1954 lines
106 KiB
Python
1954 lines
106 KiB
Python
import logging
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import numpy as np
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import datetime
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import datetime
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import os
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import json
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import glob
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import redis
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from functools import reduce
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from freqtrade.persistence import Trade
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import talib.abstract as ta
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from pandas import DataFrame
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import pandas as pd
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from typing import Dict
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from freqtrade.strategy import (DecimalParameter, IStrategy, IntParameter)
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from datetime import datetime
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logger = logging.getLogger(__name__)
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class FreqaiPrimer(IStrategy):
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"""
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基于 FreqAI 的动态阈值交易策略，集成动态加仓、减仓和自定义 ROI 逻辑，兼容最新 Freqtrade 版本
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"""
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INTERFACE_VERSION = 3 # 使用最新策略接口版本
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# --- 🧪 固定配置参数（从hyperopt优化结果获取）---
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TRAILING_STOP_START = 0.045
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TRAILING_STOP_DISTANCE = 0.0125
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BUY_THRESHOLD_MIN = -0.035
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BUY_THRESHOLD_MAX = -0.001
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SELL_THRESHOLD_MIN = 0.002
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SELL_THRESHOLD_MAX = 0.065
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# 新增：加仓和减仓参数
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ADD_POSITION_THRESHOLD = -0.021
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EXIT_POSITION_RATIO = 0.472
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COOLDOWN_PERIOD_MINUTES = 9
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MAX_ENTRY_POSITION_ADJUSTMENT = 3
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# 🟢 绿色通道开关 - 直接控制是否启用绿色通道功能
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GREEN_CHANNEL_ENABLED = False # 设置为False关闭绿色通道，True开启绿色通道
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# 趋势判定阈值参数
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TREND_BULLISH_THRESHOLD = 85
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TREND_BEARISH_THRESHOLD = 60
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# 新的加权趋势判定阈值（用于hyperopt优化）
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TREND_FINAL_BULLISH_THRESHOLD = 55 # 上涨趋势最终阈值
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TREND_FINAL_BEARISH_THRESHOLD = 13 # 下跌趋势最终阈值
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# Hyperopt 可优化参数 - 基于初步结果调整范围
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trend_final_bullish_threshold = IntParameter(20, 85, default=71, space="buy", optimize=True, load=True) # 降低上限，避免过于保守
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trend_final_bearish_threshold = IntParameter(5, 45, default=21, space="buy", optimize=True, load=True) # 扩大下限，捕获更多熊市机会
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# 趋势确认参数 - 增加交易频率
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trend_confirmation_period = IntParameter(3, 15, default=8, space="buy", optimize=True, load=True) # 趋势确认周期
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# 出场策略相关参数
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exit_divergence_multiplier = DecimalParameter(1.0, 1.3, default=1.06, space="sell", optimize=True, load=True)
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exit_adx_threshold = IntParameter(15, 35, default=20, space="sell", optimize=True, load=True)
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exit_rsi_threshold = IntParameter(55, 75, default=65, space="sell", optimize=True, load=True)
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exit_stochrsi_threshold = IntParameter(60, 80, default=70, space="sell", optimize=True, load=True)
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exit_adx_threshold_overbought = IntParameter(20, 35, default=25, space="sell", optimize=True, load=True)
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exit_min_profit_threshold = DecimalParameter(0.01, 0.08, default=0.039, space="sell", optimize=True, load=True) # 提高最小盈利要求
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exit_rapid_rise_bull = DecimalParameter(1.5, 4.0, default=2.0, space="sell", optimize=True, load=True)
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exit_rapid_rise_bear = DecimalParameter(2.5, 5.0, default=3.5, space="sell", optimize=True, load=True)
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exit_stochrsi_rapid = IntParameter(65, 85, default=70, space="sell", optimize=True, load=True)
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exit_volume_threshold = DecimalParameter(0.5, 2.0, default=1.0, space="sell", optimize=True, load=True)
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# 趋势状态相关出场参数
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exit_bullish_trend_score_max = IntParameter(90, 98, default=95, space="sell", optimize=True, load=True)
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exit_bullish_divergence_mult = DecimalParameter(1.1, 1.3, default=1.15, space="sell", optimize=True, load=True)
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exit_bullish_rsi = IntParameter(70, 85, default=75, space="sell", optimize=True, load=True)
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exit_bullish_stochrsi = IntParameter(80, 90, default=85, space="sell", optimize=True, load=True)
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exit_bullish_adx = IntParameter(25, 40, default=30, space="sell", optimize=True, load=True)
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exit_bullish_return = DecimalParameter(3.0, 7.0, default=5.0, space="sell", optimize=True, load=True)
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exit_bullish_stochrsi_rapid = IntParameter(80, 90, default=85, space="sell", optimize=True, load=True)
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exit_bearish_divergence_mult = DecimalParameter(0.8, 1.0, default=0.9, space="sell", optimize=True, load=True)
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exit_bearish_rsi = IntParameter(55, 65, default=60, space="sell", optimize=True, load=True)
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exit_bearish_stochrsi = IntParameter(65, 75, default=70, space="sell", optimize=True, load=True)
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exit_bearish_adx = IntParameter(15, 25, default=20, space="sell", optimize=True, load=True)
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exit_bearish_return = DecimalParameter(1.0, 3.0, default=1.5, space="sell", optimize=True, load=True)
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exit_bearish_stochrsi_rapid = IntParameter(70, 85, default=75, space="sell", optimize=True, load=True)
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exit_ranging_trend_score_max = IntParameter(95, 99, default=98, space="sell", optimize=True, load=True)
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exit_ranging_trend_score_threshold = IntParameter(80, 90, default=85, space="sell", optimize=True, load=True)
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# --- 🛠️ 固定配置参数 ---
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stoploss = -0.15
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timeframe = "3m"
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use_custom_stoploss = True
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position_adjustment_enable = True # 启用动态仓位调整
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trailing_stop = True
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trailing_stop_positive = 0.0125 # 🎯 降低锁定利润到2.5%
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trailing_stop_positive_offset = 0.04 # 🎯 降低启动阈值到8%
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trailing_only_offset_is_reached = False
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minimal_roi = {
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"0": 0.06, # 30分钟（0-30分钟）内，8% 盈利退出
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"30": 0.04, # 2小时（30-120分钟）内，4% 盈利退出
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"90": 0.025, # 4小时（120-240分钟）内，2% 盈利退出
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"270": 0.002 # 8小时（240-480分钟）内，0% 盈利退出
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}
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plot_config = {
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"main_plot": {
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"ema200": {"color": "blue"},
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"bb_upperband": {"color": "gray"},
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"bb_lowerband": {"color": "gray"},
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},
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"subplots": {
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"Signals": {
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"enter_long": {"color": "green", "type": "scatter"},
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"exit_long": {"color": "red", "type": "scatter"}
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},
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"Indicators": {
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"&-price_value_divergence": {"color": "purple"},
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"volume_z_score": {"color": "orange"},
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"rsi": {"color": "cyan"},
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"stochrsi_k": {"color": "magenta"},
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},
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"Bearish Signals": {
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"bearish_signal": {"type": "bar", "color": "red"},
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"stochrsi_overbought": {"type": "bar", "color": "orange"},
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}
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}
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}
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freqai_info = {
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"identifier": "freqai_primer_mixed_v7", # 更新标识符以强制重新训练
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"feature_parameters": {
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"include_timeframes": ["3m", "15m", "1h"],
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"label_period_candles": 12,
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"include_shifted_candles": 3,
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"include_corr_pairlist": [],
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"indicator_periods_candles": [10, 20, 50],
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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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"price_value_divergence": {
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"model": "LightGBMRegressor",
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"model_params": {
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"n_estimators": 200,
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"learning_rate": 0.08,
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"num_leaves": 25,
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"max_depth": 7,
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"min_child_samples": 8,
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"subsample": 0.85,
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"colsample_bytree": 0.85,
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"reg_alpha": 0.08,
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"reg_lambda": 0.08,
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"verbose": -1,
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}
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},
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"optimal_first_length": {
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"model": "LightGBMClassifier",
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"model_params": {
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"n_estimators": 200,
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"learning_rate": 0.08,
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"num_leaves": 25,
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"max_depth": 7,
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"min_child_samples": 8,
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"subsample": 0.85,
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"colsample_bytree": 0.85,
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"reg_alpha": 0.08,
