651 lines
34 KiB
Python
651 lines
34 KiB
Python
import logging
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import numpy as np
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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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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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from typing import Dict
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from freqtrade.strategy import (DecimalParameter, IStrategy, IntParameter)
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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，集成动态加仓和减仓逻辑，兼容最新 Freqtrade 版本
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"""
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# --- 🧪 Hyperopt Parameters ---
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TRAILING_STOP_START = DecimalParameter(0.01, 0.05, default=0.03, space='sell', optimize=True)
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TRAILING_STOP_DISTANCE = DecimalParameter(0.005, 0.02, default=0.01, space='sell', optimize=True)
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BUY_THRESHOLD_MIN = DecimalParameter(-0.1, -0.01, default=-0.05, space='buy', optimize=True)
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BUY_THRESHOLD_MAX = DecimalParameter(-0.02, -0.001, default=-0.005, space='buy', optimize=True)
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SELL_THRESHOLD_MIN = DecimalParameter(0.001, 0.02, default=0.005, space='sell', optimize=True)
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SELL_THRESHOLD_MAX = DecimalParameter(0.02, 0.1, default=0.05, space='sell', optimize=True)
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# 新增：加仓和减仓参数
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ADD_POSITION_THRESHOLD = DecimalParameter(-0.05, -0.01, default=-0.02, space='buy', optimize=True)
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EXIT_POSITION_RATIO = DecimalParameter(0.2, 0.7, default=0.5, space='sell', optimize=True)
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COOLDOWN_PERIOD_MINUTES = IntParameter(1, 10, default=5, space='buy', optimize=True)
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MAX_ENTRY_POSITION_ADJUSTMENT = IntParameter(1, 3, default=2, space='buy', optimize=True)
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# --- 🛠️ 固定配置参数 ---
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stoploss = -0.015
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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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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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"bb_middleband": {"color": "gray"}
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},
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"subplots": {
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"Signals": {
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"enter_long": {"color": "green"},
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"exit_long": {"color": "red"}
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},
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"Price-Value Divergence": {
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"&-price_value_divergence": {"color": "purple"}
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},
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"Volume Z-Score": {
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"volume_z_score": {"color": "orange"}
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},
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"RSI": {
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"rsi": {"color": "cyan"}
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}
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}
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}
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freqai_info = {
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"identifier": "test58",
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"model": "LightGBMRegressor",
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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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},
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"data_split_parameters": {
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"test_size": 0.2,
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"shuffle": False,
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},
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"model_training_parameters": {
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"n_estimators": 200,
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"learning_rate": 0.05,
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"num_leaves": 31,
