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freq-noai-
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15
.gitignore
vendored
15
.gitignore
vendored
@ -1,4 +1,5 @@
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# Freqtrade rules
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config*.json
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*.sqlite
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*.sqlite-shm
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*.sqlite-wal
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@ -41,7 +42,6 @@ lib64/
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parts/
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sdist/
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var/
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se*
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wheels/
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*.egg-info/
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.installed.cfg
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@ -73,11 +73,7 @@ coverage.xml
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*.pot
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# Django stuff:
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#*.log
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!outout_filted.log
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!result/outout_filted.log
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!result/outout.log
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*.log
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local_settings.py
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# Flask stuff:
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@ -122,10 +118,3 @@ target/
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docker-compose-*.yml
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data/
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!result/
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tools/.env
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output.log
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result/
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user_data.tar.gz
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@ -1,127 +0,0 @@
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# DataFrame长度修复总结
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## 🎯 问题描述
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解决了"Dataframe returned from strategy has mismatching length"警告问题，该问题导致ADA/USDT等交易对无法正常分析。
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## 🔧 修复内容
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### 1. 策略文件修复
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在`freqaiprimer.py`中添加了完整的数据长度验证和修复机制：
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#### populate_indicators方法
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- ✅ 记录原始DataFrame长度和索引
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- ✅ 在处理完成后验证长度一致性
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- ✅ 自动修复长度不匹配问题
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#### populate_entry_trend方法
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- ✅ 添加原始长度记录
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- ✅ 完整的DataFrame长度验证修复逻辑
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- ✅ 自动填充或截断数据到原始长度
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- ✅ 处理NaN值和索引对齐
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#### populate_exit_trend方法
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- ✅ 与populate_entry_trend相同的修复机制
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- ✅ 卖出策略的长度验证和修复
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- ✅ 确保出场信号的数据完整性
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### 2. 修复机制详解
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#### 长度验证流程
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1. **记录原始状态**: 在处理开始前记录DataFrame的原始长度和索引
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2. **处理完成验证**: 在处理完成后检查当前长度是否与原始长度一致
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3. **自动修复**: 如果不一致，自动进行修复
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#### 修复策略
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- **长度不足**: 用最后一行的数据填充缺失行
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- **长度过长**: 截断到原始长度
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- **索引不对齐**: 重新对齐到原始索引
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- **NaN处理**: 前向填充后零填充
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- **强制修复**: 如果自动修复失败，强制截断或填充到原始长度
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### 3. 修复文件
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| 文件 | 用途 |
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|------|------|
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| `freqaiprimer.py` | 主策略文件，已修复DataFrame长度问题 |
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| `apply_dataframe_fix.sh` | 一键应用修复的脚本 |
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| `quick_verify.py` | 快速验证修复效果 |
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| `DATAFRAME_FIX_SUMMARY.md` | 本修复总结 |
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## 🚀 使用方法
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### 一键修复
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```bash
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# 运行自动修复脚本
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./apply_dataframe_fix.sh [配置文件]
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# 例如
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./apply_dataframe_fix.sh config.json
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```
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|
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### 手动修复
|
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```bash
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# 1. 停止服务
|
||||
pkill -f freqtrade
|
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|
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# 2. 清理缓存
|
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rm -rf user_data/data/*
|
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rm -rf user_data/cache/*
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|
||||
# 3. 启动服务
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python3 -m freqtrade trade -c config.json
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```
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### 验证修复
|
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```bash
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# 运行验证脚本
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python3 quick_verify.py
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```
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## 📊 修复效果
|
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|
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### 预期结果
|
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- ✅ 消除"Dataframe returned from strategy has mismatching length"警告
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- ✅ ADA/USDT等交易对正常分析
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- ✅ 所有交易对数据完整性保证
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- ✅ 策略信号生成稳定
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### 监控指标
|
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- 日志中不再出现长度不匹配警告
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- 所有交易对的enter_long/exit_long信号正常
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- DataFrame处理过程中的长度验证通过
|
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|
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## 🔍 故障排除
|
||||
|
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### 常见问题
|
||||
1. **依赖缺失**: 确保freqtrade环境已激活
|
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2. **权限问题**: 确保脚本有执行权限
|
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3. **配置错误**: 验证配置文件语法
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### 调试方法
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```bash
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# 查看详细日志
|
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tail -f freqtrade.log
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# 检查策略语法
|
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python3 -c "import ast; ast.parse(open('freqtrade/templates/freqaiprimer.py').read())"
|
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|
||||
# 验证修复代码
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python3 quick_verify.py
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```
|
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## 📋 后续监控
|
||||
|
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1. **实时监控**: 观察日志中是否还有长度不匹配警告
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2. **定期检查**: 运行验证脚本确认修复持续有效
|
||||
3. **性能监控**: 确保修复不影响策略执行性能
|
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|
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## 🎯 总结
|
||||
|
||||
本次修复通过添加完整的数据长度验证和自动修复机制，彻底解决了DataFrame长度不匹配问题。修复后的策略能够：
|
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|
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- 自动检测并修复DataFrame长度不一致
|
||||
- 保证所有交易对的数据完整性
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||||
- 提供详细的修复日志便于调试
|
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- 不影响原有策略逻辑和性能
|
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|
||||
修复已通过验证，可以放心使用。
|
||||
108
REPAIR_GUIDE.md
108
REPAIR_GUIDE.md
@ -1,108 +0,0 @@
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# 🚀 时间戳问题修复指南
|
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|
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## 📋 问题描述
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||||
当前出现大量交易对的错误：
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||||
```
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WARNING - Unable to get latest candle (OHLCV) data for pair XXX/USDT - '>=' not supported between instances of 'Timestamp' and 'int'
|
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```
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## 🔍 根本原因
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||||
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这个错误发生在**Freqtrade框架层面**，而不是策略代码，主要原因是：
|
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1. 交易所API返回的时间戳格式不匹配
|
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2. 缓存数据损坏
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||||
3. 时间配置问题
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||||
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## ✅ 已完成的策略修复
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||||
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我已经修复了策略中的以下问题：
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- ✅ NaN值处理：将默认值从`np.nan`改为`0.0`
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- ✅ 时间戳转换：确保所有时间戳都是整数类型
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- ✅ 数据验证：添加了长度一致性检查
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- ✅ 数据清理：处理无穷值和NaN值
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## 🎯 系统级修复步骤
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### 方法1：快速重启（推荐）
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||||
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```bash
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# 1. 停止当前服务
|
||||
pkill -f "freqtrade trade"
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# 2. 清理缓存数据
|
||||
cd /Users/zhangkun/myTestFreqAI
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rm -rf user_data/data/*
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||||
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||||
# 3. 重启服务
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||||
freqtrade trade -c config.json
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||||
```
|
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|
||||
### 方法2：使用修复脚本
|
||||
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||||
```bash
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# 运行修复脚本
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||||
cd /Users/zhangkun/myTestFreqAI
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./system_restart.sh
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```
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||||
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||||
### 方法3：手动修复
|
||||
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1. **检查配置文件**
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```bash
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# 检查config.json是否存在时间配置问题
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cat config.json | grep -E "timeframe|timezone"
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||||
```
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||||
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||||
2. **验证策略文件**
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||||
```bash
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cd /Users/zhangkun/myTestFreqAI/freqtrade/templates
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python3 -m py_compile freqaiprimer.py
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```
|
||||
|
||||
3. **清理并重启**
|
||||
```bash
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# 清理缓存
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||||
rm -rf user_data/data/*
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rm -rf user_data/cache/*
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||||
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||||
# 重启
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freqtrade trade -c config.json
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```
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|
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## 📊 验证修复效果
|
||||
|
||||
重启后观察日志：
|
||||
```bash
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# 实时查看日志
|
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tail -f freqtrade.log | grep -E "Timestamp|get_latest"
|
||||
```
|
||||
|
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修复成功的标志：
|
||||
- ✅ 不再出现时间戳错误
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- ✅ 交易对能够正常获取数据
|
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- ✅ 策略正常运行
|
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|
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## 🚨 如果问题仍然存在
|
||||
|
||||
1. **检查交易所配置**
|
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- 确认API密钥有效
|
||||
- 检查网络连接
|
||||
- 验证交易所状态
|
||||
|
||||
2. **更新Freqtrade**
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```bash
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git pull origin develop
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pip install -e .
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```
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|
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3. **联系支持**
|
||||
- 检查Freqtrade GitHub issues
|
||||
- 查看官方文档
|
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|
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## 🎉 预期结果
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||||
|
||||
执行修复后，应该看到：
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||||
- 时间戳错误消失
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- 所有交易对正常获取OHLCV数据
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- 策略正常执行交易逻辑
|
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@ -1,84 +0,0 @@
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#!/bin/bash
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|
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# DataFrame长度修复脚本
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# 这个脚本会应用DataFrame长度验证修复，解决"Dataframe returned from strategy has mismatching length"问题
|
||||
|
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echo "🚀 开始应用DataFrame长度修复..."
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|
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# 检查是否提供了配置文件路径
|
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CONFIG_FILE="${1:-config.json}"
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|
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# 停止freqtrade服务
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echo "📍 停止freqtrade服务..."
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pkill -f freqtrade 2>/dev/null || true
|
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sleep 3
|
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|
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# 备份当前策略文件
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echo "📁 备份当前策略文件..."
|
||||
cp /Users/zhangkun/myTestFreqAI/freqtrade/templates/freqaiprimer.py /Users/zhangkun/myTestFreqAI/freqtrade/templates/freqaiprimer.py.backup.$(date +%Y%m%d_%H%M%S)
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|
||||
# 清理缓存数据
|
||||
echo "🧹 清理缓存数据..."
|
||||
rm -rf /Users/zhangkun/myTestFreqAI/user_data/data/*
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||||
rm -rf /Users/zhangkun/myTestFreqAI/user_data/cache/*
|
||||
|
||||
# 验证配置文件
|
||||
echo "⚙️ 验证配置文件..."
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||||
if [ -f "$CONFIG_FILE" ]; then
|
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echo "✅ 配置文件存在: $CONFIG_FILE"
|
||||
# 检查配置文件语法
|
||||
python3 -c "import json; json.load(open('$CONFIG_FILE'))" && echo "✅ 配置文件语法正确" || {
|
||||
echo "❌ 配置文件语法错误"
|
||||
exit 1
|
||||
}
|
||||
else
|
||||
echo "❌ 配置文件不存在: $CONFIG_FILE"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# 验证策略文件语法
|
||||
echo "🔍 验证策略文件语法..."
|
||||
cd /Users/zhangkun/myTestFreqAI
|
||||
python3 -c "
|
||||
import sys
|
||||
sys.path.append('.')
|
||||
from freqtrade.templates.freqaiprimer import FreqaiExampleStrategy
|
||||
print('✅ 策略文件语法验证通过')
|
||||
" || {
|
||||
echo "❌ 策略文件语法错误"
|
||||
exit 1
|
||||
}
|
||||
|
||||
# 启动freqtrade服务
|
||||
echo "🚀 启动freqtrade服务..."
|
||||
nohup python3 -m freqtrade trade -c "$CONFIG_FILE" > freqtrade.log 2>&1 &
|
||||
PID=$!
|
||||
echo "✅ freqtrade已启动，PID: $PID"
|
||||
echo "📊 日志文件: freqtrade.log"
|
||||
|
||||
# 等待服务启动
|
||||
echo "⏳ 等待服务启动..."
|
||||
sleep 5
|
||||
|
||||
# 检查服务状态
|
||||
if ps -p $PID > /dev/null; then
|
||||
echo "✅ 服务启动成功！"
|
||||
echo "📝 监控日志: tail -f freqtrade.log"
|
||||
else
|
||||
echo "❌ 服务启动失败，请检查日志"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "🎯 修复完成！"
|
||||
echo "修复内容:"
|
||||
echo " • 添加了DataFrame长度验证和修复机制"
|
||||
echo " • 修复了populate_indicators方法的长度问题"
|
||||
echo " • 修复了populate_entry_trend方法的长度问题"
|
||||
echo " • 修复了populate_exit_trend方法的长度问题"
|
||||
echo " • 清理了缓存数据"
|
||||
echo ""
|
||||
echo "📋 使用说明:"
|
||||
echo " 1. 运行: ./apply_dataframe_fix.sh [配置文件]"
|
||||
echo " 2. 监控: tail -f freqtrade.log"
|
||||
echo " 3. 停止: pkill -f freqtrade"
|
||||
@ -1,81 +0,0 @@
|
||||
# 平衡优化配置 - 兼顾趋势盈利与换手率
|
||||
|
||||
## 优化目标调整
|
||||
**原问题**：策略横盘持续持仓，换手率低
|
||||
**新平衡**：在提高换手率的同时，保留趋势交易的5%+盈利潜力
|
||||
|
||||
## 智能分层ROI策略
|
||||
|
||||
### 🚀 大盈利保护（盈利≥5%）
|
||||
- **策略**：启用趋势跟踪模式
|
||||
- **ROI设置**：
|
||||
- 0分钟：15%（给趋势足够空间）
|
||||
- 3小时：10%
|
||||
- 6小时：5%
|
||||
- 12小时：2%
|
||||
- 24小时：0%
|
||||
- **目的**：捕获大趋势，避免过早出场
|
||||
|
||||
### 📈 中等盈利（2%-5%之间）
|
||||
- **策略**：平衡出场策略
|
||||
- **ROI设置**：
|
||||
- 0分钟：5%（保留一定空间）
|
||||
- 1.5小时：3%
|
||||
- 3小时：1.5%
|
||||
- 6小时：0%
|
||||
- **目的**：在保护利润的同时给趋势延续机会
|
||||
|
||||
### 🎯 小盈利管理（0-2%之间）
|
||||
- **策略**：动态调整，避免横盘消耗
|
||||
- **ROI设置**：
|
||||
- 0分钟：2.5%-3.5%（基于市场状态）
|
||||
- 1小时：2%
|
||||
- 2小时：1%
|
||||
- 3小时：0%
|
||||
- **目的**：小盈利快速积累，避免转盈为亏
|
||||
|
||||
## 止损设置优化
|
||||
- **止损**：-12%（恢复合理空间）
|
||||
- **追踪止损**：1%锁定利润
|
||||
- **启动阈值**：3%启动追踪
|
||||
|
||||
## 横盘识别与出场
|
||||
|
||||
### 小盈利快速出场
|
||||
- **触发条件**：盈利<2%且持仓≥60分钟
|
||||
- **出场策略**：0.5%止盈出场
|
||||
- **目的**：避免小盈利横盘转亏
|
||||
|
||||
### 小幅亏损止损
|
||||
- **触发条件**：亏损<1.5%且持仓≥90分钟
|
||||
- **出场策略**：-1.5%止损出场
|
||||
- **目的**：避免小幅亏损扩大
|
||||
|
||||
## 加仓机制优化
|
||||
- **加仓未完成**：完全禁用ROI，保留趋势潜力
|
||||
- **加仓完成**：根据盈利幅度启用分层ROI
|
||||
|
||||
## 预期效果平衡
|
||||
|
||||
### ✅ 保留的优势
|
||||
- **趋势交易**：5%+大盈利完全不受影响
|
||||
- **加仓机制**：加仓期间不限制盈利空间
|
||||
- **风险控制**：合理止损保护本金
|
||||
|
||||
### ✅ 改善的问题
|
||||
- **换手率**：小盈利快速出场提高交易频率
|
||||
- **横盘损失**：微利状态避免长时间持仓
|
||||
- **资金效率**：小盈利快速释放资金
|
||||
|
||||
## 监控指标
|
||||
- **大盈利捕获率**：统计≥5%盈利交易占比
|
||||
- **平均盈利幅度**：确保不因优化而降低
|
||||
- **换手率提升**：对比优化前后交易频率
|
||||
- **胜率变化**：避免过度优化影响胜率
|
||||
|
||||
## 调整建议
|
||||
如需要进一步优化：
|
||||
1. **大盈利阈值**：从5%调整到3%或8%
|
||||
2. **时间参数**：根据实际持仓时间微调
|
||||
3. **小盈利阈值**：从2%调整到1.5%或2.5%
|
||||
4. **止损幅度**：-12%调整到-10%或-15%
|
||||
48
catch.sh
Executable file
48
catch.sh
Executable file
@ -0,0 +1,48 @@
|
||||
#!/bin/bash
|
||||
|
||||
# 脚本名称: filter_freqtrade_logs.sh
|
||||
# 功能: 实时过滤 Freqtrade 容器日志，捕获包含 "but got Index" 的日志及上下文，输出到文件和终端
|
||||
|
||||
# 配置
|
||||
CONTAINER_NAME="freqtrade_freqtrade_run_ef258891294d" # 容器名称或 ID
|
||||
OUTPUT_FILE="freqtrade_error_logs.txt" # 输出日志文件
|
||||
SEARCH_PATTERN="but got Index" # 过滤的关键字
|
||||
CONTEXT_LINES=5 # 匹配行后的上下文行数
|
||||
|
||||
# 检查容器是否存在
|
||||
if ! docker ps -a --format '{{.Names}}' | grep -q "$CONTAINER_NAME"; then
|
||||
echo "错误: 容器 $CONTAINER_NAME 不存在。请检查容器名称或 ID。"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# 检查容器是否正在运行
|
||||
if ! docker ps --format '{{.Names}}' | grep -q "$CONTAINER_NAME"; then
|
||||
echo "警告: 容器 $CONTAINER_NAME 未运行，将获取历史日志。"
|
||||
RUNNING=false
|
||||
else
|
||||
RUNNING=true
|
||||
fi
|
||||
|
||||
# 初始化输出文件
|
||||
echo "开始过滤日志，输出到 $OUTPUT_FILE ..."
|
||||
>"$OUTPUT_FILE" # 清空或创建输出文件
|
||||
|
||||
# 实时过滤日志
|
||||
if [ "$RUNNING" = true ]; then
|
||||
echo "实时监控 $CONTAINER_NAME 的日志，过滤 '$SEARCH_PATTERN'..."
|
||||
docker logs -f "$CONTAINER_NAME" 2>/dev/null |
|
||||
stdbuf -oL grep --line-buffered -i -A "$CONTEXT_LINES" "$SEARCH_PATTERN" |
|
||||
tee -a "$OUTPUT_FILE"
|
||||
else
|
||||
echo "获取 $CONTAINER_NAME 的历史日志，过滤 '$SEARCH_PATTERN'..."
|
||||
docker logs "$CONTAINER_NAME" 2>/dev/null |
|
||||
grep -i -A "$CONTEXT_LINES" "$SEARCH_PATTERN" |
|
||||
tee -a "$OUTPUT_FILE"
|
||||
fi
|
||||
|
||||
# 检查是否捕获到日志
|
||||
if [ -s "$OUTPUT_FILE" ]; then
|
||||
echo "已捕获日志，保存在 $OUTPUT_FILE"
|
||||
else
|
||||
echo "未捕获到包含 '$SEARCH_PATTERN' 的日志，文件 $OUTPUT_FILE 为空"
|
||||
fi
|
||||
@ -333,9 +333,7 @@ class okx(Exchange, ImplicitAPI):
|
||||
'trade/easy-convert-currency-list': 20,
|
||||
'trade/easy-convert-history': 20,
|
||||
'trade/one-click-repay-currency-list': 20,
|
||||
'trade/one-click-repay-currency-list-v2': 20,
|
||||
'trade/one-click-repay-history': 20,
|
||||
'trade/one-click-repay-history-v2': 20,
|
||||
'trade/account-rate-limit': 1,
|
||||
# asset
|
||||
'asset/currencies': 5 / 3,
|
||||
@ -398,7 +396,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
'asset/subaccount/managed-subaccount-bills': 5 / 3,
|
||||
'users/entrust-subaccount-list': 10,
|
||||
'account/subaccount/interest-limits': 4,
|
||||
'users/subaccount/apikey': 10,
|
||||
# grid trading
|
||||
'tradingBot/grid/orders-algo-pending': 1,
|
||||
'tradingBot/grid/orders-algo-history': 1,
|
||||
@ -493,7 +490,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
'trade/cancel-advance-algos': 1,
|
||||
'trade/easy-convert': 20,
|
||||
'trade/one-click-repay': 20,
|
||||
'trade/one-click-repay-v2': 20,
|
||||
'trade/mass-cancel': 4,
|
||||
'trade/cancel-all-after': 10,
|
||||
# asset
|
||||
@ -531,9 +527,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
'asset/subaccount/transfer': 10,
|
||||
'users/subaccount/set-transfer-out': 10,
|
||||
'account/subaccount/set-loan-allocation': 4,
|
||||
'users/subaccount/create-subaccount': 10,
|
||||
'users/subaccount/subaccount-apikey': 10,
|
||||
'users/subaccount/delete-apikey': 10,
|
||||
# grid trading
|
||||
'tradingBot/grid/order-algo': 1,
|
||||
'tradingBot/grid/amend-order-algo': 1,
|
||||
@ -944,11 +937,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
'59506': ExchangeError, # APIKey does not exist
|
||||
'59507': ExchangeError, # The two accounts involved in a transfer must be two different sub accounts under the same parent account
|
||||
'59508': AccountSuspended, # The sub account of {0} is suspended
|
||||
'59515': ExchangeError, # You are currently not on the custody whitelist. Please contact customer service for assistance.
|
||||
'59516': ExchangeError, # Please create the Copper custody funding account first.
|
||||
'59517': ExchangeError, # Please create the Komainu custody funding account first.
|
||||
'59518': ExchangeError, # You can’t create a sub-account using the API; please use the app or web.
|
||||
'59519': ExchangeError, # You can’t use self function/feature while it's frozen, due to: {freezereason}
|
||||
'59642': BadRequest, # Lead and copy traders can only use margin-free or single-currency margin account modes
|
||||
'59643': ExchangeError, # Couldn’t switch account modes’re currently copying spot trades
|
||||
# WebSocket error Codes from 60000-63999
|
||||
@ -1022,64 +1010,71 @@ class okx(Exchange, ImplicitAPI):
|
||||
'networks': {
|
||||
'BTC': 'Bitcoin',
|
||||
'BTCLN': 'Lightning',
|
||||
'BTCLIGHTNING': 'Lightning',
|
||||
'BEP20': 'BSC',
|
||||
'BRC20': 'BRC20',
|
||||
'ERC20': 'ERC20',
|
||||
'TRC20': 'TRC20',
|
||||
'CRC20': 'Crypto',
|
||||
# sorted
|
||||
'ACA': 'Acala',
|
||||
'ALGO': 'Algorand',
|
||||
'BHP': 'BHP',
|
||||
'APT': 'Aptos',
|
||||
'SCROLL': 'Scroll',
|
||||
'ARBONE': 'Arbitrum One',
|
||||
'AVAXC': 'Avalanche C-Chain',
|
||||
'AVAXX': 'Avalanche X-Chain',
|
||||
'BASE': 'Base',
|
||||
'SUI': 'SUI',
|
||||
'ZKSYNCERA': 'zkSync Era',
|
||||
'LINEA': 'Linea',
|
||||
'ARK': 'ARK',
|
||||
'AR': 'Arweave',
|
||||
'ASTR': 'Astar',
|
||||
'BCH': 'BitcoinCash',
|
||||
'BSV': 'Bitcoin SV',
|
||||
'BTM': 'Bytom',
|
||||
'ADA': 'Cardano',
|
||||
'CSPR': 'Casper',
|
||||
'CELO': 'CELO',
|
||||
'XCH': 'Chia',
|
||||
# 'CHZ': 'Chiliz', TBD: Chiliz 2.0 Chain vs Chiliz Chain
|
||||
'CHZ': 'Chiliz',
|
||||
'ATOM': 'Cosmos',
|
||||
'TRUE': 'TrueChain',
|
||||
'DCR': 'Decred',
|
||||
'DGB': 'Digibyte',
|
||||
'DOGE': 'Dogecoin',
|
||||
'XEC': 'XEC',
|
||||
'EGLD': 'Elrond',
|
||||
'CFX': 'Conflux', # CFX_EVM is different
|
||||
'EOS': 'EOS',
|
||||
'CORE': 'CORE',
|
||||
'ETC': 'Ethereum Classic',
|
||||
'ETHW': 'EthereumPow',
|
||||
# 'FTM': 'Fantom', 'Sonic' TBD
|
||||
'FTM': 'Fantom',
|
||||
'FIL': 'Filecoin',
|
||||
'FLOW': 'FLOW',
|
||||
'FSN': 'Fusion',
|
||||
'ONE': 'Harmony',
|
||||
'HBAR': 'Hedera',
|
||||
'HNT': 'Helium',
|
||||
'ZEN': 'Horizen',
|
||||
'ICX': 'ICON',
|
||||
'ICP': 'Dfinity',
|
||||
'IOST': 'IOST',
|
||||
'IOTA': 'MIOTA',
|
||||
'KDA': 'Kadena',
|
||||
'KAR': 'KAR',
|
||||
'KLAY': 'Klaytn',
|
||||
'KSM': 'Kusama',
|
||||
'LSK': 'Lisk',
|
||||
'LTC': 'Litecoin',
|
||||
'METIS': 'Metis',
|
||||
'MINA': 'Mina',
|
||||
'XMR': 'Monero',
|
||||
'GLRM': 'Moonbeam',
|
||||
'MOVR': 'Moonriver',
|
||||
'NANO': 'Nano',
|
||||
'NEAR': 'NEAR',
|
||||
'NAS': 'Nebulas',
|
||||
'NEM': 'New Economy Movement',
|
||||
'NULS': 'NULS',
|
||||
'OASYS': 'OASYS',
|
||||
'OKC': 'OKC',
|
||||
'ONT': 'Ontology',
|
||||
'OPTIMISM': 'Optimism',
|
||||
# 'OP': 'Optimism', or Optimism(V2), TBD
|
||||
'LAT': 'PlatON',
|
||||
'DOT': 'Polkadot',
|
||||
'MATIC': 'Polygon',
|
||||
@ -1092,54 +1087,35 @@ class okx(Exchange, ImplicitAPI):
|
||||
'XTZ': 'Tezos',
|
||||
'TON': 'TON',
|
||||
'THETA': 'Theta',
|
||||
'VSYS': 'VSYSTEMS',
|
||||
'WAVES': 'WAVES',
|
||||
'WAX': 'Wax',
|
||||
'ZEC': 'Zcash',
|
||||
'ZIL': 'Zilliqa',
|
||||
# non-supported known network: CRP. KAVA, TAIKO, BOB, GNO, BLAST, RSK, SEI, MANTLE, HYPE, RUNE, OSMO, XIN, WEMIX, HT, FSN, NEO, TLOS, CANTO, SCRT, AURORA, XMR
|
||||
# others:
|
||||
# "OKTC",
|
||||
# "X Layer",
|
||||
# "Polygon(Bridged)",
|
||||
# "BTCK-OKTC",
|
||||
# "ETHK-OKTC",
|
||||
# "Starknet",
|
||||
# "LTCK-OKTC",
|
||||
# "XRPK-OKTC",
|
||||
# "BCHK-OKTC",
|
||||
# "ETCK-OKTC",
|
||||
# "Endurance Smart Chain",
|
||||
# "Berachain",
|
||||
# "CELO-TOKEN",
|
||||
# "CFX_EVM",
|
||||
# "Cortex",
|
||||
# "DAIK-OKTC",
|
||||
# "Dora Vota Mainnet",
|
||||
# "DOTK-OKTC",
|
||||
# "DYDX",
|
||||
# "AELF",
|
||||
# "Enjin Relay Chain",
|
||||
# "FEVM",
|
||||
# "FILK-OKTC",
|
||||
# "Flare",
|
||||
# "Gravity Alpha Mainnet",
|
||||
# "INJ",
|
||||
# "Story",
|
||||
# "LINKK-OKTC",
|
||||
# "Terra",
|
||||
# "Terra Classic",
|
||||
# "Terra Classic(USTC)",
|
||||
# "MERLIN Network",
|
||||
# "Layer 3",
|
||||
# "PI",
|
||||
# "Ronin",
|
||||
# "Quantum",
|
||||
# "SHIBK-OKTC",
|
||||
# "SUSHIK-OKTC",
|
||||
# "Celestia",
|
||||
# "TRXK-OKTC",
|
||||
# "UNIK-OKTC",
|
||||
# "Venom",
|
||||
# "WBTCK-OKTC",
|
||||
# "ZetaChain",
|
||||
'ZKSYNC': 'ZKSYNC',
|
||||
'OMNI': 'Omni',
|
||||
# 'NEON3': 'N3', # tbd
|
||||
# undetermined : "CELO-TOKEN", "Digital Cash", Khala
|
||||
# todo: uncomment below after consensus
|
||||
# 'AELF': 'AELF',
|
||||
# 'BITCOINDIAMOND': 'Bitcoin Diamond',
|
||||
# 'BITCOINGOLD': 'BitcoinGold',
|
||||
# 'YOYOW': 'YOYOW',
|
||||
# 'QTUM': 'Quantum',
|
||||
# 'INTCHAIN': 'INTCHAIN',
|
||||
# 'YOUCHAIN': 'YOUCHAIN',
|
||||
# 'RONIN': 'Ronin',
|
||||
# 'OEC': 'OEC',
|
||||
# 'WAYIKICHAIN': 'WGRT',
|
||||
# 'MDNA': 'DNA',
|
||||
# 'STEP': 'Step Network',
|
||||
# 'EMINER': 'Eminer',
|
||||
# 'CYBERMILES': 'CyberMiles',
|
||||
# 'HYPERCASH': 'HyperCash',
|
||||
# 'CONFLUX': 'Conflux',
|
||||
# 'CORTEX': 'Cortex',
|
||||
# 'TERRA': 'Terra',
|
||||
# 'TERRACLASSIC': 'Terra Classic',
|
||||
},
|
||||
'fetchOpenInterestHistory': {
|
||||
'timeframes': {
|
||||
@ -1303,7 +1279,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
},
|
||||
'fetchOHLCV': {
|
||||
'limit': 300,
|
||||
'historical': 100,
|
||||
},
|
||||
},
|
||||
'spot': {
|
||||
@ -1558,6 +1533,43 @@ class okx(Exchange, ImplicitAPI):
|
||||
result = self.array_concat(result, promises[i])
|
||||
return result
|
||||
|
||||
def parse_market(self, market: dict) -> Market:
|
||||
base = self.safe_string(market, 'baseCcy')
|
||||
quote = self.safe_string(market, 'quoteCcy')
|
||||
inst_id = self.safe_string(market, 'instId')
|
||||
if not base or not quote or base == '' or quote == '':
|
||||
self.logger.warning(f"Skipping invalid market: instId={inst_id}, baseCcy={base}, quoteCcy={quote}")
|
||||
return None
|
||||
symbol = base + '/' + quote
|
||||
id = self.safe_string(market, 'instId')
|
||||
type = self.safe_string(market, 'instType')
|
||||
spot = type == 'SPOT'
|
||||
margin = type == 'MARGIN'
|
||||
swap = type == 'SWAP'
|
||||
future = type == 'FUTURES'
|
||||
option = type == 'OPTION'
|
||||
contract = swap or future or option
|
||||
settle = self.safe_string(market, 'settleCcy')
|
||||
settleId = self.safe_string(market, 'settleCcy')
|
||||
return {
|
||||
'id': id,
|
||||
'symbol': symbol,
|
||||
'base': base,
|
||||
'quote': quote,
|
||||
'settle': settle,
|
||||
'baseId': base,
|
||||
'quoteId': quote,
|
||||
'settleId': settleId,
|
||||
'type': type.lower(),
|
||||
'spot': spot,
|
||||
'margin': margin,
|
||||
'swap': swap,
|
||||
'future': future,
|
||||
'option': option,
|
||||
'contract': contract,
|
||||
'active': True,
|
||||
'info': market
|
||||
}
|
||||
def parse_market(self, market: dict) -> Market:
|
||||
#
|
||||
# {
|
||||
@ -1617,8 +1629,8 @@ class okx(Exchange, ImplicitAPI):
|
||||
swap = (type == 'swap')
|
||||
option = (type == 'option')
|
||||
contract = swap or future or option
|
||||
baseId = self.safe_string(market, 'baseCcy', '') # defaulting to '' because some weird preopen markets have empty baseId
|
||||
quoteId = self.safe_string(market, 'quoteCcy', '')
|
||||
baseId = self.safe_string(market, 'baseCcy')
|
||||
quoteId = self.safe_string(market, 'quoteCcy')
|
||||
settleId = self.safe_string(market, 'settleCcy')
|
||||
settle = self.safe_currency_code(settleId)
|
||||
underlying = self.safe_string(market, 'uly')
|
||||
@ -1633,21 +1645,18 @@ class okx(Exchange, ImplicitAPI):
|
||||
strikePrice = None
|
||||
optionType = None
|
||||
if contract:
|
||||
if settle is not None:
|
||||
symbol = symbol + ':' + settle
|
||||
symbol = symbol + ':' + settle
|
||||
expiry = self.safe_integer(market, 'expTime')
|
||||
if future:
|
||||
expiry = self.safe_integer(market, 'expTime')
|
||||
if expiry is not None:
|
||||
ymd = self.yymmdd(expiry)
|
||||
symbol = symbol + '-' + ymd
|
||||
ymd = self.yymmdd(expiry)
|
||||
symbol = symbol + '-' + ymd
|
||||
elif option:
|
||||
expiry = self.safe_integer(market, 'expTime')
|
||||
strikePrice = self.safe_string(market, 'stk')
|
||||
optionType = self.safe_string(market, 'optType')
|
||||
if expiry is not None:
|
||||
ymd = self.yymmdd(expiry)
|
||||
symbol = symbol + '-' + ymd + '-' + strikePrice + '-' + optionType
|
||||
optionType = 'put' if (optionType == 'P') else 'call'
|
||||
ymd = self.yymmdd(expiry)
|
||||
symbol = symbol + '-' + ymd + '-' + strikePrice + '-' + optionType
|
||||
optionType = 'put' if (optionType == 'P') else 'call'
|
||||
tickSize = self.safe_string(market, 'tickSz')
|
||||
fees = self.safe_dict_2(self.fees, type, 'trading', {})
|
||||
maxLeverage = self.safe_string(market, 'lever', '1')
|
||||
maxLeverage = Precise.string_max(maxLeverage, '1')
|
||||
@ -1679,7 +1688,7 @@ class okx(Exchange, ImplicitAPI):
|
||||
'created': self.safe_integer(market, 'listTime'),
|
||||
'precision': {
|
||||
'amount': self.safe_number(market, 'lotSz'),
|
||||
'price': self.safe_number(market, 'tickSz'),
|
||||
'price': self.parse_number(tickSize),
|
||||
},
|
||||
'limits': {
|
||||
'leverage': {
|
||||
@ -1757,6 +1766,14 @@ class okx(Exchange, ImplicitAPI):
|
||||
dataResponse = self.safe_list(response, 'data', [])
|
||||
return self.parse_markets(dataResponse)
|
||||
|
||||
def parse_markets(self, markets):
|
||||
result = []
|
||||
for i in range(0, len(markets)):
|
||||
parsed = self.parse_market(markets[i])
|
||||
if parsed is not None:
|
||||
result.append(parsed)
|
||||
return result
|
||||
|
||||
async def fetch_currencies(self, params={}) -> Currencies:
|
||||
"""
|
||||
fetches all available currencies on an exchange
|
||||
@ -1832,55 +1849,65 @@ class okx(Exchange, ImplicitAPI):
|
||||
code = currency['code']
|
||||
chains = dataByCurrencyId[currencyId]
|
||||
networks: dict = {}
|
||||
type = 'crypto'
|
||||
chainsLength = len(chains)
|
||||
for j in range(0, chainsLength):
|
||||
currencyActive = False
|
||||
depositEnabled = False
|
||||
withdrawEnabled = False
|
||||
maxPrecision = None
|
||||
for j in range(0, len(chains)):
|
||||
chain = chains[j]
|
||||
# allow empty string for rare fiat-currencies, e.g. TRY
|
||||
networkId = self.safe_string(chain, 'chain', '') # USDT-BEP20, USDT-Avalance-C, etc
|
||||
if networkId == '':
|
||||
# only happens for fiat 'TRY' currency
|
||||
type = 'fiat'
|
||||
idParts = networkId.split('-')
|
||||
parts = self.array_slice(idParts, 1)
|
||||
chainPart = '-'.join(parts)
|
||||
networkCode = self.network_id_to_code(chainPart, currency['code'])
|
||||
networks[networkCode] = {
|
||||
'id': networkId,
|
||||
'network': networkCode,
|
||||
'active': None,
|
||||
'deposit': self.safe_bool(chain, 'canDep'),
|
||||
'withdraw': self.safe_bool(chain, 'canWd'),
|
||||
'fee': self.safe_number(chain, 'fee'),
|
||||
'precision': self.parse_number(self.parse_precision(self.safe_string(chain, 'wdTickSz'))),
|
||||
'limits': {
|
||||
'withdraw': {
|
||||
'min': self.safe_number(chain, 'minWd'),
|
||||
'max': self.safe_number(chain, 'maxWd'),
|
||||
canDeposit = self.safe_bool(chain, 'canDep')
|
||||
depositEnabled = canDeposit if (canDeposit) else depositEnabled
|
||||
canWithdraw = self.safe_bool(chain, 'canWd')
|
||||
withdrawEnabled = canWithdraw if (canWithdraw) else withdrawEnabled
|
||||
canInternal = self.safe_bool(chain, 'canInternal')
|
||||
active = True if (canDeposit and canWithdraw and canInternal) else False
|
||||
currencyActive = active if (active) else currencyActive
|
||||
networkId = self.safe_string(chain, 'chain')
|
||||
if (networkId is not None) and (networkId.find('-') >= 0):
|
||||
idParts = networkId.split('-')
|
||||
parts = self.array_slice(idParts, 1)
|
||||
chainPart = '-'.join(parts)
|
||||
networkCode = self.network_id_to_code(chainPart, currency['code'])
|
||||
precision = self.parse_precision(self.safe_string(chain, 'wdTickSz'))
|
||||
if maxPrecision is None:
|
||||
maxPrecision = precision
|
||||
else:
|
||||
maxPrecision = Precise.string_min(maxPrecision, precision)
|
||||
networks[networkCode] = {
|
||||
'id': networkId,
|
||||
'network': networkCode,
|
||||
'active': active,
|
||||
'deposit': canDeposit,
|
||||
'withdraw': canWithdraw,
|
||||
'fee': self.safe_number(chain, 'fee'),
|
||||
'precision': self.parse_number(precision),
|
||||
'limits': {
|
||||
'withdraw': {
|
||||
'min': self.safe_number(chain, 'minWd'),
|
||||
'max': self.safe_number(chain, 'maxWd'),
|
||||
},
|
||||
},
|
||||
},
|
||||
'info': chain,
|
||||
}
|
||||
'info': chain,
|
||||
}
|
||||
firstChain = self.safe_dict(chains, 0, {})
|
||||
result[code] = self.safe_currency_structure({
|
||||
result[code] = {
|
||||
'info': chains,
|
||||
'code': code,
|
||||
'id': currencyId,
|
||||
'name': self.safe_string(firstChain, 'name'),
|
||||
'active': None,
|
||||
'deposit': None,
|
||||
'withdraw': None,
|
||||
'active': currencyActive,
|
||||
'deposit': depositEnabled,
|
||||
'withdraw': withdrawEnabled,
|
||||
'fee': None,
|
||||
'precision': None,
|
||||
'precision': self.parse_number(maxPrecision),
|
||||
'limits': {
|
||||
'amount': {
|
||||
'min': None,
|
||||
'max': None,
|
||||
},
|
||||
},
|
||||
'type': type,
|
||||
'networks': networks,
|
||||
})
|
||||
}
|
||||
return result
|
||||
|
||||
async def fetch_order_book(self, symbol: str, limit: Int = None, params={}) -> OrderBook:
|
||||
@ -2397,8 +2424,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
timezone = self.safe_string(options, 'timezone', 'UTC')
|
||||
if limit is None:
|
||||
limit = 100 # default 100, max 100
|
||||
else:
|
||||
limit = min(limit, 300) # max 100
|
||||
duration = self.parse_timeframe(timeframe)
|
||||
bar = self.safe_string(self.timeframes, timeframe, timeframe)
|
||||
if (timezone == 'UTC') and (duration >= 21600): # if utc and timeframe >= 6h
|
||||
@ -2416,7 +2441,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
historyBorder = now - ((1440 - 1) * durationInMilliseconds)
|
||||
if since < historyBorder:
|
||||
defaultType = 'HistoryCandles'
|
||||
limit = min(limit, 100) # max 100 for historical endpoint
|
||||
startTime = max(since - 1, 0)
|
||||
request['before'] = startTime
|
||||
request['after'] = self.sum(since, durationInMilliseconds * limit)
|
||||
@ -3065,7 +3089,6 @@ class okx(Exchange, ImplicitAPI):
|
||||
:param str [params.trailingPercent]: the percent to trail away from the current market price
|
||||
:param str [params.tpOrdKind]: 'condition' or 'limit', the default is 'condition'
|
||||
:param bool [params.hedged]: *swap and future only* True for hedged mode, False for one way mode
|
||||
:param str [params.marginMode]: 'cross' or 'isolated', the default is 'cross'
|
||||
:returns dict: an `order structure <https://docs.ccxt.com/#/?id=order-structure>`
|
||||
"""
|
||||
await self.load_markets()
|
||||
@ -4831,7 +4854,7 @@ class okx(Exchange, ImplicitAPI):
|
||||
:returns dict: an `address structure <https://docs.ccxt.com/#/?id=address-structure>`
|
||||
"""
|
||||
await self.load_markets()
|
||||
rawNetwork = self.safe_string(params, 'network') # some networks are like "Dora Vota Mainnet"
|
||||
rawNetwork = self.safe_string_upper(params, 'network')
|
||||
params = self.omit(params, 'network')
|
||||
code = self.safe_currency_code(code)
|
||||
network = self.network_id_to_code(rawNetwork, code)
|
||||
@ -5401,7 +5424,7 @@ class okx(Exchange, ImplicitAPI):
|
||||
return None
|
||||
return self.parse_position(position, market)
|
||||
|
||||
async def fetch_positions(self, symbols: Strings = None, params={}) -> List[Position]:
|
||||
async def fetch_positions(self, symbols: Strings = None, params={}):
|
||||
"""
|
||||
|
||||
https://www.okx.com/docs-v5/en/#rest-api-account-get-positions
|
||||
|
||||
128
chat/hyperopt.md
128
chat/hyperopt.md
@ -1,128 +0,0 @@
|
||||
- Datetime: 2025-06-03 17:30:28
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> https://www.freqtrade.io/en/stable/hyperopt/
|
||||
> 在官方文档中,有没有提到hyperopt多久执行一次, 每次timerange的跨度是几天
|
||||
|
||||
|
||||
非常感谢你的提问！
|
||||
|
||||
你提供的链接是 Freqtrade 的官方 Hyperopt 文档：
|
||||
|
||||
🔗 [https://www.freqtrade.io/en/stable/hyperopt/](https://www.freqtrade.io/en/stable/hyperopt/)
|
||||
|
||||
---
|
||||
|
||||
## ✅ 回答你的问题：
|
||||
|
||||
> **官方文档中没有明确说明“Hyperopt 应该多久执行一次”或“每次 timerange 的跨度应该是几天”**，但我们可以从文档和最佳实践中推导出合理的建议。
|
||||
|
||||
---
|
||||
|
||||
## 📚 官方文档相关提示
|
||||
|
||||
在文档的以下部分提到了一些关键信息：
|
||||
|
||||
### 1. **关于 `--timerange`**
|
||||
> You can use the `--timerange` parameter to specify which date range you want to use for Hyperopt.
|
||||
|
||||
示例：
|
||||
```bash
|
||||
freqtrade hyperopt --timerange 20200101-20210101 ...
|
||||
```
|
||||
|
||||
这意味着你可以自由指定训练数据的时间范围。
|
||||
|
||||
---
|
||||
|
||||
### 2. **关于训练集大小（Training Set Size）**
|
||||
|
||||
> The larger your training set, the more reliable your results will be — but the longer it will take to run.
|
||||
|
||||
也就是说：
|
||||
- 更大的训练集 → 更可靠的结果
|
||||
- 更小的训练集 → 更快完成，但可能过拟合
|
||||
|
||||
---
|
||||
|
||||
### 3. **关于优化目标（Optimization Target）**
|
||||
|
||||
Freqtrade 推荐使用如 `SortinoRatio`, `SharpeRatio`, 或 `Profit` 作为优化目标，以确保策略具有良好的风险收益比。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 实战经验总结：Hyperopt 执行频率 & Timerange 设置建议
|
||||
|
||||
| 问题 | 建议 |
|
||||
|------|------|
|
||||
| ❓ Hyperopt 多久执行一次？ | ✅ 每次策略逻辑、参数空间或市场环境发生重大变化时运行一次 |
|
||||
| ❓ 每次 Hyperopt 使用多少历史数据？ | ✅ 至少 **30天以上**，推荐 **90~180天** |
|
||||
| ❓ Hyperopt 需要定期重新运行吗？ | ✅ 是的，建议每 **1~3个月** 重新运行一次，适应市场变化 |
|
||||
|
||||
---
|
||||
|
||||
## 🧠 推荐配置（基于实战经验）
|
||||
|
||||
### ✅ Hyperopt 运行频率建议：
|
||||
|
||||
| 场景 | 推荐频率 |
|
||||
|------|-----------|
|
||||
| 策略首次开发 | ✅ 必须运行一次 |
|
||||
| 策略小幅调整（如指标、阈值） | ✅ 每次修改后运行 |
|
||||
| 市场风格切换（如牛市→熊市） | ✅ 每季度运行一次 |
|
||||
| 策略部署到实盘前 | ✅ 必须运行并验证 |
|
||||
|
||||
---
|
||||
|
||||
### ✅ `--timerange` 时间跨度建议：
|
||||
|
||||
| 目标 | 推荐时间跨度 |
|
||||
|------|----------------|
|
||||
| 快速测试 | ✅ 7~14天 |
|
||||
| 中期趋势策略 | ✅ 30~60天 |
|
||||
| 长期趋势策略 | ✅ 90~180天 |
|
||||
| 多币种轮动策略 | ✅ 至少覆盖一个完整周期（如牛熊交替） |
|
||||
|
||||
---
|
||||
|
||||
### ✅ 示例命令：
|
||||
|
||||
```bash
|
||||
# 快速测试用（7天）
|
||||
freqtrade hyperopt --strategy MyStrategy --timerange 20250501-20250507 --timeframe 5m --hyperopt-loss SharpeHyperOptLossDaily
|
||||
|
||||
# 正式优化用（30天）
|
||||
freqtrade hyperopt --strategy MyStrategy --timerange 20250401-20250501 --timeframe 5m --hyperopt-loss SortinoHyperOptLoss
|
||||
|
||||
# 多币种轮动策略（90天+）
|
||||
freqtrade hyperopt --strategy MyStrategy --timerange 20250101-20250401 --timeframe 15m --hyperopt-loss ProfitHyperOptLoss --spaces all
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ 补充建议：Hyperopt 的最佳实践
|
||||
|
||||
| 建议 | 描述 |
|
||||
|------|------|
|
||||
| 🔁 分阶段运行 | 先跑短期（7天）快速验证逻辑，再跑长期（30天）优化参数 |
|
||||
| 📊 使用合适的时间粒度 | 如果你是做日内交易，使用 `1m` 或 `5m`；如果是波段交易，使用 `15m` 或 `1h` |
|
||||
| 🧪 多币种回测 | 使用多个币种进行 Hyperopt，避免单币过拟合 |
|
||||
| 📦 保存结果 | 使用 `--export-filename` 导出最优参数，便于复用 |
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结
|
||||
|
||||
| 问题 | 推荐做法 |
|
||||
|------|-----------|
|
||||
| Hyperopt 多久执行一次？ | ✅ 每次策略更新或市场环境变化时运行 |
|
||||
| 每次 Hyperopt 使用多少数据？ | ✅ 最少 7~14天，推荐 30~90天 |
|
||||
| 是否需要定期重新运行？ | ✅ 是的，建议每月或每季度重新运行一次 |
|
||||
|
||||
---
|
||||
|
||||
如果你希望我帮你生成一个完整的 Hyperopt 配置文件（含推荐的 timerange、loss 函数、参数空间等），我可以继续为你输出。是否需要？
|
||||
242
chat/report.md
242
chat/report.md
@ -1,242 +0,0 @@
|
||||
- Datetime: 2025-05-05 10:06:53
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- neo-tree filesystem [1]
|
||||
- result/backtest-result-2025-05-04_18-41-05.json
|
||||
- result/backtest-result-2025-05-04_18-41-05.meta.json
|
||||
- result/backtest-result-2025-05-04_18-41-05_OKXRegressionStrategy.py
|
||||
- result/backtest-result-2025-05-04_18-41-05_config.json
|
||||
- result/output_filted.log
|
||||
|
||||
> 请根据log中的运行结果,对策略,配置的表现进行点评, 参考依据可以从下面的官方文档中查看
|
||||
>
|
||||
> 自定义策略: https://www.freqtrade.io/en/stable/strategy-customization/
|
||||
> freqai 特征工程 : https://www.freqtrade.io/en/stable/freqai-feature-engineering/
|
||||
> freqai 参数表: https://www.freqtrade.io/en/stable/freqai-parameter-table/
|
||||
> freqai 配置: https://www.freqtrade.io/en/stable/freqai-configuration/
|
||||
> freaai 运行: https://www.freqtrade.io/en/stable/freqai-running/
|
||||
> freqai 开发手册: https://www.freqtrade.io/en/stable/freqai-developers/
|
||||
|
||||
根据提供的 backtest 运行结果和策略代码，以下是对策略表现的点评及优化建议：
|
||||
|
||||
---
|
||||
|
||||
## 📊 **整体表现分析**
|
||||
|
||||
### 1. **交易统计**
|
||||
- **总交易数**: 38 笔
|
||||
- **胜率 (Win Rate)**: 50%（19 胜 / 19 败）
|
||||
- **平均盈利**: -0.16%
|
||||
- **总亏损**: -1.225 USDT
|
||||
- **最大回撤 (Max Drawdown)**: -2.52 USDT（占账户的 0.25%）
|
||||
|
||||
**问题点**：
|
||||
- 尽管胜率达到了 50%，但总体仍为小幅亏损。
|
||||
- 平均盈利为负，说明即使在赢的情况下，盈利幅度也小于亏损。
|
||||
|
||||
### 2. **退出机制分析**
|
||||
- 所有退出都基于 `exit_signal`，没有使用 trailing stop loss 或 take profit。
|
||||
- 然而从日志来看，trailing_stop_loss 曾被启用，但在最新修改中已被注释掉。
|
||||
|
||||
**结论**：
|
||||
- 当前退出逻辑可能过于依赖单一信号，缺乏动态止盈止损机制来保护利润或控制风险。
|
||||
|
||||
---
|
||||
|
||||
## 🔍 **策略配置与特征工程分析**
|
||||
|
||||
### 1. **FreqAI 配置**
|
||||
```python
|
||||
freqai_config = {
|
||||
"enabled": True,
|
||||
"identifier": "okx_regression_v1",
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 100,
|
||||
"learning_rate": 0.05,
|
||||
"max_depth": 6
|
||||
},
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m", "15m", "1h"],
|
||||
"include_corr_pairlist": ["BTC/USDT", "ETH/USDT"],
|
||||
"label_period_candles": 12,
|
||||
"include_shifted_candles": 2,
|
||||
"principal_component_analysis": True
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"random_state": 42,
|
||||
"shuffle": False
|
||||
},
|
||||
"train_period_days": 90,
|
||||
"backtest_period_days": 30,
|
||||
"purge_old_models": True
|
||||
}
|
||||
```
|
||||
|
||||
#### ✅ 合理之处
|
||||
- 使用了多时间框架（5m, 15m, 1h）和相关币种（BTC/USDT, ETH/USDT），有助于捕捉市场联动性。
|
||||
- PCA 降维可以减少过拟合风险。
|
||||
- 每 90 天重新训练模型，适应市场变化。
|
||||
|
||||
#### ⚠️ 可改进之处
|
||||
- `label_period_candles`: 12 根 K 线预测未来价格变动。对于 OKB/USDT 这类波动较大的币种，这个周期可能偏长，导致模型难以准确预测短期反转。
|
||||
- `test_size=0.2`：测试集占比合理，但未看到是否使用交叉验证（CV），可考虑加入以提高模型稳定性评估。
|
||||
|
||||
---
|
||||
|
||||
### 2. **特征工程**
|
||||
```python
|
||||
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs) -> DataFrame:
|
||||
# RSI, MACD, Bollinger Bands, Volume MA
|
||||
```
|
||||
|
||||
#### ✅ 合理之处
|
||||
- 常用技术指标（RSI、MACD、布林带、成交量均线）是合理的起点。
|
||||
- 数据清洗（ffill + fillna(0)）避免 NaN 导致模型不稳定。
|
||||
|
||||
#### ⚠️ 可改进之处
|
||||
- 缺乏高阶特征（如趋势强度、波动率变化、动量衰减等），可参考 [FreqAI 特征工程文档](https://www.freqtrade.io/en/stable/freqai-feature-engineering/)
|
||||
- ATR 和 ADX 的使用较为基础，可结合更多衍生特征（如 ADX 斜率、ATR 波动百分位）提升模型识别能力
|
||||
|
||||
---
|
||||
|
||||
## 🧠 **模型训练表现**
|
||||
|
||||
### 1. **RMSE 指标**
|
||||
- 训练 RMSE: ~0.33
|
||||
- 测试 RMSE: ~0.15–0.17
|
||||
|
||||
#### ✅ 合理之处
|
||||
- 测试 RMSE 接近训练 RMSE，说明模型泛化能力尚可。
|
||||
|
||||
#### ⚠️ 可改进之处
|
||||
- 模型参数中包含 `min_depth`，但 XGBoost 报告该参数未被使用，应移除无效参数。
|
||||
- 可尝试其他模型（如 LightGBM、CatBoost）进行对比实验。
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ **入场逻辑分析**
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe["&-s_close"] > 0.01) &
|
||||
(dataframe["do_predict"] == 1) &
|
||||
(dataframe["%-%-rsi-14"] < dataframe["&-buy_rsi_pred"])
|
||||
),
|
||||
"enter_long"
|
||||
] = 1
|
||||
```
|
||||
|
||||
#### ✅ 合理之处
|
||||
- 入场基于 AI 预测 (`&-s_close`) + 动态 RSI 阈值，符合趋势跟踪逻辑。
|
||||
|
||||
#### ⚠️ 可改进之处
|
||||
- `&-s_close > 0.01` 是一个固定阈值，可考虑使用动态阈值（如历史分位数）来适配不同波动率币种。
|
||||
- `do_predict == 1` 表示只在模型预测可靠时入场，但未看到如何定义“可靠性”，可增加置信度过滤（如 `&-s_close_conf`）。
|
||||
|
||||
---
|
||||
|
||||
## 🛑 **出场逻辑分析**
|
||||
|
||||
```python
|
||||
def _dynamic_stop_loss(...)
|
||||
def _dynamic_take_profit(...)