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"reg_lambda": 0.08,
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"class_weight": "balanced",
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"verbose": -1,
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}
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},
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"risk_rating": {
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"model": "LightGBMClassifier",
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"model_params": {
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"n_estimators": 200,
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"learning_rate": 0.08,
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"num_leaves": 25,
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"max_depth": 7,
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"min_child_samples": 8,
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"subsample": 0.85,
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"colsample_bytree": 0.85,
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"reg_alpha": 0.08,
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"reg_lambda": 0.08,
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"class_weight": "balanced",
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"verbose": -1,
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}
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}
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},
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"fit_live_predictions_candles": 100,
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"live_retrain_candles": 100,
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}
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@staticmethod
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def linear_map(value, from_min, from_max, to_min, to_max):
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return (value - from_min) / (from_max - from_min) * (to_max - to_min) + to_min
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def __init__(self, config: dict, *args, **kwargs):
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super().__init__(config, *args, **kwargs)
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# 读取 Redis 配置（不存储客户端实例，避免序列化问题）
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self.redis_url = config.get('redis', {}).get('url', None)
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self.stats_logged = False
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# 验证Redis配置但不创建持久化客户端
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if self.redis_url:
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logger.info(f"Redis 配置已启用: {self.redis_url}")
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else:
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logger.info("ℹ️ 未找到 Redis 配置，使用本地缓存模式")
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self.trailing_stop_enabled = False
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self.pair_stats = {}
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self.fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
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self.last_entry_time = {} # 记录每个币种的最后入场时间
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# 绿色通道开关直接使用全局变量
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self.green_channel_enabled = self.GREEN_CHANNEL_ENABLED
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logger.info(f"🟢 绿色通道开关状态: {'开启' if self.green_channel_enabled else '关闭'}（通过全局变量控制）")
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def _get_redis_client(self):
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"""延迟初始化Redis客户端，避免序列化问题"""
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if not self.redis_url:
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return None
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try:
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import redis
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client = redis.from_url(self.redis_url)
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# 快速测试连接
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client.ping()
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return client
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except Exception:
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return None
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
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dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
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real = ta.TYPPRICE(dataframe)
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upperband, middleband, lowerband = ta.BBANDS(real, timeperiod=period, nbdevup=2.0, nbdevdn=2.0)
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dataframe["bb_lowerband-period"] = lowerband
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dataframe["bb_upperband-period"] = upperband
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dataframe["bb_middleband-period"] = middleband
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dataframe["%-bb_width-period"] = (dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]) / dataframe["bb_middleband-period"]
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dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
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dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
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dataframe["%-relative_volume-period"] = dataframe["volume"] / dataframe["volume"].rolling(period).mean()
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dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
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dataframe["%-price_value_divergence"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"]
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# 新增：风险评级相关特征
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dataframe["%-atr-period"] = ta.ATR(dataframe, timeperiod=period)
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dataframe["%-volatility_ratio"] = dataframe["%-atr-period"] / dataframe["close"]
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dataframe["%-price_range"] = (dataframe["high"] - dataframe["low"]) / dataframe["close"]
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dataframe["%-volume_volatility"] = dataframe["volume"].pct_change().rolling(period).std()
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dataframe["%-price_velocity"] = dataframe["close"].pct_change().abs().rolling(period).mean()
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dataframe["%-rsi_velocity"] = dataframe["%-rsi-period"].diff().abs().rolling(3).mean()
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columns_to_clean = [
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"%-rsi-period", "%-mfi-period", "%-sma-period", "%-ema-period", "%-adx-period",
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"bb_lowerband-period", "bb_middleband-period", "bb_upperband-period",
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"%-bb_width-period", "%-relative_volume-period", "%-price_value_divergence",
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"%-atr-period", "%-volatility_ratio", "%-price_range", "%-volume_volatility",
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"%-price_velocity", "%-rsi_velocity"
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]
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for col in columns_to_clean:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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pair = metadata.get('pair', 'Unknown')
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logger.info(f"[{pair}] 特征工程完成，列：{list(dataframe.columns)}")
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return dataframe
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def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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pair = metadata.get('pair', 'Unknown')
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if len(dataframe) < 200:
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logger.warning(f"[{pair}] 数据量不足（{len(dataframe)}根K线），需要至少200根K线进行训练")
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return dataframe
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dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
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dataframe["&-price_value_divergence"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"]
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dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean()
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dataframe["volume_std_20"] = dataframe["volume"].rolling(20).std()
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dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
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dataframe["volume_z_score"] = dataframe["volume_z_score"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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# 严格清理price_value_divergence，确保数据类型正确
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price_divergence = dataframe["&-price_value_divergence"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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dataframe["&-price_value_divergence"] = price_divergence.astype(np.float64)
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# 验证数据类型和范围
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if not np.isfinite(dataframe["&-price_value_divergence"]).all():
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logger.warning(f"[{pair}] price_value_divergence包含非有限值，进行清理")
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dataframe["&-price_value_divergence"] = dataframe["&-price_value_divergence"].replace([np.inf, -np.inf, np.nan], 0.0)
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# 新增：智能风险评级分类目标
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# 基于未来波动率、成交量异常、价格动量等多维度评估风险等级
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# 0: 低风险(绿色) 1: 中低风险(蓝色) 2: 中等风险(黄色) 3: 中高风险(橙色) 4: 高风险(红色)
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# 计算未来波动率作为风险指标
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future_atr = ta.ATR(dataframe, timeperiod=14).shift(-6)
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future_volatility = (future_atr / dataframe['close']).replace([np.inf, -np.inf], 0)
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# 成交量异常检测
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volume_zscore = (dataframe['volume'] - dataframe['volume'].rolling(20).mean()) / dataframe['volume'].rolling(20).std()
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volume_anomaly = abs(volume_zscore) > 2
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# 价格动量强度
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price_momentum = abs(dataframe['close'].pct_change(5)).rolling(3).mean()
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# 综合风险评分
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risk_score = (
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future_volatility * 0.4 + # 未来波动率权重40%
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(abs(volume_zscore) / 5) * 0.3 + # 成交量异常权重30%
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price_momentum * 0.3 # 价格动量权重30%
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)
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# 风险等级分类
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risk_conditions = [
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risk_score < 0.01, # 低风险
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risk_score < 0.02, # 中低风险
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risk_score < 0.035, # 中等风险
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risk_score < 0.05, # 中高风险
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]
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risk_choices = [0, 1, 2, 3]
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dataframe["&*-risk_rating"] = np.select(risk_conditions, risk_choices, default=4).astype(np.int32)
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# 清理风险评级值
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if not np.isfinite(dataframe["&*-risk_rating"]).all():
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logger.warning(f"[{pair}] risk_rating包含非有限值，进行清理")
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dataframe["&*-risk_rating"] = dataframe["&*-risk_rating"].replace([np.inf, -np.inf, np.nan], 2).astype(np.int32)
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logger.info(f"[{pair}] 风险评级分布: {dict(pd.Series(dataframe['&*-risk_rating']).value_counts())}")
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# 新增：分类模型优化 first_length
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# 基于市场状态预测最优的 first_length 分类（2, 4, 6, 8, 10）
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# 使用技术指标和市场波动率作为特征
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atr = ta.ATR(dataframe, timeperiod=14)
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volatility_ratio = atr / dataframe['close']
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# 定义市场状态特征
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trend_strength = abs(dataframe['close'] - dataframe['ema200']) / dataframe['ema200']
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volume_anomaly = abs(dataframe['volume_z_score']) > 2
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# 基于市场状态确定最优 first_length 类别