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"verbose": -1,
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},
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"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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logger.setLevel(logging.DEBUG)
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logger.debug("✅ 策略已初始化，日志级别设置为 DEBUG")
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self.trailing_stop_enabled = False
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self.pair_stats = {}
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self.stats_logged = False
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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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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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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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]
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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.debug(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["&-price_value_divergence"] = dataframe["&-price_value_divergence"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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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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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata.get('pair', 'Unknown')
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logger.info(f"[{pair}] 当前可用列（调用FreqAI前）：{list(dataframe.columns)}")
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# 计算200周期EMA和历史价值背离
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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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# 调用FreqAI预测价值背离
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if not hasattr(self, 'freqai') or self.freqai is None:
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logger.error(f"[{pair}] FreqAI 未初始化，请确保回测命令中启用了 --freqai")
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dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
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else:
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logger.debug(f"self.freqai 类型：{type(self.freqai)}")
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dataframe = self.freqai.start(dataframe, metadata, self)
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if "&-price_value_divergence" not in dataframe.columns:
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logger.warning(f"[{pair}] 回归模型未生成 &-price_value_divergence，回退到规则计算")
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dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
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# 计算其他指标
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upperband, middleband, lowerband = ta.BBANDS(dataframe["close"], timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
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dataframe["bb_upperband"] = upperband
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dataframe["bb_middleband"] = middleband
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dataframe["bb_lowerband"] = lowerband
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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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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# 数据清理
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for col in ["ema200", "bb_upperband", "bb_middleband", "bb_lowerband", "rsi", "volume_z_score", "&-price_value_divergence", "price_value_divergence"]:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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# 添加调试日志
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logger.debug(f"[{pair}] 最新数据 - close：{dataframe['close'].iloc[-1]:.6f}, "
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f"rsi：{dataframe['rsi'].iloc[-1]:.2f}, "
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f"&-price_value_divergence：{dataframe['&-price_value_divergence'].iloc[-1]:.6f}, "
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f"volume_z_score：{dataframe['volume_z_score'].iloc[-1]:.2f}, "
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f"bb_lowerband：{dataframe['bb_lowerband'].iloc[-1]:.6f}")
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# 获取 labels_mean 和 labels_std
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labels_mean = None
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labels_std = None
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try:
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model_base_dir = os.path.join(self.config["user_data_dir"], "models", self.freqai_info["identifier"])
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pair_base = pair.split('/')[0] if '/' in pair else pair
