366 lines
17 KiB
Python
366 lines
17 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 IStrategy
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logger = logging.getLogger(__name__)
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class FreqaiPrimer(IStrategy):
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minimal_roi = {
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"0": 0.02,
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"30": 0.01,
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"60": 0
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}
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stoploss = -0.015
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timeframe = "3m"
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use_custom_stoploss = False
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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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def __init__(self, config: dict, *args, **kwargs):
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super().__init__(config, *args, **kwargs)
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logger.setLevel(logging.DEBUG) # 保持 DEBUG 级别以查看更多日志
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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.trailing_stop_start = 0.03
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self.trailing_stop_distance = 0.01
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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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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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logger.debug(f"[{pair}] 目标列生成完成，列：{list(dataframe.columns)}")
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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：打印关键指标
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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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logger.debug(f"freqai_info identifier：{self.freqai_info['identifier']}")
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logger.debug(f"user_data_dir：{self.config['user_data_dir']}")
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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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self.pair_stats[pair] = {"labels_mean": labels_mean, "labels_std": labels_std}
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if labels_std > 0.015:
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k_buy = 1.2
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k_sell = 1.5
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elif labels_std < 0.010:
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k_buy = 0.8
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k_sell = 1.0
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else:
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k_buy = 1.0
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k_sell = 1.2
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if labels_mean > 0.015:
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k_sell += 0.5
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logger.info(f"[{pair}] labels_mean 较高（{labels_mean:.4f}），增加 k_sell 到 {k_sell:.2f}")
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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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self.buy_threshold = max(self.buy_threshold, -0.05)
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self.buy_threshold = min(self.buy_threshold, -0.005)
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self.sell_threshold = min(self.sell_threshold, 0.05)
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self.sell_threshold = max(self.sell_threshold, 0.005)
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logger.info(f"[{pair}] 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}")
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logger.info(f"[{pair}] 动态卖出阈值：{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 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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if "&-price_value_divergence" in dataframe.columns:
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# 逐个检查买入条件
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
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cond2 = (dataframe["volume_z_score"] > 1.5)
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cond3 = (dataframe["rsi"] < 40)
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cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
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buy_condition = cond1 & cond2 & cond3 & cond4
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conditions.append(buy_condition)
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# 添加调试日志：打印条件是否满足
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logger.debug(f"[{pair}] 买入条件检查 - "
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f"&-price_value_divergence < {self.buy_threshold:.6f}: {cond1.iloc[-1]}, "
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f"volume_z_score > 1.5: {cond2.iloc[-1]}, "
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f"rsi < 40: {cond3.iloc[-1]}, "
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f"close <= bb_lowerband: {cond4.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:
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dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
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# 检查是否同时有卖出信号
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if 'exit_long' in dataframe.columns and (dataframe["exit_long"] == 1).any():
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logger.warning(f"[{pair}] 同时检测到买入和卖出信号，忽略买入信号")
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dataframe['enter_long'] = 0
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else:
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logger.debug(f"[{pair}] 入场信号触发，条件满足") # 改为 DEBUG 级别
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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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# 逐个检查卖出条件
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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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# 添加调试日志：打印条件是否满足
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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:
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dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
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logger.debug(f"[{pair}] 出场信号触发，条件满足") # 改为 DEBUG 级别
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return dataframe
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def adjust_trade_position(self, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float,
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min_roi: Dict[float, float], max_profit: float):
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hold_time = (current_time - trade.open_date_utc).total_seconds() / 60
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if hold_time < 15:
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logger.info(f"[{trade.pair}] 持仓时间 {hold_time:.1f} 分钟，未达到最小持仓时间 15 分钟，暂不退出")
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return None
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profit_ratio = (current_rate - trade.open_rate) / trade.open_rate
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if profit_ratio >= self.trailing_stop_start and not self.trailing_stop_enabled:
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self.trailing_stop_enabled = True
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trade.adjust_max_rate(current_rate)
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logger.info(f"[{trade.pair}] 价格上涨超过 {self.trailing_stop_start*100:.1f}%，启动 Trailing Stop")
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if self.trailing_stop_enabled:
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max_rate = trade.max_rate
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trailing_stop_price = max_rate * (1 - self.trailing_stop_distance)
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if current_rate < trailing_stop_price:
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logger.info(f"[{trade.pair}] 价格回落至 Trailing Stop 点 {trailing_stop_price:.6f}，触发卖出")
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return -1
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trade.adjust_max_rate(current_rate)
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if hold_time > 30:
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logger.info(f"[{trade.pair}] 持仓时间超过30分钟，强制平仓")
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return -1
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return None
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, **kwargs) -> bool:
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# 添加调试日志：检查是否允许买入
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recent_trades = Trade.query.filter(
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and_(
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Trade.pair == pair,
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Trade.is_open == False, # noqa: E712
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Trade.close_date > current_time - datetime.timedelta(minutes=5)
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)
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).all()
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if len(recent_trades) > 0:
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logger.info(f"[{pair}] 5分钟内有近期交易（{len(recent_trades)} 笔），跳过本次入场")
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return False
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# 检查其他限制（例如资金、仓位等）
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logger.debug(f"[{pair}] 允许买入 - 订单类型：{order_type}, 数量：{amount:.2f}, 价格：{rate:.6f}, 时间：{current_time}")
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self.trailing_stop_enabled = False
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return True
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
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rate: float, time_in_force: str, exit_reason: str,
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current_time: datetime, **kwargs) -> bool:
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logger.info(f"[{pair}] 退出交易，原因：{exit_reason}, 利润：{trade.calc_profit_ratio(rate):.2%}")
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return True
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