1763 lines
93 KiB
Python
1763 lines
93 KiB
Python
import warnings
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warnings.filterwarnings("ignore", category=UserWarning, module="pandas_ta")
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import logging
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import pandas as pd
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pd.set_option('future.no_silent_downcasting', True)
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from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
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from pandas import DataFrame
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import pandas_ta as ta
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from freqtrade.persistence import Trade
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import numpy as np
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from datetime import datetime, timezone, timedelta
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import math
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logger = logging.getLogger(__name__)
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# ========== 时区统一管理 ==========
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# FreqAI dataframe 的 date 列是 UTC 时间
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# 所有虚机/容器统一为 UTC+8
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UTC_PLUS_8 = timezone(timedelta(hours=8))
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class FreqaiPrimer(IStrategy):
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# 策略参数 - 使用custom_roi替代minimal_roi字典
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loglevel = "warning"
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minimal_roi = {}
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add_position_callback = True
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# 启用自定义ROI回调函数
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use_custom_roi = True
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# FreqAI 要求
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process_only_new_candles = True
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stoploss = -0.15 # 固定止损 -15% (大幅放宽止损以承受更大波动)
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trailing_stop = True
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trailing_stop_positive_offset = 0.005 # 追踪止损偏移量 0.5% (更容易触发跟踪止盈)
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# 用于跟踪市场状态的数据框缓存
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_dataframe_cache = None
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def __init__(self, config=None):
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"""初始化策略参数，调用父类初始化方法并接受config参数"""
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super().__init__(config) # 调用父类的初始化方法并传递config
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# 存储从配置文件加载的默认值
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self._trailing_stop_positive_default = 0.004 # 降低默认值以更容易触发跟踪止盈
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# 波动系数缓存（简化版：直接计算，无需历史序列）
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self._volatility_timestamp = {} # {pair: timestamp}
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self._volatility_cache = {} # {pair: volatility_coef}
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self._volatility_update_interval = 180 # 波动系数更新间隔（秒），3分钟
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# 入场间隔控制：记录每个交易对最近一次入场的时间
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# 格式: {pair: datetime}
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self._last_entry_time = {}
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# 冷启动保护：记录策略启动时间
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self._strategy_start_time = datetime.now()
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# ========== 数据新鲜度监控统计 ==========
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# 用于量化和诊断数据延迟问题的严重性
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self._freshness_stats = {
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'total_checks': 0, # 总检查次数
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'fresh_count': 0, # 新鲜数据次数 (< 10分钟)
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'warning_count': 0, # 警告次数 (10-30分钟)
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'stale_count': 0, # 过期次数 (> 30分钟)
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'max_delay_minutes': 0.0, # 最大延迟时间
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'total_delay_minutes': 0.0, # 累计延迟时间（用于计算平均值）
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'pair_delays': {}, # 每个币对的延迟统计 {pair: {'count': N, 'avg_delay': M, 'max_delay': X}}
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'last_report_time': None, # 上次报告时间
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}
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self._freshness_report_interval = 3600 # 每小时输出一次统计报告
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def strategy_log(self, message: str, level: str = "info") -> None:
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"""根据 config 的 enable_strategy_log 决定是否输出日志"""
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enable_log = self.config.get('enable_strategy_log', False)
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if not enable_log:
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return
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if level.lower() == "debug":
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logger.debug(message)
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elif level.lower() == "warning":
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logger.warning(message)
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elif level.lower() == "error":
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logger.error(message)
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else:
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logger.info(message)
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def calculate_data_freshness(self, data_timestamp: datetime, pair: str, dataframe: DataFrame) -> float:
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"""
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计算数据新鲜度（与当前时间比较，因为dataframe本身可能已过时）
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:param data_timestamp: 数据时间戳（来自dataframe的最新时间）
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:param pair: 交易对
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:param dataframe: 数据框
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:return: 数据延迟时间（分钟）
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"""
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try:
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# 转换为UTC+8时间
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if data_timestamp.tzinfo is None:
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data_timestamp = data_timestamp.replace(tzinfo=timezone.utc)
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data_timestamp_utc8 = data_timestamp.astimezone(UTC_PLUS_8).replace(tzinfo=None)
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# 使用当前系统时间作为基准，因为dataframe本身可能已过时
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# 这样可以检测到Freqtrade内部缓存机制导致的数据延迟
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current_time = datetime.now(UTC_PLUS_8).replace(tzinfo=None)
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freshness_minutes = (current_time - data_timestamp_utc8).total_seconds() / 60
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return max(0.0, freshness_minutes) # 确保不会返回负数
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except Exception as e:
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self.strategy_log(f"[{pair}] 数据新鲜度计算异常: {e}", "warning")
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return 3.0 # 异常时返回保守估计