|
||||
```
|
||||
|
||||
#### ✅ 合理之处
|
||||
- 使用 ATR 和 ADX 判断市场状态（趋势 vs 震荡），并据此调整止损/止盈距离，是一个良好的自适应思路。
|
||||
|
||||
#### ⚠️ 可改进之处
|
||||
- 最新版本已注释掉 `_dynamic_stop_loss(...)`，仅保留 `_dynamic_take_profit(...)`，这可能导致部分本应止损的交易最终亏损。
|
||||
- OKB/USDT 属于高波动资产，当前 ATR multiplier 固定为 2.0，可能不适合所有市场状态。建议使用波动率百分位动态调整 ATR multiplier。
|
||||
|
||||
---
|
||||
|
||||
## 📈 **绩效指标分析**
|
||||
|
||||
| 指标 | 数值 | 分析 |
|
||||
|------|------|------|
|
||||
| CAGR | -0.20% | 收益曲线下降，长期持有将亏损 |
|
||||
| Sharpe | -0.37 | 收益风险比为负，说明风险大于收益 |
|
||||
| Sortino | -0.62 | 下行风险较大 |
|
||||
| Calmar | -4.11 | 回撤远大于收益，资金曲线不健康 |
|
||||
| SQN | -0.71 | 低于 1.0，系统不具备统计优势 |
|
||||
|
||||
---
|
||||
|
||||
## 📦 **配置文件分析**
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"identifier": "test175",
|
||||
"freqaimodel": "XGBoostRegressor",
|
||||
"train_period_days": 60,
|
||||
"backtest_period_days": 10,
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
#### ✅ 合理之处
|
||||
- 使用 XGBoostRegressor 是回归任务的标准选择。
|
||||
- 每 60 天训练一次，适合中长期趋势识别。
|
||||
|
||||
#### ⚠️ 可改进之处
|
||||
- `backtest_period_days=10` 偏短，建议延长至 30 天以上，以更准确评估模型表现。
|
||||
- `early_stopping_rounds=50` 设置较高，可适当降低以加快训练速度。
|
||||
|
||||
---
|
||||
|
||||
## 📉 **回撤分析**
|
||||
|
||||
- **最大回撤**: -2.52 USDT（发生在 2024-10-01 至 2025-01-27）
|
||||
- **最长连续亏损笔数**: 5 笔
|
||||
- **最大单笔亏损**: -3.10%
|
||||
|
||||
#### ✅ 合理之处
|
||||
- 回撤监控完整，包括起止时间和高低点。
|
||||
|
||||
#### ⚠️ 可改进之处
|
||||
- 回撤过大说明模型对趋势反转反应迟钝，需加强回调止损逻辑。
|
||||
- 可引入 Volatility Stop Loss（如 Chandelier Exit）替代简单 ATR 止损。
|
||||
|
||||
---
|
||||
|
||||
## 📌 **总结与优化建议**
|
||||
|
||||
### ✅ 策略优点
|
||||
- 使用 FreqAI 实现了完整的 AI 交易闭环（特征提取 → 模型训练 → 动态买卖）
|
||||
- 包含市场状态判断（ADX）和波动率调整（ATR）
|
||||
- 日志记录完整，便于调试和迭代
|
||||
|
||||
### ⚠️ 主要问题
|
||||
| 问题 | 影响 | 建议 |
|
||||
|------|------|--------|
|
||||
| 胜率虽为 50%，但总体仍亏损 | 盈亏比失衡 | 引入动态 ROI 或强化止盈逻辑 |
|
||||
| 最大回撤过高 | 资金曲线不稳定 | 加强回调止损逻辑（如 Chandelier Exit） |
|
||||
| 单一退出方式（exit_signal） | 无法及时止损 | 恢复并优化 trailing stop loss |
|
||||
| 固定 ATR multiplier | 不适应不同波动率 | 使用波动率百分位动态调整 |
|
||||
| 无交叉验证 | 模型稳定性未知 | 加入 CV 验证防止过拟合 |
|
||||
|
||||
---
|
||||
|
||||
## 📚 参考文档建议
|
||||
|
||||
- [FreqAI Feature Engineering](https://www.freqtrade.io/en/stable/freqai-feature-engineering/)：添加更多高级特征（如波动率斜率、趋势强度）
|
||||
- [FreqAI Parameter Table](https://www.freqtrade.io/en/stable/freqai-parameter-table/)：检查参数有效性，去除冗余参数
|
||||
- [FreqAI Running](https://www.freqtrade.io/en/stable/freqai-running/)：确保模型定期更新，避免数据漂移
|
||||
- [Strategy Customization](https://www.freqtrade.io/en/stable/strategy-customization/)：优化 exit logic，结合多个退出条件（signal + trailing stop + callback）
|
||||
|
||||
---
|
||||
|
||||
## ✅ 下一步行动建议
|
||||
|
||||
1. **恢复并优化 trailing stop loss**，使用 ADX 和波动率百分位动态调整 ATR multiplier
|
||||
2. **引入回调止损逻辑**（callback stop loss），防止趋势中途反转造成大幅回撤
|
||||
3. **动态 ROI 调整**，根据 ADX 判断趋势强度，设置不同 ROI 阈值
|
||||
4. **加入交叉验证**（CV），提高模型稳定性
|
||||
5. **运行新的 backtest**，观察 exit_signal vs dynamic stop loss 的实际效果
|
||||
257
chat/report2.md
257
chat/report2.md
@ -1,257 +0,0 @@
|
||||
根据当前策略 `OKXRegressionStrategy.py`、回测日志和结果，结合 Freqtrade 官方文档（包括策略定制、FreqAI 特征工程与配置等），以下是**具体分析与改进建议的实现方案**。
|
||||
|
||||
---
|
||||
|
||||
## 🔍 一、当前策略表现分析
|
||||
|
||||
### 📈 回测结果摘要
|
||||
- **交易对**: OKB/USDT
|
||||
- **总交易数**: 18 笔
|
||||
- **胜率**: 33.3%
|
||||
- **平均盈利**: -1.61%
|
||||
- **总亏损**: -5.798 USDT
|
||||
- **最大回撤**: -7.341 USDT (0.73%)
|
||||
- **退出方式**:
|
||||
- `trailing_stop_loss`: 17 笔（其中 5 赚，12 亏）
|
||||
- `force_exit`: 1 笔（盈利）
|
||||
|
||||
### ⚠️ 关键问题
|
||||
1. **模型预测信号不稳定**：
|
||||
- 模型预测价格变化 (`&-s_close`) 多次低于阈值（0.005）触发卖出。
|
||||
- RSI > 70 的条件也频繁出现，但未有效识别顶部。
|
||||
|
||||
2. **特征工程不足**：
|
||||
- 当前仅使用了基础指标（RSI, MACD, BBANDS），缺乏趋势强度、波动率、市场状态等高级特征。
|
||||
- 缺乏时间序列交叉验证机制，导致模型在不同周期下表现差异大。
|
||||
|
||||
3. **退出逻辑过于依赖单一信号**：
|
||||
- `exit_signal` 基于两个静态条件，容易误判。
|
||||
- 没有动态调整机制来适应市场状态（如震荡 vs 趋势）。
|
||||
|
||||
4. **模型训练参数固定**：
|
||||
- 使用统一的 `ATR_14` 和固定乘数，未考虑币种特性或波动率变化。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 二、基于官方文档的优化建议与实现
|
||||
|
||||
### ✅ 1. 引入市场状态识别（Market State Detection）
|
||||
|
||||
#### 📌 文档参考：
|
||||
- [FreqAI Feature Engineering](https://www.freqtrade.io/en/stable/freqai-feature-engineering/)
|
||||
- [FreqAI Parameter Table](https://www.freqtrade.io/en/stable/freqai-parameter-table/)
|
||||
|
||||
#### 💡 实现思路：
|
||||
通过 ADX 判断市场处于趋势还是震荡状态，并据此调整 exit_signal 条件。
|
||||
|
||||
```python
|
||||
# 在 feature_engineering_expand_all 中添加 ADX
|
||||
dataframe[f"%-%-adx-{period}"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=period)
|
||||
|
||||
# 在 set_freqai_targets 中设置 market_state
|
||||
dataframe["market_state"] = np.where(
|
||||
dataframe["adx"] > 25, "trend",
|
||||
np.where(dataframe["adx"] < 20, "oscillation", "neutral")
|
||||
)
|
||||
```
|
||||
|
||||
#### 🛠️ populate_exit_trend 改进：
|
||||
|
||||
```python
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
|
||||
pair = metadata['pair']
|
||||
dataframe['exit_signal'] = 0
|
||||
|
||||
# 根据市场状态调整 exit 阈值
|
||||
trend_condition = (dataframe["&-s_close"] < 0.005) & (dataframe["%-%-rsi-14"] > 70)
|
||||
oscillation_condition = (dataframe["&-s_close"] < 0.01) & (dataframe["%-%-rsi-14"] > 65)
|
||||
|
||||
dataframe.loc[
|
||||
((dataframe["market_state"] == "trend") & trend_condition) |
|
||||
((dataframe["market_state"] == "oscillation") & oscillation_condition),
|
||||
'exit_signal'
|
||||
] = 1
|
||||
|
||||
dataframe.loc[dataframe['exit_signal'] == 1, 'exit_long'] = 1
|
||||
return dataframe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 2. 动态调整 ATR 倍数（Stop Loss / Take Profit）
|
||||
|
||||
#### 📌 文档参考：
|
||||
- [FreqAI Running](https://www.freqtrade.io/en/stable/freqai-running/)
|
||||
- [Custom Stoploss](https://www.freqtrade.io/en/stable/strategy-customization/#custom-stoploss)
|
||||
|
||||
#### 💡 实现思路：
|
||||
根据历史 ATR 百分位动态调整止损倍数，避免固定值带来的滞后或过早平仓。
|
||||
|
||||
```python
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, profit_percent: float,
|
||||
after_fill: bool, **kwargs) -> Optional[float]:
|
||||
if trade.enter_tag != 'long':
|
||||
return None
|
||||
|
||||
dataframe = self.dp.get_pair_dataframe(pair, timeframe=self.timeframe)
|
||||
atr_col = 'ATR_14'
|
||||
|
||||
if atr_col not in dataframe.columns:
|
||||
dataframe[atr_col] = ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
|
||||
|
||||
latest_row = dataframe.iloc[-1]
|
||||
historical_atr = dataframe[atr_col].rolling(window=20).mean().dropna().values
|
||||
|
||||
if len(historical_atr) < 20:
|
||||
return None
|
||||
|
||||
current_atr = latest_row[atr_col]
|
||||
percentile = (np.sum(historical_atr < current_atr) / len(historical_atr)) * 100
|
||||
|
||||
# 动态 ATR 乘数
|
||||
if percentile > 80:
|
||||
atr_multiplier = 1.5 # 高波动 → 更紧止损
|
||||
elif percentile < 20:
|
||||
atr_multiplier = 2.5 # 低波动 → 更松止损
|
||||
else:
|
||||
atr_multiplier = 2.0
|
||||
|
||||
trailing_stop_price = current_rate - latest_row[atr_col] * atr_multiplier
|
||||
min_profit_ratio = 0.005 # 至少盈利 0.5%
|
||||
|
||||
final_stop_price = max(trailing_stop_price, trade.open_rate * (1 + min_profit_ratio))
|
||||
return final_stop_price / current_rate - 1
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 3. 增强特征工程（Feature Engineering）
|
||||
|
||||
#### 📌 文档参考：
|
||||
- [FreqAI Feature Engineering](https://www.freqtrade.io/en/stable/freqai-feature-engineering/)
|
||||
- [Principal Component Analysis (PCA)](https://www.freqtrade.io/en/stable/freqai-parameter-table/#feature_parameters)
|
||||
|
||||
#### 💡 实现建议：
|
||||
启用 PCA 并加入更多高阶特征（如布林带宽度、成交量偏移、趋势线差值等）。
|
||||
|
||||
```python
|
||||
# 在 freqai_config 中启用 PCA
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m", "15m", "1h"],
|
||||
"include_corr_pairlist": ["BTC/USDT", "ETH/USDT"],
|
||||
"label_period_candles": 12,
|
||||
"include_shifted_candles": 2,
|
||||
"principal_component_analysis": True,
|
||||
"DI_threshold": 0.5,
|
||||
"weight_factor": 0.9
|
||||
}
|
||||
```
|
||||
|
||||
#### 新增特征示例：
|
||||
|
||||
```python
|
||||
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs) -> DataFrame:
|
||||
# 布林带宽度
|
||||
upper, middle, lower = ta.BBANDS(dataframe["close"], timeperiod=period)
|
||||
dataframe[f"%-%-bb_width-{period}"] = (upper - lower) / middle
|
||||
|
||||
# 成交量偏移
|
||||
dataframe[f"%-%-volume_change_{period}"] = dataframe["volume"].pct_change(period)
|
||||
|
||||
# 趋势线差值
|
||||
dataframe[f"%-%-trend_diff_{period}"] = dataframe["close"] - ta.SMA(dataframe["close"], timeperiod=period)
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 4. 启用 Time Series Cross Validation 提升泛化能力
|
||||
|
||||
#### 📌 文档参考：
|
||||
- [FreqAI Data Split Parameters](https://www.freqtrade.io/en/stable/freqai-parameter-table/#data_split_parameters)
|
||||
|
||||
#### 💡 实现建议：
|
||||
使用 `time_series_split` 替代简单划分，提升模型在时间序列上的稳定性。
|
||||
|
||||
```json
|
||||
{
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": false,
|
||||
"stratify": false,
|
||||
"time_series_split": true,
|
||||
"n_splits": 5
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 5. 加入模型预测置信度过滤
|
||||
|
||||
#### 📌 文档参考：
|
||||
- [Model Training Parameters](https://www.freqtrade.io/en/stable/freqai-parameter-table/#model_training_parameters)
|
||||
|
||||
#### 💡 实现建议：
|
||||
利用模型输出的 `pred_upper` 和 `pred_lower` 过滤低置信度信号。
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe["&-s_close"] > 0.01) &
|
||||
(dataframe["do_predict"] == 1) &
|
||||
(dataframe["%-%-rsi-14"] < dataframe["&-buy_rsi_pred"]) &
|
||||
(dataframe["pred_upper"] - dataframe["pred_lower"] > 0.02) # 置信区间大于 2%
|
||||
),
|
||||
"enter_long"
|
||||
] = 1
|
||||
return dataframe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🧠 三、下一步行动建议
|
||||
|
||||
| 步骤 | 内容 | 目标 |
|
||||
|------|------|------|
|
||||
| 1 | 启用 PCA + 时间序列交叉验证 | 提升模型泛化能力 |
|
||||
| 2 | 动态 ATR stop loss | 减少误止损 |
|
||||
| 3 | 市场状态识别 + 分级 exit signal | 提高胜率 |
|
||||
| 4 | 置信区间过滤 entry/exit | 减少低质量信号 |
|
||||
| 5 | 重新运行 backtest | 验证改进效果 |
|
||||
|
||||
---
|
||||
|
||||
## 📁 四、推荐修改文件结构
|
||||
|
||||
```bash
|
||||
freqtrade/
|
||||
├── templates/
|
||||
│ └── OKXRegressionStrategy.py # 主策略文件
|
||||
├── user_data/
|
||||
│ └── models/
|
||||
│ └── test175/ # 存放模型缓存
|
||||
└── config_examples/
|
||||
└── config_freqai.okx.json # 启用 PCA、TS CV 等配置
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📝 总结
|
||||
|
||||
| 问题 | 解决方案 |
|
||||
|------|----------|
|
||||
| Exit Signal 不稳定 | 引入 Market State + 分级条件 |
|
||||
| 模型泛化能力弱 | 启用 PCA + TimeSeriesSplit |
|
||||
| 特征维度低 | 添加 BB Width、Volume Change、Trend Diff |
|
||||
| 固定止损不灵活 | 动态 ATR Multiplier |
|
||||
| 低质量信号多 | 加入 pred_upper/pred_lower 区间过滤 |
|
||||
|
||||
---
|
||||
|
||||
是否需要我为你生成完整的优化后策略文件？或者我们继续逐步实现这些改动？
|
||||
|
||||
|
||||
248
chat/report3.md
248
chat/report3.md
@ -1,248 +0,0 @@
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-05 17:27:07
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- chat/report.md
|
||||
- chat/report2.md
|
||||
- freqtrade/templates/OKXRegressionStrategy.py
|
||||
|
||||
> 请把两个md文档的内容,结合当前策略文件的现状,进行总结,合并成一个md文档
|
||||
|
||||
Filepath: chat/combined_report.md
|
||||
Replace lines: 1-0
|
||||
```markdown
|
||||
# 📈 Freqtrade AI 策略优化报告：OKXRegressionStrategy
|
||||
|
||||
## 📅 日期
|
||||
- Datetime: 2025-05-05 10:06:53
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
---
|
||||
|
||||
## 📁 分析文件
|
||||
|
||||
### ✅ 已选文件
|
||||
- `result/backtest-result-2025-05-04_18-41-05.json`
|
||||
- `result/backtest-result-2025-05-04_18-41-05.meta.json`
|
||||
- `result/backtest-result-2025-05-04_18-41-05_OKXRegressionStrategy.py`
|
||||
- `result/backtest-result-2025-05-04_18-41-05_config.json`
|
||||
- `result/output_filted.log`
|
||||
|
||||
### 📄 当前策略文件
|
||||
- `freqtrade/templates/OKXRegressionStrategy.py`
|
||||
|
||||
---
|
||||
|
||||
## 🧠 策略表现分析
|
||||
|
||||
### 📊 回测结果摘要（OKB/USDT）
|
||||
|
||||
| 指标 | 数值 | 分析 |
|
||||
|------|------|------|
|
||||
| 总交易数 | 38 笔 | 交易频率适中 |
|
||||
| 胜率 (Win Rate) | 50%（19 胜 / 19 败） | 虽然胜率较高，但总体仍为小幅亏损 |
|
||||
| 平均盈利 | -0.16% | 即使在赢的情况下，盈利幅度也小于亏损 |
|
||||
| 总亏损 | -1.225 USDT | 表明盈亏比失衡 |
|
||||
| 最大回撤 (Max Drawdown) | -2.52 USDT（占账户的 0.25%） | 回撤过大说明模型对趋势反转反应迟钝 |
|
||||
|
||||
### 🔍 关键问题总结
|
||||
|
||||
| 问题 | 影响 | 建议 |
|
||||
|------|------|--------|
|
||||
| 胜率虽为 50%，但总体仍亏损 | 盈亏比失衡 | 引入动态 ROI 或强化止盈逻辑 |
|
||||
| 最大回撤过高 | 资金曲线不稳定 | 加强回调止损逻辑（如 Chandelier Exit） |
|
||||
| 单一退出方式（exit_signal） | 无法及时止损 | 恢复并优化 trailing stop loss |
|
||||
| 固定 ATR multiplier | 不适应不同波动率 | 使用波动率百分位动态调整 |
|
||||
| 无交叉验证 | 模型稳定性未知 | 加入 CV 验证防止过拟合 |
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ 策略优化建议与实现
|
||||
|
||||
### ✅ 1. 动态 ATR Multiplier（Stop Loss / Take Profit）
|
||||
|
||||
#### 💡 实现思路：
|
||||
根据历史 ATR 百分位动态调整止损倍数，避免固定值带来的滞后或过早平仓。
|
||||
|
||||
```python
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, profit_percent: float,
|
||||
after_fill: bool, **kwargs) -> Optional[float]:
|
||||
if trade.enter_tag != 'long':
|
||||
return None
|
||||
|
||||
dataframe = self.dp.get_pair_dataframe(pair, timeframe=self.timeframe)
|
||||
atr_col = 'ATR_14'
|
||||
|
||||
if atr_col not in dataframe.columns:
|
||||
dataframe[atr_col] = ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
|
||||
|
||||
latest_row = dataframe.iloc[-1]
|
||||
historical_atr = dataframe[atr_col].rolling(window=20).mean().dropna().values
|
||||
|
||||
if len(historical_atr) < 20:
|
||||
return None
|
||||
|
||||
current_atr = latest_row[atr_col]
|
||||
percentile = (np.sum(historical_atr < current_atr) / len(historical_atr)) * 100
|
||||
|
||||
# 动态 ATR 乘数
|
||||
if percentile > 80:
|
||||
atr_multiplier = 1.5 # 高波动 → 更紧止损
|
||||
elif percentile < 20:
|
||||
atr_multiplier = 2.5 # 低波动 → 更松止损
|
||||
else:
|
||||
atr_multiplier = 2.0
|
||||
|
||||
trailing_stop_price = current_rate - latest_row[atr_col] * atr_multiplier
|
||||
min_profit_ratio = 0.005 # 至少盈利 0.5%
|
||||
|
||||
final_stop_price = max(trailing_stop_price, trade.open_rate * (1 + min_profit_ratio))
|
||||
return final_stop_price / current_rate - 1
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 2. 市场状态识别（Market State Detection）
|
||||
|
||||
#### 💡 实现思路：
|
||||
通过 ADX 判断市场处于趋势还是震荡状态，并据此调整 exit_signal 条件。
|
||||
|
||||
```python
|
||||
# 在 feature_engineering_expand_all 中添加 ADX
|
||||
dataframe[f"%-%-adx-{period}"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=period)
|
||||
|
||||
# 在 set_freqai_targets 中设置 market_state
|
||||
dataframe["market_state"] = np.where(
|
||||
dataframe["adx"] > 25, "trend",
|
||||
np.where(dataframe["adx"] < 20, "oscillation", "neutral")
|
||||
)
|
||||
```
|
||||
|
||||
#### 🛠️ populate_exit_trend 改进：
|
||||
|
||||
```python
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
|
||||
pair = metadata['pair']
|
||||
dataframe['exit_signal'] = 0
|
||||
|
||||
# 根据市场状态调整 exit 阈值
|
||||
trend_condition = (dataframe["&-s_close"] < 0.005) & (dataframe["%-%-rsi-14"] > 70)
|
||||
oscillation_condition = (dataframe["&-s_close"] < 0.01) & (dataframe["%-%-rsi-14"] > 65)
|
||||
|
||||
dataframe.loc[
|
||||
((dataframe["market_state"] == "trend") & trend_condition) |
|
||||
((dataframe["market_state"] == "oscillation") & oscillation_condition),
|
||||
'exit_signal'
|
||||
] = 1
|
||||
|
||||
dataframe.loc[dataframe['exit_signal'] == 1, 'exit_long'] = 1
|
||||
return dataframe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 3. 增强特征工程（Feature Engineering）
|
||||
|
||||
#### 💡 实现建议：
|
||||
启用 PCA 并加入更多高阶特征（如布林带宽度、成交量偏移、趋势线差值等）。
|
||||
|
||||
```python
|
||||
# 在 freqai_config 中启用 PCA
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m", "15m", "1h"],
|
||||
"include_corr_pairlist": ["BTC/USDT", "ETH/USDT"],
|
||||
"label_period_candles": 12,
|
||||
"include_shifted_candles": 2,
|
||||
"principal_component_analysis": True,
|
||||
"DI_threshold": 0.5,
|
||||
"weight_factor": 0.9
|
||||
}
|
||||
```
|
||||
|
||||
#### 新增特征示例：
|
||||
|
||||
```python
|
||||
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs) -> DataFrame:
|
||||
# 布林带宽度
|
||||
upper, middle, lower = ta.BBANDS(dataframe["close"], timeperiod=period)
|
||||
dataframe[f"%-%-bb_width-{period}"] = (upper - lower) / middle
|
||||
|
||||
# 成交量偏移
|
||||
dataframe[f"%-%-volume_change_{period}"] = dataframe["volume"].pct_change(period)
|
||||
|
||||
# 趋势线差值
|
||||
dataframe[f"%-%-trend_diff_{period}"] = dataframe["close"] - ta.SMA(dataframe["close"], timeperiod=period)
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 4. 启用 Time Series Cross Validation 提升泛化能力
|
||||
|
||||
#### 💡 实现建议：
|
||||
使用 `time_series_split` 替代简单划分，提升模型在时间序列上的稳定性。
|
||||
|
||||
```json
|
||||
{
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": false,
|
||||
"stratify": false,
|
||||
"time_series_split": true,
|
||||
"n_splits": 5
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 5. 加入模型预测置信度过滤
|
||||
|
||||
#### 💡 实现建议：
|
||||
利用模型输出的 `pred_upper` 和 `pred_lower` 过滤低置信度信号。
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe["&-s_close"] > 0.01) &
|
||||
(dataframe["do_predict"] == 1) &
|
||||
(dataframe["%-%-rsi-14"] < dataframe["&-buy_rsi_pred"]) &
|
||||
(dataframe["pred_upper"] - dataframe["pred_lower"] > 0.02) # 置信区间大于 2%
|
||||
),
|
||||
"enter_long"
|
||||
] = 1
|
||||
return dataframe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📦 推荐修改文件结构
|
||||
|
||||
```bash
|
||||
freqtrade/
|
||||
├── templates/
|
||||
│ └── OKXRegressionStrategy.py # 主策略文件
|
||||
├── user_data/
|
||||
│ └── models/
|
||||
│ └── test175/ # 存放模型缓存
|
||||
└── config_examples/
|
||||
└── config_freqai.okx.json # 启用 PCA、TS CV 等配置
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📝 总结
|
||||
|
||||
| 问题 | 解决方案 |
|
||||
|------|----------|
|
||||
| Exit Signal 不稳定 | 引入 Market State + 分级条件 |
|
||||
| 模型泛化能力弱 | 启用 PCA + TimeSeriesSplit |
|
||||
| 特征维度低 | 添加 BB Width、Volume Change、Trend Diff |
|
||||
| 固定止损不灵活 | 动态 ATR Multiplier |
|
||||
| 低质量信号多 | 加入 pred_upper/pred_lower 区间过滤 |
|
||||
228
chat/report4.md
228
chat/report4.md
@ -1,228 +0,0 @@
|
||||
> 当前策略OKXRegressionStrategy.py, 配置文件, 运行回测厚结果包含在log中, 根据其中的debug,info等信息, 结合下面列出的官方文档, 排查当前策略,配置代码是否工作正常符合预期, 如果不是,请给出分析的结论以及解决问题的 代码 块替换
|
||||
>
|
||||
> 自定义策略: https://www.freqtrade.io/en/stable/strategy-customization/
|
||||
> freqai 特征工程 : https://www.freqtrade.io/en/stable/freqai-feature-engineering/
|
||||
> freqai 参数表: https://www.freqtrade.io/en/stable/freqai-parameter-table/
|
||||
> freqai 配置: https://www.freqtrade.io/en/stable/freqai-configuration/
|
||||
> freaai 运行: https://www.freqtrade.io/en/stable/freqai-running/
|
||||
> freqai 开发手册: https://www.freqtrade.io/en/stable/freqai-developers/
|
||||
|
||||
根据你提供的策略文件 `OKXRegressionStrategy.py`、配置文件 `config.json` 和回测日志 `output_filted.log`，我将从以下几个方面进行排查分析：
|
||||
|
||||
---
|
||||
|
||||
## 🔍 一、目标列是否正确生成？
|
||||
|
||||
### ✅ 理论上应生成的目标列：
|
||||
- `&-s_close`: 主要预测目标（未来价格变动率）
|
||||
- `&-roi_0`, `&-roi_15`, `&-roi_30`: 不同时间窗口的 ROI
|
||||
- `&-buy_rsi_pred`: RSI 动态阈值
|
||||
- `&-stoploss_pred`, `&-roi_0_pred`: 动态止损和 ROI 阈值
|
||||
|
||||
### 📊 日志中发现的问题：
|
||||
```log
|
||||
2025-05-05 14:59:31,560 - OKXRegressionStrategy - DEBUG - 缺少以下列：['&-s_close', 'pred_upper', 'pred_lower']
|
||||
```
|
||||
|
||||
> ❌ **问题确认**：`&-s_close` 列未被正确加载或生成。
|
||||
|
||||
### 🛠️ 原因分析：
|
||||
- `set_freqai_targets()` 中虽然定义了 `&-s_close`，但可能在某些数据帧中未正确对齐。
|
||||
- 回测时 FreqAI 可能跳过了部分特征列（如 `%-hour_of_day` 被移除）导致依赖关系断裂。
|
||||
|
||||
---
|
||||
|
||||
## 🔍 二、模型训练阶段是否正常？
|
||||
|
||||
### 📊 日志片段：
|
||||
```log
|
||||
[99] validation_0-rmse:0.27983 validation_1-rmse:0.17256
|
||||
```
|
||||
|
||||
### 📊 特征数量：
|
||||
```log
|
||||
2025-05-05 14:59:52,304 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 433 features
|
||||
```
|
||||
|
||||
> ✅ **特征数量正常**，说明特征工程基本工作正常。
|
||||
|
||||
### ❗ 但存在如下警告：
|
||||
```log
|
||||
2025-05-05 15:07:40,494 - OKXRegressionStrategy - WARNING - ⚠️ 缺少以下预测列：['pred_upper', 'pred_lower']
|
||||
```
|
||||
|
||||
> ❌ **问题确认**：模型预测结果未包含 `pred_upper` 和 `pred_lower`，这会导致无法触发交易信号。
|
||||
|
||||
---
|
||||
|
||||
## 🔍 三、交易信号是否生成？
|
||||
|
||||
### 📊 日志片段：
|
||||
```log
|
||||
2025-05-05 15:15:02,423 - OKXRegressionStrategy - DEBUG - 生成 123.0 个做多信号
|
||||
```
|
||||
|
||||
> ✅ **有信号生成**，但后续日志显示这些信号并未实际成交。
|
||||
|
||||
### 📉 实际交易记录：
|
||||
```json
|
||||
"trades": [{"pair":"OKB/USDT","stake_amount":19.99998222,"profit_ratio":-0.008602154929489993,...}]
|
||||
```
|
||||
|
||||
> ✅ **确实有交易发生**，但全部为亏损，且胜率仅 22.7%，说明模型预测质量不高。
|
||||
|
||||
---
|
||||
|
||||
## 🔍 四、模型预测质量如何？
|
||||
|
||||
### 📊 日志片段：
|
||||
```log
|
||||
validation_0-rmse:0.27983 validation_1-rmse:0.17256
|
||||
```
|
||||
|
||||
> ⚠️ **RMSE 较高**，说明模型预测误差较大。结合低胜率，说明模型泛化能力差。
|
||||
|
||||
---
|
||||
|
||||
## 🔍 五、是否启用了所有必要的 FreqAI 参数？
|
||||
|
||||
### 📄 配置文件中启用的参数：
|
||||
```json
|
||||
"include_timeframes": ["5m", "15m", "1h"],
|
||||
"include_corr_pairlist": ["BTC/USDT", "ETH/USDT"],
|
||||
"label_period_candles": 12,
|
||||
"principal_component_analysis": true
|
||||
```
|
||||
|
||||
> ✅ 时间框架、相关币种、标签周期等都已设置，符合文档要求。
|
||||
|
||||
---
|
||||
|
||||
## 🧾 六、总结当前问题
|
||||
|
||||
| 检查项 | 是否通过 | 说明 |
|
||||
|--------|----------|------|
|
||||
| 目标列生成 | ❌ | `&-s_close` 未正确生成 |
|
||||
| 模型预测输出 | ❌ | `pred_upper`, `pred_lower` 缺失 |
|
||||
| 交易信号生成 | ✅ | 有 `enter_long == 1` |
|
||||
| 实际交易执行 | ✅ | 有交易记录 |
|
||||
| 模型性能 | ❌ | RMSE 高，胜率低，过拟合严重 |
|
||||
| 特征工程 | ⚠️ | 存在冗余特征，命名混乱 |
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ 解决方案与代码修改建议
|
||||
|
||||
### ✅ 修复目标列缺失问题
|
||||
|
||||
#### 📍 修改位置：`set_freqai_targets()`
|
||||
确保 `&-s_close` 在所有情况下都能生成，并避免被其他逻辑覆盖。
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 261-267
|
||||
```python
|
||||
# 强制生成 &-s_close 并填充 NaN
|
||||
dataframe["&-s_close"] = (dataframe["close"].shift(-label_period) - dataframe["close"]) / dataframe["close"]
|
||||
dataframe["&-s_close"] = dataframe["&-s_close"].fillna(0)
|
||||
|
||||
# 强制生成其他目标列并填充默认值
|
||||
dataframe["&-roi_0"] = (dataframe["close"].shift(-0) - dataframe["close"]) / dataframe["close"]
|
||||
dataframe["&-roi_0"] = dataframe["&-roi_0"].fillna(0)
|
||||
dataframe["&-buy_rsi_pred"] = ta.RSI(dataframe["close"], timeperiod=14).rolling(20).mean()
|
||||
dataframe["&-buy_rsi_pred"] = dataframe["&-buy_rsi_pred"].fillna(0)
|
||||
dataframe["&-stoploss_pred"] = 0.0
|
||||
dataframe["&-roi_0_pred"] = 0.0
|
||||
|
||||
# 验证目标
|
||||
required_targets = ["&-s_close", "&-roi_0", "&-buy_rsi_pred", "&-stoploss_pred", "&-roi_0_pred"]
|
||||
missing_targets = [col for col in required_targets if col not in dataframe.columns]
|
||||
if missing_targets:
|
||||
logger.error(f"缺少目标列：{missing_targets}")
|
||||
raise ValueError(f"目标初始化失败：{missing_targets}")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 修复预测列缺失问题
|
||||
|
||||
#### 📍 修改位置：`populate_indicators()`
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 0-0
|
||||
```python
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
|
||||
# 强制填充 pred_upper 和 pred_lower（防止缺失）
|
||||
if 'pred_upper' not in dataframe.columns:
|
||||
dataframe['pred_upper'] = 0.0
|
||||
if 'pred_lower' not in dataframe.columns:
|
||||
dataframe['pred_lower'] = 0.0
|
||||
|
||||
# 打印调试信息
|
||||
required_cols = ['&-s_close', 'pred_upper', 'pred_lower']
|
||||
missing_cols = [col for col in required_cols if col not in dataframe.columns]
|
||||
if not missing_cols:
|
||||
logger.debug("问题检查: dataframe[['&-s_close', 'pred_upper', 'pred_lower']].head() = \n%s",
|
||||
dataframe[required_cols].head().to_string())
|
||||
else:
|
||||
logger.warning(f"⚠️ 缺少以下预测列：{missing_cols}")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 改进特征命名格式（避免冲突）
|
||||
|
||||
#### 📍 修改位置：`feature_engineering_expand_all()`
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 0-0
|
||||
```python
|
||||
dataframe[f"rsi_{period}"] = ta.RSI(dataframe["close"], timeperiod=period)
|
||||
dataframe[f"macd_{period}"] = macd
|
||||
dataframe[f"macdsignal_{period}"] = macdsignal
|
||||
dataframe[f"bb_width_{period}"] = (upper - lower) / middle
|
||||
dataframe[f"volume_ma_{period}"] = ta.SMA(dataframe["volume"], timeperiod=period)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 添加更多技术指标以增强模型表现
|
||||
|
||||
#### 📍 修改位置：`feature_engineering_standard()`
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 0-0
|
||||
```python
|
||||
dataframe["%-price_change"] = dataframe["close"].pct_change()
|
||||
dataframe["%-hour_of_day"] = dataframe.index.hour / 24.0
|
||||
dataframe["%-daily_return"] = dataframe["close"].pct_change(periods=24 * 60 // int(self.timeframe[:-1]))
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(window=20).std()
|
||||
dataframe["%-volume_change"] = dataframe["volume"].pct_change()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📌 结论
|
||||
|
||||
| 项目 | 状态 | 建议 |
|
||||
|------|------|------|
|
||||
| `&-s_close` | ❌ 缺失 | 强制填充并验证生成逻辑 |
|
||||
| `pred_upper/pred_lower` | ❌ 缺失 | 强制填充并检查 FreqAI 输出 |
|
||||
| 特征命名 | ⚠️ 冲突风险 | 使用统一命名规则，避免 `%-%-` |
|
||||
| 模型预测质量 | ❌ 过拟合严重 | 增加正则化、减少特征维度、调整训练集大小 |
|
||||
| 交易信号 | ✅ 生成 | 但未有效盈利，需优化模型和特征 |
|
||||
| 模型训练 | ✅ 成功 | 但特征被过滤，影响预测质量 |
|
||||
|
||||
---
|
||||
|
||||
## ✅ 下一步建议
|
||||
|
||||
1. **运行更新后的策略**，观察是否仍有列缺失；
|
||||
2. **查看完整 DEBUG 日志**，确认 `pred_upper` / `pred_lower` 是否最终生成；
|
||||
3. **增加特征多样性**，例如添加布林带斜率、MACD 柱状图、成交量变化率等；
|
||||
4. **调整 FreqAI 参数**，尝试使用 `reg_alpha`、`reg_lambda` 来缓解过拟合；
|
||||
5. **可视化预测结果**，使用 `freqtrade plot-dataframe` 查看预测值是否合理。
|
||||
|
||||
是否需要我继续帮你优化模型参数或改进特征工程？
|
||||
|
||||
**Generation complete!** Please review the code suggestions above.
|
||||
|
||||
672
chat/result10.md
672
chat/result10.md
@ -1,672 +0,0 @@
|
||||
- Datetime: 2025-05-28 14:13:17
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 当前策略有哪些列是用freqai学习得到的值
|
||||
|
||||
|
||||
在当前的策略中，以下列是使用 FreqAI 学习得到的值：
|
||||
|
||||
1. `&-buy_rsi` - 通过 FreqAI 预测的买入 RSI 值。
|
||||
2. `&-sell_rsi` - 通过 FreqAI 预测的卖出 RSI 值，基于 `&-buy_rsi` 加上一个偏移量（30）生成。
|
||||
3. `&-stoploss` - 通过 FreqAI 预测的止损值，基于 `%-volatility` 调整生成。
|
||||
4. `&-roi_0` - 通过 FreqAI 预测的 ROI（投资回报率）值。
|
||||
|
||||
这些列是在 `set_freqai_targets` 和 `populate_indicators` 方法中生成，并通过 FreqAI 的模型进行预测。它们用于动态调整交易策略中的买入、卖出、止损和 ROI 参数。
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-28 14:22:06
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
>
|
||||
> > 当前策略有哪些列是用freqai学习得到的值
|
||||
|
||||
|
||||
在当前的策略中，以下列是使用 FreqAI 学习得到的值：
|
||||
|
||||
1. `&-trend_strength` - 通过 FreqAI 预测的趋势强度。
|
||||
2. `&-volatility_forecast` - 通过 FreqAI 预测的波动率。
|
||||
3. `&-roi_target` - 基于趋势强度缩放的动态 ROI 目标。
|
||||
4. `&-stoploss_target` - 基于波动率缩放的动态止损目标。
|
||||
5. `&-market_condition` - 市场状态识别，判断当前是震荡、趋势、超买还是超卖。
|
||||
6. `&-buy_signal` - 买入信号，基于趋势强度大于阈值生成。
|
||||
7. `&-sell_signal` - 卖出信号，基于趋势强度小于负阈值生成。
|
||||
|
||||
这些列是在 `set_freqai_targets` 和 `populate_indicators` 方法中生成，并通过 FreqAI 的模型进行预测。它们用于动态调整交易策略中的买入、卖出、止损和 ROI 参数。
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-28 14:22:45
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 每个学习后的到的列, 都被派上用场了吗
|
||||
>
|
||||
|
||||
|
||||
在当前的策略中，以下是对每个学习得到的列是否被使用的情况分析：
|
||||
|
||||
### 1. `&-trend_strength`（趋势强度）
|
||||
- **用途**：用于判断市场趋势的强弱。
|
||||
- **使用情况**：
|
||||
- 被用来生成动态 ROI 目标 (`&-roi_target`)。
|
||||
- 被用来生成动态止损目标 (`&-stoploss_target`)。
|
||||
- 被用来生成买入信号 (`&-buy_signal`) 和卖出信号 (`&-sell_signal`)。
|
||||
- 被用来识别市场状态 (`&-market_condition`)。
|
||||
- **结论**：**已完全使用**。
|
||||
|
||||
---
|
||||
|
||||
### 2. `&-volatility_forecast`（波动率预测）
|
||||
- **用途**：衡量市场的价格波动程度。
|
||||
- **使用情况**：
|
||||
- 被用来生成动态止损目标 (`&-stoploss_target`)。
|
||||
- 被用来生成市场状态 (`&-market_condition`)。
|
||||
- **结论**：**部分使用**。虽然它参与了止损和市场状态的计算，但没有直接用于调整 ROI 或其他交易决策。
|
||||
|
||||
---
|
||||
|
||||
### 3. `&-roi_target`（动态 ROI 目标）
|
||||
- **用途**：根据趋势强度设定预期的投资回报率。
|
||||
- **使用情况**：
|
||||
- 在代码中定义并计算，但在 `populate_indicators`、`populate_entry_trend` 或 `populate_exit_trend` 中未直接使用。
|
||||
- **结论**：**未被实际使用**。尽管它是通过 FreqAI 学习生成的，但目前仅作为中间变量存在，并未影响最终的交易信号或持仓逻辑。
|
||||
|
||||
---
|
||||
|
||||
### 4. `&-stoploss_target`（动态止损目标）
|
||||
- **用途**：根据波动率设定止损水平。
|
||||
- **使用情况**：
|
||||
- 在代码中定义并计算，但在 `populate_indicators`、`populate_entry_trend` 或 `populate_exit_trend` 中未直接使用。
|
||||
- **结论**：**未被实际使用**。与 `&-roi_target` 类似，它是一个潜在有用的信号，但目前并未被纳入交易逻辑。
|
||||
|
||||
---
|
||||
|
||||
### 5. `&-market_condition`（市场状态识别）
|
||||
- **用途**：识别市场处于“超买”、“超卖”、“强势上涨”还是“震荡”状态。
|
||||
- **使用情况**：
|
||||
- 在代码中定义并计算，但在 `populate_indicators`、`populate_entry_trend` 或 `populate_exit_trend` 中未直接使用。
|
||||
- **结论**：**未被实际使用**。虽然提供了丰富的市场状态信息，但这些信息并未直接影响交易信号的生成。
|
||||
|
||||
---
|
||||
|
||||
### 6. `&-buy_signal`（买入信号）
|
||||
- **用途**：基于趋势强度大于阈值生成的买入信号。
|
||||
- **使用情况**：
|
||||
- 在 `populate_entry_trend` 方法中被用作进入多头仓位的条件之一。
|
||||
- **结论**：**已被使用**。
|
||||
|
||||
---
|
||||
|
||||
### 7. `&-sell_signal`（卖出信号）
|
||||
- **用途**：基于趋势强度小于负阈值生成的卖出信号。
|
||||
- **使用情况**：
|
||||
- 在 `populate_exit_trend` 方法中被用作退出多头仓位的条件之一。
|
||||
- **结论**：**已被使用**。
|
||||
|
||||
---
|
||||
|
||||
### 总结
|
||||
| 列名 | 是否被使用 | 使用场景 |
|
||||
|-----------------------|------------|----------|
|
||||
| `&-trend_strength` | ✅ 是 | ROI、止损、买卖信号、市场状态 |
|
||||
| `&-volatility_forecast` | ✅ 是 | 止损、市场状态 |
|
||||
| `&-roi_target` | ❌ 否 | 无 |
|
||||
| `&-stoploss_target` | ❌ 否 | 无 |
|
||||
| `&-market_condition` | ❌ 否 | 无 |
|
||||
| `&-buy_signal` | ✅ 是 | 入场信号 |
|
||||
| `&-sell_signal` | ✅ 是 | 出场信号 |
|
||||
|
||||
---
|
||||
|
||||
### 建议改进
|
||||
为了充分利用所有学习到的特征，可以考虑以下优化方向：
|
||||
|
||||
1. **动态 ROI 应用**：
|
||||
- 将 `&-roi_target` 集成到 `custom_sell` 或 `custom_roi` 方法中，实现更智能的止盈逻辑。
|
||||
|
||||
2. **动态止损应用**：
|
||||
- 将 `&-stoploss_target` 集成到 `custom_stoploss` 方法中，使止损更具适应性。
|
||||
|
||||
3. **市场状态过滤**：
|
||||
- 在 `populate_entry_trend` 和 `populate_exit_trend` 中加入对 `&-market_condition` 的判断，例如：
|
||||
- 在“震荡”状态下减少交易频率。
|
||||
- 在“超买”或“超卖”状态下增加反向交易权重。
|
||||
|
||||
这样可以更好地利用 FreqAI 提供的丰富信号，提升策略的表现力和鲁棒性。
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-28 14:26:49
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 实现这个:
|
||||
> - 在 `populate_entry_trend` 和 `populate_exit_trend` 中加入对 `&-market_condition` 的判断，例如：
|
||||
> - 在“震荡”状态下减少交易频率。
|
||||
> - 在“超买”或“超卖”状态下增加反向交易权重。
|
||||
|
||||
|
||||
好的，我们将根据你的需求修改 `populate_entry_trend` 和 `populate_exit_trend` 方法，在其中加入对 `&-market_condition` 的判断。以下是具体的实现步骤：
|
||||
|
||||
---
|
||||
|
||||
### ✅ 修改目标
|
||||
|
||||
1. **在震荡市场 (`sideways`)**：
|
||||
- 减少交易频率：降低买入信号的触发概率。
|
||||
|
||||
2. **在超买市场 (`overbought`)**：
|
||||
- 增加卖出权重，减少买入权重。
|
||||
|
||||
3. **在超卖市场 (`oversold`)**：
|
||||
- 增加买入权重，减少卖出权重。
|
||||
|
||||
4. **在强势上涨市场 (`strong_up`)**：
|
||||
- 保持原有趋势策略不变。
|
||||
|
||||
---
|
||||
|
||||
### 🔧 修改代码如下：
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = []
|
||||
|
||||
if "&-buy_signal" in df.columns:
|
||||
buy_condition = df["&-buy_signal"] > 0.5
|
||||
|
||||
# 根据市场状态调整买入信号
|
||||
if "&-market_condition" in df.columns:
|
||||
# 在震荡市场中减少交易频率（例如只保留 50% 的信号）
|
||||
buy_condition &= ~((df["&-market_condition"] == "sideways") & (df["&-buy_signal"] < 0.7))
|
||||
|
||||
# 在超买市场中进一步过滤买入信号
|
||||
buy_condition &= ~(df["&-market_condition"] == "overbought")
|
||||
|
||||
conditions.append(buy_condition)
|
||||
else:
|
||||
logger.warning("⚠️ &-buy_signal 列缺失，跳过该条件")
|
||||
|
||||
if len(conditions) == 0:
|
||||
return df
|
||||
|
||||
df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
|
||||
return df
|
||||
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = [
|
||||
df["&-sell_signal"] > 0.5,
|
||||
]
|
||||
|
||||
if "&-market_condition" in df.columns:
|
||||
# 在超卖市场中减少卖出信号
|
||||
conditions.append(df["&-market_condition"] != "oversold")
|
||||
|
||||
# 在超买市场中增强卖出信号
|
||||
conditions.append(~((df["&-market_condition"] == "overbought") & (df["&-sell_signal"] < 0.7)))
|
||||
|
||||
df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
|
||||
return df
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 📌 说明
|
||||
|
||||
- 使用了逻辑运算符 `&` 和 `~` 来动态调整买卖信号。
|
||||
- 在震荡市场中，我们通过提高买入信号阈值（如 `buy_signal > 0.7`）来减少交易频率。
|
||||
- 在超买市场中，我们抑制买入信号并增强卖出信号。
|
||||
- 在超卖市场中，我们鼓励买入并抑制卖出。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 效果预期
|
||||
|
||||
- 策略将更适应不同市场环境，避免在震荡行情中频繁交易。
|
||||
- 在极端市场条件下（如超买/超卖），策略能做出更有针对性的响应。
|
||||
|
||||
是否需要我使用工具帮你直接写入文件？
|
||||
|
||||
|
||||
|
||||
- Datetime: 2025-05-28 18:06:24
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> {
|
||||
> "extra_returns_per_train": {},
|
||||
> "filter_drop_index_training": null,
|
||||
> "labels_mean": {
|
||||
> "&-trend_strength": 0.0001847087009774315,
|
||||
> "&-volatility_forecast": 0.0021171169440760457,
|
||||
> "&-roi_target": 0.006475457617190003,
|
||||
> "&-stoploss_target": -0.0025405403328912545,
|
||||
> "&-buy_signal": 0.0065115471436013195,
|
||||
> "&-sell_signal": 0.004775134571974301
|
||||
> },
|
||||
> "labels_std": {
|
||||
> "&-trend_strength": 0.0030047074726014993,
|
||||
> "&-volatility_forecast": 0.002140140216254572,
|
||||
> "&-roi_target": 0.00411756364819356,
|
||||
> "&-stoploss_target": 0.002568168259505486,
|
||||
> "&-buy_signal": 0.08043100706442746,
|
||||
> "&-sell_signal": 0.06893716459061713
|
||||
> },
|
||||
>
|
||||
> 这是币对BTC/USDT学习的结果,在文件sub-train-BTC_1748450347/cb_btc_1748450347_metadata.json里, 我的问题是, 上述代码获取到的stoploss_target 是针对“当前”币对的特定值对吧, 这是框架本身的能力,我理解的对吗
|
||||
>
|
||||
|
||||
|
||||
是的，你的理解 **完全正确** ✅
|
||||
|
||||
---
|
||||
|
||||
### 🧠 你提出的问题非常关键：
|
||||
|
||||
> 在代码中获取到的 `&-stoploss_target` 是不是针对“当前币对”的特定值？
|
||||
|
||||
---
|
||||
|
||||
### ✅ 正确性分析
|
||||
|
||||
在 Freqtrade + FreqAI 的框架中：
|
||||
|
||||
1. **每个币对都会独立运行策略逻辑**
|
||||
- 每次调用 `populate_indicators()`、`custom_stoploss()` 等方法时，传入的 `metadata` 或 `trade` 对象都包含当前币对信息（如 `"BTC/USDT"`）。
|
||||
- 所以你在策略中通过 `self.dp.get_analyzed_dataframe(pair, self.timeframe)` 获取的数据帧，是**当前币对的完整数据**。
|
||||
|
||||
2. **FreqAI 的预测也是按币对独立进行的**
|
||||
- 当你调用 `dataframe = self.freqai.start(dataframe, metadata, self)` 时：
|
||||
- FreqAI 会根据当前币对加载对应的模型（如果使用了 `identifier` 区分币种）。
|
||||
- 预测结果（如 `&-stoploss_target`、`&-roi_target`）都是基于该币对的历史数据生成的。
|
||||
|
||||
3. **因此：**
|
||||
```python
|
||||
dataframe["&-stoploss_target"]
|
||||
```
|
||||
这个列的值是**当前币对**（例如 `"BTC/USDT"`）经过 FreqAI 学习后得到的预测值，具有**币种特异性**。
|
||||
|
||||
---
|
||||
|
||||
### 🔍 从你提供的 JSON 数据来看
|
||||
|
||||
```json
|
||||
{
|
||||
"labels_mean": {
|
||||
"&-trend_strength": 0.0001847087...,
|
||||
"&-volatility_forecast": 0.0021171169...,
|
||||
...
|
||||
},
|
||||
"labels_std": {
|
||||
...
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
这些统计值也说明：
|
||||
|
||||
- 这些列（包括 `&-stoploss_target`）是针对 `"BTC/USDT"` 币对训练得出的。
|
||||
- 如果你换一个币对（比如 `"ETH/USDT"`），这些值会不同，因为模型会为它单独训练或预测。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 总结
|
||||
|
||||
| 问题 | 回答 |
|
||||
|------|------|
|
||||
| `&-stoploss_target` 是否是当前币对的特定值？ | ✅ 是的，它是基于当前币对的 FreqAI 预测结果。 |
|
||||
| Freqtrade/FreqAI 是否支持币对级预测？ | ✅ 是的，这是框架原生支持的能力。 |
|
||||
| 我是否可以放心地在 `custom_stoploss()` 中使用这个值？ | ✅ 完全可以，它就是为你当前交易的币对准备的。 |
|
||||
|
||||
---
|
||||
|
||||
如果你希望进一步利用这些统计值（如 `labels_mean`, `labels_std`）来做标准化处理或参数优化，我也可以帮你设计更高级的逻辑。需要的话请告诉我 👇
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-28 18:07:43
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 当前现有逻辑,对这些列的取值, 应用是否处于合理的范围
|
||||
|
||||
|
||||
非常好的问题！
|
||||
|
||||
我们来分析一下你当前策略中使用的几个关键列的取值范围，并判断它们是否在**合理区间内**，以及是否适合用于交易逻辑。
|
||||
|
||||
---
|
||||
|
||||
## 📊 1. `&-trend_strength`（趋势强度）
|
||||
|
||||
### ✅ 取值范围：
|
||||
```json
|
||||
"labels_mean": {
|
||||
"&-trend_strength": 0.0001847087009774315,
|
||||
}
|
||||
"labels_std": {
|
||||
"&-trend_strength": 0.0030047074726014993,
|
||||
}
|
||||
```
|
||||
|
||||
### 🔍 分析：
|
||||
- 平均值接近于 0，说明整体趋势不明显。
|
||||
- 标准差较小（约 0.003），说明波动不大。
|
||||
- 在策略中你使用了如下条件：
|
||||
```python
|
||||
buy_condition = df["&-buy_signal"] > 0.5
|
||||
```
|
||||
|
||||
### ⚠️ 潜在问题：
|
||||
- 如果 `&-trend_strength` 的绝对值普遍小于 0.01，那么设置 `> 0.01` 或 `< -0.01` 的阈值可能无法触发任何信号。
|
||||
- 当前 `buy_signal` 是基于 `trend_strength > 0.01` 生成的，但平均值只有 0.00018，意味着大多数情况下不会触发买入信号。
|
||||
|
||||
### ✅ 建议：
|
||||
- 考虑使用标准化后的 Z-score 来判断趋势强弱：
|
||||
```python
|
||||
z_score = (df["&-trend_strength"] - mean) / std
|
||||
df["&-buy_signal"] = np.where(z_score > 1, 1, 0)
|
||||
```
|
||||
- 或者根据历史分位数设定动态阈值。
|
||||
|
||||
---
|
||||
|
||||
## 📊 2. `&-volatility_forecast`（波动率预测）
|
||||
|
||||
### ✅ 取值范围：
|
||||
```json
|
||||
"labels_mean": {
|
||||
"&-volatility_forecast": 0.0021171169440760457,
|
||||
}
|
||||
"labels_std": {
|
||||
"&-volatility_forecast": 0.002140140216254572,
|
||||
}
|
||||
```
|
||||
|
||||
### 🔍 分析：
|
||||
- 平均波动率为 ~0.21%，标准差为 ~0.21%，说明波动性较低且分布较集中。
|
||||
- 在策略中你用它来计算止损：
|
||||
```python
|
||||
dynamic_stoploss = min(-0.005, stoploss_target * params["stoploss_multiplier"])
|
||||
```
|
||||
|
||||
### ✅ 合理性：
|
||||
- 使用 `volatility_forecast` 来调整止损是合理的。
|
||||
- 但要注意：如果波动率太小，可能导致止损过紧，容易被“洗出去”。
|
||||
|
||||
### ✅ 建议：
|
||||
- 可以考虑对波动率做指数平滑（如 EMA）后再使用。
|
||||
- 或者加入一个最小波动率下限，防止止损过窄。
|
||||
|
||||
---
|
||||
|
||||
## 📊 3. `&-roi_target`（ROI 目标）
|
||||
|
||||
### ✅ 取值范围：
|
||||
```json
|
||||
"labels_mean": {
|
||||
"&-roi_target": 0.006475457617190003,
|
||||
}
|
||||
"labels_std": {
|
||||
"&-roi_target": 0.00411756364819356,
|
||||
}
|
||||
```
|
||||
|
||||
### 🔍 分析：
|
||||
- 平均 ROI 约为 0.65%，标准差为 0.41%，说明波动适中。
|
||||
- 在策略中你使用它来动态调整止盈目标：
|
||||
```python
|
||||
if roi_target > 0.05:
|
||||
...
|
||||
elif 0.02 <= roi_target <= 0.05:
|
||||
...
|
||||
else:
|
||||
...
|
||||
```
|
||||
|
||||
### ⚠️ 潜在问题：
|
||||
- 你的判断阈值是 `0.05` 和 `0.02`，而平均值只有 `0.0065`，这意味着大部分时间会落入最低档 ROI。
|
||||
- 这会导致止盈过于保守，错失盈利机会。
|
||||
|
||||
### ✅ 建议：
|
||||
- 将判断阈值改为基于当前币种的历史分位数或 Z-score。
|
||||
- 或者将阈值设为 `mean + k * std`，例如：
|
||||
```python
|
||||
threshold_high = mean_roi + 1 * std_roi # 如 0.0065 + 0.0041 = 0.0106
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 4. `&-stoploss_target`（止损目标）
|
||||
|
||||
### ✅ 取值范围：
|
||||
```json
|
||||
"labels_mean": {
|
||||
"&-stoploss_target": -0.0025405403328912545,
|
||||
}
|
||||
"labels_std": {
|
||||
"&-stoploss_target": 0.002568168259505486,
|
||||
}
|
||||
```
|
||||
|
||||
### 🔍 分析：
|
||||
- 平均止损目标为 `-0.25%`，标准差也为 `~0.25%`。
|
||||
- 在策略中你使用它来动态止损：
|
||||
```python
|
||||
dynamic_stoploss = min(-0.005, stoploss_target * params["stoploss_multiplier"])
|
||||
```
|
||||
|
||||
### ✅ 合理性：
|
||||
- 当前止损目标偏小，结合 `min(-0.005, ...)`，最终止损会被限制在 `-0.5%`。
|
||||
- 对于 BTC/USDT 这类波动较小的币种来说，这个止损是合理的。
|
||||
- 但如果用于波动较大的币种（如山寨币），则需要放大倍数。
|
||||
|
||||
### ✅ 建议：
|
||||
- 可以根据币种类型（主币、山寨币）设置不同的 `stoploss_multiplier`。
|
||||
- 或者使用 `&-volatility_forecast` 来缩放止损值。
|
||||
|
||||
---
|
||||
|
||||
## 📊 5. `&-buy_signal` / `&-sell_signal`（买卖信号）
|
||||
|
||||
### ✅ 取值范围：
|
||||
```json
|
||||
"&-buy_signal": 0.0065115471436013195,
|
||||
"&-sell_signal": 0.004775134571974301
|
||||
```
|
||||
|
||||
### 🔍 分析：
|
||||
- 平均值都很低，说明模型预测出的信号强度较弱。
|
||||
- 在策略中你使用了：
|
||||
```python
|
||||
buy_condition = df["&-buy_signal"] > 0.5
|
||||
```
|
||||
|
||||
### ⚠️ 潜在问题：
|
||||
- 由于平均值远低于 0.5，这个条件几乎不会触发。
|
||||
- 这会导致策略长期空仓，错过交易机会。
|
||||
|
||||
### ✅ 建议：
|
||||
- 改为使用相对阈值，比如：
|
||||
```python
|
||||
buy_condition = df["&-buy_signal"] > df["&-buy_signal"].rolling(100).quantile(0.8)
|
||||
```
|
||||
- 或者使用 Z-score 判断异常值：
|
||||
```python
|
||||
z_score = (df["&-buy_signal"] - mean) / std
|
||||
buy_condition = z_score > 1
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结：各列取值合理性评估
|
||||
|
||||
| 列名 | 是否合理 | 说明 |
|
||||
|-----------------------|-----------|------|
|
||||
| `&-trend_strength` | ⚠️ 部分合理 | 平均值太低，建议使用 Z-score 或分位数判断趋势。 |
|
||||
| `&-volatility_forecast` | ✅ 合理 | 波动适中，可用于止损控制。 |
|
||||
| `&-roi_target` | ⚠️ 部分合理 | 平均值偏低，建议使用动态阈值。 |
|
||||
| `&-stoploss_target` | ✅ 合理 | 结合 `min()` 控制后可用，适用于主流币种。 |
|
||||
| `&-buy_signal` | ❌ 不合理 | 平均值远低于 0.5，建议改用统计方法判断信号。 |
|
||||
| `&-sell_signal` | ❌ 不合理 | 同上，需优化信号判断方式。 |
|
||||
|
||||
---
|
||||
|
||||
### ✅ 下一步建议
|
||||
|
||||
你可以考虑：
|
||||
|
||||
1. **使用 Z-score 或滚动分位数替代固定阈值**。
|
||||
2. **对 ROI 和 Stoploss 使用指数平滑处理**。
|
||||
3. **根据不同币种类型（主币/山寨币）设置不同参数**。
|
||||
4. **将这些统计值（mean/std）写入配置文件，供策略运行时使用**。
|
||||
|
||||
如果你希望我帮你实现这些改进，请告诉我，我可以一步步带你完成代码修改 👇
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-28 18:13:46
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 的确有长期空仓的问题, 我希望持仓更加轻量, 这比较符合我的预期, 不过适当的提高一点(不超过现有
|
||||
> 10%)持仓的时长占比我并不介意,
|
||||
|
||||
|
||||
明白了！
|
||||
|
||||
你的目标是：
|
||||
|
||||
> **在当前“长期空仓”的基础上，适度提高持仓时间占比（不超过现有水平的 10%），但整体仍保持轻仓风格。**
|
||||
|
||||
---
|
||||
|
||||
## ✅ 策略调整方向
|
||||
|
||||
为了实现这个目标，我们可以从以下几个方面入手：
|
||||
|
||||
### 🧠 核心思路
|
||||
|
||||
- **保留轻仓风格**：不追求高胜率或高频交易。
|
||||
- **小幅提升持仓比例**：通过放宽入场条件、延迟出场等方式，让策略更愿意“持有”一段时间。
|
||||
- **避免过度拟合**：使用通用逻辑而非硬编码阈值。
|
||||
|
||||
---
|
||||
|
||||
## 🔧 具体优化建议
|
||||
|
||||
### 1. ✅ 放宽买入信号触发条件
|
||||
|
||||
你目前使用的是：
|
||||
```python
|
||||
buy_condition = df["&-buy_signal"] > 0.5
|
||||
```
|
||||
|
||||
但根据你提供的数据，`&-buy_signal` 的平均值为 `0.0065`，远低于 0.5，几乎不会触发。
|
||||
|
||||
#### ✅ 建议修改为基于滚动分位数的动态判断：
|
||||
|
||||
```python
|
||||
# 使用过去 20 根 K 线中 buy_signal 的 80 分位作为阈值
|
||||
df["buy_threshold"] = df["&-buy_signal"].rolling(20).quantile(0.8)
|
||||
buy_condition = df["&-buy_signal"] >= df["buy_threshold"]
|
||||
```
|
||||
|
||||
这样可以保证只在相对较强的信号下才入场，同时又不至于完全空仓。
|
||||
|
||||
---
|
||||
|
||||
### 2. ✅ 延迟卖出信号触发时机
|
||||
|
||||
你目前使用的是：
|
||||
```python
|
||||
conditions.append(df["&-sell_signal"] > 0.5)
|
||||
```
|
||||
|
||||
同样，由于 `&-sell_signal` 平均值也很低，容易导致过早离场。
|
||||
|
||||
#### ✅ 建议修改为：
|
||||
|
||||
```python
|
||||
# 只有当 sell_signal 持续高于某个较低阈值时才触发
|
||||
df["sell_signal_high"] = (df["&-sell_signal"] > 0.3).rolling(3).sum() >= 2
|
||||
conditions.append(df["sell_signal_high"])
|
||||
```
|
||||
|
||||
这表示：如果过去 3 根 K 线中有至少 2 根信号大于 0.3，则认为卖出信号有效。
|
||||
|
||||
---
|
||||
|
||||
### 3. ✅ 设置最小持仓时间（如 5 根 K 线）
|
||||
|
||||
如果你希望持仓时间稍微延长一点，可以设置一个最小持仓时间限制。
|
||||
|
||||
#### ✅ 示例代码：
|
||||
|
||||
```python
|
||||
from freqtrade.persistence import Trade
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
# 获取当前币种的 trade 对象（如果有）
|
||||
pair = metadata['pair']
|
||||
trade = Trade.get_trades([("pair", pair)]).first()
|
||||
|
||||
if trade and trade.is_open:
|
||||
open_candle_index = df.index.get_loc(trade.open_date.replace(tzinfo=None))
|
||||
current_index = len(df) - 1
|
||||
holding_period = current_index - open_candle_index
|
||||
|
||||
# 强制持有至少 5 根 K 线
|
||||
if holding_period < 5:
|
||||
df.loc[df.index[-1], 'exit_long'] = 0
|
||||
return df
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 4. ✅ 结合市场状态过滤卖出信号（可选）
|
||||
|
||||
你可以结合 `&-market_condition` 来抑制震荡行情下的卖出信号：
|
||||
|
||||
```python
|
||||
if "&-market_condition" in df.columns:
|
||||
conditions.append(~((df["&-market_condition"] == "sideways") & (df["&-sell_signal"] < 0.4)))
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📈 预期效果
|
||||
|
||||
| 调整项 | 效果 |
|
||||
|--------|------|
|
||||
| 动态买入信号 | 提高入场频率，但仍保持择时性 |
|
||||
| 延迟卖出信号 | 延长持仓时间，减少频繁交易 |
|
||||
| 最小持仓时间 | 防止“刚进就出”，提升持仓占比 |
|
||||
| 市场状态过滤 | 减少震荡市中的无效交易 |
|
||||
|
||||
|
||||
752
chat/result11.md
752
chat/result11.md
@ -1,752 +0,0 @@
|
||||
- Datetime: 2025-05-29 18:04:45
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- /home/ubuntu/freqtrade/freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 无法触发入场信号的原因是:
|
||||
> (`df["&-trend_strength"]` > 0.01)
|
||||
> 这个条件永远无法被满足:
|
||||
> 这是一个典型的训练结果
|
||||
> "labels_mean": {
|
||||
> "&-trend_strength": 0.00016881476792118572,
|
||||
> "&-volatility_forecast": 0.005005756262776747,
|
||||
> "&-roi_target": 0.008831010745460291,
|
||||
> "&-stoploss_target": -0.006006907515332095,
|
||||
> "&-buy_signal": 0.06259223890962758,
|
||||
> "&-sell_signal": 0.05226148103133953
|
||||
> },
|
||||
> "labels_std": {
|
||||
> "&-trend_strength": 0.0066341052806047325,
|
||||
> "&-volatility_forecast": 0.004356863038665235,
|
||||
> "&-roi_target": 0.004687195365502718,
|
||||
> "&-stoploss_target": 0.005228235646398282,
|
||||
> "&-buy_signal": 0.24222809609520457,
|
||||
> "&-sell_signal": 0.22255385557601662
|
||||
> },
|
||||
>
|
||||
> 那么根据此结果,阈值该如何判定 ,另外 labels_mean和labels_std有什么区别
|
||||
|
||||
你提出的问题非常关键，涉及到 **FreqAI 模型输出的标签分布分析** 和 **如何根据模型预测结果设定合理的交易信号阈值**。
|
||||
|
||||
---
|
||||
|
||||
## 🧠 问题总结
|
||||
|
||||
你发现：
|
||||
|
||||
```python
|
||||
buy_condition &= (df["&-trend_strength"] > 0.01)
|
||||
```
|
||||
|
||||
这个条件 **永远无法满足**，因为从你的 FreqAI 输出中可以看到：
|
||||
|
||||
```json
|
||||
"labels_mean": {
|
||||
"&-trend_strength": 0.00016881476792118572,
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
说明模型预测的趋势强度（`&-trend_strength`）平均值接近于 0，标准差也很小（`0.0066`），所以几乎不会出现大于 `0.01` 的值。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 解决方案：基于 `labels_mean` 和 `labels_std` 设置动态阈值
|
||||
|
||||
### 🔍 什么是 `labels_mean` 和 `labels_std`？
|
||||
|
||||
| 字段 | 含义 | 示例 |
|
||||
|------|------|------|
|
||||
| `labels_mean` | 所有预测目标列的均值 | `0.0001688` 表示趋势强度平均为 0.0001688 |
|
||||
| `labels_std` | 所有预测目标列的标准差 | `0.006634` 表示趋势强度波动范围约为 ±0.0066 |
|
||||
|
||||
> ✅ 这两个统计量可以帮助我们判断模型预测值的分布情况，从而设置更合理的交易信号阈值。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 如何设置合理的入场信号阈值？
|
||||
|
||||
### 方法一：使用 Z-score 判断显著性
|
||||
|
||||
你可以使用如下方式来判断当前趋势是否显著：
|
||||
|
||||
```python
|
||||
# 计算 z-score
|
||||
mean = 0.0001688
|
||||
std = 0.0066341
|
||||
df["z_score_trend"] = (df["&-trend_strength"] - mean) / std
|
||||
|
||||
# 只在趋势强度显著高于平均水平时开仓（例如 z-score > 1）
|
||||
buy_condition &= (df["z_score_trend"] > 1)
|
||||
```
|
||||
|
||||
这表示只有当趋势强度超过历史平均值 +1 个标准差时才认为是“有效趋势”。
|
||||
|
||||
---
|
||||
|
||||
### 方法二：直接使用分位数过滤
|
||||
|
||||
如果你不想用 z-score，也可以使用滚动窗口的分位数来判断：
|
||||
|
||||
```python
|
||||
# 使用过去 20 根 K 线的趋势强度分位数
|
||||
df["trend_quantile"] = df["&-trend_strength"].rolling(20).quantile(0.8)
|
||||
|
||||
# 只在趋势强度处于前 20% 时开仓
|
||||
buy_condition &= (df["&-trend_strength"] >= df["trend_quantile"])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 方法三：结合多个信号综合判断
|
||||
|
||||
由于 `&-trend_strength` 本身变化较小，可以考虑结合其他信号一起判断：
|
||||
|
||||
```python
|
||||
# 结合 buy_signal 和 trend_strength
|
||||
buy_condition &= (
|
||||
(df["&-buy_signal"] > df["buy_threshold"]) &
|
||||
((df["&-trend_strength"] > 0.005) | (df["&-volatility_forecast"] > 0.006))
|
||||
)
|
||||
```
|
||||
|
||||
这样即使趋势不强，但波动率较高或买方信号强烈时，也能触发入场。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 推荐修改后的 `populate_entry_trend()` 片段
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = []
|
||||
|
||||
if "&-buy_signal" in df.columns:
|
||||
# 基础买入信号
|
||||
df["buy_threshold"] = df["&-buy_signal"].rolling(20).quantile(0.8)
|
||||
buy_condition = (df["&-buy_signal"] >= df["buy_threshold"]) & (df["&-buy_signal"] > 0.4)
|
||||
|
||||
# 趋势强度过滤（使用 z-score）
|
||||
mean_trend = 0.0001688
|
||||
std_trend = 0.0066341
|
||||
df["z_score_trend"] = (df["&-trend_strength"] - mean_trend) / std_trend
|
||||
buy_condition &= (df["z_score_trend"] > 1)
|
||||
|
||||
# EMA 趋势过滤
|
||||
df["ema50"] = ta.EMA(df, timeperiod=50)
|
||||
buy_condition &= (df["close"] > df["ema50"])
|
||||
|
||||
# 成交量过滤
|
||||
df["vol_mean"] = df["volume"].rolling(20).mean()
|
||||
buy_condition &= (df["volume"] > df["vol_mean"] * 1.2)
|
||||
|
||||
# 市场状态过滤
|
||||
if "&-market_condition" in df.columns:
|
||||
buy_condition &= ~(
|
||||
(df["&-market_condition"] == "overbought")
|
||||
)
|
||||
|
||||
conditions.append(buy_condition)
|
||||
else:
|
||||
logger.warning("⚠️ &-buy_signal 列缺失，跳过该条件")
|
||||
|
||||
if len(conditions) == 0:
|
||||
return df
|
||||
|
||||
df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
|
||||
return df
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结
|
||||
|
||||
| 内容 | 说明 |
|
||||
|------|------|
|
||||
| ❌ 为什么 `df["&-trend_strength"] > 0.01` 不生效？ | 因为模型预测值太小，均值仅 `0.0001688`，标准差 `0.0066` |
|
||||
| ✅ 如何解决？ | 使用 z-score、分位数、多信号组合等方式替代固定阈值 |
|
||||
| 📊 `labels_mean` vs `labels_std` | `mean` 是中心位置，`std` 是离散程度，两者共同描述分布形态 |
|
||||
| 🛠 推荐做法 | 使用 z-score 或滚动分位数来判断趋势是否显著，避免硬编码阈值 |
|
||||
|
||||
---
|
||||
|
||||
如果你希望我帮你生成完整的可运行策略文件，并根据你的回测数据自动计算最佳阈值，请告诉我你想预测的目标（比如涨跌、波动率等），我可以为你定制这部分逻辑。
|
||||
|
||||
------
|
||||
|
||||
- Datetime: 2025-05-29 18:07:22
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- /home/ubuntu/freqtrade/freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> mean_trend和std_trend是否可以从dataframe里动态获取, 而不是写死
|
||||
|
||||
是的，**`mean_trend` 和 `std_trend` 完全可以从 dataframe 中动态获取**，而不是写死在代码中。实际上，这也是更推荐的做法，因为：
|
||||
|
||||
- 模型预测值会随时间变化（尤其是在模型重新训练后）
|
||||
- 不同币种可能有不同的分布特性
|
||||
- 写死数值不利于长期运行和策略适应性
|
||||
|
||||
---
|
||||
|
||||
## ✅ 推荐做法：使用滚动窗口计算趋势强度的均值和标准差
|
||||
|
||||
你可以在 `populate_entry_trend()` 方法中添加如下逻辑：
|
||||
|
||||
```python
|
||||
# 动态计算趋势强度的滚动均值和标准差（例如过去 50 根 K 线）
|
||||
window = 50
|
||||
df["trend_mean"] = df["&-trend_strength"].rolling(window).mean()
|
||||
df["trend_std"] = df["&-trend_strength"].rolling(window).std()
|
||||
|
||||
# 使用 z-score 判断当前趋势是否显著
|
||||
df["z_score_trend"] = (df["&-trend_strength"] - df["trend_mean"]) / df["trend_std"]
|
||||
|
||||
# 只在趋势强度显著高于历史平均水平时开仓（例如 z-score > 1）
|
||||
buy_condition &= (df["z_score_trend"] > 1)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 示例：完整更新后的 `populate_entry_trend()` 片段
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = []
|
||||
|
||||
if "&-buy_signal" in df.columns:
|
||||
# 基础买入信号
|
||||
df["buy_threshold"] = df["&-buy_signal"].rolling(20).quantile(0.8)
|
||||
buy_condition = (df["&-buy_signal"] >= df["buy_threshold"]) & (df["&-buy_signal"] > 0.4)
|
||||
|
||||
# 动态计算趋势强度的滚动均值和标准差
|
||||
window = 50
|
||||
df["trend_mean"] = df["&-trend_strength"].rolling(window).mean()
|
||||
df["trend_std"] = df["&-trend_strength"].rolling(window).std()
|
||||
|
||||
# 使用 z-score 判断趋势是否显著
|
||||
df["z_score_trend"] = (df["&-trend_strength"] - df["trend_mean"]) / df["trend_std"]
|
||||
buy_condition &= (df["z_score_trend"] > 1)
|
||||
|
||||
# EMA 趋势过滤
|
||||
df["ema50"] = ta.EMA(df, timeperiod=50)
|
||||
buy_condition &= (df["close"] > df["ema50"])
|
||||
|
||||
# 成交量过滤
|
||||
df["vol_mean"] = df["volume"].rolling(20).mean()
|
||||
buy_condition &= (df["volume"] > df["vol_mean"] * 1)
|
||||
|
||||
# 市场状态过滤（只排除超买）
|
||||
if "&-market_condition" in df.columns:
|
||||
buy_condition &= ~(
|
||||
(df["&-market_condition"] == "overbought")
|
||||
)
|
||||
|
||||
conditions.append(buy_condition)
|
||||
else:
|
||||
logger.warning("⚠️ &-buy_signal 列缺失，跳过该条件")
|
||||
|
||||
if len(conditions) == 0:
|
||||
return df
|
||||
|
||||
df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
|
||||
return df
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 进阶建议：使用 FreqAI 提供的 `labels_mean` 和 `labels_std`
|
||||
|
||||
FreqAI 在每次训练后都会输出 `labels_mean` 和 `labels_std`，你可以通过以下方式访问它们：
|
||||
|
||||
```python
|
||||
if self.freqai and hasattr(self.freqai, "data"):
|
||||
labels_mean = self.freqai.data.labels_mean
|
||||
labels_std = self.freqai.data.labels_std
|
||||
|
||||
mean_trend = labels_mean.get("&-trend_strength", 0)
|
||||
std_trend = labels_std.get("&-trend_strength", 1e-6)
|
||||
```
|
||||
|
||||
> ⚠️ 注意：这些值在整个回测过程中不会实时更新，仅反映模型训练时的状态。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结
|
||||
|
||||
| 方法 | 是否推荐 | 说明 |
|
||||
|------|----------|------|
|
||||
| ❌ 写死 `mean_trend` 和 `std_trend` | 否 | 不灵活，容易失效 |
|
||||
| ✅ 使用滚动窗口动态计算 | 是 | 更适合实盘和长期运行 |
|
||||
| 🧠 使用 `self.freqai.data.labels_mean` | 可选 | 适用于静态分析或调试 |
|
||||
|
||||
---
|
||||
|
||||
如果你希望我帮你生成一个 **完全自适应、自动调节阈值的版本**，请告诉我你的目标（比如你想预测涨跌、波动率、趋势强度等），我可以为你定制这部分逻辑。
|
||||
|
||||
------
|
||||
|
||||
- Datetime: 2025-05-29 18:11:48
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- /home/ubuntu/freqtrade/freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 这些值在整个回测过程中不会实时更新，仅反映模型训练时的状态。
|
||||
>
|
||||
> 如何理解上面这句话, 在trade模式下会实时更新吗
|
||||
>
|
||||
|
||||
非常好的问题！
|
||||
|
||||
你提到的这句话：
|
||||
|
||||
> **"这些值在整个回测过程中不会实时更新，仅反映模型训练时的状态。"**
|
||||
|
||||
我们来详细解释它的含义，并回答你在 **trade 模式下是否会实时更新** 的问题。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 一、在 `backtesting`（回测）模式下的行为
|
||||
|
||||
### 📌 FreqAI 的 `labels_mean` 和 `labels_std` 是什么？
|
||||
|
||||
它们是 FreqAI 在每次 **模型训练完成后** 自动计算并保存的统计信息，用于描述模型预测目标列的分布情况。
|
||||
|
||||
例如：
|
||||
```json
|
||||
{
|
||||
"labels_mean": {
|
||||
"&-trend_strength": 0.0001688,
|
||||
...
|
||||
},
|
||||
"labels_std": {
|
||||
"&-trend_strength": 0.0066341,
|
||||
...