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# 0: 激进(2) 1: 中性(4) 2: 稳健(6) 3: 保守(8) 4: 极保守(10)
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conditions = [
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(volatility_ratio > 0.02) & (trend_strength > 0.05), # 高波动+强趋势 -> 激进
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(volatility_ratio > 0.015) & (trend_strength > 0.03), # 中高波动+中强趋势 -> 中性
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(volatility_ratio > 0.01) | (volume_anomaly), # 中等波动或成交量异常 -> 稳健
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(volatility_ratio < 0.008) & (trend_strength < 0.02), # 低波动+弱趋势 -> 保守
|
||
]
|
||
choices = [0, 1, 2, 3]
|
||
|
||
# 确保分类目标列是整数类型，添加严格验证
|
||
try:
|
||
optimal_length_class = np.select(conditions, choices, default=4)
|
||
# 确保所有值都是有效的整数
|
||
optimal_length_class = np.clip(optimal_length_class, 0, 4) # 限制在0-4范围内
|
||
dataframe["&*-optimal_first_length"] = optimal_length_class.astype(np.int32)
|
||
|
||
# 验证分类值的有效性
|
||
unique_values = np.unique(optimal_length_class)
|
||
if not all(0 <= val <= 4 for val in unique_values):
|
||
logger.warning(f"[{pair}] 发现无效的first_length分类值: {unique_values}")
|
||
dataframe["&*-optimal_first_length"] = np.clip(optimal_length_class, 0, 4).astype(np.int32)
|
||
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] 计算分类目标时出错: {e}")
|
||
dataframe["&*-optimal_first_length"] = np.full(len(dataframe), 2, dtype=np.int32) # 默认值为2
|
||
|
||
# 添加调试信息
|
||
logger.info(f"[{pair}] 📈 分类目标统计: {dict(pd.Series(optimal_length_class).value_counts())}")
|
||
logger.info(f"[{pair}] 🎯 最新分类值: {optimal_length_class[-5:]}")
|
||
|
||
return dataframe
|
||
def is_stochrsi_overbought(self, dataframe: DataFrame, period=10, threshold=85) -> bool:
|
||
"""
|
||
判断当前 STOCHRSI 是否在过去 N 根 K 线中平均高于阈值（如 85）
|
||
"""
|
||
if 'stochrsi_k' not in dataframe.columns:
|
||
# 如果列不存在，返回全 False 的 Series
|
||
return pd.Series([False] * len(dataframe), index=dataframe.index)
|
||
# 计算滚动平均值并判断是否超过阈值
|
||
avg_stochrsi = dataframe['stochrsi_k'].rolling(window=period).mean()
|
||
return avg_stochrsi > threshold
|
||
|
||
def is_bearish_market(self, dataframe: DataFrame, metadata: dict, timeframe: str = "1h") -> pd.Series:
|
||
pair = metadata.get('pair', 'Unknown')
|
||
logger.info(f"[{pair}] 开始计算 1h 熊市信号")
|
||
|
||
# 防御性检查
|
||
required_columns = ['close_1h', 'high_1h', 'low_1h', 'open_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'bb_middle_1h']
|
||
missing = [col for col in required_columns if col not in dataframe.columns]
|
||
if missing:
|
||
logger.error(f"[{pair}] 缺少必要列：{missing}，返回全 False")
|
||
return pd.Series([False] * len(dataframe), index=dataframe.index)
|
||
|
||
# 检查 volume_z_score_1h 是否存在
|
||
has_volume_z_score = 'volume_z_score_1h' in dataframe.columns and not dataframe['volume_z_score_1h'].isna().all()
|
||
|
||
# 条件 a：价格远低于布林带中轨（低于中轨 1% 以上）
|
||
cond_a = dataframe['close_1h'] < dataframe['bb_middle_1h'] * 0.99
|
||
|
||
# 条件 b：STOCHRSI 超过90
|
||
cond_b = dataframe['stochrsi_k_1h'] > 90
|
||
|
||
# 条件 c：看跌蜡烛图形态
|
||
open_1h = dataframe['open_1h']
|
||
close_1h = dataframe['close_1h']
|
||
high_1h = dataframe['high_1h']
|
||
low_1h = dataframe['low_1h']
|
||
prev_open = open_1h.shift(1)
|
||
prev_close = close_1h.shift(1)
|
||
|
||
cond_engulfing = (
|
||
(prev_close > prev_open) &
|
||
(close_1h < open_1h) &
|
||
(close_1h < prev_open) &
|
||
(open_1h > prev_close)
|
||
)
|
||
cond_dark_cloud_cover = (
|
||
(prev_close > prev_open) &
|
||
(open_1h > prev_close) &
|
||
(close_1h < (prev_open + prev_close) / 2) &
|
||
(close_1h < open_1h)
|
||
)
|
||
body = abs(open_1h - close_1h)
|
||
upper_wick = high_1h - np.maximum(open_1h, close_1h)
|
||
lower_wick = np.minimum(open_1h, close_1h) - low_1h
|
||
cond_shooting_star = (upper_wick > 2 * body) & (lower_wick < body) & (close_1h < open_1h)
|
||
cond_stochrsi_high = self.is_stochrsi_overbought(dataframe, period=10, threshold=85)
|
||
cond_c = cond_engulfing | cond_dark_cloud_cover | cond_shooting_star | cond_stochrsi_high
|
||
|
||
# 条件 d：成交量显著下降
|
||
cond_d = dataframe['volume_z_score_1h'] < -1.0 if has_volume_z_score else pd.Series([False] * len(dataframe), index=dataframe.index)
|
||
|
||
# 综合熊市信号（至少满足两个条件）
|
||
bearish_signal = (cond_a & cond_b) | (cond_a & cond_c) | (cond_b & cond_c) | (cond_a & cond_d)
|
||
|
||
# 记录每个条件的触发情况
|
||
logger.info(f"[{pair}] 熊市信号 - 条件a (价格低于布林中轨): {cond_a.iloc[-1]}")
|
||
logger.info(f"[{pair}] 熊市信号 - 条件b (STOCHRSI>90): {cond_b.iloc[-1]}")
|
||
logger.info(f"[{pair}] 熊市信号 - 条件c (看跌形态): {cond_c.iloc[-1]}")
|
||
logger.info(f"[{pair}] 熊市信号 - 条件d (成交量下降): {cond_d.iloc[-1]}")
|
||
|
||
# 使用向量化操作计算得分，避免使用iloc[-1]进行业务逻辑判断
|
||
bearish_score_vector = (cond_a.astype(int) + cond_b.astype(int) + cond_c.astype(int) + cond_d.astype(int)) * 25
|
||
|
||
# 仅在日志中使用最后一行的值（这是允许的用途）
|
||
if len(dataframe) > 0:
|
||
bearish_score = bearish_score_vector.iloc[-1]
|
||
logger.info(f"[{pair}] 熊市信号总得分: {bearish_score}/100")
|
||
else:
|
||
bearish_score = 0
|
||
|
||
return bearish_signal
|
||
|
||
|
||
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
"""
|
||
计算主时间框架（3m）和 1h 时间框架的指标，并映射到主 dataframe。
|
||
包含 FreqAI 预测、布林带、RSI、成交量 Z 分数等，并确保 1h 数据列完整性。
|
||
"""
|
||
pair = metadata.get('pair', 'Unknown')
|
||
original_length = len(dataframe)
|
||
original_index = dataframe.index
|
||
|
||
logger.info(f"[{pair}] populate_indicators 开始处理，原始数据长度：{original_length}")
|
||
logger.info(f"[{pair}] 当前可用列（调用FreqAI前）：{list(dataframe.columns)}")
|
||
|
||
# 计算主时间框架（3m）指标
|
||
dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
|
||
dataframe["price_value_divergence"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"]
|
||
upperband, middleband, lowerband = ta.BBANDS(dataframe["close"], timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
|
||
dataframe["bb_upperband"] = upperband
|
||
dataframe["bb_middleband"] = middleband
|
||
dataframe["bb_lowerband"] = lowerband
|
||
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||
dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean()
|
||
dataframe["volume_std_20"] = dataframe["volume"].rolling(20).std()
|
||
dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
|
||
# 计算主时间框架的 STOCHRSI
|
||
stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
|
||
dataframe["stochrsi_k"] = stochrsi["fastk"]
|
||
dataframe["stochrsi_d"] = stochrsi["fastd"]
|
||
|
||
# 获取 1h 时间框架数据
|
||
dataframe_1h = self.dp.get_pair_dataframe(pair=pair, timeframe="1h")
|
||
if dataframe_1h.empty or len(dataframe_1h) < 50:
|
||
logger.warning(f"[{pair}] 1h 数据为空或不足（{len(dataframe_1h)} 根K线），初始化空列")
|
||
for col in ['open_1h', 'high_1h', 'low_1h', 'close_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
|
||
'bb_upper_1h', 'bb_middle_1h', 'bb_lower_1h', 'volume_z_score_1h']:
|
||
dataframe[col] = 0.0 # 使用0.0代替np.nan避免NaN值
|
||
else:
|
||
# 计算 1h 指标
|
||
if len(dataframe_1h) >= 20: # 确保有足够数据计算 rolling(20)
|
||
stochrsi_1h = ta.STOCHRSI(dataframe_1h, timeperiod=14, fastk_period=3, fastd_period=3)
|
||
dataframe_1h['stochrsi_k'] = stochrsi_1h['fastk']
|
||
dataframe_1h['stochrsi_d'] = stochrsi_1h['fastd']
|
||
real = ta.TYPPRICE(dataframe_1h)
|
||
upperband, middleband, lowerband = ta.BBANDS(real, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
|
||
dataframe_1h['bb_upper_1h'] = upperband
|
||
dataframe_1h['bb_middle_1h'] = middleband
|
||
dataframe_1h['bb_lower_1h'] = lowerband
|
||
dataframe_1h['volume_mean_20_1h'] = dataframe_1h["volume"].rolling(20).mean()
|
||
dataframe_1h['volume_std_20_1h'] = dataframe_1h["volume"].rolling(20).std()
|
||
dataframe_1h['volume_z_score_1h'] = (dataframe_1h["volume"] - dataframe_1h['volume_mean_20_1h']) / dataframe_1h['volume_std_20_1h']
|
||
# 清理 NaN 和无穷值
|
||
dataframe_1h['volume_z_score_1h'] = dataframe_1h['volume_z_score_1h'].replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||
else:
|
||
logger.warning(f"[{pair}] 1h 数据不足以计算 volume_z_score_1h（{len(dataframe_1h)} 根K线，需至少20根）")
|
||
dataframe_1h['volume_z_score_1h'] = 0.0
|
||
|
||
# 映射 1h 数据到主时间框架
|
||
for col in ['open', 'high', 'low', 'close', 'stochrsi_k', 'stochrsi_d', 'bb_upper_1h', 'bb_middle_1h', 'bb_lower_1h', 'volume_z_score_1h']:
|
||
if col in dataframe_1h.columns:
|
||
dataframe[col if col.endswith('_1h') else f"{col}_1h"] = dataframe_1h[col].reindex(dataframe.index, method='ffill').bfill()
|
||
else:
|
||
logger.warning(f"[{pair}] 1h 数据缺少列 {col}，初始化为空")
|
||
dataframe[col if col.endswith('_1h') else f"{col}_1h"] = 0.0
|
||
|
||
# 数据清理：处理 NaN 和无穷值，确保数据完整性
|
||
critical_columns = ["ema200", "bb_upperband", "bb_middleband", "bb_lowerband", "rsi", "volume_z_score",
|
||
"&-price_value_divergence", "price_value_divergence", "open_1h", "high_1h", "low_1h",
|
||
"close_1h", "stochrsi_k_1h", "stochrsi_d_1h", "bb_upper_1h", "bb_middle_1h", "bb_lower_1h",
|
||
"volume_z_score_1h", "stochrsi_k", "stochrsi_d", "close", "volume"]
|
||
|
||
for col in critical_columns:
|
||
if col in dataframe.columns:
|
||
# 处理无穷值和NaN值
|
||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
|
||
# 前向填充，然后用0填充剩余NaN
|
||
dataframe[col] = dataframe[col].ffill().fillna(0)
|
||
# 确保数据类型正确
|
||
dataframe[col] = pd.to_numeric(dataframe[col], errors='coerce').fillna(0)
|
||
|
||
# 验证并修复DataFrame长度一致性
|
||
final_length = len(dataframe)
|
||
if final_length != original_length:
|
||
logger.warning(f"[{pair}] DataFrame长度不匹配: 原始{original_length} vs 当前{final_length}")
|
||
# 重新索引确保长度一致
|
||
dataframe = dataframe.reindex(original_index)
|
||
# 填充任何NaN值
|
||
dataframe = dataframe.fillna(0)
|
||
logger.info(f"[{pair}] 已修复DataFrame长度，当前长度: {len(dataframe)}")
|
||
|
||
# 最终验证
|
||
if len(dataframe) != original_length:
|
||
logger.error(f"[{pair}] 严重错误：无法修复DataFrame长度不匹配问题")
|
||
# 强制截断或填充到原始长度
|
||
if len(dataframe) > original_length:
|
||
dataframe = dataframe.iloc[:original_length]
|
||
else:
|
||
# 填充缺失的行
|
||
missing_rows = original_length - len(dataframe)
|
||
logger.warning(f"[{pair}] 需要填充{missing_rows}行缺失数据")
|
||
# 使用最后一个有效值填充
|
||
last_row = dataframe.iloc[-1:] if len(dataframe) > 0 else dataframe.iloc[0:1]
|
||
for _ in range(missing_rows):
|
||
dataframe = pd.concat([dataframe, last_row])
|
||
dataframe = dataframe.iloc[:original_length]
|
||
dataframe.index = original_index
|
||
|
||
# 调用 FreqAI 预测 - 使用单一回归模型
|
||
if not hasattr(self, 'freqai') or self.freqai is None:
|
||
logger.error(f"[{pair}] FreqAI 未初始化，回退到规则计算")
|
||
dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
|
||
else:
|
||
logger.info(f"[{pair}] 调用 FreqAI 预测，类型：{type(self.freqai)}")
|
||
|
||
try:
|
||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||
|
||
# 检查 FreqAI 生成的列
|
||
freqai_columns = [col for col in dataframe.columns if col.startswith('&')]
|
||
logger.info(f"[{pair}] FreqAI 生成的列: {freqai_columns}")
|
||
|
||
if "&-price_value_divergence" not in dataframe.columns:
|
||
logger.warning(f"[{pair}] FreqAI 未生成 &-price_value_divergence，回退到规则计算")
|
||
dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
|
||
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] FreqAI 预测失败: {e}")
|
||
dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
|
||
|
||
# 计算 labels_mean 和 labels_std
|
||
labels_mean = None
|
||
labels_std = None
|
||
try:
|
||
model_base_dir = os.path.join(self.config["user_data_dir"], "models", self.freqai_info["identifier"])
|
||
pair_base = pair.split('/')[0] if '/' in pair else pair
|
||
sub_dirs = glob.glob(os.path.join(model_base_dir, f"sub-train-{pair_base}_*"))
|
||
if not sub_dirs:
|
||
logger.warning(f"[{pair}] 未找到任何子目录：{model_base_dir}/sub-train-{pair_base}_*")
|
||
else:
|
||
latest_sub_dir = max(sub_dirs, key=lambda x: int(x.split('_')[-1]))
|
||
pair_base_lower = pair_base.lower()
|
||
timestamp = latest_sub_dir.split('_')[-1]
|
||
metadata_file = os.path.join(latest_sub_dir, f"cb_{pair_base_lower}_{timestamp}_metadata.json")
|
||
if os.path.exists(metadata_file):
|
||
with open(metadata_file, "r") as f:
|
||
metadata = json.load(f)
|
||
labels_mean = metadata["labels_mean"]["&-price_value_divergence"]
|
||
labels_std = metadata["labels_std"]["&-price_value_divergence"]
|
||
logger.info(f"[{pair}] 从最新子目录 {latest_sub_dir} 读取 labels_mean：{labels_mean}, labels_std：{labels_std}")
|
||
else:
|
||
logger.warning(f"[{pair}] 最新的 metadata.json 文件 {metadata_file} 不存在")
|
||
except Exception as e:
|
||
logger.warning(f"[{pair}] 无法从子目录读取 labels_mean 和 labels_std：{e}，重新计算")
|
||
|
||
if labels_mean is None or labels_std is None:
|
||
logger.warning(f"[{pair}] 无法获取 labels_mean 和 labels_std，重新计算")
|
||
dataframe["&-price_value_divergence_actual"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"]
|
||
dataframe["&-price_value_divergence_actual"] = dataframe["&-price_value_divergence_actual"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||
recent_data = dataframe["&-price_value_divergence_actual"].tail(self.fit_live_predictions_candles)
|
||
labels_mean = recent_data.mean()
|
||
labels_std = recent_data.std()
|
||
if np.isnan(labels_std) or labels_std == 0:
|
||
labels_std = 0.01
|
||
logger.warning(f"[{pair}] labels_std 计算异常，使用默认值 0.01")
|
||
|
||
# 根据市场趋势得分动态调整买卖阈值
|
||
market_trend_score = self.get_market_trend(dataframe=dataframe, metadata={'pair': pair})
|
||
k_buy = self.linear_map(market_trend_score, 0, 100, 1.2, 0.8)
|
||
k_sell = self.linear_map(market_trend_score, 0, 100, 1.5, 1.0)
|
||
|
||
# 处理分类模型的预测值
|
||
if "&*-optimal_first_length" in dataframe.columns:
|
||
dataframe["optimal_first_length_pred"] = dataframe["&*-optimal_first_length"]
|
||
logger.info(f"[{pair}] ✅ 找到并复制分类模型预测列: &*-optimal_first_length -> optimal_first_length_pred")
|
||
logger.info(f"[{pair}] 分类预测值统计: {dataframe['&*-optimal_first_length'].value_counts().to_dict()}")
|
||
# 确保列存在
|
||
if "optimal_first_length_pred" in dataframe.columns:
|
||
logger.info(f"[{pair}] ✅ optimal_first_length_pred 列已创建，最后5个值: {dataframe['optimal_first_length_pred'].tail().tolist()}")