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def update_freshness_stats(self, pair: str, data_age_minutes: float) -> None:
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"""
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更新数据新鲜度统计
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:param pair: 交易对
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:param data_age_minutes: 数据延迟时间（分钟）
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"""
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stats = self._freshness_stats
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stats['total_checks'] += 1
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stats['total_delay_minutes'] += data_age_minutes
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stats['max_delay_minutes'] = max(stats['max_delay_minutes'], data_age_minutes)
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# 分类统计
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if data_age_minutes <= 10:
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stats['fresh_count'] += 1
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elif data_age_minutes <= 30:
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stats['warning_count'] += 1
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else:
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stats['stale_count'] += 1
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# 每个币对的统计
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if pair not in stats['pair_delays']:
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stats['pair_delays'][pair] = {
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'count': 0,
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'total_delay': 0.0,
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'max_delay': 0.0,
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'fresh': 0,
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'warning': 0,
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'stale': 0
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}
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pair_stat = stats['pair_delays'][pair]
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pair_stat['count'] += 1
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pair_stat['total_delay'] += data_age_minutes
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pair_stat['max_delay'] = max(pair_stat['max_delay'], data_age_minutes)
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if data_age_minutes <= 10:
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pair_stat['fresh'] += 1
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elif data_age_minutes <= 30:
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pair_stat['warning'] += 1
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else:
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pair_stat['stale'] += 1
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def report_freshness_stats(self, force: bool = False) -> None:
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"""
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输出数据新鲜度统计报告
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:param force: 强制输出（忽略时间间隔）
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"""
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current_time = datetime.now()
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stats = self._freshness_stats
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# 检查是否需要报告
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if not force and stats['last_report_time'] is not None:
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elapsed = (current_time - stats['last_report_time']).total_seconds()
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if elapsed < self._freshness_report_interval:
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return
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# 如果没有数据，不输出
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if stats['total_checks'] == 0:
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return
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# 计算统计指标
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avg_delay = stats['total_delay_minutes'] / stats['total_checks']
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fresh_rate = stats['fresh_count'] / stats['total_checks'] * 100
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warning_rate = stats['warning_count'] / stats['total_checks'] * 100
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stale_rate = stats['stale_count'] / stats['total_checks'] * 100
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# 输出总体报告
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self.strategy_log(
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f"\n"
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f"========================================\n"
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f" 📊 数据新鲜度统计报告\n"
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f"========================================\n"
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f"总检查次数: {stats['total_checks']}\n"
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f"新鲜数据 (<10min): {stats['fresh_count']} ({fresh_rate:.1f}%)\n"
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f"警告数据 (10-30min): {stats['warning_count']} ({warning_rate:.1f}%)\n"
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f"过期数据 (>30min): {stats['stale_count']} ({stale_rate:.1f}%)\n"
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f"平均延迟: {avg_delay:.2f} 分钟\n"
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f"最大延迟: {stats['max_delay_minutes']:.2f} 分钟\n"
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f"========================================",
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level="info"
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)
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# 输出每个币对的详细统计（按过期率排序）
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if stats['pair_delays']:
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pair_stats_list = []
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for pair, pair_stat in stats['pair_delays'].items():
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avg_pair_delay = pair_stat['total_delay'] / pair_stat['count']
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stale_rate_pair = pair_stat['stale'] / pair_stat['count'] * 100
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pair_stats_list.append({
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'pair': pair,
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'avg_delay': avg_pair_delay,
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'max_delay': pair_stat['max_delay'],
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'stale_rate': stale_rate_pair,
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'count': pair_stat['count']
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})
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# 按过期率排序（从高到低）
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pair_stats_list.sort(key=lambda x: x['stale_rate'], reverse=True)
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report_lines = ["📈 各币对数据新鲜度排名（按过期率降序）:"]
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for i, ps in enumerate(pair_stats_list[:10], 1): # 只显示前10个最差的
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report_lines.append(
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f" {i}. {ps['pair']:15s} | "
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f"平均: {ps['avg_delay']:5.1f}min | "
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f"最大: {ps['max_delay']:5.1f}min | "
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f"过期率: {ps['stale_rate']:5.1f}% | "
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f"检查: {ps['count']}次"
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)