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
这些值是在模型训练时基于训练数据计算出来的，**不是实时滚动更新的统计数据**。
|
||||
|
||||
---
|
||||
|
||||
### 🧨 回测中为什么不会“实时”更新？
|
||||
|
||||
因为回测是 **历史模拟**，它必须保证：
|
||||
|
||||
- 所有决策只能使用过去的数据（不能偷看未来）
|
||||
- 所有参数必须在训练阶段就确定下来
|
||||
|
||||
所以：
|
||||
|
||||
> ❗ 在回测中，`labels_mean` 和 `labels_std` 只会在模型第一次训练时计算一次，之后不会再更新。
|
||||
|
||||
即使你在策略中调用：
|
||||
|
||||
```python
|
||||
self.freqai.data.labels_mean
|
||||
```
|
||||
|
||||
它返回的也是 **模型训练那一刻的历史值**，而不是当前 K 线窗口的动态均值/标准差。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 二、在 `live/dry-run/trade`（实盘/模拟交易）模式下的行为
|
||||
|
||||
### 🚀 实盘运行时会重新训练模型
|
||||
|
||||
FreqAI 支持定期重新训练模型（通过配置 `live_retrain_hours`），这意味着：
|
||||
|
||||
> ✅ **在 trade 模式下，`labels_mean` 和 `labels_std` 是会随着模型重新训练而更新的！**
|
||||
|
||||
也就是说：
|
||||
|
||||
| 模式 | 是否更新 `labels_mean/std` | 原因 |
|
||||
|------|-----------------------------|------|
|
||||
| `backtesting` | ❌ 否 | 使用固定模型和静态统计量 |
|
||||
| `trade` / `dry_run` | ✅ 是 | 模型定期重训练，统计量随之更新 |
|
||||
|
||||
---
|
||||
|
||||
### 🛠 如何获取最新的 `labels_mean` 和 `labels_std`？
|
||||
|
||||
你可以这样访问：
|
||||
|
||||
```python
|
||||
if self.freqai and hasattr(self.freqai, "data"):
|
||||
labels_mean = self.freqai.data.labels_mean
|
||||
labels_std = self.freqai.data.labels_std
|
||||
|
||||
mean_trend = labels_mean.get("&-trend_strength", 0)
|
||||
std_trend = labels_std.get("&-trend_strength", 1e-6)
|
||||
|
||||
logger.info(f"Current trend mean: {mean_trend}, std: {std_trend}")
|
||||
```
|
||||
|
||||
> ⚠️ 注意：这些值只在模型训练或加载时更新，不是每根 K 线都更新。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 三、如何实现“实时”动态统计？
|
||||
|
||||
如果你希望根据最近一段时间的预测值动态计算趋势强度的均值和标准差（比如滚动窗口），可以这样做：
|
||||
|
||||
```python
|
||||
window = 50
|
||||
df["trend_mean"] = df["&-trend_strength"].rolling(window).mean()
|
||||
df["trend_std"] = df["&-trend_strength"].rolling(window).std()
|
||||
|
||||
# 使用 z-score 判断趋势是否显著
|
||||
df["z_score_trend"] = (df["&-trend_strength"] - df["trend_mean"]) / df["trend_std"]
|
||||
buy_condition &= (df["z_score_trend"] > 1)
|
||||
```
|
||||
|
||||
这种方式适用于所有模式（包括 backtest 和 trade），并且能更灵活地适应市场变化。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结
|
||||
|
||||
| 场景 | `labels_mean/std` 是否更新 | 是否推荐使用 |
|
||||
|------|----------------------------|--------------|
|
||||
| 回测模式 | ❌ 否（仅训练时一次） | ❌ 不适合长期依赖 |
|
||||
| 实盘/模拟交易 | ✅ 是（随模型重训练更新） | ✅ 推荐用于辅助分析 |
|
||||
| 滚动窗口动态统计 | ✅ 是（每根 K 线更新） | ✅ 强烈推荐用于信号判断 |
|
||||
|
||||
---
|
||||
|
||||
如果你想我帮你写一个 **完全自适应、自动调节阈值的版本**，请告诉我你的目标（比如你想预测涨跌、波动率、趋势强度等），我可以为你定制这部分逻辑。
|
||||
|
||||
------
|
||||
|
||||
- Datetime: 2025-05-29 18:18:57
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- /home/ubuntu/freqtrade/freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 既然已经:
|
||||
> window = 50
|
||||
> `df["trend_mean"]` = `df["&-trend_strength"]`.rolling(window).mean()
|
||||
> `df["trend_std"]` = `df["&-trend_strength"]`.rolling(window).std()
|
||||
>
|
||||
> # 使用 z-score 判断趋势是否显著
|
||||
> `df["z_score_trend"]` = (`df["&-trend_strength"]` - `df["trend_mean"]`) / `df["trend_std"]`
|
||||
> buy_condition &= (`df["z_score_trend"]` > 1)
|
||||
> 那么什么时候需要用到:
|
||||
> if self.freqai and hasattr(self.freqai, "data"):
|
||||
> labels_mean = self.freqai.data.labels_mean
|
||||
> labels_std = self.freqai.data.labels_std
|
||||
>
|
||||
> mean_trend = labels_mean.get("&-trend_strength", 0)
|
||||
> std_trend = labels_std.get("&-trend_strength", 1e-6)
|
||||
>
|
||||
> logger.info(f"Current trend mean: {mean_trend}, std: {std_trend}")
|
||||
> ```
|
||||
>
|
||||
|
||||
非常好的问题！
|
||||
|
||||
你已经正确地在 `populate_entry_trend()` 中使用了 **滚动窗口的 z-score** 来动态判断趋势是否显著，这在大多数交易场景下是更优的做法。
|
||||
|
||||
那么：
|
||||
|
||||
---
|
||||
|
||||
## 🤔 既然我已经用上了滚动窗口 z-score，那 `labels_mean` 和 `labels_std` 还有什么用？
|
||||
|
||||
### ✅ 简短回答：
|
||||
|
||||
> `labels_mean` 和 `labels_std` 是 **模型训练时的全局统计信息**，主要用于：
|
||||
>
|
||||
> - **调试和分析**
|
||||
> - **辅助设置初始阈值**
|
||||
> - **实盘中监控模型输出分布变化**
|
||||
|
||||
它们不是用于实时信号生成（你已经做得很好），而是用于 **理解模型行为、验证数据质量、检测漂移等目的**。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 具体用途详解
|
||||
|
||||
### 1. **调试模型输出分布**
|
||||
|
||||
你可以通过打印 `labels_mean["&-trend_strength"]` 来确认模型输出的趋势强度是否合理。
|
||||
|
||||
例如：
|
||||
|
||||
```python
|
||||
logger.info(f"Model trend mean: {mean_trend}, std: {std_trend}")
|
||||
```
|
||||
|
||||
如果发现 `mean_trend` 接近 0，且 `std_trend` 很小（如你的案例），说明模型预测的趋势不明显，可能需要调整标签定义或特征工程。
|
||||
|
||||
---
|
||||
|
||||
### 2. **作为回测/实盘的一致性检查**
|
||||
|
||||
如果你在回测中看到某个币种的 `z_score_trend > 1` 频繁触发，但在实盘中几乎不触发，可以对比：
|
||||
|
||||
- 实盘中的 `labels_mean/std` 是否与回测一致？
|
||||
- 模型是否发生了“漂移”？
|
||||
|
||||
这有助于判断模型是否仍然有效。
|
||||
|
||||
---
|
||||
|
||||
### 3. **作为自适应策略的一部分**
|
||||
|
||||
虽然你已经在用滚动窗口 z-score，但也可以结合 `labels_mean/std` 做一些高级逻辑，比如：
|
||||
|
||||
#### 🧠 动态调整窗口大小
|
||||
|
||||
```python
|
||||
if std_trend < 0.001:
|
||||
window = 100 # 如果标准差太小，说明趋势变化慢，延长窗口
|
||||
else:
|
||||
window = 50
|
||||
```
|
||||
|
||||
#### 📊 判断模型是否“失效”
|
||||
|
||||
```python
|
||||
if abs(mean_trend) < 1e-4 and std_trend < 1e-4:
|
||||
logger.warning("⚠️ 模型趋势预测无变化，可能需要重新训练")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结：你应该什么时候使用它？
|
||||
|
||||
| 使用场景 | 是否推荐 | 说明 |
|
||||
|----------|----------|------|
|
||||
| 实时信号生成 | ❌ 否 | 应该使用滚动窗口 z-score |
|
||||
| 调试模型输出 | ✅ 是 | 查看模型预测趋势是否合理 |
|
||||
| 监控模型漂移 | ✅ 是 | 对比不同时间点的 `labels_mean/std` |
|
||||
| 辅助策略决策 | ✅ 可选 | 如动态调整窗口长度、判断模型有效性 |
|
||||
|
||||
---
|
||||
|
||||
## ✅ 示例：保留 `labels_mean/std` 用于日志记录
|
||||
|
||||
你可以这样写代码：
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = []
|
||||
|
||||
if "&-buy_signal" in df.columns:
|
||||
# 基础买入信号
|
||||
df["buy_threshold"] = df["&-buy_signal"].rolling(20).quantile(0.8)
|
||||
buy_condition = (df["&-buy_signal"] >= df["buy_threshold"]) & (df["&-buy_signal"] > 0.4)
|
||||
|
||||
# 动态计算趋势强度的滚动均值和标准差
|
||||
window = 50
|
||||
df["trend_mean"] = df["&-trend_strength"].rolling(window).mean()
|
||||
df["trend_std"] = df["&-trend_strength"].rolling(window).std()
|
||||
|
||||
# 使用 z-score 判断趋势是否显著
|
||||
df["z_score_trend"] = (df["&-trend_strength"] - df["trend_mean"]) / df["trend_std"]
|
||||
buy_condition &= (df["z_score_trend"] > 1)
|
||||
|
||||
# 打印模型统计信息用于调试
|
||||
if self.freqai and hasattr(self.freqai, "data"):
|
||||
labels_mean = self.freqai.data.labels_mean
|
||||
labels_std = self.freqai.data.labels_std
|
||||
|
||||
mean_trend = labels_mean.get("&-trend_strength", 0)
|
||||
std_trend = labels_std.get("&-trend_strength", 1e-6)
|
||||
|
||||
logger.info(f"[{metadata['pair']}] Model trend mean: {mean_trend:.6f}, std: {std_trend:.6f}")
|
||||
|
||||
# EMA 趋势过滤
|
||||
df["ema50"] = ta.EMA(df, timeperiod=50)
|
||||
buy_condition &= (df["close"] > df["ema50"])
|
||||
|
||||
# 成交量过滤
|
||||
df["vol_mean"] = df["volume"].rolling(20).mean()
|
||||
buy_condition &= (df["volume"] > df["vol_mean"] * 1)
|
||||
|
||||
# 市场状态过滤（只排除超买）
|
||||
if "&-market_condition" in df.columns:
|
||||
buy_condition &= ~(
|
||||
(df["&-market_condition"] == "overbought")
|
||||
)
|
||||
|
||||
conditions.append(buy_condition)
|
||||
else:
|
||||
logger.warning("⚠️ &-buy_signal 列缺失，跳过该条件")
|
||||
|
||||
if len(conditions) == 0:
|
||||
return df
|
||||
|
||||
df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
|
||||
return df
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
✅ **结论：**
|
||||
|
||||
你现在的做法非常合理 —— 使用滚动窗口 z-score 来判断趋势是否显著。
|
||||
而 `labels_mean` 和 `labels_std` 更像是“仪表盘上的指示灯”，帮助你了解模型运行状态，而不是直接用于交易信号。
|
||||
|
||||
---
|
||||
|
||||
如果你想我帮你加入自动模型漂移检测、动态窗口调整等功能，请告诉我你的目标（比如你想预测涨跌、波动率等），我可以为你定制这部分逻辑。
|
||||
|
||||
------
|
||||
|
||||
- Datetime: 2025-05-29 18:37:43
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- /home/ubuntu/freqtrade/freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 当前trailing stop loss的逻辑由哪段代码或配置来决定
|
||||
>
|
||||
|
||||
在你提供的 `FreqaiPrimer` 策略中，**当前并没有启用 Freqtrade 的 trailing stop loss（追踪止损）功能**。
|
||||
|
||||
也就是说：
|
||||
|
||||
> ❌ **目前的止损逻辑完全由 `custom_stoploss()` 方法控制，而不是通过 trailing stop loss 配置。**
|
||||
|
||||
---
|
||||
|
||||
## ✅ 1. 当前使用的止损机制
|
||||
|
||||
### 📌 `custom_stoploss()` 方法说明：
|
||||
|
||||
```python
|
||||
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
"""
|
||||
动态止损回调函数，基于 FreqAI 预测的 &-stoploss_target 值。
|
||||
"""
|
||||
|
||||
# 获取当前币种的 dataframe
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1]
|
||||
|
||||
# 获取预测的止损目标
|
||||
stoploss_target = last_candle.get("&-stoploss_target", -0.01)
|
||||
|
||||
# 将 stoploss_target 转换为负数（因为 stoploss 是负值）
|
||||
dynamic_stoploss = min(-0.005, stoploss_target) # 设置最小止损限制
|
||||
|
||||
return dynamic_stoploss
|
||||
```
|
||||
|
||||
这段代码是你的策略中唯一生效的止损逻辑 —— 它根据 FreqAI 提供的 `&-stoploss_target` 来动态设置止损点。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 2. Trailing Stop Loss 是什么？
|
||||
|
||||
Trailing stop loss 是一种 **跟随价格移动的止损机制**，例如：
|
||||
|
||||
```json
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.02,
|
||||
"trailing_stop_positive_offset": 0.03,
|
||||
"trailing_only_offset_is_reached": true
|
||||
```
|
||||
|
||||
它的行为是：
|
||||
- 开仓后，止损点随着价格上涨“向上移动”
|
||||
- 可以锁定利润，防止回吐过多盈利
|
||||
|
||||
---
|
||||
|
||||
## ✅ 3. 如何判断是否启用了 trailing stop loss？
|
||||
|
||||
在你的策略类中没有看到如下配置或方法：
|
||||
|
||||
```python
|
||||
trailing_stop = True
|
||||
```
|
||||
|
||||
也没有使用以下任何与 trailing stop 相关的参数。
|
||||
|
||||
因此可以确认：
|
||||
|
||||
> 🔍 **当前使用的是 `custom_stoploss()` 的动态止损，而不是 trailing stop loss。**
|
||||
|
||||
---
|
||||
|
||||
## ✅ 4. 如果你想启用 trailing stop loss，应该怎么做？
|
||||
|
||||
你可以选择以下两种方式之一：
|
||||
|
||||
### ✅ 方式一：使用全局配置文件（如 `config.json`）
|
||||
|
||||
```json
|
||||
{
|
||||
"max_open_trades": 3,
|
||||
"stake_currency": "USDT",
|
||||
"stake_amount": 100,
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"minimal_roi": {
|
||||
"60": 0.01,
|
||||
"30": 0.02,
|
||||
"0": 0.03
|
||||
},
|
||||
"stoploss": -0.10,
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.02,
|
||||
"trailing_stop_positive_offset": 0.03,
|
||||
"trailing_only_offset_is_reached": true
|
||||
}
|
||||
```
|
||||
|
||||
这样会全局启用 trailing stop loss。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 方式二：在策略中定义（覆盖配置）
|
||||
|
||||
```python
|
||||
# 在策略类中添加如下属性
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.02
|
||||
trailing_stop_positive_offset = 0.03
|
||||
trailing_only_offset_is_reached = True
|
||||
```
|
||||
|
||||
这将覆盖配置文件中的设置。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 5. `custom_stoploss()` vs `trailing_stop`
|
||||
|
||||
| 特性 | `custom_stoploss()` | `trailing_stop` |
|
||||
|------|---------------------|------------------|
|
||||
| 是否可动态计算 | ✅ 是 | ❌ 否（固定百分比） |
|
||||
| 是否跟随价格移动 | ❌ 否 | ✅ 是 |
|
||||
| 是否支持 FreqAI 输出 | ✅ 是 | ❌ 否 |
|
||||
| 是否需要额外配置 | ❌ 否 | ✅ 是 |
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结
|
||||
|
||||
| 问题 | 回答 |
|
||||
|------|------|
|
||||
| 当前是否启用了 trailing stop loss？ | ❌ 否 |
|
||||
| 止损逻辑由哪段代码决定？ | ✅ `custom_stoploss()` 方法 |
|
||||
| 如何启用 trailing stop loss？ | ✅ 在配置文件或策略中添加相关参数 |
|
||||
| 哪个更适合结合 FreqAI 使用？ | ✅ `custom_stoploss()` 更灵活，推荐继续使用它 |
|
||||
|
||||
---
|
||||
|
||||
如果你想我帮你实现一个 **结合 FreqAI 和 trailing stop 的混合止损系统**，请告诉我你的目标（比如你想预测涨跌、波动率等），我可以为你定制这部分逻辑。
|
||||
|
||||
|
||||
2220
chat/result12.md
2220
chat/result12.md
File diff suppressed because it is too large
Load Diff
250
chat/result13.md
250
chat/result13.md
@ -1,250 +0,0 @@
|
||||
> 请实现代码:
|
||||
> 1. **将 `&-divergence_score` 改为连续值形式**
|
||||
> 2. **移除任何离散值列（如 market_condition 编码）**
|
||||
> 3. **保持使用 `LightGBMMultiTargetRegressor` 模型**
|
||||
> 4. **在策略中使用这些连续值进行动态交易决策**
|
||||
|
||||
|
||||
Error: {
|
||||
exit = 56,
|
||||
message = 'post https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions - curl error exit_code=56 stderr={ "curl: (56) OpenSSL SSL_read: error:0A000126:SSL routines::unexpected eof while reading, errno 0" }',
|
||||
stderr = '{ "curl: (56) OpenSSL SSL_read: error:0A000126:SSL routines::unexpected eof while reading, errno 0" }'
|
||||
}
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-31 08:40:04
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- freqtrade/templates/freqaiprimer.py
|
||||
|
||||
> 中断了,请继续
|
||||
|
||||
|
||||
非常理解，我们继续完成你的需求。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 你的目标
|
||||
|
||||
你希望：
|
||||
|
||||
1. **将 `&-divergence_score` 改为连续值形式**
|
||||
2. **移除任何离散值列（如 `&-market_condition`）**
|
||||
3. **保持使用 `LightGBMMultiTargetRegressor` 模型**
|
||||
4. **在策略中使用这些连续值进行动态交易决策**
|
||||
|
||||
---
|
||||
|
||||
## ✅ 实现步骤概览
|
||||
|
||||
我们将完成以下修改：
|
||||
|
||||
| 步骤 | 内容 |
|
||||
|------|------|
|
||||
| 🧮 Step 1 | 修改 `set_freqai_targets()`：生成连续背离评分 |
|
||||
| 🧹 Step 2 | 移除所有离散值列（如 `&-market_condition`） |
|
||||
| 📈 Step 3 | 配置 FreqAI 使用 `LightGBMMultiTargetRegressor` |
|
||||
| 🛠 Step 4 | 在 `populate_entry_trend()` 和 `populate_exit_trend()` 中使用连续背离信号 |
|
||||
|
||||
---
|
||||
|
||||
## 🧮 Step 1：修改 `set_freqai_targets()`，生成连续背离评分
|
||||
|
||||
```python
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
"""
|
||||
使用历史窗口预测未来趋势和波动率作为辅助信号，
|
||||
并新增一个连续值的背离评分列 &-divergence_score
|
||||
"""
|
||||
|
||||
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
|
||||
|
||||
# 1. 趋势强度：使用过去 N 根 K 线的收益率判断当前趋势
|
||||
dataframe["&-trend_strength"] = dataframe["close"].pct_change(label_period)
|
||||
|
||||
# 2. 波动率预测：使用过去 N 根 K 线的价格变动绝对值
|
||||
dataframe["&-volatility_forecast"] = dataframe["close"].pct_change(label_period).abs()
|
||||
|
||||
# 3. ROI 目标：基于趋势强度缩放
|
||||
dataframe["&-roi_target"] = np.where(
|
||||
dataframe["&-trend_strength"] > 0,
|
||||
dataframe["&-trend_strength"] * 1.5,
|
||||
0.01 # 最小 ROI
|
||||
)
|
||||
|
||||
# 4. 动态止损目标：基于波动率缩放
|
||||
dataframe["&-stoploss_target"] = -dataframe["&-volatility_forecast"] * 1.2
|
||||
|
||||
# 5. 新增：连续背离评分（价格与趋势的偏离程度）
|
||||
dataframe["price_change"] = dataframe["close"].pct_change(label_period)
|
||||
dataframe["trend_change"] = dataframe["&-trend_strength"].pct_change(label_period)
|
||||
|
||||
# 连续背离评分：正值表示正向背离，负值表示负向背离
|
||||
dataframe["&-divergence_score"] = dataframe["price_change"] - dataframe["trend_change"]
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🧹 Step 2：移除所有离散值列（如 `&-market_condition`）
|
||||
|
||||
如果你之前定义了 `&-market_condition` 或其他分类列，请从 `set_freqai_targets()` 和 `populate_indicators()` 中删除它们。
|
||||
|
||||
### ❌ 删除示例：
|
||||
```python
|
||||
# 删除以下代码（如果存在）
|
||||
if "bb_upperband-period" in dataframe.columns and "bb_lowerband-period" in dataframe.columns:
|
||||
dataframe["&-market_condition"] = np.select([
|
||||
(dataframe["close"] > dataframe["bb_upperband-period"]),
|
||||
(dataframe["close"] < dataframe["bb_lowerband-period"]),
|
||||
(dataframe["&-trend_strength"] > 0.03),
|
||||
], [
|
||||
"overbought",
|
||||
"oversold",
|
||||
"strong_up",
|
||||
], default="sideways")
|
||||
```
|
||||
|
||||
同时确保不再使用这些列做任何逻辑判断。
|
||||
|
||||
---
|
||||
|
||||
## 📈 Step 3：配置 FreqAI 使用 `LightGBMMultiTargetRegressor`
|
||||
|
||||
在你的策略类中设置：
|
||||
|
||||
```python
|
||||
freqai_info = {
|
||||
"model": "LightGBMMultiTargetRegressor", # ✅ 多目标回归模型
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m", "15m", "1h"],
|
||||
"label_period_candles": 24,
|
||||
"include_shifted_candles": 3,
|
||||
"principal_component_analysis": True, # 可选：启用 PCA 提升泛化能力
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": False,
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 200,
|
||||
"learning_rate": 0.05,
|
||||
"num_leaves": 31,
|
||||
"verbose": -1,
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
这样 FreqAI 将使用 LightGBM 的多输出回归模型来同时预测多个连续目标列。
|
||||
|
||||
---
|
||||
|
||||
## 🛠 Step 4：在策略中使用连续背离信号进行交易决策
|
||||
|
||||
### ✅ 示例：在 `populate_entry_trend()` 中使用 `&-divergence_score`
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = []
|
||||
|
||||
if "&-divergence_score" in df.columns:
|
||||
|
||||
# 正向背离：价格下跌但趋势上升（背离评分为正值）
|
||||
buy_condition = df["&-divergence_score"] > 0.01
|
||||
|
||||
# 结合趋势强度过滤
|
||||
if "&-trend_strength" in df.columns:
|
||||
buy_condition &= df["&-trend_strength"] > 0.005
|
||||
|
||||
# 成交量过滤：成交量需大于 20 日均值的 1 倍
|
||||
df["vol_mean"] = df["volume"].rolling(20).mean()
|
||||
buy_condition &= (df["volume"] > df["vol_mean"] * 1)
|
||||
|
||||
conditions.append(buy_condition)
|
||||
|
||||
if len(conditions):
|
||||
df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
|
||||
|
||||
return df
|
||||
```
|
||||
|
||||
### ✅ 示例：在 `populate_exit_trend()` 中使用 `&-divergence_score`
|
||||
|
||||
```python
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
conditions = []
|
||||
|
||||
if "&-divergence_score" in df.columns:
|
||||
|
||||
# 负向背离：价格上涨但趋势下降（背离评分为负值）
|
||||
sell_condition = df["&-divergence_score"] < -0.01
|
||||
|
||||
# 结合趋势强度过滤
|
||||
if "&-trend_strength" in df.columns:
|
||||
sell_condition &= df["&-trend_strength"] < -0.005
|
||||
|
||||
conditions.append(sell_condition)
|
||||
|
||||
if len(conditions):
|
||||
df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
|
||||
|
||||
return df
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📦 Step 5：更新 `plot_config`（可选）
|
||||
|
||||
你可以将背离评分加入绘图配置，方便可视化观察：
|
||||
|
||||
```python
|
||||
plot_config = {
|
||||
"main_plot": {},
|
||||
"subplots": {
|
||||
"Signals": {
|
||||
"enter_long": {"color": "green"},
|
||||
"exit_long": {"color": "red"}
|
||||
},
|
||||
"FreqAI Predictions": {
|
||||
"&-trend_strength": {"color": "blue"},
|
||||
"&-volatility_forecast": {"color": "orange"},
|
||||
"&-divergence_score": {"color": "purple"}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结改进点
|
||||
|
||||
| 改进项 | 描述 |
|
||||
|--------|------|
|
||||
| ✅ `&-divergence_score` 改为连续值 | 更适合回归模型学习 |
|
||||
| ✅ 移除所有离散值列 | 如 `&-market_condition` |
|
||||
| ✅ 使用 `LightGBMMultiTargetRegressor` | 同时预测多个连续目标 |
|
||||
| ✅ 在策略中使用背离评分进行交易决策 | 提高反转识别能力 |
|
||||
|
||||
---
|
||||
|
||||
## ✅ 下一步建议
|
||||
|
||||
你可以：
|
||||
|
||||
- 使用 `freqtrade plot-dataframe` 查看背离信号是否合理
|
||||
- 使用 `freqtrade backtesting` 测试新策略表现
|
||||
- 使用 `freqtrade hyperopt` 对背离阈值进行优化
|
||||
|
||||
---
|
||||
|
||||
如果你希望我帮你生成完整的策略文件，并提供对应的 `config.json` 示例，请告诉我 👇 我可以一步步帮你完成。
|
||||
|
||||
freqtrade plot-dataframe --strategy DivergenceRegressionStrategy \
|
||||
--timerange=20250501-20250510 \
|
||||
--timeframe=5m \
|
||||
--exchange=binance \
|
||||
--pair=ETH/USDT
|
||||
708
chat/result14.md
708
chat/result14.md
@ -1,708 +0,0 @@
|
||||
- Datetime: 2025-06-03 02:21:39
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- result/backtest-result-2025-06-03_01-41-33_FreqaiPrimer.py
|
||||
- result/analytic.log
|
||||
- result/backtest-result-2025-06-03_01-41-33.json
|
||||
- result/backtest-result-2025-06-03_01-41-33.meta.json
|
||||
- result/backtest-result-2025-06-03_01-41-33_FreqaiPrimer.json
|
||||
- result/backtest-result-2025-06-03_01-41-33_config.json
|
||||
- result/backtest_trades.csv
|
||||
- result/output.log
|
||||
|
||||
> 当前策略的配置,代码,执行结果都在上下文中, 结合官方文档, 请给出对于执行结果的评价以及修改建议
|
||||
>
|
||||
> 自定义策略: https://www.freqtrade.io/en/stable/strategy-customization/
|
||||
> freqai 特征工程 : https://www.freqtrade.io/en/stable/freqai-feature-engineering/
|
||||
> freqai 参数表: https://www.freqtrade.io/en/stable/freqai-parameter-table/
|
||||
> freqai 配置: https://www.freqtrade.io/en/stable/freqai-configuration/
|
||||
> freaai 运行: https://www.freqtrade.io/en/stable/freqai-running/
|
||||
> freqai 开发手册: https://www.freqtrade.io/en/stable/freqai-developers/
|
||||
> freqtrade 策略例子: https://github.com/freqtrade/freqtrade/tree/develop/freqtrade/templates
|
||||
> freqtrade 优化: https://github.com/freqtrade/freqtrade/tree/develop/freqtrade/optimize
|
||||
> freqai 超参数优化: https://www.freqtrade.io/en/stable/hyperopt/
|
||||
>
|
||||
>
|
||||
|
||||
|
||||
# 策略执行结果分析与优化建议
|
||||
|
||||
## 一、执行结果关键指标分析
|
||||
|
||||
### 总体表现
|
||||
- 总交易次数：8次
|
||||
- 盈利交易：4次 (50%胜率)
|
||||
- 总利润：8.87 USDT (+0.89%)
|
||||
- 平均持仓时间：21分钟
|
||||
- 最大连续亏损：2次
|
||||
- 利润因子：2.15
|
||||
|
||||
### 分币种表现
|
||||
| 币种 | 交易次数 | 平均利润 | 胜率 |
|
||||
|------------|----------|----------|--------|
|
||||
| KAITO/USDT | 4 | +0.77% | 50% |
|
||||
| TRUMP/USDT | 3 | +0.95% | 66.7% |
|
||||
| WCT/USDT | 1 | 0% | 0% |
|
||||
|
||||
### 分退出原因表现
|
||||
| 原因 | 交易次数 | 平均利润 | 胜率 |
|
||||
|-------------|----------|----------|--------|
|
||||
| ROI | 7 | +1.58% | 100% |
|
||||
| 止损 | 1 | -5.15% | 0% |
|
||||
|
||||
## 二、策略配置分析
|
||||
|
||||
### 1. FreqAI配置
|
||||
```json
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"data_kitchen": {"fillna": "ffill"},
|
||||
"freqaimodel": "LightGBMMultiTargetRegressor",
|
||||
"purge_old_models": 2,
|
||||
"identifier": "test58",
|
||||
"train_period_days": 7,
|
||||
"backtest_period_days": 1,
|
||||
"live_retrain_hours": 2,
|
||||
"outlier_detection": {"method": "IsolationForest", "contamination": 0.1},
|
||||
"feature_selection": {"method": "recursive_elimination"},
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["3m","15m","1h"],
|
||||
"include_corr_pairlist": ["BTC/USDT","SOL/USDT"],
|
||||
"outlier_protection_percentage": 0.1,
|
||||
"label_period_candles": 24,
|
||||
"include_shifted_candles": 3,
|
||||
"DI_threshold": 0.7,
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": false,
|
||||
"use_SVM_to_remove_outliers": true,
|
||||
"indicator_periods_candles": [10,20,50],
|
||||
"plot_feature_importances": 10
|
||||
},
|
||||
"data_split_parameters": {"test_size": 0.2, "shuffle": false},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 120,
|
||||
"learning_rate": 0.05,
|
||||
"num_leaves": 31,
|
||||
"verbose": -1,
|
||||
"max_depth": 8
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 2. 策略参数
|
||||
```python
|
||||
minimal_roi = {
|
||||
"0": 0.02,
|
||||
"30": 0.01,
|
||||
"60": 0
|
||||
}
|
||||
|
||||
stoploss = -0.015
|
||||
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.053
|
||||
trailing_stop_positive_offset = 0.152
|
||||
```
|
||||
|
||||
## 三、执行结果评价
|
||||
|
||||
### 1. 优势
|
||||
- **快速交易特性**：平均持仓时间仅21分钟，适合高频交易
|
||||
- **ROI设置合理**：最终盈利交易都实现了正收益
|
||||
- **止损机制有效**：唯一亏损交易及时止损(-5.15%)
|
||||
|
||||
### 2. 存在的问题
|
||||
- **交易机会少**：整个回测期间仅有8次交易，交易频率过低
|
||||
- **过度依赖FreqAI预测**：当`&-price_value_divergence`缺失时，策略几乎无法产生信号
|
||||
- **动态阈值调整不足**：虽然有labels_mean和labels_std的动态计算，但实际买卖点分布较集中
|
||||
- **特征工程冗余**：生成了大量技术指标，但很多未被使用
|
||||
- **日志记录过多**：DEBUG级别日志可能影响性能
|
||||
- **自定义价格机制效果有限**：买入价降低0.5%、卖出价提高0.25%，对整体收益贡献较小
|
||||
|
||||
## 四、优化建议
|
||||
|
||||
### 1. 提高交易频率
|
||||
- 缩小`fit_live_predictions_candles`从100到50，增加交易机会
|
||||
- 降低入场条件门槛：
|
||||
```python
|
||||
cond2 = (dataframe["volume_z_score"] > 1.0) # 原为1.5
|
||||
cond3 = (dataframe["rsi"] < 45) # 原为40
|
||||
```
|
||||
|
||||
### 2. 增强入场信号
|
||||
- 添加备用信号源，当FreqAI数据不可用时：
|
||||
```python
|
||||
if "&-price_value_divergence" not in dataframe.columns:
|
||||
buy_condition = (dataframe["rsi"] < 30) & (dataframe["close"] <= dataframe["bb_lowerband"])
|
||||
logger.warning(f"[{pair}] 使用备用策略: RSI+布林带")
|
||||
```
|
||||
|
||||
### 3. 优化动态阈值计算
|
||||
- 引入时间衰减因子，使阈值随市场波动自动调节：
|
||||
```python
|
||||
volatility = dataframe["bb_upperband-period"] / dataframe["bb_lowerband-period"] - 1
|
||||
k_volatility = 1 + (volatility.iloc[-1] * 10) # 波动率越高，k值越大
|
||||
|
||||
self.buy_threshold = labels_mean - k_buy * labels_std * k_volatility
|
||||
self.sell_threshold = labels_mean + k_sell * labels_std * k_volatility
|
||||
```
|
||||
|
||||
### 4. 简化特征工程
|
||||
- 移除未使用的指标：
|
||||
```python
|
||||
# 删除以下未被使用的指标
|
||||
del dataframe["%-sma-period"]
|
||||
del dataframe["%-mfi-period"]
|
||||
del dataframe["%-adx-period"]
|
||||
```
|
||||
|
||||
### 5. 改进Trailing Stop逻辑
|
||||
- 实现动态追踪止损：
|
||||
```python
|
||||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float,
|
||||
min_roi: Dict[float, float], max_profit: float):
|
||||
# 动态调整追踪止损参数
|
||||
hold_time = (current_time - trade.open_date_utc).total_seconds() / 60
|
||||
|
||||
# 持仓前5分钟使用保守止损
|
||||
if hold_time < 5:
|
||||
trailing_stop_start = 0.02
|
||||
trailing_stop_distance = 0.008
|
||||
# 持仓5-15分钟使用激进追涨
|
||||
elif hold_time < 15:
|
||||
trailing_stop_start = 0.03
|
||||
trailing_stop_distance = 0.01
|
||||
# 持仓超过15分钟后使用保守追踪
|
||||
else:
|
||||
trailing_stop_start = 0.015
|
||||
trailing_stop_distance = 0.005
|
||||
|
||||
# 剩余代码保持不变...
|
||||
```
|
||||
|
||||
### 6. 优化Hyperopt参数空间（用于后续优化）
|
||||
```python
|
||||
# ROI 参数空间
|
||||
def roi_space():
|
||||
return [
|
||||
Integer(0, 60, name='roi_t1'),
|
||||
Integer(30, 120, name='roi_t2'),
|
||||
Integer(60, 180, name='roi_t3'),
|
||||
Real(0.01, 0.05, name='roi_p1'),
|
||||
Real(0.005, 0.03, name='roi_p2'),
|
||||
Real(0.0, 0.02, name='roi_p3'),
|
||||
]
|
||||
|
||||
# Trailing Stop 参数空间
|
||||
def trailing_space():
|
||||
return [
|
||||
Real(0.01, 0.05, name='trailing_stop_start'),
|
||||
Real(0.005, 0.02, name='trailing_stop_distance'),
|
||||
]
|
||||
```
|
||||
|
||||
### 7. 风险控制增强
|
||||
- 添加最大单日亏损限制：
|
||||
```python
|
||||
def custom_stake_amount(self, pair, current_time, current_rate, proposed_stake, min_stake, max_stake):
|
||||
# 计算当日已实现亏损
|
||||
daily_loss = sum(trade.calc_profit() for trade in Trade.get_trades()
|
||||
if trade.close_date and trade.close_date.date() == current_time.date() and trade.loss)
|
||||
|
||||
if daily_loss < -100: # 当日亏损超过$100停止交易
|
||||
return 0
|
||||
|
||||
return proposed_stake * 0.5 # 默认使用50%推荐仓位
|
||||
```
|
||||
|
||||
## 五、FreqAI专项优化建议
|
||||
|
||||
### 1. 特征工程优化
|
||||
- 根据官方文档[FreqAI特征工程](https://www.freqtrade.io/en/stable/freqai-feature-engineering/)建议：
|
||||
```python
|
||||
def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
|
||||
# 保留核心特征
|
||||
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
|
||||
|
||||
# 添加趋势特征
|
||||
dataframe[f"%-sma-period-ratio"] = (dataframe["close"] / ta.SMA(dataframe, timeperiod=period)) - 1
|
||||
|
||||
# 添加波动率特征
|
||||
dataframe[f"%-volatility"] = (ta.ATR(dataframe, timeperiod=period) / dataframe["close"])
|
||||
|
||||
# 清理NaN值
|
||||
dataframe.fillna(method='ffill', inplace=True)
|
||||
dataframe.fillna(0, inplace=True)
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
### 2. 标签(Label)优化
|
||||
- 根据[官方建议](https://www.freqtrade.io/en/stable/freqai-parameter-table/)，可以尝试更复杂的标签：
|
||||
```python
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
# 多目标回归
|
||||
dataframe["&-target-profit"] = (dataframe["close"].shift(-self.freqai_info["label_period_candles"]) / dataframe["close"]) - 1
|
||||
dataframe["&-target-direction"] = np.where(
|
||||
dataframe["close"].shift(-self.freqai_info["label_period_candles"]) > dataframe["close"], 1, 0
|
||||
)
|
||||
|
||||
# 数据清理
|
||||
dataframe.replace([np.inf, -np.inf], 0, inplace=True)
|
||||
dataframe.fillna(0, inplace=True)
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
## 六、具体修改建议
|
||||
|
||||
### 1. 修改后的`__init__`方法
|
||||
```python
|
||||
def __init__(self, config: dict, *args, **kwargs):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
logger.setLevel(logging.INFO) # 降低默认日志级别
|
||||
logger.info("✅ 策略已初始化，日志级别设置为 INFO")
|
||||
self.trailing_stop_enabled = False
|
||||
self.pair_stats = {}
|
||||
self.stats_logged = False
|
||||
self.fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 50) # 减少到50根K线
|
||||
```
|
||||
|
||||
### 2. 修改后的`confirm_trade_entry`方法
|
||||
```python
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, **kwargs) -> bool:
|
||||
# 在 Hyperopt 或 Dry Run 模式下避免数据库查询
|
||||
if 'hyperopt' in self.config.get('runmode', '') or not hasattr(Trade, 'query') or Trade.query is None:
|
||||
return True
|
||||
|
||||
# 降低检查窗口到2分钟
|
||||
recent_trades = Trade.query.filter(
|
||||
Trade.pair == pair,
|
||||
Trade.is_open == False, # noqa: E712
|
||||
Trade.close_date > current_time - datetime.timedelta(minutes=2)
|
||||
).all()
|
||||
|
||||
if len(recent_trades) > 0:
|
||||
logger.debug(f"[{pair}] 2分钟内有{len(recent_trades)}笔近期交易，跳过本次入场")
|
||||
return False
|
||||
|
||||
self.trailing_stop_enabled = False
|
||||
return True
|
||||
```
|
||||
|
||||
### 3. 修改后的`adjust_trade_position`方法
|
||||
```python
|
||||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float,
|
||||
min_roi: Dict[float, float], max_profit: float):
|
||||
hold_time = (current_time - trade.open_date_utc).total_seconds() / 60
|
||||
|
||||
# 动态调整追踪止损
|
||||
if hold_time < 5:
|
||||
trailing_stop_start = 0.02
|
||||
trailing_stop_distance = 0.008
|
||||
elif hold_time < 15:
|
||||
trailing_stop_start = 0.03
|
||||
trailing_stop_distance = 0.01
|
||||
else:
|
||||
trailing_stop_start = 0.015
|
||||
trailing_stop_distance = 0.005
|
||||
|
||||
profit_ratio = (current_rate - trade.open_rate) / trade.open_rate
|
||||
|
||||
if profit_ratio >= trailing_stop_start and not self.trailing_stop_enabled:
|
||||
self.trailing_stop_enabled = True
|
||||
trade.adjust_max_rate(current_rate)
|
||||
logger.info(f"[{trade.pair}] 价格上涨超过 {trailing_stop_start*100:.1f}%，启动 Trailing Stop")
|
||||
|
||||
if self.trailing_stop_enabled:
|
||||
max_rate = trade.max_rate
|
||||
trailing_stop_price = max_rate * (1 - trailing_stop_distance)
|
||||
|
||||
if current_rate < trailing_stop_price:
|
||||
logger.info(f"[{trade.pair}] 价格回落至 Trailing Stop 点 {trailing_stop_price:.6f}，触发卖出")
|
||||
return -1
|
||||
|
||||
trade.adjust_max_rate(current_rate)
|
||||
|
||||
# 动态延长最大持仓时间
|
||||
if hold_time > 30 and current_profit < 0.005: # 小于0.5%收益时强制平仓
|
||||
logger.info(f"[{trade.pair}] 持仓时间超过30分钟且无明显收益，强制平仓")
|
||||
return -1
|
||||
|
||||
return None
|
||||
```
|
||||
|
||||
## 七、总结
|
||||
|
||||
该策略具有良好的风险控制能力，特别是追踪止损机制有效防止了大幅亏损。然而，策略过于保守导致交易机会较少，且对FreqAI预测结果依赖过高。
|
||||
|
||||
建议优先改进方向：
|
||||
1. 增加备用交易信号以应对FreqAI数据缺失情况
|
||||
2. 优化特征工程，移除冗余指标
|
||||
3. 实施动态仓位管理
|
||||
4. 细化追踪止损逻辑，根据持仓时间动态调整参数
|
||||
5. 降低最小持仓时间要求，增加交易机会
|
||||
|
||||
下一步建议进行Hyperopt优化，重点优化ROI和追踪止损参数，同时考虑加入更多市场状态判断来动态调整策略行为。
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-06-03 02:44:15
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- result/backtest-result-2025-06-03_01-41-33_FreqaiPrimer.py
|
||||
- result/analytic.log
|
||||
- result/backtest-result-2025-06-03_01-41-33.json
|
||||
- result/backtest-result-2025-06-03_01-41-33.meta.json
|
||||
- result/backtest-result-2025-06-03_01-41-33_FreqaiPrimer.json
|
||||
- result/backtest-result-2025-06-03_01-41-33_config.json
|
||||
- result/backtest_trades.csv
|
||||
- result/output.log
|
||||
|
||||
> 实际使用中,本策略往往在某个下跌趋势的过程中给出多个买入信号, 虽然还没有跌入到谷底,但也差的不多, 然后趋势开始反转, 反转之前一些买入已经被止损了, 没被止损的订单,在小幅上升过程中也都roi了, 真正能获利超过1%的并不多
|
||||
>
|
||||
|
||||
|
||||
你描述的这个现象非常典型：**策略在下跌趋势末期频繁尝试抄底，但多数交易因止损或小幅盈利退出，真正能捕捉到反转趋势的交易很少**。
|
||||
|
||||
---
|
||||
|
||||
## 📉 问题本质分析
|
||||
|
||||
### ✅ 现象总结：
|
||||
- **下跌趋势中频繁给出买入信号**
|
||||
- **部分订单被止损（stop_loss）**
|
||||
- **未止损的订单在小幅反弹后触发 ROI 卖出**
|
||||
- **最终实现超过1%利润的交易很少**
|
||||
|
||||
这说明：
|
||||
|
||||
| 问题类型 | 描述 |
|
||||
|---------|------|
|
||||
| ❌ 过早入场 | 在趋势尚未明确反转时就开始抄底 |
|
||||
| ❌ 止损设置不合理 | 在震荡行情中容易被洗出去 |
|
||||
| ❌ ROI 设置过紧 | 小幅反弹就卖出，错过后续上涨空间 |
|
||||
| ❌ 缺乏趋势过滤机制 | 没有判断当前是下跌、震荡还是上升趋势 |
|
||||
|
||||
---
|
||||
|
||||
## 🔍 结合你的策略代码和回测结果分析
|
||||
|
||||
从你的原始策略来看：
|
||||
|
||||
```python
|
||||
buy_condition = (dataframe["&-price_value_divergence"] < self.buy_threshold) & \
|
||||
(dataframe["volume_z_score"] > 1.5) & \
|
||||
(dataframe["rsi"] < 40) & \
|
||||
(dataframe["close"] <= dataframe["bb_lowerband"])
|
||||
```
|
||||
|
||||
这些条件在下跌趋势末端会频繁满足，因为：
|
||||
|
||||
- `&-price_value_divergence` 偏离越来越大
|
||||
- RSI 经常低于 40
|
||||
- 价格经常触及布林带下轨
|
||||
|
||||
所以导致了**频繁抄底失败**。
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ 改进建议（实战级）
|
||||
|
||||
### ✅ 1. 添加趋势过滤器（Trend Filter）
|
||||
|
||||
避免在下跌趋势中盲目抄底，只允许在特定趋势中开仓。
|
||||
|
||||
#### 示例：使用 EMA 判断趋势方向
|
||||
|
||||
```python
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ...原有指标计算...
|
||||
|
||||
# 趋势判断
|
||||
dataframe['trend_ema'] = ta.EMA(dataframe, timeperiod=50)
|
||||
dataframe['trend_direction'] = np.where(
|
||||
dataframe['close'] > dataframe['trend_ema'], 'up',
|
||||
np.where(dataframe['close'] < dataframe['trend_ema'], 'down', 'sideways')
|
||||
)
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
然后在 `populate_entry_trend` 中加入趋势判断：
|
||||
|
||||
```python
|
||||
if "&-price_value_divergence" in dataframe.columns:
|
||||
buy_condition = (
|
||||
(dataframe["&-price_value_divergence"] < self.buy_threshold) &
|
||||
(dataframe["volume_z_score"] > 1.5) &
|
||||
(dataframe["rsi"] < 40) &
|
||||
(dataframe["close"] <= dataframe["bb_lowerband"]) &
|
||||
(dataframe['trend_direction'] != 'down') # 避免在下降趋势中买入
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 2. 动态调整止损和ROI（Adaptive Exit）
|
||||
|
||||
不要固定止损和ROI，而是根据市场波动率动态调整。
|
||||
|
||||
#### 示例：基于ATR的动态止损
|
||||
|
||||
```python
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ...其他指标...
|
||||
|
||||
# ATR用于动态止损
|
||||
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
|
||||
dataframe['dynamic_stoploss'] = -0.01 - (dataframe['atr'] / dataframe['close']) * 1.5
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
然后在策略中使用它：
|
||||
|
||||
```python
|
||||
def __init__(self, config: dict, *args, **kwargs):
|
||||
super().__init__(config, *args, **kwargs)
|
||||
self.stoploss = RealParameter(-0.03, -0.01, default=-0.015, space="all")
|
||||
```
|
||||
|
||||
这样可以防止在波动大的时候被轻易止损。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 3. 延迟入场（Delay Entry Until Confirmation）
|
||||
|
||||
不要一满足条件就入场，等待确认信号。
|
||||
|
||||
#### 示例：RSI + MACD 双重确认
|
||||
|
||||
```python
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# ...其他指标...
|
||||
dataframe['macd'], dataframe['signal'], _ = ta.MACD(dataframe)
|
||||
dataframe['macd_cross_up'] = (dataframe['macd'] > dataframe['signal']) & \
|
||||
(dataframe['macd'].shift(1) <= dataframe['signal'].shift(1))
|
||||
return dataframe
|
||||
```
|
||||
|
||||
然后修改入场逻辑：
|
||||
|
||||
```python
|
||||
buy_condition = (
|
||||
(dataframe["&-price_value_divergence"] < self.buy_threshold) &
|
||||
(dataframe["volume_z_score"] > 1.5) &
|
||||
(data
|
||||
|
||||
|
||||
Error: {
|
||||
exit = 56,
|
||||
message = 'post https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions - curl error exit_code=56 stderr={ "curl: (56) OpenSSL SSL_read: error:0A000126:SSL routines::unexpected eof while reading, errno 0" }',
|
||||
stderr = '{ "curl: (56) OpenSSL SSL_read: error:0A000126:SSL routines::unexpected eof while reading, errno 0" }'
|
||||
}
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-06-03 02:45:22
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- result/backtest-result-2025-06-03_01-41-33_FreqaiPrimer.py
|
||||
- result/analytic.log
|
||||
- result/backtest-result-2025-06-03_01-41-33.json
|
||||
- result/backtest-result-2025-06-03_01-41-33.meta.json
|
||||
- result/backtest-result-2025-06-03_01-41-33_FreqaiPrimer.json
|
||||
- result/backtest-result-2025-06-03_01-41-33_config.json
|
||||
- result/backtest_trades.csv
|
||||
- result/output.log
|
||||
|
||||
> 输出中断了,请重新输出
|
||||
>
|
||||
|
||||
|
||||
### ✅ 3. 延迟入场（Delay Entry Until Confirmation）（续）
|
||||
|
||||
```python
|
||||
(dataframe["rsi"] < 40) &
|
||||
(dataframe["close"] <= dataframe["bb_lowerband"]) &
|
||||
(dataframe['trend_direction'] != 'down') &
|
||||
(dataframe['macd_cross_up']) # MACD金叉作为确认信号
|
||||
)
|
||||
```
|
||||
|
||||
这样可以避免在下跌趋势中过早抄底。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 4. 放宽 ROI 设置，让利润奔跑（Let Profits Run）
|
||||
|
||||
你提到“小幅反弹就ROI了”，说明 ROI 设置太紧。我们可以：
|
||||
|
||||
- **延长 ROI 时间**
|
||||
- **使用动态 ROI**
|
||||
|
||||
#### 示例：基于波动率的动态 ROI
|
||||
|
||||
```python
|
||||
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
|
||||
base_roi = {
|
||||
"0": params.get('roi_p1', 0.02),
|
||||
"7": params.get('roi_t1', 0.015),
|
||||
"21": params.get('roi_t2', 0.008),
|
||||
"27": 0
|
||||
}
|
||||
|
||||
# 根据市场波动率调整ROI
|
||||
volatility = self.current_volatility # 来自历史ATR或标准差
|
||||
if volatility > 0.02: # 高波动时放宽ROI
|
||||
base_roi["0"] *= 1.5
|
||||
base_roi["7"] *= 1.5
|
||||
|
||||
return base_roi
|
||||
```
|
||||
|
||||
或者直接设置更宽松的默认值：
|
||||
|
||||
```python
|
||||
minimal_roi = {
|
||||
"0": 0.092,
|
||||
"7": 0.056,
|
||||
"21": 0.024,
|
||||
"27": 0
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 5. 使用 FreqAI 的预测方向作为过滤器
|
||||
|
||||
既然你已经在用 FreqAI，可以利用它的预测值来判断趋势方向：
|
||||
|
||||
```python
|
||||
if "&-price_value_divergence" in dataframe.columns:
|
||||
# 只有当预测值开始上升时才允许买入
|
||||
dataframe["prediction_up"] = (dataframe["&-price_value_divergence"].diff() > 0)
|
||||
|
||||
buy_condition = (
|
||||
(dataframe["&-price_value_divergence"] < self.buy_threshold) &
|
||||
(dataframe["volume_z_score"] > 1.5) &
|
||||
(dataframe["rsi"] < 40) &
|
||||
(dataframe["close"] <= dataframe["bb_lowerband"]) &
|
||||
(dataframe["prediction_up"])
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 6. 加入“连续亏损”保护机制
|
||||
|
||||
防止在连续止损后继续交易，建议加入一个冷却期：
|
||||
|
||||
```python
|
||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time: datetime, **kwargs) -> bool:
|
||||
recent_trades = Trade.query.filter(
|
||||
Trade.pair == pair,
|
||||
Trade.is_open == False,
|
||||
Trade.close_date > current_time - datetime.timedelta(minutes=30)
|
||||
).all()
|
||||
|
||||
losing_trades = [t for t in recent_trades if t.calc_profit_ratio(rate) < 0]
|
||||
|
||||
if len(losing_trades) >= 2:
|
||||
logger.info(f"[{pair}] 最近2次交易亏损，暂停入场")
|
||||
return False
|
||||
|
||||
self.trailing_stop_enabled = False
|
||||
return True
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 7. 自定义出场逻辑优化
|
||||
|
||||
目前你的出场逻辑是：
|
||||
|
||||
```python
|
||||
sell_condition = (dataframe["&-price_value_divergence"] > self.sell_threshold) | (dataframe["rsi"] > 75)
|
||||
```
|
||||
|
||||
建议改为**分阶段卖出**，比如：
|
||||
|
||||
```python
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
if "&-price_value_divergence" in dataframe.columns:
|
||||
# 分阶段退出
|
||||
dataframe.loc[
|
||||
(dataframe["&-price_value_divergence"] > self.sell_threshold * 1.2),
|
||||
'exit_long_partial'
|
||||
] = 1 # 先卖一半
|
||||
|
||||
dataframe.loc[
|
||||
(dataframe["&-price_value_divergence"] > self.sell_threshold) |
|
||||
(dataframe["rsi"] > 75),
|
||||
'exit_long_full'
|
||||
] = 1 # 再卖另一半
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
然后结合 `custom_sell` 实现部分卖出逻辑。
|
||||
|
||||
---
|
||||
|
||||
## 📊 总结：策略改进优先级建议
|
||||
|
||||
| 改进点 | 优先级 | 说明 |
|
||||
|--------|--------|------|
|
||||
| ✅ 添加趋势过滤器 | ⭐⭐⭐⭐⭐ | 避免在下降趋势中盲目抄底 |
|
||||
| ✅ 动态止损和ROI | ⭐⭐⭐⭐ | 提高止损能力，让利润奔跑 |
|
||||
| ✅ 延迟入场确认 | ⭐⭐⭐⭐ | 等待MACD、RSI等指标确认反转 |
|
||||
| ✅ 利用FreqAI预测方向 | ⭐⭐⭐ | 过滤掉预测值仍在下降的信号 |
|
||||
| ✅ 分阶段出场 | ⭐⭐⭐ | 锁定部分利润，保留上涨空间 |
|
||||
| ✅ 连续亏损保护 | ⭐⭐ | 防止连败扩大回撤 |
|
||||
|
||||
---
|
||||
|
||||
## 🧪 下一步建议
|
||||
|
||||
你可以先从以下两个改动入手，快速验证效果：
|
||||
|
||||
### 🔧 修改1：添加趋势过滤器
|
||||
|
||||
```python
|
||||
# 在 populate_indicators 中添加
|
||||
dataframe['trend_ema'] = ta.EMA(dataframe, timeperiod=50)
|
||||
dataframe['in_uptrend'] = dataframe['close'] > dataframe['trend_ema']
|
||||
|
||||
# 在 populate_entry_trend 中添加
|
||||
buy_condition = buy_condition & (dataframe['in_uptrend'])
|
||||
```
|
||||
|
||||
### 🔧 修改2：放宽止损和ROI
|
||||
|
||||
```python
|
||||
minimal_roi = {
|
||||
"0": 0.092,
|
||||
"7": 0.056,
|
||||
"21": 0.024,
|
||||
"27": 0
|
||||
}
|
||||
|
||||
stoploss = -0.05 # 原为 -0.015，提高容忍度
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
如果你希望我帮你生成完整的修改后策略代码文件，我可以直接输出一份**优化后的完整策略文件**供你测试。是否需要？
|
||||
|
||||
543
chat/result15.md
543
chat/result15.md
@ -1,543 +0,0 @@
|
||||
> 当前策略的config, freqai部分, 如何调整配置能提升策略表现
|
||||
>
|
||||
|
||||
你当前的 FreqAI 配置已经非常完整，涵盖了数据预处理、特征工程、模型训练、异常检测等多个方面。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 当前配置亮点
|
||||
|
||||
| 模块 | 说明 |
|
||||
|------|------|
|
||||
| 多时间帧特征 | `include_timeframes`: ["3m", "15m", "1h"] |
|
||||
| 相关币种特征 | 使用 BTC/USDT 和 SOL/USDT 做相关性分析 |
|
||||
| 标签周期 | `label_period_candles`: 24（预测未来 24 根 K 线） |
|
||||
| 特征选择 | 使用递归消除法（recursive_elimination） |
|
||||
| 异常检测 | 使用 IsolationForest + SVM 过滤异常值 |
|
||||
| 模型参数 | LightGBMRegressor，n_estimators=400 |
|
||||
|
||||
---
|
||||
|
||||
## 🚀 如何优化 FreqAI 配置以提升策略表现？
|
||||
|
||||
以下是 **6 个关键优化方向**，每个都附有建议配置和理由：
|
||||
|
||||
---
|
||||
|
||||
### ✅ 1. **增加标签周期长度**
|
||||
|
||||
> 当前：`label_period_candles: 24`
|
||||
|
||||
#### 🔧 建议：
|
||||
```json
|
||||
"label_period_candles": 48
|
||||
```
|
||||
|
||||
#### 📌 理由：
|
||||
- 更长的标签周期可以让模型学习更远期的趋势
|
||||
- 对于中长期趋势策略更有帮助
|
||||
- 可结合 ROI 设置进行匹配
|
||||
|
||||
---
|
||||
|
||||
### ✅ 2. **使用 PCA 降维（Principal Component Analysis）**
|
||||
|
||||
> 当前：`principal_component_analysis`: false
|
||||
|
||||
#### 🔧 建议：
|
||||
```json
|
||||
"principal_component_analysis": true
|
||||
```
|
||||
|
||||
#### 📌 理由：
|
||||
- 减少冗余特征，提高模型泛化能力
|
||||
- 对抗过拟合
|
||||
- 尤其适用于多时间帧 + 多币种场景
|
||||
|
||||
---
|
||||
|
||||
### ✅ 3. **调整模型训练参数（LightGBM）**
|
||||
|
||||
> 当前：`n_estimators`: 400, `learning_rate`: 0.01
|
||||
|
||||
#### 🔧 建议：
|
||||
```json
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 600,
|
||||
"learning_rate": 0.005,
|
||||
"num_leaves": 40,
|
||||
"max_depth": 10,
|
||||
"min_data_in_leaf": 20,
|
||||
"feature_fraction": 0.8,
|
||||
"bagging_fraction": 0.8,
|
||||
"bagging_freq": 5
|
||||
}
|
||||
```
|
||||
|
||||
#### 📌 理由：
|
||||
- 更多迭代次数 + 更低学习率 = 更稳定收敛
|
||||
- 增加叶子数和深度可以捕捉更复杂的模式
|
||||
- 添加正则化项防止过拟合
|
||||
|
||||
---
|
||||
|
||||
### ✅ 4. **增加更多相关币种用于特征提取**
|
||||
|
||||
> 当前：`include_corr_pairlist`: ["BTC/USDT", "SOL/USDT"]
|
||||
|
||||
#### 🔧 建议：
|
||||
```json
|
||||
"include_corr_pairlist": ["BTC/USDT", "ETH/USDT", "SOL/USDT", "XRP/USDT"]
|
||||
```
|
||||
|
||||
#### 📌 理由：
|
||||
- 不同币种之间存在联动效应
|
||||
- 增加更多主流币种可以增强市场状态识别能力
|
||||
- 尤其在牛市或熊市中效果显著
|
||||
|
||||
---
|
||||
|
||||
### ✅ 5. **启用增量训练（Incremental Training）**
|
||||
|
||||
> 当前未启用
|
||||
|
||||
#### 🔧 建议：
|
||||
```json
|
||||
"incremental_train": true
|
||||
```
|
||||
|
||||
#### 📌 理由：
|
||||
- 在 live trading 中持续更新模型，而不是完全重训练
|
||||
- 提高模型对最新市场的适应速度
|
||||
- 减少 retrain 时间开销
|
||||
|
||||
---
|
||||
|
||||
### ✅ 6. **调整 outlier_protection_percentage**
|
||||
|
||||
> 当前：`outlier_protection_percentage`: 0.1
|
||||
|
||||
#### 🔧 建议：
|
||||
```json
|
||||
"outlier_protection_percentage": 0.15
|
||||
```
|
||||
|
||||
#### 📌 理由：
|
||||
- 市场波动剧烈时，适当放宽异常值过滤范围
|
||||
- 防止误删极端行情下的有效信号
|
||||
- 可结合 volatility_forecast 动态调整
|
||||
|
||||
---
|
||||
|
||||
## ✅ 推荐的优化后 FreqAI 配置（只展示修改部分）
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"identifier": "test58",
|
||||
"train_period_days": 7,
|
||||
"backtest_period_days": 1,
|
||||
"live_retrain_hours": 2,
|
||||
"purge_old_models": 2,
|
||||
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["3m", "15m", "1h"],
|
||||
"include_corr_pairlist": ["BTC/USDT", "ETH/USDT", "SOL/USDT", "XRP/USDT"],
|
||||
"label_period_candles": 48,
|
||||
"include_shifted_candles": 3,
|
||||
"DI_threshold": 0.7,
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": true,
|
||||
"use_SVM_to_remove_outliers": true,
|
||||
"indicator_periods_candles": [10, 20, 50],
|
||||
"plot_feature_importances": 10,
|
||||
"outlier_protection_percentage": 0.15
|
||||
},
|
||||
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": false
|
||||
},
|
||||
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 600,
|
||||
"learning_rate": 0.005,
|
||||
"num_leaves": 40,
|
||||
"max_depth": 10,
|
||||
"min_data_in_leaf": 20,
|
||||
"feature_fraction": 0.8,
|
||||
"bagging_fraction": 0.8,
|
||||
"bagging_freq": 5,
|
||||
"verbose": -1
|
||||
},
|
||||
|
||||
"incremental_train": true
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 总结：推荐优化一览表
|
||||
|
||||
| 优化点 | 是否推荐 | 描述 |
|
||||
|--------|----------|------|
|
||||
| 增加 label_period_candles | ✅ 是 | 学习更远期趋势 |
|
||||
| 启用 PCA 降维 | ✅ 是 | 减少冗余特征 |
|
||||
| 调整 LightGBM 参数 | ✅ 是 | 提高模型性能 |
|
||||
| 增加相关币种 | ✅ 是 | 增强市场感知 |
|
||||
| 启用增量训练 | ✅ 是 | 提升实时适应性 |
|
||||
| 放宽异常值过滤 | ✅ 是 | 避免误删极端行情 |
|
||||
|
||||
---
|
||||
|
||||
🎯 **下一步建议：**
|
||||
|
||||
你可以先尝试以下组合进行 backtest 测试：
|
||||
|
||||
```bash
|
||||
freqtrade backtesting --strategy freqaiprimer --config config_examples/freqaiprimer.json --timerange 20240101-20250101 --freqaimodel LightGBMMultiTargetRegressor
|
||||
```
|
||||
|
||||
如果你希望我帮你：
|
||||
- 实现动态调整 label_period_candles
|
||||
- 加入模型漂移检测
|
||||
- 自动调整 outlier_protection_percentage
|
||||
|
||||
请告诉我你想优先实现哪个功能，我可以为你定制这部分逻辑。
|
||||
|
||||
------
|
||||
|
||||
- Datetime: 2025-06-07 02:11:35
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- result/backtest-result-2025-06-06_17-55-14.json
|
||||
- result/analytic.log
|
||||
- result/backtest-result-2025-06-06_17-55-14.meta.json
|
||||
- result/backtest-result-2025-06-06_17-55-14_FreqaiPrimer.json
|
||||
- result/backtest-result-2025-06-06_17-55-14_FreqaiPrimer.py
|
||||
- result/backtest-result-2025-06-06_17-55-14_config.json
|
||||
- result/backtest_trades.csv
|
||||
- result/output.log
|
||||
|
||||
> 在下行行情下,这个策略会亏损严重, 请给分析原因和解决策略
|
||||
|
||||
你当前的策略在**下行行情中表现不佳，亏损严重**，这是一个非常典型的问题。我们来从多个角度分析原因，并提出针对性的优化建议。
|
||||
|
||||
---
|
||||
|
||||
## 📉 一、回测结果总结（关键数据）
|
||||
|
||||
| 指标 | 数值 | 说明 |
|
||||
|------|------|------|
|
||||
| 总交易数 | 10 | 少量交易但亏损集中 |
|
||||
| 胜率 | 20% | 非常低，仅 2 赢，5 输 |
|
||||
| 平均盈利 | -1.96% | 所有交易平均亏损近 2% |
|
||||
| 最大单笔亏损 | -5.15%（TRUMP/USDT） | 单笔最大亏损超过 5% |
|
||||
| ROI 设置 | `{"0": 0.028, "12": 0.016, "48": 0.009, "120": 0}` | 初始目标为 2.8%，但未达成 |
|
||||
| Trailing Stop 设置 | `trailing_stop_positive = 0.237` | 追踪止损偏高，可能无法及时止盈 |
|
||||
|
||||
---
|
||||
|
||||
## 🔍 二、亏损原因分析
|
||||
|
||||
### ✅ 1. **趋势判断滞后 + 止损设置不合理**
|
||||
|
||||
- 策略使用了 EMA200 和价格背离作为主要信号
|
||||
- 在下行行情中：
|
||||
- EMA200 反应慢，导致入场点位滞后
|
||||
- 价格背离信号容易“假突破”，造成频繁误判
|
||||
- 当前 trailing stop 设置为 `0.237`，但在下跌行情中根本无法触发，反而被提前止损
|
||||
|
||||
#### 📌 示例：
|
||||
|
||||
```json
|
||||
"trailing_stop_positive": 0.237,
|
||||
"trailing_stop_positive_offset": 0.318,
|
||||
```
|
||||
|
||||
这意味着只有当价格反弹到比最低价高出 23.7% 才会开始追踪，这在下跌趋势中几乎不可能实现。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 2. **模型预测偏差 + 动态阈值失效**
|
||||
|
||||
你的策略依赖 FreqAI 的 `&-price_value_divergence` 来判断买卖点，但在下行行情中：
|
||||
|
||||
- 模型输出的 `labels_mean` 和 `labels_std` 偏移较大
|
||||
- 动态阈值计算逻辑过于简单，没有考虑市场状态变化
|
||||
- 导致买入信号过早触发，卖出信号延迟或缺失
|
||||
|
||||
#### 📌 示例代码段：
|
||||
|
||||
```python
|
||||
self.buy_threshold = labels_mean - k_buy * labels_std
|
||||
self.sell_threshold = labels_mean + k_sell * labels_std
|
||||
```
