251 lines
7.6 KiB
Markdown
251 lines
7.6 KiB
Markdown
> 请实现代码:
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> 1. **将 `&-divergence_score` 改为连续值形式**
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> 2. **移除任何离散值列（如 market_condition 编码）**
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> 3. **保持使用 `LightGBMMultiTargetRegressor` 模型**
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> 4. **在策略中使用这些连续值进行动态交易决策**
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Error: {
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exit = 56,
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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" }',
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stderr = '{ "curl: (56) OpenSSL SSL_read: error:0A000126:SSL routines::unexpected eof while reading, errno 0" }'
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}
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-------
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- Datetime: 2025-05-31 08:40:04
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- Model: _qwen3/qwen-plus-latest
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- Selected files:
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- freqtrade/templates/freqaiprimer.py
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> 中断了,请继续
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非常理解，我们继续完成你的需求。
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---
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## ✅ 你的目标
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你希望：
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1. **将 `&-divergence_score` 改为连续值形式**
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2. **移除任何离散值列（如 `&-market_condition`）**
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3. **保持使用 `LightGBMMultiTargetRegressor` 模型**
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4. **在策略中使用这些连续值进行动态交易决策**
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---
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## ✅ 实现步骤概览
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我们将完成以下修改：
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| 步骤 | 内容 |
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|------|------|
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| 🧮 Step 1 | 修改 `set_freqai_targets()`：生成连续背离评分 |
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| 🧹 Step 2 | 移除所有离散值列（如 `&-market_condition`） |
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| 📈 Step 3 | 配置 FreqAI 使用 `LightGBMMultiTargetRegressor` |
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| 🛠 Step 4 | 在 `populate_entry_trend()` 和 `populate_exit_trend()` 中使用连续背离信号 |
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---
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## 🧮 Step 1：修改 `set_freqai_targets()`，生成连续背离评分
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```python
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def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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"""
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使用历史窗口预测未来趋势和波动率作为辅助信号，
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并新增一个连续值的背离评分列 &-divergence_score
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"""
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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# 1. 趋势强度：使用过去 N 根 K 线的收益率判断当前趋势
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dataframe["&-trend_strength"] = dataframe["close"].pct_change(label_period)
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# 2. 波动率预测：使用过去 N 根 K 线的价格变动绝对值
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dataframe["&-volatility_forecast"] = dataframe["close"].pct_change(label_period).abs()
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# 3. ROI 目标：基于趋势强度缩放
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dataframe["&-roi_target"] = np.where(
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dataframe["&-trend_strength"] > 0,
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dataframe["&-trend_strength"] * 1.5,
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0.01 # 最小 ROI
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)
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# 4. 动态止损目标：基于波动率缩放
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dataframe["&-stoploss_target"] = -dataframe["&-volatility_forecast"] * 1.2
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# 5. 新增：连续背离评分（价格与趋势的偏离程度）
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dataframe["price_change"] = dataframe["close"].pct_change(label_period)
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dataframe["trend_change"] = dataframe["&-trend_strength"].pct_change(label_period)
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# 连续背离评分：正值表示正向背离，负值表示负向背离
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dataframe["&-divergence_score"] = dataframe["price_change"] - dataframe["trend_change"]
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return dataframe
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```
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---
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## 🧹 Step 2：移除所有离散值列（如 `&-market_condition`）
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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。
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### ❌ 删除示例：
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```python
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# 删除以下代码（如果存在）
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if "bb_upperband-period" in dataframe.columns and "bb_lowerband-period" in dataframe.columns:
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dataframe["&-market_condition"] = np.select([
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(dataframe["close"] > dataframe["bb_upperband-period"]),
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(dataframe["close"] < dataframe["bb_lowerband-period"]),
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(dataframe["&-trend_strength"] > 0.03),
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], [
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"overbought",
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"oversold",
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"strong_up",
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], default="sideways")
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```
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同时确保不再使用这些列做任何逻辑判断。
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---
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## 📈 Step 3：配置 FreqAI 使用 `LightGBMMultiTargetRegressor`
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在你的策略类中设置：
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```python
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freqai_info = {
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"model": "LightGBMMultiTargetRegressor", # ✅ 多目标回归模型
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"feature_parameters": {
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"include_timeframes": ["5m", "15m", "1h"],
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"label_period_candles": 24,
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"include_shifted_candles": 3,
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"principal_component_analysis": True, # 可选：启用 PCA 提升泛化能力
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},
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"data_split_parameters": {
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"test_size": 0.2,
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"shuffle": False,
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},
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"model_training_parameters": {
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"n_estimators": 200,
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"learning_rate": 0.05,
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"num_leaves": 31,
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"verbose": -1,
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},
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}
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```
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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。
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---
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## 🛠 Step 4：在策略中使用连续背离信号进行交易决策
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### ✅ 示例：在 `populate_entry_trend()` 中使用 `&-divergence_score`
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```python
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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if "&-divergence_score" in df.columns:
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# 正向背离：价格下跌但趋势上升（背离评分为正值）
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buy_condition = df["&-divergence_score"] > 0.01
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# 结合趋势强度过滤
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if "&-trend_strength" in df.columns:
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buy_condition &= df["&-trend_strength"] > 0.005
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# 成交量过滤：成交量需大于 20 日均值的 1 倍
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df["vol_mean"] = df["volume"].rolling(20).mean()
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buy_condition &= (df["volume"] > df["vol_mean"] * 1)
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conditions.append(buy_condition)
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if len(conditions):
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df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
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return df
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```
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### ✅ 示例：在 `populate_exit_trend()` 中使用 `&-divergence_score`
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```python
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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if "&-divergence_score" in df.columns:
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# 负向背离：价格上涨但趋势下降（背离评分为负值）
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sell_condition = df["&-divergence_score"] < -0.01
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# 结合趋势强度过滤
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if "&-trend_strength" in df.columns:
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sell_condition &= df["&-trend_strength"] < -0.005
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conditions.append(sell_condition)
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if len(conditions):
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df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
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return df
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```
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---
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## 📦 Step 5：更新 `plot_config`（可选）
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你可以将背离评分加入绘图配置，方便可视化观察：
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```python
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plot_config = {
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"main_plot": {},
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"subplots": {
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"Signals": {
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"enter_long": {"color": "green"},
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"exit_long": {"color": "red"}
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},
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"FreqAI Predictions": {
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"&-trend_strength": {"color": "blue"},
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"&-volatility_forecast": {"color": "orange"},
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"&-divergence_score": {"color": "purple"}
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}
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}
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}
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```
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---
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## ✅ 总结改进点
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| 改进项 | 描述 |
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|--------|------|
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| ✅ `&-divergence_score` 改为连续值 | 更适合回归模型学习 |
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| ✅ 移除所有离散值列 | 如 `&-market_condition` |
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| ✅ 使用 `LightGBMMultiTargetRegressor` | 同时预测多个连续目标 |
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| ✅ 在策略中使用背离评分进行交易决策 | 提高反转识别能力 |
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---
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## ✅ 下一步建议
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你可以：
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- 使用 `freqtrade plot-dataframe` 查看背离信号是否合理
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- 使用 `freqtrade backtesting` 测试新策略表现
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- 使用 `freqtrade hyperopt` 对背离阈值进行优化
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---
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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。
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freqtrade plot-dataframe --strategy DivergenceRegressionStrategy \
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--timerange=20250501-20250510 \
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--timeframe=5m \
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--exchange=binance \
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--pair=ETH/USDT
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