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self.strategy_log("\n".join(report_lines), level="info")
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# 更新报告时间
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stats['last_report_time'] = current_time
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# 只用于adjust_trade_position方法的波动系数获取
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def get_volatility_coefficient(self, pair: str) -> float:
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"""
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获取币对的波动系数（简化版：直接计算，无需历史序列）
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- USDT/USDT 波动系数设置为0
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- BTC/USDT 波动系数设置为1
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- 其他币对：
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计算当前波动系数 = 该币对波动率 / BTC/USDT波动率
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（基于最近200根1h K线，足够稳定，无需额外平滑）
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波动系数表示某币对与BTC/USDT相比的波动幅度倍数
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- 山寨币的波动系数可能大于3
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- 稳定性较高的币对（如DOT/USDT）波动系数可能小于1
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添加了缓存机制，每3分钟更新一次，避免频繁计算
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"""
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# 检查特殊币对
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if pair == 'USDT/USDT':
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return 0.0
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elif pair == 'BTC/USDT':
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return 1.0
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try:
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# 获取当前时间戳
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current_time = datetime.now().timestamp()
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# 检查缓存：如果距离上次计算时间小于更新间隔，则直接返回缓存值
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if (pair in self._volatility_cache and
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pair in self._volatility_timestamp and
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current_time - self._volatility_timestamp[pair] < self._volatility_update_interval):
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return self._volatility_cache[pair]
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# 直接计算当前波动系数（基于最近200根1h K线）
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current_volatility_coef = self._calculate_current_volatility_coef(pair)
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# 更新缓存和时间戳
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self._volatility_cache[pair] = current_volatility_coef
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self._volatility_timestamp[pair] = current_time
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self.strategy_log(f"波动系数计算完成 {pair}: 系数={current_volatility_coef:.4f} (基于最近200根1h K线)")
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return current_volatility_coef
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except Exception as e:
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logger.warning(f"计算波动系数时出错 {pair}: {str(e)}")
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# 如果出错，尝试返回缓存值，否则返回默认值1.0
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return self._volatility_cache.get(pair, 1.0)
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def _calculate_current_volatility_coef(self, pair: str) -> float:
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"""
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计算当前的波动系数（该币对波动率 / BTC/USDT波动率）
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"""
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try:
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# 获取当前币对的1小时k线数据
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current_pair_df, _ = self.dp.get_analyzed_dataframe(pair, '1h')
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# 获取BTC/USDT的1小时k线数据
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btc_df, _ = self.dp.get_analyzed_dataframe('BTC/USDT', '1h')
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# 确保有足够的数据点
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if len(current_pair_df) < 2 or len(btc_df) < 2:
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return 1.0 # 如果没有足够数据，返回默认值1.0
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# 对于数据点少于200个的情况，使用所有可用数据
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# 对于数据点多于200个的情况，使用最近200个数据点
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current_data = current_pair_df.iloc[-min(200, len(current_pair_df)):]
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btc_data = btc_df.iloc[-min(200, len(btc_df)):]
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# 计算当前币对的对数收益率和波动率
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current_data['returns'] = current_data['close'].pct_change()
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current_volatility = current_data['returns'].std() * 100 # 转换为百分比
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# 计算BTC/USDT的对数收益率和波动率
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btc_data['returns'] = btc_data['close'].pct_change()
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btc_volatility = btc_data['returns'].std() * 100 # 转换为百分比
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# 避免除以零的情况
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if btc_volatility == 0:
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return 1.0
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# 计算波动系数：当前币对波动率 / BTC/USDT波动率
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volatility_coef = current_volatility / btc_volatility
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# 设置合理的上下限，避免极端值影响策略
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# 上限设置为5.0（非常高波动的币对）
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# 下限设置为0.1（非常稳定的币对）
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return max(0.1, min(5.0, volatility_coef))
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except Exception as e:
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logger.warning(f"计算当前波动系数时出错 {pair}: {str(e)}")
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return 1.0 # 出错时返回默认值1.0
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# 其他辅助方法可以在这里添加
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@property
|
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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": 15 # 45分钟冷却期（15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 = "15m" # 主时间框架为 15 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（分钟）
|
||
entry_interval_minutes = IntParameter(20, 200, default=42, optimize=True, load=True, space='buy')
|
||
|
||
# EMA20斜率阈值参数（用于趋势强度过滤）
|
||
ema20_slope_threshold = DecimalParameter(0.0005, 0.005, decimals=4, default=0.001, optimize=True, load=True, space='buy')
|
||
|
||
# ML 审核官：entry_signal 拒绝入场的阈值（越高越宽松，越低越严格）
|
||
ml_entry_signal_threshold = DecimalParameter(0.05, 0.85, decimals=2, default=0.37, optimize=True, load=True, space='buy')
|
||
|
||
# ML 审核官：exit_signal 拒绝出场的阈值（越高越宽松，越低越严格）
|
||
ml_exit_signal_threshold = DecimalParameter(0.05, 0.85, decimals=2, default=0.68, optimize=True, load=True, space='buy')
|
||
|
||
# FreqAI 标签定义：entry_signal 的洛底上涨幅度（%）
|
||
freqai_entry_up_percent = DecimalParameter(0.3, 2.0, decimals=2, default=0.5, optimize=True, load=True, space='buy')
|
||
|
||
# FreqAI 标签定义：exit_signal 的洛底下跌幅度（%）
|
||
freqai_exit_down_percent = DecimalParameter(0.3, 2.0, decimals=2, default=0.5, optimize=True, load=True, space='buy')
|
||
|
||
# 定义可优化参数
|
||
# 初始入场金额: 75.00
|
||
|
||
# 加仓次数 相对降幅间隔 加仓金额
|
||
# ------- ------------ --------
|
||
# 0 N/A 75
|
||
# 1 0.045000 36.29
|
||
# 2 0.051750 163.31
|
||
# 3 0.059513 734.88
|
||
# 4 0.068439 3306.96
|
||
#
|
||
# 累计投入金额: 4316.43
|
||