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sub_dirs = glob.glob(os.path.join(model_base_dir, f"sub-train-{pair_base}_*"))
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if not sub_dirs:
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logger.warning(f"[{pair}] 未找到任何子目录：{model_base_dir}/sub-train-{pair_base}_*")
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else:
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latest_sub_dir = max(sub_dirs, key=lambda x: int(x.split('_')[-1]))
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pair_base_lower = pair_base.lower()
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timestamp = latest_sub_dir.split('_')[-1]
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metadata_file = os.path.join(latest_sub_dir, f"cb_{pair_base_lower}_{timestamp}_metadata.json")
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if os.path.exists(metadata_file):
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with open(metadata_file, "r") as f:
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metadata = json.load(f)
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labels_mean = metadata["labels_mean"]["&-price_value_divergence"]
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labels_std = metadata["labels_std"]["&-price_value_divergence"]
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logger.info(f"[{pair}] 从最新子目录 {latest_sub_dir} 读取 labels_mean：{labels_mean}, labels_std：{labels_std}")
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else:
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logger.warning(f"[{pair}] 最新的 metadata.json 文件 {metadata_file} 不存在")
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except Exception as e:
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logger.warning(f"[{pair}] 无法从子目录读取 labels_mean 和 labels_std：{e}，重新计算")
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if labels_mean is None or labels_std is None:
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logger.warning(f"[{pair}] 无法获取 labels_mean 和 labels_std，重新计算")
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dataframe["&-price_value_divergence_actual"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"]
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dataframe["&-price_value_divergence_actual"] = dataframe["&-price_value_divergence_actual"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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recent_data = dataframe["&-price_value_divergence_actual"].tail(self.fit_live_predictions_candles)
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labels_mean = recent_data.mean()
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labels_std = recent_data.std()
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if np.isnan(labels_std) or labels_std == 0:
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labels_std = 0.01
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logger.warning(f"[{pair}] labels_std 计算异常，使用默认值 0.01")
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# 根据市场趋势得分动态调整买卖阈值
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market_trend_score = self.get_market_trend()
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k_buy = FreqaiPrimer.linear_map(market_trend_score, 0, 100, 1.2, 0.8)
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k_sell = FreqaiPrimer.linear_map(market_trend_score, 0, 100, 1.5, 1.0)
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self.buy_threshold = labels_mean - k_buy * labels_std
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self.sell_threshold = labels_mean + k_sell * labels_std
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# 使用 Hyperopt 参数限制阈值
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self.buy_threshold = max(self.buy_threshold, self.BUY_THRESHOLD_MIN.value)
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self.buy_threshold = min(self.buy_threshold, self.BUY_THRESHOLD_MAX.value)
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self.sell_threshold = min(self.sell_threshold, self.SELL_THRESHOLD_MAX.value)
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self.sell_threshold = max(self.sell_threshold, self.SELL_THRESHOLD_MIN.value)
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logger.info(f"[{pair}] 市场趋势得分：{market_trend_score}, labels_mean：{labels_mean:.4f}, labels_std：{labels_std:.4f}")
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logger.info(f"[{pair}] k_buy：{k_buy:.2f}, k_sell：{k_sell:.2f}")
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logger.info(f"[{pair}] 动态买入阈值：{self.buy_threshold:.4f}, 卖出阈值：{self.sell_threshold:.4f}")
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if not self.stats_logged:
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logger.info("===== 所有币对的 labels_mean 和 labels_std 汇总 =====")
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for p, stats in self.pair_stats.items():