|
||
else:
|
||
logger.error(f"[{pair}] ❌ optimal_first_length_pred 列创建失败")
|
||
else:
|
||
logger.warning(f"[{pair}] 未找到分类模型预测列 &*-optimal_first_length")
|
||
# 创建默认列
|
||
dataframe["optimal_first_length_pred"] = 2.0
|
||
logger.info(f"[{pair}] 创建默认 optimal_first_length_pred 列，值=2.0")
|
||
|
||
self.buy_threshold = labels_mean - k_buy * labels_std
|
||
self.sell_threshold = labels_mean + k_sell * labels_std
|
||
|
||
# 使用固定参数限制阈值
|
||
self.buy_threshold = max(self.buy_threshold, self.BUY_THRESHOLD_MIN)
|
||
self.buy_threshold = min(self.buy_threshold, self.BUY_THRESHOLD_MAX)
|
||
self.sell_threshold = min(self.sell_threshold, self.SELL_THRESHOLD_MAX)
|
||
self.sell_threshold = max(self.sell_threshold, self.SELL_THRESHOLD_MIN)
|
||
|
||
# 调试日志
|
||
# 处理风险评级预测
|
||
if "&*-risk_rating" in dataframe.columns:
|
||
risk_rating = dataframe["&*-risk_rating"].iloc[-1] if len(dataframe) > 0 else 2
|
||
|
||
# 基于风险评级调整仓位和止损
|
||
risk_multipliers = {
|
||
0: {"position": 1.5, "stoploss": 0.8, "label": "🟢低风险"}, # 低风险：加大仓位，收紧止损
|
||
1: {"position": 1.2, "stoploss": 0.9, "label": "🔵中低风险"}, # 中低风险：适中仓位
|
||
2: {"position": 1.0, "stoploss": 1.0, "label": "🟡中等风险"}, # 中等风险：标准参数
|
||
3: {"position": 0.7, "stoploss": 1.2, "label": "🟠中高风险"}, # 中高风险：减小仓位，放宽止损
|
||
4: {"position": 0.5, "stoploss": 1.5, "label": "🔴高风险"}, # 高风险：最小仓位，最宽止损
|
||
}
|
||
|
||
risk_config = risk_multipliers.get(risk_rating, risk_multipliers[2])
|
||
|
||
# 应用到策略参数
|
||
self.risk_position_multiplier = risk_config["position"]
|
||
self.risk_stoploss_multiplier = risk_config["stoploss"]
|
||
|
||
logger.info(f"[{pair}] {risk_config['label']} - 仓位倍数: {self.risk_position_multiplier}, "
|
||
f"止损倍数: {self.risk_stoploss_multiplier}")
|
||
else:
|
||
# 默认值
|
||
self.risk_position_multiplier = 1.0
|
||
self.risk_stoploss_multiplier = 1.0
|
||
|
||
logger.info(f"[{pair}] 市场趋势得分：{market_trend_score}, labels_mean：{labels_mean:.4f}, labels_std：{labels_std:.4f}")
|
||
logger.info(f"[{pair}] k_buy：{k_buy:.2f}, k_sell：{k_sell:.2f}")
|
||
logger.info(f"[{pair}] 动态买入阈值：{self.buy_threshold:.4f}, 卖出阈值：{self.sell_threshold:.4f}")
|
||
logger.info(f"[{pair}] 最新数据 - close: {dataframe['close'].iloc[-1]:.6f}, "
|
||
f"rsi: {dataframe['rsi'].iloc[-1]:.2f}, "
|
||
f"&-price_value_divergence: {dataframe['&-price_value_divergence'].iloc[-1]:.6f}, "
|
||
f"volume_z_score: {dataframe['volume_z_score'].iloc[-1]:.2f}, "
|
||
f"bb_lowerband: {dataframe['bb_lowerband'].iloc[-1]:.6f}, "
|
||
f"risk_rating: {dataframe['&*-risk_rating'].iloc[-1] if '&*-risk_rating' in dataframe else 'N/A'}")
|
||
|
||
if not self.stats_logged:
|
||
logger.info("===== 所有币对的 labels_mean 和 labels_std 汇总 =====")
|
||
for p, stats in self.pair_stats.items():
|
||
logger.info(f"[{p}] labels_mean：{stats['labels_mean']:.4f}, labels_std：{stats['labels_std']:.4f}")
|
||
logger.info("==============================================")
|
||
self.stats_logged = True
|
||
|
||
|
||
|
||
return dataframe
|
||
|
||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
pair = metadata.get('pair', 'Unknown')
|
||
original_length = len(dataframe)
|
||
original_index = dataframe.index
|
||
|
||
conditions = []
|
||
logger.info(f"[{pair}] populate_entry_trend 被调用，原始数据长度：{original_length}，时间：{dataframe.index[-1]}")
|
||
# 获取市场趋势得分
|
||
trend_score = self.get_market_trend(dataframe=dataframe, metadata=metadata)
|
||
|
||
# 动态调整成交量和 RSI 阈值
|
||
volume_z_score_min = 0.5
|
||
volume_z_score_max = 1.5
|
||
volume_z_score_threshold = self.linear_map(trend_score, 0, 100, volume_z_score_max, volume_z_score_min)
|
||
rsi_min = 35
|
||
rsi_max = 55
|
||
rsi_threshold = self.linear_map(trend_score, 0, 100, rsi_max, rsi_min)
|
||
stochrsi_min = 25
|
||
stochrsi_max = 45
|
||
stochrsi_threshold = self.linear_map(trend_score, 0, 100, stochrsi_max, stochrsi_min)
|
||
|
||
# 计算熊市信号和 STOCHRSI 超买信号
|
||
bearish_signal = self.is_bearish_market(dataframe, metadata, timeframe="1h")
|
||
bearish_signal_aligned = bearish_signal.reindex(dataframe.index, method='ffill').fillna(False)
|
||
stochrsi_overbought = self.is_stochrsi_overbought(dataframe, period=10, threshold=85)
|
||
stochrsi_overbought_aligned = stochrsi_overbought.reindex(dataframe.index, method='ffill').fillna(True)
|
||
|
||
# 检测趋势状态
|
||
trend_status = self.detect_trend_status(dataframe, metadata)
|
||
|
||
# 根据趋势状态调整入场策略
|
||
if "&-price_value_divergence" in dataframe.columns:
|
||
# 检测当前持仓订单数量（用于绿色通道判断）
|
||
open_trades = len(self.active_trades) if hasattr(self, 'active_trades') else 0
|
||
is_green_channel = (trend_status == "bullish" and open_trades <= 2 and self.GREEN_CHANNEL_ENABLED)
|
||
|
||
# 获取风险评级并调整入场条件
|
||
risk_rating = dataframe["&*-risk_rating"].iloc[-1] if "&*-risk_rating" in dataframe.columns else 2
|
||
|
||
# 风险评级影响入场严格程度
|
||
risk_adjustments = {
|
||
0: {"threshold_mult": 1.5, "strict_mult": 0.7}, # 低风险：更宽松入场
|
||
1: {"threshold_mult": 1.2, "strict_mult": 0.8}, # 中低风险：较宽松
|
||
2: {"threshold_mult": 1.0, "strict_mult": 1.0}, # 中等风险：标准
|
||
3: {"threshold_mult": 0.8, "strict_mult": 1.2}, # 中高风险：更严格
|
||
4: {"threshold_mult": 0.6, "strict_mult": 1.5}, # 高风险：最严格
|
||
}
|
||
|
||
risk_adj = risk_adjustments.get(risk_rating, risk_adjustments[2])
|
||
|
||
logger.info(f"[{pair}] 风险评级: {risk_rating}, 阈值调整: {risk_adj['threshold_mult']}, 严格度调整: {risk_adj['strict_mult']}")
|
||
|
||
if is_green_channel:
|
||
# 🟢 牛市绿色通道：持仓≤2个，25USDT入场，5条件需要满足4个
|
||
cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.8) # 超宽松偏离度
|
||
cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 0.4) # 超低成交量要求
|
||
cond3 = (dataframe["rsi"] < rsi_threshold * 1.4) # 超宽松RSI
|
||
cond4 = (dataframe["close"] <= dataframe["bb_upperband"] * 1.05) # 允许上轨附近
|
||
cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 1.4) # 超宽松STOCHRSI
|
||
|
||
core_conditions = [cond1, cond2, cond3, cond4, cond5]
|
||
# 使用向量化操作计算满足条件的数量
|
||
satisfied_count_vector = cond1.astype(int) + cond2.astype(int) + cond3.astype(int) + cond4.astype(int) + cond5.astype(int)
|
||
buy_condition = satisfied_count_vector >= 4
|
||
|
||
# 仅在日志中使用最后一行的值
|
||
if len(dataframe) > 0:
|
||
satisfied_count = satisfied_count_vector.iloc[-1]
|
||
logger.info(f"[{pair}] 🟢 牛市绿色通道：持仓{open_trades}≤2个，25USDT入场，5条件需要满足{satisfied_count}/4个")
|
||
|
||
elif trend_status == "bullish":
|
||
# 牛市正常通道：持仓>2个，75USDT入场，必须满足全部7个条件
|
||
cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.5) # 放宽到1.5倍
|
||
cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 0.7) # 降低成交量要求
|
||
cond3 = (dataframe["rsi"] < rsi_threshold * 1.2) # 放宽RSI要求
|
||
cond4 = (dataframe["close"] <= dataframe["bb_upperband"]) # 可以在上轨附近入场
|
||
cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 1.2) # 放宽STOCHRSI要求
|
||
cond6 = pd.Series([True] * len(dataframe), index=dataframe.index) # 取消熊市过滤
|
||
cond7 = pd.Series([True] * len(dataframe), index=dataframe.index) # 取消超买过滤
|
||
buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7
|
||
logger.info(f"[{pair}] 🚀 牛市正常通道：持仓{open_trades}>2个，75USDT入场，必须满足全部7个条件")
|
||
|
||
elif trend_status == "bearish":
|
||
# 下跌趋势：严格入场条件，只抄底
|
||
cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 0.7) # 严格到0.7倍
|
||
cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold * 1.3) # 提高成交量要求
|
||
cond3 = (dataframe["rsi"] < rsi_threshold * 0.8) # 严格RSI要求
|
||
cond4 = (dataframe["close"] <= dataframe["bb_lowerband"] * 0.95) # 必须跌破下轨
|
||
cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 0.8) # 严格STOCHRSI要求
|
||
cond6 = ~bearish_signal_aligned # 保持熊市过滤
|
||
cond7 = ~stochrsi_overbought_aligned # 保持超买过滤
|
||
buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7
|
||
logger.info(f"[{pair}] 📉 下跌趋势策略：严格入场条件")
|
||
|
||
else: # ranging
|
||
# 震荡趋势：使用原策略
|
||
cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
|
||
cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold)
|
||
cond3 = (dataframe["rsi"] < rsi_threshold)
|
||
cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
|
||
cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
|
||
cond6 = ~bearish_signal_aligned
|
||
cond7 = ~stochrsi_overbought_aligned
|
||
buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7
|
||
logger.info(f"[{pair}] ⚖️ 震荡趋势策略：标准入场条件")
|
||
|
||
# 绿色通道和趋势状态的条件已经设置好buy_condition
|
||
conditions.append(buy_condition)
|
||
|
||
# 调试日志 - 仅在日志中使用最后一行的值
|
||
if len(dataframe) > 0:
|
||
divergence_value = dataframe["&-price_value_divergence"].iloc[-1]
|
||
volume_z_score_value = dataframe["volume_z_score"].iloc[-1]
|
||
rsi_value = dataframe["rsi"].iloc[-1]
|
||
stochrsi_value = dataframe["stochrsi_k"].iloc[-1]
|
||
bb_close_value = dataframe["close"].iloc[-1]
|
||
bb_lower_value = dataframe["bb_lowerband"].iloc[-1]
|
||
bb_upper_value = dataframe["bb_upperband"].iloc[-1]
|
||
|
||
# 输出关键指标以便调试
|
||
logger.info(f"[{pair}] 📊 关键指标 - 偏离度: {divergence_value:.6f}, RSI: {rsi_value:.2f}, STOCHRSI: {stochrsi_value:.2f}, 价格: {bb_close_value:.4f}, 下轨: {bb_lower_value:.4f}, 上轨: {bb_upper_value:.4f}")
|
||
logger.info(f"[{pair}] 🔍 买入阈值 - 偏离度: {self.buy_threshold:.6f}, RSI: {rsi_threshold:.2f}, STOCHRSI: {stochrsi_threshold:.2f}")
|
||
|
||
# 获取条件结果的最后一行值（仅用于日志）
|
||
cond1_last = cond1.iloc[-1]
|
||
cond2_last = cond2.iloc[-1]
|
||
cond3_last = cond3.iloc[-1]
|
||
cond4_last = cond4.iloc[-1]
|
||
cond5_last = cond5.iloc[-1]
|
||
cond6_last = cond6.iloc[-1] if trend_status != "bullish" or open_trades > 2 else False
|
||
cond7_last = cond7.iloc[-1] if trend_status != "bullish" or open_trades > 2 else False
|
||
|
||
# 定义条件名称和状态（仅用于日志）
|
||
conditions_summary = [
|
||
("&-price_value_divergence", divergence_value, "<", self.buy_threshold, cond1_last),
|
||
("volume_z_score", volume_z_score_value, ">", volume_z_score_threshold, cond2_last),
|
||
("rsi", rsi_value, "<", rsi_threshold, cond3_last),
|
||
("close <= bb_lowerband", bb_close_value, "<=", bb_lower_value, cond4_last),
|
||
("stochrsi_k", stochrsi_value, "<", stochrsi_threshold, cond5_last),
|
||
]
|
||
|
||
# 根据趋势状态添加对应的条件6和7
|
||
if trend_status != "bullish" or open_trades > 2:
|
||
conditions_summary.extend([
|
||
("非熊市", None, None, None, cond6_last),
|
||
("STOCHRSI未持续超买", None, None, None, cond7_last),
|
||
])
|
||
|
||
# 输出每个条件的状态
|
||
logger.info(f"[{pair}] === 买入条件检查 ===")
|
||
failed_conditions = []
|
||
for name, value, operator, threshold, result in conditions_summary:
|
||
status = "✅" if result else "❌"
|
||
if value is not None and threshold is not None:
|
||
logger.info(f"[{pair}] {status} {name}: {value:.6f} {operator} {threshold:.6f}")
|
||
else:
|
||
logger.info(f"[{pair}] {status} {name}")
|
||
if not result:
|
||
failed_conditions.append(name)
|
||
else:
|
||
logger.warning(f"[{pair}] &-price_value_divergence 列缺失，跳过买入信号生成")
|
||
|
||
if conditions:
|
||
combined_condition = reduce(lambda x, y: x & y, conditions)
|
||
dataframe.loc[combined_condition, 'enter_long'] = 1
|
||
|
||
# 输出每个条件的状态
|
||
logger.info(f"[{pair}] === 买入条件检查 ===")
|
||
satisfied_conditions = []
|
||
for name, value, operator, threshold, result in conditions_summary:
|
||
status = "✅" if result else "❌"
|
||
if value is not None and threshold is not None:
|
||
logger.info(f"[{pair}] {status} {name}: {value:.6f} {operator} {threshold:.6f}")
|
||
else:
|
||
logger.info(f"[{pair}] {status} {name}")
|
||
if result:
|
||
satisfied_conditions.append(name)
|
||
|
||
# 总结满足的条件
|
||
if combined_condition.any():
|
||
logger.info(f"[{pair}] ✅ 买入信号触发，满足条件: {', '.join(satisfied_conditions)}，趋势得分：{trend_score:.2f}")
|
||
else:
|
||
logger.info(f"[{pair}] ❌ 买入条件未满足")
|
||
|
||
else:
|
||
logger.info(f"[{pair}] 无有效买入条件")
|
||
|
||
# 记录各条件触发率
|
||
logger.info(f"[{pair}] 各条件触发率 - "
|
||
f"cond1: {cond1.mean():.2%}, "
|
||
f"cond2: {cond2.mean():.2%}, "
|
||
f"cond3: {cond3.mean():.2%}, "
|
||
f"cond4: {cond4.mean():.2%}, "
|
||
f"cond5: {cond5.mean():.2%}, "
|
||
f"buy_condition: {buy_condition.mean():.2%}")
|
||
# 记录 enter_long 信号统计
|
||
enter_long_count = dataframe['enter_long'].sum() if 'enter_long' in dataframe.columns else 0
|
||
logger.info(f"[{pair}] enter_long 信号总数：{enter_long_count}")
|
||
|
||
# 数据完整性检查和长度验证修复
|
||
if len(dataframe) > 0:
|
||
# 验证数据长度一致性
|
||
current_length = len(dataframe)
|
||
if current_length != original_length:
|
||
logger.warning(f"[{pair}] ⚠️ DataFrame长度不匹配！原始长度: {original_length}, 当前长度: {current_length}")
|
||
|
||
# 修复数据长度
|
||
if current_length < original_length:
|
||
# 数据行数不足，需要填充
|
||
missing_rows = original_length - current_length
|
||
logger.info(f"[{pair}] 填充缺失的 {missing_rows} 行数据...")
|
||
|
||
# 创建缺失行的索引
|
||
missing_index = original_index.difference(dataframe.index)
|
||
if len(missing_index) > 0:
|
||
# 用最后一行的数据填充缺失行
|
||
last_row = dataframe.iloc[-1:].copy()
|
||
filled_rows = pd.DataFrame(index=missing_index)
|
||
for col in dataframe.columns:
|
||
filled_rows[col] = last_row[col].iloc[0] if len(last_row) > 0 else 0
|
||
|
||
# 合并数据
|
||
dataframe = pd.concat([dataframe, filled_rows])
|
||
dataframe = dataframe.reindex(original_index)
|
||
logger.info(f"[{pair}] 数据填充完成，新长度: {len(dataframe)}")
|
||
|
||
elif current_length > original_length:
|
||
# 数据行数过多，截断到原始长度
|
||
excess_rows = current_length - original_length
|
||
logger.info(f"[{pair}] 截断多余的 {excess_rows} 行数据...")
|
||
dataframe = dataframe.iloc[:original_length].copy()
|
||
dataframe = dataframe.reindex(original_index)
|
||
logger.info(f"[{pair}] 数据截断完成，新长度: {len(dataframe)}")
|
||
|
||
else:
|
||
# 长度一致但索引可能不同，重新对齐索引
|
||
dataframe = dataframe.reindex(original_index)
|
||
logger.info(f"[{pair}] 索引重新对齐完成，长度: {len(dataframe)}")
|
||
|
||
# 处理NaN值
|
||
nan_columns = [col for col in dataframe.columns if dataframe[col].isna().any()]
|
||
if nan_columns:
|
||
logger.warning(f"[{pair}] 发现NaN值的列: {nan_columns}")
|
||
for col in nan_columns:
|
||
nan_count = dataframe[col].isna().sum()
|
||
if nan_count > 0:
|
||
logger.warning(f"[{pair}] 列 {col} 有 {nan_count} 个NaN值，正在清理...")