max_entry_adjustments = IntParameter(2, 5, default=4, optimize=False, load=True, space='buy') # 最大加仓次数
|
||
add_position_callback = DecimalParameter(0.02, 0.06, decimals=3, default=0.047, optimize=False, load=True, space='buy') # 加仓回调百分比
|
||
add_position_growth = DecimalParameter(1.5, 5.0, decimals=2, default=4.5, optimize=False, load=True, space='buy') # 加仓金额增长因子，保留2位小数用于hyperopt优化
|
||
add_position_multiplier = DecimalParameter(0.2, 2, decimals=2, default=1.35, optimize=False, load=True, space='buy') # 加仓间隔系数，保留2位小数用于hyperopt优化
|
||
stake_divisor = DecimalParameter(2.0, 12.0, decimals=2, default=9.3, optimize=False, load=True, space='buy') # 加仓金额分母（小数类型，保留2位小数）
|
||
|
||
# 线性ROI参数 - 用于线性函数: y = (a * (x + k)) + t
|
||
roi_param_a = DecimalParameter(-0.0002, -0.00005, decimals=5, default=-0.0001, optimize=True, load=True, space='sell') # 系数a
|
||
roi_param_k = IntParameter(20, 150, default=50, optimize=True, load=True, space='sell') # 偏移量k
|
||
roi_param_t = DecimalParameter(0.02, 0.18, decimals=3, default=0.06, optimize=True, load=True, space='sell') # 常数项t
|
||
# 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}")
|
||
|
||
# ========================= FreqAI 特征与标签定义 =========================
|
||
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
|
||
"""FreqAI 全量特征：这里先用简单技术指标，后续可逐步扩展。"""
|
||
# 使用 rolling 计算 RSI（减少看前偏差）
|
||
delta = dataframe["close"].diff()
|
||
gain = delta.where(delta > 0, 0).rolling(window=period).mean()
|
||
loss = -delta.where(delta < 0, 0).rolling(window=period).mean()
|
||
rs = gain / loss
|
||
dataframe[f"%-rsi-{period}"] = 100 - (100 / (1 + rs))
|
||
|
||
dataframe[f"%-mfi-{period}"] = ta.mfi(dataframe["high"], dataframe["low"], dataframe["close"], dataframe["volume"], length=period)
|
||
adx_df = ta.adx(dataframe["high"], dataframe["low"], dataframe["close"], length=period)
|
||
adx_col = f"ADX_{period}"
|
||
if adx_col in adx_df.columns:
|
||
dataframe[f"%-adx-{period}"] = adx_df[adx_col]
|
||
return dataframe
|
||
|
||
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||
"""FreqAI 基础特征。"""
|
||
dataframe["%-pct_change"] = dataframe["close"].pct_change().fillna(0)
|
||
dataframe["%-raw_volume"] = dataframe["volume"].fillna(0)
|
||
dataframe["%-raw_price"] = dataframe["close"].ffill() # 使用 ffill() 替代 fillna(method="ffill")
|
||
return dataframe
|
||
|
||
def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
|
||
"""FreqAI 标准时间类特征。"""
|
||
if "date" in dataframe.columns:
|
||
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:
|
||
"""定义 FreqAI 训练标签：简单二分类版本 + 持仓时长预测。"""
|
||
# 从配置中读取预测窗口参数（禁止硬编码）
|
||
label_horizon = self.freqai_info.get('feature_parameters', {}).get('label_period_candles', 24)
|
||
|
||
# 动态计算上涨/下跌阈值
|
||
entry_up_percent = self.freqai_entry_up_percent.value / 100.0 # 转换为小数（如 0.01 表示 1%）
|
||
exit_down_percent = self.freqai_exit_down_percent.value / 100.0
|
||
|
||
entry_up_threshold = 1.0 + entry_up_percent # 例如 1.01 表示 +1%
|
||
exit_down_threshold = 1.0 - exit_down_percent # 例如 0.99 表示 -1%
|
||
|
||
# 入场标签：未来窗口内的最高价是否超过 +1%
|
||
future_max = dataframe["close"].rolling(window=label_horizon, min_periods=1).max().shift(-label_horizon + 1)
|
||
dataframe["&s-entry_signal"] = np.where(
|
||
future_max > dataframe["close"] * entry_up_threshold,
|
||
1,
|
||
0,
|
||
)
|
||
|
||
# 出场标签：未来窗口内的最低价是否跌破 -1%
|
||
future_min = dataframe["close"].rolling(window=label_horizon, min_periods=1).min().shift(-label_horizon + 1)
|
||
dataframe["&s-exit_signal"] = np.where(
|
||
future_min < dataframe["close"] * exit_down_threshold,
|
||
1,
|
||
0,
|
||
)
|
||
|
||
# 新增：未来波动率预测标签（极端化方案）
|
||
# 计算当前波动率（过10根K线的收盘价波动）
|
||
current_volatility = dataframe["close"].pct_change().rolling(window=10, min_periods=5).std()
|
||
|
||
# 计算未来10根K线的波动率（向未来移动）
|
||
future_pct_change = dataframe["close"].pct_change().shift(-1) # 未来的收盘价变化
|
||
future_volatility = future_pct_change.rolling(window=10, min_periods=5).std().shift(-9) # 未来10根K线的波动率
|
||
|
||
# 标签：未来波动率 > 当前波动率 * 1.5 则标记为高波动（趋势启动）
|
||
volatility_ratio = future_volatility / (current_volatility + 1e-8) # 避免除以0
|
||
dataframe["&s-future_volatility"] = np.where(
|
||
volatility_ratio > 1.5,
|
||
1, # 未来高波动（趋势启动），继续持有
|
||
0 # 未来低波动（震荡市），快速止盈
|
||
)
|
||
|
||
return dataframe
|
||
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# ========== 新增：ML目标变量数据新鲜度监控 ==========
|
||
# 检查FreqAI模型预测数据的新鲜度，确保使用最新的预测结果
|
||
# 注意：ML预测数据通常比普通量化数据滞后一个timeframe（约3分钟）
|
||
# 这是FreqAI的正常工作机制，不影响策略有效性
|
||
try:
|
||
# 获取FreqAI分析过的数据帧
|
||
analyzed_df, _ = self.dp.get_analyzed_dataframe(metadata['pair'], self.timeframe)
|
||
if len(analyzed_df) > 0:
|
||
latest_analyzed_row = analyzed_df.iloc[-1]
|
||
latest_date = latest_analyzed_row.get('date')
|
||
|
||
if latest_date is not None:
|
||
# 确保日期是datetime对象
|
||
if not isinstance(latest_date, datetime):
|
||
try:
|
||
from pandas import Timestamp
|
||
if isinstance(latest_date, Timestamp):
|
||
latest_date = latest_date.to_pydatetime()
|
||
except:
|
||
pass
|
||
# 计算ML数据新鲜度（使用统一函数）
|
||
if isinstance(latest_date, datetime):
|
||
ml_age_minutes = self.calculate_data_freshness(latest_date, metadata['pair'], dataframe)
|
||
|
||
# 检查目标变量的类型和新鲜度
|
||
target_vars = []
|
||
for col in analyzed_df.columns:
|
||
if col.startswith('target_') or col.startswith('pred_'):
|
||
target_vars.append(col)
|
||
|
||
# 如果有多个目标变量，分别检查
|
||
if target_vars:
|
||
# 检查所有目标变量的列是否存在
|
||
available_targets = []
|
||
for var in target_vars:
|
||
if var in analyzed_df.columns:
|
||
available_targets.append(var)
|
||
|
||
if available_targets:
|
||
# 输出每个目标变量的检查结果
|
||
target_info = []
|
||
for target in available_targets[:3]: # 只显示前3个目标变量
|
||
# 由于所有目标变量共享同一时间戳，我们可以统一显示
|
||
if ml_age_minutes <= 10:
|
||
icon = "✅"
|
||
elif ml_age_minutes <= 30:
|
||
icon = "⚠️"
|
||
else:
|
||
icon = "❌"
|
||
target_info.append(f"{target}({icon}{ml_age_minutes:.1f}min)")
|
||
|
||
# 如果有更多目标变量，显示总数
|
||
if len(available_targets) > 3:
|
||
target_info.append(f"...共{len(available_targets)}个目标变量")
|
||
|
||
self.strategy_log(f"[{metadata['pair']}] ML目标变量数据新鲜度: {', '.join(target_info)}")
|
||
else:
|
||
# 如果没有目标变量列，显示通用信息
|
||
if ml_age_minutes <= 10:
|
||
ml_freshness_icon = "✅"
|
||
elif ml_age_minutes <= 30:
|
||
ml_freshness_icon = "⚠️"
|
||
else:
|
||
ml_freshness_icon = "❌"
|
||
|
||
self.strategy_log(f"[{metadata['pair']}] ML目标变量数据新鲜度: {ml_freshness_icon} {ml_age_minutes:.1f}min")
|
||
else:
|
||
# 如果没有检测到目标变量，显示通用信息
|
||
if ml_age_minutes <= 10:
|
||
ml_freshness_icon = "✅"
|
||
elif ml_age_minutes <= 30:
|
||
ml_freshness_icon = "⚠️"
|
||
else:
|
||
ml_freshness_icon = "❌"
|
||
|
||
self.strategy_log(f"[{metadata['pair']}] ML目标变量数据新鲜度: {ml_freshness_icon} {ml_age_minutes:.1f}min")
|
||
except Exception as e:
|
||
self.strategy_log(f"[{metadata['pair']}] ML数据新鲜度检查失败: {e}")
|
||
|
||
# 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
|
||
|
||
# 使用 rolling 计算布林带（减少看前偏差）
|
||
bb_ma_3m = dataframe['close'].rolling(window=bb_length_value).mean()
|
||
bb_std_3m = dataframe['close'].rolling(window=bb_length_value).std()
|
||
dataframe['bb_lower_3m'] = bb_ma_3m - (bb_std_value * bb_std_3m)
|
||
dataframe['bb_upper_3m'] = bb_ma_3m + (bb_std_value * bb_std_3m)
|
||
|
||
# 使用 rolling 计算 RSI（减少看前偏差）
|
||
delta_3m = dataframe['close'].diff()
|
||
gain_3m = delta_3m.where(delta_3m > 0, 0).rolling(window=rsi_length_value).mean()
|
||
loss_3m = -delta_3m.where(delta_3m < 0, 0).rolling(window=rsi_length_value).mean()
|
||
rs_3m = gain_3m / loss_3m
|
||
dataframe['rsi_3m'] = 100 - (100 / (1 + rs_3m))
|
||
|
||
# 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']
|
||
|
||
# 使用 ewm 计算 EMA（减少看前偏差，adjust=False 确保实时计算）
|
||
dataframe['ema_50_3m'] = dataframe['close'].ewm(span=50, adjust=False).mean()
|
||
dataframe['ema_200_3m'] = dataframe['close'].ewm(span=200, adjust=False).mean()
|
||
|
||
# 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')
|
||
|
||
# 使用 rolling 计算 RSI（减少看前偏差）
|
||
delta_15m = df_15m['close'].diff()
|
||
gain_15m = delta_15m.where(delta_15m > 0, 0).rolling(window=rsi_length_value).mean()
|
||
loss_15m = -delta_15m.where(delta_15m < 0, 0).rolling(window=rsi_length_value).mean()