|
||||
|
||||
在下行行情中，`labels_mean` 下降，但 `k_buy * labels_std` 仍保持不变，导致买点不准确。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 3. **缺乏空头保护机制**
|
||||
|
||||
你的策略是纯多头策略（`is_short: false`），但在下跌行情中：
|
||||
|
||||
- 没有做空机制
|
||||
- 没有对冲手段
|
||||
- 没有根据大盘走势动态调整仓位或暂停交易
|
||||
|
||||
#### 📌 回测日志显示：
|
||||
|
||||
```log
|
||||
Market change: -6.96%
|
||||
```
|
||||
|
||||
大盘整体下跌近 7%，而你的策略仍然坚持做多，导致系统性亏损。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 4. **入场条件过于宽松**
|
||||
|
||||
你的入场条件包括：
|
||||
|
||||
```python
|
||||
cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
|
||||
cond2 = (dataframe["volume_z_score"] > 1.5)
|
||||
cond3 = (dataframe["rsi"] < 40)
|
||||
cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
|
||||
buy_condition = cond1 & cond2 & cond3 & cond4
|
||||
```
|
||||
|
||||
这些条件在下跌行情中很容易满足，尤其是 RSI < 40 和 close 在布林下轨以下，容易形成“抄底陷阱”。
|
||||
|
||||
---
|
||||
|
||||
### ✅ 5. **出场逻辑单一 + ROI 不灵活**
|
||||
|
||||
- ROI 设置为固定时间衰减曲线，但在下跌行情中需要更激进的止损
|
||||
- trailing stop 设置过高，无法有效锁定利润
|
||||
- sell_condition 中只有一个 RSI > 75 的条件，太弱
|
||||
|
||||
---
|
||||
|
||||
## 🚀 三、解决方案与优化建议
|
||||
|
||||
### ✅ 1. **加入大盘趋势过滤器（Market Trend Filter）**
|
||||
|
||||
你可以通过 BTC/USDT 或 ETH/USDT 的趋势来判断是否开启交易。
|
||||
|
||||
#### 🔧 修改 `populate_indicators()`：
|
||||
|
||||
```python
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# 获取 BTC/USDT 的趋势
|
||||
btc_df = self.dp.get_pair_dataframe("BTC/USDT", self.timeframe)
|
||||
dataframe["btc_ema200"] = ta.EMA(btc_df, timeperiod=200)
|
||||
dataframe["btc_trend"] = (btc_df["close"] > dataframe["btc_ema200"]).astype(int)
|
||||
|
||||
# 如果 BTC 处于下降趋势，则禁止交易
|
||||
if dataframe["btc_trend"].iloc[-1] == 0:
|
||||
logger.info(f"[{metadata['pair']}] BTC 处于下降趋势，暂停交易")
|
||||
dataframe["enter_long"] = 0
|
||||
return dataframe
|
||||
|
||||
# 否则继续执行原有逻辑...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 2. **动态调整 trailing stop 参数**
|
||||
|
||||
在下跌行情中，适当降低 trailing stop 的距离，提高退出速度。
|
||||
|
||||
#### 🔧 修改 `adjust_trade_position()`：
|
||||
|
||||
```python
|
||||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float,
|
||||
min_roi: Dict[float, float], max_profit: float):
|
||||
hold_time = (current_time - trade.open_date_utc).total_seconds() / 60
|
||||
|
||||
if hold_time < 15:
|
||||
return None
|
||||
|
||||
profit_ratio = (current_rate - trade.open_rate) / trade.open_rate
|
||||
|
||||
# 如果大盘处于下跌趋势，缩短 trailing stop 距离
|
||||
market_trend = self.get_market_trend()
|
||||
if market_trend == 'bear':
|
||||
self.trailing_stop_distance = 0.005 # 更快退出
|
||||
self.trailing_stop_start = 0.01
|
||||
else:
|
||||
self.trailing_stop_distance = 0.01
|
||||
self.trailing_stop_start = 0.03
|
||||
|
||||
if profit_ratio >= self.trailing_stop_start and not self.trailing_stop_enabled:
|
||||
self.trailing_stop_enabled = True
|
||||
trade.adjust_max_rate(current_rate)
|
||||
|
||||
if self.trailing_stop_enabled:
|
||||
max_rate = trade.max_rate
|
||||
trailing_stop_price = max_rate * (1 - self.trailing_stop_distance)
|
||||
if current_rate < trailing_stop_price:
|
||||
return -1
|
||||
trade.adjust_max_rate(current_rate)
|
||||
|
||||
if hold_time > 30:
|
||||
return -1
|
||||
|
||||
return None
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 3. **优化入场条件：增加趋势确认机制**
|
||||
|
||||
避免在下跌趋势中盲目抄底。
|
||||
|
||||
#### 🔧 修改 `populate_entry_trend()`：
|
||||
|
||||
```python
|
||||
if "&-price_value_divergence" in dataframe.columns:
|
||||
# 新增：只在价格高于短期均线时才允许入场
|
||||
dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
|
||||
cond5 = (dataframe["close"] > dataframe["ema20"])
|
||||
|
||||
cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
|
||||
cond2 = (dataframe["volume_z_score"] > 1.5)
|
||||
cond3 = (dataframe["rsi"] < 40)
|
||||
cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
|
||||
|
||||
buy_condition = cond1 & cond2 & cond3 & cond4 & cond5
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 4. **动态调整 ROI 目标**
|
||||
|
||||
在下跌行情中，ROI 应该更快衰减，甚至设为负值以强制止损。
|
||||
|
||||
#### 🔧 修改 `generate_roi_table()`：
|
||||
|
||||
```python
|
||||
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
|
||||
market_trend = self.get_market_trend()
|
||||
|
||||
if market_trend == 'bear':
|
||||
roi_table = {
|
||||
"0": -0.01, # 强制止损
|
||||
"5": 0.005, # 极端情况下微利退出
|
||||
"15": 0.01,
|
||||
"30": 0.015
|
||||
}
|
||||
else:
|
||||
roi_table = {
|
||||
"0": params.get("roi_t0", 0.02),
|
||||
"30": params.get("roi_t1", 0.01),
|
||||
"60": params.get("roi_t2", 0.0),
|
||||
}
|
||||
|
||||
return roi_table
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 5. **加入大盘联动因子（Pair Correlation）**
|
||||
|
||||
你可以利用 BTC/USDT 的波动率来动态调整其他币种的止损幅度。
|
||||
|
||||
#### 🔧 示例逻辑：
|
||||
|
||||
```python
|
||||
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
|
||||
# 获取 BTC 波动率
|
||||
btc_df = self.dp.get_pair_dataframe("BTC/USDT", self.timeframe)
|
||||
volatility = (btc_df["close"].pct_change(24)).abs().mean()
|
||||
|
||||
base_stop = -0.05 # 基础止损
|
||||
adjusted_stop = base_stop * (1 + volatility * 2) # 波动越大，止损越紧
|
||||
|
||||
return max(adjusted_stop, -0.1) # 最大不超过 -10%
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 6. **加入“市场状态识别”模块**
|
||||
|
||||
你可以使用 RSI + ADX + Volatility 综合判断市场状态。
|
||||
|
||||
#### 🔧 示例逻辑：
|
||||
|
||||
```python
|
||||
def get_market_trend(self, dataframe: DataFrame) -> str:
|
||||
rsi = dataframe["rsi"].iloc[-1]
|
||||
adx = dataframe["adx"].iloc[-1]
|
||||
trend_strength = abs(dataframe["trend_strength"].iloc[-1])
|
||||
|
||||
if adx > 25 and trend_strength < -0.01:
|
||||
return "bear"
|
||||
elif adx > 25 and trend_strength > 0.01:
|
||||
return "bull"
|
||||
else:
|
||||
return "sideways"
|
||||
```
|
||||
|
||||
然后在 `populate_entry_trend()` 和 `custom_stoploss()` 中使用这个状态。
|
||||
|
||||
---
|
||||
|
||||
## ✅ 四、推荐配置修改（config.json）
|
||||
|
||||
```json
|
||||
{
|
||||
"stoploss": -0.1,
|
||||
"minimal_roi": {
|
||||
"0": -0.01,
|
||||
"5": 0.005,
|
||||
"15": 0.01,
|
||||
"30": 0.015
|
||||
},
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.01,
|
||||
"trailing_stop_positive_offset": 0.02,
|
||||
"trailing_only_offset_is_reached": true
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 五、总结：亏损原因 & 对策一览表
|
||||
|
||||
| 问题 | 原因 | 解决方案 |
|
||||
|------|------|----------|
|
||||
| 趋势判断滞后 | 使用 EMA200 + price divergence | 加入 BTC 趋势过滤器 |
|
||||
| 动态阈值失效 | 没有考虑市场状态 | 根据市场状态调整 buy/sell threshold |
|
||||
| 止损设置不合理 | trailing_stop_positive 过高 | 动态调整 trailing stop |
|
||||
| 入场条件宽松 | 容易抄底失败 | 加入 EMA20 过滤 |
|
||||
| 缺乏空头保护 | 没有做空机制 | 加入大盘趋势过滤器 |
|
||||
| ROI 设置不合理 | 固定衰减，未适应下跌行情 | 动态 ROI 表 |
|
||||
| 模型漂移 | labels_mean/std 偏移 | 加入 Z-score 标准化 |
|
||||
|
||||
---
|
||||
|
||||
🎯 **下一步建议：**
|
||||
|
||||
你可以优先实现以下功能之一，我为你定制具体代码：
|
||||
|
||||
| 功能 | 推荐指数 | 描述 |
|
||||
|------|----------|------|
|
||||
| BTC 趋势过滤器 | ⭐⭐⭐⭐⭐ | 防止在熊市中盲目做多 |
|
||||
| 动态 trailing stop | ⭐⭐⭐⭐ | 提高退出效率 |
|
||||
| 动态 ROI 表 | ⭐⭐⭐⭐ | 自适应不同市场环境 |
|
||||
| 市场状态识别 | ⭐⭐⭐ | 提升策略鲁棒性 |
|
||||
| 模型漂移检测 | ⭐⭐⭐ | 防止模型退化 |
|
||||
|
||||
---
|
||||
|
||||
如果你希望我帮你实现其中某一个功能，请告诉我你想优先实现哪个功能，我可以为你生成完整代码。
|
||||
|
||||
------
|
||||
@ -1,84 +0,0 @@
|
||||
根据提供的日志、策略代码和配置文件，我将从以下几个方面进行排查分析：
|
||||
|
||||
---
|
||||
|
||||
## 🔍 一、FreqAI 是否正常训练与预测
|
||||
|
||||
### ✅ 日志中的关键信息
|
||||
- **模型初始化成功**：
|
||||
```log
|
||||
freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True
|
||||
```
|
||||
- **特征工程完成**：
|
||||
```log
|
||||
OKXRegressionStrategy - DEBUG - 目标初始化完成。DataFrame 形状：(43250, 475)
|
||||
```
|
||||
- **模型训练完成**：
|
||||
```log
|
||||
BaseRegressionModel - INFO - -------------------- Done training OKB/USDT (219.77 secs) --------------------
|
||||
```
|
||||
|
||||
### 📌 分析结论
|
||||
✅ FreqAI 模型确实完成了训练，并尝试进行了预测。
|
||||
|
||||
---
|
||||
|
||||
## ❗ 二、预测列未正确加载导致信号缺失
|
||||
|
||||
### ⚠️ 日志中发现的问题
|
||||
```log
|
||||
OKXRegressionStrategy - WARNING - ⚠️ 缺少以下预测列：['pred_upper', 'pred_lower']
|
||||
```
|
||||
这说明在 `populate_indicators` 中尝试访问的 `pred_upper`, `pred_lower` 列不存在。
|
||||
|
||||
### 📄 策略代码片段（问题所在）
|
||||
```python
|
||||
required_pred_cols = ['&-s_close', 'pred_upper', 'pred_lower']
|
||||
missing_pred_cols = [col for col in required_pred_cols if col not in dataframe.columns]
|
||||
|
||||
if not missing_pred_cols:
|
||||
logger.debug("✅ 预测列已就绪：")
|
||||
logger.debug(dataframe[['&-s_close', 'pred_upper', 'pred_lower']].head().to_string())
|
||||
else:
|
||||
logger.warning(f"⚠️ 缺少以下预测列：{missing_pred_cols}")
|
||||
```
|
||||
|
||||
### 🧩 原因分析
|
||||
FreqAI 的预测结果默认列名可能不是 `pred_upper` 和 `pred_lower`，而是类似 `pred_upper_&-s_close` 这样的格式，尤其是当你使用了多个目标标签时。
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ 解决方案：更新列名匹配逻辑
|
||||
|
||||
### ✅ 修改建议
|
||||
将 `pred_upper` 和 `pred_lower` 替换为完整的列名模式，例如：
|
||||
```python
|
||||
'pred_upper_&-s_close'
|
||||
'pred_lower_&-s_close'
|
||||
```
|
||||
|
||||
### 🔁 SEARCH/REPLACE 块如下：
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 331-332
|
||||
```python
|
||||
required_pred_cols = ['&-s_close', 'pred_upper_&-s_close', 'pred_lower_&-s_close']
|
||||
missing_pred_cols = [col for col in required_pred_cols if col not in dataframe.columns]
|
||||
```
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 338-338
|
||||
```python
|
||||
logger.warning(f"⚠️ 缺少以下预测列：{missing_pred_cols} | 当前可用列：{list(dataframe.columns)}")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📈 三、交易信号生成是否依赖预测值？
|
||||
|
||||
### 📉 日志显示没有生成有效信号
|
||||
```log
|
||||
OKXRegressionStrategy - DEBUG - 生成 0 个做多信号
|
||||
```
|
||||
|
||||
### 🧾 策略代码片段（问题所在）
|
||||
278
chat/result6.md
278
chat/result6.md
@ -1,278 +0,0 @@
|
||||
|
||||
|
||||
- Datetime: 2025-05-06 10:24:16
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- neo-tree filesystem [1]
|
||||
- result/backtest-result-2025-05-06_02-15-16.json
|
||||
- result/backtest-result-2025-05-06_02-15-16.meta.json
|
||||
- result/backtest-result-2025-05-06_02-15-16_OKXRegressionStrategy.py
|
||||
- result/backtest-result-2025-05-06_02-15-16_config.json
|
||||
- result/output_filted.log
|
||||
- freqtrade/templates/OKXRegressionStrategy.py
|
||||
- config_examples/config_freqai.okx.json
|
||||
|
||||
> 当前策略OKXRegressionStrategy.py, 配置文件, 运行回测厚结果包含在log中, 根据其中的debug,info等信息, 结合下面列出的官方文档, 排查当前策略,配置代码是否工作正常符合预期, 如果不是,请给出分析的结论以及解决问题的 代码 块替换
|
||||
>
|
||||
> 自定义策略: https://www.freqtrade.io/en/stable/strategy-customization/
|
||||
> freqai 特征工程 : https://www.freqtrade.io/en/stable/freqai-feature-engineering/
|
||||
> freqai 参数表: https://www.freqtrade.io/en/stable/freqai-parameter-table/
|
||||
> freqai 配置: https://www.freqtrade.io/en/stable/freqai-configuration/
|
||||
> freaai 运行: https://www.freqtrade.io/en/stable/freqai-running/
|
||||
> freqai 开发手册: https://www.freqtrade.io/en/stable/freqai-developers/
|
||||
>
|
||||
|
||||
根据你提供的策略代码 (`OKXRegressionStrategy.py`)、配置文件 (`config_freqai.okx.json`) 和回测日志 (`output_filted.log`)，我进行了以下分析：
|
||||
|
||||
---
|
||||
|
||||
## 🔍 **问题诊断**
|
||||
|
||||
### ✅ 1. 模型加载状态
|
||||
在 `OKXRegressionStrategy.py` 的 `__init__` 方法中，你添加了模型路径和存在性检查逻辑：
|
||||
```python
|
||||
model_file = f"{freqai_model_path}/{model_identifier}/cb_okb_*.pkl"
|
||||
if any(os.path.exists(f.replace('*', '1743465600')) for f in [model_file]):
|
||||
logger.info("✅ 模型文件存在，准备加载")
|
||||
else:
|
||||
logger.warning("⚠️ 模型文件未找到，请确认是否训练完成")
|
||||
```
|
||||
|
||||
但在日志中出现如下警告：
|
||||
```
|
||||
2025-05-06 02:12:04,218 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-OKB_1743465600/cb_okb_1743465600
|
||||
```
|
||||
|
||||
➡️ **结论：模型未成功加载**。
|
||||
虽然策略尝试从指定路径加载模型，但实际路径 `/freqtrade/user_data/models/test175/sub-train-OKB_1743465600/cb_okb_1745971200.pkl` 并不存在或未被正确生成。
|
||||
|
||||
---
|
||||
|
||||
### ❌ 2. 预测列缺失
|
||||
|
||||
在 `populate_indicators()` 中有如下逻辑：
|
||||
```python
|
||||
required_pred_cols = ['pred_upper', 'pred_lower', '&-s_close', '&-roi_0_pred']
|
||||
missing_pred_cols = [col for col in required_pred_cols if col not in dataframe.columns]
|
||||
if missing_pred_cols:
|
||||
logger.warning(f"⚠️ 缺失预测列：{missing_pred_cols}，尝试回退默认值")
|
||||
for col in missing_pred_cols:
|
||||
dataframe[col] = np.nan
|
||||
```
|
||||
|
||||
日志中也出现了该警告：
|
||||
```
|
||||
2025-05-06 02:15:15,695 - OKXRegressionStrategy - WARNING - ⚠️ 缺失预测列：['pred_upper', 'pred_lower']，尝试回退默认值
|
||||
```
|
||||
|
||||
➡️ **结论：模型未输出关键预测列（如 pred_upper, pred_lower）**。
|
||||
这导致交易信号无法正常触发，因为 `enter_long` 是基于 `&-s_close` 和 `pred_upper` 的组合判断的。
|
||||
|
||||
---
|
||||
|
||||
### 📉 3. 回测结果表现异常
|
||||
|
||||
回测结果显示：
|
||||
```
|
||||
Avg Profit %: -0.26%
|
||||
Tot Profit USDT: -3.811
|
||||
Winrate: 47.3%
|
||||
Profit Factor: 0.48
|
||||
```
|
||||
|
||||
➡️ **结论：策略整体表现不佳，胜率低于 50%，且盈利因子小于 1，说明策略不具备正期望收益。**
|
||||
|
||||
---
|
||||
|
||||
## 🧰 **根本原因分析**
|
||||
|
||||
| 原因 | 描述 |
|
||||
|------|------|
|
||||
| 模型未保存/加载 | 日志显示模型路径下没有 `.pkl` 文件，说明 FreqAI 可能未成功保存模型 |
|
||||
| 特征工程与目标列不匹配 | 策略定义了多个特征列，但模型可能未输出对应预测列（如 `pred_upper`, `pred_lower`） |
|
||||
| 预测列缺失导致信号失效 | `enter_long` 依赖于 `&-s_close > 0.005` 或 `rsi < 30`，但由于预测列缺失，信号未能有效生成 |
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ **解决方案**
|
||||
|
||||
### ✅ 1. 确保模型训练并保存
|
||||
|
||||
在 `config_freqai.okx.json` 中确保：
|
||||
```json
|
||||
"save_backtesting_prediction": true,
|
||||
"purge_old_models": false,
|
||||
"load_trained_model": true
|
||||
```
|
||||
|
||||
同时，在命令行运行时加上 `--freqaimodel-path` 参数以指定模型路径：
|
||||
```bash
|
||||
freqtrade backtesting --strategy OKXRegressionStrategy --config config_freqai.okx.json --freqaimodel-path user_data/models/
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 2. 在策略中强制等待模型加载
|
||||
|
||||
修改 `set_freqai_targets()` 方法，确保模型已加载后再继续执行：
|
||||
```python
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
|
||||
# 等待模型加载
|
||||
while not hasattr(self, "freqai") or self.freqai is None:
|
||||
time.sleep(1)
|
||||
|
||||
# 正常设置目标列
|
||||
dataframe["&-s_close"] = (dataframe["close"].shift(-self.label_period_candles) - dataframe["close"]) / dataframe["close"]
|
||||
...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 3. 添加模型训练失败处理逻辑
|
||||
|
||||
在 `fit()` 方法中增加重试机制和错误提示：
|
||||
```python
|
||||
def fit(self, data_dictionary: Dict, metadata: Dict, **kwargs) -> None:
|
||||
try:
|
||||
if not hasattr(self, 'model') or self.model is None:
|
||||
self.model = XGBRegressor(**self.freqai_config["model_training_parameters"])
|
||||
self.freqai.fit(data_dictionary, metadata, **kwargs)
|
||||
except Exception as e:
|
||||
logger.error(f"FreqAI 模型训练失败：{str(e)}")
|
||||
raise RuntimeError("模型训练失败，请检查数据完整性或重新训练模型")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### ✅ 4. 强制填充预测列（用于调试）
|
||||
|
||||
在 `populate_indicators()` 中加入模拟预测列逻辑，防止空列影响信号生成：
|
||||
```python
|
||||
# 如果预测列缺失，用随机数模拟
|
||||
if 'pred_upper' not in dataframe.columns:
|
||||
dataframe['pred_upper'] = dataframe['close'].rolling(window=10).mean() * 1.01
|
||||
if 'pred_lower' not in dataframe.columns:
|
||||
dataframe['pred_lower'] = dataframe['close'].rolling(window=10).mean() * 0.99
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 最终建议
|
||||
|
||||
| 动作 | 建议 |
|
||||
|------|------|
|
||||
| ✅ 模型训练 | 使用 `freqtrade train` 显式训练模型，并确认模型保存到 `user_data/models/` |
|
||||
| ✅ 模型加载 | 修改策略逻辑，确保模型加载完成再进行指标计算 |
|
||||
| ✅ 数据完整性 | 在 `set_freqai_targets()` 中验证所有目标列是否存在 |
|
||||
| ✅ 模拟预测列 | 在 `populate_indicators()` 中加入模拟预测列，防止空列导致信号丢失 |
|
||||
| ✅ 调整参数 | 尝试不同 `n_estimators`, `learning_rate`, `label_period_candles` 提高模型表现 |
|
||||
|
||||
---
|
||||
|
||||
## 📦 SEARCH/REPLACE 块
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 0-0
|
||||
```python
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
|
||||
"""
|
||||
设置回归模型的目标变量，为不同币对设置动态止损和ROI阈值。
|
||||
输入：dataframe（K线数据：close, high, low），metadata（交易对信息），config（FreqAI配置）
|
||||
输出：更新后的dataframe，包含目标标签
|
||||
"""
|
||||
# 等待模型加载完成
|
||||
import time
|
||||
while not hasattr(self, "freqai") or self.freqai is None:
|
||||
time.sleep(1)
|
||||
|
||||
# 获取配置参数
|
||||
label_period = self.freqai_config["feature_parameters"]["label_period_candles"] # 标签预测周期（如5分钟K线的N根）
|
||||
pair = metadata["pair"] # 当前交易对（如DOGE/USDT）
|
||||
|
||||
# 计算未来价格变化率（现有逻辑）
|
||||
dataframe["&-s_close"] = (dataframe["close"].shift(-label_period) - dataframe["close"]) / dataframe["close"]
|
||||
|
||||
# 计算不同时间窗口的ROI（现有逻辑）
|
||||
for minutes in [0, 15, 30]:
|
||||
candles = int(minutes / 5) # 假设5分钟K线
|
||||
if candles > 0:
|
||||
dataframe[f"&-roi_{minutes}"] = (dataframe["close"].shift(-candles) - dataframe["close"]) / dataframe["close"]
|
||||
else:
|
||||
dataframe[f"&-roi_{minutes}"] = 0.0
|
||||
|
||||
# 计算市场状态指标：ADX（14周期，与label_period_candles对齐）
|
||||
dataframe["adx"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
|
||||
|
||||
# 定义币对特定的ADX阈值和止损/ROI范围
|
||||
pair_thresholds = {
|
||||
"DOGE/USDT": {
|
||||
"adx_trend": 20,
|
||||
"adx_oscillation": 15,
|
||||
"stoploss_trend": -0.08,
|
||||
"stoploss_oscillation": -0.04,
|
||||
"stoploss_mid": -0.06,
|
||||
"roi_trend": 0.06,
|
||||
"roi_oscillation": 0.025,
|
||||
"roi_mid": 0.04
|
||||
},
|
||||
"BTC/USDT": {
|
||||
"adx_trend": 25,
|
||||
"adx_oscillation": 20,
|
||||
"stoploss_trend": -0.03,
|
||||
"stoploss_oscillation": -0.015,
|
||||
"stoploss_mid": -0.02,
|
||||
"roi_trend": 0.03,
|
||||
"roi_oscillation": 0.015,
|
||||
"roi_mid": 0.02
|
||||
},
|
||||
"SOL/USDT": {
|
||||
"adx_trend": 22,
|
||||
"adx_oscillation": 18,
|
||||
"stoploss_trend": -0.06,
|
||||
"stoploss_oscillation": -0.03,
|
||||
"stoploss_mid": -0.045,
|
||||
"roi_trend": 0.045,
|
||||
"roi_oscillation": 0.02,
|
||||
"roi_mid": 0.03
|
||||
},
|
||||
"XRP/USDT": {
|
||||
"adx_trend": 22,
|
||||
"adx_oscillation": 18,
|
||||
"stoploss_trend": -0.06,
|
||||
"stoploss_oscillation": -0.03,
|
||||
"stoploss_mid": -0.045,
|
||||
"roi_trend": 0.045,
|
||||
"roi_oscillation": 0.02,
|
||||
"roi_mid": 0.03
|
||||
},
|
||||
"OKB/USDT": {
|
||||
"adx_trend": 18,
|
||||
"adx_oscillation": 12,
|
||||
"stoploss_trend": -0.10,
|
||||
"stoploss_oscillation": -0.06,
|
||||
"stoploss_mid": -0.08,
|
||||
"roi_trend": 0.07,
|
||||
"roi_oscillation": 0.04,
|
||||
"roi_mid": 0.05
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# 动态化 &-roi_0_pred（基于市场状态和币对）
|
||||
dataframe["&-roi_0_pred"] = 0.0
|
||||
for index, row in dataframe.iterrows():
|
||||
thresholds = pair_thresholds.get(pair, {})
|
||||
if not thresholds:
|
||||
continue
|
||||
adx_value = row["adx"]
|
||||
if adx_value > thresholds["adx_trend"]: # 趋势市场
|
||||
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_trend"] # 较高ROI目标
|
||||
elif adx_value < thresholds["adx_oscillation"]: # 震荡市场
|
||||
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_oscillation"] # 较低ROI目标
|
||||
else:
|
||||
# 中性市场：使用中间值
|
||||
dataframe.at[index, "&-roi_0_pred"] = thresholds["roi_mid"]
|
||||
|
||||
return dataframe
|
||||
244
chat/result7.md
244
chat/result7.md
@ -1,244 +0,0 @@
|
||||
根据当前提供的策略文件 `OKXRegressionStrategy.py`、配置文件 `config_freqai.okx.json` 和回测日志 `output_filted.log`，结合 Freqtrade 官方文档和实际运行逻辑，我进行了以下分析：
|
||||
|
||||
---
|
||||
|
||||
## ✅ **1. 策略是否正常工作？**
|
||||
|
||||
### ❌ **结论：策略未按预期工作。**
|
||||
|
||||
尽管代码结构基本符合 FreqAI 的要求，但存在几个关键问题导致模型预测未被正确使用，最终交易信号仅依赖 RSI。
|
||||
|
||||
---
|
||||
|
||||
## 🧨 **2. 主要问题分析**
|
||||
|
||||
### 🔴 2.1 模型未加载成功
|
||||
|
||||
**日志证据：**
|
||||
```
|
||||
2025-05-06 03:03:03,093 - OKXRegressionStrategy - WARNING - ⚠️ 模型文件未找到，请确认是否训练完成
|
||||
2025-05-06 03:04:29,843 - freqtrade.plot.plotting - INFO - Stored plot as /freqtrade/user_data/models/test175/sub-train-OKB_1743465600/cb_okb_1743465600--s_close.html
|
||||
2025-05-06 03:05:53,910 - OKXRegressionStrategy - WARNING - ⚠️ pred_upper 列全为 NaN，可能模型未加载成功
|
||||
```
|
||||
|
||||
**原因：**
|
||||
- 日志显示模型路径 `/freqtrade/user_data/models/test175/` 下没有 `.pkl` 文件；
|
||||
- 虽然回测过程中重新训练了模型（生成了 HTML 图表），但这些模型未被保存或加载到策略中用于预测；
|
||||
- 回测结束后，`pred_upper`, `pred_lower` 列仍为 `NaN`，说明模型输出未生效。
|
||||
|
||||
**官方文档参考：**
|
||||
- [FreqAI Running](https://www.freqtrade.io/en/stable/freqai-running/)：需确保模型在训练后保存，并在回测时通过 `load_trained_model: true` 加载；
|
||||
- [FreqAI Configuration](https://www.freqtrade.io/en/stable/freqai-configuration/)：检查 `model_filename` 是否指向正确的模型路径。
|
||||
|
||||
---
|
||||
|
||||
### 🔴 2.2 预测列缺失或无效
|
||||
|
||||
**日志证据：**
|
||||
```
|
||||
2025-05-06 03:05:53,910 - OKXRegressionStrategy - WARNING - ⚠️ pred_upper 列全为 NaN，可能模型未加载成功
|
||||
2025-05-06 03:05:53,917 - OKXRegressionStrategy - DEBUG - &-s_close pred_upper pred_lower
|
||||
0 0.0 NaN NaN
|
||||
1 0.0 NaN NaN
|
||||
2 0.0 NaN NaN
|
||||
3 0.0 NaN NaN
|
||||
4 0.0 NaN NaN
|
||||
```
|
||||
|
||||
**后果：**
|
||||
- `populate_entry_trend()` 中的 `valid_prediction = (~dataframe['&-s_close'].isna()) & (dataframe['&-s_close'] > 0.005)` 实际上只依赖常量 `0.0`；
|
||||
- 所有做多信号仅由 RSI < 30 触发，与模型无关。
|
||||
|
||||
**官方文档参考：**
|
||||
- [FreqAI Feature Engineering](https://www.freqtrade.io/en/stable/freqai-feature-engineering/)：必须确保目标列如 `&-s_close` 被正确识别并用于训练；
|
||||
- [Custom Strategy](https://www.freqtrade.io/en/stable/strategy-customization/)：交易信号应基于有效预测值。
|
||||
|
||||
---
|
||||
|
||||
### 🔴 2.3 模型训练未持久化
|
||||
|
||||
**日志证据：**
|
||||
```
|
||||
2025-05-06 03:04:43,510 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-OKB_1744329600/cb_okb_1744329600
|
||||
```
|
||||
|
||||
**后果：**
|
||||
- 每次回测都重新训练模型，且未保存模型供后续使用；
|
||||
- 导致无法复用已有模型进行稳定回测。
|
||||
|
||||
**建议：**
|
||||
- 使用 `freqtrade train` 显式训练模型并保存；
|
||||
- 在回测前确保模型已存在；
|
||||
- 设置 `"purge_old_models": false` 保留历史模型。
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ **3. 解决方案与代码修复**
|
||||
|
||||
### ✅ **3.1 修改策略类名以匹配文件名**
|
||||
|
||||
**问题：**
|
||||
- 当前策略类名为 `OKXRegressionStrategy`，但文件名也为 `OKXRegressionStrategy.py`；
|
||||
- 若从模板导入，需确保类名一致。
|
||||
|
||||
**修改建议：**
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 18-18
|
||||
```python
|
||||
class OKXRegressionStrategy(IStrategy):
|
||||
```
|
||||
|
||||
✅ 无需更改，当前命名已正确。
|
||||
|
||||
---
|
||||
|
||||
### ✅ **3.2 强制启用模型保存与加载**
|
||||
|
||||
**问题：**
|
||||
- 回测时未加载已有模型；
|
||||
- 每次都重新训练，影响可重复性。
|
||||
|
||||
**修改建议：**
|
||||
|
||||
Filepath: config_examples/config_freqai.okx.json
|
||||
Replace lines: 62-68
|
||||
```json
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"identifier": "test175",
|
||||
"freqaimodel": "XGBoostRegressor",
|
||||
"save_backtesting_prediction": true,
|
||||
"purge_old_models": false,
|
||||
"load_trained_model": true,
|
||||
"model_filename": "/freqtrade/user_data/models/test175/cb_okb_*.pkl"
|
||||
```
|
||||
|
||||
📌 **说明：**
|
||||
- 增加 `model_filename` 字段，明确指定模型路径；
|
||||
- 确保模型能被正确加载用于预测。
|
||||
|
||||
---
|
||||
|
||||
### ✅ **3.3 补充模型训练命令以生成 `.pkl` 文件**
|
||||
|
||||
**问题：**
|
||||
- 当前直接运行回测会触发训练，但不会持久化模型；
|
||||
- 应先显式训练模型再进行回测。
|
||||
|
||||
**解决方案：**
|
||||
|
||||
```bash
|
||||
# 先训练模型
|
||||
docker-compose run --rm freqtrade train --config config_examples/config_freqai.okx.json --freqaimodel XGBoostRegressor --strategy OKXRegressionStrategy
|
||||
|
||||
# 再运行回测
|
||||
docker-compose run --rm freqtrade backtesting --config config_examples/config_freqai.okx.json --freqaimodel XGBoostRegressor --strategy OKXRegressionStrategy
|
||||
```
|
||||
|
||||
📌 **说明：**
|
||||
- 训练完成后会在 `/freqtrade/user_data/models/test175/` 下生成 `.pkl` 模型文件；
|
||||
- 回测时将加载该模型进行预测。
|
||||
|
||||
---
|
||||
|
||||
### ✅ **3.4 确保 `set_freqai_targets()` 正确设置目标列**
|
||||
|
||||
**问题：**
|
||||
- 目标列 `&-s_close`, `&-roi_0_pred` 已定义；
|
||||
- 但在某些情况下未被 FreqAI 正确识别。
|
||||
|
||||
**修改建议：**
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 0-0
|
||||
```python
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
|
||||
# 设置回归目标
|
||||
label_period = self.freqai_config["feature_parameters"]["label_period_candles"]
|
||||
dataframe["&-s_close"] = (dataframe["close"].shift(-label_period) - dataframe["close"]) / dataframe["close"]
|
||||
|
||||
# 设置动态 ROI 和止损阈值
|
||||
dataframe["&-roi_0_pred"] = 0.03 # 默认值，防止空值
|
||||
dataframe["&-stoploss_pred"] = -0.05
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
📌 **说明：**
|
||||
- 提供默认值避免空列；
|
||||
- 确保列名与 `feature_engineering_expand_all()` 中的特征一致；
|
||||
- 可进一步扩展为基于 ADX 的动态设定。
|
||||
|
||||
---
|
||||
|
||||
### ✅ **3.5 确保 `populate_entry_trend()` 使用模型预测值**
|
||||
|
||||
**问题：**
|
||||
- 当前 `valid_prediction` 条件过于宽松，仅判断 `&-s_close > 0.005`；
|
||||
- 若模型未加载，此条件始终成立，导致信号仅由 RSI 控制。
|
||||
|
||||
**修改建议：**
|
||||
|
||||
Filepath: freqtrade/templates/OKXRegressionStrategy.py
|
||||
Replace lines: 365-365
|
||||
```python
|
||||
valid_prediction = (~dataframe['&-s_close'].isna()) & (dataframe['&-s_close'] > 0.005) & (dataframe['&-s_close'].abs() > 0.001)
|
||||
```
|
||||
|
||||
📌 **说明：**
|
||||
- 添加 `abs() > 0.001` 过滤掉恒定为零的预测值；
|
||||
- 确保只有当模型真正输出有意义数值时才生成信号。
|
||||
|
||||
---
|
||||
|
||||
## 📊 **4. 验证建议**
|
||||
|
||||
请执行以下操作验证模型是否加载成功并参与预测：
|
||||
|
||||
1. **运行训练命令生成模型：**
|
||||
```bash
|
||||
docker-compose run --rm freqtrade train --config config_examples/config_freqai.okx.json --freqaimodel XGBoostRegressor --strategy OKXRegressionStrategy
|
||||
```
|
||||
|
||||
2. **查看模型是否生成：**
|
||||
```bash
|
||||
ls -la /home/ubuntu/freqtrade/user_data/models/test175/
|
||||
```
|
||||
应看到类似 `cb_okb_*.pkl` 的模型文件。
|
||||
|
||||
3. **运行回测并检查预测列：**
|
||||
```bash
|
||||
docker-compose run --rm freqtrade backtesting --config config_examples/config_freqai.okx.json --freqaimodel XGBoostRegressor --strategy OKXRegressionStrategy
|
||||
```
|
||||
|
||||
4. **打开 `result/backtest-result-*.json` 查看预测列：**
|
||||
- 检查 `&-s_close`, `pred_upper`, `pred_lower` 是否随时间变化；
|
||||
- 若仍为恒定值或全为 NaN，则说明模型仍未加载成功。
|
||||
|
||||
---
|
||||
|
||||
## ✅ **5. 总结**
|
||||
|
||||
| 问题 | 是否解决 | 说明 |
|
||||
|------|----------|------|
|
||||
| 模型未加载 | ❌ | 缺少 `.pkl` 模型文件 |
|
||||
| 预测列为 NaN | ❌ | 模型未加载导致预测失败 |
|
||||
| 交易信号仅由 RSI 触发 | ❌ | `&-s_close` 恒定为 0 |
|
||||
| 模型未持久化 | ❌ | 每次回测都重新训练 |
|
||||
| 策略类名不一致 | ✅ | 类名与文件名一致 |
|
||||
|
||||
---
|
||||
|
||||
## 📌 **下一步建议**
|
||||
|
||||
请优先执行以下任意一项操作以继续诊断：
|
||||
|
||||
1. **提供 `result/backtest-result-*.json` 中部分预测数据片段；**
|
||||
2. **提供 `output_filted.log` 中关于模型加载和预测值的关键日志；**
|
||||
3. **告知是否可以运行新的训练 + 回测流程并启用 `save_backtesting_prediction: true`。**
|
||||
|
||||
这将帮助我们进一步确认模型是否加载成功以及预测值是否合理。
|
||||
|
||||
**Generation complete!** Please review the code suggestions above.
|
||||
|
||||
@ -1,6 +0,0 @@
|
||||
### 🛠️ 可能的改进方向
|
||||
|
||||
如果你希望进一步优化这个替代逻辑，可以考虑：
|
||||
1. **引入更多技术指标**（如 MACD、布林带等）来增强模拟预测的智能性。
|
||||
2. **使用简单机器学习模型**（如线性回归）基于历史 RSI 和价格走势训练一个轻量级替代模型。
|
||||
3. **动态调整窗口大小**（rolling window），根据市场波动率自动调节平滑程度。
|
||||
8554
chat/result9.md
8554
chat/result9.md
File diff suppressed because it is too large
Load Diff
@ -1,68 +0,0 @@
|
||||
> 根据当前问题, 制定一个优化路线图, 每次只完成一个功能点改动, 路线图只包含要优化的问题,和解决思路,不包含代码,请输出此路线图
|
||||
>
|
||||
|
||||
### ✅ **OKXRegressionStrategy 优化路线图**
|
||||
|
||||
| 步骤 | 待优化问题 | 解决思路 |
|
||||
|------|------------|----------|
|
||||
| 1 | **胜率低（33%–50%）** | 引入模型预测置信度过滤，仅在 `pred_upper - pred_lower > 0.02` 时允许交易，减少低质量信号。 |
|
||||
| 2 | **退出逻辑单一（仅依赖 exit_signal）** | 增加回调止损（Chandelier Exit）和动态 ROI 结合使用，提升止盈/止损效率。 |
|
||||
| 3 | **固定 ATR multiplier 不适应波动变化** | 使用历史 ATR 百分位动态调整 multiplier：高波动 → 更紧止损；低波动 → 更松止损。 |
|
||||
| 4 | **缺乏市场状态识别（趋势 vs 震荡）** | 利用 ADX 判断市场状态，并据此调整 exit_signal 条件阈值，提高趋势跟踪能力。 |
|
||||
| 5 | **特征工程维度不足** | 添加 BB Width、Volume Change、Trend Diff 等高阶特征，增强模型对价格行为的理解。 |
|
||||
| 6 | **模型训练未启用交叉验证（CV）** | 启用 TimeSeriesSplit CV，提升模型稳定性与泛化能力。 |
|
||||
| 7 | **回撤过大（最大达 7.341 USDT）** | 引入回调止损逻辑（callback stop loss），防止趋势中途反转造成大幅亏损。 |
|
||||
| 8 | **无动态仓位管理** | 根据账户风险暴露和波动率动态调整 stake_amount，降低极端行情下的单笔损失。 |
|
||||
|
||||
---
|
||||
|
||||
📌 **说明**：
|
||||
- 每次只完成一个功能点改动，确保每次 backtest 变量唯一。
|
||||
- 每个改动后运行 backtest 并记录关键指标（胜率、平均盈利、最大回撤等）。
|
||||
- 若某项改动效果显著，可继续深入调参；若无效，则跳过或尝试替代方案。
|
||||
|
||||
是否需要我开始第一步，即“引入模型预测置信度过滤”？
|
||||
|
||||
-------
|
||||
|
||||
- Datetime: 2025-05-05 17:37:56
|
||||
|
||||
- Model: _qwen3/qwen-plus-latest
|
||||
|
||||
- Selected files:
|
||||
- chat/report.md
|
||||
- chat/report2.md
|
||||
- freqtrade/templates/OKXRegressionStrategy.py
|
||||
|
||||
> 根据路线图的内容描述,当前代码中的部分功能点是否要被删掉,如果需要,请在路线图中明确说明,否则新版代码将新旧混杂,难以维护
|
||||
|
||||
你提出了一个非常关键的问题：**当前代码中确实存在一些“新旧混杂”的功能点，如果不清理，将影响策略的可维护性和逻辑一致性。**
|
||||
|
||||
---
|
||||
|
||||
## ✅ **确认需要删除的功能点（已在路线图中覆盖）**
|
||||
|
||||
| 功能点 | 是否保留 | 说明 |
|
||||
|--------|----------|------|
|
||||
| `exit_signal` 基于固定 RSI 和预测值下降 | ❌ 删除 | 已被市场状态 + Chandelier Exit 替代 |
|
||||
| `_dynamic_stop_loss` 方法 | ❌ 删除 | 已被 `custom_stoploss` + ATR 百分位替代 |
|
||||
| `populate_exit_trend` 中硬编码 exit_signal | ❌ 删除 | 已由回调止损和动态 ROI 替代 |
|
||||
| `check_data_freshness()` 简化版 | ❌ 删除 | 实际运行中依赖 Freqtrade 数据源质量，无需额外检查 |
|
||||
| `confirm_trade_entry()` 中 OKX 数据验证 | ❌ 删除 | 同上，且 OKX 不是唯一数据来源 |
|
||||
| `fetch_okx_order_book()` 注释掉的订单簿逻辑 | ✅ 保留（注释） | 可作为未来扩展参考 |
|
||||
| `feature_engineering_expand_all` 中冗余缓存逻辑 | ✅ 保留 | 缓存机制仍有效，提升性能 |
|
||||
| `fit()` 方法中的手动模型初始化 | ✅ 保留 | 与 FreqAI 兼容性良好 |
|
||||
|
||||
|
||||
### ✅ **OKXRegressionStrategy 优化路线图**
|
||||
|
||||
| 步骤 | 待优化问题 | 解决思路 | 是否需要删除旧代码 |
|
||||
|------|------------|----------|------------------|
|
||||
| 1 | **胜率低（33%–50%）** | 引入模型预测置信度过滤，仅在 `pred_upper - pred_lower > 0.02` 时允许交易，减少低质量信号。 | 否 |
|
||||
| 2 | **退出逻辑单一（仅依赖 exit_signal）** | 增加回调止损（Chandelier Exit）和动态 ROI 结合使用，提升止盈/止损效率。 | 是（`exit_signal` 已被替代） |
|
||||
| 3 | **固定 ATR multiplier 不适应波动变化** | 使用历史 ATR 百分位动态调整 multiplier：高波动 → 更紧止损；低波动 → 更松止损。 | 是（`_dynamic_stop_loss` 方法已废弃） |
|
||||
| 4 | **缺乏市场状态识别（趋势 vs 震荡）** | 利用 ADX 判断市场状态，并据此调整 exit_signal 条件阈值，提高趋势跟踪能力。 | 否 |
|
||||
| 5 | **特征工程维度不足** | 添加 BB Width、Volume Change、Trend Diff 等高阶特征，增强模型对价格行为的理解。 | 否 |
|
||||
| 6 | **模型训练未启用交叉验证（CV）** | 启用 TimeSeriesSplit CV，提升模型稳定性与泛化能力。 | 否 |
|
||||
| 7 | **回撤过大（最大达 7.341 USDT）** | 引入回调止损逻辑（callback stop loss），防止趋势中途反转造成大幅亏损。 | 是（原 `populate_exit_trend` 中硬编码 exit_signal 被替代） |
|
||||
| 8 | **无动态仓位管理** | 根据账户风险暴露和波动率动态调整 stake_amount，降低极端行情下的单笔损失。 | 否 |
|
||||
101
code.txt
101
code.txt
@ -1,101 +0,0 @@
|
||||
FUNCTION set_freqai_targets(dataframe, metadata, config):
|
||||
"""
|
||||
设置回归模型的目标变量，为不同币对设置动态止损和ROI阈值。
|
||||
输入：dataframe（K线数据：close, high, low），metadata（交易对信息），config（FreqAI配置）
|
||||
输出：更新后的dataframe，包含目标标签
|
||||
"""
|
||||
# 获取配置参数
|
||||
label_period = `config["feature_parameters"]`["label_period_candles"] # 标签预测周期（如5分钟K线的N根）
|
||||
pair = `metadata["pair"]` # 当前交易对（如DOGE/USDT）
|
||||
|
||||
# 计算未来价格变化率（现有逻辑）
|
||||
SET `dataframe["&-s_close"]` = (`dataframe["close"]`.shift(-label_period) - `dataframe["close"]`) / `dataframe["close"]`
|
||||
|
||||
# 计算不同时间窗口的ROI（现有逻辑）
|
||||
FOR minutes IN [0, 15, 30]:
|
||||
candles = INTEGER(minutes / 5) # 假设5分钟K线
|
||||
IF candles > 0:
|
||||
SET `dataframe["&-roi_{minutes}"]` = (`dataframe["close"]`.shift(-candles) - `dataframe["close"]`) / `dataframe["close"]`
|
||||
ELSE:
|
||||
SET `dataframe["&-roi_{minutes}"]` = 0.0
|
||||
|
||||
# 计算市场状态指标：ADX（14周期，与label_period_candles对齐）
|
||||
SET `dataframe["adx"]` = CALCULATE_ADX(`dataframe["high"]`, `dataframe["low"]`, `dataframe["close"]`, period=14)
|
||||
|
||||
# 定义币对特定的ADX阈值和止损/ROI范围
|
||||
INITIALIZE pair_thresholds = {
|
||||
"DOGE/USDT": {
|
||||
"adx_trend": 20, # 趋势市场ADX阈值
|
||||
"adx_oscillation": 15, # 震荡市场ADX阈值
|
||||
"stoploss_trend": -0.08, # 趋势市场止损：-8%
|
||||
"stoploss_oscillation": -0.04, # 震荡市场止损：-4%
|
||||
"stoploss_mid": -0.06, # 中间状态止损：-6%
|
||||
"roi_trend": 0.06, # 趋势市场ROI：6%
|
||||
"roi_oscillation": 0.025, # 震荡市场ROI：2.5%
|
||||
"roi_mid": 0.04 # 中间状态ROI：4%
|
||||
},
|
||||
"BTC/USDT": {
|
||||
"adx_trend": 25,
|
||||
"adx_oscillation": 20,
|
||||
"stoploss_trend": -0.03,
|
||||
"stoploss_oscillation": -0.015,
|
||||
"stoploss_mid": -0.02,
|
||||
"roi_trend": 0.03,
|
||||
"roi_oscillation": 0.015,
|
||||
"roi_mid": 0.02
|
||||
},
|
||||
"SOL/USDT": {
|
||||
"adx_trend": 22,
|
||||
"adx_oscillation": 18,
|
||||
"stoploss_trend": -0.06,
|
||||
"stoploss_oscillation": -0.03,
|
||||
"stoploss_mid": -0.045,
|
||||
"roi_trend": 0.045,
|
||||
"roi_oscillation": 0.02,
|
||||
"roi_mid": 0.03
|
||||
},
|
||||
"XRP/USDT": {
|
||||
"adx_trend": 22,
|
||||
"adx_oscillation": 18,
|
||||
"stoploss_trend": -0.06,
|
||||
"stoploss_oscillation": -0.03,
|
||||
"stoploss_mid": -0.045,
|
||||
"roi_trend": 0.045,
|
||||
"roi_oscillation": 0.02,
|
||||
"roi_mid": 0.03
|
||||
}
|
||||
}
|
||||
|
||||
# 动态化 &-stoploss_pred（基于市场状态和币对）
|
||||
INITIALIZE `dataframe["&-stoploss_pred"]` = 0.0
|
||||
FOR EACH row IN dataframe:
|
||||
thresholds = `pair_thresholds[pair]` # 获取当前币对的阈值
|
||||
IF `row["adx"]` > `thresholds["adx_trend"]`: # 趋势市场
|
||||
SET `row["&-stoploss_pred"]` = `thresholds["stoploss_trend"]` # 宽松止损
|
||||
ELSE IF `row["adx"]` < `thresholds["adx_oscillation"]`: # 震荡市场
|
||||
SET `row["&-stoploss_pred"]` = `thresholds["stoploss_oscillation"]` # 严格止损
|
||||
ELSE: # 中间状态
|
||||
SET `row["&-stoploss_pred"]` = `thresholds["stoploss_mid"]` # 中等止损
|
||||
# 风险控制：设置止损下限
|
||||
IF `row["&-stoploss_pred"]` < -0.10: # 防止过大止损
|
||||
SET `row["&-stoploss_pred"]` = -0.10
|
||||
|
||||
# 动态化 &-roi_0_pred（基于市场趋势和币对）
|
||||
INITIALIZE `dataframe["&-roi_0_pred"]` = 0.0
|
||||
FOR EACH row IN dataframe:
|
||||
thresholds = `pair_thresholds[pair]` # 获取当前币对的阈值
|
||||
IF `row["adx"]` > `thresholds["adx_trend"]`: # 强趋势市场
|
||||
SET `row["&-roi_0_pred"]` = `thresholds["roi_trend"]` # 高ROI
|
||||
ELSE IF `row["adx"]` < `thresholds["adx_oscillation"]`: # 震荡市场
|
||||
SET `row["&-roi_0_pred"]` = `thresholds["roi_oscillation"]` # 低ROI
|
||||
ELSE: # 中间状态
|
||||
SET `row["&-roi_0_pred"]` = `thresholds["roi_mid"]` # 中等ROI
|
||||
# 风险控制：设置ROI上限
|
||||
IF `row["&-roi_0_pred"]` > 0.10: # 防止过高目标
|
||||
SET `row["&-roi_0_pred"]` = 0.10
|
||||
|
||||
# 计算RSI预测（现有逻辑）
|
||||
SET `dataframe["&-buy_rsi_pred"]` = CALCULATE_RSI(`dataframe["close"]`, period=14).ROLLING_MEAN(window=20)
|
||||
|
||||
RETURN dataframe
|
||||
|
||||
@ -1,76 +0,0 @@
|
||||
{
|
||||
"exchange": {
|
||||
"name": "okx",
|
||||
"key": "your_api_key",
|
||||
"secret": "your_api_secret",
|
||||
"ccxt_config": {
|
||||
"enableRateLimit": true,
|
||||
"defaultType": "spot",
|
||||
"rateLimit": 2000,
|
||||
"timeout": 30000
|
||||
},
|
||||
"pair_whitelist": ["BTC/USDT", "TON/USDT", "DOT/USDT", "XRP/USDT", "OKB/USDT", "SOL/USDT", "DOGE/USDT", "WCT/USDT", "TRUMP/USDT", "SUI/USDT", "PEPE/USDT", "TRB/USDT", "MASK/USDT", "UNI/USDT", "KAITO/USDT"]
|
||||
},
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "StaticPairList"
|
||||
}
|
||||
],
|
||||
"timeframe": "15m",
|
||||
"strategy": "NostalgiaForInfinityV8",
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"freqaimodel": "LightGBMRegressor",
|
||||
"train_period_days": 7,
|
||||
"backtest_period_days": 0.2,
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["15m"],
|
||||
"include_corr_pairlist": ["OKB/USDT", "SOL/USDT"],
|
||||
"label_period_candles": 4,
|
||||
"include_shifted_candles": 0,
|
||||
"indicator_periods_candles": [5, 10, 20],
|
||||
"include_default_features": ["close", "open", "high", "low", "volume", "rsi", "ema", "sma"],
|
||||
"DI_threshold": 0.3
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": false
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 100,
|
||||
"learning_rate": 0.1,
|
||||
"num_leaves": 15,
|
||||
"verbose": -1
|
||||
}
|
||||
},
|
||||
"order_types": {
|
||||
"entry": "market",
|
||||
"exit": "market",
|
||||
"stoploss": "limit",
|
||||
"stoploss_on_exchange": false
|
||||
},
|
||||
"order_time_in_force": {
|
||||
"entry": "gtc",
|
||||
"exit": "gtc"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "other",
|
||||
"use_order_book": false,
|
||||
"price_last_balance": 0.0
|
||||
},
|
||||
"exit_pricing": {
|
||||
"price_side": "other",
|
||||
"use_order_book": false
|
||||
},
|
||||
"use_exit_signal": true,
|
||||
"exit_profit_only": false,
|
||||
"stake_currency": "USDT",
|
||||
"stake_amount": 150,
|
||||
"max_open_trades": 4,
|
||||
"unfilledtimeout": {
|
||||
"entry": 5,
|
||||
"exit": 15,
|
||||
"unit": "minutes"
|
||||
},
|
||||
"fee": 0.0008
|
||||
}
|
||||
84
config_examples/config_binance.example.json_diabled
Normal file
84
config_examples/config_binance.example.json_diabled
Normal file
@ -0,0 +1,84 @@
|
||||
{
|
||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
||||
"max_open_trades": 3,
|
||||
"stake_currency": "USDT",
|
||||
"stake_amount": 0.05,
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"fiat_display_currency": "USD",
|
||||
"timeframe": "5m",
|
||||
"dry_run": true,
|
||||
"cancel_open_orders_on_exit": false,
|
||||
"unfilledtimeout": {
|
||||
"entry": 10,
|
||||
"exit": 10,
|
||||
"exit_timeout_count": 0,
|
||||
"unit": "minutes"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0,
|
||||
"check_depth_of_market": {
|
||||
"enabled": false,
|
||||
"bids_to_ask_delta": 1
|
||||
}
|
||||
},
|
||||
"exit_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"exchange": {
|
||||
"name": "binance",
|
||||
"key": "your_exchange_key",
|
||||
"secret": "your_exchange_secret",
|
||||
"ccxt_config": {},
|
||||
"ccxt_async_config": {
|
||||
},
|
||||
"pair_whitelist": [
|
||||
"ALGO/USDT",
|
||||
"ATOM/USDT",
|
||||
"BAT/USDT",
|
||||
"BCH/USDT",
|
||||
"BRD/USDT",
|
||||
"EOS/USDT",
|
||||
"ETH/USDT",
|
||||
"IOTA/USDT",
|
||||
"LINK/USDT",
|
||||
"LTC/USDT",
|
||||
"NEO/USDT",
|
||||
"NXS/USDT",
|
||||
"XMR/USDT",
|
||||
"XRP/USDT",
|
||||
"XTZ/USDT"
|
||||
],
|
||||
"pair_blacklist": [
|
||||
"BNB/.*"
|
||||
]
|
||||
},
|
||||
"pairlists": [
|
||||
{"method": "StaticPairList"}
|
||||
],
|
||||
"telegram": {
|
||||
"enabled": false,
|
||||
"token": "your_telegram_token",
|
||||
"chat_id": "your_telegram_chat_id"
|
||||
},
|
||||
"api_server": {
|
||||
"enabled": false,
|
||||
"listen_ip_address": "127.0.0.1",
|
||||
"listen_port": 8080,
|
||||
"verbosity": "error",
|
||||
"jwt_secret_key": "somethingrandom",
|
||||
"CORS_origins": [],
|
||||
"username": "freqtrader",
|
||||
"password": "SuperSecurePassword"
|
||||
},
|
||||
"bot_name": "freqtrade",
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 5
|
||||
}
|
||||
}
|
||||
@ -9,9 +9,10 @@
|
||||
"tradable_balance_ratio": 1,
|
||||
"fiat_display_currency": "USD",
|
||||
"dry_run": true,
|
||||
"timeframe": "3m",
|
||||
"timeframe": "5m",
|
||||
"dry_run_wallet": 1000,
|
||||
"cancel_open_orders_on_exit": true,
|
||||
"stoploss": -0.05,
|
||||
"unfilledtimeout": {
|
||||
"entry": 5,
|
||||
"exit": 15
|
||||
@ -23,16 +24,20 @@
|
||||
"enable_ws": false,
|
||||
"ccxt_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 500,
|
||||
"rateLimit": 800,
|
||||
"options": {
|
||||
"defaultType": "spot"
|
||||
}
|
||||
},
|
||||
"ccxt_async_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 500,
|
||||
"rateLimit": 800,
|
||||
"timeout": 20000
|
||||
},
|
||||
"pair_whitelist": [
|
||||
"BTC/USDT",
|
||||
"SOL/USDT"
|
||||
]
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
@ -49,47 +54,56 @@
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "StaticPairList"
|
||||
}
|
||||
],
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"data_kitchen": {
|
||||
"fillna": "ffill",
|
||||
"feature_parameters": {
|
||||
"DI_threshold": 0.9,
|
||||
"weight_factor": 0.9
|
||||
}
|
||||
"fillna": "ffill"
|
||||
},
|
||||
"freqaimodel": "XGBoostRegressor",
|
||||
"freqaimodel": "CatboostClassifier",
|
||||
"purge_old_models": 2,
|
||||
"identifier": "test175",
|
||||
"identifier": "test178",
|
||||
"train_period_days": 30,
|
||||
"backtest_period_days": 10,
|
||||
"live_retrain_hours": 0,
|
||||
"feature_selection": {
|
||||
"method": "recursive_elimination",
|
||||
"threshold": 0.01
|
||||
"method": "recursive_elimination"
|
||||
},
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m", "1h"],
|
||||
"include_corr_pairlist": ["BTC/USDT"],
|
||||
"label_period_candles": 10,
|
||||
"use_SVM_to_remove_outliers": true,
|
||||
"principal_component_analysis": true
|
||||
"include_timeframes": [
|
||||
"5m",
|
||||
"1h"
|
||||
],
|
||||
"include_corr_pairlist": [
|
||||
"BTC/USDT",
|
||||
"SOL/USDT"
|
||||
],
|
||||
"label_period_candles": 12,
|
||||
"include_shifted_candles": 3,
|
||||
"DI_threshold": 0.9,
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": false,
|
||||
"use_SVM_to_remove_outliers": false,
|
||||
"indicator_periods_candles": [
|
||||
10,
|
||||
20,
|
||||
50
|
||||
],
|
||||
"plot_feature_importances": 0
|
||||
},
|
||||
"model": "XGBoostRegressor",
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": true,
|
||||
"random_state": 42
|
||||
"shuffle": false,
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 200,
|
||||
"learning_rate": 0.05,
|
||||
"max_depth": 5,
|
||||
"subsample": 0.8,
|
||||
"colsample_bytree": 0.8,
|
||||
"objective": "reg:squarederror",
|
||||
"eval_metric": "rmse",
|
||||
"early_stopping_rounds": 50
|
||||
"n_estimators": 100,
|
||||
"learning_rate": 0.1,
|
||||
"num_leaves": 15,
|
||||
"verbose": -1
|
||||
}
|
||||
},
|
||||
"api_server": {
|
||||
@ -108,7 +122,7 @@
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 5,
|
||||
"process_throttle_secs": 10,
|
||||
"heartbeat_interval": 20,
|
||||
"loglevel": "DEBUG"
|
||||
}
|
||||
212
config_examples/config_full.example.json_disabled
Normal file
212
config_examples/config_full.example.json_disabled
Normal file
@ -0,0 +1,212 @@
|
||||
{
|
||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
||||
"max_open_trades": 3,
|
||||
"stake_currency": "BTC",
|
||||
"stake_amount": 0.05,
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"fiat_display_currency": "USD",
|
||||
"amount_reserve_percent": 0.05,
|
||||
"available_capital": 1000,
|
||||
"amend_last_stake_amount": false,
|
||||
"last_stake_amount_min_ratio": 0.5,
|
||||
"dry_run": true,
|
||||
"dry_run_wallet": 1000,
|
||||
"cancel_open_orders_on_exit": false,
|
||||
"timeframe": "5m",
|
||||
"trailing_stop": false,
|
||||
"trailing_stop_positive": 0.005,
|
||||
"trailing_stop_positive_offset": 0.0051,
|
||||
"trailing_only_offset_is_reached": false,
|
||||
"use_exit_signal": true,
|
||||
"exit_profit_only": false,
|
||||
"exit_profit_offset": 0.0,
|
||||
"ignore_roi_if_entry_signal": false,
|
||||
"ignore_buying_expired_candle_after": 300,
|
||||
"trading_mode": "spot",
|
||||
"margin_mode": "",
|
||||
"minimal_roi": {
|
||||
"40": 0.0,
|
||||
"30": 0.01,
|
||||
"20": 0.02,
|
||||
"0": 0.04
|
||||
},
|
||||
"stoploss": -0.10,
|
||||
"unfilledtimeout": {
|
||||
"entry": 10,
|
||||
"exit": 10,
|
||||
"exit_timeout_count": 0,
|
||||
"unit": "minutes"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0,
|
||||
"check_depth_of_market": {
|
||||
"enabled": false,
|
||||
"bids_to_ask_delta": 1
|
||||
}
|
||||
},
|
||||
"exit_pricing":{
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0
|
||||
},
|
||||
"order_types": {
|
||||
"entry": "limit",
|
||||
"exit": "limit",
|
||||
"emergency_exit": "market",
|
||||
"force_exit": "market",
|
||||
"force_entry": "market",
|
||||
"stoploss": "market",
|
||||
"stoploss_on_exchange": false,
|
||||
"stoploss_price_type": "last",
|
||||
"stoploss_on_exchange_interval": 60,
|
||||
"stoploss_on_exchange_limit_ratio": 0.99
|
||||
},
|
||||
"order_time_in_force": {
|
||||
"entry": "GTC",
|
||||
"exit": "GTC"
|
||||
},
|
||||
"pairlists": [
|
||||
{"method": "StaticPairList"},
|
||||
{"method": "FullTradesFilter"},
|
||||
{
|
||||
"method": "VolumePairList",
|
||||
"number_assets": 20,
|
||||
"sort_key": "quoteVolume",
|
||||
"refresh_period": 1800
|
||||
},
|
||||
{"method": "AgeFilter", "min_days_listed": 10},
|
||||
{"method": "PrecisionFilter"},
|
||||
{"method": "PriceFilter", "low_price_ratio": 0.01, "min_price": 0.00000010},
|
||||
{"method": "SpreadFilter", "max_spread_ratio": 0.005},
|
||||
{
|
||||
"method": "RangeStabilityFilter",
|
||||
"lookback_days": 10,
|
||||
"min_rate_of_change": 0.01,
|
||||
"refresh_period": 1440
|
||||
}
|
||||
],
|
||||
"exchange": {
|
||||
"name": "binance",
|
||||
"key": "your_exchange_key",
|
||||
"secret": "your_exchange_secret",
|
||||
"password": "",
|
||||
"log_responses": false,
|
||||
// "unknown_fee_rate": 1,
|
||||
"ccxt_config": {},
|
||||
"ccxt_async_config": {},
|
||||
"pair_whitelist": [
|
||||
"ALGO/BTC",
|
||||
"ATOM/BTC",
|
||||
"BAT/BTC",
|
||||
"BCH/BTC",
|
||||
"BRD/BTC",
|
||||
"EOS/BTC",
|
||||
"ETH/BTC",
|
||||
"IOTA/BTC",
|
||||
"LINK/BTC",
|
||||
"LTC/BTC",
|
||||
"NEO/BTC",
|
||||
"NXS/BTC",
|
||||
"XMR/BTC",
|
||||
"XRP/BTC",
|
||||
"XTZ/BTC"
|
||||
],
|
||||
"pair_blacklist": [
|
||||
"DOGE/BTC"
|
||||
],
|
||||
"outdated_offset": 5,
|
||||
"markets_refresh_interval": 60
|
||||
},
|
||||
"edge": {
|
||||
"enabled": false,
|
||||
"process_throttle_secs": 3600,
|
||||
"calculate_since_number_of_days": 7,
|
||||
"allowed_risk": 0.01,
|
||||
"stoploss_range_min": -0.01,
|
||||
"stoploss_range_max": -0.1,
|
||||
"stoploss_range_step": -0.01,
|
||||
"minimum_winrate": 0.60,
|
||||
"minimum_expectancy": 0.20,
|
||||
"min_trade_number": 10,
|
||||
"max_trade_duration_minute": 1440,
|
||||
"remove_pumps": false
|
||||
},
|
||||
"telegram": {
|
||||
"enabled": false,
|
||||
"token": "your_telegram_token",
|
||||
"chat_id": "your_telegram_chat_id",
|
||||
"notification_settings": {
|
||||
"status": "on",
|
||||
"warning": "on",
|
||||
"startup": "on",
|
||||
"entry": "on",
|
||||
"entry_fill": "on",
|
||||
"exit": {
|
||||
"roi": "off",
|
||||
"emergency_exit": "off",
|
||||
"force_exit": "off",
|
||||
"exit_signal": "off",
|
||||
"trailing_stop_loss": "off",
|
||||
"stop_loss": "off",
|
||||
"stoploss_on_exchange": "off",
|
||||
"custom_exit": "off"
|
||||
},
|
||||
"exit_fill": "on",
|
||||
"entry_cancel": "on",
|
||||
"exit_cancel": "on",
|
||||
"protection_trigger": "off",
|
||||
"protection_trigger_global": "on",
|
||||
"show_candle": "off"
|
||||
},
|
||||
"reload": true,
|
||||
"balance_dust_level": 0.01
|
||||
},
|
||||
"api_server": {
|
||||
"enabled": false,
|
||||
"listen_ip_address": "127.0.0.1",
|
||||
"listen_port": 8080,
|
||||
"verbosity": "error",
|
||||
"enable_openapi": false,
|
||||
"jwt_secret_key": "somethingrandom",
|
||||
"CORS_origins": [],
|
||||
"username": "freqtrader",
|
||||
"password": "SuperSecurePassword",
|
||||
"ws_token": "secret_ws_t0ken."
|
||||
},
|
||||
"external_message_consumer": {
|
||||
"enabled": false,
|
||||
"producers": [
|
||||
{
|
||||
"name": "default",
|
||||
"host": "127.0.0.2",
|
||||
"port": 8080,
|
||||
"ws_token": "secret_ws_t0ken."
