275 lines
13 KiB
Python
275 lines
13 KiB
Python
import logging
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import numpy as np
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import pandas as pd
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from functools import reduce
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import talib.abstract as ta
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from typing import Dict, List, Optional
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from pandas import DataFrame
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from technical import qtpylib
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from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
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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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stoploss = 0.0
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trailing_stop = True
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process_only_new_candles = True
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use_exit_signal = True
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startup_candle_count: int = 40
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can_short = False
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# Hyperopt 参数
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buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True)
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sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True)
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roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
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roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
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roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
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stoploss_param = DecimalParameter(low=-0.25, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
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# 保护机制
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protections = [
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{"method": "StoplossGuard", "stop_duration": 60, "lookback_period": 120},
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{"method": "MaxDrawdown", "lookback_period": 120, "max_allowed_drawdown": 0.05}
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]
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# FreqAI 配置
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freqai_info = {
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"model": "LightGBMRegressor",
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"feature_parameters": {
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"include_timeframes": ["5m", "15m", "1h"],
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"include_corr_pairlist": [],
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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": 100,
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"learning_rate": 0.1,
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"num_leaves": 15, # 降低以减少警告
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"verbose": -1,
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},
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}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
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"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
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"&-stoploss": {"&-stoploss": {"color": "purple"}},
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"&-roi_0": {"&-roi_0": {"color": "orange"}},
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"do_predict": {"do_predict": {"color": "brown"}},
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},
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}
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def __init__(self, config: Dict):
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super().__init__(config)
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# 初始化特征缓存
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self.feature_cache = {}
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# 设置日志级别
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logger.setLevel(logging.DEBUG)
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# 输出模型路径用于调试
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freqai_model_path = self.config.get("freqai", {}).get("model_path", "/freqtrade/user_data/models")
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logger.info(f"FreqAI 模型路径：{freqai_model_path}")
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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["%-mfi-period"] = ta.MFI(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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dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2)
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dataframe["bb_lowerband-period"] = bollinger["lower"]
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dataframe["bb_middleband-period"] = bollinger["mid"]
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dataframe["bb_upperband-period"] = bollinger["upper"]
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dataframe["%-bb_width-period"] = (
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dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
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) / dataframe["bb_middleband-period"]
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dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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dataframe["%-relative_volume-period"] = (
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dataframe["volume"] / dataframe["volume"].rolling(period).mean()
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)
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dataframe = dataframe.replace([np.inf, -np.inf], 0)
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dataframe = dataframe.ffill()
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dataframe = dataframe.fillna(0)
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logger.info(f"最终数据框列：\n{dataframe.columns.to_list()}")
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return dataframe
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def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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dataframe["%-raw_price"] = dataframe["close"]
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dataframe = dataframe.replace([np.inf, -np.inf], 0)
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dataframe = dataframe.ffill()
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dataframe = dataframe.fillna(0)
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return dataframe
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def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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dataframe = dataframe.replace([np.inf, -np.inf], 0)
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dataframe = dataframe.ffill()
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dataframe = dataframe.fillna(0)
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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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logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
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if "close" not in dataframe.columns:
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logger.error("数据框缺少必要的 'close' 列")
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raise ValueError("数据框缺少必要的 'close' 列")
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try:
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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logger.info(f"Generated %-volatility column: {dataframe['%-volatility'].head().to_string()}")
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dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14).shift(-label_period)
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for col in ["&-buy_rsi", "%-volatility"]:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
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dataframe[col] = dataframe[col].ffill()
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dataframe[col] = dataframe[col].fillna(0)
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if dataframe[col].isna().any():
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logger.warning(f"目标列 {col} 仍包含 NaN，检查数据生成逻辑")
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except Exception as e:
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logger.error(f"创建 FreqAI 目标失败：{str(e)}")
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raise
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logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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logger.info(f"处理交易对：{metadata['pair']}")
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logger.info(f"开始 FreqAI 处理，交易对：{metadata['pair']}")
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logger.info(f"输入数据框列：\n{dataframe.columns.to_list()}")
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dataframe = self.freqai.start(dataframe, metadata, self)
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logger.info(f"FreqAI 处理后数据框列：\n{dataframe.columns.to_list()}")
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# 计算传统指标
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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dataframe["bb_upperband"] = bollinger["upper"]
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dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
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# 生成 up_or_down 信号
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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dataframe["up_or_down"] = np.where(
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dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
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)
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# 预填充 NaN
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dataframe = dataframe.ffill()
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dataframe = dataframe.fillna(0)
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if "&-buy_rsi" in dataframe.columns:
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if "%-volatility" not in dataframe.columns or dataframe["%-volatility"].isna().any():
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logger.error("Critical column '%-volatility' is missing or contains NaN values.")
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raise ValueError("Missing or invalid '%-volatility' column")
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# 派生其他目标
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dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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dataframe["&-stoploss"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
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dataframe["&-roi_0"] = (dataframe["close"].shift(-label_period) / dataframe["close"] - 1).clip(0, 0.2)
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# 计算预测值并减少 NaN
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dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(5, min_periods=1).mean().clip(10, 50)
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dataframe["sell_rsi_pred"] = dataframe["&-sell_rsi"].rolling(5, min_periods=1).mean().clip(50, 90)
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dataframe["stoploss_pred"] = dataframe["&-stoploss"].clip(-0.25, -0.05)
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dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
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# 处理 NaN
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for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
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if dataframe[col].isna().any():
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logger.warning(f"列 {col} 包含 NaN，填充为默认值")
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mean_value = dataframe[col].mean()
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if pd.isna(mean_value):
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logger.warning(f"列 {col} 均值仍为 NaN，使用默认值")
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mean_value = {
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"buy_rsi_pred": 30,
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"sell_rsi_pred": 70,
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"stoploss_pred": -0.1,
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"roi_0_pred": 0.05,
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"&-sell_rsi": 70,
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"&-stoploss": -0.1,
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"&-roi_0": 0.05
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}.get(col, 0)
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dataframe[col] = dataframe[col].fillna(mean_value)
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# 动态追踪止盈
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dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
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dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.75).clip(0.02, 0.4)
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# 设置动态参数
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self.stoploss = float(dataframe["stoploss_pred"].iloc[-1])
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self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
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self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
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self.minimal_roi = {
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0: float(self.roi_0.value),
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15: float(self.roi_15.value),
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30: float(self.roi_30.value),
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60: 0.0
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}
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self.trailing_stop_positive = float(dataframe["trailing_stop_positive"].iloc[-1])
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self.trailing_stop_positive_offset = float(dataframe["trailing_stop_positive_offset"].iloc[-1])
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logger.info(f"minimal_roi 键：{list(self.minimal_roi.keys())}")
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logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
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f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
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dataframe = dataframe.replace([np.inf, -np.inf], 0)
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dataframe = dataframe.ffill()
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dataframe = dataframe.fillna(0)
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logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}")
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logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}")
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [
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qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
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df["tema"] > df["tema"].shift(1),
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["up_or_down"] == 1
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]
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions),
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["enter_long", "enter_tag"]
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] = (1, "long")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [
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(qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
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(df["close"] < df["close"].shift(1) * 0.98) |
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(df["close"] < df["bb_lowerband"]),
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["up_or_down"] == 0
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]
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time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
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df.loc[
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(reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
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"exit_long"
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] = 1
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return df
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def confirm_trade_entry(
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self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time, entry_tag, side: str, **kwargs
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) -> bool:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == "long":
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if rate > (last_candle["close"] * (1 + 0.001)):
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return False
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return True
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