|
||
# 使用前向填充，然后零填充
|
||
dataframe[col] = dataframe[col].ffill().fillna(0)
|
||
|
||
# 最终验证
|
||
final_length = len(dataframe)
|
||
if final_length != original_length:
|
||
logger.error(f"[{pair}] ❌ 数据长度修复失败！期望: {original_length}, 实际: {final_length}")
|
||
# 强制截断或填充到原始长度
|
||
if final_length > original_length:
|
||
dataframe = dataframe.iloc[:original_length]
|
||
elif final_length < original_length:
|
||
# 复制最后一行填充
|
||
last_row = dataframe.iloc[-1:].copy()
|
||
while len(dataframe) < original_length:
|
||
dataframe = pd.concat([dataframe, last_row])
|
||
dataframe = dataframe.iloc[:original_length]
|
||
logger.info(f"[{pair}] 强制修复完成，最终长度: {len(dataframe)}")
|
||
else:
|
||
logger.info(f"[{pair}] ✅ 数据长度验证通过，最终长度: {final_length}")
|
||
|
||
return dataframe
|
||
|
||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
pair = metadata.get('pair', 'Unknown')
|
||
|
||
# 记录原始数据长度和索引
|
||
original_length = len(dataframe)
|
||
original_index = dataframe.index
|
||
logger.info(f"[{pair}] populate_exit_trend 开始处理，原始数据长度：{original_length}")
|
||
|
||
conditions = []
|
||
|
||
if "&-price_value_divergence" in dataframe.columns:
|
||
# 计算额外指标：StochRSI、ADX 和短期价格变化
|
||
stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
|
||
dataframe["stochrsi_k"] = stochrsi["fastk"]
|
||
dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
|
||
# 计算短期价格涨幅（最近 5 根 K 线，约 15 分钟）
|
||
dataframe["short_term_return"] = dataframe["close"].pct_change(5, fill_method=None) * 100 # 百分比回报
|
||
|
||
# 清理新计算列的NaN值
|
||
for col in ["stochrsi_k", "adx", "short_term_return"]:
|
||
if col in dataframe.columns:
|
||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
|
||
|
||
# 获取市场趋势得分
|
||
trend_score = self.get_market_trend(dataframe=dataframe, metadata={'pair': pair})
|
||
|
||
# 条件 1：高阈值 &-price_value_divergence
|
||
cond1 = (
|
||
(dataframe["&-price_value_divergence"] > self.sell_threshold * float(self.exit_divergence_multiplier.value)) &
|
||
(dataframe["adx"] > float(self.exit_adx_threshold.value)) # 趋势强度过滤
|
||
)
|
||
|
||
# 条件 2：超买信号
|
||
cond2 = (
|
||
(dataframe["rsi"] > float(self.exit_rsi_threshold.value)) &
|
||
(dataframe["stochrsi_k"] > float(self.exit_stochrsi_threshold.value)) & # StochRSI 超买
|
||
(dataframe["adx"] > float(self.exit_adx_threshold_overbought.value)) # 趋势强度
|
||
)
|
||
|
||
# 条件 3：快速拉升退出
|
||
# 检测最近 5 根 K 线（约 15 分钟）涨幅超过阈值，且有最低利润要求
|
||
min_profit = float(self.exit_min_profit_threshold.value) # 最低利润要求
|
||
rapid_rise_threshold = self.linear_map(trend_score, 0, 100, float(self.exit_rapid_rise_bear.value), float(self.exit_rapid_rise_bull.value))
|
||
cond3 = (
|
||
(dataframe["short_term_return"] > rapid_rise_threshold) & # 短期快速拉升
|
||
(dataframe["close"] / dataframe["close"].shift(5) - 1 > min_profit) & # 确保最低利润
|
||
(dataframe["stochrsi_k"] > float(self.exit_stochrsi_rapid.value)) & # 超买确认
|
||
(dataframe["volume_z_score"] > float(self.exit_volume_threshold.value))
|
||
)
|
||
|
||
# 检测趋势状态
|
||
trend_status = self.detect_trend_status(dataframe, metadata)
|
||
|
||
# 根据趋势状态调整出场策略
|
||
if trend_status == "bullish":
|
||
# 上涨趋势：严格出场条件，让利润奔跑
|
||
if trend_score > float(self.exit_bullish_trend_score_max.value):
|
||
logger.info(f"[{pair}] 🚀 强劲上涨趋势，拒绝卖出")
|
||
return dataframe
|
||
|
||
# 上涨趋势下需要更强的卖出信号
|
||
cond1_bullish = (dataframe["&-price_value_divergence"] > self.sell_threshold * float(self.exit_bullish_divergence_mult.value))
|
||
cond2_bullish = (dataframe["rsi"] > float(self.exit_bullish_rsi.value)) & (dataframe["stochrsi_k"] > float(self.exit_bullish_stochrsi.value)) & (dataframe["adx"] > float(self.exit_bullish_adx.value))
|
||
cond3_bullish = (dataframe["short_term_return"] > float(self.exit_bullish_return.value)) & (dataframe["stochrsi_k"] > float(self.exit_bullish_stochrsi_rapid.value))
|
||
|
||
sell_condition = (cond1_bullish & cond2_bullish) | (cond1_bullish & cond3_bullish) | (cond2_bullish & cond3_bullish)
|
||
logger.info(f"[{pair}] 🚀 上涨趋势策略：严格出场条件")
|
||
|
||
elif trend_status == "bearish":
|
||
# 下跌趋势：宽松出场条件，快速止盈止损
|
||
cond1_bearish = (dataframe["&-price_value_divergence"] > self.sell_threshold * float(self.exit_bearish_divergence_mult.value))
|
||
cond2_bearish = (dataframe["rsi"] > float(self.exit_bearish_rsi.value)) & (dataframe["stochrsi_k"] > float(self.exit_bearish_stochrsi.value)) & (dataframe["adx"] > float(self.exit_bearish_adx.value))
|
||
cond3_bearish = (dataframe["short_term_return"] > float(self.exit_bearish_return.value)) & (dataframe["stochrsi_k"] > float(self.exit_bearish_stochrsi_rapid.value))
|
||
|
||
sell_condition = cond1_bearish | cond2_bearish | cond3_bearish # 任一条件即可卖出
|
||
logger.info(f"[{pair}] 📉 下跌趋势策略：宽松出场条件")
|
||
|
||
else: # ranging
|
||
# 震荡趋势：使用原策略
|
||
if trend_score > float(self.exit_ranging_trend_score_max.value):
|
||
logger.info(f"[{pair}] ⚖️ 震荡趋势但得分较高，拒绝卖出")
|
||
return dataframe
|
||
|
||
if trend_score > float(self.exit_ranging_trend_score_threshold.value):
|
||
sell_condition = (cond1 & cond2) | (cond1 & cond3) | (cond2 & cond3) # 中等趋势，至少两个条件满足
|
||
logger.info(f"[{pair}] ⚖️ 震荡趋势策略：标准出场条件")
|
||
else:
|
||
sell_condition = cond1 | cond2 | cond3 # 弱势趋势，任一条件满足
|
||
logger.info(f"[{pair}] ⚖️ 弱势震荡，任一条件满足")
|
||
|
||
conditions.append(sell_condition)
|
||
|
||
# 调试日志 - 仅在日志中使用最后一行的值（这是允许的用途）
|
||
if len(dataframe) > 0:
|
||
divergence_value = dataframe["&-price_value_divergence"].iloc[-1] if not dataframe["&-price_value_divergence"].isna().iloc[-1] else 0.0
|
||
rsi_value = dataframe["rsi"].iloc[-1] if not dataframe["rsi"].isna().iloc[-1] else 0.0
|
||
stochrsi_value = dataframe["stochrsi_k"].iloc[-1] if not dataframe["stochrsi_k"].isna().iloc[-1] else 0.0
|
||
adx_value = dataframe["adx"].iloc[-1] if not dataframe["adx"].isna().iloc[-1] else 0.0
|
||
short_term_return = dataframe["short_term_return"].iloc[-1] if not dataframe["short_term_return"].isna().iloc[-1] else 0.0
|
||
|
||
# 获取条件结果的最后一行值（仅用于日志）
|
||
cond1_last = cond1.iloc[-1]
|
||
cond2_last = cond2.iloc[-1]
|
||
cond3_last = cond3.iloc[-1]
|
||
|
||
logger.info(f"[{pair}] 卖出条件检查 - "
|
||
f"&-price_value_divergence={divergence_value:.6f} > {self.sell_threshold * float(self.exit_divergence_multiplier.value):.6f}: {cond1_last}, "
|
||
f"rsi={rsi_value:.2f} > {float(self.exit_rsi_threshold.value)} & stochrsi_k={stochrsi_value:.2f} > {float(self.exit_stochrsi_threshold.value)}: {cond2_last}, "
|
||
f"short_term_return={short_term_return:.2f}% > {rapid_rise_threshold:.2f}% & profit > {min_profit*100:.2f}%: {cond3_last}, "
|
||
f"adx={adx_value:.2f}, trend_score={trend_score:.2f}")
|
||
else:
|
||
logger.warning(f"[{pair}] ⚠️ &-price_value_divergence 列缺失，跳过该条件")
|
||
|
||
if len(conditions) > 0:
|
||
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
|
||
logger.info(f"[{pair}] 出场信号触发，条件满足，趋势得分：{trend_score:.2f}")
|
||
else:
|
||
logger.info(f"[{pair}] 无有效卖出条件")
|
||
|
||
# 数据完整性检查和长度验证修复
|
||
if len(dataframe) > 0:
|
||
# 验证数据长度一致性
|
||
current_length = len(dataframe)
|
||
if current_length != original_length:
|
||
logger.warning(f"[{pair}] ⚠️ 卖出DataFrame长度不匹配！原始长度: {original_length}, 当前长度: {current_length}")
|
||
|
||
# 修复数据长度
|
||
if current_length < original_length:
|
||
# 数据行数不足，需要填充
|
||
missing_rows = original_length - current_length
|
||
logger.info(f"[{pair}] 卖出填充缺失的 {missing_rows} 行数据...")
|
||
|
||
# 创建缺失行的索引
|
||
missing_index = original_index.difference(dataframe.index)
|
||
if len(missing_index) > 0:
|
||
# 用最后一行的数据填充缺失行
|
||
last_row = dataframe.iloc[-1:].copy()
|
||
filled_rows = pd.DataFrame(index=missing_index)
|
||
for col in dataframe.columns:
|
||
filled_rows[col] = last_row[col].iloc[0] if len(last_row) > 0 else 0
|
||
|
||
# 合并数据
|
||
dataframe = pd.concat([dataframe, filled_rows])
|
||
dataframe = dataframe.reindex(original_index)
|
||
logger.info(f"[{pair}] 卖出数据填充完成，新长度: {len(dataframe)}")
|
||
|
||
elif current_length > original_length:
|
||
# 数据行数过多，截断到原始长度
|
||
excess_rows = current_length - original_length
|
||
logger.info(f"[{pair}] 卖出截断多余的 {excess_rows} 行数据...")
|
||
dataframe = dataframe.iloc[:original_length].copy()
|
||
dataframe = dataframe.reindex(original_index)
|
||
logger.info(f"[{pair}] 卖出数据截断完成，新长度: {len(dataframe)}")
|
||
|
||
else:
|
||
# 长度一致但索引可能不同，重新对齐索引
|
||
dataframe = dataframe.reindex(original_index)
|
||
logger.info(f"[{pair}] 卖出索引重新对齐完成，长度: {len(dataframe)}")
|
||
|
||
# 处理NaN值
|
||
nan_columns = [col for col in dataframe.columns if dataframe[col].isna().any()]
|
||
if nan_columns:
|
||
logger.warning(f"[{pair}] 卖出检查 - 发现NaN值的列: {nan_columns}")
|
||
for col in nan_columns:
|
||
nan_count = dataframe[col].isna().sum()
|
||
if nan_count > 0:
|
||
logger.warning(f"[{pair}] 卖出检查 - 列 {col} 有 {nan_count} 个NaN值，正在清理...")
|
||
# 使用前向填充，然后零填充
|
||
dataframe[col] = dataframe[col].ffill().fillna(0)
|
||
|
||
# 最终验证
|
||
final_length = len(dataframe)
|
||
if final_length != original_length:
|
||
logger.error(f"[{pair}] ❌ 卖出数据长度修复失败！期望: {original_length}, 实际: {final_length}")
|
||
# 强制截断或填充到原始长度
|
||
if final_length > original_length:
|
||
dataframe = dataframe.iloc[:original_length]
|
||
elif final_length < original_length:
|
||
# 复制最后一行填充
|
||
last_row = dataframe.iloc[-1:].copy()
|
||
while len(dataframe) < original_length:
|
||
dataframe = pd.concat([dataframe, last_row])
|
||
dataframe = dataframe.iloc[:original_length]
|
||
logger.info(f"[{pair}] 卖出强制修复完成，最终长度: {len(dataframe)}")
|
||
else:
|
||
logger.info(f"[{pair}] ✅ 卖出数据长度验证通过，最终长度: {final_length}")
|
||
|
||
# 记录exit_long信号
|
||
exit_long_count = dataframe['exit_long'].sum() if 'exit_long' in dataframe.columns else 0
|
||
logger.info(f"[{pair}] exit_long 信号总数：{exit_long_count}")
|
||
|
||
return dataframe
|
||
|
||
def buy_space(self):
|
||
return [
|
||
DecimalParameter(-0.1, -0.01, name="buy_threshold_min"),
|
||
DecimalParameter(-0.02, -0.001, name="buy_threshold_max"),
|
||
DecimalParameter(-0.05, -0.01, name="add_position_threshold", default=-0.02),
|
||
IntParameter(1, 10, name="cooldown_period_minutes", default=5),
|
||
IntParameter(1, 3, name="max_entry_position_adjustment", default=2)
|
||
]
|
||
|
||
def sell_space(self):
|
||
return [
|
||
DecimalParameter(0.001, 0.02, name="sell_threshold_min"),
|
||
DecimalParameter(0.02, 0.1, name="sell_threshold_max"),
|
||
DecimalParameter(0.2, 0.7, name="exit_position_ratio", default=0.5),
|
||
# 出场策略参数
|