|
||
rs_15m = gain_15m / loss_15m
|
||
df_15m['rsi_15m'] = 100 - (100 / (1 + rs_15m))
|
||
|
||
# 使用 ewm 计算 EMA（减少看前偏差）
|
||
df_15m['ema_20_15m'] = df_15m['close'].ewm(span=20, adjust=False).mean()
|
||
df_15m['ema_50_15m'] = df_15m['close'].ewm(span=50, adjust=False).mean()
|
||
df_15m['ema_200_15m'] = df_15m['close'].ewm(span=200, adjust=False).mean()
|
||
|
||
# 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_20_15m', 'ema_50_15m', 'ema_200_15m']].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')
|
||
|
||
# 使用 rolling 计算布林带（减少看前偏差）
|
||
bb_ma_1h = df_1h['close'].rolling(window=bb_length_value).mean()
|
||
bb_std_1h = df_1h['close'].rolling(window=bb_length_value).std()
|
||
df_1h['bb_lower_1h'] = bb_ma_1h - (bb_std_value * bb_std_1h)
|
||
df_1h['bb_upper_1h'] = bb_ma_1h + (bb_std_value * bb_std_1h)
|
||
|
||
# 添加 EMA5 和 EMA20 用于趋势过滤（方案2：宽松条件）
|
||
df_1h['ema_5_1h'] = df_1h['close'].ewm(span=5, adjust=False).mean()
|
||
df_1h['ema_20_1h'] = df_1h['close'].ewm(span=20, adjust=False).mean()
|
||
|
||
# 计算 EMA20 斜率（用于趋势强度过滤）
|
||
df_1h['ema20_slope'] = df_1h['ema_20_1h'].diff()
|
||
# 归一化斜率，使其相对于价格水平标准化
|
||
df_1h['ema20_slope_normalized'] = df_1h['ema20_slope'] / df_1h['ema_20_1h']
|
||
|
||
# 检测 EMA5 向上穿越 EMA20（添加安全检查）
|
||
if len(df_1h) >= 2:
|
||
df_1h['ema5_cross_above_ema20'] = (
|
||
(df_1h['ema_5_1h'] > df_1h['ema_20_1h']) &
|
||
(df_1h['ema_5_1h'].shift(1) <= df_1h['ema_20_1h'].shift(1))
|
||
)
|
||
else:
|
||
# 数据不足时，默认为False
|
||
df_1h['ema5_cross_above_ema20'] = False
|
||
|
||
# 使用 rolling 计算 RSI（减少看前偏差）
|
||
delta_1h = df_1h['close'].diff()
|
||
gain_1h = delta_1h.where(delta_1h > 0, 0).rolling(window=rsi_length_value).mean()
|
||
loss_1h = -delta_1h.where(delta_1h < 0, 0).rolling(window=rsi_length_value).mean()
|
||
rs_1h = gain_1h / loss_1h
|
||
df_1h['rsi_1h'] = 100 - (100 / (1 + rs_1h))
|
||
|
||
# 使用 ewm 计算 EMA（减少看前偏差）
|
||
df_1h['ema_50_1h'] = df_1h['close'].ewm(span=50, adjust=False).mean()
|
||
df_1h['ema_200_1h'] = df_1h['close'].ewm(span=200, adjust=False).mean()
|
||
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
|
||
#self.strategy_log(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
|
||
# 注意：只使用 ffill()（向前填充），不要用 bfill()（避免数据前瞻偏差）
|
||
df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().reset_index()
|
||
df_1h = df_1h.rename(columns={'index': 'date'})
|
||
# Include macd_1h, macd_signal_1h, ema_5_1h, ema_20_1h, ema5_cross_above_ema20 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', 'macd_hist_1h', 'ema_5_1h', 'ema_20_1h', 'ema20_slope_normalized', 'ema5_cross_above_ema20']].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', 'macd_hist_1h', 'ema_5_1h', 'ema_20_1h', 'ema20_slope_normalized', 'ema5_cross_above_ema20']
|
||
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', 'macd_hist_1h', 'ema_5_1h', 'ema_20_1h', 'ema20_slope_normalized', 'ema5_cross_above_ema20']
|
||
|
||
# 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}")
|
||
|
||
# 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', 'macd_hist_1h', 'ema_5_1h', 'ema_20_1h', 'ema20_slope_normalized', 'ema5_cross_above_ema20']
|
||
|
||
df_1h = df_1h[required_columns] # 确保包含所有必需的列（包括EMA过滤相关列）
|
||
|
||
# 合并 1h 数据
|
||
dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
|
||
|
||
# 验证合并后的列
|
||
#self.strategy_log(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'] = dataframe['prev_market_state'].fillna('neutral')
|
||
|
||
# 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]
|
||
#self.strategy_log(f"[{metadata['pair']}] 熊牛得分: {current_score:.1f}, 市场状态: {current_state}")
|
||
|
||
# 计算并显示基于未来波动率的最终阈值
|
||
if len(dataframe) > 0:
|
||
# 检查是否有未来波动率列
|
||
future_volatility = None
|
||
if '&s-future_volatility' in dataframe.columns:
|
||
future_volatility = dataframe['&s-future_volatility'].iloc[-1]
|
||
elif '&-future_volatility' in dataframe.columns:
|
||
future_volatility = dataframe['&-future_volatility'].iloc[-1]
|
||
|
||
if future_volatility is not None:
|
||
# 确保 future_volatility 在 [0, 1] 范围内
|
||
future_volatility = max(0.0, min(1.0, future_volatility))
|
||
|
||
# 使用与 confirm_trade_entry 相同的线性函数计算阈值
|
||
base_signal_ratio_threshold = 1.33
|
||
x_min = 0.3
|
||
x_max = 0.9
|
||
y_min = 1.6
|
||
y_max = 1.1
|
||
|
||
if future_volatility >= x_max:
|
||
signal_ratio_threshold = y_max
|
||
elif future_volatility <= x_min:
|
||
signal_ratio_threshold = y_min
|
||
else:
|
||
signal_ratio_threshold = y_max + (y_min - y_max) * (future_volatility - x_max) / (x_min - x_max)
|
||
|
||
# 限制阈值范围
|
||
signal_ratio_threshold = max(1.1, min(1.6, signal_ratio_threshold))
|
||
|
||
self.strategy_log(f"[{metadata['pair']}] 基于未来波动率的最终阈值: {signal_ratio_threshold:.2f} (波动率: {future_volatility:.2f})")
|
||
#self.strategy_log(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
|
||
#self.strategy_log(f"[{metadata['pair']}] 最终数据框列名: {list(dataframe.columns)}")
|
||
|
||
# 检查FreqAI是否启用
|
||
freqai_enabled = False
|
||
try:
|
||
# 更可靠的检查方法：检查config中是否启用了FreqAI
|
||
freqai_enabled = self.config.get('freqai', {}).get('enabled', False)
|
||
# 同时检查freqai属性是否存在且有效
|
||
if freqai_enabled and hasattr(self, 'freqai') and self.freqai is not None:
|
||
freqai_enabled = True
|
||
else:
|
||
freqai_enabled = False
|
||
except Exception as e:
|
||
self.strategy_log(f"[{metadata['pair']}] 检查FreqAI启用状态时出错: {str(e)}")
|
||
freqai_enabled = False
|
||
|
||
# 只有在FreqAI启用时才调用
|
||
if freqai_enabled:
|
||
self.strategy_log(f"[{metadata['pair']}] FreqAI已启用，启动FreqAI处理")
|
||
# 启用 FreqAI：在所有指标计算完成后调用
|
||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||
|
||
# ========== 新增：记录FreqAI多目标预测结果 ==========
|
||
# 检查并记录所有可用的预测列，确认XGBoostRegressorMultiTarget是否为所有目标变量生成预测
|
||
try:
|
||
pred_columns = []
|
||
for col in dataframe.columns:
|
||
if col.startswith('&-') or col.startswith('&s-'): # FreqAI预测列通常以&-或&s-开头
|
||
pred_columns.append(col)
|
||
|
||
if pred_columns:
|
||
# 获取最后一行的预测值
|
||
last_row = dataframe.iloc[-1]
|
||
pred_values = {}
|
||
for col in pred_columns:
|
||
if col in last_row:
|
||
pred_values[col] = round(float(last_row[col]), 4)
|
||
|
||
if pred_values:
|
||
pred_info = ", ".join([f"{k}:{v}" for k, v in pred_values.items()])
|
||
self.strategy_log(f"[{metadata['pair']}] FreqAI多目标预测: {pred_info}")
|
||
|
||
# 特别记录三个主要目标变量
|
||
entry_val = pred_values.get('&s-entry_signal', 'N/A')
|
||
exit_val = pred_values.get('&s-exit_signal', 'N/A')
|
||
vol_val = pred_values.get('&s-future_volatility', 'N/A')
|
||
|
||
self.strategy_log(f"[{metadata['pair']}] FreqAI三目标预测 - 入场:{entry_val}, 出场:{exit_val}, 波动率:{vol_val}")
|
||
|
||
# ========== 新增：发送HTTP请求 ==========
|
||
# 从配置中读取API URL
|
||
api_url = self.config.get('freqai', {}).get('ml_prediction_api_url', '')
|
||
if api_url:
|
||
import json
|
||
import urllib.request
|
||
import urllib.error
|
||
import os
|
||
|
||
# 从环境变量获取virtualHostName
|
||
virtual_host_name = os.environ.get('VIRTUAL_HOST_NAME')
|
||
|
||
# 只有正确获取到virtualHostName时才发送请求
|
||
if virtual_host_name and virtual_host_name != 'default-vhost':
|
||
# 准备请求数据
|
||
request_data = {
|
||
"hostName": "freqtrade-live-c6b6f357-20260215212649",
|
||
"virtualHostName": virtual_host_name,
|
||
"pairName": metadata['pair'],
|
||
"timestamp": int(last_row['date'].timestamp()),
|
||
"values": {
|
||
"&s-entry_signal": pred_values.get('&s-entry_signal', 0),
|
||
"&s-exit_signal": pred_values.get('&s-exit_signal', 0),
|
||
"&s-future_volatility": pred_values.get('&s-future_volatility', 0)
|
||
}
|
||
}
|
||
|
||
# 转换为JSON
|
||
json_data = json.dumps(request_data).encode('utf-8')
|
||
|
||
# 发送请求
|
||
try:
|
||
req = urllib.request.Request(api_url, data=json_data, method='POST')
|
||
req.add_header('Content-Type', 'application/json')
|
||
with urllib.request.urlopen(req, timeout=5) as response:
|
||
status_code = response.getcode()
|
||
if status_code == 200:
|
||
self.strategy_log(f"[{metadata['pair']}] 成功发送ML预测数据到API (virtualHostName: {virtual_host_name})")
|
||
else:
|
||
self.strategy_log(f"[{metadata['pair']}] 发送ML预测数据失败，状态码: {status_code}")