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logger.info(f"[{p}] labels_mean：{stats['labels_mean']:.4f}, labels_std：{stats['labels_std']:.4f}")
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logger.info("==============================================")
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self.stats_logged = True
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return dataframe
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def trailing_space(self):
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return [
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DecimalParameter(0.01, 0.05, name="trailing_stop_start"),
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DecimalParameter(0.005, 0.02, name="trailing_stop_distance")
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]
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def leverage_space(self):
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return [
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DecimalParameter(-0.05, -0.01, name="add_position_threshold", default=-0.02),
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DecimalParameter(0.2, 0.7, name="exit_position_ratio", default=0.5),
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IntParameter(1, 10, name="cooldown_period_minutes", default=5),
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IntParameter(1, 3, name="max_entry_position_adjustment", default=2)
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]
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata.get('pair', 'Unknown')
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conditions = []
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# 获取市场趋势得分
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trend_score = self.get_market_trend(dataframe=dataframe, metadata=metadata)
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# 动态调整成交量阈值：牛市（trend_score=100）-> 0.5，熊市（trend_score=0）-> 1.5
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volume_z_score_min = 0.5
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volume_z_score_max = 1.5
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volume_z_score_threshold = self.linear_map(trend_score, 0, 100, volume_z_score_max, volume_z_score_min)
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# 动态调整 RSI 阈值：牛市（trend_score=100）-> 40，熊市（trend_score=0）-> 60
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rsi_min = 40
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rsi_max = 60
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rsi_threshold = self.linear_map(trend_score, 0, 100, rsi_max, rsi_min)
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# 新增：动态调整 STOCHRSI 阈值，牛市 -> 30，熊市 -> 50
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stochrsi_min = 30
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stochrsi_max = 50
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stochrsi_threshold = self.linear_map(trend_score, 0, 100, stochrsi_max, stochrsi_min)
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if "&-price_value_divergence" in dataframe.columns:
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# 计算 STOCHRSI
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stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
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dataframe["stochrsi_k"] = stochrsi["fastk"]
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold)
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cond3 = (dataframe["rsi"] < rsi_threshold)
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cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold) # 新增 STOCHRSI 条件
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buy_condition = cond1 & cond2 & cond3 & cond4 & cond5
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conditions.append(buy_condition)
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divergence_value = dataframe['&-price_value_divergence'].iloc[-1] if not dataframe['&-price_value_divergence'].isna().all() else np.nan
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volume_z_score_value = dataframe['volume_z_score'].iloc[-1] if not dataframe['volume_z_score'].isna().all() else np.nan
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rsi_value = dataframe['rsi'].iloc[-1] if not dataframe['rsi'].isna().all() else np.nan
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stochrsi_value = dataframe['stochrsi_k'].iloc[-1] if not dataframe['stochrsi_k'].isna().all() else np.nan
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logger.debug(f"[{pair}] 买入条件检查 - "
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f"&-price_value_divergence={divergence_value:.6f} < {self.buy_threshold:.6f}: {cond1.iloc[-1]}, "
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f"volume_z_score={volume_z_score_value:.2f} > {volume_z_score_threshold:.2f}: {cond2.iloc[-1]}, "
