# source: https://raw.githubusercontent.com/tellyoung/money_game/9a55b7d1b9242eeb79f7209be9375eae9c89b130/Trading/user_data/strategies/MyStrategy.py import sys import importlib import json import os import pandas as pd import numpy as np from freqtrade.strategy import IStrategy from typing import Dict, Any import logging # 添加logger属性，兼容因子加载等日志输出 # 将factor_mining目录加入sys.path，确保importlib能找到factors包。 factor_mining_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../factor_mining')) sys.path.insert(0, factor_mining_path) class Github_tellyoung_money_game__MyStrategy__20250529_143720(IStrategy): """ 在策略类初始化时加载factor_logic.json，动态注册所有有效因子。 在populate_indicators中，利用这些因子逻辑生成因子数据。 在populate_entry_trend和populate_exit_trend中，加载买卖信号生成逻辑（如zscore、quantile、ML模型），并据此生成买卖信号。 支持ML模型时，自动加载pkl模型文件并预测信号。 这样策略类就能无缝衔接你的因子挖掘pipeline，直接用于回测。 """ INTERFACE_VERSION = 3 can_short = True timeframe = "5m" # ROI table (hyperoptable) minimal_roi = { "0": 0.03, # Default values, will be overridden by hyperopt "30": 0.02, "60": 0.01 } # Stoploss (hyperoptable) stoploss = -0.01 # Default value, will be overridden by hyperopt # Trailing stop: trailing_stop = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.06 trailing_only_offset_is_reached = False def __init__(self, config: Dict[str, Any]) -> None: super().__init__(config) self.logger = logging.getLogger('Github_tellyoung_money_game__MyStrategy__20250529_143720') # 加载因子逻辑 self.factors = {} self.best_factor_logic_path = os.path.join("/Users/yutieyang/Documents/yuty/yuty_projects/money_game/Trading/factor_mining/factor_data/factor_logic.json") self.load_factor_logic(self.best_factor_logic_path) # 加载best_factors顺序 best_factors_path = os.path.join("/Users/yutieyang/Documents/yuty/yuty_projects/money_game/Trading/factor_mining/factor_data/best_factors.json") with open(best_factors_path, 'r', encoding='utf-8') as f: self.best_factors = json.load(f) # 预加载联合多因子ML模型（如有） self.all_factors_ml_model = self.load_factors_ml_model() def load_factor_logic(self, filepath): with open(filepath, 'r', encoding='utf-8') as f: factor_logic = json.load(f) # 记录原始顺序 self.factor_order = list(factor_logic.keys()) # 按类型分组 self.basic_factors = {} self.advanced_factors = {} self.parametric_dynamic_factors = {} self.dynamic_factors = {} for name, info in factor_logic.items(): try: module = importlib.import_module(info["module"]) cls = getattr(module, info["class"]) factor = cls(**info["params"]) # 分类 if info["module"].endswith("basic_factors"): self.basic_factors[name] = factor elif info["module"].endswith("advanced_factors"): self.advanced_factors[name] = factor elif info["class"] == "ParametricDynamicFactor": self.parametric_dynamic_factors[name] = factor elif info["class"] == "DynamicFactor": self.dynamic_factors[name] = factor else: self.dynamic_factors[name] = factor # 兜底 self.logger.info(f"因子加载成功: {name}") except Exception as e: self.logger.error(f"因子加载失败: {name}, 错误: {e}") # 合并所有因子，便于后续兼容 self.factors = {**self.basic_factors, **self.advanced_factors, **self.parametric_dynamic_factors, **self.dynamic_factors} self.logger.info(f"已加载因子逻辑: {list(self.factors.keys())}") def load_factors_ml_model(self): # 支持加载联合多因子ML模型 model_path = os.path.join("/Users/yutieyang/Documents/yuty/yuty_projects/money_game/Trading/factor_mining/factor_data/ml_model.pkl") if os.path.exists(model_path): import pickle with open(model_path, 'rb') as f: return pickle.load(f) return None def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ 按依赖顺序分阶段生成所有因子，最后按原始顺序排列。 """ # 1. basic_factors for name, factor in self.basic_factors.items(): try: dataframe[name] = factor.generate(dataframe.copy()) self.logger.info(f"因子生成成功: {name}") except Exception as e: self.logger.error(f"因子生成失败: {name}, 错误: {e}") dataframe[name] = 0 # 2. advanced_factors for name, factor in self.advanced_factors.items(): try: dataframe[name] = factor.generate(dataframe.copy()) self.logger.info(f"因子生成成功: {name}") except Exception as e: self.logger.error(f"因子生成失败: {name}, 错误: {e}") dataframe[name] = 0 # 3. parametric_dynamic_factors for name, factor in self.parametric_dynamic_factors.items(): try: dataframe[name] = factor.generate(dataframe.copy()) self.logger.info(f"因子生成成功: {name}") except Exception as e: self.logger.error(f"因子生成失败: {name}, 错误: {e}") dataframe[name] = 0 # 4. dynamic_factors for name, factor in self.dynamic_factors.items(): try: dataframe[name] = factor.generate(dataframe.copy()) self.logger.info(f"因子生成成功: {name}") except Exception as e: self.logger.error(f"因子生成失败: {name}, 错误: {e}") dataframe[name] = 0 return dataframe def generate_signal(self, dataframe: pd.DataFrame, factor_name: str = None, method: str = 'zscore', buy_thr=1, sell_thr=-1, window=60, use_all_factors=False): """ 支持单因子和多因子ML推理。use_all_factors=True时，使用联合ML模型。 """ if use_all_factors and self.all_factors_ml_model is not None: # 多因子ML推理，严格按best_factors顺序取特征 X = dataframe[self.best_factors].values X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0) # 置信度阈值 prob_thr = 0.8 if hasattr(self.all_factors_ml_model, 'predict_proba'): probas = self.all_factors_ml_model.predict_proba(X) preds = self.all_factors_ml_model.predict(X) preds = np.where(preds == 0, -1, preds) # 只在最大概率大于阈值时才输出预测，否则为0 max_proba = np.max(probas, axis=1) preds = np.where(max_proba >= prob_thr, preds, 0) else: preds = self.all_factors_ml_model.predict(X) return pd.Series(preds, index=dataframe.index) if factor_name is None: return pd.Series(0, index=dataframe.index) def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: # 支持ML联合信号和单因子信号 use_ml = self.all_factors_ml_model is not None if use_ml: signal = self.generate_signal(dataframe, use_all_factors=True) dataframe.loc[signal == 1, 'enter_long'] = 1 dataframe.loc[signal == -1, 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: # 支持ML联合信号和单因子信号 use_ml = self.all_factors_ml_model is not None if use_ml: signal = self.generate_signal(dataframe, use_all_factors=True) dataframe.loc[signal == -1, 'exit_long'] = 1 dataframe.loc[signal == 1, 'exit_short'] = 1 return dataframe