|
||
except urllib.error.URLError as e:
|
||
self.strategy_log(f"[{metadata['pair']}] 发送ML预测数据网络错误: {str(e)}")
|
||
except Exception as e:
|
||
self.strategy_log(f"[{metadata['pair']}] 发送ML预测数据其他错误: {str(e)}")
|
||
else:
|
||
self.strategy_log(f"[{metadata['pair']}] 未正确获取到virtualHostName，跳过发送ML预测数据")
|
||
else:
|
||
self.strategy_log(f"[{metadata['pair']}] ML预测API URL未配置，跳过发送")
|
||
except Exception as e:
|
||
self.strategy_log(f"[{metadata['pair']}] 记录FreqAI预测结果时出错: {str(e)}")
|
||
else:
|
||
self.strategy_log(f"[{metadata['pair']}] FreqAI未启用，跳过FreqAI处理")
|
||
|
||
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
|
||
|
||
# 设置出场价格：上浮1.25%（使用乘法避免除零风险）
|
||
# Freqtrade 会优先使用 exit_price 列作为限价单价格
|
||
final_exit_condition = dataframe['exit_long'] == 1
|
||
#dataframe.loc[final_exit_condition, 'exit_price'] = dataframe.loc[final_exit_condition, 'close'] * 1.0125
|
||
|
||
# 增强调试信息
|
||
#self.strategy_log(f"[{metadata['pair']}] 出场条件检查:")
|
||
#self.strategy_log(f" - 价格突破布林带上轨: {breakout_condition.sum()} 次")
|
||
#self.strategy_log(f" - 成交量显著放大: {volume_spike.sum()} 次")
|
||
#self.strategy_log(f" - MACD 下降趋势: {macd_downward.sum()} 次")
|
||
#self.strategy_log(f" - RSI 超买: {rsi_overbought.sum()} 次")
|
||
#self.strategy_log(f" - 最终条件: {final_condition.sum()} 次")
|
||
#self.strategy_log(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:
|
||
# self.strategy_log(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)
|
||
|
||
# 新增：EMA趋势过滤条件（方案2：宽松版本）
|
||
# 条件1：EMA5保持在EMA20之上 或 条件2：最近20根1h K线内发生过向上穿越
|
||
# 这样既能捕捉趋势启动，又能在趋势延续时继续入场
|
||
if 'ema_5_1h' in dataframe.columns and 'ema_20_1h' in dataframe.columns:
|
||
# 条件1：EMA5保持在EMA20之上
|
||
ema5_above_ema20 = dataframe['ema_5_1h'] > dataframe['ema_20_1h']
|
||
|
||
# 条件2：最近20根1h K线内发生过向上穿越
|
||
if 'ema5_cross_above_ema20' in dataframe.columns:
|
||
# 使用rolling.max检查最近20根K线内是否有True值
|
||
recent_cross = dataframe['ema5_cross_above_ema20'].rolling(window=20, min_periods=1).max() == 1
|
||
# 两个条件满足其一即可
|
||
ema5_20_condition = ema5_above_ema20 | recent_cross
|
||
else:
|
||
# 如果没有交叉列，只用保持在上方的条件
|
||
ema5_20_condition = ema5_above_ema20
|
||
|
||
# 新增条件：EMA20斜率阈值检查
|
||
if 'ema20_slope_normalized' in dataframe.columns:
|
||
# EMA20斜率为正（上升趋势）且超过阈值
|
||
ema20_positive_slope = dataframe['ema20_slope_normalized'] > self.ema20_slope_threshold.value
|
||
# 组合条件：EMA5/EMA20条件 AND EMA20斜率条件
|
||
ema_trend_filter = ema5_20_condition & ema20_positive_slope
|
||
else:
|
||
# 如果没有斜率列，只用EMA5/EMA20条件
|
||
ema_trend_filter = ema5_20_condition
|
||
else:
|
||
# 如果列不存在，创建一个全False的Series（不允许入场）
|
||
self.strategy_log(f"[{metadata['pair']}] 警告：ema_5_1h或ema_20_1h列不存在，过滤条件设为False")
|
||
ema_trend_filter = pd.Series(False, index=dataframe.index)
|
||
|
||
# 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，并且必须满足EMA趋势过滤
|
||
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)
|
||
)
|
||
|
||
# 条件1：超卖反弹入场（原逻辑）
|
||
# 要求：基本条件满足 + EMA趋势过滤 + 价格低于15分钟EMA20
|
||
price_below_ema20_15m = dataframe['close'] < dataframe['ema_20_15m']
|
||
basic_condition = condition_count >= self.min_condition_count.value
|
||
pullback_entry = basic_condition & ema_trend_filter & price_below_ema20_15m
|
||
|
||
# 条件2：趋势跟踪入场（新增逻辑）
|
||
# 不要求价格低于EMA20，而是要求：
|
||
# - EMA趋势过滤满足
|
||
# - MACD上升趋势
|
||
# - 趋势确认（3m或15m趋势向上）
|
||
# - 价格高于15分钟EMA20（表明处于上涨趋势）
|
||
price_above_ema20_15m = dataframe['close'] >= dataframe['ema_20_15m']
|
||
# 趋势跟踪需要的条件数量较少，因为已经在明显上涨趋势中
|
||
trend_condition_count = (
|
||
macd_condition_1h.astype(int) +
|
||
trend_confirmation.astype(int) +
|
||
(volume_spike | bb_width_condition).astype(int)
|
||
)
|
||
# 趋势跟踪只需要满足2个条件即可，降低门槛
|
||
trend_basic_condition = trend_condition_count >= 2
|
||
trend_following_entry = trend_basic_condition & ema_trend_filter & price_above_ema20_15m
|
||
|
||
# 最终条件：超卖反弹入场 或 趋势跟踪入场
|
||
final_condition = pullback_entry | trend_following_entry
|
||
|
||
# 设置入场信号
|
||
dataframe.loc[final_condition, 'enter_long'] = 1
|
||
|
||
# ========== 新增：入场诊断统计（回测可用） ==========
|
||
# 先输出最新数据时间（dataframe最后一行），用于判断数据新鲜度
|
||
if 'date' in dataframe.columns and len(dataframe) > 0:
|
||
latest_data_date = dataframe['date'].iloc[-1]
|
||
try:
|
||
if isinstance(latest_data_date, datetime):
|
||
display_latest = latest_data_date
|
||
if display_latest.tzinfo is None:
|
||
display_latest = display_latest.replace(tzinfo=timezone.utc)
|
||
display_latest = display_latest.astimezone(UTC_PLUS_8)
|
||
latest_time_str = display_latest.strftime('%H:%M:%S')
|
||
# 计算数据新鲜度（使用统一函数）
|
||
data_freshness = self.calculate_data_freshness(latest_data_date, metadata['pair'], dataframe)
|
||
|
||
# 数据新鲜度判断
|
||
if data_freshness <= 3: # 3分钟内为新鲜数据
|
||
fresh_icon = "✅"
|
||
elif data_freshness <= 10: # 3-10分钟为可接受延迟
|
||
fresh_icon = "⚠️"
|
||
else: # 超过10分钟为过期数据
|
||
fresh_icon = "❌"
|
||
self.strategy_log(
|
||
f"[{metadata['pair']}] 数据新鲜度: {fresh_icon} {data_freshness:.1f}min | "
|
||
f"最新K线: {latest_time_str} | 总行数: {len(dataframe)}"
|
||
)
|
||
else:
|
||
from pandas import Timestamp
|
||
if isinstance(latest_data_date, Timestamp):
|
||
display_latest = latest_data_date.to_pydatetime()
|
||
if display_latest.tzinfo is None:
|
||
display_latest = display_latest.replace(tzinfo=timezone.utc)
|
||
display_latest = display_latest.astimezone(UTC_PLUS_8)
|
||
latest_time_str = display_latest.strftime('%H:%M:%S')
|
||
latest_naive = display_latest.replace(tzinfo=None)
|
||
|
||
# 计算数据新鲜度（使用统一函数）
|
||
data_freshness = self.calculate_data_freshness(latest_data_date, metadata['pair'], dataframe)
|
||
|
||
# 数据新鲜度判断
|
||
if data_freshness <= 3: # 3分钟内为新鲜数据
|
||
fresh_icon = "✅"
|
||
elif data_freshness <= 10: # 3-10分钟为可接受延迟
|
||
fresh_icon = "⚠️"
|
||
else: # 超过10分钟为过期数据
|
||
fresh_icon = "❌"
|
||
self.strategy_log(
|
||
f"[{metadata['pair']}] 数据新鲜度: {fresh_icon} {data_freshness:.1f}min | "
|
||
f"最新K线: {latest_time_str} | 总行数: {len(dataframe)}"
|
||
)
|
||
except Exception as e:
|
||
self.strategy_log(f"[{metadata['pair']}] 数据新鲜度计算异常: {e}")
|
||
|
||
# ========== 诊断统计结束 ==========
|
||
|
||
# 设置入场价格：下调1.67%（使用乘法避免除零风险）
|
||
final_condition_updated = dataframe['enter_long'] == 1
|
||
#dataframe.loc[final_condition_updated, 'enter_price'] = dataframe.loc[final_condition_updated, 'close'] * 0.9833
|
||
|
||
# 增强调试信息
|
||
# 确保ema_trend_filter是Series类型才能调用sum()
|
||
if isinstance(ema_trend_filter, pd.Series):
|
||
ema_trend_count = ema_trend_filter.sum()
|
||
else:
|
||
ema_trend_count = 0
|
||
|
||
basic_condition_count = basic_condition.sum()
|
||
final_condition_count = final_condition.sum()
|
||
|
||
self.strategy_log(f"[{metadata['pair']}] 入场条件检查:")
|
||
self.strategy_log(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
|
||
self.strategy_log(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
|
||
self.strategy_log(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
|
||
self.strategy_log(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
|
||
self.strategy_log(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
|
||
self.strategy_log(f" - 价格低于15分钟EMA20: {price_below_ema20_15m.sum()} 次")
|
||
self.strategy_log(f" - 价格高于15分钟EMA20: {price_above_ema20_15m.sum()} 次")
|
||
self.strategy_log(f" - 趋势确认: {trend_confirmation.sum()} 次")
|
||
self.strategy_log(f" - EMA趋势过滤(在上方或20根K线内穿越): {ema_trend_count} 次")
|
||
self.strategy_log(f" - 超卖反弹入场信号: {pullback_entry.sum()} 次")
|
||
self.strategy_log(f" - 趋势跟踪入场信号: {trend_following_entry.sum()} 次")
|
||
self.strategy_log(f" - 最终入场信号(总计): {final_condition.sum()} 次")
|
||
|
||
# ========== 新增：最近5个时间点的条件满足状况可视化 ==========
|
||
# 显示最近5个时间点的条件满足情况（更直观）
|
||
if len(dataframe) >= 5:
|
||
recent_indices = dataframe.index[-5:]
|
||
self.strategy_log(f"[{metadata['pair']}] 最近5个时间点条件满足状况:")
|
||
|
||
# 辅助函数：根据数值距离阈值的程度返回颜色emoji
|
||
def get_color_emoji(value, threshold, condition_type='less_than'):
|
||
"""
|
||
根据数值距离阈值的程度返回颜色emoji
|
||
condition_type: 'less_than' 表示值小于阈值为满足条件
|
||
'greater_than' 表示值大于阈值为满足条件
|
||
"""
|
||
if condition_type == 'less_than':
|
||
# value越小越满足条件，所以距离阈值越远(值越大)越红
|
||
distance = value - threshold # 距离阈值的距离，正值表示未满足，负值表示已满足