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f"rsi={rsi_value:.2f} < {rsi_threshold:.2f}: {cond3.iloc[-1]}, "
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f"close={dataframe['close'].iloc[-1]:.6f} <= bb_lowerband={dataframe['bb_lowerband'].iloc[-1]:.6f}: {cond4.iloc[-1]}, "
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f"stochrsi_k={stochrsi_value:.2f} < {stochrsi_threshold:.2f}: {cond5.iloc[-1]}")
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else:
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logger.warning(f"[{pair}] ⚠️ &-price_value_divergence 列缺失，跳过买入信号生成")
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if len(conditions) > 0:
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combined_condition = reduce(lambda x, y: x & y, conditions)
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if combined_condition.any():
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dataframe.loc[combined_condition, 'enter_long'] = 1
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logger.info(f"[{pair}] 买入信号触发，条件满足，趋势得分：{trend_score:.2f}")
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else:
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logger.debug(f"[{pair}] 买入条件未满足，无买入信号")
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else:
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logger.debug(f"[{pair}] 无有效买入条件")
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata.get('pair', 'Unknown')
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conditions = []
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if "&-price_value_divergence" in dataframe.columns:
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cond1 = (dataframe["&-price_value_divergence"] > self.sell_threshold)
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cond2 = (dataframe["rsi"] > 75)
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sell_condition = cond1 | cond2
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conditions.append(sell_condition)
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logger.debug(f"[{pair}] 卖出条件检查 - "
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f"&-price_value_divergence > {self.sell_threshold:.6f}: {cond1.iloc[-1]}, "
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f"rsi > 75: {cond2.iloc[-1]}")
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else:
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logger.warning(f"[{pair}] ⚠️ &-price_value_divergence 列缺失，跳过该条件")
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if len(conditions) > 0:
|
||
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
|
||
logger.debug(f"[{pair}] 出场信号触发，条件满足")
|
||
else:
|
||
logger.debug(f"[{pair}] 无有效卖出条件")
|
||
|
||
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)
|
||
]
|
||
|
||
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
|
||
|
||
initial_stake_amount = trade.stake_amount / 3
|
||
logger.debug(f"{pair} 首次入场金额: {initial_stake_amount:.2f}, 当前持仓金额: {trade.stake_amount:.2f}, "
|
||
f"加仓次数: {trade.nr_of_successful_entries - 1}")
|
||
|
||
# 加仓逻辑
|
||
max_entry_adjustments = self.MAX_ENTRY_POSITION_ADJUSTMENT.value
|
||
if trade.nr_of_successful_entries <= max_entry_adjustments + 1:
|
||
add_position_threshold = self.ADD_POSITION_THRESHOLD.value
|
||
# 线性映射加仓阈值，趋势值越高，加仓越严格
|
||
add_threshold = 80 - 30 * (trend_score / 100) # 趋势值 100 -> 50, 0 -> 80
|
||
if profit_ratio <= add_position_threshold and hold_time > 5 and trend_score <= add_threshold:
|
||
logger.debug(f"{pair} 初始下注金额: {initial_stake_amount:.2f}, trend_score: {trend_score:.2f}, add_threshold: {add_threshold} ")
|
||
|
||
# 计算加仓金额
|
||
add_count = trade.nr_of_successful_entries - 1
|
||
multipliers = [2, 4, 8]
|
||
if add_count < len(multipliers):
|
||
multiplier = multipliers[add_count]
|
||
add_amount = initial_stake_amount * multiplier
|
||
logger.debug(f"{pair} 第 {add_count + 1} 次加仓，倍数={multiplier}, "
|
||
f"金额 = {initial_stake_amount:.2f} * {multiplier} = {add_amount:.2f}")
|
||
logger.debug(f"{pair} 加仓计算: 第 {add_count + 1} 次加仓，倍数={multiplier}, "
|
||
f"金额 = {initial_stake_amount:.2f} * {multiplier} = {add_amount:.2f}")
|
||
|
||
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}，取消加仓")
|
||
return (None, f"Add amount {add_amount:.2f} below min_stake {min_stake:.2f}")
|
||
if add_amount > max_stake:
|
||
logger.warning(f"{pair} 加仓金额 {add_amount:.2f} 超出最大可用金额 {max_stake:.2f}，调整为 {max_stake:.2f}")
|
||
add_amount = max_stake
|
||
logger.info(f"{pair} 价格下跌 {profit_ratio*100:.2f}%，触发第 {add_count + 1} 次加仓 {add_amount:.2f}")
|
||
return (add_amount, f"Price dropped {profit_ratio*100:.2f}%, add {add_amount:.2f}")
|
||
|
||
# 减仓逻辑
|
||
exit_position_ratio = self.EXIT_POSITION_RATIO.value
|
||
if profit_ratio >= 0.03:
|
||
# 趋势值越高，减仓比例越低
|
||
reduce_factor = 0.6 + 0.4 * (1 - trend_score / 100) # 牛市(100) -> 0.6, 熊市(0) -> 1.0
|
||
reduce_amount = -exit_position_ratio * reduce_factor * trade.stake_amount
|
||
logger.info(f"{pair} 趋势值 {trend_score:.2f}，利润 {profit_ratio*100:.2f}%，减仓 {abs(reduce_amount):.2f}")