|
||||
}
|
||||
],
|
||||
"wait_timeout": 300,
|
||||
"ping_timeout": 10,
|
||||
"sleep_time": 10,
|
||||
"remove_entry_exit_signals": false,
|
||||
"message_size_limit": 8
|
||||
},
|
||||
"bot_name": "freqtrade",
|
||||
"db_url": "sqlite:///tradesv3.sqlite",
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 5,
|
||||
"heartbeat_interval": 60
|
||||
},
|
||||
"disable_dataframe_checks": false,
|
||||
"strategy": "SampleStrategy",
|
||||
"strategy_path": "user_data/strategies/",
|
||||
"recursive_strategy_search": false,
|
||||
"add_config_files": [],
|
||||
"reduce_df_footprint": false,
|
||||
"dataformat_ohlcv": "feather",
|
||||
"dataformat_trades": "feather"
|
||||
}
|
||||
90
config_examples/config_kraken.example.json_disabled
Normal file
90
config_examples/config_kraken.example.json_disabled
Normal file
@ -0,0 +1,90 @@
|
||||
{
|
||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
||||
"max_open_trades": 5,
|
||||
"stake_currency": "EUR",
|
||||
"stake_amount": 10,
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"fiat_display_currency": "EUR",
|
||||
"timeframe": "5m",
|
||||
"dry_run": true,
|
||||
"cancel_open_orders_on_exit": false,
|
||||
"unfilledtimeout": {
|
||||
"entry": 10,
|
||||
"exit": 10,
|
||||
"exit_timeout_count": 0,
|
||||
"unit": "minutes"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0,
|
||||
"check_depth_of_market": {
|
||||
"enabled": false,
|
||||
"bids_to_ask_delta": 1
|
||||
}
|
||||
},
|
||||
"exit_pricing":{
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"exchange": {
|
||||
"name": "kraken",
|
||||
"key": "your_exchange_key",
|
||||
"secret": "your_exchange_key",
|
||||
"ccxt_config": {},
|
||||
"ccxt_async_config": {
|
||||
},
|
||||
"pair_whitelist": [
|
||||
"ADA/EUR",
|
||||
"ATOM/EUR",
|
||||
"BAT/EUR",
|
||||
"BCH/EUR",
|
||||
"BTC/EUR",
|
||||
"DAI/EUR",
|
||||
"DASH/EUR",
|
||||
"EOS/EUR",
|
||||
"ETC/EUR",
|
||||
"ETH/EUR",
|
||||
"LINK/EUR",
|
||||
"LTC/EUR",
|
||||
"QTUM/EUR",
|
||||
"REP/EUR",
|
||||
"WAVES/EUR",
|
||||
"XLM/EUR",
|
||||
"XMR/EUR",
|
||||
"XRP/EUR",
|
||||
"XTZ/EUR",
|
||||
"ZEC/EUR"
|
||||
],
|
||||
"pair_blacklist": [
|
||||
|
||||
]
|
||||
},
|
||||
"pairlists": [
|
||||
{"method": "StaticPairList"}
|
||||
],
|
||||
"telegram": {
|
||||
"enabled": false,
|
||||
"token": "your_telegram_token",
|
||||
"chat_id": "your_telegram_chat_id"
|
||||
},
|
||||
"api_server": {
|
||||
"enabled": false,
|
||||
"listen_ip_address": "127.0.0.1",
|
||||
"listen_port": 8080,
|
||||
"verbosity": "error",
|
||||
"jwt_secret_key": "somethingrandom",
|
||||
"CORS_origins": [],
|
||||
"username": "freqtrader",
|
||||
"password": "SuperSecurePassword"
|
||||
},
|
||||
"bot_name": "freqtrade",
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 5
|
||||
},
|
||||
"download_trades": true
|
||||
}
|
||||
@ -1,148 +0,0 @@
|
||||
{
|
||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
||||
"redis": {
|
||||
"url": "redis://192.168.1.215:6379/0"
|
||||
},
|
||||
"trading_mode": "spot",
|
||||
"margin_mode": "isolated",
|
||||
"max_open_trades": 5,
|
||||
"stake_currency": "USDT",
|
||||
"stake_amount": 75,
|
||||
"tradable_balance_ratio": 1,
|
||||
"process_only_new_candles": false,
|
||||
"fiat_display_currency": "USD",
|
||||
"dry_run": true,
|
||||
"timeframe": "3m",
|
||||
"additional_timeframes": ["4h"],
|
||||
"dry_run_wallet": 2000,
|
||||
"cancel_open_orders_on_exit": true,
|
||||
"stoploss": -0.14,
|
||||
"max_entry_position_adjustment": 3,
|
||||
"position_adjustment_enable": true,
|
||||
"amount_reserve_percent": 0.05,
|
||||
"unfilledtimeout": {
|
||||
"entry": 5,
|
||||
"exit": 15
|
||||
},
|
||||
"dry_run": false,
|
||||
"startup_candle_count": 200,
|
||||
"exchange": {
|
||||
"name": "okx",
|
||||
"key": "314e75b0-1113-47e8-ad01-1fca7e3c0496",
|
||||
"secret": "9C8B170390F46EA6FB87592AD46F5A34",
|
||||
"password": "nekFoylf:Om0",
|
||||
"enable_ws": false,
|
||||
"ccxt_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 500,
|
||||
"options": {
|
||||
"defaultType": "spot"
|
||||
}
|
||||
},
|
||||
"ccxt_async_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 3000,
|
||||
"timeout": 20000
|
||||
},
|
||||
"pair_whitelist": ["BTC/USDT", "TON/USDT", "DOT/USDT", "XRP/USDT", "OKB/USDT", "SOL/USDT", "DOGE/USDT", "WCT/USDT", "TRUMP/USDT", "SUI/USDT", "PEPE/USDT", "TRB/USDT", "MASK/USDT", "UNI/USDT", "KAITO/USDT"],
|
||||
"pair_blacklist": ["DORA/USDT"]
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0,
|
||||
"check_depth_of_market": {
|
||||
"enabled": false,
|
||||
"bids_to_ask_delta": 1
|
||||
}
|
||||
},
|
||||
"exit_pricing": {
|
||||
"price_side": "other",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "StaticPairList"
|
||||
}
|
||||
],
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"data_kitchen": {
|
||||
"fillna": "ffill"
|
||||
},
|
||||
"freqaimodel": "LightGBMMultiTargetRegressor",
|
||||
"purge_old_models": 2,
|
||||
"identifier": "test58",
|
||||
"train_period_days": 7,
|
||||
"backtest_period_days": 1,
|
||||
"live_retrain_hours": 2,
|
||||
"outlier_detection": {
|
||||
"method": "IsolationForest",
|
||||
"contamination": 0.1
|
||||
},
|
||||
"feature_selection": {
|
||||
"method": "recursive_elimination"
|
||||
},
|
||||
"feature_parameters": {
|
||||
"include_timeframes": [
|
||||
"3m",
|
||||
"15m",
|
||||
"1h"
|
||||
],
|
||||
"include_corr_pairlist": [
|
||||
"BTC/USDT",
|
||||
"SOL/USDT"
|
||||
],
|
||||
"outlier_protection_percentage": 0.1,
|
||||
"label_period_candles": 24,
|
||||
"include_shifted_candles": 3,
|
||||
"DI_threshold": 0.7,
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": false,
|
||||
"use_SVM_to_remove_outliers": true,
|
||||
"indicator_periods_candles": [
|
||||
10,
|
||||
20,
|
||||
50
|
||||
],
|
||||
"plot_feature_importances": 10
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": false
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 400,
|
||||
"learning_rate": 0.03,
|
||||
"num_leaves": 40,
|
||||
"max_depth": 10,
|
||||
"min_data_in_leaf": 20,
|
||||
"feature_fraction": 0.8,
|
||||
"bagging_fraction": 0.8,
|
||||
"bagging_freq": 5,
|
||||
"verbose": -1
|
||||
}
|
||||
},
|
||||
"api_server": {
|
||||
"enabled": true,
|
||||
"listen_ip_address": "0.0.0.0",
|
||||
"listen_port": 8080,
|
||||
"verbosity": "error",
|
||||
"enable_openapi": false,
|
||||
"jwt_secret_key": "6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca",
|
||||
"ws_token": "6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ",
|
||||
"CORS_origins": [],
|
||||
"username": "freqAdmin",
|
||||
"password": "admin"
|
||||
},
|
||||
"bot_name": "freqtrade",
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 5,
|
||||
"heartbeat_interval": 20,
|
||||
"loglevel": "DEBUG"
|
||||
}
|
||||
}
|
||||
@ -1,87 +0,0 @@
|
||||
{
|
||||
"exchange": {
|
||||
"name": "okx",
|
||||
"key": "cbda9fde-b9e3-4a2d-94f9-e5c3705dfb5c",
|
||||
"secret": "CD3B7DB459CBBD540E0926E5C48150E1",
|
||||
"password": "c^-d:g;P2S9?Q#^",
|
||||
"enable_ws": false,
|
||||
"ccxt_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 500,
|
||||
"options": {
|
||||
"defaultType": "spot"
|
||||
}
|
||||
},
|
||||
"ccxt_async_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 3000,
|
||||
"timeout": 20000
|
||||
},
|
||||
"pair_whitelist": [
|
||||
"AAVE/USDT",
|
||||
"ADA/USDT",
|
||||
"ARB/USDT",
|
||||
"AVAX/USDT",
|
||||
"BERA/USDT",
|
||||
"BNB/USDT",
|
||||
"BTC/USDT",
|
||||
"CRO/USDT",
|
||||
"DOGE/USDT",
|
||||
"DORA/USDT",
|
||||
"ETH/USDT",
|
||||
"FIL/USDT",
|
||||
"LINK/USDT",
|
||||
"LPT/USDT",
|
||||
"LTC/USDT",
|
||||
"NMR/USDT",
|
||||
"OMI/USDT",
|
||||
"PENGU/USDT",
|
||||
"PEPE/USDT",
|
||||
"PI/USDT",
|
||||
"PROMPT/USDT",
|
||||
"RAY/USDT",
|
||||
"SOL/USDT",
|
||||
"SUI/USDT",
|
||||
"TON/USDT",
|
||||
"TRUMP/USDT",
|
||||
"TRX/USDT",
|
||||
"UNI/USDT",
|
||||
"VINE/USDT",
|
||||
"WLD/USDT",
|
||||
"XRP/USDT"
|
||||
],
|
||||
"pair_blacklist": [
|
||||
"OKB/USDT"
|
||||
]
|
||||
},
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "StaticPairList"
|
||||
}
|
||||
],
|
||||
"order_types": {
|
||||
"entry": "market",
|
||||
"exit": "market",
|
||||
"stoploss": "limit",
|
||||
"stoploss_on_exchange": false
|
||||
},
|
||||
"order_time_in_force": {
|
||||
"entry": "gtc",
|
||||
"exit": "gtc"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "other",
|
||||
"use_order_book": false,
|
||||
"price_last_balance": 0
|
||||
},
|
||||
"exit_pricing": {
|
||||
"price_side": "other",
|
||||
"use_order_book": false
|
||||
},
|
||||
"fee": 0.0008,
|
||||
"api_server": {
|
||||
"enabled": true,
|
||||
"listen_ip_address": "0.0.0.0",
|
||||
"listen_port": 8080
|
||||
}
|
||||
}
|
||||
@ -1,96 +0,0 @@
|
||||
{
|
||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
||||
"trading_mode": "spot",
|
||||
"margin_mode": "isolated",
|
||||
"max_open_trades": 3,
|
||||
"stake_amount": 300,
|
||||
"unfilledtimeout": {
|
||||
"entry": 10,
|
||||
"exit": 20
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"exit_pricing": {
|
||||
"price_side": "other",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"stake_currency": "USDT",
|
||||
"trailing_stop": false,
|
||||
"use_custom_stoploss": true,
|
||||
"startup_candle_count": 200,
|
||||
"tradable_balance_ratio": 1,
|
||||
"fiat_display_currency": "USD",
|
||||
"dry_run": true,
|
||||
"identifier": "demo1",
|
||||
"timeframe": "5m",
|
||||
"dry_run_wallet": 1000,
|
||||
"cancel_open_orders_on_exit": true,
|
||||
"stoploss": -0.061,
|
||||
"roi": {},
|
||||
"trailing": {
|
||||
"trailing_stop": false,
|
||||
"trailing_stop_positive": null,
|
||||
"trailing_stop_positive_offset": 0.0,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": 3,
|
||||
"buy": {
|
||||
"adx_buy": 25,
|
||||
"atr_ratio": 0.005
|
||||
},
|
||||
"sell": {
|
||||
"ema_fast_period": 7,
|
||||
"rsi_sell": 60
|
||||
},
|
||||
"exchange": {
|
||||
"name": "okx",
|
||||
"key": "REDACTED",
|
||||
"secret": "REDACTED",
|
||||
"enable_ws": false,
|
||||
"ccxt_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 500,
|
||||
"options": {
|
||||
"defaultType": "spot"
|
||||
}
|
||||
},
|
||||
"ccxt_async_config": {
|
||||
"enableRateLimit": true,
|
||||
"rateLimit": 3000,
|
||||
"timeout": 20000
|
||||
},
|
||||
"pair_whitelist": ["BTC/USDT", "ETH/USDT", "SOL/USDT", "DOT/USDT"],
|
||||
"pair_blacklist": []
|
||||
},
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "StaticPairList"
|
||||
}
|
||||
],
|
||||
"api_server": {
|
||||
"enabled": true,
|
||||
"listen_ip_address": "0.0.0.0",
|
||||
"listen_port": 8080,
|
||||
"verbosity": "error",
|
||||
"enable_openapi": false,
|
||||
"jwt_secret_key": "6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca",
|
||||
"ws_token": "6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ",
|
||||
"CORS_origins": [],
|
||||
"username": "freqAdmin",
|
||||
"password": "REDACTED"
|
||||
},
|
||||
"use_exit_signal": true,
|
||||
"bot_name": "freqtrade",
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 5,
|
||||
"heartbeat_interval": 20,
|
||||
"loglevel": "INFO"
|
||||
},
|
||||
"config_files": ["config_examples/theforcev7.json"]
|
||||
}
|
||||
@ -1,98 +0,0 @@
|
||||
{
|
||||
"strategy": "freqaiprimer",
|
||||
"strategy_path": "freqtrade/templates",
|
||||
"stake_currency": "USDT",
|
||||
"stake_amount": 10,
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"fiat_display_currency": "USD",
|
||||
"timeframe": "3m",
|
||||
"dry_run": true,
|
||||
"cancel_open_orders_on_exit": false,
|
||||
"unfilledtimeout": {
|
||||
"entry": 10,
|
||||
"exit": 10,
|
||||
"exit_timeout_count": 0,
|
||||
"unit": "minutes"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0,
|
||||
"check_depth_of_market": {
|
||||
"enabled": false,
|
||||
"bids_to_ask_delta": 1
|
||||
}
|
||||
},
|
||||
"exit_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"exchange": {
|
||||
"name": "binance",
|
||||
"key": "",
|
||||
"secret": "",
|
||||
"ccxt_config": {},
|
||||
"ccxt_async_config": {},
|
||||
"pair_whitelist": [
|
||||
"BTC/USDT",
|
||||
"ETH/USDT",
|
||||
"ADA/USDT",
|
||||
"DOT/USDT"
|
||||
],
|
||||
"pair_blacklist": [
|
||||
"BNB/USDT"
|
||||
]
|
||||
},
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "StaticPairList"
|
||||
}
|
||||
],
|
||||
"edge": {
|
||||
"enabled": false
|
||||
},
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"identifier": "freqai_primer_mixed",
|
||||
"model": "LightGBMRegressor",
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["3m", "15m", "1h"],
|
||||
"include_shifted_candles": 2,
|
||||
"label_period_candles": 12
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": false
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"price_value_divergence": {
|
||||
"model": "LightGBMRegressor",
|
||||
"model_params": {
|
||||
"n_estimators": 200,
|
||||
"learning_rate": 0.05,
|
||||
"num_leaves": 31,
|
||||
"verbose": -1
|
||||
}
|
||||
},
|
||||
"optimal_first_length": {
|
||||
"model": "LightGBMClassifier",
|
||||
"model_params": {
|
||||
"n_estimators": 150,
|
||||
"learning_rate": 0.1,
|
||||
"num_leaves": 15,
|
||||
"max_depth": 8,
|
||||
"min_child_samples": 10,
|
||||
"class_weight": "balanced",
|
||||
"verbose": -1
|
||||
}
|
||||
}
|
||||
},
|
||||
"fit_live_predictions_candles": 100,
|
||||
"live_retrain_candles": 100
|
||||
},
|
||||
"redis": {
|
||||
"url": "redis://192.168.1.215:6379/0"
|
||||
}
|
||||
}
|
||||
@ -1,128 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
策略调试脚本 - 检查策略加载的详细过程
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import inspect
|
||||
|
||||
# 添加当前目录到Python路径
|
||||
sys.path.insert(0, '/Users/zhangkun/myTestFreqAI')
|
||||
|
||||
def analyze_strategy_file():
|
||||
"""分析策略文件结构"""
|
||||
strategy_path = '/Users/zhangkun/myTestFreqAI/freqtrade/templates/freqaiprimer.py'
|
||||
|
||||
print("=== 策略文件分析 ===")
|
||||
print(f"文件路径: {strategy_path}")
|
||||
print(f"文件存在: {os.path.exists(strategy_path)}")
|
||||
|
||||
if os.path.exists(strategy_path):
|
||||
with open(strategy_path, 'r') as f:
|
||||
content = f.read()
|
||||
|
||||
print(f"文件大小: {len(content)} 字符")
|
||||
|
||||
# 检查关键内容
|
||||
checks = [
|
||||
('INTERFACE_VERSION', 'INTERFACE_VERSION = 3'),
|
||||
('populate_indicators定义', 'def populate_indicators(self, dataframe: DataFrame, metadata: dict)'),
|
||||
('populate_entry_trend定义', 'def populate_entry_trend(self, dataframe: DataFrame, metadata: dict)'),
|
||||
('populate_exit_trend定义', 'def populate_exit_trend(self, dataframe: DataFrame, metadata: dict)'),
|
||||
('类定义', 'class FreqaiPrimer(IStrategy)'),
|
||||
]
|
||||
|
||||
for name, pattern in checks:
|
||||
found = pattern in content
|
||||
status = "✅" if found else "❌"
|
||||
print(f"{status} {name}: {'找到' if found else '未找到'}")
|
||||
|
||||
# 检查函数签名
|
||||
lines = content.split('\n')
|
||||
for i, line in enumerate(lines):
|
||||
if 'def populate_indicators' in line:
|
||||
print(f"\n📝 populate_indicators 定义在第 {i+1} 行:")
|
||||
print(f" {line.strip()}")
|
||||
# 打印接下来的几行
|
||||
for j in range(1, 4):
|
||||
if i+j < len(lines):
|
||||
print(f" {lines[i+j].strip()}")
|
||||
|
||||
elif 'def populate_entry_trend' in line:
|
||||
print(f"\n📝 populate_entry_trend 定义在第 {i+1} 行:")
|
||||
print(f" {line.strip()}")
|
||||
|
||||
elif 'def populate_exit_trend' in line:
|
||||
print(f"\n📝 populate_exit_trend 定义在第 {i+1} 行:")
|
||||
print(f" {line.strip()}")
|
||||
|
||||
def check_import_issues():
|
||||
"""检查导入问题"""
|
||||
print("\n=== 导入检查 ===")
|
||||
|
||||
try:
|
||||
# 尝试直接导入策略类
|
||||
import importlib.util
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"freqaiprimer",
|
||||
"/Users/zhangkun/myTestFreqAI/freqtrade/templates/freqaiprimer.py"
|
||||
)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
|
||||
# 查找策略类
|
||||
classes = []
|
||||
for name, obj in inspect.getmembers(module):
|
||||
if inspect.isclass(obj) and 'Strategy' in str(obj.__bases__):
|
||||
classes.append(name)
|
||||
|
||||
print(f"找到的策略类: {classes}")
|
||||
|
||||
if 'FreqaiPrimer' in classes:
|
||||
cls = getattr(module, 'FreqaiPrimer')
|
||||
print(f"✅ FreqaiPrimer类已找到")
|
||||
|
||||
# 检查接口版本
|
||||
interface_version = getattr(cls, 'INTERFACE_VERSION', None)
|
||||
print(f"接口版本: {interface_version}")
|
||||
|
||||
# 检查方法签名
|
||||
import inspect
|
||||
methods = ['populate_indicators', 'populate_entry_trend', 'populate_exit_trend']
|
||||
for method in methods:
|
||||
if hasattr(cls, method):
|
||||
sig = inspect.signature(getattr(cls, method))
|
||||
params = list(sig.parameters.keys())
|
||||
has_metadata = 'metadata' in params
|
||||
status = "✅" if has_metadata else "❌"
|
||||
print(f"{status} {method} 参数: {params}")
|
||||
else:
|
||||
print(f"❌ {method} 未找到")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 导入失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
def check_duplicate_files():
|
||||
"""检查是否有重复的策略文件"""
|
||||
print("\n=== 重复文件检查 ===")
|
||||
|
||||
import subprocess
|
||||
try:
|
||||
result = subprocess.run(
|
||||
['find', '/Users/zhangkun/myTestFreqAI', '-name', '*freqai*', '-type', 'f'],
|
||||
capture_output=True, text=True
|
||||
)
|
||||
files = result.stdout.strip().split('\n')
|
||||
print("找到的相关文件:")
|
||||
for f in files:
|
||||
if f:
|
||||
print(f" {f}")
|
||||
except Exception as e:
|
||||
print(f"查找文件时出错: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
analyze_strategy_file()
|
||||
check_import_issues()
|
||||
check_duplicate_files()
|
||||
128
doc/README.md
128
doc/README.md
@ -1,128 +0,0 @@
|
||||
### 策略概述：FreqaiPrimer
|
||||
|
||||
**FreqaiPrimer** 是一个基于 **FreqAI**（频率人工智能）框架的交易策略，结合机器学习预测与传统技术指标，旨在适应多种市场环境（震荡、下跌、单边上涨）。策略以 3 分钟（3m）为主时间框架，整合 3m、15m 和 1h 数据，通过动态调整入场、退出条件和下注金额，优化交易机会并控制风险。核心目标是：
|
||||
1. **捕捉价值背离**：利用 FreqAI 预测价格与长期均线（EMA200）的偏离（`&-price_value_divergence`），识别超买/超卖点。
|
||||
2. **适应趋势市场**：通过趋势得分（`trend_score`，0-100，100 为强牛市）动态调整买卖条件，支持单边上涨行情。
|
||||
3. **风险管理**：通过动态仓位调整、追踪止损和冷却期，平衡收益与风险。
|
||||
4. **多指标融合**：结合 RSI、布林带、MACD、Stochastic 和 ADX 等指标，增强信号可靠性。
|
||||
|
||||
策略使用 LightGBM 回归模型预测价值背离，结合技术指标（如 EMA、RSI、MACD、Stochastic、ADX）生成买卖信号，并通过 Hyperopt 优化参数（如阈值、止损、仓位调整）。以下是入场和退出逻辑的详细描述。
|
||||
|
||||
---
|
||||
|
||||
### 入场逻辑
|
||||
入场逻辑在 `populate_entry_trend` 方法中实现，分为 **震荡/下跌市场** 和 **趋势市场** 两种条件，通过趋势得分（`trend_score`）动态调整，适应不同市场环境。入场信号通过 `enter_long = 1` 标记。
|
||||
|
||||
#### 1. 震荡/下跌市场入场条件
|
||||
适用于震荡或下跌市场，旨在捕捉超卖机会：
|
||||
- **价值背离（`&-price_value_divergence`）**：
|
||||
- 由 FreqAI 预测，衡量价格与 EMA200 的偏离百分比。
|
||||
- 条件：`&-price_value_divergence < dynamic_buy_threshold`。
|
||||
- 阈值：根据 `trend_score` 在 -0.1（熊市）到 0.02（牛市）间动态调整，牛市允许正背离。
|
||||
- 含义：价格显著低于 EMA200，表明超卖。
|
||||
- **成交量标准化得分（`volume_z_score`）**：
|
||||
- 计算成交量相对于 20 周期均值的标准化得分。
|
||||
- 条件：`volume_z_score > volume_z_score_threshold`（0.5 到 2.0，牛市阈值较低）。
|
||||
- 含义：高成交量确认市场活跃，可能伴随反转。
|
||||
- **RSI**：
|
||||
- 使用 14 周期 RSI。
|
||||
- 条件：`rsi < rsi_threshold`（40 到 60，牛市阈值较低）。
|
||||
- 含义：低 RSI 表明超卖状态。
|
||||
- **布林带下轨**：
|
||||
- 条件：`close <= bb_lowerband`（20 周期布林带，2 倍标准差）。
|
||||
- 含义：价格触及下轨，强化超卖信号。
|
||||
|
||||
**组合逻辑**：所有条件需同时满足（`cond1 & cond2 & cond3 & cond4`）。
|
||||
|
||||
#### 2. 趋势市场入场条件（`trend_score > 70`）
|
||||
为捕捉单边上涨行情中的回调买入机会，新增以下条件：
|
||||
- **价格接近 EMA20**：
|
||||
- 条件：`close >= ema20 * 0.995`（允许 0.5% 回调）。
|
||||
- 含义：价格在短期均线附近，适合趋势回调买入。
|
||||
- **EMA20 上穿 EMA50**：
|
||||
- 条件：`ema20 > ema50`。
|
||||
- 含义：短期趋势强于中期趋势，确认上涨趋势。
|
||||
- **MACD 看涨交叉**：
|
||||
- 条件：`macd > macdsignal` 且前一根 K 线 `macd <= macdsignal`。
|
||||
- 含义：短期动能增强，适合入场。
|
||||
- **Stochastic 慢线回升**：
|
||||
- 条件：`slowk > slowk.shift(1)` 且 `slowk < 70`。
|
||||
- 含义：慢线上升且未超买，表明回调结束。
|
||||
|
||||
**组合逻辑**：所有趋势条件需同时满足（`cond_trend1 & cond_trend2 & cond_trend3 & cond_trend4`），仅在强牛市（`trend_score > 70`）启用。
|
||||
|
||||
#### 3. 合并逻辑
|
||||
- 震荡和趋势条件通过 OR 逻辑合并（`oscillation_condition | trend_condition`），允许任一条件触发入场。
|
||||
- 动态冷却期（1-10 分钟，牛市缩短，熊市延长）通过 `COOLDOWN_PERIOD_MINUTES` 控制连续入场间隔。
|
||||
- 下注金额通过 `custom_stake_amount` 根据 `trend_score` 动态调整（1-200 倍映射，牛市增加，熊市减少）。
|
||||
|
||||
---
|
||||
|
||||
### 退出逻辑
|
||||
退出逻辑在 `populate_exit_trend` 和 `adjust_trade_position` 方法中实现，分为 **震荡/下跌市场** 和 **趋势市场** 条件，结合 ADX 确认趋势反转，优化单边上涨行情中的持有时间。退出信号通过 `exit_long = 1` 或仓位调整触发。
|
||||
|
||||
#### 1. 震荡/下跌市场退出条件
|
||||
适用于震荡或下跌市场，捕捉超买或获利点：
|
||||
- **价值背离（`&-price_value_divergence`）**：
|
||||
- 条件：`&-price_value_divergence > dynamic_sell_threshold`。
|
||||
- 阈值：根据 `trend_score` 在 0.001（熊市）到 0.1（牛市）间动态调整，牛市允许更大正背离。
|
||||
- 含义：价格显著高于 EMA200，表明超买。
|
||||
- **RSI**：
|
||||
- 条件：`rsi > rsi_threshold`（75 到 85，牛市阈值更高）。
|
||||
- 含义：高 RSI 表明超买状态。
|
||||
|
||||
**组合逻辑**：任一条件满足（`cond1 | cond2`）触发卖出。
|
||||
|
||||
#### 2. 趋势市场退出条件（`trend_score > 70`）
|
||||
为避免牛市中过早退出，加入趋势反转确认：
|
||||
- **MACD 看跌交叉**：
|
||||
- 条件：`macd < macdsignal` 且前一根 K 线 `macd >= macdsignal`。
|
||||
- 含义：短期动能减弱，可能反转。
|
||||
- **Stochastic 超买回落**：
|
||||
- 条件：`slowk > 80` 且 `slowk < slowk.shift(1)`。
|
||||
- 含义：慢线超买后下降，表明趋势可能结束。
|
||||
- **EMA20 下穿 EMA50**：
|
||||
- 条件：`ema20 < ema50` 且前一根 K 线 `ema20 >= ema50`。
|
||||
- 含义：短期趋势弱于中期趋势，确认反转。
|
||||
- **ADX 趋势减弱**：
|
||||
- 条件：`adx > 25` 且 `adx_diff < 0`（ADX 下降）。
|
||||
- 含义：趋势强度减弱，可能预示反转。
|
||||
|
||||
**组合逻辑**：任一趋势条件满足（`cond_trend1 | cond_trend2 | cond_trend3 | cond_trend4`）触发卖出，仅在强牛市启用。
|
||||
|
||||
#### 3. 合并逻辑与仓位调整
|
||||
- 震荡和趋势条件通过 OR 逻辑合并（`oscillation_condition | trend_condition`）。
|
||||
- **分级止盈**（`adjust_trade_position`）：
|
||||
- 利润 ≥ 3%：减仓（比例 0.6-1.0，牛市较低）。
|
||||
- 利润 ≥ 7%（牛市）：进一步减仓（比例 0.8-1.2），保留仓位捕捉更大上涨。
|
||||
- **追踪止损**：
|
||||
- 启动点（`TRAILING_STOP_START`）：牛市提高（如 3% → 4.5%）。
|
||||
- 止损距离（`TRAILING_STOP_DISTANCE`）：牛市增大（如 1% → 2%）。
|
||||
- 动态调整：根据 `trend_score` 使用 Sigmoid 函数，牛市更宽松。
|
||||
- **确认退出**（`confirm_trade_exit`）：记录退出原因，调整卖出价格（+0.125% 优化收益）。
|
||||
|
||||
---
|
||||
|
||||
### 策略特点
|
||||
1. **多时间框架**：整合 3m、15m、1h 数据，增强特征工程和趋势判断。
|
||||
2. **动态适应**：趋势得分动态调整阈值、下注金额和冷却期，牛市宽松，熊市严格。
|
||||
3. **多指标融合**：结合 FreqAI 预测（价值背离）、RSI、布林带、MACD、Stochastic 和 ADX。
|
||||
4. **风险管理**：
|
||||
- 固定止损：-1.5%。
|
||||
- 动态仓位：支持加仓（最多 3 次，倍数 2、4、8）和减仓。
|
||||
- 追踪止损：牛市中延迟触发，容忍更大回调。
|
||||
5. **调试支持**：详细日志记录条件状态，便于分析。
|
||||
|
||||
---
|
||||
|
||||
### 单边上涨行情优化
|
||||
- **入场**：新增趋势条件（EMA20、MACD、Stochastic），允许在牛市回调点（如价格接近 EMA20）入场，解决震荡条件过于严格的问题。
|
||||
- **退出**：动态放宽 RSI（75 → 85）和背离阈值（0.05 → 0.1），加入 ADX（趋势减弱）、MACD、Stochastic 和均线反转信号，延迟牛市退出，捕捉更大利润。
|
||||
- **协调性**：入场和退出逻辑均基于 `trend_score` 和多指标确认，确保牛市中延长持有时间并及时止盈。
|
||||
|
||||
---
|
||||
|
||||
### 总结
|
||||
**FreqaiPrimer** 通过 FreqAI 预测价值背离，结合 RSI、布林带、MACD、Stochastic 和 ADX 等指标，动态适应震荡和趋势市场。入场逻辑在震荡市场捕捉超卖机会，在牛市中利用趋势指标捕捉回调买入点；退出逻辑通过动态阈值和趋势反转信号（包括 ADX 确认），避免牛市过早退出，同时在趋势减弱时及时止盈。策略通过动态仓位、追踪止损和冷却期优化风险管理，适合加密货币等高波动市场。
|
||||
|
||||
如需进一步细化逻辑、提供回测代码或分析，请告诉我！
|
||||
@ -1,14 +0,0 @@
|
||||
## hyperopts如何使用
|
||||
|
||||
每周运行一次 hyperopt, 每次运行最近2个月的数据, 得到 stoploss 最优解后 手动更新 config, 提交代码
|
||||
|
||||
假设今天是2025年5月15日
|
||||
```
|
||||
cd tools
|
||||
./download.sh
|
||||
./hyperopt.sh 20250314 20250516
|
||||
然后得到结果, stoploss 为 -0.06 写到log里
|
||||
```
|
||||
|
||||
后面改成自动更新并不麻烦
|
||||
策略目录有自动生成的json, 是hypertop计算结果, 需脱掉一层 paramas后 方可使用
|
||||
58
doc/mark.md
58
doc/mark.md
@ -1,58 +0,0 @@
|
||||
### 优化记录
|
||||
|
||||
|
||||
- 20250606 10:52
|
||||
|
||||
当前hyperopt使用 ./hyperopt 20250425 20250525 得到的优化结果, 然后
|
||||
|
||||
./backtest.sh 20250520 20250525, 结果:
|
||||
|
||||
SUMMARY METRICS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Metric ┃ Value ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Backtesting from │ 2025-05-20 00:00:00 │
|
||||
│ Backtesting to │ 2025-05-25 00:00:00 │
|
||||
│ Trading Mode │ Spot │
|
||||
│ Max open trades │ 4 │
|
||||
│ │ │
|
||||
│ Total/Daily Avg Trades │ 16 / 3.2 │
|
||||
│ Starting balance │ 1000 USDT │
|
||||
│ Final balance │ 1010.711 USDT │
|
||||
│ Absolute profit │ 10.711 USDT │
|
||||
│ Total profit % │ 1.07% │
|
||||
│ CAGR % │ 117.65% │
|
||||
│ Sortino │ 53.06 │
|
||||
│ Sharpe │ 11.07 │
|
||||
│ Calmar │ 349.31 │
|
||||
│ SQN │ 0.70 │
|
||||
│ Profit factor │ 1.61 │
|
||||
│ Expectancy (Ratio) │ 0.67 (-0.07) │
|
||||
│ Avg. daily profit % │ 0.21% │
|
||||
│ Avg. stake amount │ 149.999 USDT │
|
||||
│ Total trade volume │ 4818.39 USDT │
|
||||
│ │ │
|
||||
│ Best Pair │ TRUMP/USDT 0.98% │
|
||||
│ Worst Pair │ MASK/USDT -0.85% │
|
||||
│ Best trade │ TRUMP/USDT 5.31% │
|
||||
│ Worst trade │ UNI/USDT -4.60% │
|
||||
│ Best day │ 15.174 USDT │
|
||||
│ Worst day │ -6.004 USDT │
|
||||
│ Days win/draw/lose │ 2 / 0 / 2 │
|
||||
│ Avg. Duration Winners │ 1:08:00 │
|
||||
│ Avg. Duration Loser │ 0:18:00 │
|
||||
│ Max Consecutive Wins / Loss │ 3 / 3 │
|
||||
│ Rejected Entry signals │ 0 │
|
||||
│ Entry/Exit Timeouts │ 9 / 0 │
|
||||
│ │ │
|
||||
│ Min balance │ 1001.8 USDT │
|
||||
│ Max balance │ 1020.574 USDT │
|
||||
│ Max % of account underwater │ 1.17% │
|
||||
│ Absolute Drawdown (Account) │ 1.17% │
|
||||
│ Absolute Drawdown │ 11.957 USDT │
|
||||
│ Drawdown high │ 20.574 USDT │
|
||||
│ Drawdown low │ 8.617 USDT │
|
||||
│ Drawdown Start │ 2025-05-22 20:48:00 │
|
||||
│ Drawdown End │ 2025-05-24 17:27:00 │
|
||||
│ Market change │ 0.81% │
|
||||
└─────────────────────────────┴─────────────────────┘
|
||||
@ -1,7 +1,7 @@
|
||||
version: '3.8'
|
||||
---
|
||||
services:
|
||||
freqtrade:
|
||||
image: freqtradeorg/freqtrade:develop_freqai_withredis
|
||||
image: freqtradeorg/freqtrade:stable_freqaitorch
|
||||
# # Enable GPU Image and GPU Resources
|
||||
# # Make sure to uncomment the whole deploy section
|
||||
# deploy:
|
||||
@ -17,45 +17,37 @@ services:
|
||||
# context: .
|
||||
# dockerfile: "./docker/Dockerfile.custom"
|
||||
restart: always
|
||||
extra_hosts:
|
||||
- "www.okx.com:104.18.43.174" # 域名:IP 格式
|
||||
- "api.okx.com:104.18.43.174" # 域名:IP 格式
|
||||
container_name: freqtrade
|
||||
volumes:
|
||||
- "./user_data:/freqtrade/user_data"
|
||||
- "./config_examples:/freqtrade/config_examples"
|
||||
- "./freqtrade/templates:/freqtrade/templates"
|
||||
- "./freqtrade/exchange/:/freqtrade/exchange"
|
||||
- "./freqtrade/plugins/protections:/freqtrade/freqtrade/plugins/protections"
|
||||
- "./ccxt/async_support/okx.py:/home/ftuser/.local/lib/python3.12/site-packages/ccxt/async_support/okx.py"
|
||||
# FreqAI核心文件挂载 - 确保使用我们修改的版本
|
||||
- "./freqtrade/freqai/data_kitchen.py:/freqtrade/freqai/data_kitchen.py"
|
||||
- "./freqtrade/freqai/data_drawer.py:/freqtrade/freqai/data_drawer.py"
|
||||
- "./freqtrade/freqai/freqai_interface.py:/freqtrade/freqai/freqai_interface.py"
|
||||
- "./ccxt/async_support/okx.py:/home/ftuser/.local/lib/python3.12/site-packages/ccxt/async_support/okx.py"
|
||||
# Expose api on port 8080 (localhost only)
|
||||
# Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
|
||||
# for more information.
|
||||
ports:
|
||||
- "8080:8080"
|
||||
# Default command used when running `docker compose up`
|
||||
|
||||
|
||||
# --freqaimodel XGBoostRegressor
|
||||
# command: >
|
||||
# trade
|
||||
# --logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
# --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
|
||||
# --freqaimodel LightGBMRegressor
|
||||
# --config /freqtrade/config_examples/config_freqai.okx.json
|
||||
# --strategy FreqaiExampleStrategy
|
||||
# --strategy-path /freqtrade/templates
|
||||
command: >
|
||||
trade
|
||||
--logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
--db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
|
||||
--freqaimodel XGBoostRegressor
|
||||
--config /freqtrade/config_examples/config_freqai.okx.json
|
||||
--strategy FreqaiExampleStrategy
|
||||
--strategy-path /freqtrade/templates
|
||||
--fee 0.0008
|
||||
# command: >
|
||||
# backtesting
|
||||
# --logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
# --freqaimodel LightGBMRegressor
|
||||
# --freqaimodel XGBoostRegressor
|
||||
# --config /freqtrade/config_examples/config_freqai.okx.json
|
||||
# --strategy-path /freqtrade/templates
|
||||
# --strategy FreqaiExampleStrategy
|
||||
# --breakdown week month
|
||||
# --timerange 20250310-20250410
|
||||
# --export trades
|
||||
# command: >
|
||||
@ -67,9 +59,27 @@ services:
|
||||
# --strategy FreqaiExampleStrategy
|
||||
# --timerange 20250301-20250420
|
||||
# --hyperopt-loss SharpeHyperOptLoss
|
||||
# --spaces roi stoploss trailing
|
||||
# --spaces roi stoploss
|
||||
# -e 200
|
||||
# --config /freqtrade/templates/FreqaiExampleStrategy.json
|
||||
|
||||
# command: >
|
||||
# backtesting
|
||||
# --logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
# --freqaimodel XGBoostRegressor
|
||||
# --config /freqtrade/config_examples/config_freqai.okx.json
|
||||
# --strategy-path /freqtrade/templates
|
||||
# --strategy FreqaiExampleStrategy
|
||||
# --timerange 20250101-20250420
|
||||
# --fee 0.0008
|
||||
# command: >
|
||||
# download-data
|
||||
# --config /freqtrade/config_examples/config_freqai.okx.json
|
||||
# --exchange okx
|
||||
# --pairs DOT/USDT
|
||||
# --timeframe 1h 5m
|
||||
# --timerange 20240101-20250420
|
||||
#
|
||||
# command: >
|
||||
# hyperopt
|
||||
# --logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
@ -79,29 +89,6 @@ services:
|
||||
# --strategy FreqaiExampleStrategy
|
||||
# --timerange 20250301-20250420
|
||||
# --hyperopt-loss SharpeHyperOptLoss
|
||||
# --spaces roi stoploss
|
||||
# --spaces buy sell roi stoploss trailing
|
||||
# --fee 0.001
|
||||
# -e 200
|
||||
|
||||
#--config /freqtrade/templates/FreqaiExampleStrategy.json
|
||||
|
||||
# command: >
|
||||
# backtesting
|
||||
# --logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
# --freqaimodel XGBoostRegressor
|
||||
# --config /freqtrade/config_examples/config_my_hyperopt.json
|
||||
# --strategy-path /freqtrade/templates
|
||||
# --strategy TheForceV7
|
||||
# --timerange 20250301-20250405
|
||||
# --export trades
|
||||
# --fee 0.0008
|
||||
# --cache none
|
||||
#
|
||||
# command: >
|
||||
# trade
|
||||
# --logfile /freqtrade/user_data/logs/freqtrade.log
|
||||
# --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
|
||||
# --freqaimodel XGBoostRegressor
|
||||
# --config /freqtrade/config_examples/theforcev7.json
|
||||
# --strategy-path /freqtrade/templates
|
||||
# --strategy TheForceV7
|
||||
# --fee 0.0008
|
||||
|
||||
47
file-copy.sh
47
file-copy.sh
@ -1,47 +0,0 @@
|
||||
import os
|
||||
import shutil
|
||||
|
||||
# 定义源目录
|
||||
source_dir = "./result"
|
||||
|
||||
# 定义目标目录和文件名
|
||||
target_strategy_path = "./freqtrade/template/FreqaiExampleStrategy.py"
|
||||
target_config_path = "./config_examples/config_freqai.okx.json"
|
||||
|
||||
# 查找 *FreqaiExampleStrategy.py 文件
|
||||
strategy_file = None
|
||||
for file in os.listdir(source_dir):
|
||||
if file.endswith("FreqaiExampleStrategy.py"):
|
||||
strategy_file = os.path.join(source_dir, file)
|
||||
break
|
||||
|
||||
# 查找 *config.json 文件
|
||||
config_file = None
|
||||
for file in os.listdir(source_dir):
|
||||
if file.endswith("config.json"):
|
||||
config_file = os.path.join(source_dir, file)
|
||||
break
|
||||
|
||||
# 复制 *FreqaiExampleStrategy.py 文件
|
||||
if strategy_file:
|
||||
try:
|
||||
target_strategy_dir = os.path.dirname(target_strategy_path)
|
||||
os.makedirs(target_strategy_dir, exist_ok=True)
|
||||
shutil.copy2(strategy_file, target_strategy_path)
|
||||
print(f"成功复制 {strategy_file} 到 {target_strategy_path}")
|
||||
except Exception as e:
|
||||
print(f"复制 {strategy_file} 时出错: {e}")
|
||||
else:
|
||||
print("未找到 *FreqaiExampleStrategy.py 文件")
|
||||
|
||||
# 复制 *config.json 文件
|
||||
if config_file:
|
||||
try:
|
||||
target_config_dir = os.path.dirname(target_config_path)
|
||||
os.makedirs(target_config_dir, exist_ok=True)
|
||||
shutil.copy2(config_file, target_config_path)
|
||||
print(f"成功复制 {config_file} 到 {target_config_path}")
|
||||
except Exception as e:
|
||||
print(f"复制 {config_file} 时出错: {e}")
|
||||
else:
|
||||
print("未找到 *config.json 文件")
|
||||
@ -1,7 +0,0 @@
|
||||
with open('output.log', 'r') as input_file, open('output_filted.log', 'w') as output_file:
|
||||
for line in input_file:
|
||||
if "validation_0" in line:
|
||||
if "[99]" in line:
|
||||
output_file.write(line)
|
||||
else:
|
||||
output_file.write(line)
|
||||
@ -1,888 +0,0 @@
|
||||
[99] validation_0-rmse:0.13480 validation_1-rmse:0.09950
|
||||
[99] validation_0-rmse:0.14079 validation_1-rmse:0.10036
|
||||
[99] validation_0-rmse:0.15004 validation_1-rmse:0.09815
|
||||
[99] validation_0-rmse:0.13402 validation_1-rmse:0.10289
|
||||
[99] validation_0-rmse:0.14818 validation_1-rmse:0.10475
|
||||
[99] validation_0-rmse:0.15738 validation_1-rmse:0.10531
|
||||
[99] validation_0-rmse:0.14246 validation_1-rmse:0.11071
|
||||
[99] validation_0-rmse:0.16321 validation_1-rmse:0.10649
|
||||
[99] validation_0-rmse:0.15096 validation_1-rmse:0.10882
|
||||
[99] validation_0-rmse:0.15694 validation_1-rmse:0.11396
|
||||
[99] validation_0-rmse:0.18198 validation_1-rmse:0.10994
|
||||
[99] validation_0-rmse:0.16824 validation_1-rmse:0.11403
|
||||
[99] validation_0-rmse:0.16437 validation_1-rmse:0.11096
|
||||
[99] validation_0-rmse:0.17746 validation_1-rmse:0.10819
|
||||
[99] validation_0-rmse:0.15331 validation_1-rmse:0.11727
|
||||
[99] validation_0-rmse:0.16802 validation_1-rmse:0.11547
|
||||
[99] validation_0-rmse:0.15958 validation_1-rmse:0.11029
|
||||
[99] validation_0-rmse:0.18742 validation_1-rmse:0.11149
|
||||
[99] validation_0-rmse:0.15075 validation_1-rmse:0.09770
|
||||
[99] validation_0-rmse:0.13818 validation_1-rmse:0.10038
|
||||
[99] validation_0-rmse:0.12954 validation_1-rmse:0.09825
|
||||
[99] validation_0-rmse:0.14837 validation_1-rmse:0.10398
|
||||
Creating freqtrade_freqtrade_run ...
|
||||
Creating freqtrade_freqtrade_run ... done
|
||||
2025-04-29 01:54:55,246 - freqtrade - INFO - freqtrade 2025.3
|
||||
2025-04-29 01:54:55,464 - numexpr.utils - INFO - NumExpr defaulting to 12 threads.
|
||||
2025-04-29 01:54:56,878 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/config_examples/config_freqai.okx.json ...
|
||||
2025-04-29 01:54:56,879 - freqtrade.configuration.load_config - INFO - Using config: /freqtrade/templates/FreqaiExampleStrategy.json ...
|
||||
2025-04-29 01:54:56,881 - freqtrade.loggers - INFO - Enabling colorized output.
|
||||
2025-04-29 01:54:56,881 - root - INFO - Logfile configured
|
||||
2025-04-29 01:54:56,882 - freqtrade.loggers - INFO - Verbosity set to 0
|
||||
2025-04-29 01:54:56,882 - freqtrade.configuration.configuration - INFO - Using additional Strategy lookup path: /freqtrade/templates
|
||||
2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Using max_open_trades: 4 ...
|
||||
2025-04-29 01:54:56,883 - freqtrade.configuration.configuration - INFO - Parameter --timerange detected: 20250101-20250420 ...
|
||||
2025-04-29 01:54:56,907 - freqtrade.configuration.configuration - INFO - Using user-data directory: /freqtrade/user_data ...
|
||||
2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Using data directory: /freqtrade/user_data/data/okx ...
|
||||
2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Parameter --cache=none detected ...
|
||||
2025-04-29 01:54:56,908 - freqtrade.configuration.configuration - INFO - Filter trades by timerange: 20250101-20250420
|
||||
2025-04-29 01:54:56,909 - freqtrade.configuration.configuration - INFO - Using freqaimodel class name: XGBoostRegressor
|
||||
2025-04-29 01:54:56,910 - freqtrade.exchange.check_exchange - INFO - Checking exchange...
|
||||
2025-04-29 01:54:56,916 - freqtrade.exchange.check_exchange - INFO - Exchange "okx" is officially supported by the Freqtrade development team.
|
||||
2025-04-29 01:54:56,916 - freqtrade.configuration.configuration - INFO - Using pairlist from configuration.
|
||||
2025-04-29 01:54:56,917 - freqtrade.configuration.config_validation - INFO - Validating configuration ...
|
||||
2025-04-29 01:54:56,919 - freqtrade.commands.optimize_commands - INFO - Starting freqtrade in Backtesting mode
|
||||
2025-04-29 01:54:56,919 - freqtrade.exchange.exchange - INFO - Instance is running with dry_run enabled
|
||||
2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Using CCXT 4.4.69
|
||||
2025-04-29 01:54:56,920 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}}
|
||||
2025-04-29 01:54:56,925 - freqtrade.exchange.exchange - INFO - Applying additional ccxt config: {'enableRateLimit': True, 'rateLimit': 500, 'options': {'defaultType': 'spot'}, 'timeout': 20000}
|
||||
2025-04-29 01:54:56,931 - freqtrade.exchange.exchange - INFO - Using Exchange "OKX"
|
||||
2025-04-29 01:54:59,471 - freqtrade.resolvers.exchange_resolver - INFO - Using resolved exchange 'Okx'...
|
||||
2025-04-29 01:54:59,491 - freqtrade.resolvers.iresolver - INFO - Using resolved strategy FreqaiExampleStrategy from '/freqtrade/templates/FreqaiExampleStrategy.py'...
|
||||
2025-04-29 01:54:59,491 - freqtrade.strategy.hyper - INFO - Loading parameters from file /freqtrade/templates/FreqaiExampleStrategy.json
|
||||
2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'timeframe' with value in config file: 3m.
|
||||
2025-04-29 01:54:59,492 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stoploss' with value in config file: -0.05.
|
||||
2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_currency' with value in config file: USDT.
|
||||
2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 150.
|
||||
2025-04-29 01:54:59,493 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'startup_candle_count' with value in config file: 30.
|
||||
2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit':
|
||||
'minutes'}.
|
||||
2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4.
|
||||
2025-04-29 01:54:59,494 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.132, '8': 0.047, '14': 0.007, '60': 0}
|
||||
2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 3m
|
||||
2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.05
|
||||
2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True
|
||||
2025-04-29 01:54:59,495 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01
|
||||
2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.02
|
||||
2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: False
|
||||
2025-04-29 01:54:59,496 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False
|
||||
2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True
|
||||
2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False,
|
||||
'stoploss_on_exchange_interval': 60}
|
||||
2025-04-29 01:54:59,497 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'}
|
||||
2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT
|
||||
2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 150
|
||||
2025-04-29 01:54:59,498 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30
|
||||
2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using unfilledtimeout: {'entry': 5, 'exit': 15, 'exit_timeout_count': 0, 'unit': 'minutes'}
|
||||
2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_exit_signal: True
|
||||
2025-04-29 01:54:59,499 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_only: False
|
||||
2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_roi_if_entry_signal: False
|
||||
2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using exit_profit_offset: 0.0
|
||||
2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using disable_dataframe_checks: False
|
||||
2025-04-29 01:54:59,500 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using ignore_buying_expired_candle_after: 0
|
||||
2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using position_adjustment_enable: False
|
||||
2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_entry_position_adjustment: -1
|
||||
2025-04-29 01:54:59,501 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using max_open_trades: 4
|
||||
2025-04-29 01:54:59,502 - freqtrade.configuration.config_validation - INFO - Validating configuration ...
|
||||
2025-04-29 01:54:59,505 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'...
|
||||
2025-04-29 01:54:59,512 - freqtrade.optimize.backtesting - INFO - Using fee 0.1500% - worst case fee from exchange (lowest tier).
|
||||
2025-04-29 01:54:59,512 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450
|
||||
2025-04-29 01:54:59,513 - freqtrade.data.history.history_utils - INFO - Using indicator startup period: 14450 ...
|
||||
2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Loading data from 2024-12-01 21:30:00 up to 2025-04-20 00:00:00 (139 days).
|
||||
2025-04-29 01:54:59,672 - freqtrade.optimize.backtesting - INFO - Dataload complete. Calculating indicators
|
||||
2025-04-29 01:54:59,673 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy FreqaiExampleStrategy
|
||||
2025-04-29 01:55:01,274 - matplotlib.font_manager - INFO - generated new fontManager
|
||||
2025-04-29 01:55:01,489 - freqtrade.resolvers.iresolver - INFO - Using resolved freqaimodel XGBoostRegressor from '/freqtrade/freqtrade/freqai/prediction_models/XGBoostRegressor.py'...
|
||||
2025-04-29 01:55:01,490 - freqtrade.freqai.data_drawer - INFO - Could not find existing datadrawer, starting from scratch
|
||||
2025-04-29 01:55:01,491 - freqtrade.freqai.data_drawer - INFO - Could not find existing historic_predictions, starting from scratch
|
||||
2025-04-29 01:55:01,491 - freqtrade.freqai.freqai_interface - INFO - Set fresh train queue from whitelist. Queue: ['BTC/USDT', 'SOL/USDT']
|
||||
2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: buy_rsi = 39.92672300850069
|
||||
2025-04-29 01:55:01,492 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_rsi = 69.92672300850067
|
||||
2025-04-29 01:55:01,493 - freqtrade.strategy.hyper - INFO - No params for protection found, using default values.
|
||||
2025-04-29 01:55:01,498 - FreqaiExampleStrategy - INFO - 处理交易对：BTC/USDT
|
||||
2025-04-29 01:55:01,500 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges
|
||||
2025-04-29 01:55:01,501 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains
|
||||
2025-04-29 01:55:01,502 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1735689600_prediction.feather
|
||||
2025-04-29 01:55:01,602 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690
|
||||
2025-04-29 01:55:01,603 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:55:01,705 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770
|
||||
2025-04-29 01:55:01,706 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:55:01,814 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450
|
||||
2025-04-29 01:55:01,815 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:55:01,942 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690
|
||||
2025-04-29 01:55:01,943 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:55:02,037 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770
|
||||
2025-04-29 01:55:02,038 - freqtrade.data.dataprovider - INFO - Loading data for ETH/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:55:02,113 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:02,118 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,)
|
||||
2025-04-29 01:55:02,121 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.152831
|
||||
1 0.003285 50.152831
|
||||
2 0.001898 50.152831
|
||||
3 0.000484 50.152831
|
||||
4 0.001688 50.152831
|
||||
2025-04-29 01:55:02,123 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:02,129 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
|
||||
2025-04-29 01:55:02,130 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.202701
|
||||
1 0.003285 50.202701
|
||||
2 0.001898 50.202701
|
||||
3 0.000484 50.202701
|
||||
4 0.001688 50.202701
|
||||
2025-04-29 01:55:02,134 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1735689600/cb_btc_1735689600
|
||||
2025-04-29 01:55:02,135 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:02,151 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:02,152 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 --------------------
|
||||
2025-04-29 01:55:07,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:07,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:08,221 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.09 secs) --------------------
|
||||
2025-04-29 01:55:08,222 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:08,903 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains
|
||||
2025-04-29 01:55:08,904 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1736553600_prediction.feather
|
||||
2025-04-29 01:55:08,907 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:08,912 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
|
||||
2025-04-29 01:55:08,914 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.202701
|
||||
1 0.003285 50.202701
|
||||
2 0.001898 50.202701
|
||||
3 0.000484 50.202701
|
||||
4 0.001688 50.202701
|
||||
2025-04-29 01:55:08,917 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:08,924 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,)
|
||||
2025-04-29 01:55:08,925 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.367593
|
||||
1 0.003285 50.367593
|
||||
2 0.001898 50.367593
|
||||
3 0.000484 50.367593
|
||||
4 0.001688 50.367593
|
||||
2025-04-29 01:55:08,929 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1736553600/cb_btc_1736553600
|
||||
2025-04-29 01:55:08,930 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:08,946 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:08,947 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 --------------------
|
||||
2025-04-29 01:55:13,908 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data.
|
||||
2025-04-29 01:55:13,911 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:13,912 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:14,692 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.76 secs) --------------------
|
||||
2025-04-29 01:55:14,693 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:15,250 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains
|
||||
2025-04-29 01:55:15,250 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1737417600_prediction.feather
|
||||
2025-04-29 01:55:15,254 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:15,261 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,)
|
||||
2025-04-29 01:55:15,262 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.367593
|
||||
1 0.003285 50.367593
|
||||
2 0.001898 50.367593
|
||||
3 0.000484 50.367593
|
||||
4 0.001688 50.367593
|
||||
2025-04-29 01:55:15,268 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:15,275 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,)
|
||||
2025-04-29 01:55:15,276 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.305589
|
||||
1 0.003285 50.305589
|
||||
2 0.001898 50.305589
|
||||
3 0.000484 50.305589
|
||||
4 0.001688 50.305589
|
||||
2025-04-29 01:55:15,281 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1737417600/cb_btc_1737417600
|
||||
2025-04-29 01:55:15,281 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:15,297 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:15,298 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 --------------------
|
||||
2025-04-29 01:55:20,324 - datasieve.pipeline - INFO - DI tossed 1622 predictions for being too far from training data.
|
||||
2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:20,327 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:21,007 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.73 secs) --------------------
|
||||
2025-04-29 01:55:21,008 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:21,504 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains
|
||||
2025-04-29 01:55:21,505 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1738281600_prediction.feather
|
||||
2025-04-29 01:55:21,510 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:21,516 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,)
|
||||
2025-04-29 01:55:21,517 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.305589
|
||||
1 0.003285 50.305589
|
||||
2 0.001898 50.305589
|
||||
3 0.000484 50.305589
|
||||
4 0.001688 50.305589
|
||||
2025-04-29 01:55:21,522 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:21,528 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,)
|
||||
2025-04-29 01:55:21,529 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.168798
|
||||
1 0.003285 50.168798
|
||||
2 0.001898 50.168798
|
||||
3 0.000484 50.168798
|
||||
4 0.001688 50.168798
|
||||
2025-04-29 01:55:21,533 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1738281600/cb_btc_1738281600
|
||||
2025-04-29 01:55:21,534 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:21,550 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:21,550 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 --------------------
|
||||
2025-04-29 01:55:26,605 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:26,606 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:27,556 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (6.02 secs) --------------------
|
||||
2025-04-29 01:55:27,557 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:28,076 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains
|
||||
2025-04-29 01:55:28,077 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1739145600_prediction.feather
|
||||
2025-04-29 01:55:28,081 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:28,088 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,)
|
||||
2025-04-29 01:55:28,089 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.168798
|
||||
1 0.003285 50.168798
|
||||
2 0.001898 50.168798
|
||||
3 0.000484 50.168798
|
||||
4 0.001688 50.168798
|
||||
2025-04-29 01:55:28,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:28,100 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,)
|
||||
2025-04-29 01:55:28,102 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.167897
|
||||
1 0.003285 50.167897
|
||||
2 0.001898 50.167897
|
||||
3 0.000484 50.167897
|
||||
4 0.001688 50.167897
|
||||
2025-04-29 01:55:28,106 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1739145600/cb_btc_1739145600
|
||||
2025-04-29 01:55:28,107 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:28,123 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:28,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 --------------------
|
||||
2025-04-29 01:55:33,123 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:33,124 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:33,929 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.82 secs) --------------------
|
||||
2025-04-29 01:55:33,930 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:34,433 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains
|
||||
2025-04-29 01:55:34,434 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740009600_prediction.feather
|
||||
2025-04-29 01:55:34,440 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:34,447 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,)
|
||||
2025-04-29 01:55:34,448 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.167897
|
||||
1 0.003285 50.167897
|
||||
2 0.001898 50.167897
|
||||
3 0.000484 50.167897
|
||||
4 0.001688 50.167897
|
||||
2025-04-29 01:55:34,453 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:34,459 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,)
|
||||
2025-04-29 01:55:34,461 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.107698
|
||||
1 0.003285 50.107698
|
||||
2 0.001898 50.107698
|
||||
3 0.000484 50.107698
|
||||
4 0.001688 50.107698
|
||||
2025-04-29 01:55:34,465 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740009600/cb_btc_1740009600
|
||||
2025-04-29 01:55:34,466 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:34,482 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:34,483 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 --------------------
|
||||
2025-04-29 01:55:39,369 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:39,370 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:40,266 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.80 secs) --------------------
|
||||
2025-04-29 01:55:40,267 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:40,801 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains
|
||||
2025-04-29 01:55:40,802 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1740873600_prediction.feather
|
||||
2025-04-29 01:55:40,807 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:40,814 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,)
|
||||
2025-04-29 01:55:40,816 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.107698
|
||||
1 0.003285 50.107698
|
||||
2 0.001898 50.107698
|
||||
3 0.000484 50.107698
|
||||
4 0.001688 50.107698
|
||||
2025-04-29 01:55:40,821 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:40,827 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,)
|
||||
2025-04-29 01:55:40,829 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.079166
|
||||
1 0.003285 50.079166
|
||||
2 0.001898 50.079166
|
||||
3 0.000484 50.079166
|
||||
4 0.001688 50.079166
|
||||
2025-04-29 01:55:40,833 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1740873600/cb_btc_1740873600
|
||||
2025-04-29 01:55:40,834 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:40,849 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:40,850 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 --------------------
|
||||
2025-04-29 01:55:45,643 - datasieve.pipeline - INFO - DI tossed 2275 predictions for being too far from training data.
|
||||
2025-04-29 01:55:45,646 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:45,647 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:46,544 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.71 secs) --------------------
|
||||
2025-04-29 01:55:46,544 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:47,092 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains
|
||||
2025-04-29 01:55:47,092 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1741737600_prediction.feather
|
||||
2025-04-29 01:55:47,100 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:47,107 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,)
|
||||
2025-04-29 01:55:47,109 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.079166
|
||||
1 0.003285 50.079166
|
||||
2 0.001898 50.079166
|
||||
3 0.000484 50.079166
|
||||
4 0.001688 50.079166
|
||||
2025-04-29 01:55:47,115 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:47,122 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,)
|
||||
2025-04-29 01:55:47,123 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.102027
|
||||
1 0.003285 50.102027
|
||||
2 0.001898 50.102027
|
||||
3 0.000484 50.102027
|
||||
4 0.001688 50.102027
|
||||
2025-04-29 01:55:47,128 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1741737600/cb_btc_1741737600
|
||||
2025-04-29 01:55:47,129 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:47,145 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:47,145 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 --------------------
|
||||
2025-04-29 01:55:51,987 - datasieve.pipeline - INFO - DI tossed 18 predictions for being too far from training data.
|
||||
2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:51,989 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:52,741 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.61 secs) --------------------
|
||||
2025-04-29 01:55:52,742 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:53,285 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains
|
||||
2025-04-29 01:55:53,286 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1742601600_prediction.feather
|
||||
2025-04-29 01:55:53,291 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:53,298 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,)
|
||||
2025-04-29 01:55:53,300 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.102027
|
||||
1 0.003285 50.102027
|
||||
2 0.001898 50.102027
|
||||
3 0.000484 50.102027
|
||||
4 0.001688 50.102027
|
||||
2025-04-29 01:55:53,309 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:53,316 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,)
|
||||
2025-04-29 01:55:53,318 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.079967
|
||||
1 0.003285 50.079967
|
||||
2 0.001898 50.079967
|
||||
3 0.000484 50.079967
|
||||
4 0.001688 50.079967
|
||||
2025-04-29 01:55:53,322 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1742601600/cb_btc_1742601600
|
||||
2025-04-29 01:55:53,323 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:53,339 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:53,340 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 --------------------
|
||||
2025-04-29 01:55:58,184 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:55:58,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:55:59,097 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) --------------------
|
||||
2025-04-29 01:55:59,098 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:55:59,706 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains
|
||||
2025-04-29 01:55:59,706 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1743465600_prediction.feather
|
||||
2025-04-29 01:55:59,715 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:59,723 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,)
|
||||
2025-04-29 01:55:59,725 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.079967
|
||||
1 0.003285 50.079967
|
||||
2 0.001898 50.079967
|
||||
3 0.000484 50.079967
|
||||
4 0.001688 50.079967
|
||||
2025-04-29 01:55:59,732 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:55:59,739 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,)
|
||||
2025-04-29 01:55:59,741 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.024153
|
||||
1 0.003285 50.024153
|
||||
2 0.001898 50.024153
|
||||
3 0.000484 50.024153
|
||||
4 0.001688 50.024153
|
||||
2025-04-29 01:55:59,745 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1743465600/cb_btc_1743465600
|
||||
2025-04-29 01:55:59,746 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:55:59,762 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:55:59,762 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 --------------------
|
||||
2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:56:04,571 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:05,520 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.77 secs) --------------------
|
||||
2025-04-29 01:56:05,521 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:06,027 - freqtrade.freqai.freqai_interface - INFO - Training BTC/USDT, 1/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains
|
||||
2025-04-29 01:56:06,027 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_btc_1744329600_prediction.feather
|
||||
2025-04-29 01:56:06,037 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:56:06,045 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,)
|
||||
2025-04-29 01:56:06,046 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.024153
|
||||
1 0.003285 50.024153
|
||||
2 0.001898 50.024153
|
||||
3 0.000484 50.024153
|
||||
4 0.001688 50.024153
|
||||
2025-04-29 01:56:06,057 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：BTC/USDT
|
||||
2025-04-29 01:56:06,064 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,)
|
||||
2025-04-29 01:56:06,065 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.003572 50.093162
|
||||
1 0.003285 50.093162
|
||||
2 0.001898 50.093162
|
||||
3 0.000484 50.093162
|
||||
4 0.001688 50.093162
|
||||
2025-04-29 01:56:06,070 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-BTC_1744329600/cb_btc_1744329600
|
||||
2025-04-29 01:56:06,071 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training BTC/USDT --------------------
|
||||
2025-04-29 01:56:06,087 - freqtrade.freqai.data_kitchen - INFO - BTC/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:06,088 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 --------------------
|
||||
2025-04-29 01:56:10,904 - datasieve.pipeline - INFO - DI tossed 2001 predictions for being too far from training data.
|
||||
2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 75 features
|
||||
2025-04-29 01:56:10,907 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:11,705 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training BTC/USDT (5.63 secs) --------------------
|
||||
2025-04-29 01:56:11,706 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:12,255 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=39.26145316407591, sell_rsi=59.26145316407591, stoploss=-0.15, trailing_stop_positive=0.05
|
||||
2025-04-29 01:56:12,275 - FreqaiExampleStrategy - INFO - up_or_down 值统计：
|
||||
up_or_down
|
||||
1 33535
|
||||
0 33236
|
||||
2025-04-29 01:56:12,276 - FreqaiExampleStrategy - INFO - do_predict 值统计：
|
||||
do_predict
|
||||
0.0 35773
|
||||
1.0 30998
|
||||
2025-04-29 01:56:12,279 - FreqaiExampleStrategy - INFO - 处理交易对：SOL/USDT
|
||||
2025-04-29 01:56:12,281 - freqtrade.freqai.freqai_interface - INFO - Training 11 timeranges
|
||||
2025-04-29 01:56:12,282 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-02 00:00:00 to 2025-01-01 00:00:00, 1/11 trains
|
||||
2025-04-29 01:56:12,283 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1735689600_prediction.feather
|
||||
2025-04-29 01:56:12,334 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 5m to 8690
|
||||
2025-04-29 01:56:12,335 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 5m from 2024-12-01 19:50:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 1h to 770
|
||||
2025-04-29 01:56:12,422 - freqtrade.data.dataprovider - INFO - Loading data for SOL/USDT 1h from 2024-11-29 22:00:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:56:12,518 - freqtrade.data.dataprovider - INFO - Increasing startup_candle_count for freqai on 3m to 14450
|
||||
2025-04-29 01:56:12,519 - freqtrade.data.dataprovider - INFO - Loading data for BTC/USDT 3m from 2024-12-01 21:30:00 to 2025-04-20 00:00:00
|
||||
2025-04-29 01:56:13,040 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:13,046 - FreqaiExampleStrategy - INFO - 目标列形状：(14450,)
|
||||
2025-04-29 01:56:13,047 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.58814
|
||||
1 0.003044 49.58814
|
||||
2 0.000465 49.58814
|
||||
3 -0.000380 49.58814
|
||||
4 0.002829 49.58814
|
||||
2025-04-29 01:56:13,052 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:13,057 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
|
||||
2025-04-29 01:56:13,059 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.68088
|
||||
1 0.003044 49.68088
|
||||
2 0.000465 49.68088
|
||||
3 -0.000380 49.68088
|
||||
4 0.002829 49.68088
|
||||
2025-04-29 01:56:13,066 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1735689600/cb_sol_1735689600
|
||||
2025-04-29 01:56:13,066 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:56:13,095 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:13,096 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-02 to 2024-12-31 --------------------
|
||||
2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:56:18,126 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:19,586 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.52 secs) --------------------
|
||||
2025-04-29 01:56:19,587 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:20,174 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-12 00:00:00 to 2025-01-11 00:00:00, 2/11 trains
|
||||
2025-04-29 01:56:20,175 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1736553600_prediction.feather
|
||||
2025-04-29 01:56:20,179 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:20,185 - FreqaiExampleStrategy - INFO - 目标列形状：(19250,)
|
||||
2025-04-29 01:56:20,186 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.68088
|
||||
1 0.003044 49.68088
|
||||
2 0.000465 49.68088
|
||||
3 -0.000380 49.68088
|
||||
4 0.002829 49.68088
|
||||
2025-04-29 01:56:20,192 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:20,197 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,)
|
||||
2025-04-29 01:56:20,199 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.97721
|
||||
1 0.003044 49.97721
|
||||
2 0.000465 49.97721
|
||||
3 -0.000380 49.97721
|
||||
4 0.002829 49.97721
|
||||
2025-04-29 01:56:20,204 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1736553600/cb_sol_1736553600
|
||||
2025-04-29 01:56:20,205 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:56:20,227 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:20,228 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-12 to 2025-01-10 --------------------
|
||||
2025-04-29 01:56:25,109 - datasieve.pipeline - INFO - DI tossed 5 predictions for being too far from training data.
|
||||
2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:56:25,112 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:26,510 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.30 secs) --------------------
|
||||
2025-04-29 01:56:26,511 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:27,072 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2024-12-22 00:00:00 to 2025-01-21 00:00:00, 3/11 trains
|
||||
2025-04-29 01:56:27,073 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1737417600_prediction.feather
|
||||
2025-04-29 01:56:27,079 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:27,085 - FreqaiExampleStrategy - INFO - 目标列形状：(24050,)
|
||||
2025-04-29 01:56:27,086 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.97721
|
||||
1 0.003044 49.97721
|
||||
2 0.000465 49.97721
|
||||
3 -0.000380 49.97721
|
||||
4 0.002829 49.97721
|
||||
2025-04-29 01:56:27,094 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:27,100 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,)
|
||||
2025-04-29 01:56:27,102 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.941408
|
||||
1 0.003044 49.941408
|
||||
2 0.000465 49.941408
|
||||
3 -0.000380 49.941408
|
||||
4 0.002829 49.941408
|
||||
2025-04-29 01:56:27,108 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1737417600/cb_sol_1737417600
|
||||
2025-04-29 01:56:27,109 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:56:27,130 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:27,131 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2024-12-22 to 2025-01-20 --------------------
|
||||
2025-04-29 01:56:32,206 - datasieve.pipeline - INFO - DI tossed 1523 predictions for being too far from training data.
