286 lines
13 KiB
Python
286 lines
13 KiB
Python
import logging
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import numpy as np
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import datetime
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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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"""
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策略说明：
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- 只使用回归模型预测价值背离（&-price_value_divergence）
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- 动态调整阈值，基于 price_value_divergence 的历史数据
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- 放宽过滤条件，增加信号触发频率
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- 使用RSI和布林带作为辅助过滤条件
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"""
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# 策略参数
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minimal_roi = {
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"0": 0.02, # 固定止盈2%
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"30": 0.01,
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"60": 0
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}
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stoploss = -0.015 # 固定止损-1.5%
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timeframe = "3m" # 3分钟K线
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use_custom_stoploss = False # 不使用动态止损，改为固定止损
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# 绘图配置
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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 配置：回归模型（预测价值背离）
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freqai_info = {
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"identifier": "divergence_model",
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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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}
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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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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
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"""
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特征工程：计算技术指标作为FreqAI的输入特征
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"""
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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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# 计算价值背离作为特征
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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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logger.debug(f"[{metadata['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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"""
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设置FreqAI的训练目标：只预测价值背离
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"""
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if len(dataframe) < 200:
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logger.warning(f"[{metadata['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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# 计算200周期EMA作为公平价值
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dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
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# 计算价值背离（回归目标）
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dataframe["&-price_value_divergence"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"]
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# 成交量Z分数
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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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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"[{metadata['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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"""
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计算所有指标，包括FreqAI预测结果，并基于历史数据动态调整阈值
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"""
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logger.info(f"[{metadata['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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# 动态计算阈值：基于 price_value_divergence 的历史均值和标准差
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if "price_value_divergence" in dataframe.columns:
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divergence_mean = dataframe["price_value_divergence"].mean()
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divergence_std = dataframe["price_value_divergence"].std()
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k = 1.5 # 标准差倍数，可以调整
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self.buy_threshold = max(divergence_mean - k * divergence_std, -0.05) # 设置下限，避免过低
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self.sell_threshold = min(divergence_mean + k * divergence_std, 0.05) # 设置上限，避免过高
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# 确保阈值至少有一定宽度
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self.buy_threshold = min(self.buy_threshold, -0.005)
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self.sell_threshold = max(self.sell_threshold, 0.005)
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logger.info(f"[{metadata['pair']}] price_value_divergence 历史均值：{divergence_mean:.4f}")
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logger.info(f"[{metadata['pair']}] price_value_divergence 历史标准差：{divergence_std:.4f}")
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logger.info(f"[{metadata['pair']}] 动态买入阈值：{self.buy_threshold:.4f}")
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logger.info(f"[{metadata['pair']}] 动态卖出阈值：{self.sell_threshold:.4f}")
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else:
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self.buy_threshold = -0.015 # 回退阈值
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self.sell_threshold = 0.015
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logger.warning(f"[{metadata['pair']}] 无法计算动态阈值，使用默认阈值 ±0.015")
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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"[{metadata['pair']}] FreqAI 未初始化，请确保回测命令中启用了 --freqai")
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# 回退到规则计算
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dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
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else:
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dataframe = self.freqai.start(dataframe, metadata, self)
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# 检查预测结果
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if "&-price_value_divergence" not in dataframe.columns:
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logger.warning(f"[{metadata['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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logger.info(f"[{metadata['pair']}] 指标计算完成，列：{list(dataframe.columns)}")
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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"""
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入场逻辑：基于动态阈值和放宽的过滤条件
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"""
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conditions = []
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if "&-price_value_divergence" in df.columns:
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# 买入条件：价格低估且放量
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buy_condition = (df["&-price_value_divergence"] < self.buy_threshold)
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buy_condition &= (df["volume_z_score"] > 1.5) # 降低成交量要求
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# RSI超卖过滤（放宽条件）
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buy_condition &= (df["rsi"] < 40)
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# 价格触及布林带下轨
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buy_condition &= (df["close"] <= df["bb_lowerband"])
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conditions.append(buy_condition)
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else:
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logger.warning("⚠️ &-price_value_divergence 列缺失，跳过该条件")
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if len(conditions) > 0:
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df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
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logger.info(f"[{metadata['pair']}] 入场信号触发，条件满足")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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"""
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出场逻辑：基于动态阈值和放宽的过滤条件
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"""
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conditions = []
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if "&-price_value_divergence" in df.columns:
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# 卖出条件：价格高估
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sell_condition = (df["&-price_value_divergence"] > self.sell_threshold)
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sell_condition &= (df["volume_z_score"] > 1.5) # 降低成交量要求
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# RSI超买过滤（放宽条件）
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sell_condition |= (df["rsi"] > 60)
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conditions.append(sell_condition)
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else:
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logger.warning("⚠️ &-price_value_divergence 列缺失，跳过该条件")
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if len(conditions) > 0:
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df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
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logger.info(f"[{metadata['pair']}] 出场信号触发，条件满足")
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return df
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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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"""
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动态调整仓位：持仓时间超过30分钟后强制平仓
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"""
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hold_time = (current_time - trade.open_date_utc).total_seconds() / 60
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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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"""
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交易确认：确保没有快速重复交易
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"""
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recent_trades = Trade.get_trades(
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pair=pair,
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is_open=False,
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close_date=current_time - datetime.timedelta(minutes=5)
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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，跳过本次入场")
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return 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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"""
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交易退出确认：记录退出原因
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"""
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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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