||
DecimalParameter(1.0, 1.3, name="exit_divergence_multiplier"),
|
||
IntParameter(15, 35, name="exit_adx_threshold"),
|
||
IntParameter(55, 75, name="exit_rsi_threshold"),
|
||
IntParameter(60, 80, name="exit_stochrsi_threshold"),
|
||
IntParameter(20, 35, name="exit_adx_threshold_overbought"),
|
||
DecimalParameter(0.005, 0.05, name="exit_min_profit_threshold"),
|
||
DecimalParameter(1.5, 4.0, name="exit_rapid_rise_bull"),
|
||
DecimalParameter(2.5, 5.0, name="exit_rapid_rise_bear"),
|
||
IntParameter(65, 85, name="exit_stochrsi_rapid"),
|
||
DecimalParameter(0.5, 2.0, name="exit_volume_threshold"),
|
||
# 趋势状态相关出场参数
|
||
IntParameter(90, 98, name="exit_bullish_trend_score_max"),
|
||
DecimalParameter(1.1, 1.3, name="exit_bullish_divergence_mult"),
|
||
IntParameter(70, 85, name="exit_bullish_rsi"),
|
||
IntParameter(80, 90, name="exit_bullish_stochrsi"),
|
||
IntParameter(25, 40, name="exit_bullish_adx"),
|
||
DecimalParameter(3.0, 7.0, name="exit_bullish_return"),
|
||
IntParameter(80, 90, name="exit_bullish_stochrsi_rapid"),
|
||
IntParameter(95, 99, name="exit_ranging_trend_score_max"),
|
||
IntParameter(80, 90, name="exit_ranging_trend_score_threshold"),
|
||
DecimalParameter(0.8, 1.0, name="exit_bearish_divergence_mult"),
|
||
IntParameter(55, 65, name="exit_bearish_rsi"),
|
||
IntParameter(65, 75, name="exit_bearish_stochrsi"),
|
||
IntParameter(15, 25, name="exit_bearish_adx"),
|
||
DecimalParameter(1.0, 3.0, name="exit_bearish_return"),
|
||
IntParameter(70, 85, name="exit_bearish_stochrsi_rapid")
|
||
]
|
||
|
||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||
current_rate: float, current_profit: float,
|
||
min_stake: float | None, max_stake: float,
|
||
current_entry_rate: float, current_exit_rate: float,
|
||
current_entry_profit: float, current_exit_profit: float,
|
||
**kwargs) -> float | None | tuple[float | None, str | None]:
|
||
"""
|
||
动态调整仓位：支持加仓、减仓、追踪止损和最大持仓时间限制
|
||
参数：
|
||
- trade: 当前交易对象
|
||
- current_time: 当前时间
|
||
- current_rate: 当前价格
|
||
- current_profit: 当前总盈利
|
||
- min_stake: 最小下注金额
|
||
- max_stake: 最大下注金额
|
||
- current_entry_rate: 当前入场价格
|
||
- current_exit_rate: 当前退出价格
|
||
- current_entry_profit: 当前入场盈利
|
||
- current_exit_profit: 当前退出盈利
|
||
返回：
|
||
- 调整金额（正数为加仓，负数为减仓）或 None
|
||
"""
|
||
pair = trade.pair
|
||
dataframe = self.dp.get_pair_dataframe(pair, self.timeframe)
|
||
trend_score = self.get_market_trend(dataframe=dataframe, metadata={'pair': pair})
|
||
hold_time = (current_time - trade.open_date_utc).total_seconds() / 60
|
||
profit_ratio = (current_rate - trade.open_rate) / trade.open_rate
|
||
|
||
# 检测趋势状态（必须先定义才能使用）
|
||
trend_status = self.detect_trend_status(dataframe, {'pair': pair})
|
||
logger.info(f"{pair} 当前趋势状态: {trend_status}")
|
||
|
||
# 检测当前持仓订单数量
|
||
open_trades = len(self.active_trades) if hasattr(self, 'active_trades') else 0
|
||
|
||
# 牛市绿色通道判断：持仓≤2个且牛市趋势，且绿色通道开关开启
|
||
is_green_channel = (trend_status == "bullish" and open_trades <= 2 and self.GREEN_CHANNEL_ENABLED)
|
||
|
||
# 根据绿色通道调整入场金额
|
||
if is_green_channel:
|
||
initial_stake_amount = 25.0 # 绿色通道：25USDT入场
|
||
logger.info(f"{pair} 🟢 牛市绿色通道：25USDT入场，当前持仓{open_trades}个")
|
||
else:
|
||
initial_stake_amount = 75.0 # 正常通道：75USDT入场
|
||
logger.info(f"{pair} 首次入场金额: {initial_stake_amount:.2f}, 当前持仓金额: {trade.stake_amount:.2f}, "
|
||
f"加仓次数: {trade.nr_of_successful_entries - 1}, 趋势得分: {trend_score:.2f}")
|
||
|
||
# 根据趋势状态调整仓位管理参数
|
||
if trend_status == "bullish":
|
||
# 上涨趋势：积极加仓，放宽止盈
|
||
max_entry_adjustments = min(self.MAX_ENTRY_POSITION_ADJUSTMENT + 1, 5) # 允许更多加仓
|
||
add_position_threshold = self.ADD_POSITION_THRESHOLD * 1.3 # 更宽松的加仓条件
|
||
exit_position_ratio = self.EXIT_POSITION_RATIO * 1.4 # 更高的止盈目标
|
||
trailing_stop_start = self.TRAILING_STOP_START * 1.2 # 更高的启动阈值
|
||
trailing_stop_distance = self.TRAILING_STOP_DISTANCE * 1.5 # 更大的回撤容忍
|
||
logger.info(f"{pair} 🚀 上涨趋势仓位管理参数: max_entries={max_entry_adjustments}, add_thresh={add_position_threshold:.4f}, exit_ratio={exit_position_ratio:.2%}")
|
||
|
||
elif trend_status == "bearish":
|
||
# 下跌趋势：谨慎加仓，严格止盈
|
||
max_entry_adjustments = max(self.MAX_ENTRY_POSITION_ADJUSTMENT - 1, 1) # 减少加仓次数
|
||
add_position_threshold = self.ADD_POSITION_THRESHOLD * 0.7 # 更严格的加仓条件
|
||
exit_position_ratio = self.EXIT_POSITION_RATIO * 0.8 # 更低的止盈目标
|
||
trailing_stop_start = self.TRAILING_STOP_START * 0.8 # 更低的启动阈值
|
||
trailing_stop_distance = self.TRAILING_STOP_DISTANCE * 0.7 # 更严格的止损
|
||
logger.info(f"{pair} 📉 下跌趋势仓位管理参数: max_entries={max_entry_adjustments}, add_thresh={add_position_threshold:.4f}, exit_ratio={exit_position_ratio:.2%}")
|
||
|
||
else: # ranging
|
||
# 震荡趋势：使用标准参数
|
||
max_entry_adjustments = self.MAX_ENTRY_POSITION_ADJUSTMENT
|
||
add_position_threshold = self.ADD_POSITION_THRESHOLD
|
||
exit_position_ratio = self.EXIT_POSITION_RATIO
|
||
trailing_stop_start = self.TRAILING_STOP_START
|
||
trailing_stop_distance = self.TRAILING_STOP_DISTANCE
|
||
logger.info(f"{pair} ⚖️ 震荡趋势仓位管理参数: max_entries={max_entry_adjustments}, add_thresh={add_position_threshold:.4f}, exit_ratio={exit_position_ratio:.2%}")
|
||
|
||
# 加仓逻辑
|
||
if trade.nr_of_successful_entries <= max_entry_adjustments + 1:
|
||
# 动态调整加仓阈值
|
||
if trend_status == "bullish":
|
||
add_threshold = 90 - 20 * (trend_score / 100) # 上涨趋势下更宽松
|
||
elif trend_status == "bearish":
|
||
add_threshold = 70 - 30 * (trend_score / 100) # 下跌趋势下更谨慎
|
||
else:
|
||
add_threshold = 80 - 30 * (trend_score / 100) # 震荡趋势标准
|
||
|
||
if profit_ratio <= add_position_threshold and hold_time > 5 and trend_score <= add_threshold:
|
||
add_count = trade.nr_of_successful_entries - 1
|
||
|
||
# 根据趋势状态调整加仓倍数
|
||
if trend_status == "bullish":
|
||
multipliers = [1.5, 3, 6] # 上涨趋势下更保守的加仓
|
||
elif trend_status == "bearish":
|
||
multipliers = [1, 2, 4] # 下跌趋势下更激进的加仓（抄底）
|
||
else:
|
||
multipliers = [2, 4, 8] # 震荡趋势标准加仓
|
||
|
||
if add_count < len(multipliers):
|
||
multiplier = multipliers[add_count]
|
||
add_amount = initial_stake_amount * multiplier
|
||
|
||
if min_stake is not None and add_amount < min_stake:
|
||
logger.warning(f"{pair} 加仓金额 {add_amount:.2f} 低于最小下注金额 {min_stake:.2f}")
|
||
return (None, f"Add amount {add_amount:.2f} below min_stake {min_stake:.2f}")
|
||
if add_amount > max_stake:
|
||
add_amount = max_stake
|
||
|
||
logger.info(f"{pair} 趋势状态: {trend_status}, 价格下跌 {profit_ratio*100:.2f}%，触发第 {add_count + 1} 次加仓 {add_amount:.2f}")
|
||
return (add_amount, f"Trend: {trend_status}, Price dropped {profit_ratio*100:.2f}%, add {add_amount:.2f}")
|
||
|
||
# 减仓逻辑
|
||
if profit_ratio >= exit_position_ratio:
|
||
# 根据趋势状态调整减仓比例
|
||
if trend_status == "bullish":
|
||
reduce_factor = 0.5 # 上涨趋势下只减仓50%
|
||
elif trend_status == "bearish":
|
||
reduce_factor = 1.0 # 下跌趋势下全部减仓
|
||
else:
|
||
reduce_factor = 0.8 # 震荡趋势下减仓80%
|
||
|
||
reduce_amount = -trade.stake_amount * reduce_factor
|
||
logger.info(f"{pair} 趋势状态: {trend_status}, 利润 {profit_ratio*100:.2f}%，减仓 {abs(reduce_amount):.2f} ({reduce_factor*100:.0f}%)")
|
||
return (reduce_amount, f"Trend: {trend_status}, Profit {profit_ratio*100:.2f}%, reduce {abs(reduce_amount):.2f}")
|
||
|
||
# 追踪止损逻辑
|
||
if profit_ratio >= trailing_stop_start and not self.trailing_stop_enabled:
|
||
self.trailing_stop_enabled = True
|
||
trade.adjust_min_max_rates(current_rate, current_rate)
|
||
logger.info(f"{pair} 趋势状态: {trend_status}, 价格上涨超过 {trailing_stop_start*100:.1f}%，启动追踪止损")
|
||
return None
|
||
|
||
if self.trailing_stop_enabled:
|
||
max_rate = trade.max_rate or current_rate
|
||
trailing_stop_price = max_rate * (1 - trailing_stop_distance)
|
||
if current_rate < trailing_stop_price:
|
||
logger.info(f"{pair} 趋势状态: {trend_status}, 价格回落至 {trailing_stop_price:.6f}，触发全部卖出")
|
||
return (-trade.stake_amount, f"Trend: {trend_status}, Trailing stop at {trailing_stop_price:.6f}")
|
||
trade.adjust_min_max_rates(current_rate, trade.min_rate)
|
||
return None
|
||
|
||
return None
|
||
|
||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
||
time_in_force: str, current_time: datetime, **kwargs) -> bool:
|
||
# 调试日志：记录输入参数
|
||
logger.info(f"[{pair}] confirm_trade_entry called with rate={rate}, type(rate)={type(rate)}, "
|
||
f"amount={amount}, order_type={order_type}, time_in_force={time_in_force}")
|
||
|
||
# 检查 rate 是否有效
|
||
if not isinstance(rate, (float, int)) or rate is None:
|
||
logger.error(f"[{pair}] Invalid rate value: {rate} (type: {type(rate)}). Skipping trade entry.")
|
||
return False
|
||
|
||
# 获取当前数据
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1]
|
||
|
||
market_trend_score = self.get_market_trend(dataframe=DataFrame, metadata={'pair': pair})
|
||
# 修正逻辑：趋势得分越低（熊市），冷却期越长；得分越高（牛市），冷却期越短
|
||
cooldown_period_minutes = self.COOLDOWN_PERIOD_MINUTES if market_trend_score < 50 else self.COOLDOWN_PERIOD_MINUTES // 2
|
||
|
||
if pair in self.last_entry_time:
|
||
last_time = self.last_entry_time[pair]
|
||
if (current_time - last_time).total_seconds() < cooldown_period_minutes * 60:
|
||
logger.info(f"[{pair}] 冷却期内（{cooldown_period_minutes} 分钟），跳过本次入场")
|
||
return False
|
||
|
||
self.last_entry_time[pair] = current_time
|
||
self.trailing_stop_enabled = False
|
||
try:
|
||
logger.info(f"[{pair}] 确认入场，价格：{float(rate):.6f}")
|
||
except (ValueError, TypeError) as e:
|
||
logger.error(f"[{pair}] Failed to format rate: {rate} (type: {type(rate)}), error: {e}")
|
||
return False
|
||
|
||
return True
|
||
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:
|
||
adjusted_rate = rate * (1 + 0.0025)
|
||
logger.info(f"[{pair}] 退出交易，原因：{exit_reason}, 原始利润：{trade.calc_profit_ratio(rate):.2%},"f"调整后卖出价：{adjusted_rate:.6f}")
|
||
return True
|
||
|
||
def custom_roi(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int,
|
||
current_rate: float = None, min_stake: float | None = None, max_stake: float | None = None) -> dict:
|
||
"""
|
||
动态调整 ROI 表格，基于 FreqAI 预测的 &-price_value_divergence 和 RSI。
|
||
- 负的 divergence（预测上涨）或低 RSI 时提高 ROI。
|
||
- 正的 divergence（预测下跌）或高 RSI 时降低 ROI。
|
||
- 长时间持仓降低 ROI 目标。
|
||
"""
|
||
pair = trade.pair
|
||
logger.info(f"[{pair}] 计算自定义 ROI，当前盈利: {current_profit:.2%}, 持仓时间: {trade_dur} 分钟")
|
||
|
||
# 获取最新数据
|
||
dataframe = self.dp.get_pair_dataframe(pair=pair, timeframe=self.timeframe)
|
||
dataframe = self.populate_indicators(dataframe, {'pair': pair}) # 计算指标