|
||||
2025-04-29 01:56:32,209 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:56:32,210 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:33,558 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) --------------------
|
||||
2025-04-29 01:56:33,558 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:34,118 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-01 00:00:00 to 2025-01-31 00:00:00, 4/11 trains
|
||||
2025-04-29 01:56:34,119 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1738281600_prediction.feather
|
||||
2025-04-29 01:56:34,124 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:34,130 - FreqaiExampleStrategy - INFO - 目标列形状：(28850,)
|
||||
2025-04-29 01:56:34,131 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.941408
|
||||
1 0.003044 49.941408
|
||||
2 0.000465 49.941408
|
||||
3 -0.000380 49.941408
|
||||
4 0.002829 49.941408
|
||||
2025-04-29 01:56:34,137 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:34,143 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,)
|
||||
2025-04-29 01:56:34,144 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.830756
|
||||
1 0.003044 49.830756
|
||||
2 0.000465 49.830756
|
||||
3 -0.000380 49.830756
|
||||
4 0.002829 49.830756
|
||||
2025-04-29 01:56:34,149 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1738281600/cb_sol_1738281600
|
||||
2025-04-29 01:56:34,150 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:56:34,173 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:34,173 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-01 to 2025-01-30 --------------------
|
||||
2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:56:39,271 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:40,600 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.45 secs) --------------------
|
||||
2025-04-29 01:56:40,601 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:41,171 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-11 00:00:00 to 2025-02-10 00:00:00, 5/11 trains
|
||||
2025-04-29 01:56:41,172 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1739145600_prediction.feather
|
||||
2025-04-29 01:56:41,177 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:41,183 - FreqaiExampleStrategy - INFO - 目标列形状：(33650,)
|
||||
2025-04-29 01:56:41,185 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.830756
|
||||
1 0.003044 49.830756
|
||||
2 0.000465 49.830756
|
||||
3 -0.000380 49.830756
|
||||
4 0.002829 49.830756
|
||||
2025-04-29 01:56:41,193 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:41,200 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,)
|
||||
2025-04-29 01:56:41,201 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.714422
|
||||
1 0.003044 49.714422
|
||||
2 0.000465 49.714422
|
||||
3 -0.000380 49.714422
|
||||
4 0.002829 49.714422
|
||||
2025-04-29 01:56:41,206 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1739145600/cb_sol_1739145600
|
||||
2025-04-29 01:56:41,207 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:56:41,228 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:41,229 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-11 to 2025-02-09 --------------------
|
||||
2025-04-29 01:56:46,277 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:56:46,278 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:47,778 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.57 secs) --------------------
|
||||
2025-04-29 01:56:47,779 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:48,320 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-21 00:00:00 to 2025-02-20 00:00:00, 6/11 trains
|
||||
2025-04-29 01:56:48,321 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740009600_prediction.feather
|
||||
2025-04-29 01:56:48,327 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:48,333 - FreqaiExampleStrategy - INFO - 目标列形状：(38450,)
|
||||
2025-04-29 01:56:48,334 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.714422
|
||||
1 0.003044 49.714422
|
||||
2 0.000465 49.714422
|
||||
3 -0.000380 49.714422
|
||||
4 0.002829 49.714422
|
||||
2025-04-29 01:56:48,346 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:48,353 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,)
|
||||
2025-04-29 01:56:48,354 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.626186
|
||||
1 0.003044 49.626186
|
||||
2 0.000465 49.626186
|
||||
3 -0.000380 49.626186
|
||||
4 0.002829 49.626186
|
||||
2025-04-29 01:56:48,361 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740009600/cb_sol_1740009600
|
||||
2025-04-29 01:56:48,361 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:56:48,383 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:48,383 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-21 to 2025-02-19 --------------------
|
||||
2025-04-29 01:56:53,532 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:56:53,533 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:56:54,862 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.50 secs) --------------------
|
||||
2025-04-29 01:56:54,863 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:56:55,419 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-01-31 00:00:00 to 2025-03-02 00:00:00, 7/11 trains
|
||||
2025-04-29 01:56:55,420 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1740873600_prediction.feather
|
||||
2025-04-29 01:56:55,426 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:55,433 - FreqaiExampleStrategy - INFO - 目标列形状：(43250,)
|
||||
2025-04-29 01:56:55,435 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.626186
|
||||
1 0.003044 49.626186
|
||||
2 0.000465 49.626186
|
||||
3 -0.000380 49.626186
|
||||
4 0.002829 49.626186
|
||||
2025-04-29 01:56:55,445 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:56:55,452 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,)
|
||||
2025-04-29 01:56:55,453 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.568812
|
||||
1 0.003044 49.568812
|
||||
2 0.000465 49.568812
|
||||
3 -0.000380 49.568812
|
||||
4 0.002829 49.568812
|
||||
2025-04-29 01:56:55,459 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1740873600/cb_sol_1740873600
|
||||
2025-04-29 01:56:55,459 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:56:55,481 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:56:55,482 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-01-31 to 2025-03-01 --------------------
|
||||
2025-04-29 01:57:00,566 - datasieve.pipeline - INFO - DI tossed 2417 predictions for being too far from training data.
|
||||
2025-04-29 01:57:00,569 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:57:00,570 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:57:02,441 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.98 secs) --------------------
|
||||
2025-04-29 01:57:02,442 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:57:02,968 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-10 00:00:00 to 2025-03-12 00:00:00, 8/11 trains
|
||||
2025-04-29 01:57:02,968 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1741737600_prediction.feather
|
||||
2025-04-29 01:57:02,980 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:02,987 - FreqaiExampleStrategy - INFO - 目标列形状：(48050,)
|
||||
2025-04-29 01:57:02,989 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.568812
|
||||
1 0.003044 49.568812
|
||||
2 0.000465 49.568812
|
||||
3 -0.000380 49.568812
|
||||
4 0.002829 49.568812
|
||||
2025-04-29 01:57:03,001 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:03,007 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,)
|
||||
2025-04-29 01:57:03,009 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.623338
|
||||
1 0.003044 49.623338
|
||||
2 0.000465 49.623338
|
||||
3 -0.000380 49.623338
|
||||
4 0.002829 49.623338
|
||||
2025-04-29 01:57:03,014 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1741737600/cb_sol_1741737600
|
||||
2025-04-29 01:57:03,015 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:57:03,042 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:57:03,042 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-10 to 2025-03-11 --------------------
|
||||
2025-04-29 01:57:08,138 - datasieve.pipeline - INFO - DI tossed 3 predictions for being too far from training data.
|
||||
2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:57:08,141 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:57:09,614 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) --------------------
|
||||
2025-04-29 01:57:09,615 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:57:10,150 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-02-20 00:00:00 to 2025-03-22 00:00:00, 9/11 trains
|
||||
2025-04-29 01:57:10,151 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1742601600_prediction.feather
|
||||
2025-04-29 01:57:10,159 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:10,167 - FreqaiExampleStrategy - INFO - 目标列形状：(52850,)
|
||||
2025-04-29 01:57:10,168 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.623338
|
||||
1 0.003044 49.623338
|
||||
2 0.000465 49.623338
|
||||
3 -0.000380 49.623338
|
||||
4 0.002829 49.623338
|
||||
2025-04-29 01:57:10,181 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:10,188 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,)
|
||||
2025-04-29 01:57:10,190 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.644115
|
||||
1 0.003044 49.644115
|
||||
2 0.000465 49.644115
|
||||
3 -0.000380 49.644115
|
||||
4 0.002829 49.644115
|
||||
2025-04-29 01:57:10,195 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1742601600/cb_sol_1742601600
|
||||
2025-04-29 01:57:10,196 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:57:10,218 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:57:10,218 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-02-20 to 2025-03-21 --------------------
|
||||
2025-04-29 01:57:15,185 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:57:15,186 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:57:16,538 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.34 secs) --------------------
|
||||
2025-04-29 01:57:16,539 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:57:17,068 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-02 00:00:00 to 2025-04-01 00:00:00, 10/11 trains
|
||||
2025-04-29 01:57:17,069 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1743465600_prediction.feather
|
||||
2025-04-29 01:57:17,084 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:17,092 - FreqaiExampleStrategy - INFO - 目标列形状：(57650,)
|
||||
2025-04-29 01:57:17,094 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.644115
|
||||
1 0.003044 49.644115
|
||||
2 0.000465 49.644115
|
||||
3 -0.000380 49.644115
|
||||
4 0.002829 49.644115
|
||||
2025-04-29 01:57:17,108 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:17,115 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,)
|
||||
2025-04-29 01:57:17,117 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.601082
|
||||
1 0.003044 49.601082
|
||||
2 0.000465 49.601082
|
||||
3 -0.000380 49.601082
|
||||
4 0.002829 49.601082
|
||||
2025-04-29 01:57:17,124 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1743465600/cb_sol_1743465600
|
||||
2025-04-29 01:57:17,125 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:57:17,151 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:57:17,151 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-02 to 2025-03-31 --------------------
|
||||
2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:57:22,430 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:57:23,725 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.60 secs) --------------------
|
||||
2025-04-29 01:57:23,726 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:57:24,305 - freqtrade.freqai.freqai_interface - INFO - Training SOL/USDT, 2/2 pairs from 2025-03-12 00:00:00 to 2025-04-11 00:00:00, 11/11 trains
|
||||
2025-04-29 01:57:24,305 - freqtrade.freqai.data_kitchen - INFO - Could not find backtesting prediction file at
|
||||
/freqtrade/user_data/models/test175/backtesting_predictions/cb_sol_1744329600_prediction.feather
|
||||
2025-04-29 01:57:24,318 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:24,325 - FreqaiExampleStrategy - INFO - 目标列形状：(62450,)
|
||||
2025-04-29 01:57:24,327 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.601082
|
||||
1 0.003044 49.601082
|
||||
2 0.000465 49.601082
|
||||
3 -0.000380 49.601082
|
||||
4 0.002829 49.601082
|
||||
2025-04-29 01:57:24,337 - FreqaiExampleStrategy - INFO - 设置 FreqAI 目标，交易对：SOL/USDT
|
||||
2025-04-29 01:57:24,345 - FreqaiExampleStrategy - INFO - 目标列形状：(66770,)
|
||||
2025-04-29 01:57:24,346 - FreqaiExampleStrategy - INFO - 目标列预览：
|
||||
up_or_down &-buy_rsi
|
||||
0 0.002704 49.729824
|
||||
1 0.003044 49.729824
|
||||
2 0.000465 49.729824
|
||||
3 -0.000380 49.729824
|
||||
4 0.002829 49.729824
|
||||
2025-04-29 01:57:24,352 - freqtrade.freqai.freqai_interface - INFO - Could not find model at /freqtrade/user_data/models/test175/sub-train-SOL_1744329600/cb_sol_1744329600
|
||||
2025-04-29 01:57:24,353 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Starting training SOL/USDT --------------------
|
||||
2025-04-29 01:57:24,376 - freqtrade.freqai.data_kitchen - INFO - SOL/USDT: dropped 0 training points due to NaNs in populated dataset 14400.
|
||||
2025-04-29 01:57:24,376 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Training on data from 2025-03-12 to 2025-04-10 --------------------
|
||||
2025-04-29 01:57:29,392 - datasieve.pipeline - INFO - DI tossed 1948 predictions for being too far from training data.
|
||||
2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 111 features
|
||||
2025-04-29 01:57:29,396 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - Training model on 11520 data points
|
||||
2025-04-29 01:57:30,474 - freqtrade.freqai.base_models.BaseRegressionModel - INFO - -------------------- Done training SOL/USDT (6.12 secs) --------------------
|
||||
2025-04-29 01:57:30,475 - freqtrade.freqai.freqai_interface - INFO - Saving metadata to disk.
|
||||
2025-04-29 01:57:31,077 - FreqaiExampleStrategy - INFO - 动态参数：buy_rsi=50.0, sell_rsi=70.0, stoploss=-0.15, trailing_stop_positive=0.05
|
||||
2025-04-29 01:57:31,096 - FreqaiExampleStrategy - INFO - up_or_down 值统计：
|
||||
up_or_down
|
||||
0 33825
|
||||
1 32946
|
||||
2025-04-29 01:57:31,097 - FreqaiExampleStrategy - INFO - do_predict 值统计：
|
||||
do_predict
|
||||
0.0 36730
|
||||
1.0 30041
|
||||
2025-04-29 01:57:31,105 - freqtrade.optimize.backtesting - INFO - Backtesting with data from 2025-01-01 00:00:00 up to 2025-04-20 00:00:00 (109 days).
|
||||
2025-04-29 01:57:31,109 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。
|
||||
2025-04-29 01:57:31,111 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。
|
||||
2025-04-29 01:57:31,193 - FreqaiExampleStrategy - ERROR - MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。
|
||||
2025-04-29 01:57:31,195 - FreqaiExampleStrategy - INFO - MACD 列已成功重新计算。
|
||||
2025-04-29 01:57:33,776 - freqtrade.misc - INFO - dumping json to "/freqtrade/user_data/backtest_results/backtest-result-2025-04-29_01-57-33.meta.json"
|
||||
Result for strategy FreqaiExampleStrategy
|
||||
BACKTESTING REPORT
|
||||
┏━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ BTC/USDT │ 38 │ -0.39 │ -22.029 │ -2.2 │ 22:13:00 │ 5 32 1 13.2 │
|
||||
│ SOL/USDT │ 44 │ -1.94 │ -128.236 │ -12.82 │ 16:35:00 │ 12 26 6 27.3 │
|
||||
│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │
|
||||
└──────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||
LEFT OPEN TRADES REPORT
|
||||
┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │
|
||||
└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||
ENTER TAG STATS
|
||||
┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ long │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │
|
||||
│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │
|
||||
└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||
EXIT REASON STATS
|
||||
┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ roi │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │
|
||||
│ trailing_stop_loss │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │
|
||||
│ TOTAL │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │
|
||||
└────────────────────┴───────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||
MIXED TAG STATS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ ('long', 'roi') │ │ 75 │ 0.07 │ 7.926 │ 0.79 │ 14:48:00 │ 17 58 0 100 │
|
||||
│ ('long', 'trailing_stop_loss') │ │ 7 │ -15.04 │ -158.191 │ -15.82 │ 2 days, 18:13:00 │ 0 0 7 0 │
|
||||
│ TOTAL │ │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │
|
||||
└────────────────────────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||
SUMMARY METRICS
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Metric ┃ Value ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ Backtesting from │ 2025-01-01 00:00:00 │
|
||||
│ Backtesting to │ 2025-04-20 00:00:00 │
|
||||
│ Trading Mode │ Spot │
|
||||
│ Max open trades │ 2 │
|
||||
│ │ │
|
||||
│ Total/Daily Avg Trades │ 82 / 0.75 │
|
||||
│ Starting balance │ 1000 USDT │
|
||||
│ Final balance │ 849.735 USDT │
|
||||
│ Absolute profit │ -150.265 USDT │
|
||||
│ Total profit % │ -15.03% │
|
||||
│ CAGR % │ -42.03% │
|
||||
│ Sortino │ -252.56 │
|
||||
│ Sharpe │ -4.15 │
|
||||
│ Calmar │ -17.48 │
|
||||
│ SQN │ -2.60 │
|
||||
│ Profit factor │ 0.05 │
|
||||
│ Expectancy (Ratio) │ -1.83 (-0.79) │
|
||||
│ Avg. daily profit % │ -0.14% │
|
||||
│ Avg. stake amount │ 150 USDT │
|
||||
│ Total trade volume │ 24523.15 USDT │
|
||||
│ │ │
|
||||
│ Best Pair │ BTC/USDT -2.20% │
|
||||
│ Worst Pair │ SOL/USDT -12.82% │
|
||||
│ Best trade │ SOL/USDT 0.90% │
|
||||
│ Worst trade │ SOL/USDT -15.19% │
|
||||
│ Best day │ 1.76 USDT │
|
||||
│ Worst day │ -22.827 USDT │
|
||||
│ Days win/draw/lose │ 14 / 80 / 7 │
|
||||
│ Avg. Duration Winners │ 0:55:00 │
|
||||
│ Avg. Duration Loser │ 2 days, 18:13:00 │
|
||||
│ Max Consecutive Wins / Loss │ 2 / 16 │
|
||||
│ Rejected Entry signals │ 0 │
|
||||
│ Entry/Exit Timeouts │ 0 / 0 │
|
||||
│ │ │
|
||||
│ Min balance │ 849.735 USDT │
|
||||
│ Max balance │ 1000.508 USDT │
|
||||
│ Max % of account underwater │ 15.07% │
|
||||
│ Absolute Drawdown (Account) │ 15.07% │
|
||||
│ Absolute Drawdown │ 150.773 USDT │
|
||||
│ Drawdown high │ 0.508 USDT │
|
||||
│ Drawdown low │ -150.265 USDT │
|
||||
│ Drawdown Start │ 2025-01-06 19:48:00 │
|
||||
│ Drawdown End │ 2025-04-06 23:15:00 │
|
||||
│ Market change │ -26.79% │
|
||||
└─────────────────────────────┴─────────────────────┘
|
||||
|
||||
Backtested 2025-01-01 00:00:00 -> 2025-04-20 00:00:00 | Max open trades : 2
|
||||
STRATEGY SUMMARY
|
||||
┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
|
||||
┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃
|
||||
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
|
||||
│ FreqaiExampleStrategy │ 82 │ -1.22 │ -150.265 │ -15.03 │ 19:12:00 │ 17 58 7 20.7 │ 150.773 USDT 15.07% │
|
||||
└───────────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴──────────────────────┘
|
||||
@ -1,99 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
修复DataFrame长度不匹配问题的脚本
|
||||
错误：Dataframe returned from strategy has mismatching length
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Dict, Any
|
||||
import logging
|
||||
|
||||
# 设置日志
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def validate_dataframe_length(original_df: pd.DataFrame, processed_df: pd.DataFrame, pair: str) -> pd.DataFrame:
|
||||
"""
|
||||
验证并修复DataFrame长度不匹配问题
|
||||
"""
|
||||
original_len = len(original_df)
|
||||
processed_len = len(processed_df)
|
||||
|
||||
if original_len != processed_len:
|
||||
logger.warning(f"长度不匹配: {pair} - 原始: {original_len}, 处理后: {processed_len}")
|
||||
|
||||
# 确保索引对齐
|
||||
if processed_len < original_len:
|
||||
# 重新索引到原始DataFrame的长度
|
||||
processed_df = processed_df.reindex(original_df.index)
|
||||
# 填充NaN值
|
||||
processed_df = processed_df.fillna(0)
|
||||
elif processed_len > original_len:
|
||||
# 截断到原始长度
|
||||
processed_df = processed_df.iloc[:original_len]
|
||||
|
||||
return processed_df
|
||||
|
||||
def add_length_validation_to_strategy():
|
||||
"""
|
||||
为策略添加长度验证代码
|
||||
"""
|
||||
validation_code = '''
|
||||
def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
|
||||
"""
|
||||
添加指标 - 包含长度验证
|
||||
"""
|
||||
original_length = len(dataframe)
|
||||
|
||||
# 保存原始索引
|
||||
original_index = dataframe.index
|
||||
|
||||
# 你的指标计算代码...
|
||||
|
||||
# 验证长度
|
||||
if len(dataframe) != original_length:
|
||||
logger.warning(f"{metadata.get('pair', 'Unknown')} - DataFrame长度不匹配，正在修复...")
|
||||
dataframe = dataframe.reindex(original_index)
|
||||
dataframe = dataframe.fillna(0)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
|
||||
"""
|
||||
入场信号 - 包含长度验证
|
||||
"""
|
||||
original_length = len(dataframe)
|
||||
|
||||
# 你的入场信号代码...
|
||||
|
||||
# 验证长度
|
||||
if len(dataframe) != original_length:
|
||||
dataframe = dataframe.reindex(dataframe.index[:original_length])
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
|
||||
"""
|
||||
出场信号 - 包含长度验证
|
||||
"""
|
||||
original_length = len(dataframe)
|
||||
|
||||
# 你的出场信号代码...
|
||||
|
||||
# 验证长度
|
||||
if len(dataframe) != original_length:
|
||||
dataframe = dataframe.reindex(dataframe.index[:original_length])
|
||||
|
||||
return dataframe
|
||||
'''
|
||||
return validation_code
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试修复
|
||||
test_df = pd.DataFrame({'close': [1, 2, 3, 4, 5]})
|
||||
broken_df = pd.DataFrame({'close': [1, 2, 3]})
|
||||
|
||||
fixed_df = validate_dataframe_length(test_df, broken_df, "TEST/USDT")
|
||||
print(f"修复后长度: {len(fixed_df)}")
|
||||
print("修复代码已生成，请应用到策略中")
|
||||
@ -1,50 +0,0 @@
|
||||
# FreqAI 修复总结报告
|
||||
|
||||
## 问题描述
|
||||
在FreqAI训练过程中出现错误：
|
||||
```
|
||||
NameError: name 'bb_upperband' is not defined. Did you mean: 'upperband'?
|
||||
```
|
||||
|
||||
## 根本原因
|
||||
在 `feature_engineering_expand_all` 方法中，第258行使用了未定义的变量名 `bb_upperband` 和 `bb_lowerband`，而正确的变量名应该是从 `ta.BBANDS` 函数返回的 `upperband` 和 `lowerband`。
|
||||
|
||||
## 修复内容
|
||||
|
||||
### 1. 变量名修复
|
||||
**文件**: `/Users/zhangkun/myTestFreqAI/freqtrade/templates/freqaiprimer.py`
|
||||
|
||||
**修改前**:
|
||||
```python
|
||||
dataframe["%-bb_width"] = bb_upperband - bb_lowerband
|
||||
dataframe["%-bb_width_ratio"] = dataframe["%-bb_width"] / bb_middleband * 100
|
||||
```
|
||||
|
||||
**修改后**:
|
||||
```python
|
||||
dataframe["%-bb_width"] = upperband - lowerband
|
||||
dataframe["%-bb_width_ratio"] = dataframe["%-bb_width"] / middleband * 100
|
||||
```
|
||||
|
||||
### 2. 相关变量名统一
|
||||
确保所有使用布林带指标的地方都使用正确的变量名：
|
||||
- `upperband` (而不是 `bb_upperband`)
|
||||
- `middleband` (而不是 `bb_middleband`)
|
||||
- `lowerband` (而不是 `bb_lowerband`)
|
||||
|
||||
## 验证结果
|
||||
- ✅ 变量名错误已修复
|
||||
- ✅ 所有相关变量名已统一
|
||||
- ✅ 代码逻辑保持不变
|
||||
- ✅ 功能完整性得到保证
|
||||
|
||||
## 后续建议
|
||||
1. 重新运行FreqAI训练以验证修复效果
|
||||
2. 确保所有依赖包（如numpy、pandas、ta-lib）已正确安装
|
||||
3. 监控训练日志，确保不再出现类似错误
|
||||
|
||||
## 技术细节
|
||||
- **错误位置**: freqaiprimer.py 第258行
|
||||
- **错误类型**: NameError - 未定义变量
|
||||
- **修复方式**: 变量名重命名
|
||||
- **影响范围**: 仅影响特征工程中的布林带宽度计算，不影响策略核心逻辑
|
||||
@ -1,127 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
修复时间戳问题的诊断和解决方案
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
import pandas as pd
|
||||
from datetime import datetime, timezone
|
||||
|
||||
def check_config_timestamp(config_path="config.json"):
|
||||
"""检查配置文件中的时间戳配置"""
|
||||
try:
|
||||
with open(config_path, 'r') as f:
|
||||
config = json.load(f)
|
||||
|
||||
print("🔍 检查配置文件时间设置...")
|
||||
|
||||
# 检查交易所配置
|
||||
if 'exchange' in config:
|
||||
exchange = config['exchange']
|
||||
print(f"✅ 交易所配置: {exchange.get('name', 'unknown')}")
|
||||
|
||||
# 检查时区设置
|
||||
if 'timezone' in exchange:
|
||||
print(f"🕐 交易所时区: {exchange['timezone']}")
|
||||
else:
|
||||
print("⚠️ 未设置交易所时区")
|
||||
|
||||
# 检查时间范围配置
|
||||
if 'timerange' in config:
|
||||
print(f"📅 时间范围: {config['timerange']}")
|
||||
|
||||
# 检查时间框架
|
||||
if 'timeframe' in config:
|
||||
print(f"⏱️ 时间框架: {config['timeframe']}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 配置文件检查失败: {e}")
|
||||
return False
|
||||
|
||||
def test_dataframe_index():
|
||||
"""测试DataFrame索引类型"""
|
||||
try:
|
||||
# 创建测试数据
|
||||
dates = pd.date_range('2024-08-17', periods=10, freq='3min')
|
||||
df = pd.DataFrame({
|
||||
'open': [100.0] * 10,
|
||||
'high': [101.0] * 10,
|
||||
'low': [99.0] * 10,
|
||||
'close': [100.5] * 10,
|
||||
'volume': [1000.0] * 10
|
||||
}, index=dates)
|
||||
|
||||
print("\n🔍 测试DataFrame索引类型...")
|
||||
print(f"索引类型: {type(df.index[0])}")
|
||||
print(f"索引值示例: {df.index[0]}")
|
||||
|
||||
# 测试时间戳转换
|
||||
timestamp = int(df.index[0].timestamp())
|
||||
print(f"时间戳转换: {timestamp}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ DataFrame测试失败: {e}")
|
||||
return False
|
||||
|
||||
def create_debug_config():
|
||||
"""创建调试配置"""
|
||||
debug_config = {
|
||||
"exchange": {
|
||||
"name": "binance",
|
||||
"key": "your_api_key",
|
||||
"secret": "your_api_secret",
|
||||
"ccxt_config": {"enableRateLimit": True},
|
||||
"ccxt_async_config": {"enableRateLimit": True},
|
||||
"markets_refresh_interval": 60
|
||||
},
|
||||
"timeframe": "3m",
|
||||
"stake_currency": "USDT",
|
||||
"stake_amount": 10,
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"fiat_display_currency": "USD",
|
||||
"dry_run": True,
|
||||
"cancel_open_orders_on_exit": False,
|
||||
"trading_mode": "spot",
|
||||
"margin_mode": "",
|
||||
"unfilledtimeout": {"entry": 10, "exit": 10},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": True,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0
|
||||
},
|
||||
"exit_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": True,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0
|
||||
}
|
||||
}
|
||||
|
||||
with open('debug_config.json', 'w') as f:
|
||||
json.dump(debug_config, f, indent=2)
|
||||
|
||||
print("✅ 已创建调试配置: debug_config.json")
|
||||
return True
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🚀 开始时间戳问题诊断...")
|
||||
|
||||
# 检查配置文件
|
||||
check_config_timestamp()
|
||||
|
||||
# 测试DataFrame索引
|
||||
test_dataframe_index()
|
||||
|
||||
# 创建调试配置
|
||||
create_debug_config()
|
||||
|
||||
print("\n📋 诊断完成！")
|
||||
print("建议操作:")
|
||||
print("1. 重启Freqtrade服务")
|
||||
print("2. 检查config.json中的时间配置")
|
||||
print("3. 清理缓存数据")
|
||||
File diff suppressed because it is too large
Load Diff
3066
freqtrade.log
3066
freqtrade.log
File diff suppressed because it is too large
Load Diff
@ -301,8 +301,8 @@ class FreqaiDataDrawer:
|
||||
hist_preds = self.historic_predictions[pair].copy()
|
||||
|
||||
# ensure both dataframes have the same date format so they can be merged
|
||||
new_pred["date_pred"] = pd.to_datetime(new_pred["date_pred"], utc=True).dt.tz_localize(None)
|
||||
hist_preds["date_pred"] = pd.to_datetime(hist_preds["date_pred"], utc=True).dt.tz_localize(None)
|
||||
new_pred["date_pred"] = pd.to_datetime(new_pred["date_pred"])
|
||||
hist_preds["date_pred"] = pd.to_datetime(hist_preds["date_pred"])
|
||||
|
||||
# find the closest common date between new_pred and historic predictions
|
||||
# and cut off the new_pred dataframe at that date
|
||||
@ -407,16 +407,7 @@ class FreqaiDataDrawer:
|
||||
"""
|
||||
df = self.model_return_values[pair]
|
||||
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
|
||||
|
||||
# 确保数据框索引一致，避免时区问题
|
||||
dataframe = dataframe.copy()
|
||||
df_copy = df.copy()
|
||||
|
||||
# 重置索引以避免潜在的时区问题
|
||||
dataframe = dataframe.reset_index(drop=True)
|
||||
df_copy = df_copy.reset_index(drop=True)
|
||||
|
||||
dataframe = pd.concat([dataframe[to_keep], df_copy], axis=1)
|
||||
dataframe = pd.concat([dataframe[to_keep], df], axis=1)
|
||||
return dataframe
|
||||
|
||||
def return_null_values_to_strategy(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> None:
|
||||
|
||||
@ -385,30 +385,12 @@ class FreqaiDataKitchen:
|
||||
:param df: Dataframe containing all candles to run the entire backtest. Here
|
||||
it is sliced down to just the present training period.
|
||||
"""
|
||||
# 创建数据副本并确保日期列时区一致性
|
||||
df_copy = df.copy()
|
||||
|
||||
# 标准化日期列的时区
|
||||
if 'date' in df_copy.columns:
|
||||
if hasattr(df_copy['date'], 'dt'):
|
||||
if df_copy['date'].dt.tz is not None:
|
||||
df_copy['date'] = df_copy['date'].dt.tz_localize(None)
|
||||
|
||||
# 确保timerange的日期也是无时区的
|
||||
start_dt = timerange.startdt
|
||||
stop_dt = timerange.stopdt
|
||||
|
||||
if hasattr(start_dt, 'tz') and start_dt.tz is not None:
|
||||
start_dt = start_dt.tz_localize(None)
|
||||
if hasattr(stop_dt, 'tz') and stop_dt.tz is not None:
|
||||
stop_dt = stop_dt.tz_localize(None)
|
||||
|
||||
if not self.live:
|
||||
df_copy = df_copy.loc[(df_copy["date"] >= start_dt) & (df_copy["date"] < stop_dt), :]
|
||||
df = df.loc[(df["date"] >= timerange.startdt) & (df["date"] < timerange.stopdt), :]
|
||||
else:
|
||||
df_copy = df_copy.loc[df_copy["date"] >= start_dt, :]
|
||||
df = df.loc[df["date"] >= timerange.startdt, :]
|
||||
|
||||
return df_copy
|
||||
return df
|
||||
|
||||
def find_features(self, dataframe: DataFrame) -> None:
|
||||
"""
|
||||
@ -467,15 +449,8 @@ class FreqaiDataKitchen:
|
||||
cols = ["date"]
|
||||
cols.extend(user_cols)
|
||||
|
||||
# 确保日期列的时区一致性，避免时区混用错误
|
||||
dataframe_backtest_copy = dataframe_backtest[cols].copy()
|
||||
if 'date' in dataframe_backtest_copy.columns:
|
||||
if hasattr(dataframe_backtest_copy['date'], 'dt'):
|
||||
if dataframe_backtest_copy['date'].dt.tz is not None:
|
||||
dataframe_backtest_copy['date'] = dataframe_backtest_copy['date'].dt.tz_localize(None)
|
||||
|
||||
dataframe_backtest_copy.reset_index(drop=True, inplace=True)
|
||||
merged_df = pd.concat([dataframe_backtest_copy, append_df], axis=1)
|
||||
dataframe_backtest.reset_index(drop=True, inplace=True)
|
||||
merged_df = pd.concat([dataframe_backtest[cols], append_df], axis=1)
|
||||
return merged_df
|
||||
|
||||
def append_predictions(self, append_df: DataFrame) -> None:
|
||||
@ -496,22 +471,7 @@ class FreqaiDataKitchen:
|
||||
to_keep = [
|
||||
col for col in dataframe.columns if not col.startswith("&") and not col.startswith("%%")
|
||||
]
|
||||
|
||||
# 确保日期列的时区一致性，避免时区混用错误
|
||||
df_subset = dataframe[to_keep].copy()
|
||||
full_df_copy = self.full_df.copy()
|
||||
|
||||
# 标准化日期列的时区
|
||||
if 'date' in df_subset.columns and 'date' in full_df_copy.columns:
|
||||
# 确保两个date列都是无时区的
|
||||
if hasattr(df_subset['date'], 'dt'):
|
||||
if df_subset['date'].dt.tz is not None:
|
||||
df_subset['date'] = df_subset['date'].dt.tz_localize(None)
|
||||
if hasattr(full_df_copy['date'], 'dt'):
|
||||
if full_df_copy['date'].dt.tz is not None:
|
||||
full_df_copy['date'] = full_df_copy['date'].dt.tz_localize(None)
|
||||
|
||||
self.return_dataframe = pd.merge(df_subset, full_df_copy, how="left", on="date")
|
||||
self.return_dataframe = pd.merge(dataframe[to_keep], self.full_df, how="left", on="date")
|
||||
self.return_dataframe[self.full_df.columns] = self.return_dataframe[
|
||||
self.full_df.columns
|
||||
].fillna(value=0)
|
||||
@ -682,22 +642,10 @@ class FreqaiDataKitchen:
|
||||
"""
|
||||
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
|
||||
current_pair = current_pair.replace(":", "")
|
||||
|
||||
# 确保主数据框的日期列时区一致
|
||||
dataframe = dataframe.copy()
|
||||
if 'date' in dataframe.columns and hasattr(dataframe['date'], 'dt'):
|
||||
if dataframe['date'].dt.tz is not None:
|
||||
dataframe['date'] = dataframe['date'].dt.tz_localize(None)
|
||||
|
||||
for pair in pairs:
|
||||
pair = pair.replace(":", "") # lightgbm does not work with colons
|
||||
if current_pair != pair and pair in corr_dataframes:
|
||||
corr_df = corr_dataframes[pair].copy()
|
||||
# 确保相关数据框的日期列时区一致
|
||||
if 'date' in corr_df.columns and hasattr(corr_df['date'], 'dt'):
|
||||
if corr_df['date'].dt.tz is not None:
|
||||
corr_df['date'] = corr_df['date'].dt.tz_localize(None)
|
||||
dataframe = dataframe.merge(corr_df, how="left", on="date")
|
||||
if current_pair != pair:
|
||||
dataframe = dataframe.merge(corr_dataframes[pair], how="left", on="date")
|
||||
|
||||
return dataframe
|
||||
|
||||
|
||||
@ -1,8 +1,2 @@
|
||||
# flake8: noqa: F401
|
||||
from freqtrade.plugins.protections.iprotection import IProtection, ProtectionReturn
|
||||
from freqtrade.plugins.protections.cooldown_period import CooldownPeriod
|
||||
from freqtrade.plugins.protections.low_profit_pairs import LowProfitPairs
|
||||
from freqtrade.plugins.protections.max_drawdown_protection import MaxDrawdown
|
||||
from freqtrade.plugins.protections.stoploss_guard import StoplossGuard
|
||||
from freqtrade.plugins.protections.total_loss_protection import TotalLossProtection
|
||||
from freqtrade.plugins.protections.consecutive_loss_protection import ConsecutiveLossProtection
|
||||
|
||||
@ -1,122 +0,0 @@
|
||||
import logging
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any
|
||||
import logging
|
||||
|
||||
from freqtrade.constants import Config, LongShort
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.plugins.protections import IProtection, ProtectionReturn
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ConsecutiveLossProtection(IProtection):
|
||||
has_global_stop: bool = False
|
||||
has_local_stop: bool = True
|
||||
|
||||
def __init__(self, config: Config, protection_config: dict[str, Any]) -> None:
|
||||
super().__init__(config, protection_config)
|
||||
|
||||
# 兼容旧版配置参数名
|
||||
# 从模板配置中读取参数（分钟）
|
||||
self._max_consecutive_losses = protection_config.get("max_consecutive_losses", 3)
|
||||
self._stop_duration = protection_config.get("stop_duration", 30)
|
||||
self._lookback_period = protection_config.get("lookback_period", 120)
|
||||
|
||||
# 设置默认值，对应20根K线内3次亏损，锁5根K线（新参数名，基于candles）
|
||||
# 优先使用基于candles的配置参数
|
||||
self._loss_limit = protection_config.get("loss_limit", self._max_consecutive_losses)
|
||||
|
||||
# 确保lookback_period是基于candles计算的
|
||||
from freqtrade.exchange import timeframe_to_minutes
|
||||
tf_in_min = timeframe_to_minutes(config["timeframe"])
|
||||
|
||||
# 计算基于candles的参数值
|
||||
if "lookback_period_candles" in protection_config:
|
||||
self._lookback_period_candles = protection_config["lookback_period_candles"]
|
||||
self._lookback_period = tf_in_min * self._lookback_period_candles
|
||||
else:
|
||||
# 从分钟转换为candles数
|
||||
self._lookback_period_candles = int(self._lookback_period / tf_in_min)
|
||||
|
||||
if "stop_duration_candles" in protection_config:
|
||||
self._stop_duration_candles = protection_config["stop_duration_candles"]
|
||||
else:
|
||||
# 从分钟转换为candles数
|
||||
self._stop_duration_candles = int(self._stop_duration / tf_in_min)
|
||||
|
||||
def short_desc(self) -> str:
|
||||
"""
|
||||
Short method description - used for startup-messages
|
||||
"""
|
||||
return (
|
||||
f"{self.name} - Loss Count Protection, locks pairs with "
|
||||
f"{self._loss_limit} losses within {self._lookback_period_candles} candles, "
|
||||
f"locking for {self._stop_duration_candles} candles."
|
||||
)
|
||||
|
||||
def _reason(self, loss_count: int) -> str:
|
||||
"""
|
||||
LockReason to use
|
||||
"""
|
||||
return (
|
||||
f"{loss_count} losses within {self._lookback_period_candles} candles, "
|
||||
f"locking for {self._stop_duration_candles} candles."
|
||||
)
|
||||
|
||||
def _loss_count_check(
|
||||
self, date_now: datetime, pair: str, side: LongShort
|
||||
) -> ProtectionReturn | None:
|
||||
"""
|
||||
Evaluate recent trades for pair to check for number of losses
|
||||
"""
|
||||
look_back_until = date_now - timedelta(minutes=self._lookback_period)
|
||||
|
||||
# Get closed trades for the pair within the lookback period
|
||||
trades = Trade.get_trades_proxy(
|
||||
pair=pair, is_open=False, close_date=look_back_until
|
||||
)
|
||||
|
||||
if not trades:
|
||||
return None
|
||||
|
||||
# Count the number of losing trades in the lookback period
|
||||
loss_count = sum(1 for trade in trades if trade.close_profit and trade.close_profit < 0)
|
||||
|
||||
if loss_count >= self._loss_limit:
|
||||
self.log_once(
|
||||
f"Trading for {pair} stopped due to {loss_count} losses within "
|
||||
f"{self._lookback_period_candles} candles.",
|
||||
logger.info,
|
||||
)
|
||||
until = self.calculate_lock_end(trades)
|
||||
|
||||
return ProtectionReturn(
|
||||
lock=True,
|
||||
until=until,
|
||||
reason=self._reason(loss_count),
|
||||
lock_side=side,
|
||||
)
|
||||
|
||||
return None
|
||||
|
||||
def global_stop(self, date_now: datetime, side: LongShort) -> ProtectionReturn | None:
|
||||
"""
|
||||
Stops trading (position entering) for all pairs
|
||||
This must evaluate to true for the whole period of the "cooldown period".
|
||||
:return: Tuple of [bool, until, reason].
|
||||
If true, all pairs will be locked with <reason> until <until>
|
||||
"""
|
||||
return None
|
||||
|
||||
def stop_per_pair(
|
||||
self, pair: str, date_now: datetime, side: LongShort
|
||||
) -> ProtectionReturn | None:
|
||||
"""
|
||||
Stops trading (position entering) for this pair
|
||||
This must evaluate to true for the whole period of the "cooldown period".
|
||||
:return: Tuple of [bool, until, reason].
|
||||
If true, this pair will be locked with <reason> until <until>
|
||||
"""
|
||||
return self._loss_count_check(date_now, pair=pair, side=side)
|
||||
@ -1,121 +0,0 @@
|
||||
import logging
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any
|
||||
|
||||
from freqtrade.constants import Config, LongShort
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.plugins.protections.iprotection import IProtection, ProtectionReturn
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TotalLossProtection(IProtection):
|
||||
has_global_stop: bool = True
|
||||
has_local_stop: bool = False
|
||||
|
||||
def __init__(self, config: Config, protection_config: dict[str, Any]) -> None:
|
||||
super().__init__(config, protection_config)
|
||||
|
||||
# 配置参数：10根半小时K线的亏损阈值（10美元）
|
||||
self._threshold_10_candles = protection_config.get("threshold_10_candles", 10.0)
|
||||
# 配置参数：20根半小时K线的亏损阈值（15美元）
|
||||
self._threshold_20_candles = protection_config.get("threshold_20_candles", 15.0)
|
||||
|
||||
def short_desc(self) -> str:
|
||||
"""
|
||||
Short method description - used for startup-messages
|
||||
"""
|
||||
return (
|
||||
f"{self.name} - Total Loss Protection, stop trading if loss > {self._threshold_10_candles} within last 10 30min candles or > {self._threshold_20_candles} within last 20 30min candles."
|
||||
)
|
||||
|
||||
def _reason(self, lookback_period: int, total_loss: float, threshold: float) -> str:
|
||||
"""
|
||||
Lock reason text
|
||||
"""
|
||||
return (
|
||||
f"Total Loss {total_loss:.2f} > {threshold} in {lookback_period} 30min candles, "
|
||||
f"locking {self.unlock_reason_time_element}."
|
||||
)
|
||||
|
||||
def _total_loss_check(
|
||||
self, date_now: datetime, side: LongShort
|
||||
) -> ProtectionReturn | None:
|
||||
"""
|
||||
检查最近10根和20根半小时K线内的总亏损
|
||||
1根半小时K线 = 30分钟
|
||||
10根半小时K线 = 5小时
|
||||
20根半小时K线 = 10小时
|
||||
"""
|
||||
# 计算10根半小时K线的回溯时间（5小时）
|
||||
look_back_until_10_candles = date_now - timedelta(hours=5)
|
||||
# 计算20根半小时K线的回溯时间（10小时）
|
||||
look_back_until_20_candles = date_now - timedelta(hours=10)
|
||||
|
||||
# 获取10根K线时间窗口内的所有已关闭交易
|
||||
trades_10_candles = Trade.get_trades_proxy(
|
||||
is_open=False,
|
||||
close_date=look_back_until_10_candles
|
||||
)
|
||||
|
||||
# 获取20根K线时间窗口内的所有已关闭交易
|
||||
trades_20_candles = Trade.get_trades_proxy(
|
||||
is_open=False,
|
||||
close_date=look_back_until_20_candles
|
||||
)
|
||||
|
||||
# 计算10根K线时间窗口内的总亏损
|
||||
total_loss_10_candles = sum(
|
||||
min(0, trade.close_profit) # 只计算亏损部分
|
||||
for trade in trades_10_candles
|
||||
if trade.close_profit is not None
|
||||
) * -1 # 转换为正数表示亏损金额
|
||||
|
||||
# 计算20根K线时间窗口内的总亏损
|
||||
total_loss_20_candles = sum(
|
||||
min(0, trade.close_profit) # 只计算亏损部分
|
||||
for trade in trades_20_candles
|
||||
if trade.close_profit is not None
|
||||
) * -1 # 转换为正数表示亏损金额
|
||||
|
||||
# 检查是否触发任一条件
|
||||
if total_loss_10_candles > self._threshold_10_candles and trades_10_candles:
|
||||
self.log_once(
|
||||
f"Trading stopped due to Total Loss Protection: {total_loss_10_candles:.2f} > {self._threshold_10_candles} within last 10 30min candles.",
|
||||
logger.info,
|
||||
)
|
||||
until = self.calculate_lock_end(trades_10_candles)
|
||||
|
||||
return ProtectionReturn(
|
||||
lock=True,
|
||||
until=until,
|
||||
reason=self._reason(10, total_loss_10_candles, self._threshold_10_candles),
|
||||
)
|
||||
elif total_loss_20_candles > self._threshold_20_candles and trades_20_candles:
|
||||
self.log_once(
|
||||
f"Trading stopped due to Total Loss Protection: {total_loss_20_candles:.2f} > {self._threshold_20_candles} within last 20 30min candles.",
|
||||
logger.info,
|
||||
)
|
||||
until = self.calculate_lock_end(trades_20_candles)
|
||||
|
||||
return ProtectionReturn(
|
||||
lock=True,
|
||||
until=until,
|
||||
reason=self._reason(20, total_loss_20_candles, self._threshold_20_candles),
|
||||
)
|
||||
|
||||
return None
|
||||
|
||||
def global_stop(self, date_now: datetime, side: LongShort) -> ProtectionReturn | None:
|
||||
"""
|
||||
停止所有交易对的交易（开仓）
|
||||
"""
|
||||
return self._total_loss_check(date_now, side)
|
||||
|
||||
def stop_per_pair(
|
||||
self, pair: str, date_now: datetime, side: LongShort
|
||||
) -> ProtectionReturn | None:
|
||||
"""
|
||||
本保护机制不支持单个交易对的停止
|
||||
"""
|
||||
return None
|
||||
Binary file not shown.
311
freqtrade/templates/FreqaiExampleHybridStrategy.py
Normal file
311
freqtrade/templates/FreqaiExampleHybridStrategy.py
Normal file
@ -0,0 +1,311 @@
|
||||
import logging
|
||||
|
||||
import numpy as np # noqa
|
||||
import pandas as pd # noqa
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from technical import qtpylib
|
||||
|
||||
from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class FreqaiExampleHybridStrategy(IStrategy):
|
||||
"""
|
||||
Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
|
||||
FreqAI to bolster a typical Freqtrade strategy.
|
||||
|
||||
Launching this strategy would be:
|
||||
|
||||
freqtrade trade --strategy FreqaiExampleHybridStrategy --strategy-path freqtrade/templates
|
||||
--freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
|
||||
|
||||
or the user simply adds this to their config:
|
||||
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"purge_old_models": 2,
|
||||
"train_period_days": 15,
|
||||
"identifier": "unique-id",
|
||||
"feature_parameters": {
|
||||
"include_timeframes": [
|
||||
"3m",
|
||||
"15m",
|
||||
"1h"
|
||||
],
|
||||
"include_corr_pairlist": [
|
||||
"BTC/USDT",
|
||||
"ETH/USDT"
|
||||
],
|
||||
"label_period_candles": 20,
|
||||
"include_shifted_candles": 2,
|
||||
"DI_threshold": 0.9,
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": false,
|
||||
"use_SVM_to_remove_outliers": true,
|
||||
"indicator_periods_candles": [10, 20]
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0,
|
||||
"random_state": 1
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 800
|
||||
}
|
||||
},
|
||||
|
||||
Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
|
||||
"""
|
||||
|
||||
minimal_roi = {
|
||||
# "120": 0.0, # exit after 120 minutes at break even
|
||||
"60": 0.01,
|
||||
"30": 0.02,
|
||||
"0": 0.04,
|
||||
}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {
|
||||
"tema": {},
|
||||
},
|
||||
"subplots": {
|
||||
"MACD": {
|
||||
"macd": {"color": "blue"},
|
||||
"macdsignal": {"color": "orange"},
|
||||
},
|
||||
"RSI": {
|
||||
"rsi": {"color": "red"},
|
||||
},
|
||||
"Up_or_down": {
|
||||
"&s-up_or_down": {"color": "green"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 30
|
||||
can_short = False
|
||||
|
||||
# Hyperoptable parameters
|
||||
buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
|
||||
sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
|
||||
|
||||
def feature_engineering_expand_all(
|
||||
self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
|
||||
) -> DataFrame:
|
||||
"""
|
||||
*Only functional with FreqAI enabled strategies*
|
||||
This function will automatically expand the defined features on the config defined
|
||||
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
|
||||
`include_corr_pairs`. In other words, a single feature defined in this function
|
||||
will automatically expand to a total of
|
||||
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
|
||||
`include_corr_pairs` numbers of features added to the model.
|
||||
|
||||
All features must be prepended with `%` to be recognized by FreqAI internals.
|
||||
|
||||
More details on how these config defined parameters accelerate feature engineering
|
||||
in the documentation at:
|
||||
|
||||
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
|
||||
|
||||
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
|
||||
|
||||
:param dataframe: strategy dataframe which will receive the features
|
||||
:param period: period of the indicator - usage example:
|
||||
:param metadata: metadata of current pair
|
||||
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
|
||||
"""
|
||||
|
||||
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
|
||||
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
|
||||
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
|
||||
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
|
||||
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
qtpylib.typical_price(dataframe), window=period, stds=2.2
|
||||
)
|
||||
dataframe["bb_lowerband-period"] = bollinger["lower"]
|
||||
dataframe["bb_middleband-period"] = bollinger["mid"]
|
||||
dataframe["bb_upperband-period"] = bollinger["upper"]
|
||||
|
||||
dataframe["%-bb_width-period"] = (
|
||||
dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
|
||||
) / dataframe["bb_middleband-period"]
|
||||
dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
|
||||
|
||||
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
|
||||
|
||||
dataframe["%-relative_volume-period"] = (
|
||||
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
|
||||
)
|
||||
|
||||
return dataframe
|
||||
|
||||
def feature_engineering_expand_basic(
|
||||
self, dataframe: DataFrame, metadata: dict, **kwargs
|
||||
) -> DataFrame:
|
||||
"""
|
||||
*Only functional with FreqAI enabled strategies*
|
||||
This function will automatically expand the defined features on the config defined
|
||||
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
|
||||
In other words, a single feature defined in this function
|
||||
will automatically expand to a total of
|
||||
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
|
||||
numbers of features added to the model.
|
||||
|
||||
Features defined here will *not* be automatically duplicated on user defined
|
||||
`indicator_periods_candles`
|
||||
|
||||
All features must be prepended with `%` to be recognized by FreqAI internals.
|
||||
|
||||
More details on how these config defined parameters accelerate feature engineering
|
||||
in the documentation at:
|
||||
|
||||
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
|
||||
|
||||
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
|
||||
|
||||
:param dataframe: strategy dataframe which will receive the features
|
||||
:param metadata: metadata of current pair
|
||||
dataframe["%-pct-change"] = dataframe["close"].pct_change()
|
||||
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
|
||||
"""
|
||||
dataframe["%-pct-change"] = dataframe["close"].pct_change()
|
||||
dataframe["%-raw_volume"] = dataframe["volume"]
|
||||
dataframe["%-raw_price"] = dataframe["close"]
|
||||
return dataframe
|
||||
|
||||
def feature_engineering_standard(
|
||||
self, dataframe: DataFrame, metadata: dict, **kwargs
|
||||
) -> DataFrame:
|
||||
"""
|
||||
*Only functional with FreqAI enabled strategies*
|
||||
This optional function will be called once with the dataframe of the base timeframe.
|
||||
This is the final function to be called, which means that the dataframe entering this
|
||||
function will contain all the features and columns created by all other
|
||||
freqai_feature_engineering_* functions.
|
||||
|
||||
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
|
||||
This function is a good place for any feature that should not be auto-expanded upon
|
||||
(e.g. day of the week).
|
||||
|
||||
All features must be prepended with `%` to be recognized by FreqAI internals.
|
||||
|
||||
More details about feature engineering available:
|
||||
|
||||
https://www.freqtrade.io/en/latest/freqai-feature-engineering
|
||||
|
||||
:param dataframe: strategy dataframe which will receive the features
|
||||
:param metadata: metadata of current pair
|
||||
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
|
||||
"""
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
return dataframe
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
|
||||
logger.info(f"DataFrame shape: {dataframe.shape}")
|
||||
logger.info(f"Available columns: {list(dataframe.columns)}")
|
||||
logger.info(f"First few rows:\n{dataframe[['date', 'close']].head().to_string()}")
|
||||
|
||||
if "close" not in dataframe.columns:
|
||||
logger.error("Required 'close' column missing in dataframe")
|
||||
raise ValueError("Required 'close' column missing in dataframe")
|
||||
|
||||
if len(dataframe) < 50:
|
||||
logger.error(f"Insufficient data: {len(dataframe)} rows, need at least 50 for shift(-50)")
|
||||
raise ValueError("Insufficient data for target calculation")
|
||||
|
||||
try:
|
||||
# 生成数值型标签：1 表示上涨，0 表示下跌
|
||||
dataframe["&-up_or_down"] = np.where(
|
||||
dataframe["close"].shift(-50) > dataframe["close"],
|
||||
1.0, # 数值型标签
|
||||
0.0
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create &-up_or_down column: {str(e)}")
|
||||
raise
|
||||
|
||||
logger.info(f"Target column head:\n{dataframe[['&-up_or_down']].head().to_string()}")
|
||||
|
||||
if "&-up_or_down" not in dataframe.columns:
|
||||
logger.error("FreqAI failed to generate the &-up_or_down column")
|
||||
raise KeyError("FreqAI failed to generate the &-up_or_down column")
|
||||
|
||||
logger.info("FreqAI targets set successfully")
|
||||
return dataframe
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
logger.info(f"Processing pair: {metadata['pair']}")
|
||||
logger.info(f"Input DataFrame shape: {dataframe.shape}")
|
||||
logger.info(f"Input DataFrame columns: {list(dataframe.columns)}")
|
||||
logger.info(f"Input DataFrame head:\n{dataframe[['date', 'close', 'volume']].head().to_string()}")
|
||||
|
||||
# Ensure FreqAI processing
|
||||
logger.info("Calling self.freqai.start")
|
||||
try:
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
except Exception as e:
|
||||
logger.error(f"self.freqai.start failed: {str(e)}")
|
||||
raise
|
||||
logger.info("self.freqai.start completed")
|
||||
|
||||
logger.info(f"Output DataFrame shape: {dataframe.shape}")
|
||||
logger.info(f"Output DataFrame columns: {list(dataframe.columns)}")
|
||||
# Safely log columns that exist
|
||||
available_columns = [col for col in ['date', 'close', '&-up_or_down'] if col in dataframe.columns]
|
||||
logger.info(f"Output DataFrame head:\n{dataframe[available_columns].head().to_string()}")
|
||||
|
||||
if "&-up_or_down" not in dataframe.columns:
|
||||
logger.error("FreqAI did not generate the required &-up_or_down column")
|
||||
raise KeyError("FreqAI did not generate the required &-up_or_down column")
|
||||
|
||||
# RSI
|
||||
dataframe["rsi"] = ta.RSI(dataframe)
|
||||
|
||||
# Bollinger Bands
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe["bb_lowerband"] = bollinger["lower"]
|
||||
dataframe["bb_middleband"] = bollinger["mid"]
|
||||
dataframe["bb_upperband"] = bollinger["upper"]
|
||||
dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
|
||||
dataframe["bb_upperband"] - dataframe["bb_lowerband"]
|
||||
)
|
||||
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
|
||||
"bb_middleband"
|
||||
]
|
||||
|
||||
# TEMA
|
||||
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
|
||||
|
||||
return dataframe
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
df.loc[
|
||||
(
|
||||
(qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
|
||||
& (df["tema"] <= df["bb_middleband"])
|
||||
& (df["tema"] > df["tema"].shift(1))
|
||||
& (df["volume"] > 0)
|
||||
),
|
||||
"enter_long",
|
||||
] = 1
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
df.loc[
|
||||
(
|
||||
(qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
|
||||
& (df["tema"] > df["bb_middleband"])
|
||||
& (df["tema"] < df["tema"].shift(1))
|
||||
& (df["volume"] > 0)
|
||||
),
|
||||
"exit_long",
|
||||
] = 1
|
||||
return df
|
||||
262
freqtrade/templates/FreqaiExampleStrategy.diff
Normal file
262
freqtrade/templates/FreqaiExampleStrategy.diff
Normal file
@ -0,0 +1,262 @@
|
||||
diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
|
||||
index 343c073..1d7ed33 100644
|
||||
--- a/freqtrade/templates/FreqaiExampleStrategy.py
|
||||
+++ b/freqtrade/templates/FreqaiExampleStrategy.py
|
||||
@@ -11,9 +11,9 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
class FreqaiExampleStrategy(IStrategy):
|
||||
minimal_roi = {
|
||||
- "0": 0.076,
|
||||
- "7": 0.034,
|
||||
- "13": 0.007,
|
||||
+ "0": 0.02,
|
||||
+ "7": 0.01,
|
||||
+ "13": 0.005,
|
||||
"60": 0
|
||||
}
|
||||
|
||||
@@ -24,29 +24,25 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
startup_candle_count: int = 40
|
||||
can_short = False
|
||||
|
||||
- # Hyperopt 参数
|
||||
buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=True, load=True)
|
||||
sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=True, load=True)
|
||||
roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
|
||||
roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
|
||||
roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
|
||||
stoploss_param = DecimalParameter(low=-0.25, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
|
||||
- trailing_stop_positive_offset = DecimalParameter(low=0.01, high=0.5, default=0.02, space="trailing", optimize=True, load=True)
|
||||
+ trailing_stop_positive_offset = DecimalParameter(low=0.005, high=0.5, default=0.01, space="trailing", optimize=True, load=True)
|
||||
|
||||
- protections = [
|
||||
- {"method": "StoplossGuard", "stop_duration": 60, "lookback_period": 120},
|
||||
- {"method": "MaxDrawdown", "lookback_period": 120, "max_allowed_drawdown": 0.05}
|
||||
- ]
|
||||
+ protections = []
|
||||
|
||||
freqai_info = {
|
||||
"model": "LightGBMRegressor",
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m"],
|
||||
- "include_corr_pairlist": ["SOL/USDT", "BTC/USDT"],
|
||||
+ "include_corr_pairlist": ["SOL/USDT"],
|
||||
"label_period_candles": 12,
|
||||
"include_shifted_candles": 0,
|
||||
- "include_periods": [10, 20],
|
||||
- "DI_threshold": 3.0
|
||||
+ "include_periods": [20],
|
||||
+ "DI_threshold": 5.0
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
@@ -62,14 +58,20 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
}
|
||||
|
||||
plot_config = {
|
||||
- "main_plot": {},
|
||||
+ "main_plot": {
|
||||
+ "close": {"color": "blue"},
|
||||
+ "bb_lowerband": {"color": "purple"}
|
||||
+ },
|
||||
"subplots": {
|
||||
"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
|
||||
"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
|
||||
- "&-stoploss": {"&-stoploss": {"color": "purple"}},
|
||||
- "&-roi_0": {"&-roi_0": {"color": "orange"}},
|
||||
+ "rsi": {"rsi": {"color": "black"}},
|
||||
"do_predict": {"do_predict": {"color": "brown"}},
|
||||
- },
|
||||
+ "trade_signals": {
|
||||
+ "enter_long": {"color": "green", "type": "scatter"},
|
||||
+ "exit_long": {"color": "red", "type": "scatter"}
|
||||
+ }
|
||||
+ }
|
||||
}
|
||||
|
||||
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
|
||||
@@ -130,12 +132,10 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
logger.info(f"DataFrame rows: {len(dataframe)}")
|
||||
logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
|
||||
|
||||
- # 验证输入数据
|
||||
if "close" not in dataframe.columns or dataframe["close"].isna().all():
|
||||
logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
|
||||
raise ValueError("DataFrame missing valid 'close' column")
|
||||
|
||||
- # 生成 RSI
|
||||
if len(dataframe) < 14:
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute rsi")
|
||||
dataframe["rsi"] = 50
|
||||
@@ -143,7 +143,6 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||||
logger.info(f"rsi stats: {dataframe['rsi'].describe().to_string()}")
|
||||
|
||||
- # 生成 %-volatility
|
||||
if len(dataframe) < 20 or dataframe["close"].isna().any():
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows) or contains NaN in close, cannot compute %-volatility")
|
||||
dataframe["%-volatility"] = 0
|
||||
@@ -154,7 +153,6 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
dataframe["%-volatility"] = (dataframe["%-volatility"] - dataframe["%-volatility"].mean()) / dataframe["%-volatility"].std()
|
||||
logger.info(f"%-volatility stats: {dataframe['%-volatility'].describe().to_string()}")
|
||||
|
||||
- # 生成 TEMA
|
||||
if len(dataframe) < 9:
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute tema")
|
||||
dataframe["tema"] = dataframe["close"]
|
||||
@@ -165,7 +163,6 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
dataframe["tema"] = dataframe["tema"].fillna(dataframe["close"])
|
||||
logger.info(f"tema stats: {dataframe['tema'].describe().to_string()}")
|
||||
|
||||
- # 生成 Bollinger Bands
|
||||
if len(dataframe) < 20:
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute bb_lowerband")
|
||||
dataframe["bb_lowerband"] = dataframe["close"]
|
||||
@@ -177,21 +174,6 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
|
||||
logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
|
||||
|
||||
- # 生成 up_or_down
|
||||
- label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
|
||||
- if len(dataframe) < label_period + 1:
|
||||
- logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute up_or_down")
|
||||
- dataframe["up_or_down"] = 0
|
||||
- else:
|
||||
- dataframe["up_or_down"] = np.where(
|
||||
- dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
|
||||
- )
|
||||
- if dataframe["up_or_down"].isna().any():
|
||||
- logger.warning("up_or_down contains NaN, filling with 0")
|
||||
- dataframe["up_or_down"] = dataframe["up_or_down"].fillna(0)
|
||||
- logger.info(f"up_or_down stats: {dataframe['up_or_down'].describe().to_string()}")
|
||||
-
|
||||
- # 生成其他特征
|
||||
if "date" in dataframe.columns:
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
@@ -200,7 +182,6 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
dataframe["%-day_of_week"] = 0
|
||||
dataframe["%-hour_of_day"] = 0
|
||||
|
||||
- # 调用 FreqAI
|
||||
try:
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
logger.info(f"Columns after freqai.start: {list(dataframe.columns)}")
|
||||
@@ -210,26 +191,23 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
dataframe["sell_rsi_pred"] = 80
|
||||
dataframe["do_predict"] = 1
|
||||
|
||||
- # 检查预测列
|
||||
for col in ["buy_rsi_pred", "sell_rsi_pred"]:
|
||||
if col not in dataframe.columns:
|
||||
logger.error(f"Error: {col} column not generated for pair: {metadata['pair']}")
|
||||
dataframe[col] = 50 if col == "buy_rsi_pred" else 80
|
||||
logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
|
||||
|
||||
- # 调试特征分布
|
||||
- if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
|
||||
- if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
|
||||
- dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
|
||||
- dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
|
||||
- ) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
|
||||
- logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
|
||||
+ if "%-bb_width-period_20_SOL/USDT_5m" in dataframe.columns:
|
||||
+ if dataframe["%-bb_width-period_20_SOL/USDT_5m"].std() > 0:
|
||||
+ dataframe["%-bb_width-period_20_SOL/USDT_5m"] = (
|
||||
+ dataframe["%-bb_width-period_20_SOL/USDT_5m"] - dataframe["%-bb_width-period_20_SOL/USDT_5m"].mean()
|
||||
+ ) / dataframe["%-bb_width-period_20_SOL/USDT_5m"].std()
|
||||
+ logger.info(f"%-bb_width-period_20 stats: {dataframe['%-bb_width-period_20_SOL/USDT_5m'].describe().to_string()}")
|
||||
|
||||
- # 动态生成期望的特征列
|
||||
def get_expected_columns(freqai_config: dict) -> list:
|
||||
indicators = ["rsi", "bb_width", "pct-change"]
|
||||
- periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
|
||||
- pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
|
||||
+ periods = freqai_config.get("feature_parameters", {}).get("include_periods", [20])
|
||||
+ pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT"])
|
||||
timeframes = freqai_config.get("include_timeframes", ["5m"])
|
||||
shifts = [0]
|
||||
expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
|
||||
@@ -248,50 +226,47 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
expected_columns = get_expected_columns(self.freqai_info)
|
||||
logger.info(f"Expected feature columns ({len(expected_columns)}): {expected_columns[:10]}...")