|
||
else: # greater_than
|
||
# value越大越满足条件，所以距离阈值越远(值越小)越红
|
||
distance = threshold - value # 距离阈值的距离，正值表示未满足，负值表示已满足
|
||
|
||
# 根据距离阈值的程度返回不同颜色
|
||
if condition_type == 'less_than':
|
||
is_satisfied = value < threshold
|
||
else: # greater_than
|
||
is_satisfied = value > threshold
|
||
|
||
if is_satisfied:
|
||
# 条件已满足，使用绿色系
|
||
if distance < -threshold * 0.5: # 远超阈值
|
||
return '🟢' # 深绿
|
||
elif distance < -threshold * 0.2: # 明显超越阈值
|
||
return '🟢' # 绿色
|
||
else: # 接近阈值但仍满足
|
||
return '🟡' # 黄绿色
|
||
else:
|
||
# 条件未满足，使用红色系
|
||
if distance > abs(threshold) * 0.5: # 远离阈值
|
||
return '🔴' # 深红
|
||
elif distance > abs(threshold) * 0.2: # 接近阈值
|
||
return '🟠' # 橙色
|
||
else: # 非常接近阈值
|
||
return '🟡' # 黄色（接近满足）
|
||
|
||
# RSI 超卖条件可视化（RSI越小越好）
|
||
rsi_values = dataframe['rsi_1h'].iloc[-5:]
|
||
rsi_threshold = self.rsi_bull_threshold.value # 使用牛市阈值作为参考
|
||
rsi_visual = ''.join([get_color_emoji(val, rsi_threshold, 'less_than') for val in rsi_values])
|
||
self.strategy_log(f" - [{rsi_visual}]#RSI 超卖(越红越大)")
|
||
|
||
# StochRSI 超卖条件可视化（StochRSI越小越好）
|
||
stochrsi_values = dataframe['stochrsi_k_1h'].iloc[-5:]
|
||
stochrsi_threshold = 0.2 # 固定阈值
|
||
stochrsi_visual = ''.join([get_color_emoji(val, stochrsi_threshold, 'less_than') for val in stochrsi_values])
|
||
self.strategy_log(f" - [{stochrsi_visual}]#StochRSI超卖(越红越大)")
|
||
|
||
# MACD 上升趋势条件可视化（MACD直方图越大越好）
|
||
macd_values = dataframe['macd_hist_1h'].iloc[-5:]
|
||
macd_threshold = 0 # 直方图大于0为上升趋势
|
||
macd_visual = ''.join([get_color_emoji(val, macd_threshold, 'greater_than') for val in macd_values])
|
||
self.strategy_log(f" - [{macd_visual}]#MACD上升趋势(越绿越大)")
|
||
|
||
# 趋势确认条件可视化（布尔值，使用原来的✅❌）
|
||
trend_recent = trend_confirmation.iloc[-5:].tolist()
|
||
trend_visual = ''.join(['✅' if x else '❌' for x in trend_recent])
|
||
self.strategy_log(f" - [{trend_visual}]#趋势确认")
|
||
|
||
# 价格接近布林带下轨条件可视化（价格相对于布林带下轨的比例，越小越好）
|
||
price_bb_ratios = (dataframe['close'] / dataframe['bb_lower_1h']).iloc[-5:]
|
||
bb_threshold = self.bb_lower_deviation.value # 价格/布林带下轨应该小于这个值
|
||
bb_lower_visual = ''.join([get_color_emoji(val, bb_threshold, 'less_than') for val in price_bb_ratios])
|
||
self.strategy_log(f" - [{bb_lower_visual}]#价格接近布林带下轨(越绿越近)")
|
||
|
||
# 如果EMA条件满足但最终条件未满足，输出详细信息
|
||
if ema_trend_count > 0 and final_condition_count == 0:
|
||
self.strategy_log(f"[{metadata['pair']}] 注意：检测到 {ema_trend_count} 次EMA趋势过滤满足，但由于其他条件不足未能生成入场信号")
|
||
# 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,
|
||
'ema_trend': ema_trend_filter
|
||
})
|
||
correlation = conditions.corr().mean().mean()
|
||
#self.strategy_log(f"[{metadata['pair']}] 条件平均相关性: {correlation:.2f}")
|
||
# 日志记录
|
||
#if dataframe['enter_long'].sum() > 0:
|
||
# self.strategy_log(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
|
||
#self.strategy_log(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]
|
||
#self.strategy_log(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if rapid_rise_detected else '稳固'}")
|
||
#self.strategy_log(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
|
||
此处实现从API获取数据、高波动禁止入场、时间戳和RMSE检查逻辑
|
||
"""
|
||
self.strategy_log(f"[{pair}] confirm_trade_entry 被调用 - 价格: {rate:.8f}, 时间: {current_time}")
|
||
|
||
# 仅对多头交易进行检查
|
||
if side != 'long':
|
||
self.strategy_log(f"[{pair}] 非多头交易，跳过入场检查")
|
||
return False
|
||
|
||
# 记录所有拒绝条件的列表
|
||
rejected_conditions = []
|
||
|
||
# 冷启动保护：程序启动后前20分钟内不允许入场
|
||
cold_start_rejected = False
|
||
if hasattr(self, "_strategy_start_time"):
|
||
# 在回测模式下不启用冷启动保护
|
||
if not self.config.get("dry_run", False):
|
||
warmup_minutes = 20
|
||
# current_time 来自框架，是 offset-aware (UTC)
|
||
# _strategy_start_time 是 offset-naive (本地时间)
|
||
# 需要统一处理：把 current_time 转换为本地时间并移除时区信息
|
||
if current_time.tzinfo is not None:
|
||
local_current_time = current_time.astimezone(UTC_PLUS_8).replace(tzinfo=None)
|
||
else:
|
||
local_current_time = current_time
|
||
elapsed_minutes = (local_current_time - self._strategy_start_time).total_seconds() / 60.0
|
||
if elapsed_minutes < warmup_minutes:
|
||
rejected_conditions.append(f"冷启动保护: 策略启动未满 {warmup_minutes} 分钟（已运行 {elapsed_minutes:.1f} 分钟）")
|
||
self.strategy_log(f"[{pair}] {rejected_conditions[-1]}")
|
||
cold_start_rejected = True
|
||
|
||
# 检查1：入场间隔控制（使用hyperopt参数）
|
||
interval_rejected = False
|
||
if pair in self._last_entry_time and not cold_start_rejected:
|
||
last_entry = self._last_entry_time[pair]
|
||
time_diff = (current_time - last_entry).total_seconds() * 0.0166666667 # 转换为分钟（使用乘法避免除法）
|
||
if time_diff < self.entry_interval_minutes.value:
|
||
rejected_conditions.append(f"入场间隔不足: 距离上次入场 {time_diff:.1f}分钟 < {self.entry_interval_minutes.value}分钟")
|
||
self.strategy_log(f"[{pair}] {rejected_conditions[-1]}")
|
||
interval_rejected = True
|
||
|
||
# 检查2：检查是否处于剧烈拉升的不稳固区域
|
||
unstable_rejected = False
|
||
if not cold_start_rejected and not interval_rejected:
|
||
is_unstable_region = self.detect_h1_rapid_rise(pair)
|
||
if is_unstable_region:
|
||
rejected_conditions.append("剧烈拉升检测: 检测到1小时图剧烈拉升")
|
||
self.strategy_log(f"[{pair}] {rejected_conditions[-1]}")
|
||
unstable_rejected = True
|
||
|
||
# 如果有任何拒绝条件，直接返回 False
|
||
if rejected_conditions:
|
||
self.strategy_log(f"[{pair}] 入场被拒绝，原因: {', '.join(rejected_conditions)}")
|
||
return False
|
||
|
||
# 所有条件通过，允许入场并记录入场时间
|
||
self.strategy_log(f"[{pair}] 所有条件通过，允许入场")
|
||
self._last_entry_time[pair] = current_time
|
||
self.strategy_log(f"[{pair}] 更新入场时间为: {current_time}")
|
||
|
||
return True
|
||
|
||
|
||
|
||
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'
|
||
|
||
# 渐进式止损策略
|
||
if current_profit > 0.05: # 利润超过5%时
|
||
return -3.0 * atr / current_rate
|
||
elif current_profit > 0.03: # 利润超过3%时
|
||
return -2.5 * atr / current_rate
|
||
elif current_profit > 0.01: # 利润超过1%时
|
||
return -2.0 * atr / current_rate
|
||
|
||
# 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
|
||
return self.stoploss
|
||
|
||
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
|
||
rate: float, time_in_force: str, exit_reason: str,
|
||
current_time: datetime, **kwargs) -> bool:
|
||
"""
|
||
智能出场确认：结合ML预测信号来决定是否出场
|
||
"""
|
||
# 计算持仓时长（分钟）
|
||
trade_age_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
|
||
|
||
# 设置最小持仓时间保护，避免交易刚开仓就立刻被平仓
|
||
min_hold_minutes = 3 # 至少持有3分钟
|
||
if trade_age_minutes < min_hold_minutes:
|
||
self.strategy_log(f"[{pair}] 交易开仓时间仅{trade_age_minutes:.1f}分钟，小于最小持仓时间{min_hold_minutes}分钟，阻止出场")
|
||
return False
|
||
|
||
# 检查FreqAI是否启用
|
||
freqai_enabled = False
|
||
try:
|
||
# 更可靠的检查方法：检查config中是否启用了FreqAI
|
||
freqai_enabled = self.config.get('freqai', {}).get('enabled', False)
|
||
# 同时检查freqai属性是否存在且有效
|
||
if freqai_enabled and hasattr(self, 'freqai') and self.freqai is not None:
|
||
freqai_enabled = True
|
||
else:
|
||
freqai_enabled = False
|
||
except Exception as e:
|
||
self.strategy_log(f"[{pair}] 检查FreqAI启用状态时出错: {str(e)}")
|
||
freqai_enabled = False
|
||
|
||
try:
|
||
if freqai_enabled:
|
||
self.strategy_log(f"[{pair}] FreqAI已启用，使用ML信号进行出场决策")
|
||
# 获取当前的ML预测数据
|
||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if len(df) > 0:
|
||
last_row = df.iloc[-1]
|
||
|
||
# 获取当前入场信号概率
|
||
current_entry_prob = None
|
||
if '&s-entry_signal' in df.columns:
|
||
current_entry_prob = float(last_row['&s-entry_signal'])
|
||
elif '&-entry_signal_prob' in df.columns:
|
||
current_entry_prob = float(last_row['&-entry_signal_prob'])
|
||
elif '&-s-entry_signal_prob' in df.columns:
|
||
current_entry_prob = float(last_row['&-s-entry_signal_prob'])
|
||
|
||
# 获取当前出场信号概率
|
||
current_exit_prob = None
|
||
if '&s-exit_signal' in df.columns:
|
||
current_exit_prob = float(last_row['&s-exit_signal'])
|
||
elif '&-exit_signal_prob' in df.columns:
|
||
current_exit_prob = float(last_row['&-exit_signal_prob'])
|
||
elif '&-s-exit_signal_prob' in df.columns:
|
||
current_exit_prob = float(last_row['&-s-exit_signal_prob'])
|
||
|
||
# 获取波动率预测
|
||
predicted_volatility = None
|
||
if '&-target_volatility' in df.columns:
|
||