|
||
|
||
# 获取 FreqAI 预测和 RSI
|
||
divergence = dataframe["&-price_value_divergence"].iloc[-1] if "&-price_value_divergence" in dataframe else 0
|
||
rsi = dataframe["rsi"].iloc[-1] if "rsi" in dataframe else 50
|
||
|
||
# 计算调整系数
|
||
# 1. Divergence 调整：负值（预测上涨）-> 提高 ROI，正值（预测下跌）-> 降低 ROI
|
||
divergence_factor = self.linear_map(divergence, -0.1, 0.1, 1.2, 0.8)
|
||
|
||
# 2. RSI 调整：低 RSI（超卖）-> 提高 ROI，高 RSI（超买）-> 降低 ROI
|
||
rsi_factor = self.linear_map(rsi, 30, 70, 1.2, 0.8)
|
||
|
||
# 3. 时间调整：持仓时间越长，ROI 目标降低
|
||
time_factor = self.linear_map(trade_dur, 0, 240, 1.0, 0.7) # 4小时后 ROI 降低到 70%
|
||
|
||
# 综合调整系数
|
||
roi_factor = divergence_factor * rsi_factor * time_factor
|
||
|
||
# 默认 ROI 表格
|
||
base_roi = {
|
||
0: 0.06,
|
||
30: 0.04,
|
||
90: 0.025,
|
||
270: 0.002
|
||
}
|
||
|
||
# 动态调整 ROI，限制在 0% 到 20% 之间
|
||
dynamic_roi = {time: min(max(roi * roi_factor, 0.0), 0.2) for time, roi in base_roi.items()}
|
||
|
||
logger.info(f"[{pair}] Divergence: {divergence:.4f}, RSI: {rsi:.2f}, 持仓时间: {trade_dur} 分钟, "
|
||
f"调整系数: divergence={divergence_factor:.2f}, rsi={rsi_factor:.2f}, time={time_factor:.2f}, "
|
||
f"总系数={roi_factor:.2f}, 动态 ROI 表格: {dynamic_roi}")
|
||
return dynamic_roi
|
||
|
||
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
|
||
entry_tag: str | None, side: str, **kwargs) -> float:
|
||
adjusted_rate = proposed_rate * (1 - 0.005)
|
||
logger.info(f"[{pair}] 自定义买入价：{adjusted_rate:.6f}（原价：{proposed_rate:.6f}）")
|
||
return adjusted_rate
|
||
|
||
def custom_exit_price(self, pair: str, trade: Trade,
|
||
current_time: datetime, proposed_rate: float,
|
||
current_profit: float, exit_tag: str | None, **kwargs) -> float:
|
||
adjusted_rate = proposed_rate * (1 + 0.0025)
|
||
logger.info(f"[{pair}] 自定义卖出价：{adjusted_rate:.6f}（原价：{proposed_rate:.6f}）")
|
||
return adjusted_rate
|
||
|
||
def get_market_trend(self, dataframe: DataFrame = None, metadata: dict = None) -> int:
|
||
try:
|
||
timeframes = ["3m", "15m", "1h"]
|
||
weights = {"3m": 0.3, "15m": 0.35, "1h": 0.35}
|
||
trend_scores = {}
|
||
pair = metadata.get('pair', 'Unknown') if metadata else 'Unknown'
|
||
|
||
# 检查 pair 是否有效
|
||
if pair == 'Unknown':
|
||
logger.error(f"[{pair}] Invalid pair in metadata: {metadata}. Returning default score 50")
|
||
return 50
|
||
|
||
logger.info(f"[{pair}] 正在计算多时间框架市场趋势得分")
|
||
|
||
for tf in timeframes:
|
||
# 优先使用传入的 dataframe（如果匹配主时间框架），否则加载目标币对数据
|
||
pair_df = dataframe if tf == self.timeframe and dataframe is not None else self.dp.get_pair_dataframe(pair=pair, timeframe=tf)
|
||
|
||
min_candles = 200 if tf == "3m" else 100 if tf == "15m" else 50
|
||
if pair_df.empty or len(pair_df) < min_candles:
|
||
logger.warning(f"[{pair}] 数据不足（{tf}），使用默认得分：50")
|
||
trend_scores[tf] = 50
|
||
continue
|
||
|
||
# 价格趋势
|
||
ema_short_period = 50 if tf == "3m" else 20 if tf == "15m" else 12
|
||
ema_long_period = 200 if tf == "3m" else 80 if tf == "15m" else 50
|
||
pair_df["ema_short"] = ta.EMA(pair_df, timeperiod=ema_short_period)
|
||
pair_df["ema_long"] = ta.EMA(pair_df, timeperiod=ema_long_period)
|
||
pair_df["ema_short_slope"] = (pair_df["ema_short"] - pair_df["ema_short"].shift(10)) / pair_df["ema_short"].shift(10)
|
||
|
||
price_above_ema = pair_df["close"].iloc[-1] > pair_df["ema_long"].iloc[-1]
|
||
ema_short_above_ema_long = pair_df["ema_short"].iloc[-1] > pair_df["ema_long"].iloc[-1]
|
||
ema_short_slope = pair_df["ema_short_slope"].iloc[-1]
|
||
|
||
price_score = 0
|
||
if price_above_ema:
|
||
price_score += 20
|
||
if ema_short_above_ema_long:
|
||
price_score += 20
|
||
if ema_short_slope > 0.005:
|
||
price_score += 15
|
||
elif ema_short_slope < -0.005:
|
||
price_score -= 15
|
||
|
||
# K线形态
|
||
pair_df["bullish_engulfing"] = (
|
||
(pair_df["close"].shift(1) < pair_df["open"].shift(1)) &
|
||
(pair_df["close"] > pair_df["open"]) &
|
||
(pair_df["close"] > pair_df["open"].shift(1)) &
|
||
(pair_df["open"] < pair_df["close"].shift(1))
|
||
).fillna(False)
|
||
pair_df["bearish_engulfing"] = (
|
||
(pair_df["close"].shift(1) > pair_df["open"].shift(1)) &
|
||
(pair_df["close"] < pair_df["open"]) &
|
||
(pair_df["close"] < pair_df["open"].shift(1)) &
|
||
(pair_df["open"] > pair_df["close"].shift(1))
|
||
).fillna(False)
|
||
|
||
kline_score = 0
|
||
if pair_df["bullish_engulfing"].iloc[-1]:
|
||
kline_score += 15
|
||
elif pair_df["bearish_engulfing"].iloc[-1]:
|
||
kline_score -= 15
|
||
volatility = pair_df["close"].pct_change(10, fill_method=None).std() * 100
|
||
if volatility > 0.5:
|
||
kline_score += 10 if price_score > 0 else -10
|
||
|
||
# StochRSI
|
||
stochrsi = ta.STOCHRSI(pair_df, timeperiod=14, fastk_period=3, fastd_period=3)
|
||
pair_df["stochrsi_k"] = stochrsi["fastk"]
|
||
pair_df["stochrsi_d"] = stochrsi["fastd"]
|
||
|
||
stochrsi_score = 0
|
||
stochrsi_k = pair_df["stochrsi_k"].iloc[-1]
|
||
stochrsi_d = pair_df["stochrsi_d"].iloc[-1]
|
||
if stochrsi_k > 80 and stochrsi_k < stochrsi_d:
|
||
stochrsi_score -= 15
|
||
elif stochrsi_k < 20 and stochrsi_k > stochrsi_d:
|
||
stochrsi_score += 15
|
||
elif stochrsi_k > 50:
|
||
stochrsi_score += 5
|
||
elif stochrsi_k < 50:
|
||
stochrsi_score -= 5
|
||
|
||
# 量价关系
|
||
pair_df["volume_mean_20"] = pair_df["volume"].rolling(20).mean()
|
||
pair_df["volume_std_20"] = pair_df["volume"].rolling(20).std()
|
||
pair_df["volume_z_score"] = (pair_df["volume"] - pair_df["volume_mean_20"]) / pair_df["volume_std_20"]
|
||
pair_df["adx"] = ta.ADX(pair_df, timeperiod=14)
|
||
|
||
volume_score = 0
|
||
if pair_df["volume_z_score"].iloc[-1] > 1.5:
|
||
volume_score += 10 if price_score > 0 else -10
|
||
if pair_df["adx"].iloc[-1] > 25:
|
||
volume_score += 10 if price_score > 0 else -10
|
||
|
||
# 综合得分
|
||
raw_score = price_score + kline_score + stochrsi_score + volume_score
|
||
raw_score = max(min(raw_score, 50), -50)
|
||
|
||
# 对数映射到 [0, 100]
|
||
if raw_score >= 0:
|
||
mapped_score = 50 + 50 * (np.log1p(raw_score / 50) / np.log1p(1))
|
||
else:
|
||
mapped_score = 50 * (np.log1p(-raw_score / 50) / np.log1p(1))
|
||
|
||
trend_scores[tf] = max(0, min(100, int(round(mapped_score))))
|
||
logger.info(f"[{pair}] {tf} 趋势得分：{trend_scores[tf]}, 原始得分：{raw_score}, "
|
||
f"价格得分：{price_score}, K线得分：{kline_score}, "
|
||
f"StochRSI得分：{stochrsi_score}, 量价得分：{volume_score}")
|
||
|
||
# 动态调整权重
|
||
if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20 or trend_scores.get("15m", 50) - trend_scores.get("3m", 50) > 20:
|
||
weights = {"3m": 0.2, "15m": 0.35, "1h": 0.45}
|
||
logger.info(f"[{pair}] 1h 趋势得分（{trend_scores.get('1h', 50)}）显著高于 3m（{trend_scores.get('3m', 50)}），调整权重为 {weights}")
|
||
|
||
# 加权融合
|
||
final_score = sum(trend_scores[tf] * weights[tf] for tf in timeframes)
|
||
final_score = int(round(final_score))
|
||
final_score = max(0, min(100, final_score))
|
||
|
||
logger.info(f"[{pair}] 最终趋势得分：{final_score}, "
|
||
f"3m得分：{trend_scores.get('3m', 50)}, 15m得分：{trend_scores.get('15m', 50)}, "
|
||
f"1h得分：{trend_scores.get('1h', 50)}")
|
||
return final_score
|
||
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] 获取市场趋势失败：{e}", exc_info=True)
|
||
return 50
|
||
|
||
def get_trend_score_with_cache(self, pair: str, timeframe: str, timestamp: int, dataframe: DataFrame, metadata: dict) -> float:
|
||
"""
|
||
三级缓存架构获取趋势得分：
|
||
1. 本地内存缓存（一级缓存）- 最快，无网络开销
|
||
2. Redis缓存（二级缓存）- 局域网共享，减少重复计算
|
||
3. 实时计算（三级缓存）- 最慢，但保证准确性
|
||
"""
|
||
|
||
# 初始化本地缓存（如果尚未初始化）
|
||
if not hasattr(self, '_local_cache'):
|
||
self._local_cache = {}
|
||
self._local_cache_stats = {'hits': 0, 'misses': 0, 'redis_hits': 0, 'computes': 0}
|
||
logger.info("🚀 初始化本地内存缓存")
|
||
|
||
# 创建 Redis 客户端（如果尚未创建）
|
||
if not hasattr(self, 'redis_client') and self.redis_url:
|
||
try:
|
||
self.redis_client = redis.from_url(self.redis_url)
|
||
logger.info("✅ Redis 客户端已成功初始化")
|
||
except Exception as e:
|
||
logger.error(f"❌ 初始化 Redis 客户端失败: {e}")
|
||
# Redis 失败时继续运行，降级为本地缓存
|
||
self.redis_client = None
|
||
|
||
# 生成统一的缓存键
|
||
strategy_name = "freqaiprimer"
|
||
timeframes_str = "3m-15m-1h"
|
||
# 将时间戳精度从秒改为分钟，去掉最后两位秒数
|
||
minute_timestamp = int(timestamp // 60) * 60 # 向下取整到分钟
|
||
cache_key = f"{strategy_name}|trend_score|{pair.replace('/', '-')}|{timeframes_str}|{minute_timestamp}"
|
||
logger.debug(f"[{pair}] 生成缓存键：{cache_key} (时间戳: {minute_timestamp})")
|
||
|
||
# 🎯 一级缓存：本地内存检查
|
||
if cache_key in self._local_cache:
|
||
cached_score = self._local_cache[cache_key]
|
||
self._local_cache_stats['hits'] += 1
|
||
# 只在调试模式下显示单个缓存命中，正常模式下只统计
|
||
logger.debug(f"[{pair}] 🟢 本地缓存命中：key={cache_key}, value={cached_score:.2f}")
|
||
return cached_score
|
||
|
||
self._local_cache_stats['misses'] += 1
|
||
logger.debug(f"[{pair}] 本地缓存未命中：key={cache_key}")
|
||
|
||
# 🎯 二级缓存：Redis检查
|
||
redis_client = self._get_redis_client()
|
||
if redis_client:
|
||
logger.debug(f"[{pair}] 尝试从 Redis 查询趋势得分，key={cache_key}")
|
||
try:
|
||
cached_score = redis_client.get(cache_key)
|
||
if cached_score is not None:
|
||
score = float(cached_score)
|
||
# 同时写入本地缓存，加速后续访问
|
||
self._local_cache[cache_key] = score
|
||
self._local_cache_stats['redis_hits'] += 1
|
||
logger.debug(f"[{pair}] 🟡 Redis 缓存命中：key={cache_key}, value={score:.2f} (已同步到本地缓存)")
|
||
return score
|
||
else:
|
||
logger.debug(f"[{pair}] 🔴 Redis 缓存未命中：key={cache_key}")
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] Redis 查询失败，key={cache_key}, 错误: {e}")
|
||
# Redis 失败时继续运行，降级为本地缓存
|
||
|
||
# 🎯 三级缓存：实时计算
|
||
logger.info(f"[{pair}] 开始计算趋势得分 (时间戳: {timestamp})")
|
||
trend_score = self.get_market_trend(dataframe=dataframe, metadata=metadata)
|
||
self._local_cache_stats['computes'] += 1
|
||
logger.info(f"[{pair}] ✅ 成功计算趋势得分: {trend_score:.2f}")
|
||
|
||
# 将结果写入两级缓存
|
||
self._local_cache[cache_key] = trend_score
|
||
|
||
redis_client = self._get_redis_client()
|
||
if redis_client:
|
||
logger.debug(f"[{pair}] 尝试将趋势得分写入 Redis，key={cache_key}, value={trend_score:.2f}")
|
||
try:
|
||
redis_client.setex(cache_key, 86400, trend_score)
|
||
logger.info(f"[{pair}] ✅ 成功将趋势得分存储到 Redis，key={cache_key}, value={trend_score:.2f}")
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] Redis 写入失败，key={cache_key}, 错误: {e}")
|
||
# Redis 失败时继续运行
|
||
|
||
# 定期打印缓存统计信息（更频繁地显示本轮命中统计）
|
||
total_requests = sum(self._local_cache_stats.values())