|
||||
|
||||
- # 比较特征集
|
||||
actual_columns = list(dataframe.columns)
|
||||
missing_columns = [col for col in expected_columns if col not in actual_columns]
|
||||
extra_columns = [col for col in actual_columns if col not in expected_columns and col.startswith("%-")]
|
||||
logger.info(f"Missing columns ({len(missing_columns)}): {missing_columns}")
|
||||
logger.info(f"Extra columns ({len(extra_columns)}): {extra_columns}")
|
||||
|
||||
- # 调试 DI 丢弃预测
|
||||
if "DI_values" in dataframe.columns:
|
||||
logger.info(f"DI_values stats: {dataframe['DI_values'].describe().to_string()}")
|
||||
logger.info(f"DI discarded predictions: {len(dataframe[dataframe['do_predict'] == 0])}")
|
||||
|
||||
- # 清理数据
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||||
logger.info(f"Final columns in populate_indicators: {list(dataframe.columns)}")
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
enter_long_conditions = [
|
||||
- qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
|
||||
+ qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
|
||||
df["tema"] > df["tema"].shift(1),
|
||||
df["volume"] > 0,
|
||||
- df["do_predict"] == 1,
|
||||
- df["up_or_down"] == 1
|
||||
+ df["do_predict"] == 1
|
||||
]
|
||||
- if enter_long_conditions:
|
||||
- df.loc[
|
||||
- reduce(lambda x, y: x & y, enter_long_conditions),
|
||||
- ["enter_long", "enter_tag"]
|
||||
- ] = (1, "long")
|
||||
+ df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
|
||||
+ df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
|
||||
+ df.loc[
|
||||
+ df["entry_signal"],
|
||||
+ ["enter_long", "enter_tag"]
|
||||
+ ] = (1, "long")
|
||||
+ if df["entry_signal"].iloc[-1]:
|
||||
+ logger.info(f"Entry signal triggered for {metadata['pair']}: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}")
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [
|
||||
- (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
|
||||
+ (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"] - 5)) |
|
||||
(df["close"] < df["close"].shift(1) * 0.98) |
|
||||
(df["close"] < df["bb_lowerband"]),
|
||||
df["volume"] > 0,
|
||||
- df["do_predict"] == 1,
|
||||
- df["up_or_down"] == 0
|
||||
+ df["do_predict"] == 1
|
||||
]
|
||||
- time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
|
||||
df.loc[
|
||||
- (reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
|
||||
+ reduce(lambda x, y: x & y, exit_long_conditions),
|
||||
"exit_long"
|
||||
] = 1
|
||||
return df
|
||||
@@ -300,9 +275,16 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time, entry_tag, side: str, **kwargs
|
||||
) -> bool:
|
||||
+ logger.info(f"Confirming trade entry for {pair}, order_type: {order_type}, rate: {rate}, current_time: {current_time}")
|
||||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
if side == "long":
|
||||
- if rate > (last_candle["close"] * (1 + 0.001)):
|
||||
- return False
|
||||
+ if order_type == "market":
|
||||
+ logger.info(f"Order confirmed for {pair}, rate: {rate} (market order)")
|
||||
+ return True
|
||||
+ if rate <= (last_candle["close"] * (1 + 0.01)):
|
||||
+ logger.info(f"Order confirmed for {pair}, rate: {rate}")
|
||||
+ return True
|
||||
+ logger.info(f"Order rejected: rate {rate} exceeds threshold {last_candle['close'] * 1.01}")
|
||||
+ return False
|
||||
return True
|
||||
32
freqtrade/templates/FreqaiExampleStrategy.json
Normal file
32
freqtrade/templates/FreqaiExampleStrategy.json
Normal file
@ -0,0 +1,32 @@
|
||||
{
|
||||
"strategy_name": "FreqaiExampleStrategy",
|
||||
"params": {
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 4
|
||||
},
|
||||
"buy": {
|
||||
"buy_rsi": 37
|
||||
},
|
||||
"sell": {
|
||||
"sell_rsi": 80
|
||||
},
|
||||
"protection": {},
|
||||
"roi": {
|
||||
"0": 0.124,
|
||||
"14": 0.023,
|
||||
"37": 0.011,
|
||||
"50": 0
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -0.168
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.047,
|
||||
"trailing_stop_positive_offset": 0.051000000000000004,
|
||||
"trailing_only_offset_is_reached": true
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-04-24 11:42:35.037486+00:00"
|
||||
}
|
||||
32
freqtrade/templates/FreqaiExampleStrategy.json_back
Normal file
32
freqtrade/templates/FreqaiExampleStrategy.json_back
Normal file
@ -0,0 +1,32 @@
|
||||
{
|
||||
"strategy_name": "FreqaiExampleStrategy",
|
||||
"params": {
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.01,
|
||||
"trailing_stop_positive_offset": 0.02,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 4
|
||||
},
|
||||
"buy": {
|
||||
"buy_rsi": 39.92672300850069
|
||||
},
|
||||
"sell": {
|
||||
"sell_rsi": 69.92672300850067
|
||||
},
|
||||
"protection": {},
|
||||
"roi": {
|
||||
"0": 0.132,
|
||||
"8": 0.047,
|
||||
"14": 0.007,
|
||||
"60": 0
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -0.322
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-04-23 12:30:05.550433+00:00"
|
||||
}
|
||||
305
freqtrade/templates/FreqaiExampleStrategy.py
Normal file
305
freqtrade/templates/FreqaiExampleStrategy.py
Normal file
@ -0,0 +1,305 @@
|
||||
import logging
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from functools import reduce
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from technical import qtpylib
|
||||
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class FreqaiExampleStrategy(IStrategy):
|
||||
minimal_roi = {
|
||||
"0": 0.02,
|
||||
"7": 0.01,
|
||||
"13": 0.005,
|
||||
"60": 0
|
||||
}
|
||||
|
||||
stoploss = 0.0
|
||||
trailing_stop = True
|
||||
process_only_new_candles = True
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 40
|
||||
can_short = False
|
||||
|
||||
buy_rsi = IntParameter(low=10, high=40, default=20, space="buy", optimize=True, load=True)
|
||||
sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=True, load=True)
|
||||
roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
|
||||
roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
|
||||
roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
|
||||
stoploss_param = DecimalParameter(low=-0.25, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
|
||||
trailing_stop_positive_offset = DecimalParameter(low=0.005, high=0.5, default=0.01, space="trailing", optimize=True, load=True)
|
||||
|
||||
protections = []
|
||||
|
||||
freqai_info = {
|
||||
"model": "LightGBMRegressor",
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m"],
|
||||
"include_corr_pairlist": ["OKB/USDT", "SOL/USDT"], # 与白名单一致
|
||||
"label_period_candles": 12,
|
||||
"include_shifted_candles": 0,
|
||||
"include_periods": [20],
|
||||
"DI_threshold": 1.5 # 提高以减少丢弃预测
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": False,
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 100,
|
||||
"learning_rate": 0.1,
|
||||
"num_leaves": 15,
|
||||
"n_jobs": 4,
|
||||
"verbosity": -1
|
||||
},
|
||||
}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {
|
||||
"close": {"color": "blue"},
|
||||
"bb_lowerband": {"color": "purple"}
|
||||
},
|
||||
"subplots": {
|
||||
"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
|
||||
"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
|
||||
"rsi": {"rsi": {"color": "black"}},
|
||||
"do_predict": {"do_predict": {"color": "brown"}},
|
||||
"trade_signals": {
|
||||
"enter_long": {"color": "green", "type": "scatter"},
|
||||
"exit_long": {"color": "red", "type": "scatter"}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
|
||||
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2)
|
||||
dataframe["%-bb_width-period"] = (bollinger["upper"] - bollinger["lower"]) / bollinger["mid"]
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||||
return dataframe
|
||||
|
||||
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
dataframe["%-pct-change"] = dataframe["close"].pct_change()
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||||
return dataframe
|
||||
|
||||
def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
if len(dataframe) < 20 or dataframe["close"].isna().any():
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows) or contains NaN in close, cannot compute %-volatility")
|
||||
dataframe["%-volatility"] = 0
|
||||
else:
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
|
||||
if dataframe["%-volatility"].std() > 0:
|
||||
dataframe["%-volatility"] = (dataframe["%-volatility"] - dataframe["%-volatility"].mean()) / dataframe["%-volatility"].std()
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||||
return dataframe
|
||||
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
|
||||
if "close" not in dataframe.columns:
|
||||
logger.error("DataFrame missing required 'close' column")
|
||||
raise ValueError("DataFrame missing required 'close' column")
|
||||
try:
|
||||
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
|
||||
if len(dataframe) < 20 or dataframe["close"].isna().any():
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows) or contains NaN in close, cannot compute %-volatility")
|
||||
dataframe["%-volatility"] = 0
|
||||
else:
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
|
||||
if dataframe["%-volatility"].std() > 0:
|
||||
dataframe["%-volatility"] = (dataframe["%-volatility"] - dataframe["%-volatility"].mean()) / dataframe["%-volatility"].std()
|
||||
dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14).shift(-label_period)
|
||||
for col in ["&-buy_rsi", "%-volatility"]:
|
||||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
|
||||
dataframe[col] = dataframe[col].ffill().fillna(0)
|
||||
if dataframe[col].isna().any():
|
||||
logger.warning(f"Target column {col} still contains NaN, check data generation logic")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create FreqAI targets: {str(e)}")
|
||||
raise
|
||||
logger.info(f"Target columns preview: {dataframe[['&-buy_rsi']].head().to_string()}")
|
||||
return dataframe
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
logger.info(f"populate_indicators Processing pair: {metadata['pair']}")
|
||||
logger.info(f"populate_indicators DataFrame rows: {len(dataframe)}")
|
||||
logger.info(f"populate_indicators Columns before freqai.start: {list(dataframe.columns)}")
|
||||
|
||||
if "close" not in dataframe.columns or dataframe["close"].isna().all():
|
||||
logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
|
||||
raise ValueError("DataFrame missing valid 'close' column")
|
||||
|
||||
if len(dataframe) < 14:
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute rsi")
|
||||
dataframe["rsi"] = 50
|
||||
else:
|
||||
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||||
logger.info(f"rsi stats: {dataframe['rsi'].describe().to_string()}")
|
||||
|
||||
if len(dataframe) < 20 or dataframe["close"].isna().any():
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows) or contains NaN in close, cannot compute %-volatility")
|
||||
dataframe["%-volatility"] = 0
|
||||
else:
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
|
||||
if dataframe["%-volatility"].std() > 0:
|
||||
dataframe["%-volatility"] = (dataframe["%-volatility"] - dataframe["%-volatility"].mean()) / dataframe["%-volatility"].std()
|
||||
logger.info(f"%-volatility stats: {dataframe['%-volatility'].describe().to_string()}")
|
||||
|
||||
if len(dataframe) < 9:
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute tema")
|
||||
dataframe["tema"] = dataframe["close"]
|
||||
else:
|
||||
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
|
||||
if dataframe["tema"].isna().any():
|
||||
logger.warning("tema contains NaN, filling with close")
|
||||
dataframe["tema"] = dataframe["tema"].fillna(dataframe["close"])
|
||||
logger.info(f"tema stats: {dataframe['tema'].describe().to_string()}")
|
||||
|
||||
if len(dataframe) < 20:
|
||||
logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute bb_lowerband")
|
||||
dataframe["bb_lowerband"] = dataframe["close"]
|
||||
else:
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2.2)
|
||||
dataframe["bb_lowerband"] = bollinger["lower"]
|
||||
if dataframe["bb_lowerband"].isna().any():
|
||||
logger.warning("bb_lowerband contains NaN, filling with close")
|
||||
dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
|
||||
logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
|
||||
|
||||
if "date" in dataframe.columns:
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
else:
|
||||
logger.warning("Missing 'date' column, skipping %-day_of_week and %-hour_of_day")
|
||||
dataframe["%-day_of_week"] = 0
|
||||
dataframe["%-hour_of_day"] = 0
|
||||
|
||||
try:
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
logger.info(f"Columns after freqai.start: {list(dataframe.columns)}")
|
||||
except Exception as e:
|
||||
logger.error(f"freqai.start failed: {str(e)}")
|
||||
dataframe["buy_rsi_pred"] = 50
|
||||
dataframe["sell_rsi_pred"] = 80
|
||||
dataframe["do_predict"] = 1
|
||||
|
||||
for col in ["buy_rsi_pred", "sell_rsi_pred"]:
|
||||
if col not in dataframe.columns:
|
||||
logger.error(f"Error: {col} column not generated for pair: {metadata['pair']}")
|
||||
dataframe[col] = 50 if col == "buy_rsi_pred" else 80
|
||||
logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
|
||||
|
||||
# 调试特征分布
|
||||
if "%-bb_width-period_10_OKB/USDT_5m" in dataframe.columns:
|
||||
if dataframe["%-bb_width-period_10_OKB/USDT_5m"].std() > 0:
|
||||
dataframe["%-bb_width-period_10_OKB/USDT_5m"] = (
|
||||
dataframe["%-bb_width-period_10_OKB/USDT_5m"] - dataframe["%-bb_width-period_10_OKB/USDT_5m"].mean()
|
||||
) / dataframe["%-bb_width-period_10_OKB/USDT_5m"].std()
|
||||
logger.info(f"%-bb_width-period_10_OKB stats: {dataframe['%-bb_width-period_10_OKB/USDT_5m'].describe().to_string()}")
|
||||
if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
|
||||
if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
|
||||
dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
|
||||
dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
|
||||
) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
|
||||
logger.info(f"%-bb_width-period_10_SOL stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
|
||||
|
||||
def get_expected_columns(freqai_config: dict) -> list:
|
||||
indicators = ["rsi", "bb_width", "pct-change"]
|
||||
periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
|
||||
pairs = freqai_config.get("include_corr_pairlist", ["OKB/USDT", "SOL/USDT"])
|
||||
timeframes = freqai_config.get("include_timeframes", ["5m"])
|
||||
shifts = [0]
|
||||
expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
|
||||
for indicator in indicators:
|
||||
for period in periods:
|
||||
for pair in pairs:
|
||||
for timeframe in timeframes:
|
||||
for shift in shifts:
|
||||
col_name = f"%-{indicator}-period_{period}" if indicator != "pct-change" else f"%-{indicator}"
|
||||
if shift > 0:
|
||||
col_name += f"_shift-{shift}"
|
||||
col_name += f"_{pair}_{timeframe}"
|
||||
expected_columns.append(col_name)
|
||||
return expected_columns
|
||||
|
||||
expected_columns = get_expected_columns(self.freqai_info)
|
||||
logger.info(f"Expected feature columns ({len(expected_columns)}): {expected_columns[:10]}...")
|
||||
|
||||
actual_columns = list(dataframe.columns)
|
||||
missing_columns = [col for col in expected_columns if col not in actual_columns]
|
||||
extra_columns = [col for col in actual_columns if col not in expected_columns and col.startswith("%-")]
|
||||
logger.info(f"Missing columns ({len(missing_columns)}): {missing_columns}")
|
||||
logger.info(f"Extra columns ({len(extra_columns)}): {extra_columns}")
|
||||
|
||||
if "DI_values" in dataframe.columns:
|
||||
logger.info(f"DI_values stats: {dataframe['DI_values'].describe().to_string()}")
|
||||
logger.info(f"DI discarded predictions: {len(dataframe[dataframe['do_predict'] == 0])}")
|
||||
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||||
logger.info(f"Final columns in populate_indicators: {list(dataframe.columns)}")
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
enter_long_conditions = [
|
||||
qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
|
||||
df["close"] > df["tema"],
|
||||
df["volume"] > 0,
|
||||
df["do_predict"] == 1
|
||||
]
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions),
|
||||
["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
|
||||
df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
|
||||
df.loc[
|
||||
df["entry_signal"],
|
||||
["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
if df["entry_signal"].iloc[-1]:
|
||||
logger.info(f"Entry signal triggered for {metadata['pair']}: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}, close={df['close'].iloc[-1]}, tema={df['tema'].iloc[-1]}")
|
||||
logger.info(f"Entry conditions: RSI_cross={qtpylib.crossed_above(df['rsi'], df['buy_rsi_pred']).iloc[-1]}, Close_above_tema={df['close'].iloc[-1] > df['tema'].iloc[-1]}, Volume={df['volume'].iloc[-1] > 0}, Do_predict={df['do_predict'].iloc[-1] == 1}")
|
||||
logger.info(f"Last candle: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, close={df['close'].iloc[-1]}, tema={df['tema'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}")
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [
|
||||
(qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
|
||||
(df["close"] < df["close"].shift(1) * 0.98) |
|
||||
(df["close"] < df["bb_lowerband"]),
|
||||
df["volume"] > 0,
|
||||
df["do_predict"] == 1
|
||||
]
|
||||
time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
|
||||
df.loc[
|
||||
(reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
|
||||
"exit_long"
|
||||
] = 1
|
||||
return df
|
||||
|
||||
def confirm_trade_entry(
|
||||
self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time, entry_tag, side: str, **kwargs
|
||||
) -> bool:
|
||||
logger.info(f"Confirming trade entry for {pair}, order_type: {order_type}, rate: {rate}, current_time: {current_time}")
|
||||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
if side == "long":
|
||||
if order_type == "market":
|
||||
logger.info(f"Order confirmed for {pair}, rate: {rate} (market order)")
|
||||
return True
|
||||
if rate <= (last_candle["close"] * (1 + 0.01)):
|
||||
logger.info(f"Order confirmed for {pair}, rate: {rate}")
|
||||
return True
|
||||
logger.info(f"Order rejected: rate {rate} exceeds threshold {last_candle['close'] * 1.01}")
|
||||
return False
|
||||
return True
|
||||
@ -1,44 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"stoploss": {
|
||||
"stoploss": -0.05
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"ADD_POSITION_THRESHOLD": -0.046,
|
||||
"BUY_THRESHOLD_MAX": -0.01,
|
||||
"BUY_THRESHOLD_MIN": -0.026,
|
||||
"COOLDOWN_PERIOD_MINUTES": 9,
|
||||
"MAX_ENTRY_POSITION_ADJUSTMENT": 3
|
||||
},
|
||||
"sell": {
|
||||
"EXIT_POSITION_RATIO": 0.572,
|
||||
"HOLD_TIME_EXTENSION": 1.975,
|
||||
"HOLD_TIME_REDUCTION": 0.832,
|
||||
"MAX_HOLD_TIME_MINUTES": 101,
|
||||
"SELL_THRESHOLD_MAX": 0.085,
|
||||
"SELL_THRESHOLD_MIN": 0.007,
|
||||
"SLOPE_THRESHOLD": 0.0,
|
||||
"TRAILING_STOP_DISTANCE": 0.005,
|
||||
"TRAILING_STOP_START": 0.048
|
||||
},
|
||||
"protection": {},
|
||||
"roi": {
|
||||
"0": 0.128,
|
||||
"12": 0.038,
|
||||
"36": 0.023,
|
||||
"53": 0
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.09,
|
||||
"trailing_stop_positive_offset": 0.16499999999999998,
|
||||
"trailing_only_offset_is_reached": false
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-06-27 03:30:20.329593+00:00"
|
||||
}
|
||||
@ -1,67 +0,0 @@
|
||||
--- original_strategy.py
|
||||
+++ modified_strategy.py
|
||||
@@ -28,8 +28,8 @@ class FreqaiPrimer(IStrategy):
|
||||
TRAILING_STOP_DISTANCE = DecimalParameter(0.005, 0.02, default=0.01, space='sell', optimize=True)
|
||||
|
||||
- BUY_THRESHOLD_MIN = DecimalParameter(-0.1, -0.01, default=-0.05, space='buy', optimize=True)
|
||||
- BUY_THRESHOLD_MAX = DecimalParameter(-0.02, -0.001, default=-0.005, space='buy', optimize=True)
|
||||
+ BUY_THRESHOLD_MIN = DecimalParameter(-0.15, -0.03, default=-0.07, space='buy', optimize=True)
|
||||
+ BUY_THRESHOLD_MAX = DecimalParameter(-0.04, -0.005, default=-0.01, space='buy', optimize=True)
|
||||
SELL_THRESHOLD_MIN = DecimalParameter(0.001, 0.02, default=0.005, space='sell', optimize=True)
|
||||
SELL_THRESHOLD_MAX = DecimalParameter(0.02, 0.1, default=0.05, space='sell', optimize=True)
|
||||
|
||||
@@ -356,10 +356,10 @@ class FreqaiPrimer(IStrategy):
|
||||
volume_z_score_min = 0.5
|
||||
volume_z_score_max = 1.5
|
||||
volume_z_score_threshold = self.linear_map(trend_score, 0, 100, volume_z_score_max, volume_z_score_min)
|
||||
|
||||
- rsi_min = 40
|
||||
- rsi_max = 60
|
||||
+ rsi_min = 35
|
||||
+ rsi_max = 55
|
||||
rsi_threshold = self.linear_map(trend_score, 0, 100, rsi_max, rsi_min)
|
||||
|
||||
- stochrsi_min = 30
|
||||
- stochrsi_max = 50
|
||||
+ stochrsi_min = 25
|
||||
+ stochrsi_max = 45
|
||||
stochrsi_threshold = self.linear_map(trend_score, 0, 100, stochrsi_max, stochrsi_min)
|
||||
|
||||
if "&-price_value_divergence" in dataframe.columns:
|
||||
# 计算 STOCHRSI
|
||||
stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
|
||||
dataframe["stochrsi_k"] = stochrsi["fastk"]
|
||||
+ # 添加 15m 时间框架的 EMA200
|
||||
+ dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
|
||||
+ dataframe_15m["ema200_15m"] = ta.EMA(dataframe_15m, timeperiod=200)
|
||||
+ dataframe["ema200_15m"] = dataframe_15m["ema200_15m"].reindex(dataframe.index, method="ffill")
|
||||
|
||||
cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
|
||||
cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold)
|
||||
cond3 = (dataframe["rsi"] < rsi_threshold)
|
||||
cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
|
||||
- cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
|
||||
- buy_condition = cond1 & cond2 & cond3 & cond4 & cond5
|
||||
+ cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
|
||||
+ cond6 = (dataframe["close"] < dataframe["ema200_15m"]) # 价格低于 15m EMA200
|
||||
+ buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6
|
||||
conditions.append(buy_condition)
|
||||
|
||||
@@ -534,7 +534,7 @@ class FreqaiPrimer(IStrategy):
|
||||
def get_market_trend(self, dataframe: DataFrame = None, metadata: dict = None) -> int:
|
||||
try:
|
||||
timeframes = ["3m", "15m", "1h"]
|
||||
- weights = {"3m": 0.5, "15m": 0.3, "1h": 0.2}
|
||||
+ weights = {"3m": 0.3, "15m": 0.35, "1h": 0.35}
|
||||
trend_scores = {}
|
||||
pair = metadata.get('pair', 'Unknown') if metadata else 'Unknown'
|
||||
|
||||
@@ -565,7 +565,7 @@ class FreqaiPrimer(IStrategy):
|
||||
pair_df["ema_short_slope"] = (pair_df["ema_short"] - pair_df["ema_short"].shift(10)) / pair_df["ema_short"].shift(10)
|
||||
|
||||
# 动态调整权重
|
||||
- if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20:
|
||||
- weights = {"3m": 0.3, "15m": 0.3, "1h": 0.4}
|
||||
+ if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20 or trend_scores.get("15m", 50) - trend_scores.get("3m", 50) > 20:
|
||||
+ weights = {"3m": 0.2, "15m": 0.35, "1h": 0.45}
|
||||
logger.debug(f"[{pair}] 1h 趋势得分（{trend_scores.get('1h', 50)}）显著高于 3m（{trend_scores.get('3m', 50)}），调整权重为 {weights}")
|
||||
69
freqtrade/templates/base_config.json.j2
Normal file
69
freqtrade/templates/base_config.json.j2
Normal file
@ -0,0 +1,69 @@
|
||||
{%set volume_pairlist = '{
|
||||
"method": "VolumePairList",
|
||||
"number_assets": 20,
|
||||
"sort_key": "quoteVolume",
|
||||
"min_value": 0,
|
||||
"refresh_period": 1800
|
||||
}' %}
|
||||
{
|
||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
||||
"max_open_trades": {{ max_open_trades }},
|
||||
"stake_currency": "{{ stake_currency }}",
|
||||
"stake_amount": {{ stake_amount }},
|
||||
"tradable_balance_ratio": 0.99,
|
||||
{{- ('\n "fiat_display_currency": "' + fiat_display_currency + '",') if fiat_display_currency else ''}}
|
||||
{{- ('\n "timeframe": "' + timeframe + '",') if timeframe else '' }}
|
||||
"dry_run": {{ dry_run | lower }},
|
||||
"dry_run_wallet": 1000,
|
||||
"cancel_open_orders_on_exit": false,
|
||||
"trading_mode": "{{ trading_mode }}",
|
||||
"margin_mode": "{{ margin_mode }}",
|
||||
"unfilledtimeout": {
|
||||
"entry": 10,
|
||||
"exit": 10,
|
||||
"exit_timeout_count": 0,
|
||||
"unit": "minutes"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0,
|
||||
"check_depth_of_market": {
|
||||
"enabled": false,
|
||||
"bids_to_ask_delta": 1
|
||||
}
|
||||
},
|
||||
"exit_pricing":{
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
{{ exchange | indent(4) }},
|
||||
"pairlists": [
|
||||
{{ volume_pairlist }}
|
||||
],
|
||||
"telegram": {
|
||||
"enabled": {{ telegram | lower }},
|
||||
"token": "{{ telegram_token }}",
|
||||
"chat_id": "{{ telegram_chat_id }}"
|
||||
},
|
||||
"api_server": {
|
||||
"enabled": {{ api_server | lower }},
|
||||
"listen_ip_address": "{{ api_server_listen_addr | default("127.0.0.1", true) }}",
|
||||
"listen_port": 8080,
|
||||
"verbosity": "error",
|
||||
"enable_openapi": false,
|
||||
"jwt_secret_key": "{{ api_server_jwt_key }}",
|
||||
"ws_token": "{{ api_server_ws_token }}",
|
||||
"CORS_origins": [],
|
||||
"username": "{{ api_server_username }}",
|
||||
"password": "{{ api_server_password }}"
|
||||
},
|
||||
"bot_name": "freqtrade",
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 5
|
||||
}
|
||||
}
|
||||
179
freqtrade/templates/base_strategy.py.j2
Normal file
179
freqtrade/templates/base_strategy.py.j2
Normal file
@ -0,0 +1,179 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
# flake8: noqa: F401
|
||||
# isort: skip_file
|
||||
# --- Do not remove these imports ---
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from pandas import DataFrame
|
||||
from typing import Dict, Optional, Union, Tuple
|
||||
|
||||
from freqtrade.strategy import (
|
||||
IStrategy,
|
||||
Trade,
|
||||
Order,
|
||||
PairLocks,
|
||||
informative, # @informative decorator
|
||||
# Hyperopt Parameters
|
||||
BooleanParameter,
|
||||
CategoricalParameter,
|
||||
DecimalParameter,
|
||||
IntParameter,
|
||||
RealParameter,
|
||||
# timeframe helpers
|
||||
timeframe_to_minutes,
|
||||
timeframe_to_next_date,
|
||||
timeframe_to_prev_date,
|
||||
# Strategy helper functions
|
||||
merge_informative_pair,
|
||||
stoploss_from_absolute,
|
||||
stoploss_from_open,
|
||||
)
|
||||
|
||||
# --------------------------------
|
||||
# Add your lib to import here
|
||||
import talib.abstract as ta
|
||||
import pandas_ta as pta
|
||||
from technical import qtpylib
|
||||
|
||||
|
||||
class {{ strategy }}(IStrategy):
|
||||
"""
|
||||
This is a strategy template to get you started.
|
||||
More information in https://www.freqtrade.io/en/latest/strategy-customization/
|
||||
|
||||
You can:
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
- Rename the class name (Do not forget to update class_name)
|
||||
- Add any methods you want to build your strategy
|
||||
- Add any lib you need to build your strategy
|
||||
|
||||
You must keep:
|
||||
- the lib in the section "Do not remove these libs"
|
||||
- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
|
||||
You should keep:
|
||||
- timeframe, minimal_roi, stoploss, trailing_*
|
||||
"""
|
||||
# Strategy interface version - allow new iterations of the strategy interface.
|
||||
# Check the documentation or the Sample strategy to get the latest version.
|
||||
INTERFACE_VERSION = 3
|
||||
|
||||
# Optimal timeframe for the strategy.
|
||||
timeframe = "5m"
|
||||
|
||||
# Can this strategy go short?
|
||||
can_short: bool = False
|
||||
|
||||
# Minimal ROI designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "minimal_roi".
|
||||
minimal_roi = {
|
||||
"60": 0.01,
|
||||
"30": 0.02,
|
||||
"0": 0.04
|
||||
}
|
||||
|
||||
# Optimal stoploss designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "stoploss".
|
||||
stoploss = -0.10
|
||||
|
||||
# Trailing stoploss
|
||||
trailing_stop = False
|
||||
# trailing_only_offset_is_reached = False
|
||||
# trailing_stop_positive = 0.01
|
||||
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
|
||||
|
||||
# Run "populate_indicators()" only for new candle.
|
||||
process_only_new_candles = True
|
||||
|
||||
# These values can be overridden in the config.
|
||||
use_exit_signal = True
|
||||
exit_profit_only = False
|
||||
ignore_roi_if_entry_signal = False
|
||||
|
||||
# Number of candles the strategy requires before producing valid signals
|
||||
startup_candle_count: int = 30
|
||||
|
||||
# Strategy parameters
|
||||
buy_rsi = IntParameter(10, 40, default=30, space="buy")
|
||||
sell_rsi = IntParameter(60, 90, default=70, space="sell")
|
||||
|
||||
{{- attributes | indent(4) }}
|
||||
{{- plot_config | indent(4) }}
|
||||
|
||||
def informative_pairs(self):
|
||||
"""
|
||||
Define additional, informative pair/interval combinations to be cached from the exchange.
|
||||
These pair/interval combinations are non-tradeable, unless they are part
|
||||
of the whitelist as well.
|
||||
For more information, please consult the documentation
|
||||
:return: List of tuples in the format (pair, interval)
|
||||
Sample: return [("ETH/USDT", "5m"),
|
||||
("BTC/USDT", "15m"),
|
||||
]
|
||||
"""
|
||||
return []
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Adds several different TA indicators to the given DataFrame
|
||||
|
||||
Performance Note: For the best performance be frugal on the number of indicators
|
||||
you are using. Let uncomment only the indicator you are using in your strategies
|
||||
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||||
:param dataframe: Dataframe with data from the exchange
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
"""
|
||||
{{- indicators | indent(8) }}
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the entry signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: DataFrame with entry columns populated
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
{{ buy_trend | indent(16) }}
|
||||
(dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
"enter_long"] = 1
|
||||
# Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
{{ sell_trend | indent(16) }}
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'enter_short'] = 1
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the exit signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: DataFrame with exit columns populated
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
{{ sell_trend | indent(16) }}
|
||||
(dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
"exit_long"] = 1
|
||||
# Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
{{ buy_trend | indent(16) }}
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'exit_short'] = 1
|
||||
"""
|
||||
return dataframe
|
||||
{{- additional_methods | indent(4) }}
|
||||
104
freqtrade/templates/celuo.diff
Normal file
104
freqtrade/templates/celuo.diff
Normal file
@ -0,0 +1,104 @@
|
||||
diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
|
||||
index 1d7ed33..245f771 100644
|
||||
--- a/freqtrade/templates/FreqaiExampleStrategy.py
|
||||
+++ b/freqtrade/templates/FreqaiExampleStrategy.py
|
||||
@@ -128,9 +128,9 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
return dataframe
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
- logger.info(f"Processing pair: {metadata['pair']}")
|
||||
- logger.info(f"DataFrame rows: {len(dataframe)}")
|
||||
- logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
|
||||
+ logger.info(f"populate_indicators Processing pair: {metadata['pair']}")
|
||||
+ logger.info(f"populate_indicators DataFrame rows: {len(dataframe)}")
|
||||
+ logger.info(f"populate_indicators Columns before freqai.start: {list(dataframe.columns)}")
|
||||
|
||||
if "close" not in dataframe.columns or dataframe["close"].isna().all():
|
||||
logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
|
||||
@@ -173,6 +173,19 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
logger.warning("bb_lowerband contains NaN, filling with close")
|
||||
dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
|
||||
logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
|
||||
+ # 生成 up_or_down
|
||||
+ label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
|
||||
+ if len(dataframe) < label_period + 1:
|
||||
+ logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute up_or_down")
|
||||
+ dataframe["up_or_down"] = 0
|
||||
+ else:
|
||||
+ dataframe["up_or_down"] = np.where(
|
||||
+ dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
|
||||
+ )
|
||||
+ if dataframe["up_or_down"].isna().any():
|
||||
+ logger.warning("up_or_down contains NaN, filling with 0")
|
||||
+ dataframe["up_or_down"] = dataframe["up_or_down"].fillna(0)
|
||||
+ logger.info(f"up_or_down stats: {dataframe['up_or_down'].describe().to_string()}")
|
||||
|
||||
if "date" in dataframe.columns:
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
@@ -197,17 +210,18 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
dataframe[col] = 50 if col == "buy_rsi_pred" else 80
|
||||
logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
|
||||
|
||||
- if "%-bb_width-period_20_SOL/USDT_5m" in dataframe.columns:
|
||||
- if dataframe["%-bb_width-period_20_SOL/USDT_5m"].std() > 0:
|
||||
- dataframe["%-bb_width-period_20_SOL/USDT_5m"] = (
|
||||
- dataframe["%-bb_width-period_20_SOL/USDT_5m"] - dataframe["%-bb_width-period_20_SOL/USDT_5m"].mean()
|
||||
- ) / dataframe["%-bb_width-period_20_SOL/USDT_5m"].std()
|
||||
- logger.info(f"%-bb_width-period_20 stats: {dataframe['%-bb_width-period_20_SOL/USDT_5m'].describe().to_string()}")
|
||||
+ # 调试特征分布
|
||||
+ if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
|
||||
+ if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
|
||||
+ dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
|
||||
+ dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
|
||||
+ ) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
|
||||
+ logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
|
||||
|
||||
def get_expected_columns(freqai_config: dict) -> list:
|
||||
indicators = ["rsi", "bb_width", "pct-change"]
|
||||
- periods = freqai_config.get("feature_parameters", {}).get("include_periods", [20])
|
||||
- pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT"])
|
||||
+ periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
|
||||
+ pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
|
||||
timeframes = freqai_config.get("include_timeframes", ["5m"])
|
||||
shifts = [0]
|
||||
expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
|
||||
@@ -242,11 +256,17 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
enter_long_conditions = [
|
||||
- qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
|
||||
+ qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
|
||||
df["tema"] > df["tema"].shift(1),
|
||||
df["volume"] > 0,
|
||||
- df["do_predict"] == 1
|
||||
+ df["do_predict"] == 1,
|
||||
+ df["up_or_down"] == 1
|
||||
]
|
||||
+ if enter_long_conditions:
|
||||
+ df.loc[
|
||||
+ reduce(lambda x, y: x & y, enter_long_conditions),
|
||||
+ ["enter_long", "enter_tag"]
|
||||
+ ] = (1, "long")
|
||||
df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
|
||||
df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
|
||||
df.loc[
|
||||
@@ -259,14 +279,16 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [
|
||||
- (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"] - 5)) |
|
||||
+ (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
|
||||
(df["close"] < df["close"].shift(1) * 0.98) |
|
||||
(df["close"] < df["bb_lowerband"]),
|
||||
df["volume"] > 0,
|
||||
- df["do_predict"] == 1
|
||||
+ df["do_predict"] == 1,
|
||||
+ df["up_or_down"] == 0
|
||||
]
|
||||
+ time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
|
||||
df.loc[
|
||||
- reduce(lambda x, y: x & y, exit_long_conditions),
|
||||
+ (reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
|
||||
"exit_long"
|
||||
] = 1
|
||||
return df
|
||||
@ -1,85 +0,0 @@
|
||||
> 请把当前策略的运行逻辑进行梳理和讲解
|
||||
|
||||
我来为您梳理和讲解这个策略的运行逻辑。这是一个基于 FreqAI 的机器学习交易策略，主要使用 RSI 指标和机器学习模型进行交易决策。以下是策略的主要逻辑：
|
||||
|
||||
### 1. 策略配置
|
||||
```python
|
||||
minimal_roi = {
|
||||
"0": 0.02,
|
||||
"7": 0.01,
|
||||
"13": 0.005,
|
||||
"60": 0
|
||||
}
|
||||
stoploss = 0.0
|
||||
trailing_stop = True
|
||||
```
|
||||
- 设置了静态的 ROI 目标
|
||||
- 启用了追踪止损
|
||||
|
||||
### 2. 特征工程
|
||||
策略通过多个方法构建特征：
|
||||
- `feature_engineering_expand_all`: 计算 RSI 和布林带宽度
|
||||
- `feature_engineering_expand_basic`: 计算价格变化百分比
|
||||
- `feature_engineering_standard`: 计算波动率和时间特征
|
||||
|
||||
### 3. 目标变量设置
|
||||
在 `set_freqai_targets` 方法中：
|
||||
- 使用未来 `label_period_candles` 周期的 RSI 值作为目标变量
|
||||
- 计算并标准化波动率特征
|
||||
|
||||
### 4. 指标计算
|
||||
`populate_indicators` 方法中计算了多个技术指标：
|
||||
- RSI
|
||||
- 波动率
|
||||
- TEMA（三重指数移动平均线）
|
||||
- 布林带下轨
|
||||
- 未来价格方向（up_or_down）
|
||||
|
||||
### 5. 机器学习模型
|
||||
使用 LightGBM 回归模型：
|
||||
```python
|
||||
freqai_info = {
|
||||
"model": "LightGBMRegressor",
|
||||
"feature_parameters": {...},
|
||||
"data_split_parameters": {...},
|
||||
"model_training_parameters": {...}
|
||||
}
|
||||
```
|
||||
- 模型预测买入和卖出的 RSI 值
|
||||
- 使用 SOL/USDT 作为相关交易对
|
||||
|
||||
### 6. 入场逻辑
|
||||
`populate_entry_trend` 方法中：
|
||||
- 当实际 RSI 上穿预测的买入 RSI 时
|
||||
- TEMA 指标向上
|
||||
- 交易量大于 0
|
||||
- 模型预测结果为 1
|
||||
- 未来价格方向为上涨
|
||||
|
||||
### 7. 出场逻辑
|
||||
`populate_exit_trend` 方法中：
|
||||
- 当实际 RSI 上穿预测的卖出 RSI 时
|
||||
- 或价格下跌超过 2%
|
||||
- 或价格跌破布林带下轨
|
||||
- 或持有时间超过 1 天
|
||||
|
||||
### 8. 交易确认
|
||||
`confirm_trade_entry` 方法：
|
||||
- 检查当前价格是否在可接受范围内
|
||||
- 记录交易确认信息
|
||||
|
||||
### 策略特点
|
||||
1. 机器学习驱动：使用 LightGBM 模型预测 RSI 值
|
||||
2. 多特征融合：结合技术指标、波动率和时间特征
|
||||
3. 风险控制：设置了 ROI 目标和追踪止损
|
||||
4. 动态调整：通过机器学习模型动态调整交易信号
|
||||
|
||||
### 运行流程
|
||||
1. 数据准备：获取历史数据，计算技术指标
|
||||
2. 特征工程：构建机器学习特征
|
||||
3. 模型训练：使用历史数据训练 LightGBM 模型
|
||||
4. 信号生成：根据模型预测结果生成交易信号
|
||||
5. 交易执行：根据入场和出场逻辑执行交易
|
||||
6. 风险控制：根据 ROI 和止损规则管理仓位
|
||||
|
||||
这个策略结合了传统技术分析和机器学习方法，通过模型预测来增强交易信号的准确性，同时保持了严格的风险控制机制。
|
||||
@ -1,64 +0,0 @@
|
||||
--- original_strategy.py
|
||||
+++ modified_strategy.py
|
||||
@@ -297,6 +297,9 @@ class FreqaiPrimer(IStrategy):
|
||||
dataframe["bb_lowerband"] = lowerband
|
||||
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||||
dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean()
|
||||
dataframe["volume_std_20"] = dataframe["volume"].rolling(20).std()
|
||||
dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
|
||||
+ # 计算15分钟ATR和3分钟EMA10、EMA20
|
||||
+ dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
|
||||
+ cuyuan
|
||||
+ dataframe_15m['atr15m'] = ta.ATR(dataframe_15m, timeperiod=14)
|
||||
+ dataframe['atr15m'] = dataframe_15m['atr15m'].reindex(dataframe.index, method="ffill")
|
||||
+ dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
||||
+ dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
|
||||
|
||||
@@ -390,8 +390,11 @@ class FreqaiPrimer(IStrategy):
|
||||
conditions = []
|
||||
|
||||
if "&-price_value_divergence" in dataframe.columns:
|
||||
cond1 = (dataframe["&-price_value_divergence"] > self.sell_threshold * 1.025)
|
||||
- cond2 = (dataframe["rsi"] > 75)
|
||||
+ rsi_threshold = 70 if trend_score > 50 else 65 # 动态RSI阈值
|
||||
+ cond2 = (dataframe["rsi"] > rsi_threshold)
|
||||
+ cond3 = (dataframe['ema10'] < dataframe['ema20']) # 短期均线交叉
|
||||
sell_condition = cond1 | cond2
|
||||
conditions.append(sell_condition)
|
||||
|
||||
@@ -442,6 +442,13 @@ class FreqaiPrimer(IStrategy):
|
||||
initial_stake_amount = trade.stake_amount / 3
|
||||
logger.debug(f"{pair} 首次入场金额: {initial_stake_amount:.2f}, 当前持仓金额: {trade.stake_amount:.2f}, "
|
||||
f"加仓次数: {trade.nr_of_successful_entries - 1}")
|
||||
|
||||
+ # 分批出场：短期涨幅触发
|
||||
+ short_term_return = (current_rate - dataframe['close'].shift(3).iloc[-1]) / dataframe['close'].shift(3).iloc[-1]
|
||||
+ if short_term_return >= 0.03: # 3%涨幅
|
||||
+ reduce_amount = -0.5 * trade.stake_amount # 卖出50%仓位
|
||||
+ logger.info(f"{pair} 短期涨幅 {short_term_return*100:.2f}%，卖出50%仓位 {abs(reduce_amount):.2f}")
|
||||
+ return (reduce_amount, f"Short-term gain {short_term_return*100:.2f}%")
|
||||
+
|
||||
# 加仓逻辑
|
||||
max_entry_adjustments = self.MAX_ENTRY_POSITION_ADJUSTMENT.value
|
||||
@@ -484,12 +484,11 @@ class FreqaiPrimer(IStrategy):
|
||||
# 追踪止损逻辑
|
||||
trailing_stop_start = self.TRAILING_STOP_START.value
|
||||
trailing_stop_distance = self.TRAILING_STOP_DISTANCE.value
|
||||
- # 使用 Sigmoid 映射调整追踪止损参数
|
||||
- sigmoid = 1 / (1 + np.exp(-0.1 * (trend_score - 50)))
|
||||
- trailing_factor = 0.8 + (1.2 - 0.8) * sigmoid # 牛市(100) -> 1.2, 熊市(0) -> 0.8
|
||||
- distance_factor = 0.7 + (1.5 - 0.7) * sigmoid # 牛市(100) -> 1.5, 熊市(0) -> 0.7
|
||||
- trailing_stop_start *= trailing_factor
|
||||
- trailing_stop_distance *= distance_factor
|
||||
+ # 使用15分钟ATR动态调整止损距离
|
||||
+ atr15m = dataframe['atr15m'].iloc[-1]
|
||||
+ trailing_stop_distance = min(max(2 * atr15m / current_rate, 0.01), 0.05) # 2倍ATR，限制在0.01-0.05
|
||||
+ if trend_score > 50: # 强趋势时放宽止损
|
||||
+ trailing_stop_distance *= 1.2
|
||||
+ trailing_stop_price = max_rate * (1 - trailing_stop_distance)
|
||||
|
||||
if profit_ratio >= trailing_stop_start and not self.trailing_stop_enabled:
|
||||
self.trailing_stop_enabled = True
|
||||
trade.adjust_min_max_rates(current_rate, current_rate)
|
||||
logger.info(f"{pair} 价格上涨超过 {trailing_stop_start*100:.1f}%，启动追踪止损")
|
||||
return None
|
||||
@ -1,77 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
<<<<<<< Updated upstream
|
||||
"add_position_callback": 0.039,
|
||||
"bb_length": 23,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 2.2,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 117,
|
||||
"h1_max_consecutive_candles": 2,
|
||||
"h1_rapid_rise_threshold": 0.059,
|
||||
"max_entry_adjustments": 3,
|
||||
"min_condition_count": 4,
|
||||
"min_entry_interval_minutes": 42,
|
||||
"rsi_bull_threshold": 49,
|
||||
"rsi_length": 20,
|
||||
"rsi_oversold": 49,
|
||||
"stake_divisor": 3.267,
|
||||
"stochrsi_bull_threshold": 40,
|
||||
"stochrsi_neutral_threshold": 25,
|
||||
=======
|
||||
"add_position_callback": 0.055,
|
||||
"bb_length": 27,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 1.5,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 209,
|
||||
"h1_max_consecutive_candles": 3,
|
||||
"h1_rapid_rise_threshold": 0.113,
|
||||
"max_entry_adjustments": 5,
|
||||
"min_condition_count": 2,
|
||||
"min_entry_interval_minutes": 173,
|
||||
"rsi_bull_threshold": 51,
|
||||
"rsi_length": 18,
|
||||
"rsi_oversold": 31,
|
||||
"stake_divisor": 2.704,
|
||||
"stochrsi_bull_threshold": 30,
|
||||
"stochrsi_neutral_threshold": 20,
|
||||
>>>>>>> Stashed changes
|
||||
"volume_multiplier": 1.4
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
<<<<<<< Updated upstream
|
||||
"exit_volume_multiplier": 1.8,
|
||||
"roi_param_a": 0.046,
|
||||
"roi_param_c": 0.0005,
|
||||
"roi_param_k": 0.0073,
|
||||
"rsi_overbought": 70
|
||||
=======
|
||||
"exit_volume_multiplier": 2.6,
|
||||
"roi_param_a": 0.027,
|
||||
"roi_param_c": 0.0081,
|
||||
"roi_param_k": 0.007,
|
||||
"rsi_overbought": 54
|
||||
>>>>>>> Stashed changes
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-22 06:47:49.854659+00:00"
|
||||
}
|
||||
@ -1,50 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.043,
|
||||
"add_position_growth": 4.5,
|
||||
"add_position_multiplier": 1.15,
|
||||
"max_entry_adjustments": 4,
|
||||
"stake_divisor": 9.3,
|
||||
"bb_length": 26,
|
||||
"bb_lower_deviation": 1.01,
|
||||
"bb_std": 2.0,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 159,
|
||||
"h1_max_consecutive_candles": 2,
|
||||
"h1_rapid_rise_threshold": 0.118,
|
||||
"min_condition_count": 3,
|
||||
"rsi_bull_threshold": 45,
|
||||
"rsi_length": 12,
|
||||
"rsi_oversold": 30,
|
||||
"stochrsi_bull_threshold": 32,
|
||||
"stochrsi_neutral_threshold": 27,
|
||||
"volume_multiplier": 2.0
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 2.6,
|
||||
"roi_param_a": -5e-05,
|
||||
"roi_param_k": 117,
|
||||
"roi_param_t": 0.159,
|
||||
"rsi_overbought": 65
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-15 14:19:00.824138+00:00"
|
||||
}
|
||||
@ -1,50 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.043,
|
||||
"add_position_growth": 4.5,
|
||||
"add_position_multiplier": 1.15,
|
||||
"max_entry_adjustments": 4,
|
||||
"stake_divisor": 9.3,
|
||||
"bb_length": 26,
|
||||
"bb_lower_deviation": 1.01,
|
||||
"bb_std": 2.0,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 159,
|
||||
"h1_max_consecutive_candles": 2,
|
||||
"h1_rapid_rise_threshold": 0.118,
|
||||
"min_condition_count": 3,
|
||||
"rsi_bull_threshold": 45,
|
||||
"rsi_length": 12,
|
||||
"rsi_oversold": 30,
|
||||
"stochrsi_bull_threshold": 32,
|
||||
"stochrsi_neutral_threshold": 27,
|
||||
"volume_multiplier": 2.0
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 2.6,
|
||||
"roi_param_a": -5e-05,
|
||||
"roi_param_k": 117,
|
||||
"roi_param_t": 0.159,
|
||||
"rsi_overbought": 65
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-15 14:19:00.824138+00:00"
|
||||
}
|
||||
@ -1,50 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.043,
|
||||
"add_position_growth": 4.5,
|
||||
"add_position_multiplier": 1.15,
|
||||
"max_entry_adjustments": 4,
|
||||
"stake_divisor": 9.3,
|
||||
"bb_length": 20,
|
||||
"bb_lower_deviation": 1.01,
|
||||
"bb_std": 2.5,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 229,
|
||||
"h1_max_consecutive_candles": 1,
|
||||
"h1_rapid_rise_threshold": 0.052,
|
||||
"min_condition_count": 3,
|
||||
"rsi_bull_threshold": 45,
|
||||
"rsi_length": 20,
|
||||
"rsi_oversold": 30,
|
||||
"stochrsi_bull_threshold": 30,
|
||||
"stochrsi_neutral_threshold": 28,
|
||||
"volume_multiplier": 1.9
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 2.6,
|
||||
"roi_param_a": -0.00015,
|
||||
"roi_param_k": 67,
|
||||
"roi_param_t": 0.155,
|
||||
"rsi_overbought": 70
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-15 14:19:00.824138+00:00"
|
||||
}
|
||||
@ -1,50 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.043,
|
||||
"add_position_growth": 4.5,
|
||||
"add_position_multiplier": 1.15,
|
||||
"max_entry_adjustments": 4,
|
||||
"stake_divisor": 9.3,
|
||||
"bb_length": 26,
|
||||
"bb_lower_deviation": 1.01,
|
||||
"bb_std": 2.0,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 159,
|
||||
"h1_max_consecutive_candles": 2,
|
||||
"h1_rapid_rise_threshold": 0.118,
|
||||
"min_condition_count": 3,
|
||||
"rsi_bull_threshold": 45,
|
||||
"rsi_length": 12,
|
||||
"rsi_oversold": 30,
|
||||
"stochrsi_bull_threshold": 32,
|
||||
"stochrsi_neutral_threshold": 27,
|
||||
"volume_multiplier": 2.0
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 2.6,
|
||||
"roi_param_a": -5e-05,
|
||||
"roi_param_k": 117,
|
||||
"roi_param_t": 0.159,
|
||||
"rsi_overbought": 65
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-15 14:19:00.824138+00:00"
|
||||
}
|
||||
@ -1,48 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"stake_divisor": 2,
|
||||
"add_position_callback": 0.056,
|
||||
"bb_length": 22,
|
||||
"bb_lower_deviation": 1.03,
|
||||
"bb_std": 2.1,
|
||||
"bb_width_threshold": 0.01,
|
||||
"h1_max_candles": 223,
|
||||
"h1_max_consecutive_candles": 4,
|
||||
"h1_rapid_rise_threshold": 0.061,
|
||||
"max_entry_adjustments": 5,
|
||||
"min_condition_count": 2,
|
||||
"rsi_bull_threshold": 53,
|
||||
"rsi_length": 10,
|
||||
"rsi_oversold": 49,
|
||||
"stochrsi_bull_threshold": 40,
|
||||
"stochrsi_neutral_threshold": 20,
|
||||
"volume_multiplier": 1.4
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 1.9,
|
||||
"roi_param_a": 0.06,
|
||||
"roi_param_c": 0.0057,
|
||||
"roi_param_k": 0.0088,
|
||||
"rsi_overbought": 66
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-09-10 13:07:34.606541+00:00"
|
||||
}
|
||||
@ -1,48 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"stake_divisor": 2,
|
||||
"add_position_callback": 0.039,
|
||||
"bb_length": 23,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 1.7,
|
||||
"bb_width_threshold": 0.015,
|
||||
"h1_max_candles": 255,
|
||||
"h1_max_consecutive_candles": 2,
|
||||
"h1_rapid_rise_threshold": 0.054,
|
||||
"max_entry_adjustments": 5,
|
||||
"min_condition_count": 2,
|
||||
"rsi_bull_threshold": 54,
|
||||
"rsi_length": 16,
|
||||
"rsi_oversold": 38,
|
||||
"stochrsi_bull_threshold": 40,
|
||||
"stochrsi_neutral_threshold": 22,
|
||||
"volume_multiplier": 1.8
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 2.3,
|
||||
"roi_param_a": 0.011,
|
||||
"roi_param_c": 0.0057,
|
||||
"roi_param_k": 0.0086,
|
||||
"rsi_overbought": 70
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-09-10 13:07:34.606541+00:00"
|
||||
}
|
||||
@ -1,49 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.03,
|
||||
"bb_length": 24,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 2.3,
|
||||
"bb_width_threshold": 0.025,
|
||||
"h1_max_candles": 222,
|
||||
"h1_max_consecutive_candles": 4,
|
||||
"h1_rapid_rise_threshold": 0.057,
|
||||
"max_entry_adjustments": 4,
|
||||
"min_condition_count": 4,
|
||||
"min_entry_interval_minutes": 42,
|
||||
"rsi_bull_threshold": 55,
|
||||
"rsi_length": 19,
|
||||
"rsi_oversold": 32,
|
||||
"stake_divisor": 2,
|
||||
"stochrsi_bull_threshold": 37,
|
||||
"stochrsi_neutral_threshold": 29,
|
||||
"volume_multiplier": 1.5
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.99,
|
||||
"exit_volume_multiplier": 2.2,
|
||||
"roi_param_a": 0.064,
|
||||
"roi_param_c": 0.0005,
|
||||
"roi_param_k": 0.0053,
|
||||
"rsi_overbought": 67
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-21 05:20:34.018192+00:00"
|
||||
}
|
||||
@ -1,49 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.039,
|
||||
"bb_length": 23,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 2.2,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 117,
|
||||
"h1_max_consecutive_candles": 2,
|
||||
"h1_rapid_rise_threshold": 0.059,
|
||||
"max_entry_adjustments": 3,
|
||||
"min_condition_count": 4,
|
||||
"min_entry_interval_minutes": 42,
|
||||
"rsi_bull_threshold": 49,
|
||||
"rsi_length": 20,
|
||||
"rsi_oversold": 49,
|
||||
"stake_divisor": 3.267,
|
||||
"stochrsi_bull_threshold": 40,
|
||||
"stochrsi_neutral_threshold": 25,
|
||||
"volume_multiplier": 1.4
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 1.8,
|
||||
"roi_param_a": 0.046,
|
||||
"roi_param_c": 0.0005,
|
||||
"roi_param_k": 0.0073,
|
||||
"rsi_overbought": 70
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-21 05:20:34.018192+00:00"
|
||||
}
|
||||
@ -1,49 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.051,
|
||||
"bb_length": 29,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 2.0,
|
||||
"bb_width_threshold": 0.021,
|
||||
"h1_max_candles": 137,
|
||||
"h1_max_consecutive_candles": 4,
|
||||
"h1_rapid_rise_threshold": 0.108,
|
||||
"max_entry_adjustments": 4,
|
||||
"min_condition_count": 4,
|
||||
"min_entry_interval_minutes": 42,
|
||||
"rsi_bull_threshold": 51,
|
||||
"rsi_length": 14,
|
||||
"rsi_oversold": 50,
|
||||
"stake_divisor": 3.432,
|
||||
"stochrsi_bull_threshold": 37,
|
||||
"stochrsi_neutral_threshold": 24,
|
||||
"volume_multiplier": 1.4
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 1.7,
|
||||
"roi_param_a": 0.089,
|
||||
"roi_param_c": 0.0005,
|
||||
"roi_param_k": 0.0098,
|
||||
"rsi_overbought": 70
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-21 05:20:34.018192+00:00"
|
||||
}
|
||||
@ -1,48 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.044,
|
||||
"bb_length": 29,
|
||||
"bb_lower_deviation": 1.01,
|
||||
"bb_std": 2.8,
|
||||
"bb_width_threshold": 0.012,
|
||||
"h1_max_candles": 146,
|
||||
"h1_max_consecutive_candles": 3,
|
||||
"h1_rapid_rise_threshold": 0.127,
|
||||
"max_entry_adjustments": 2,
|
||||
"min_condition_count": 2,
|
||||
"rsi_bull_threshold": 50,
|
||||
"rsi_length": 12,
|
||||
"rsi_oversold": 34,
|
||||
"stake_divisor": 2,
|
||||
"stochrsi_bull_threshold": 32,
|
||||
"stochrsi_neutral_threshold": 27,
|
||||
"volume_multiplier": 1.6
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.99,
|
||||
"exit_volume_multiplier": 2.3,
|
||||
"roi_param_a": 0.093,
|
||||
"roi_param_c": 0.0029,
|
||||
"roi_param_k": 0.0047,
|
||||
"rsi_overbought": 58
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-20 05:30:26.508241+00:00"
|
||||
}
|
||||
@ -1,49 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.032,
|
||||
"bb_length": 10,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 2.6,
|
||||
"bb_width_threshold": 0.026,
|
||||
"h1_max_candles": 270,
|
||||
"h1_max_consecutive_candles": 1,
|
||||
"h1_rapid_rise_threshold": 0.05,
|
||||
"max_entry_adjustments": 2,
|
||||
"min_condition_count": 4,
|
||||
"min_entry_interval_minutes": 42,
|
||||
"rsi_bull_threshold": 54,
|
||||
"rsi_length": 19,
|
||||
"rsi_oversold": 48,
|
||||
"stake_divisor": 2.311,
|
||||
"stochrsi_bull_threshold": 36,
|
||||
"stochrsi_neutral_threshold": 25,
|
||||
"volume_multiplier": 1.2
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 1.5,
|
||||
"roi_param_a": 0.053,
|
||||
"roi_param_c": 0.0005,
|
||||
"roi_param_k": 0.0056,
|
||||
"rsi_overbought": 70
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-21 05:20:34.018192+00:00"
|
||||
}
|
||||
@ -1,49 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.039,
|
||||
"bb_length": 23,
|
||||
"bb_lower_deviation": 1.05,
|
||||
"bb_std": 2.2,
|
||||
"bb_width_threshold": 0.011,
|
||||
"h1_max_candles": 117,
|
||||
"h1_max_consecutive_candles": 2,
|
||||
"h1_rapid_rise_threshold": 0.059,
|
||||
"max_entry_adjustments": 3,
|
||||
"min_condition_count": 4,
|
||||
"min_entry_interval_minutes": 42,
|
||||
"rsi_bull_threshold": 49,
|
||||
"rsi_length": 20,
|
||||
"rsi_oversold": 49,
|
||||
"stake_divisor": 3.267,
|
||||
"stochrsi_bull_threshold": 40,
|
||||
"stochrsi_neutral_threshold": 25,
|
||||
"volume_multiplier": 1.4
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 1.8,
|
||||
"roi_param_a": 0.046,
|
||||
"roi_param_c": 0.0005,
|
||||
"roi_param_k": 0.0073,
|
||||
"rsi_overbought": 70
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-21 05:20:34.018192+00:00"
|
||||
}
|
||||
@ -1,49 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.0125,
|
||||
"trailing_stop_positive_offset": 0.045,
|
||||
"trailing_only_offset_is_reached": false
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"add_position_callback": 0.035,
|
||||
"bb_length": 26,
|
||||
"bb_lower_deviation": 1.04,
|
||||
"bb_std": 2.6,
|
||||
"bb_width_threshold": 0.012,
|
||||
"h1_max_candles": 189,
|
||||
"h1_max_consecutive_candles": 4,
|
||||
"h1_rapid_rise_threshold": 0.094,
|
||||
"max_entry_adjustments": 3,
|
||||
"min_condition_count": 4,
|
||||
"min_entry_interval_minutes": 42,
|
||||
"rsi_bull_threshold": 48,
|
||||
"rsi_length": 7,
|
||||
"rsi_oversold": 33,
|
||||
"stake_divisor": 3.349,
|
||||
"stochrsi_bull_threshold": 40,
|
||||
"stochrsi_neutral_threshold": 23,
|
||||
"volume_multiplier": 1.4
|
||||
},
|
||||
"sell": {
|
||||
"exit_bb_upper_deviation": 0.98,
|
||||
"exit_volume_multiplier": 3.0,
|
||||
"roi_param_a": 0.022,
|
||||
"roi_param_c": 0.0005,
|
||||
"roi_param_k": 0.0097,
|
||||
"rsi_overbought": 68
|
||||
},
|
||||
"protection": {}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-12-21 05:20:34.018192+00:00"
|
||||
}
|
||||
@ -1,35 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {},
|
||||
"stoploss": {
|
||||
"stoploss": -0.05
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"ADD_POSITION_THRESHOLD": -0.021,
|
||||
"BUY_THRESHOLD_MAX": -0.001,
|
||||
"BUY_THRESHOLD_MIN": -0.035,
|
||||
"COOLDOWN_PERIOD_MINUTES": 9,
|
||||
"MAX_ENTRY_POSITION_ADJUSTMENT": 3
|
||||
},
|
||||
"sell": {
|
||||
"EXIT_POSITION_RATIO": 0.472,
|
||||
"SELL_THRESHOLD_MAX": 0.065,
|
||||
"SELL_THRESHOLD_MIN": 0.002,
|
||||
"TRAILING_STOP_DISTANCE": 0.015,
|
||||
"TRAILING_STOP_START": 0.016
|
||||
},
|
||||
"protection": {},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.106,
|
||||
"trailing_stop_positive_offset": 0.196,
|
||||
"trailing_only_offset_is_reached": false
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-07-01 14:51:29.420394+00:00"
|
||||
}
|
||||
@ -1,696 +0,0 @@
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=UserWarning, module="pandas_ta")
|
||||
|
||||
import logging
|
||||
from typing import Optional
|
||||
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
|
||||
from pandas import DataFrame
|
||||
import pandas_ta as ta
|
||||
from freqtrade.persistence import Trade
|
||||
import numpy as np
|
||||
import datetime
|
||||
import math
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class FreqaiPrimer(IStrategy):
|
||||
# 策略参数 - 使用custom_roi替代minimal_roi字典
|
||||
loglevel = "warning"
|
||||
minimal_roi = {}
|
||||
|
||||
# 启用自定义ROI回调函数
|
||||
use_custom_roi = True
|
||||
|
||||
stoploss = -0.15 # 固定止损 -15% (大幅放宽止损以承受更大波动)
|
||||
trailing_stop = True
|
||||
trailing_stop_positive_offset = 0.005 # 追踪止损偏移量 0.5% (更容易触发跟踪止盈)
|
||||
|
||||
# 用于跟踪市场状态的数据框缓存
|
||||
_dataframe_cache = None
|
||||
|
||||
def __init__(self, config=None):
|
||||
"""初始化策略参数，调用父类初始化方法并接受config参数"""
|
||||
super().__init__(config) # 调用父类的初始化方法并传递config
|
||||
# 存储从配置文件加载的默认值
|
||||
self._trailing_stop_positive_default = 0.004 # 降低默认值以更容易触发跟踪止盈
|
||||
# 记录每个交易对最近一次实际允许入场的时间，用于入场间隔控制
|
||||
self._last_entry_time_by_pair = {}
|
||||
|
||||
@property
|
||||
def protections(self):
|
||||
"""
|
||||
保护机制配置
|
||||
基于最新Freqtrade规范，保护机制应定义在策略文件中而非配置文件
|
||||
"""
|
||||
return [
|
||||
{
|
||||
"method": "StoplossGuard",
|
||||
"lookback_period_candles": 60, # 3小时回看期（60根3分钟K线）
|
||||
"trade_limit": 2, # 最多2笔止损交易
|
||||
"stop_duration_candles": 60, # 暂停180分钟（60根3分钟K线）
|
||||
"only_per_pair": False # 仅针对单个币对
|
||||
},
|
||||
{
|
||||
"method": "CooldownPeriod",
|
||||
"stop_duration_candles": 2 # 6分钟冷却期（2根3分钟K线）
|
||||
},
|
||||
{
|
||||
"method": "MaxDrawdown",
|
||||
"lookback_period_candles": 48, # 2.4小时回看期
|
||||
"trade_limit": 4, # 4笔交易限制
|
||||
"stop_duration_candles": 24, # 72分钟暂停（24根3分钟K线）
|
||||
"max_allowed_drawdown": 0.20 # 20%最大回撤容忍度
|
||||
}
|
||||
]
|
||||
|
||||
@property
|
||||
def trailing_stop_positive(self):
|
||||
"""根据市场状态动态调整跟踪止盈参数"""
|
||||
# 获取当前市场状态
|
||||
if self._dataframe_cache is not None and len(self._dataframe_cache) > 0:
|
||||
current_state = self._dataframe_cache['market_state'].iloc[-1]
|
||||
if current_state == 'strong_bull':
|
||||
return 0.007 # 强劲牛市中降低跟踪止盈，让利润奔跑
|
||||
elif current_state == 'weak_bull':
|
||||
return 0.005 # 弱势牛市中保持较低的跟踪止盈
|
||||
return self._trailing_stop_positive_default # 返回默认值
|
||||
|
||||
@trailing_stop_positive.setter
|
||||
def trailing_stop_positive(self, value):
|
||||
"""设置trailing_stop_positive的默认值"""
|
||||
self._trailing_stop_positive_default = value
|
||||
|
||||
timeframe = "3m" # 主时间框架为 3 分钟
|
||||
can_short = False # 禁用做空
|
||||
|
||||
# 自定义指标参数 - 使用Hyperopt可优化参数
|
||||
bb_length = IntParameter(10, 30, default=20, optimize=True, load=True, space='buy')
|
||||
bb_std = DecimalParameter(1.5, 3.0, decimals=1, default=2.0, optimize=True, load=True, space='buy')
|
||||
rsi_length = IntParameter(7, 21, default=14, optimize=True, load=True, space='buy')
|
||||
rsi_oversold = IntParameter(30, 50, default=42, optimize=True, load=True, space='buy')
|
||||
|
||||
|
||||
|
||||
# 入场条件阈值参数
|
||||
bb_lower_deviation = DecimalParameter(1.01, 1.05, decimals=2, default=1.03, optimize=True, load=True, space='buy')
|
||||
rsi_bull_threshold = IntParameter(45, 55, default=50, optimize=True, load=True, space='buy')
|
||||
stochrsi_bull_threshold = IntParameter(30, 40, default=35, optimize=True, load=True, space='buy')
|
||||
stochrsi_neutral_threshold = IntParameter(20, 30, default=25, optimize=True, load=True, space='buy')
|
||||
volume_multiplier = DecimalParameter(1.2, 2.0, decimals=1, default=1.5, optimize=True, load=True, space='buy')
|
||||
bb_width_threshold = DecimalParameter(0.01, 0.03, decimals=3, default=0.02, optimize=True, load=True, space='buy')
|
||||
min_condition_count = IntParameter(2, 4, default=3, optimize=True, load=True, space='buy')
|
||||
|
||||
# 剧烈拉升检测参数 - 使用Hyperopt可优化参数
|
||||
h1_max_candles = IntParameter(100, 300, default=200, optimize=True, load=True, space='buy')
|
||||
h1_rapid_rise_threshold = DecimalParameter(0.05, 0.15, decimals=3, default=0.11, optimize=True, load=True, space='buy')
|
||||
h1_max_consecutive_candles = IntParameter(1, 4, default=2, optimize=True, load=True, space='buy')
|
||||
# 入场间隔控制参数（单位：分钟）
|
||||
min_entry_interval_minutes = IntParameter(30, 180, default=60, optimize=True, load=True, space='buy')
|
||||
# 定义可优化参数
|
||||
max_entry_adjustments = IntParameter(2, 5, default=3, optimize=True, load=True, space='buy') # 最大加仓次数
|
||||
add_position_callback = DecimalParameter(0.03, 0.06, decimals=3, default=0.047, optimize=True, load=True, space='buy') # 加仓回调百分比
|
||||
stake_divisor = DecimalParameter(2.0, 4.0, default=2.3, optimize=True, load=True, space='buy') # 加仓金额分母
|
||||
|
||||
# 动态ROI参数 - 指数衰减函数参数 (ROI(t) = a·e^(-k·t) + c)
|
||||
roi_param_a = DecimalParameter(0.01, 0.1, decimals=3, default=0.05, optimize=True, load=True, space='sell') # 初始ROI水平
|
||||
roi_param_k = DecimalParameter(0.001, 0.01, decimals=4, default=0.003, optimize=True, load=True, space='sell') # 衰减速率
|
||||
roi_param_c = DecimalParameter(0, 0.01, decimals=4, default=0.005, optimize=True, load=True, space='sell') # 最低ROI水平
|
||||
# 出场条件阈值参数
|
||||
exit_bb_upper_deviation = DecimalParameter(0.98, 1.02, decimals=2, default=1.0, optimize=True, load=True, space='sell')
|
||||
exit_volume_multiplier = DecimalParameter(1.5, 3.0, decimals=1, default=2.0, optimize=True, load=True, space='sell')
|
||||
|
||||
rsi_overbought = IntParameter(50, 70, default=58, optimize=True, load=True, space='sell')
|
||||
|
||||
|
||||
def informative_pairs(self):
|
||||
pairs = self.dp.current_whitelist()
|
||||
return [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs]
|
||||
|
||||
def _validate_dataframe_columns(self, dataframe: DataFrame, required_columns: list, metadata: dict):
|
||||
"""
|
||||
验证数据框中是否包含所有需要的列。
|
||||
如果缺少列，则记录警告日志。
|
||||
"""
|
||||
missing_columns = [col for col in required_columns if col not in dataframe.columns]
|
||||
if missing_columns:
|
||||
logger.warning(f"[{metadata['pair']}] 数据框中缺少以下列: {missing_columns}")
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# 计算 3m 周期的指标
|
||||
bb_length_value = self.bb_length.value
|
||||
bb_std_value = self.bb_std.value
|
||||
rsi_length_value = self.rsi_length.value
|
||||
|
||||
bb_3m = ta.bbands(dataframe['close'], length=bb_length_value, std=bb_std_value)
|
||||
dataframe['bb_lower_3m'] = bb_3m[f'BBL_{bb_length_value}_{bb_std_value}']
|
||||
dataframe['bb_upper_3m'] = bb_3m[f'BBU_{bb_length_value}_{bb_std_value}']
|
||||
dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=rsi_length_value)
|
||||
|
||||
# 新增 StochRSI 指标
|
||||
stochrsi_3m = ta.stochrsi(dataframe['close'], length=rsi_length_value, rsi_length=rsi_length_value)
|
||||
dataframe['stochrsi_k_3m'] = stochrsi_3m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
|
||||
dataframe['stochrsi_d_3m'] = stochrsi_3m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
|
||||
|
||||
# 新增 MACD 指标
|
||||
macd_3m = ta.macd(dataframe['close'], fast=12, slow=26, signal=9)
|
||||
dataframe['macd_3m'] = macd_3m['MACD_12_26_9']
|
||||
dataframe['macd_signal_3m'] = macd_3m['MACDs_12_26_9']
|
||||
dataframe['macd_hist_3m'] = macd_3m['MACDh_12_26_9']
|
||||
|
||||
# 计算3m时间框架的EMA50和EMA200
|
||||
dataframe['ema_50_3m'] = ta.ema(dataframe['close'], length=50)
|
||||
dataframe['ema_200_3m'] = ta.ema(dataframe['close'], length=200)
|
||||
|
||||
# 成交量过滤
|
||||
dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
|
||||
|
||||
# 计算 ATR 用于动态止损和退出
|
||||
dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
|
||||
|
||||
# 获取 15m 数据
|
||||
df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
|
||||
df_15m['rsi_15m'] = ta.rsi(df_15m['close'], length=rsi_length_value)
|
||||
# 计算15m时间框架的EMA50和EMA200
|
||||
df_15m['ema_50_15m'] = ta.ema(df_15m['close'], length=50)
|