predicted_volatility = float(last_row['&-target_volatility'])
|
||
|
||
# 获取未来波动率信号
|
||
future_volatility = None
|
||
if '&s-future_volatility' in df.columns:
|
||
future_volatility = float(last_row['&s-future_volatility'])
|
||
elif '&-future_volatility' in df.columns:
|
||
future_volatility = float(last_row['&-future_volatility'])
|
||
|
||
# 获取趋势预测
|
||
trend_prediction = None
|
||
if '&-target_trend' in df.columns:
|
||
trend_prediction = float(last_row['&-target_trend'])
|
||
|
||
# 应用新规则：当exit_signal > entry_signal × 阈值时退出
|
||
# 基础信号比率阈值
|
||
base_signal_ratio_threshold = 1.33
|
||
signal_ratio_threshold = base_signal_ratio_threshold
|
||
|
||
# 根据未来波动率调整阈值（线性函数）
|
||
if future_volatility is not None:
|
||
# 确保 future_volatility 在 [0, 1] 范围内
|
||
future_volatility = max(0.0, min(1.0, future_volatility))
|
||
|
||
# 使用线性函数：x范围 0.9～0.3，y范围 1.1～1.6
|
||
# 当波动率为0.9时，阈值为1.1；当波动率为0.3时，阈值为1.6
|
||
x_min = 0.3
|
||
x_max = 0.9
|
||
y_min = 1.6
|
||
y_max = 1.1
|
||
|
||
# 线性插值
|
||
if future_volatility >= x_max:
|
||
# 高于最高波动率阈值，使用最小阈值
|
||
signal_ratio_threshold = y_max
|
||
elif future_volatility <= x_min:
|
||
# 低于最低波动率阈值，使用最大阈值
|
||
signal_ratio_threshold = y_min
|
||
else:
|
||
# 中间值线性插值
|
||
signal_ratio_threshold = y_max + (y_min - y_max) * (future_volatility - x_max) / (x_min - x_max)
|
||
|
||
# 限制阈值范围
|
||
signal_ratio_threshold = max(1.1, min(1.6, signal_ratio_threshold))
|
||
if current_entry_prob is not None and current_exit_prob is not None:
|
||
# 确保概率在 [0, 1] 范围内
|
||
current_entry_prob = max(0.0, min(1.0, current_entry_prob))
|
||
current_exit_prob = max(0.0, min(1.0, current_exit_prob))
|
||
|
||
if current_exit_prob > current_entry_prob * signal_ratio_threshold:
|
||
volatility_info = f"，未来波动率: {future_volatility:.2f}，调整后阈值: {signal_ratio_threshold:.2f}" if future_volatility is not None else ""
|
||
self.strategy_log(f"[{pair}] exit_signal ({current_exit_prob:.2f}) 超过 entry_signal ({current_entry_prob:.2f}) 的 {signal_ratio_threshold:.2f} 倍，支持出场{volatility_info}")
|
||
return True
|
||
|
||
# 原有ML预测逻辑作为补充
|
||
if current_entry_prob is not None and predicted_volatility is not None:
|
||
# 如果当前入场信号概率很高，说明市场条件仍然良好，可以继续持有
|
||
if current_entry_prob > 0.6:
|
||
self.strategy_log(f"[{pair}] 当前入场信号概率高({current_entry_prob:.2f})，建议继续持有")
|
||
return False # 阻止出场
|
||
|
||
# 如果当前入场信号概率很低，支持出场
|
||
if current_entry_prob < 0.25:
|
||
self.strategy_log(f"[{pair}] 当前入场信号概率低({current_entry_prob:.2f})，支持出场")
|
||
return True # 支持出场
|
||
|
||
# 如果波动率预测很低但入场信号概率较高，可能不应该立即出场
|
||
if predicted_volatility < 0.35 and current_entry_prob > 0.55:
|
||
self.strategy_log(f"[{pair}] 低波动+高入场信号({current_entry_prob:.2f})，建议继续持有等待机会")
|
||
return False # 阻止因低波动而强制出场
|
||
|
||
# 如果波动率预测很低且入场信号概率也低，支持出场
|
||
if predicted_volatility < 0.35 and current_entry_prob < 0.3:
|
||
self.strategy_log(f"[{pair}] 低波动+低入场信号({current_entry_prob:.2f})，支持出场")
|
||
return True # 支持出场
|
||
|
||
# 如果有趋势预测且预测为下跌趋势，支持出场
|
||
if trend_prediction is not None and trend_prediction < 0.3:
|
||
self.strategy_log(f"[{pair}] 趋势预测为下跌({trend_prediction:.2f})，支持出场")
|
||
return True
|
||
else:
|
||
self.strategy_log(f"[{pair}] FreqAI未启用，使用默认出场逻辑")
|
||
|
||
# 如果无法获取ML数据或FreqAI未启用，使用默认逻辑
|
||
# 只有当exit_reason是exit_signal时才允许出场
|
||
return exit_reason == 'exit_signal'
|
||
|
||
except Exception as e:
|
||
self.strategy_log(f"[{pair}] ML出场确认检查失败，使用默认逻辑: {e}")
|
||
# 发生错误时，使用默认逻辑
|
||
return exit_reason == 'exit_signal'
|
||
|
||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
|
||
current_profit: float, **kwargs) -> float:
|
||
if trade.is_short:
|
||
return 0.0
|
||
|
||
trade_age_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
|
||
if trade_age_minutes < 0:
|
||
trade_age_minutes = 0
|
||
|
||
# 使用可优化的线性函数: y = (a * (x + k)) + t
|
||
a = self.roi_param_a.value # 系数a (可优化参数)
|
||
k = self.roi_param_k.value # 偏移量k (可优化参数)
|
||
t = self.roi_param_t.value # 常数项t (可优化参数)
|
||
|
||
dynamic_roi_threshold = (a * (trade_age_minutes + k)) + t
|
||
# 确保ROI阈值不小于0
|
||
if dynamic_roi_threshold < 0:
|
||
dynamic_roi_threshold = 0.0
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
|
||
# 获取市场状态，使用多种回退方案
|
||
if 'market_state' in dataframe.columns:
|
||
current_state = dataframe['market_state'].iloc[-1]
|
||
elif 'sma' in dataframe.columns and len(dataframe) > 1:
|
||
# 使用 SMA 斜率判断市场状态
|
||
sma_diff = dataframe['sma'].diff().iloc[-1]
|
||
if sma_diff > 0.01:
|
||
current_state = 'strong_bull'
|
||
elif sma_diff > 0:
|
||
current_state = 'weak_bull'
|
||
else:
|
||
current_state = 'neutral'
|
||
else:
|
||
# 默认为中性
|
||
current_state = 'neutral'
|
||
|
||
entry_tag = trade.enter_tag if hasattr(trade, 'enter_tag') else None
|
||
profit_ratio = current_profit / dynamic_roi_threshold if dynamic_roi_threshold > 0 else 0
|
||
|
||
exit_ratio = 0.0
|
||
if profit_ratio >= 1.0:
|
||
if current_state == 'strong_bull':
|
||
exit_ratio = 0.5 if profit_ratio < 1.5 else 0.8
|
||
elif current_state == 'weak_bull':
|
||
exit_ratio = 0.6 if profit_ratio < 1.2 else 0.9
|
||
else:
|
||
exit_ratio = 1.0
|
||
if entry_tag == 'strong_trend':
|
||
exit_ratio *= 0.8
|
||
|
||
if dynamic_roi_threshold < 0:
|
||
exit_ratio = 1.0
|
||
|
||
#self.strategy_log(f"[{pair}] 动态止盈: 持仓时间={trade_age_minutes:.1f}分钟, 当前利润={current_profit:.2%}, "
|
||
# f"动态ROI阈值={dynamic_roi_threshold:.4f}, 利润比值={profit_ratio:.2f}, "
|
||
# f"市场状态={current_state}, entry_tag={entry_tag}, 退出比例={exit_ratio:.0%}")
|
||
|
||
# 当决定退出时，输出出场价格信息（但不调整价格）
|
||
if exit_ratio > 0:
|
||
self.strategy_log(f"[{pair}] 准备出场 - 市场价: {current_rate:.8f}, 退出比例: {exit_ratio:.0%}")
|
||
|
||
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
|
||
|
||
# 计算加仓次数（从1开始计数）
|
||
adjustment_count = entry_count - 1 # 已加仓次数
|
||
|
||
# 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'
|
||
|
||
# 计算当前所需的加仓间隔百分比 = 基础间隔 * (系数 ^ 已加仓次数)
|
||
# 获取当前币对的波动系数，用于动态调整回调百分比
|
||
volatility_coef = self.get_volatility_coefficient(pair)
|
||
# 回调百分比 = 基础回调 * (系数 ^ 已加仓次数) * 波动系数
|
||
current_callback = self.add_position_callback.value * (self.add_position_multiplier.value ** adjustment_count) * volatility_coef
|
||
|
||
if price_diff_pct <= -current_callback:
|
||
# 计算初始入场金额
|
||
initial_stake = trade.orders[0].cost # 第一笔订单的成本
|
||
|
||
# 计算加仓金额: (initial_stake / stake_divisor) ^ (adjustment_count + 1)
|
||
additional_stake = (initial_stake / self.stake_divisor.value) * (self.add_position_growth.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))
|
||
|
||
#self.strategy_log(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
|
||
|
||
def custom_stake_amount(self, pair: str, current_time: datetime, **kwargs) -> float:
|
||
"""
|
||
定义初始仓位大小
|
||
"""
|
||
# 获取默认的基础仓位大小
|
||
default_stake = self.stake_amount
|
||
|
||
# 从kwargs获取最小和最大仓位限制
|
||
min_stake = kwargs.get('min_stake', 0.0)
|
||
max_stake = kwargs.get('max_stake', default_stake)
|
||
|
||
# 确保仓位在允许的范围内
|
||
adjusted_stake = max(min_stake, min(default_stake, max_stake))
|
||
|
||
return adjusted_stake
|
||
|
||
def custom_exit_price(self, pair: str, trade: Trade, current_time: datetime, proposed_rate: float,
|
||
current_profit: float, exit_tag: str | None, **kwargs) -> float:
|
||
"""
|
||
自定义出场价格
|
||
根据文档，价格调整逻辑应该放在这里，而不是在 custom_exit 中
|
||
"""
|
||
# 计算出场价格上浮比例（0.25%）
|
||
price_markup_percent = 0.25
|
||
adjusted_exit_price = proposed_rate * (1 + price_markup_percent / 100)
|
||
|
||
self.strategy_log(f"[{pair}] 自定义出场价格: 原价格 {proposed_rate:.8f} -> 调整后价格 {adjusted_exit_price:.8f}, 上浮: {price_markup_percent}%")
|
||
|
||
return adjusted_exit_price
|
||
|
||
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime,
|
||
proposed_rate: float, entry_tag: str | None, side: str, **kwargs) -> float:
|
||
"""
|
||
自定义入场价格
|
||
将入场价格调低0.25%，以提高成交概率
|
||
"""
|
||
# 计算入场价格下调比例（0.25%）
|
||
price_discount_percent = 0.25
|
||
adjusted_entry_price = proposed_rate * (1 - price_discount_percent / 100)
|
||
|
||
self.strategy_log(f"[{pair}] 自定义入场价格: 原价格 {proposed_rate:.8f} -> 调整后价格 {adjusted_entry_price:.8f}, 下调: {price_discount_percent}%")
|
||
|
||
return adjusted_entry_price
|
||
|