|
||
if total_requests % 50 == 0 and total_requests > 0: # 每50次显示一次统计
|
||
local_hits = self._local_cache_stats['hits']
|
||
redis_hits = self._local_cache_stats['redis_hits']
|
||
hit_rate = (local_hits + redis_hits) / total_requests * 100
|
||
logger.info(f"📊 本轮缓存统计 - 本地命中: {local_hits}次, "
|
||
f"Redis命中: {redis_hits}次, "
|
||
f"计算: {self._local_cache_stats['computes']}次, "
|
||
f"总命中率: {hit_rate:.1f}%")
|
||
|
||
return trend_score
|
||
|
||
def cleanup_local_cache(self):
|
||
"""
|
||
清理本地缓存，防止内存泄漏
|
||
"""
|
||
if hasattr(self, '_local_cache'):
|
||
cache_size = len(self._local_cache)
|
||
self._local_cache.clear()
|
||
logger.info(f"🧹 已清理本地缓存，清除条目数: {cache_size}")
|
||
|
||
def get_current_round_cache_hits(self) -> int:
|
||
"""
|
||
获取当前轮次的缓存命中总数（本地+Redis）
|
||
"""
|
||
if hasattr(self, '_local_cache_stats'):
|
||
return self._local_cache_stats['hits'] + self._local_cache_stats['redis_hits']
|
||
return 0
|
||
if hasattr(self, '_local_cache_stats'):
|
||
total_requests = sum(self._local_cache_stats.values())
|
||
if total_requests > 0:
|
||
local_hits = self._local_cache_stats['hits']
|
||
redis_hits = self._local_cache_stats['redis_hits']
|
||
return {
|
||
'local_hits': local_hits,
|
||
'redis_hits': redis_hits,
|
||
'misses': self._local_cache_stats['misses'],
|
||
'computes': self._local_cache_stats['computes'],
|
||
'total_requests': total_requests,
|
||
'local_hit_rate': local_hits / total_requests * 100,
|
||
'redis_hit_rate': redis_hits / total_requests * 100,
|
||
'overall_hit_rate': (local_hits + redis_hits) / total_requests * 100,
|
||
'current_round_hits': local_hits + redis_hits # 当前轮次总命中数
|
||
}
|
||
return {'local_hits': 0, 'redis_hits': 0, 'misses': 0, 'computes': 0, 'total_requests': 0, 'current_round_hits': 0}
|
||
|
||
def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
|
||
"""
|
||
每个交易循环开始时调用，用于自动清理本地缓存
|
||
"""
|
||
if hasattr(self, '_local_cache') and self._local_cache:
|
||
cache_size = len(self._local_cache)
|
||
|
||
# 每1000次循环清理一次，或当缓存超过500条时清理
|
||
if not hasattr(self, '_loop_counter'):
|
||
self._loop_counter = 0
|
||
|
||
self._loop_counter += 1
|
||
|
||
if self._loop_counter % 1000 == 0 or cache_size > 500:
|
||
logger.info(f"🧹 自动清理本地缓存 - 循环次数: {self._loop_counter}, 缓存大小: {cache_size}")
|
||
self.cleanup_local_cache()
|
||
|
||
# 打印缓存统计
|
||
stats = self.get_cache_stats()
|
||
if stats['total_requests'] > 0:
|
||
logger.info(f"📊 缓存性能统计: 本地命中率 {stats['local_hit_rate']:.1f}%, "
|
||
f"Redis命中率 {stats['redis_hit_rate']:.1f}%, "
|
||
f"总命中率 {stats['overall_hit_rate']:.1f}%")
|
||
|
||
def bot_start(self, **kwargs) -> None:
|
||
"""
|
||
机器人启动时调用，初始化缓存相关设置
|
||
"""
|
||
logger.info("🚀 策略启动 - 初始化多级缓存系统")
|
||
|
||
# 初始化本地缓存
|
||
if not hasattr(self, '_local_cache'):
|
||
self._local_cache = {}
|
||
self._local_cache_stats = {'hits': 0, 'misses': 0, 'redis_hits': 0, 'computes': 0}
|
||
logger.info("✅ 本地内存缓存已初始化")
|
||
|
||
# 设置最大本地缓存大小
|
||
self._max_local_cache_size = 500
|
||
|
||
# 初始化循环计数器
|
||
self._loop_counter = 0
|
||
|
||
def bot_stop(self, **kwargs) -> None:
|
||
"""
|
||
机器人停止时调用，清理所有缓存
|
||
"""
|
||
logger.info("🛑 策略停止 - 清理所有缓存")
|
||
|
||
# 清理本地缓存
|
||
if hasattr(self, '_local_cache'):
|
||
cache_size = len(self._local_cache)
|
||
self.cleanup_local_cache()
|
||
|
||
# 打印最终统计
|
||
stats = self.get_cache_stats()
|
||
if stats['total_requests'] > 0:
|
||
logger.info(f"📊 最终缓存统计 - 总请求: {stats['total_requests']}, "
|
||
f"本地命中: {stats['local_hits']}, "
|
||
f"Redis命中: {stats['redis_hits']}, "
|
||
f"计算次数: {stats['computes']}, "
|
||
f"总命中率: {stats['overall_hit_rate']:.1f}%")
|
||
|
||
# 注意：由于使用延迟初始化，无需关闭持久化的Redis连接
|
||
|
||
def detect_trend_status(self, dataframe: DataFrame, metadata: dict) -> str:
|
||
"""
|
||
基于分类模型优化 first_length 的趋势检测
|
||
规则：
|
||
- 使用 LightGBMClassifier 预测最优 first_length 类别
|
||
- 类别映射：0:激进(2), 1:中性(4), 2:稳健(6), 3:保守(8), 4:极保守(10)
|
||
- 根据预测的类别动态调整段长，而非固定使用2
|
||
"""
|
||
pair = metadata.get('pair', 'Unknown')
|
||
|
||
# 检查数据完整性
|
||
if len(dataframe) == 0:
|
||
logger.warning(f"[{pair}] ⚠️ 数据为空，返回震荡趋势")
|
||
return "ranging"
|
||
|
||
try:
|
||
# 使用分类模型预测最优 first_length
|
||
# 检查所有可能的分类列（精确匹配实际列名格式）
|
||
classification_cols = [col for col in dataframe.columns
|
||
if col in ["&*-optimal_first_length", "optimal_first_length_pred"]]
|
||
if classification_cols:
|
||
logger.info(f"[{pair}] 📊 检测到分类相关列: {classification_cols}")
|
||
else:
|
||
logger.debug(f"[{pair}] 📊 未检测到分类相关列")
|
||
|
||
# 调试：显示所有包含"optimal"的列名
|
||
all_optimal_cols = [col for col in dataframe.columns if "optimal" in str(col).lower()]
|
||
if all_optimal_cols:
|
||
logger.debug(f"[{pair}] 所有包含'optimal'的列: {all_optimal_cols}")
|
||
else:
|
||
logger.debug(f"[{pair}] 未找到任何包含'optimal'的列")
|
||
|
||
if "optimal_first_length_pred" in dataframe.columns:
|
||
predicted_value = dataframe["optimal_first_length_pred"].iloc[-1]
|
||
if pd.notna(predicted_value):
|
||
# 处理浮点预测值，四舍五入到最近的整数类别
|
||
optimal_length_class = int(round(float(predicted_value)))
|
||
length_mapping = {0: 2, 1: 4, 2: 6, 3: 8, 4: 10}
|
||
first_length = length_mapping.get(optimal_length_class, 2)
|
||
logger.info(f"[{pair}] ✅ 使用 optimal_first_length_pred: first_length={first_length} (原始值: {predicted_value}, 类别: {optimal_length_class})")
|
||
else:
|
||
first_length = 2
|
||
logger.warning(f"[{pair}] ⚠️ optimal_first_length_pred 为NaN，使用默认值: {first_length}")
|
||
elif "&*-optimal_first_length" in dataframe.columns:
|
||
# 直接检查原始列
|
||
predicted_value = dataframe["&*-optimal_first_length"].iloc[-1]
|
||
if pd.notna(predicted_value):
|
||
optimal_length_class = int(round(float(predicted_value)))
|
||
length_mapping = {0: 2, 1: 4, 2: 6, 3: 8, 4: 10}
|
||
first_length = length_mapping.get(optimal_length_class, 2)
|
||
logger.info(f"[{pair}] ✅ 使用 &*-optimal_first_length: first_length={first_length} (原始值: {predicted_value}, 类别: {optimal_length_class})")
|
||
else:
|
||
first_length = 2
|
||
logger.warning(f"[{pair}] ⚠️ &*-optimal_first_length 为NaN，使用默认值: {first_length}")
|
||
else:
|
||
first_length = 2
|
||
# 只在首次或特定条件下显示警告
|
||
if not hasattr(self, '_classification_warning_shown'):
|
||
logger.warning(f"[{pair}] ⚠️ 未找到分类模型列，使用默认值: {first_length}")
|
||
self._classification_warning_shown = True
|
||
else:
|
||
logger.debug(f"[{pair}] 未找到分类模型列，使用默认值: {first_length}")
|
||
|
||
# 根据 first_length 动态调整其他段长
|
||
second_length = first_length * 3
|
||
third_length = first_length * 5
|
||
|
||
# 计算总长度
|
||
total_length_needed = first_length + second_length + third_length
|
||
|
||
# 检查数据是否充足
|
||
if len(dataframe) < total_length_needed:
|
||
logger.warning(f"[{pair}] 数据不足{total_length_needed}个周期，返回震荡趋势")
|
||
return "ranging"
|
||
|
||
# 获取所需长度的trend_score历史
|
||
trend_scores = []
|
||
actual_total_length = len(dataframe)
|
||
|
||
for i in range(-total_length_needed, 0):
|
||
# 确保索引在有效范围内
|
||
if abs(i) > actual_total_length:
|
||
logger.warning(f"[{pair}] 索引 {i} 超出数据范围，使用默认趋势得分 50")
|
||
trend_scores.append(50)
|
||
continue
|
||
|
||
# 获取历史数据片段
|
||
end_idx = i + 1 if i != -1 else None
|
||
hist_df = dataframe.iloc[:end_idx]
|
||
|
||
if hist_df.empty:
|
||
logger.warning(f"[{pair}] 历史数据片段为空，使用默认趋势得分 50")
|
||
trend_scores.append(50)
|
||
continue
|
||
|
||
# 获取时间戳 - 统一使用整数时间戳
|
||
try:
|
||
last_idx = hist_df.index[-1]
|
||
if isinstance(last_idx, pd.Timestamp):
|
||
# 确保时间戳是无时区的整数
|
||
ts = last_idx.tz_localize(None) if last_idx.tz else last_idx
|
||
timestamp = int(ts.timestamp())
|
||
elif isinstance(last_idx, (int, np.integer)):
|
||
# 如果索引已经是整数，直接使用
|
||
timestamp = int(last_idx)
|
||
elif hasattr(last_idx, 'timestamp'):
|
||
timestamp = int(last_idx.timestamp())
|
||
else:
|
||
# 使用当前时间的整数时间戳
|
||
timestamp = int(pd.Timestamp.utcnow().timestamp())
|
||
except Exception as e:
|
||
# 使用当前时间的整数时间戳作为fallback
|
||
timestamp = int(pd.Timestamp.utcnow().timestamp())
|
||
|
||
# 获取趋势得分
|
||
score = self.get_trend_score_with_cache(
|
||
pair=pair,
|
||
timeframe=self.timeframe,
|
||
timestamp=timestamp,
|
||
dataframe=hist_df,
|
||
metadata=metadata
|
||
)
|
||
trend_scores.append(score)
|
||
|
||
# 验证结果数量
|
||
if len(trend_scores) < total_length_needed:
|
||
logger.warning(f"[{pair}] 只获取到 {len(trend_scores)} 个趋势得分，需要{total_length_needed}个")
|
||
while len(trend_scores) < total_length_needed:
|
||
trend_scores.append(50)
|
||
|
||
# 分段计算加权得分
|
||
# 第一段：最近first_length个周期
|
||
segment1 = trend_scores[-first_length:]
|
||
weighted_score1 = sum(score * 10 for score in segment1) / len(segment1)
|
||
|
||
# 第二段：接下来的second_length个周期
|
||
segment2 = trend_scores[-(first_length + second_length):-first_length]
|
||
weighted_score2 = sum(score * 7 for score in segment2) / len(segment2)
|
||
|
||
# 第三段：最后的third_length个周期
|
||
segment3 = trend_scores[-total_length_needed:-(first_length + second_length)]
|
||
weighted_score3 = sum(score * 3 for score in segment3) / len(segment3)
|
||
|
||
# 计算最终加权得分
|
||
final_weighted_score = (weighted_score1 + weighted_score2 + weighted_score3) / (10 + 7 + 3)
|
||
|
||
# 将得分映射到0-100区间
|
||
final_score = max(0, min(100, final_weighted_score))
|
||
|
||
# 使用hyperopt优化的阈值判断趋势状态
|
||
bullish_threshold = self.trend_final_bullish_threshold.value
|
||
bearish_threshold = self.trend_final_bearish_threshold.value
|
||
|
||
# 判定趋势状态
|
||
if final_score >= bullish_threshold:
|
||
trend_status = "bullish"
|
||
logger.info(f"[{pair}] 🚀 检测到上涨趋势: 最终加权得分={final_score:.2f}, 阈值≥{bullish_threshold}, 段长={first_length},{second_length},{third_length}")
|
||
elif final_score <= bearish_threshold:
|
||
trend_status = "bearish"
|
||
logger.info(f"[{pair}] 📉 检测到下跌趋势: 最终加权得分={final_score:.2f}, 阈值≤{bearish_threshold}, 段长={first_length},{second_length},{third_length}")
|
||
else:
|
||
trend_status = "ranging"
|
||
logger.info(f"[{pair}] ⚖️ 检测到震荡趋势: 最终加权得分={final_score:.2f}, 阈值范围({bearish_threshold}, {bullish_threshold}), 段长={first_length},{second_length},{third_length}")
|
||
|
||
# 输出分段详细信息用于调试
|
||
logger.debug(f"[{pair}] 趋势分析详情 - "
|
||
f"第一段({first_length}个,权重10): {[f'{s:.1f}' for s in segment1]}, "
|
||
f"第二段({second_length}个,权重7): {[f'{s:.1f}' for s in segment2]}, "
|
||
f"第三段({third_length}个,权重3): {[f'{s:.1f}' for s in segment3]}")
|
||
|
||
return trend_status
|
||
|
||
except Exception as e:
|
||
logger.error(f"[{pair}] 趋势状态检测失败: {e}")
|
||
return "ranging"
|