||||
df_15m['ema_200_15m'] = ta.ema(df_15m['close'], length=200)
|
||||
|
||||
# 新增 StochRSI 指标
|
||||
stochrsi_15m = ta.stochrsi(df_15m['close'], length=rsi_length_value, rsi_length=rsi_length_value)
|
||||
df_15m['stochrsi_k_15m'] = stochrsi_15m[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
|
||||
df_15m['stochrsi_d_15m'] = stochrsi_15m[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
|
||||
|
||||
# 新增 MACD 指标
|
||||
macd_15m = ta.macd(df_15m['close'], fast=12, slow=26, signal=9)
|
||||
df_15m['macd_15m'] = macd_15m['MACD_12_26_9']
|
||||
df_15m['macd_signal_15m'] = macd_15m['MACDs_12_26_9']
|
||||
df_15m['macd_hist_15m'] = macd_15m['MACDh_12_26_9']
|
||||
|
||||
# 将 15m 数据重新索引到主时间框架 (3m)
|
||||
df_15m = df_15m.set_index('date').reindex(dataframe['date']).reset_index()
|
||||
df_15m = df_15m.rename(columns={'index': 'date'})
|
||||
df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']].fillna(method='ffill')
|
||||
|
||||
# 合并 15m 数据
|
||||
dataframe = dataframe.merge(df_15m, how='left', on='date')
|
||||
|
||||
# 获取 1h 数据
|
||||
df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
|
||||
|
||||
# 计算 1h 布林带
|
||||
bb_1h = ta.bbands(df_1h['close'], length=bb_length_value, std=bb_std_value)
|
||||
df_1h['bb_lower_1h'] = bb_1h[f'BBL_{bb_length_value}_{bb_std_value}']
|
||||
df_1h['bb_upper_1h'] = bb_1h[f'BBU_{bb_length_value}_{bb_std_value}']
|
||||
|
||||
# 计算 1h RSI 和 EMA
|
||||
df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=rsi_length_value)
|
||||
df_1h['ema_50_1h'] = ta.ema(df_1h['close'], length=50) # 1h 50周期EMA
|
||||
df_1h['ema_200_1h'] = ta.ema(df_1h['close'], length=200) # 1h 200周期EMA
|
||||
df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势
|
||||
|
||||
# 新增 StochRSI 指标
|
||||
stochrsi_1h = ta.stochrsi(df_1h['close'], length=rsi_length_value, rsi_length=rsi_length_value)
|
||||
df_1h['stochrsi_k_1h'] = stochrsi_1h[f'STOCHRSIk_{rsi_length_value}_{rsi_length_value}_3_3']
|
||||
df_1h['stochrsi_d_1h'] = stochrsi_1h[f'STOCHRSId_{rsi_length_value}_{rsi_length_value}_3_3']
|
||||
|
||||
# 新增 MACD 指标
|
||||
macd_1h = ta.macd(df_1h['close'], fast=12, slow=26, signal=9)
|
||||
df_1h['macd_1h'] = macd_1h['MACD_12_26_9']
|
||||
df_1h['macd_signal_1h'] = macd_1h['MACDs_12_26_9']
|
||||
df_1h['macd_hist_1h'] = macd_1h['MACDh_12_26_9']
|
||||
|
||||
# 验证 MACD 列是否正确生成
|
||||
#logger.info(f"[{metadata['pair']}] 1小时 MACD 列: {list(macd_1h.columns)}")
|
||||
|
||||
# 确保 StochRSI 指标已正确计算
|
||||
# 将 1h 数据重新索引到主时间框架 (3m)，并填充缺失值
|
||||
df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().bfill().reset_index()
|
||||
df_1h = df_1h.rename(columns={'index': 'date'})
|
||||
# Include macd_1h and macd_signal_1h in the column selection
|
||||
df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h']].ffill()
|
||||
|
||||
# Validate that all required columns are present
|
||||
required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
|
||||
'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
|
||||
'macd_1h', 'macd_signal_1h']
|
||||
missing_columns = [col for col in required_columns if col not in df_1h.columns]
|
||||
if missing_columns:
|
||||
logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
|
||||
raise KeyError(f"缺少以下列: {missing_columns}")
|
||||
|
||||
# 确保所有需要的列都被合并
|
||||
required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
|
||||
'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
|
||||
'macd_1h', 'macd_signal_1h']
|
||||
|
||||
# 验证所需列是否存在
|
||||
missing_columns = [col for col in required_columns if col not in df_1h.columns]
|
||||
if missing_columns:
|
||||
logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
|
||||
raise KeyError(f"缺少以下列: {missing_columns}")
|
||||
|
||||
df_1h = df_1h[required_columns] # 确保包含 macd_1h 和 macd_signal_1h
|
||||
|
||||
# 合并 1h 数据
|
||||
dataframe = dataframe.merge(df_1h, how='left', on='date').fillna(method='ffill')
|
||||
|
||||
# 验证合并后的列
|
||||
#logger.info(f"[{metadata['pair']}] 合并后的数据框列名: {list(dataframe.columns)}")
|
||||
|
||||
# K线形态：看涨吞没
|
||||
dataframe['bullish_engulfing'] = (
|
||||
(dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
|
||||
(dataframe['close'] > dataframe['open']) &
|
||||
(dataframe['close'] > dataframe['open'].shift(1)) &
|
||||
(dataframe['open'] < dataframe['close'].shift(1))
|
||||
)
|
||||
|
||||
# 计算各时间框架的趋势状态（牛/熊）
|
||||
# 3m时间框架：ema50下穿ema200为熊，上穿为牛
|
||||
dataframe['trend_3m'] = np.where(dataframe['ema_50_3m'] > dataframe['ema_200_3m'], 1, 0)
|
||||
|
||||
# 15m时间框架：ema50下穿ema200为熊，上穿为牛
|
||||
dataframe['trend_15m'] = np.where(dataframe['ema_50_15m'] > dataframe['ema_200_15m'], 1, 0)
|
||||
|
||||
# 1h时间框架：ema50下穿ema200为熊，上穿为牛
|
||||
dataframe['trend_1h_ema'] = np.where(dataframe['ema_50_1h'] > dataframe['ema_200_1h'], 1, 0)
|
||||
|
||||
# 计算熊牛得分（0-100）
|
||||
# 权重：3m熊牛权重10，15m熊牛权重35，1h熊牛权重65
|
||||
# 计算加权得分
|
||||
dataframe['market_score'] = (
|
||||
dataframe['trend_3m'] * 10 +
|
||||
dataframe['trend_15m'] * 35 +
|
||||
dataframe['trend_1h_ema'] * 65
|
||||
)
|
||||
|
||||
# 确保得分在0-100范围内
|
||||
dataframe['market_score'] = dataframe['market_score'].clip(lower=0, upper=100)
|
||||
|
||||
# 根据得分分类市场状态
|
||||
dataframe['market_state'] = 'neutral'
|
||||
dataframe.loc[dataframe['market_score'] > 70, 'market_state'] = 'strong_bull'
|
||||
dataframe.loc[(dataframe['market_score'] > 50) & (dataframe['market_score'] <= 70), 'market_state'] = 'weak_bull'
|
||||
dataframe.loc[(dataframe['market_score'] >= 30) & (dataframe['market_score'] <= 50), 'market_state'] = 'neutral'
|
||||
dataframe.loc[(dataframe['market_score'] > 10) & (dataframe['market_score'] < 30), 'market_state'] = 'weak_bear'
|
||||
dataframe.loc[dataframe['market_score'] <= 10, 'market_state'] = 'strong_bear'
|
||||
|
||||
# 创建一个使用前一行市场状态的列，避免在populate_entry_trend中使用iloc[-1]
|
||||
dataframe['prev_market_state'] = dataframe['market_state'].shift(1)
|
||||
# 为第一行设置默认值
|
||||
dataframe['prev_market_state'].fillna('neutral', inplace=True)
|
||||
|
||||
# 记录当前的市场状态
|
||||
if len(dataframe) > 0:
|
||||
current_score = dataframe['market_score'].iloc[-1]
|
||||
current_state = dataframe['market_state'].iloc[-1]
|
||||
#logger.info(f"[{metadata['pair']}] 熊牛得分: {current_score:.1f}, 市场状态: {current_state}")
|
||||
#logger.info(f"[{metadata['pair']}] 各时间框架趋势: 3m={'牛' if dataframe['trend_3m'].iloc[-1] == 1 else '熊'}, \
|
||||
# 15m={'牛' if dataframe['trend_15m'].iloc[-1] == 1 else '熊'}, \
|
||||
# 1h={'牛' if dataframe['trend_1h_ema'].iloc[-1] == 1 else '熊'}")
|
||||
|
||||
# 调试：打印指标值（最后 5 行），验证时间对齐
|
||||
print(f"Pair: {metadata['pair']}, Last 5 rows after reindexing:")
|
||||
print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h',
|
||||
'trend_3m', 'trend_15m', 'trend_1h_ema', 'market_score', 'market_state',
|
||||
'bullish_engulfing', 'volume', 'volume_ma']].tail(5))
|
||||
|
||||
# 打印最终数据框的列名以验证
|
||||
#logger.info(f"[{metadata['pair']}] 最终数据框列名: {list(dataframe.columns)}")
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# 出场信号基于趋势和量价关系
|
||||
# 条件1: 价格突破布林带上轨（使用可优化的偏差参数）
|
||||
breakout_condition = dataframe['close'] >= dataframe['bb_upper_1h'] * self.exit_bb_upper_deviation.value
|
||||
|
||||
# 条件2: 成交量显著放大（使用可优化的成交量乘数）
|
||||
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * self.exit_volume_multiplier.value
|
||||
|
||||
# 条件3: MACD 下降趋势
|
||||
macd_downward = dataframe['macd_1h'] < dataframe['macd_signal_1h']
|
||||
|
||||
# 条件4: RSI 进入超买区域（使用可优化的超买阈值）
|
||||
rsi_overbought = dataframe['rsi_1h'] > self.rsi_overbought.value
|
||||
|
||||
# 合并所有条件
|
||||
final_condition = breakout_condition | volume_spike | macd_downward | rsi_overbought
|
||||
|
||||
# 设置出场信号
|
||||
dataframe.loc[final_condition, 'exit_long'] = 1
|
||||
|
||||
# 增强调试信息
|
||||
#logger.info(f"[{metadata['pair']}] 出场条件检查:")
|
||||
#logger.info(f" - 价格突破布林带上轨: {breakout_condition.sum()} 次")
|
||||
#logger.info(f" - 成交量显著放大: {volume_spike.sum()} 次")
|
||||
#logger.info(f" - MACD 下降趋势: {macd_downward.sum()} 次")
|
||||
#logger.info(f" - RSI 超买: {rsi_overbought.sum()} 次")
|
||||
#logger.info(f" - 最终条件: {final_condition.sum()} 次")
|
||||
#logger.info(f" - 使用参数: exit_bb_upper_deviation={self.exit_bb_upper_deviation.value}, exit_volume_multiplier={self.exit_volume_multiplier.value}, rsi_overbought={self.rsi_overbought.value}")
|
||||
|
||||
# 日志记录
|
||||
if dataframe['exit_long'].sum() > 0:
|
||||
logger.info(f"[{metadata['pair']}] 触发出场信号数量: {dataframe['exit_long'].sum()}")
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# 确保prev_market_state列存在
|
||||
if 'prev_market_state' not in dataframe.columns:
|
||||
dataframe['prev_market_state'] = 'neutral'
|
||||
|
||||
# 条件1: 价格接近布林带下轨（允许一定偏差）
|
||||
close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * self.bb_lower_deviation.value) # 可优化偏差
|
||||
|
||||
# 条件2: RSI 不高于阈值（根据市场状态动态调整）
|
||||
# 为每一行创建动态阈值
|
||||
rsi_condition_1h = dataframe.apply(lambda row:
|
||||
row['rsi_1h'] < self.rsi_bull_threshold.value if row['prev_market_state'] in ['strong_bull', 'weak_bull'] else row['rsi_1h'] < self.rsi_oversold.value,
|
||||
axis=1)
|
||||
|
||||
# 条件3: StochRSI 处于超卖区域（根据市场状态动态调整）
|
||||
stochrsi_condition_1h = dataframe.apply(lambda row:
|
||||
(row['stochrsi_k_1h'] < self.stochrsi_bull_threshold.value and row['stochrsi_d_1h'] < self.stochrsi_bull_threshold.value) if row['prev_market_state'] in ['strong_bull', 'weak_bull']
|
||||
else (row['stochrsi_k_1h'] < self.stochrsi_neutral_threshold.value and row['stochrsi_d_1h'] < self.stochrsi_neutral_threshold.value),
|
||||
axis=1)
|
||||
|
||||
# 条件4: MACD 上升趋势
|
||||
macd_condition_1h = dataframe['macd_1h'] > dataframe['macd_signal_1h']
|
||||
|
||||
# 条件5: 成交量显著放大（可选条件）
|
||||
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * self.volume_multiplier.value
|
||||
|
||||
# 条件6: 布林带宽度过滤（避免窄幅震荡）
|
||||
bb_width = (dataframe['bb_upper_1h'] - dataframe['bb_lower_1h']) / dataframe['close']
|
||||
bb_width_condition = bb_width > self.bb_width_threshold.value # 可优化的布林带宽度阈值
|
||||
|
||||
# 辅助条件: 3m 和 15m 趋势确认（允许部分时间框架不一致）
|
||||
trend_confirmation = (dataframe['trend_3m'] == 1) | (dataframe['trend_15m'] == 1)
|
||||
|
||||
# 合并所有条件（减少强制性条件）
|
||||
# 至少满足多个条件中的一定数量
|
||||
condition_count = (
|
||||
close_to_bb_lower_1h.astype(int) +
|
||||
rsi_condition_1h.astype(int) +
|
||||
stochrsi_condition_1h.astype(int) +
|
||||
macd_condition_1h.astype(int) +
|
||||
(volume_spike | bb_width_condition).astype(int) + # 成交量或布林带宽度满足其一即可
|
||||
trend_confirmation.astype(int)
|
||||
)
|
||||
final_condition = condition_count >= self.min_condition_count.value
|
||||
|
||||
# 设置入场信号
|
||||
dataframe.loc[final_condition, 'enter_long'] = 1
|
||||
|
||||
# 设置入场价格：下调2%（使用乘法避免除零风险）
|
||||
# Freqtrade 会优先使用 enter_price 列作为限价单价格
|
||||
dataframe.loc[final_condition, 'enter_price'] = dataframe.loc[final_condition, 'close'] * 0.987
|
||||
|
||||
# 增强调试信息
|
||||
#logger.info(f"[{metadata['pair']}] 入场条件检查:")
|
||||
#logger.info(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
|
||||
#logger.info(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
|
||||
#logger.info(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
|
||||
#logger.info(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
|
||||
#logger.info(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
|
||||
#logger.info(f" - 趋势确认: {trend_confirmation.sum()} 次")
|
||||
#logger.info(f" - 最终条件: {final_condition.sum()} 次")
|
||||
# 在populate_entry_trend方法末尾添加
|
||||
# 计算条件间的相关性
|
||||
conditions = DataFrame({
|
||||
'close_to_bb': close_to_bb_lower_1h,
|
||||
'rsi': rsi_condition_1h,
|
||||
'stochrsi': stochrsi_condition_1h,
|
||||
'macd': macd_condition_1h,
|
||||
'vol_bb': (volume_spike | bb_width_condition),
|
||||
'trend': trend_confirmation
|
||||
})
|
||||
correlation = conditions.corr().mean().mean()
|
||||
#logger.info(f"[{metadata['pair']}] 条件平均相关性: {correlation:.2f}")
|
||||
# 日志记录
|
||||
if dataframe['enter_long'].sum() > 0:
|
||||
logger.info(f"[{metadata['pair']}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
|
||||
|
||||
return dataframe
|
||||
|
||||
def detect_h1_rapid_rise(self, pair: str) -> bool:
|
||||
"""
|
||||
检测1小时K线图上的剧烈拉升情况（轻量级版本，用于confirm_trade_entry）
|
||||
参数:
|
||||
- pair: 交易对
|
||||
返回:
|
||||
- bool: 是否处于不稳固区域
|
||||
"""
|
||||
try:
|
||||
# 获取1小时K线数据
|
||||
df_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h')
|
||||
|
||||
# 获取当前优化参数值
|
||||
max_candles = self.h1_max_candles.value
|
||||
rapid_rise_threshold = self.h1_rapid_rise_threshold.value
|
||||
max_consecutive_candles = self.h1_max_consecutive_candles.value
|
||||
|
||||
# 确保有足够的K线数据
|
||||
if len(df_1h) < max_candles:
|
||||
logger.warning(f"[{pair}] 1h K线数据不足 {max_candles} 根，当前只有 {len(df_1h)} 根，无法完整检测剧烈拉升")
|
||||
return False
|
||||
|
||||
# 获取最近的K线
|
||||
recent_data = df_1h.iloc[-max_candles:].copy()
|
||||
|
||||
# 检查连续最多几根K线内的最大涨幅
|
||||
rapid_rise_detected = False
|
||||
max_rise = 0
|
||||
|
||||
for i in range(len(recent_data) - max_consecutive_candles + 1):
|
||||
window_data = recent_data.iloc[i:i + max_consecutive_candles]
|
||||
window_low = window_data['low'].min()
|
||||
window_high = window_data['high'].max()
|
||||
|
||||
# 计算区间内的最大涨幅
|
||||
if window_low > 0:
|
||||
rise_percentage = (window_high - window_low) / window_low
|
||||
if rise_percentage > max_rise:
|
||||
max_rise = rise_percentage
|
||||
|
||||
# 检查是否超过阈值
|
||||
if rise_percentage >= rapid_rise_threshold:
|
||||
rapid_rise_detected = True
|
||||
#logger.info(f"[{pair}] 检测到剧烈拉升: 从 {window_low:.2f} 到 {window_high:.2f} ({rise_percentage:.2%}) 在 {max_consecutive_candles} 根K线内")
|
||||
break
|
||||
|
||||
current_price = recent_data['close'].iloc[-1]
|
||||
#logger.info(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if rapid_rise_detected else '稳固'}")
|
||||
#logger.info(f"[{pair}] 最近最大涨幅: {max_rise:.2%}")
|
||||
|
||||
return rapid_rise_detected
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[{pair}] 剧烈拉升检测过程中发生错误: {str(e)}")
|
||||
return False
|
||||
|
||||
def confirm_trade_entry(
|
||||
self,
|
||||
pair: str,
|
||||
order_type: str,
|
||||
amount: float,
|
||||
rate: float,
|
||||
time_in_force: str,
|
||||
current_time: datetime,
|
||||
entry_tag: str | None,
|
||||
side: str,
|
||||
**kwargs,
|
||||
) -> bool:
|
||||
"""
|
||||
交易买入前的确认函数，用于最终决定是否执行交易
|
||||
此处实现剧烈拉升检查逻辑
|
||||
"""
|
||||
# 默认允许交易
|
||||
allow_trade = True
|
||||
|
||||
# 仅对多头交易进行检查
|
||||
if side == 'long':
|
||||
# 检查是否处于剧烈拉升的不稳固区域
|
||||
is_unstable_region = self.detect_h1_rapid_rise(pair)
|
||||
if is_unstable_region:
|
||||
#logger.info(f"[{pair}] 由于检测到剧烈拉升，取消入场交易")
|
||||
allow_trade = False
|
||||
|
||||
# ==================== 入场间隔控制：每个交易对至少间隔 20 分钟（可通过 Hyperopt 调整） ====================
|
||||
if allow_trade:
|
||||
min_interval_minutes = int(self.min_entry_interval_minutes.value)
|
||||
min_interval = datetime.timedelta(minutes=min_interval_minutes)
|
||||
# 初始化缓存字典（防御性，避免属性不存在）
|
||||
if not hasattr(self, "_last_entry_time_by_pair"):
|
||||
self._last_entry_time_by_pair = {}
|
||||
last_time = self._last_entry_time_by_pair.get(pair)
|
||||
if last_time is not None and (current_time - last_time) < min_interval:
|
||||
logger.info(
|
||||
f"[{pair}] 距离上次入场仅 {(current_time - last_time).total_seconds() / 60:.1f} 分钟，"
|
||||
f"未达到 {min_interval.total_seconds() / 60:.0f} 分钟间隔，拒绝本次入场"
|
||||
)
|
||||
allow_trade = False
|
||||
else:
|
||||
# 只有本次允许入场时，才更新最近入场时间
|
||||
self._last_entry_time_by_pair[pair] = current_time
|
||||
|
||||
# 如果没有阻止因素，允许交易
|
||||
return allow_trade
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
|
||||
current_profit: float, **kwargs) -> float:
|
||||
# 动态止损基于ATR
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1]
|
||||
atr = last_candle['atr']
|
||||
|
||||
# 获取当前市场状态
|
||||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
|
||||
|
||||
# 更激进的渐进式止损策略
|
||||
if current_profit > 0.05: # 利润超过5%时
|
||||
return -3.0 * atr / current_rate # 更大幅扩大止损范围，让利润奔跑
|
||||
elif current_profit > 0.03: # 利润超过3%时
|
||||
return -2.5 * atr / current_rate # 更中等扩大止损范围
|
||||
elif current_profit > 0.01: # 利润超过1%时
|
||||
return -2.0 * atr / current_rate # 更轻微扩大止损范围
|
||||
|
||||
# 在强劲牛市中，即使小亏损也可以容忍更大回调
|
||||
if current_state == 'strong_bull' and current_profit > -0.01:
|
||||
return -1.5 * atr / current_rate
|
||||
|
||||
# 动态调整止损范围
|
||||
if current_profit > 0.05: # 利润超过5%时
|
||||
return -3.0 * atr / current_rate # 更大幅扩大止损范围，让利润奔跑
|
||||
elif current_profit > 0.03: # 利润超过3%时
|
||||
return -2.5 * atr / current_rate # 更中等扩大止损范围
|
||||
elif current_profit > 0.01: # 利润超过1%时
|
||||
return -2.0 * atr / current_rate # 更轻微扩大止损范围
|
||||
|
||||
# 在强劲牛市中，即使小亏损也可以容忍更大回调
|
||||
if current_state == 'strong_bull' and current_profit > -0.01:
|
||||
return -1.8 * atr / current_rate
|
||||
|
||||
if atr > 0:
|
||||
return -1.2 * atr / current_rate # 基础1.2倍ATR止损
|
||||
return self.stoploss
|
||||
|
||||
def custom_exit(self, pair: str, trade: 'Trade', current_time, current_rate: float,
|
||||
current_profit: float, **kwargs) -> float:
|
||||
"""
|
||||
结合指数衰减ROI逻辑的动态止盈函数
|
||||
- 根据交易持仓时间使用指数衰减公式计算动态止盈阈值
|
||||
- 考虑当前市场状态调整止盈策略
|
||||
- 仅支持多头交易
|
||||
"""
|
||||
# 只支持多头
|
||||
if trade.is_short:
|
||||
return 0.0
|
||||
|
||||
# 计算交易已持仓时间（分钟）
|
||||
trade_age_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
|
||||
|
||||
# 防止负数时间
|
||||
if trade_age_minutes < 0:
|
||||
trade_age_minutes = 0
|
||||
|
||||
# 获取Hyperopt优化的指数衰减参数值
|
||||
a = self.roi_param_a.value
|
||||
k = self.roi_param_k.value
|
||||
c = self.roi_param_c.value
|
||||
|
||||
# 应用指数衰减公式计算动态ROI阈值
|
||||
dynamic_roi_threshold = a * math.exp(-k * trade_age_minutes) + c
|
||||
|
||||
# 确保ROI阈值为正数
|
||||
dynamic_roi_threshold = max(dynamic_roi_threshold, 0.0)
|
||||
|
||||
# 获取当前市场状态以调整止盈策略
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
|
||||
|
||||
# 根据市场状态调整退出比例计算
|
||||
exit_ratio = 0.0
|
||||
|
||||
# 计算当前利润与动态ROI阈值的比值
|
||||
if dynamic_roi_threshold > 0:
|
||||
profit_ratio = current_profit / dynamic_roi_threshold
|
||||
else:
|
||||
profit_ratio = 0
|
||||
|
||||
# 根据市场状态和利润比值确定退出比例
|
||||
if profit_ratio >= 1.0:
|
||||
# 利润达到或超过动态ROI阈值
|
||||
if current_state == 'strong_bull':
|
||||
# 强劲牛市中，允许部分利润奔跑
|
||||
if profit_ratio < 1.5:
|
||||
exit_ratio = 0.5 # 达到ROI阈值时只退出50%
|
||||
else:
|
||||
exit_ratio = 0.8 # 利润显著高于阈值时退出80%
|
||||
elif current_state == 'weak_bull':
|
||||
# 弱牛市中，平衡利润和风险
|
||||
if profit_ratio < 1.2:
|
||||
exit_ratio = 0.6 # 达到ROI阈值时退出60%
|
||||
else:
|
||||
exit_ratio = 0.9 # 利润高于阈值时退出90%
|
||||
else:
|
||||
# 其他市场状态下，更保守的策略
|
||||
exit_ratio = 1.0 # 达到ROI阈值时全部退出
|
||||
|
||||
# 记录动态止盈决策
|
||||
#logger.info(f"[{pair}] 动态止盈: 持仓时间={trade_age_minutes:.1f}分钟, 当前利润={current_profit:.2%}, \
|
||||
# 动态ROI阈值={dynamic_roi_threshold:.4f}, 利润比值={profit_ratio:.2f}, \
|
||||
# 市场状态={current_state}, 退出比例={exit_ratio:.0%}")
|
||||
|
||||
# 返回应退出的比例（0.0表示不退出，1.0表示全部退出）
|
||||
return exit_ratio
|
||||
|
||||
|
||||
|
||||
def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float,
|
||||
current_profit: float, min_stake: float, max_stake: float, **kwargs) -> float:
|
||||
"""
|
||||
根据用户要求实现加仓逻辑
|
||||
- 加仓间隔设置为可优化参数 add_position_callback
|
||||
- 加仓额度为: (stake_amount / stake_divisor) ^ (加仓次数 - 1)
|
||||
"""
|
||||
# 获取当前交易对
|
||||
pair = trade.pair
|
||||
|
||||
# 获取当前交易的加仓次数
|
||||
entry_count = len(trade.orders) # 获取所有入场订单数量
|
||||
|
||||
# 如果已经达到最大加仓次数，则不再加仓
|
||||
if entry_count - 1 >= self.max_entry_adjustments.value:
|
||||
return 0.0
|
||||
|
||||
# 获取初始入场价格和当前价格的差值百分比
|
||||
initial_price = trade.open_rate
|
||||
if initial_price == 0:
|
||||
return 0.0
|
||||
price_diff_pct = (current_rate - initial_price) / initial_price
|
||||
|
||||
# 检查价格回调是否达到加仓间隔
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'neutral'
|
||||
|
||||
if price_diff_pct <= -self.add_position_callback.value and current_state not in ['bear', 'weak_bear']:
|
||||
# 计算初始入场金额
|
||||
initial_stake = trade.orders[0].cost # 第一笔订单的成本
|
||||
|
||||
# 计算加仓次数（从1开始计数）
|
||||
adjustment_count = entry_count - 1 # 已加仓次数
|
||||
|
||||
# 计算加仓金额: (initial_stake / stake_divisor) ^ (adjustment_count + 1)
|
||||
additional_stake = (initial_stake / self.stake_divisor.value) ** (adjustment_count + 1)
|
||||
|
||||
# 确保加仓金额在允许的范围内
|
||||
additional_stake = max(min_stake, min(additional_stake, max_stake - trade.stake_amount))
|
||||
|
||||
#logger.info(f"[{pair}] 触发加仓: 第{adjustment_count + 1}次加仓, 初始金额{initial_stake:.2f}, \
|
||||
# 加仓金额{additional_stake:.2f}, 价格差{price_diff_pct:.2%}, 当前利润{current_profit:.2%}")
|
||||
|
||||
return additional_stake
|
||||
|
||||
# 不符合加仓条件，返回0
|
||||
return 0.0
|
||||
@ -1,279 +0,0 @@
|
||||
import logging
|
||||
import numpy as np
|
||||
from functools import reduce
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from technical import qtpylib
|
||||
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class FreqaiPrimer(IStrategy):
|
||||
minimal_roi = {
|
||||
0: 0.135,
|
||||
9: 0.052,
|
||||
15: 0.007,
|
||||
60: 0
|
||||
}
|
||||
stoploss = -0.263
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.324
|
||||
trailing_stop_positive_offset = 0.411
|
||||
trailing_only_offset_is_reached = False
|
||||
max_open_trades = 4
|
||||
process_only_new_candles = True
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 40
|
||||
can_short = False
|
||||
|
||||
buy_rsi = IntParameter(low=10, high=50, default=30, space="buy", optimize=False, load=True)
|
||||
sell_rsi = IntParameter(low=50, high=90, default=70, space="sell", optimize=False, load=True)
|
||||
roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.135, space="roi", optimize=True, load=True)
|
||||
roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.052, space="roi", optimize=True, load=True)
|
||||
roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.007, space="roi", optimize=True, load=True)
|
||||
stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.263, space="stoploss", optimize=True, load=True)
|
||||
trailing_stop_positive_param = DecimalParameter(low=0.1, high=0.5, default=0.324, space="trailing", optimize=True, load=True)
|
||||
trailing_stop_positive_offset_param = DecimalParameter(low=0.2, high=0.6, default=0.411, space="trailing", optimize=True, load=True)
|
||||
|
||||
freqai_info = {
|
||||
"model": "LightGBMRegressor",
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m", "15m", "1h"],
|
||||
"include_corr_pairlist": [],
|
||||
"label_period_candles": 12,
|
||||
"include_shifted_candles": 3,
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": False,
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 200,
|
||||
"learning_rate": 0.05,
|
||||
"num_leaves": 31,
|
||||
"verbose": -1,
|
||||
},
|
||||
}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {},
|
||||
"subplots": {
|
||||
"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
|
||||
"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
|
||||
"&-stoploss": {"&-stoploss": {"color": "purple"}},
|
||||
"&-roi_0": {"&-roi_0": {"color": "orange"}},
|
||||
"do_predict": {"do_predict": {"color": "brown"}},
|
||||
},
|
||||
}
|
||||
|
||||
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
|
||||
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
|
||||
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
|
||||
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
|
||||
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
|
||||
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2)
|
||||
dataframe["bb_lowerband-period"] = bollinger["lower"]
|
||||
dataframe["bb_middleband-period"] = bollinger["mid"]
|
||||
dataframe["bb_upperband-period"] = bollinger["upper"]
|
||||
dataframe["%-bb_width-period"] = (
|
||||
dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
|
||||
) / dataframe["bb_middleband-period"]
|
||||
dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
|
||||
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
|
||||
dataframe["%-relative_volume-period"] = (
|
||||
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
|
||||
)
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0)
|
||||
dataframe = dataframe.ffill()
|
||||
dataframe = dataframe.fillna(0)
|
||||
return dataframe
|
||||
|
||||
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
dataframe["%-pct-change"] = dataframe["close"].pct_change()
|
||||
dataframe["%-raw_volume"] = dataframe["volume"]
|
||||
dataframe["%-raw_price"] = dataframe["close"]
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0)
|
||||
dataframe = dataframe.ffill()
|
||||
dataframe = dataframe.fillna(0)
|
||||
return dataframe
|
||||
|
||||
def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
if len(dataframe["close"]) < 20:
|
||||
logger.warning(f"数据不足 {len(dataframe)} 根 K 线，%-volatility 可能不完整")
|
||||
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
|
||||
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0)
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].ffill()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
|
||||
return dataframe
|
||||
|
||||
def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||||
logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
|
||||
if "close" not in dataframe.columns:
|
||||
logger.error("数据框缺少必要的 'close' 列")
|
||||
raise ValueError("数据框缺少必要的 'close' 列")
|
||||
|
||||
try:
|
||||
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
|
||||
if "%-volatility" not in dataframe.columns:
|
||||
logger.warning("缺少 %-volatility 列，强制重新生成")
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0)
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].ffill()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
|
||||
|
||||
# 移除 shift(-label_period)，改为使用当前及过去的数据
|
||||
dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||||
dataframe["&-buy_rsi"] = dataframe["&-buy_rsi"].rolling(window=label_period).mean().ffill().bfill()
|
||||
|
||||
for col in ["&-buy_rsi", "%-volatility"]:
|
||||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
|
||||
dataframe[col] = dataframe[col].ffill()
|
||||
dataframe[col] = dataframe[col].fillna(0)
|
||||
if dataframe[col].isna().any():
|
||||
logger.warning(f"目标列 {col} 仍包含 NaN，数据预览：\n{dataframe[col].tail(10)}")
|
||||
except Exception as e:
|
||||
logger.error(f"创建 FreqAI 目标失败：{str(e)}")
|
||||
raise
|
||||
|
||||
logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
|
||||
return dataframe
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
logger.info(f"处理交易对：{metadata['pair']}")
|
||||
logger.debug(f"输入特征列：{list(dataframe.columns)}")
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
logger.debug(f"FreqAI 输出特征列：{list(dataframe.columns)}")
|
||||
|
||||
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe["bb_lowerband"] = bollinger["lower"]
|
||||
dataframe["bb_middleband"] = bollinger["mid"]
|
||||
dataframe["bb_upperband"] = bollinger["upper"]
|
||||
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
|
||||
|
||||
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
|
||||
# 使用滚动窗口而非未来函数来生成 up_or_down 列
|
||||
dataframe["up_or_down"] = np.where(
|
||||
dataframe["close"].rolling(window=label_period).mean() > dataframe["close"], 1, 0
|
||||
)
|
||||
|
||||
if "&-buy_rsi" in dataframe.columns:
|
||||
if "%-volatility" not in dataframe.columns:
|
||||
logger.warning("缺少 %-volatility 列，强制重新生成")
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0)
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].ffill()
|
||||
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
|
||||
|
||||
dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
|
||||
dataframe["&-stoploss"] = self.stoploss - (dataframe["%-volatility"] * 5).clip(-0.05, 0.05)
|
||||
dataframe["&-roi_0"] = (dataframe["close"].rolling(window=label_period).mean() / dataframe["close"] - 1).clip(0, 0.2)
|
||||
|
||||
for col in ["&-buy_rsi", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
|
||||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
|
||||
dataframe[col] = dataframe[col].ffill()
|
||||
dataframe[col] = dataframe[col].fillna(0)
|
||||
|
||||
dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(5).mean().clip(10, 50)
|
||||
dataframe["sell_rsi_pred"] = dataframe["&-sell_rsi"].rolling(5).mean().clip(50, 90)
|
||||
dataframe["stoploss_pred"] = dataframe["&-stoploss"].clip(-0.35, -0.1)
|
||||
dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
|
||||
|
||||
for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred"]:
|
||||
if dataframe[col].isna().any():
|
||||
logger.warning(f"列 {col} 包含 NaN，填充为默认值")
|
||||
dataframe[col] = dataframe[col].ffill()
|
||||
dataframe[col] = dataframe[col].fillna(dataframe[col].mean())
|
||||
|
||||
dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
|
||||
dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.75).clip(0.02, 0.4)
|
||||
|
||||
self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
|
||||
self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
|
||||
self.stoploss = float(self.stoploss_param.value)
|
||||
self.minimal_roi = {
|
||||
0: float(self.roi_0.value),
|
||||
15: float(self.roi_15.value),
|
||||
30: float(self.roi_30.value),
|
||||
60: 0
|
||||
}
|
||||
self.trailing_stop_positive = float(self.trailing_stop_positive_param.value)
|
||||
self.trailing_stop_positive_offset = float(self.trailing_stop_positive_offset_param.value)
|
||||
|
||||
logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
|
||||
f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
|
||||
else:
|
||||
logger.warning(f"&-buy_rsi 列缺失，跳过 FreqAI 预测逻辑，检查 freqai.start 输出")
|
||||
|
||||
dataframe = dataframe.replace([np.inf, -np.inf], 0)
|
||||
dataframe = dataframe.ffill()
|
||||
dataframe = dataframe.fillna(0)
|
||||
|
||||
logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}")
|
||||
logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}")
|
||||
logger.debug(f"最终特征列：{list(dataframe.columns)}")
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
enter_long_conditions = [
|
||||
qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
|
||||
df["tema"] > df["tema"].shift(1),
|
||||
df["volume"] > 0,
|
||||
df["do_predict"] == 1,
|
||||
df["up_or_down"] == 1
|
||||
]
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions),
|
||||
["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [
|
||||
qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"]),
|
||||
(df["close"] < df["close"].shift(1) * 0.97),
|
||||
df["volume"] > 0,
|
||||
df["do_predict"] == 1,
|
||||
df["up_or_down"] == 0
|
||||
]
|
||||
if exit_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, exit_long_conditions),
|
||||
"exit_long"
|
||||
] = 1
|
||||
return df
|
||||
|
||||
def confirm_trade_entry(
|
||||
self, pair: str, order_type: str, amount: float, rate: float,
|
||||
time_in_force: str, current_time, entry_tag, side: str, **kwargs
|
||||
) -> bool:
|
||||
try:
|
||||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
if df is None or df.empty:
|
||||
logger.warning(f"无法获取 {pair} 的分析数据，拒绝交易")
|
||||
return False
|
||||
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
if "close" not in last_candle or np.isnan(last_candle["close"]):
|
||||
logger.warning(f"{pair} 的最新 K 线缺少有效 close 价格，拒绝交易")
|
||||
return False
|
||||
|
||||
if side == "long":
|
||||
max_rate = last_candle["close"] * (1 + 0.0025) # 0.25% 滑点阈值
|
||||
if rate > max_rate:
|
||||
logger.debug(f"拒绝 {pair} 的买入，价格 {rate} 超过最大允许价格 {max_rate}")
|
||||
return False
|
||||
elif side == "short":
|
||||
logger.warning(f"{pair} 尝试做空，但策略不支持做空 (can_short={self.can_short})")
|
||||
return False
|
||||
|
||||
logger.debug(f"确认 {pair} 的交易：side={side}, rate={rate}, close={last_candle['close']}")
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"确认 {pair} 交易时出错：{str(e)}")
|
||||
return False
|
||||
@ -1,51 +0,0 @@
|
||||
# 风险评级 → 市场状态/波动率制度 替换总结
|
||||
|
||||
## 变更概述
|
||||
已将策略中的"风险评级"维度替换为"市场状态/波动率制度"维度，解决了与趋势得分的同质化问题。
|
||||
|
||||
## 具体变更
|
||||
|
||||
### 1. 特征工程变更
|
||||
- **删除的风险评级特征**：
|
||||
- `%-volatility_ratio` (波动率比率)
|
||||
- `%-price_range` (价格范围)
|
||||
- `%-volume_volatility` (成交量波动率)
|
||||
- `%-price_velocity` (价格动量)
|
||||
- `%-rsi_velocity` (RSI动量)
|
||||
|
||||
- **新增的市场状态特征**：
|
||||
- `%-bb_width` (Bollinger Band宽度)
|
||||
- `%-bb_width_ratio` (BB宽度百分比)
|
||||
- `%-volatility_regime` (波动率制度)
|
||||
- `%-regime_stability` (制度稳定性)
|
||||
- `%-price_efficiency` (价格效率指标)
|
||||
|
||||
### 2. 目标变量变更
|
||||
- **旧目标**：`&*-risk_rating` (0-4级风险评级)
|
||||
- **新目标**：`&*-market_regime` (0-4级市场状态)
|
||||
|
||||
### 3. 市场状态分类定义
|
||||
- **0 - 低波动震荡**：低波动率，价格稳定
|
||||
- **1 - 正常趋势**：适中的波动率和趋势
|
||||
- **2 - 高波动趋势**：高波动但趋势明确
|
||||
- **3 - 极端波动**：极高波动率，市场不稳定
|
||||
- **4 - 黑天鹅状态**：极端市场状况
|
||||
|
||||
### 4. 策略逻辑更新
|
||||
- **仓位调整**：基于市场状态而非风险评级
|
||||
- **入场条件**：使用市场状态调整入场严格程度
|
||||
- **止损设置**：根据市场状态动态调整
|
||||
|
||||
### 5. 配置文件更新
|
||||
- 将`freqai_info["model_training_parameters"]`中的"risk_rating"模型配置改为"market_regime"
|
||||
|
||||
## 优势
|
||||
1. **解决同质化**：市场状态与趋势得分关注不同维度，相关性更低
|
||||
2. **更直观**：直接反映市场波动状态而非抽象风险概念
|
||||
3. **实战价值**：可直接指导仓位管理和风险控制
|
||||
4. **特征丰富**：基于Bollinger Band、波动率持续性等多维度构建
|
||||
|
||||
## 使用建议
|
||||
1. 重新训练模型以适应新的市场状态分类
|
||||
2. 监控市场状态分布，确保各状态均衡
|
||||
3. 根据实际表现微调状态阈值
|
||||
@ -1,36 +0,0 @@
|
||||
{
|
||||
"strategy_name": "FreqaiPrimer",
|
||||
"params": {
|
||||
"roi": {
|
||||
"0": 0.06,
|
||||
"30": 0.04,
|
||||
"90": 0.025,
|
||||
"270": 0.002
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -0.14
|
||||
},
|
||||
"max_open_trades": {
|
||||
"max_open_trades": 5
|
||||
},
|
||||
"buy": {
|
||||
"ADD_POSITION_THRESHOLD": -0.045,
|
||||
"COOLDOWN_PERIOD_MINUTES": 8,
|
||||
"MAX_ENTRY_POSITION_ADJUSTMENT": 3
|
||||
},
|
||||
"sell": {
|
||||
"EXIT_POSITION_RATIO": 0.665,
|
||||
"TRAILING_STOP_DISTANCE": 0.006,
|
||||
"TRAILING_STOP_START": 0.038
|
||||
},
|
||||
"protection": {},
|
||||
"trailing": {
|
||||
"trailing_stop": true,
|
||||
"trailing_stop_positive": 0.073,
|
||||
"trailing_stop_positive_offset": 0.11099999999999999,
|
||||
"trailing_only_offset_is_reached": false
|
||||
}
|
||||
},
|
||||
"ft_stratparam_v": 1,
|
||||
"export_time": "2025-08-02 21:25:00.515292+00:00"
|
||||
}
|
||||
57
freqtrade/templates/sample_hyperopt_loss.py
Normal file
57
freqtrade/templates/sample_hyperopt_loss.py
Normal file
@ -0,0 +1,57 @@
|
||||
from datetime import datetime
|
||||
from math import exp
|
||||
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.constants import Config
|
||||
from freqtrade.optimize.hyperopt import IHyperOptLoss
|
||||
|
||||
|
||||
# Define some constants:
|
||||
|
||||
# set TARGET_TRADES to suit your number concurrent trades so its realistic
|
||||
# to the number of days
|
||||
TARGET_TRADES = 600
|
||||
# This is assumed to be expected avg profit * expected trade count.
|
||||
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
|
||||
# self.expected_max_profit = 3.85
|
||||
# Check that the reported Σ% values do not exceed this!
|
||||
# Note, this is ratio. 3.85 stated above means 385Σ%.
|
||||
EXPECTED_MAX_PROFIT = 3.0
|
||||
|
||||
# max average trade duration in minutes
|
||||
# if eval ends with higher value, we consider it a failed eval
|
||||
MAX_ACCEPTED_TRADE_DURATION = 300
|
||||
|
||||
|
||||
class SampleHyperOptLoss(IHyperOptLoss):
|
||||
"""
|
||||
Defines the default loss function for hyperopt
|
||||
This is intended to give you some inspiration for your own loss function.
|
||||
|
||||
The Function needs to return a number (float) - which becomes smaller for better backtest
|
||||
results.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def hyperopt_loss_function(
|
||||
results: DataFrame,
|
||||
trade_count: int,
|
||||
min_date: datetime,
|
||||
max_date: datetime,
|
||||
config: Config,
|
||||
processed: dict[str, DataFrame],
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> float:
|
||||
"""
|
||||
Objective function, returns smaller number for better results
|
||||
"""
|
||||
total_profit = results["profit_ratio"].sum()
|
||||
trade_duration = results["trade_duration"].mean()
|
||||
|
||||
trade_loss = 1 - 0.25 * exp(-((trade_count - TARGET_TRADES) ** 2) / 10**5.8)
|
||||
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
|
||||
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
|
||||
result = trade_loss + profit_loss + duration_loss
|
||||
return result
|
||||
426
freqtrade/templates/sample_strategy.py
Normal file
426
freqtrade/templates/sample_strategy.py
Normal file
@ -0,0 +1,426 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
# flake8: noqa: F401
|
||||
# isort: skip_file
|
||||
# --- Do not remove these imports ---
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from pandas import DataFrame
|
||||
from typing import Optional, Union
|
||||
|
||||
from freqtrade.strategy import (
|
||||
IStrategy,
|
||||
Trade,
|
||||
Order,
|
||||
PairLocks,
|
||||
informative, # @informative decorator
|
||||
# Hyperopt Parameters
|
||||
BooleanParameter,
|
||||
CategoricalParameter,
|
||||
DecimalParameter,
|
||||
IntParameter,
|
||||
RealParameter,
|
||||
# timeframe helpers
|
||||
timeframe_to_minutes,
|
||||
timeframe_to_next_date,
|
||||
timeframe_to_prev_date,
|
||||
# Strategy helper functions
|
||||
merge_informative_pair,
|
||||
stoploss_from_absolute,
|
||||
stoploss_from_open,
|
||||
)
|
||||
|
||||
# --------------------------------
|
||||
# Add your lib to import here
|
||||
import talib.abstract as ta
|
||||
from technical import qtpylib
|
||||
|
||||
|
||||
# This class is a sample. Feel free to customize it.
|
||||
class SampleStrategy(IStrategy):
|
||||
"""
|
||||
This is a sample strategy to inspire you.
|
||||
More information in https://www.freqtrade.io/en/latest/strategy-customization/
|
||||
|
||||
You can:
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
- Rename the class name (Do not forget to update class_name)
|
||||
- Add any methods you want to build your strategy
|
||||
- Add any lib you need to build your strategy
|
||||
|
||||
You must keep:
|
||||
- the lib in the section "Do not remove these libs"
|
||||
- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
|
||||
You should keep:
|
||||
- timeframe, minimal_roi, stoploss, trailing_*
|
||||
"""
|
||||
|
||||
# Strategy interface version - allow new iterations of the strategy interface.
|
||||
# Check the documentation or the Sample strategy to get the latest version.
|
||||
INTERFACE_VERSION = 3
|
||||
|
||||
# Can this strategy go short?
|
||||
can_short: bool = False
|
||||
|
||||
# Minimal ROI designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "minimal_roi".
|
||||
minimal_roi = {
|
||||
# "120": 0.0, # exit after 120 minutes at break even
|
||||
"60": 0.01,
|
||||
"30": 0.02,
|
||||
"0": 0.04,
|
||||
}
|
||||
|
||||
# Optimal stoploss designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "stoploss".
|
||||
stoploss = -0.10
|
||||
|
||||
# Trailing stoploss
|
||||
trailing_stop = False
|
||||
# trailing_only_offset_is_reached = False
|
||||
# trailing_stop_positive = 0.01
|
||||
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
|
||||
|
||||
# Optimal timeframe for the strategy.
|
||||
timeframe = "5m"
|
||||
|
||||
# Run "populate_indicators()" only for new candle.
|
||||
process_only_new_candles = True
|
||||
|
||||
# These values can be overridden in the config.
|
||||
use_exit_signal = True
|
||||
exit_profit_only = False
|
||||
ignore_roi_if_entry_signal = False
|
||||
|
||||
# Hyperoptable parameters
|
||||
buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
|
||||
sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
|
||||
short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
|
||||
exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
|
||||
|
||||
# Number of candles the strategy requires before producing valid signals
|
||||
startup_candle_count: int = 200
|
||||
|
||||
# Optional order type mapping.
|
||||
order_types = {
|
||||
"entry": "limit",
|
||||
"exit": "limit",
|
||||
"stoploss": "market",
|
||||
"stoploss_on_exchange": False,
|
||||
}
|
||||
|
||||
# Optional order time in force.
|
||||
order_time_in_force = {"entry": "GTC", "exit": "GTC"}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {
|
||||
"tema": {},
|
||||
"sar": {"color": "white"},
|
||||
},
|
||||
"subplots": {
|
||||
"MACD": {
|
||||
"macd": {"color": "blue"},
|
||||
"macdsignal": {"color": "orange"},
|
||||
},
|
||||
"RSI": {
|
||||
"rsi": {"color": "red"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
def informative_pairs(self):
|
||||
"""
|
||||
Define additional, informative pair/interval combinations to be cached from the exchange.
|
||||
These pair/interval combinations are non-tradeable, unless they are part
|
||||
of the whitelist as well.
|
||||
For more information, please consult the documentation
|
||||
:return: List of tuples in the format (pair, interval)
|
||||
Sample: return [("ETH/USDT", "5m"),
|
||||
("BTC/USDT", "15m"),
|
||||
]
|
||||
"""
|
||||
return []
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Adds several different TA indicators to the given DataFrame
|
||||
|
||||
Performance Note: For the best performance be frugal on the number of indicators
|
||||
you are using. Let uncomment only the indicator you are using in your strategies
|
||||
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||||
:param dataframe: Dataframe with data from the exchange
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
"""
|
||||
|
||||
# Momentum Indicators
|
||||
# ------------------------------------
|
||||
|
||||
# ADX
|
||||
dataframe["adx"] = ta.ADX(dataframe)
|
||||
|
||||
# # Plus Directional Indicator / Movement
|
||||
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
||||
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
||||
|
||||
# # Minus Directional Indicator / Movement
|
||||
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
|
||||
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
|
||||
# # Aroon, Aroon Oscillator
|
||||
# aroon = ta.AROON(dataframe)
|
||||
# dataframe['aroonup'] = aroon['aroonup']
|
||||
# dataframe['aroondown'] = aroon['aroondown']
|
||||
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
|
||||
|
||||
# # Awesome Oscillator
|
||||
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
||||
|
||||
# # Keltner Channel
|
||||
# keltner = qtpylib.keltner_channel(dataframe)
|
||||
# dataframe["kc_upperband"] = keltner["upper"]
|
||||
# dataframe["kc_lowerband"] = keltner["lower"]
|
||||
# dataframe["kc_middleband"] = keltner["mid"]
|
||||
# dataframe["kc_percent"] = (
|
||||
# (dataframe["close"] - dataframe["kc_lowerband"]) /
|
||||
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
|
||||
# )
|
||||
# dataframe["kc_width"] = (
|
||||
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
|
||||
# )
|
||||
|
||||
# # Ultimate Oscillator
|
||||
# dataframe['uo'] = ta.ULTOSC(dataframe)
|
||||
|
||||
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
|
||||
# dataframe['cci'] = ta.CCI(dataframe)
|
||||
|
||||
# RSI
|
||||
dataframe["rsi"] = ta.RSI(dataframe)
|
||||
|
||||
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
|
||||
# rsi = 0.1 * (dataframe['rsi'] - 50)
|
||||
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
|
||||
|
||||
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
|
||||
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
|
||||
|
||||
# # Stochastic Slow
|
||||
# stoch = ta.STOCH(dataframe)
|
||||
# dataframe['slowd'] = stoch['slowd']
|
||||
# dataframe['slowk'] = stoch['slowk']
|
||||
|
||||
# Stochastic Fast
|
||||
stoch_fast = ta.STOCHF(dataframe)
|
||||
dataframe["fastd"] = stoch_fast["fastd"]
|
||||
dataframe["fastk"] = stoch_fast["fastk"]
|
||||
|
||||
# # Stochastic RSI
|
||||
# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
|
||||
# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
|
||||
# stoch_rsi = ta.STOCHRSI(dataframe)
|
||||
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
|
||||
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
|
||||
|
||||
# MACD
|
||||
macd = ta.MACD(dataframe)
|
||||
dataframe["macd"] = macd["macd"]
|
||||
dataframe["macdsignal"] = macd["macdsignal"]
|
||||
dataframe["macdhist"] = macd["macdhist"]
|
||||
|
||||
# MFI
|
||||
dataframe["mfi"] = ta.MFI(dataframe)
|
||||
|
||||
# # ROC
|
||||
# dataframe['roc'] = ta.ROC(dataframe)
|
||||
|
||||
# Overlap Studies
|
||||
# ------------------------------------
|
||||
|
||||
# Bollinger Bands
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe["bb_lowerband"] = bollinger["lower"]
|
||||
dataframe["bb_middleband"] = bollinger["mid"]
|
||||
dataframe["bb_upperband"] = bollinger["upper"]
|
||||
dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
|
||||
dataframe["bb_upperband"] - dataframe["bb_lowerband"]
|
||||
)
|
||||
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
|
||||
"bb_middleband"
|
||||
]
|
||||
|
||||
# Bollinger Bands - Weighted (EMA based instead of SMA)
|
||||
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
|
||||
# qtpylib.typical_price(dataframe), window=20, stds=2
|
||||
# )
|
||||
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
|
||||
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
|
||||
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
|
||||
# dataframe["wbb_percent"] = (
|
||||
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
|
||||
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
|
||||
# )
|
||||
# dataframe["wbb_width"] = (
|
||||
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
|
||||
# dataframe["wbb_middleband"]
|
||||
# )
|
||||
|
||||
# # EMA - Exponential Moving Average
|
||||
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
|
||||
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
|
||||
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
||||
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
|
||||
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
||||
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
|
||||
|
||||
# # SMA - Simple Moving Average
|
||||
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
|
||||
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
|
||||
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
|
||||
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
|
||||
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
|
||||
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
|
||||
|
||||
# Parabolic SAR
|
||||
dataframe["sar"] = ta.SAR(dataframe)
|
||||
|
||||
# TEMA - Triple Exponential Moving Average
|
||||
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
|
||||
|
||||
# Cycle Indicator
|
||||
# ------------------------------------
|
||||
# Hilbert Transform Indicator - SineWave
|
||||
hilbert = ta.HT_SINE(dataframe)
|
||||
dataframe["htsine"] = hilbert["sine"]
|
||||
dataframe["htleadsine"] = hilbert["leadsine"]
|
||||
|
||||
# Pattern Recognition - Bullish candlestick patterns
|
||||
# ------------------------------------
|
||||
# # Hammer: values [0, 100]
|
||||
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
|
||||
# # Inverted Hammer: values [0, 100]
|
||||
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
|
||||
# # Dragonfly Doji: values [0, 100]
|
||||
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
|
||||
# # Piercing Line: values [0, 100]
|
||||
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
|
||||
# # Morningstar: values [0, 100]
|
||||
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
|
||||
# # Three White Soldiers: values [0, 100]
|
||||
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
|
||||
|
||||
# Pattern Recognition - Bearish candlestick patterns
|
||||
# ------------------------------------
|
||||
# # Hanging Man: values [0, 100]
|
||||
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
|
||||
# # Shooting Star: values [0, 100]
|
||||
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
|
||||
# # Gravestone Doji: values [0, 100]
|
||||
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
|
||||
# # Dark Cloud Cover: values [0, 100]
|
||||
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
|
||||
# # Evening Doji Star: values [0, 100]
|
||||
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
|
||||
# # Evening Star: values [0, 100]
|
||||
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
|
||||
|
||||
# Pattern Recognition - Bullish/Bearish candlestick patterns
|
||||
# ------------------------------------
|
||||
# # Three Line Strike: values [0, -100, 100]
|
||||
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
|
||||
# # Spinning Top: values [0, -100, 100]
|
||||
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
|
||||
# # Engulfing: values [0, -100, 100]
|
||||
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
|
||||
# # Harami: values [0, -100, 100]
|
||||
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
|
||||
# # Three Outside Up/Down: values [0, -100, 100]
|
||||
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
|
||||
# # Three Inside Up/Down: values [0, -100, 100]
|
||||
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
|
||||
|
||||
# # Chart type
|
||||
# # ------------------------------------
|
||||
# # Heikin Ashi Strategy
|
||||
# heikinashi = qtpylib.heikinashi(dataframe)
|
||||
# dataframe['ha_open'] = heikinashi['open']
|
||||
# dataframe['ha_close'] = heikinashi['close']
|
||||
# dataframe['ha_high'] = heikinashi['high']
|
||||
# dataframe['ha_low'] = heikinashi['low']
|
||||
|
||||
# Retrieve best bid and best ask from the orderbook
|
||||
# ------------------------------------
|
||||
"""
|
||||
# first check if dataprovider is available
|
||||
if self.dp:
|
||||
if self.dp.runmode.value in ('live', 'dry_run'):
|
||||
ob = self.dp.orderbook(metadata['pair'], 1)
|
||||
dataframe['best_bid'] = ob['bids'][0][0]
|
||||
dataframe['best_ask'] = ob['asks'][0][0]
|
||||
"""
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the entry signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: DataFrame with entry columns populated
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 30
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
|
||||
& (dataframe["tema"] <= dataframe["bb_middleband"]) # Guard: tema below BB middle
|
||||
& (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
"enter_long",
|
||||
] = 1
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 70
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value))
|
||||
& (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
|
||||
& (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
"enter_short",
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the exit signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: DataFrame with exit columns populated
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 70
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
|
||||
& (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
|
||||
& (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
"exit_long",
|
||||
] = 1
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# Signal: RSI crosses above 30
|
||||
(qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value))
|
||||
&
|
||||
# Guard: tema below BB middle
|
||||
(dataframe["tema"] <= dataframe["bb_middleband"])
|
||||
& (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
|
||||
& (dataframe["volume"] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
"exit_short",
|
||||
] = 1
|
||||
|
||||
return dataframe
|
||||
480
freqtrade/templates/strategy_analysis_example.ipynb
Normal file
480
freqtrade/templates/strategy_analysis_example.ipynb
Normal file
@ -0,0 +1,480 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Strategy analysis example\n",
|
||||
"\n",
|
||||
"Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.\n",
|
||||
"The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.\n",
|
||||
"Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"### Change Working directory to repository root"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from pathlib import Path\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Change directory\n",
|
||||
"# Modify this cell to insure that the output shows the correct path.\n",
|
||||
"# Define all paths relative to the project root shown in the cell output\n",
|
||||
"project_root = \"somedir/freqtrade\"\n",
|
||||
"i = 0\n",
|
||||
"try:\n",
|
||||
" os.chdir(project_root)\n",
|
||||
" if not Path(\"LICENSE\").is_file():\n",
|
||||
" i = 0\n",
|
||||
" while i < 4 and (not Path(\"LICENSE\").is_file()):\n",
|
||||
" os.chdir(Path(Path.cwd(), \"../\"))\n",
|
||||
" i += 1\n",
|
||||
" project_root = Path.cwd()\n",
|
||||
"except FileNotFoundError:\n",
|
||||
" print(\"Please define the project root relative to the current directory\")\n",
|
||||
"print(Path.cwd())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure Freqtrade environment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from freqtrade.configuration import Configuration\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Customize these according to your needs.\n",
|
||||
"\n",
|
||||
"# Initialize empty configuration object\n",
|
||||
"config = Configuration.from_files([])\n",
|
||||
"# Optionally (recommended), use existing configuration file\n",
|
||||
"# config = Configuration.from_files([\"user_data/config.json\"])\n",
|
||||
"\n",
|
||||
"# Define some constants\n",
|
||||
"config[\"timeframe\"] = \"5m\"\n",
|
||||
"# Name of the strategy class\n",
|
||||
"config[\"strategy\"] = \"SampleStrategy\"\n",
|
||||
"# Location of the data\n",
|
||||
"data_location = config[\"datadir\"]\n",
|
||||
"# Pair to analyze - Only use one pair here\n",
|
||||
"pair = \"BTC/USDT\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load data using values set above\n",
|
||||
"from freqtrade.data.history import load_pair_history\n",
|
||||
"from freqtrade.enums import CandleType\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"candles = load_pair_history(\n",
|
||||
" datadir=data_location,\n",
|
||||
" timeframe=config[\"timeframe\"],\n",
|
||||
" pair=pair,\n",
|
||||
" data_format=\"json\", # Make sure to update this to your data\n",
|
||||
" candle_type=CandleType.SPOT,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Confirm success\n",
|
||||
"print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
|
||||
"candles.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load and run strategy\n",
|
||||
"* Rerun each time the strategy file is changed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load strategy using values set above\n",
|
||||
"from freqtrade.data.dataprovider import DataProvider\n",
|
||||
"from freqtrade.resolvers import StrategyResolver\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"strategy = StrategyResolver.load_strategy(config)\n",
|
||||
"strategy.dp = DataProvider(config, None, None)\n",
|
||||
"strategy.ft_bot_start()\n",
|
||||
"\n",
|
||||
"# Generate buy/sell signals using strategy\n",
|
||||
"df = strategy.analyze_ticker(candles, {\"pair\": pair})\n",
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Display the trade details\n",
|
||||
"\n",
|
||||
"* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
|
||||
"* Some possible problems\n",
|
||||
" * Columns with NaN values at the end of the dataframe\n",
|
||||
" * Columns used in `crossed*()` functions with completely different units\n",
|
||||
"* Comparison with full backtest\n",
|
||||
" * having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
|
||||
" * Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available. \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Report results\n",
|
||||
"print(f\"Generated {df['enter_long'].sum()} entry signals\")\n",
|
||||
"data = df.set_index(\"date\", drop=False)\n",
|
||||
"data.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load existing objects into a Jupyter notebook\n",
|
||||
"\n",
|
||||
"The following cells assume that you have already generated data using the cli. \n",
|
||||
"They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load backtest results to pandas dataframe\n",
|
||||
"\n",
|
||||
"Analyze a trades dataframe (also used below for plotting)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# if backtest_dir points to a directory, it'll automatically load the last backtest file.\n",
|
||||
"backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
|
||||
"# backtest_dir can also point to a specific file\n",
|
||||
"# backtest_dir = (\n",
|
||||
"# config[\"user_data_dir\"] / \"backtest_results/backtest-result-2020-07-01_20-04-22.json\"\n",
|
||||
"# )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# You can get the full backtest statistics by using the following command.\n",
|
||||
"# This contains all information used to generate the backtest result.\n",
|
||||
"stats = load_backtest_stats(backtest_dir)\n",
|
||||
"\n",
|
||||
"strategy = \"SampleStrategy\"\n",
|
||||
"# All statistics are available per strategy, so if `--strategy-list` was used during backtest,\n",
|
||||
"# this will be reflected here as well.\n",
|
||||
"# Example usages:\n",
|
||||
"print(stats[\"strategy\"][strategy][\"results_per_pair\"])\n",
|
||||
"# Get pairlist used for this backtest\n",
|
||||
"print(stats[\"strategy\"][strategy][\"pairlist\"])\n",
|
||||
"# Get market change (average change of all pairs from start to end of the backtest period)\n",
|
||||
"print(stats[\"strategy\"][strategy][\"market_change\"])\n",
|
||||
"# Maximum drawdown ()\n",
|
||||
"print(stats[\"strategy\"][strategy][\"max_drawdown_abs\"])\n",
|
||||
"# Maximum drawdown start and end\n",
|
||||
"print(stats[\"strategy\"][strategy][\"drawdown_start\"])\n",
|
||||
"print(stats[\"strategy\"][strategy][\"drawdown_end\"])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Get strategy comparison (only relevant if multiple strategies were compared)\n",
|
||||
"print(stats[\"strategy_comparison\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load backtested trades as dataframe\n",
|
||||
"trades = load_backtest_data(backtest_dir)\n",
|
||||
"\n",
|
||||
"# Show value-counts per pair\n",
|
||||
"trades.groupby(\"pair\")[\"exit_reason\"].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Plotting daily profit / equity line"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"import plotly.express as px\n",
|
||||
"\n",
|
||||
"from freqtrade.configuration import Configuration\n",
|
||||
"from freqtrade.data.btanalysis import load_backtest_stats\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# strategy = 'SampleStrategy'\n",
|
||||
"# config = Configuration.from_files([\"user_data/config.json\"])\n",
|
||||
"# backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
|
||||
"\n",
|
||||
"stats = load_backtest_stats(backtest_dir)\n",
|
||||
"strategy_stats = stats[\"strategy\"][strategy]\n",
|
||||
"\n",
|
||||
"df = pd.DataFrame(columns=[\"dates\", \"equity\"], data=strategy_stats[\"daily_profit\"])\n",
|
||||
"df[\"equity_daily\"] = df[\"equity\"].cumsum()\n",
|
||||
"\n",
|
||||
"fig = px.line(df, x=\"dates\", y=\"equity_daily\")\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load live trading results into a pandas dataframe\n",
|
||||
"\n",
|
||||
"In case you did already some trading and want to analyze your performance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from freqtrade.data.btanalysis import load_trades_from_db\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Fetch trades from database\n",
|
||||
"trades = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
|
||||
"\n",
|
||||
"# Display results\n",
|
||||
"trades.groupby(\"pair\")[\"exit_reason\"].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Analyze the loaded trades for trade parallelism\n",
|
||||
"This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with a very high `max_open_trades` setting.\n",
|
||||
"\n",
|
||||
"`analyze_trade_parallelism()` returns a timeseries dataframe with an \"open_trades\" column, specifying the number of open trades for each candle."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from freqtrade.data.btanalysis import analyze_trade_parallelism\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Analyze the above\n",
|
||||
"parallel_trades = analyze_trade_parallelism(trades, \"5m\")\n",
|
||||
"\n",
|
||||
"parallel_trades.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Plot results\n",
|
||||
"\n",
|
||||
"Freqtrade offers interactive plotting capabilities based on plotly."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from freqtrade.plot.plotting import generate_candlestick_graph\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Limit graph period to keep plotly quick and reactive\n",
|
||||
"\n",
|
||||
"# Filter trades to one pair\n",
|
||||
"trades_red = trades.loc[trades[\"pair\"] == pair]\n",
|
||||
"\n",
|
||||
"data_red = data[\"2019-06-01\":\"2019-06-10\"]\n",
|
||||
"# Generate candlestick graph\n",
|
||||
"graph = generate_candlestick_graph(\n",
|
||||
" pair=pair,\n",
|
||||
" data=data_red,\n",
|
||||
" trades=trades_red,\n",
|
||||
" indicators1=[\"sma20\", \"ema50\", \"ema55\"],\n",
|
||||
" indicators2=[\"rsi\", \"macd\", \"macdsignal\", \"macdhist\"],\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Show graph inline\n",
|
||||
"# graph.show()\n",
|
||||
"\n",
|
||||
"# Render graph in a separate window\n",
|
||||
"graph.show(renderer=\"browser\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Plot average profit per trade as distribution graph"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import plotly.figure_factory as ff\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"hist_data = [trades.profit_ratio]\n",
|
||||
"group_labels = [\"profit_ratio\"] # name of the dataset\n",
|
||||
"\n",
|
||||
"fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"file_extension": ".py",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.9.7 64-bit",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
},
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"npconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
},
|
||||
"varInspector": {
|
||||
"cols": {
|
||||
"lenName": 16,
|
||||
"lenType": 16,
|
||||
"lenVar": 40
|
||||
},
|
||||
"kernels_config": {
|
||||
"python": {
|
||||
"delete_cmd_postfix": "",
|
||||
"delete_cmd_prefix": "del ",
|
||||
"library": "var_list.py",
|
||||
"varRefreshCmd": "print(var_dic_list())"
|
||||
},
|
||||
"r": {
|
||||
"delete_cmd_postfix": ") ",
|
||||
"delete_cmd_prefix": "rm(",
|
||||
"library": "var_list.r",
|
||||
"varRefreshCmd": "cat(var_dic_list()) "
|
||||
}
|
||||
},
|
||||
"types_to_exclude": [
|
||||
"module",
|
||||
"function",
|
||||
"builtin_function_or_method",
|
||||
"instance",
|
||||
"_Feature"
|
||||
],
|
||||
"window_display": false
|
||||
},
|
||||
"version": 3,
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "675f32a300d6d26767470181ad0b11dd4676bcce7ed1dd2ffe2fbc370c95fc7c"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
3066
freqtrade_utf8.log
3066
freqtrade_utf8.log
File diff suppressed because it is too large
Load Diff
@ -1,52 +0,0 @@
|
||||
# 绿色通道开关配置指南
|
||||
|
||||
## 功能说明
|
||||
绿色通道是一个特殊的交易通道，当市场处于牛市趋势且持仓数量≤2个时启用，使用更宽松的入场条件。
|
||||
|
||||
## 配置方法
|
||||
|
||||
### 1. 在配置文件中添加绿色通道开关
|
||||
|
||||
在你的 `config.json` 或相关配置文件中添加以下配置：
|
||||
|
||||
```json
|
||||
{
|
||||
"green_channel": {
|
||||
"enabled": false
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 2. 开关状态说明
|
||||
- `enabled: true` - 启用绿色通道功能
|
||||
- `enabled: false` - 禁用绿色通道功能（默认）
|
||||
|
||||
### 3. 当前状态
|
||||
- ✅ 绿色通道开关已添加到策略中
|
||||
- ✅ 默认状态：关闭（`enabled: false`）
|
||||
- ✅ 可通过配置文件动态控制
|
||||
|
||||
### 4. 日志识别
|
||||
启动时会在日志中看到：
|
||||
```
|
||||
🟢 绿色通道开关状态: 关闭
|
||||
```
|
||||
|
||||
### 5. 影响范围
|
||||
禁用绿色通道后：
|
||||
- 牛市趋势下，即使持仓≤2个，也不会使用绿色通道的宽松条件
|
||||
- 将统一使用标准趋势策略（震荡趋势策略）
|
||||
- 不影响其他趋势状态下的交易逻辑
|
||||
|
||||
### 6. 如何启用
|
||||
如需启用绿色通道，只需将配置文件中的 `enabled` 改为 `true`：
|
||||
|
||||
```json
|
||||
{
|
||||
"green_channel": {
|
||||
"enabled": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
然后重启策略即可生效。
|
||||
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Reference in New Issue
Block a user