|
||
return (reduce_amount, f"Profit {profit_ratio*100:.2f}%")
|
||
elif profit_ratio >= 0.05:
|
||
reduce_factor = 1.4 - 0.4 * (trend_score / 100) # 牛市(100) -> 1.0, 熊市(0) -> 1.4
|
||
reduce_amount = -exit_position_ratio * reduce_factor * trade.stake_amount
|
||
logger.info(f"{pair} 趋势值 {trend_score:.2f}，利润 {profit_ratio*100:.2f}%，减仓 {abs(reduce_amount):.2f}")
|
||
return (reduce_amount, f"Profit {profit_ratio*100:.2f}%")
|
||
|
||
# 追踪止损逻辑
|
||
trailing_stop_start = self.TRAILING_STOP_START.value
|
||
trailing_stop_distance = self.TRAILING_STOP_DISTANCE.value
|
||
# 使用 Sigmoid 映射调整追踪止损参数
|
||
sigmoid = 1 / (1 + np.exp(-0.1 * (trend_score - 50)))
|
||
trailing_factor = 0.8 + (1.2 - 0.8) * sigmoid # 牛市(100) -> 1.2, 熊市(0) -> 0.8
|
||
distance_factor = 0.7 + (1.5 - 0.7) * sigmoid # 牛市(100) -> 1.5, 熊市(0) -> 0.7
|
||
trailing_stop_start *= trailing_factor
|
||
trailing_stop_distance *= distance_factor
|
||
|
||
if profit_ratio >= trailing_stop_start and not self.trailing_stop_enabled:
|
||
self.trailing_stop_enabled = True
|
||
trade.adjust_min_max_rates(current_rate, current_rate)
|
||
logger.info(f"{pair} 价格上涨超过 {trailing_stop_start*100:.1f}%，启动追踪止损")
|
||
return None
|
||
|
||
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} 价格回落至 {trailing_stop_price:.6f}，触发全部卖出")
|
||
return (-trade.stake_amount, f"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.debug(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
|
||
|
||
market_trend_score = self.get_market_trend()
|
||
cooldown_period_minutes = self.COOLDOWN_PERIOD_MINUTES.value if market_trend_score > 50 else self.COOLDOWN_PERIOD_MINUTES.value // 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_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.debug(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.debug(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": 0.5, "15m": 0.3, "1h": 0.2} # 默认权重
|
||
trend_scores = {}
|
||
pair = metadata.get('pair', 'Unknown') if metadata else 'Unknown'
|
||
logger.debug(f"[{pair}] 正在计算多时间框架市场趋势得分")
|
||
|
||
for tf in timeframes:
|
||
if tf == "3m" and dataframe is not None:
|
||
btc_df = dataframe
|
||
else:
|
||
btc_df = self.dp.get_pair_dataframe("BTC/USDT", tf)
|
||
|
||
min_candles = 200 if tf == "3m" else 100 if tf == "15m" else 50
|
||
if len(btc_df) < min_candles:
|
||
logger.warning(f"BTC 数据不足（{tf}，{len(btc_df)} 根K线），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
|
||
btc_df["ema_short"] = ta.EMA(btc_df, timeperiod=ema_short_period)
|
||
btc_df["ema_long"] = ta.EMA(btc_df, timeperiod=ema_long_period)
|
||
btc_df["ema_short_slope"] = (btc_df["ema_short"] - btc_df["ema_short"].shift(10)) / btc_df["ema_short"].shift(10)
|
||
|
||
price_above_ema = btc_df["close"].iloc[-1] > btc_df["ema_long"].iloc[-1]
|
||
ema_short_above_ema_long = btc_df["ema_short"].iloc[-1] > btc_df["ema_long"].iloc[-1]
|
||
ema_short_slope = btc_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
|
||
btc_df["bullish_engulfing"] = (
|
||
(btc_df["close"].shift(1) < btc_df["open"].shift(1)) &
|
||
(btc_df["close"] > btc_df["open"]) &
|
||
(btc_df["close"] > btc_df["open"].shift(1)) &
|
||
(btc_df["open"] < btc_df["close"].shift(1))
|
||
)
|
||
btc_df["bearish_engulfing"] = (
|
||
(btc_df["close"].shift(1) > btc_df["open"].shift(1)) &
|
||
(btc_df["close"] < btc_df["open"]) &
|
||
(btc_df["close"] < btc_df["open"].shift(1)) &
|
||
(btc_df["open"] > btc_df["close"].shift(1))
|
||
)
|
||
|
||
kline_score = 0
|
||
if btc_df["bullish_engulfing"].iloc[-1]:
|
||
kline_score += 15
|
||
elif btc_df["bearish_engulfing"]:
|
||
kline_score -= 15
|
||
volatility = btc_df["close"].pct_change(10).std() * 100
|
||
if volatility > 0.5:
|
||
kline_score += 10 if price_score > 0 else -10
|
||
|
||
# StochRSI
|
||
stochrsi = ta.STOCHRSI(btc_df, timeperiod=14, fastk_period=3, fastd_period=3)
|
||
btc_df["stochrsi_k"] = stochrsi["fastk"]
|
||
btc_df["stochrsi_d"] = stochrsi["fastd"]
|
||
|
||
stochrsi_score = 0
|
||
stochrsi_k = btc_df["stochrsi_k"].iloc[-1]
|
||
stochrsi_d = btc_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
|
||
btc_df["volume_mean_20"] = btc_df["volume"].rolling(20).mean()
|
||
btc_df["volume_std_20"] = btc_df["volume"].rolling(20).std()
|
||
btc_df["volume_z_score"] = (btc_df["volume"] - btc_df["volume_mean_20"]) / btc_df["volume_std_20"]
|
||
btc_df["adx"] = ta.ADX(btc_df, timeperiod=14)
|
||
|
||
volume_score = 0
|
||
if btc_df["volume_z_score"].iloc[-1] > 1.5:
|
||
volume_score += 10 if price_score > 0 else -10
|
||
if btc_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.debug(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：当 1h 得分显著高于 3m（差值 > 20），提高 1h 权重
|
||
if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20:
|
||
weights = {"3m": 0.3, "15m": 0.3, "1h": 0.4}
|
||
logger.debug(f"[{pair}] 1h 趋势得分（{trend_scores.get('1h', 50)}）显著高于 3m（{trend_scores.get('3m', 50)}），调整权重为 {weights}")
|
||
|
||
# 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
|