diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py index 24814425..5f840bff 100644 --- a/freqtrade/templates/freqaiprimer.py +++ b/freqtrade/templates/freqaiprimer.py @@ -38,6 +38,14 @@ class FreqaiPrimer(IStrategy): # 趋势判定阈值参数 TREND_BULLISH_THRESHOLD = 85 TREND_BEARISH_THRESHOLD = 60 + + # 新的加权趋势判定阈值（用于hyperopt优化） + TREND_FINAL_BULLISH_THRESHOLD = 70 # 上涨趋势最终阈值 + TREND_FINAL_BEARISH_THRESHOLD = 30 # 下跌趋势最终阈值 + + # Hyperopt 可优化参数 + trend_final_bullish_threshold = IntParameter(50, 90, default=87, space="buy", optimize=True, load=True) + trend_final_bearish_threshold = IntParameter(10, 50, default=53, space="buy", optimize=True, load=True) # --- 🛠️ 固定配置参数 --- stoploss = -0.15 @@ -1022,11 +1030,11 @@ class FreqaiPrimer(IStrategy): def detect_trend_status(self, dataframe: DataFrame, metadata: dict) -> str: """ - 基于最近20个周期的trend_score判断趋势状态 + 基于加权分段的trend_score判断趋势状态 规则： - - 最近20个周期中75%以上得分≥TREND_BULLISH_THRESHOLD，且最近10个周期中90%以上得分≥TREND_BULLISH_THRESHOLD → 上涨趋势 - - 最近20个周期中75%以上得分≤TREND_BEARISH_THRESHOLD，且最近10个周期中90%以上得分≤TREND_BEARISH_THRESHOLD → 下跌趋势 - - 其他情况 → 震荡趋势 + - 将最近20个trend_score分为3段：1-3(权重10)，4-10(权重7)，11-20(权重3) + - 计算加权平均得分，映射到0-100区间 + - 根据两个可优化的阈值判断趋势状态 """ pair = metadata.get('pair', 'Unknown') @@ -1047,26 +1055,45 @@ class FreqaiPrimer(IStrategy): else: trend_scores_20.append(50) # 默认值 - # 最近10个周期 - trend_scores_10 = trend_scores_20[-10:] + # 分段计算加权得分 + # 第一段：最近1-3个周期 (索引-3到-1) + segment1 = trend_scores_20[-3:] + weighted_score1 = sum(score * 10 for score in segment1) / len(segment1) - # 计算满足条件的比例 - strong_bull_20 = sum(1 for score in trend_scores_20 if score >= self.TREND_BULLISH_THRESHOLD) / len(trend_scores_20) - strong_bull_10 = sum(1 for score in trend_scores_10 if score >= self.TREND_BULLISH_THRESHOLD) / len(trend_scores_10) + # 第二段：4-10个周期 (索引-10到-4) + segment2 = trend_scores_20[-10:-3] + weighted_score2 = sum(score * 7 for score in segment2) / len(segment2) - strong_bear_20 = sum(1 for score in trend_scores_20 if score <= self.TREND_BEARISH_THRESHOLD) / len(trend_scores_20) - strong_bear_10 = sum(1 for score in trend_scores_10 if score <= self.TREND_BEARISH_THRESHOLD) / len(trend_scores_10) + # 第三段：11-20个周期 (索引-20到-11) + segment3 = trend_scores_20[-20:-10] + weighted_score3 = sum(score * 3 for score in segment3) / len(segment3) - # 判定趋势 - if strong_bull_20 >= 0.75 and strong_bull_10 >= 0.9: + # 计算最终加权得分 + final_weighted_score = (weighted_score1 + weighted_score2 + weighted_score3) / (10 + 7 + 3) + + # 将得分映射到0-100区间（确保在合理范围内） + final_score = max(0, min(100, final_weighted_score)) + + # 使用hyperopt优化的阈值判断趋势状态 + bullish_threshold = self.trend_final_bullish_threshold.value + bearish_threshold = self.trend_final_bearish_threshold.value + + # 判定趋势状态 + if final_score >= bullish_threshold: trend_status = "bullish" - logger.info(f"[{pair}] 🚀 检测到上涨趋势: 20周期强势比例={strong_bull_20:.1%}, 10周期强势比例={strong_bull_10:.1%}, 阈值={self.TREND_BULLISH_THRESHOLD}") - elif strong_bear_20 >= 0.75 and strong_bear_10 >= 0.9: + logger.info(f"[{pair}] 🚀 检测到上涨趋势: 最终加权得分={final_score:.2f}, 阈值≥{bullish_threshold}") + elif final_score <= bearish_threshold: trend_status = "bearish" - logger.info(f"[{pair}] 📉 检测到下跌趋势: 20周期弱势比例={strong_bear_20:.1%}, 10周期弱势比例={strong_bear_10:.1%}, 阈值={self.TREND_BEARISH_THRESHOLD}") + logger.info(f"[{pair}] 📉 检测到下跌趋势: 最终加权得分={final_score:.2f}, 阈值≤{bearish_threshold}") else: trend_status = "ranging" - logger.info(f"[{pair}] ⚖️ 检测到震荡趋势: 20周期强势比例={strong_bull_20:.1%}, 弱势比例={strong_bear_20:.1%}, 阈值={self.TREND_BULLISH_THRESHOLD}/{self.TREND_BEARISH_THRESHOLD}") + logger.info(f"[{pair}] ⚖️ 检测到震荡趋势: 最终加权得分={final_score:.2f}, 阈值范围({bearish_threshold}, {bullish_threshold})") + + # 输出分段详细信息用于调试 + logger.debug(f"[{pair}] 趋势分析详情 - " + f"第一段(1-3,权重10): {[f'{s:.1f}' for s in segment1]}, " + f"第二段(4-10,权重7): {[f'{s:.1f}' for s in segment2]}, " + f"第三段(11-20,权重3): {[f'{s:.1f}' for s in segment3]}") return trend_status diff --git a/hyperopt_config_example.py b/hyperopt_config_example.py new file mode 100644 index 00000000..00289503 --- /dev/null +++ b/hyperopt_config_example.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +""" +Hyperopt 配置示例，用于优化新的趋势判定参数 + +使用方法: +1. 将此文件保存为 hyperopt_trend_params.py +2. 运行: freqtrade hyperopt --config config.json --strategy freqaiprimer --hyperopt hyperopt_trend_params +""" + +from freqtrade.optimize.hyperopt_interface import IHyperOpt +from typing import Dict, Any +import logging + +logger = logging.getLogger(__name__) + +class TrendParamsHyperopt(IHyperOpt): + """ + 优化趋势判定参数的 Hyperopt 类 + """ + + @staticmethod + def generate_roi_table(params: Dict) -> Dict[int, float]: + """生成ROI表格""" + return { + 0: params['roi_p1'] + params['roi_p2'] + params['roi_p3'], + params['roi_t3']: params['roi_p1'] + params['roi_p2'], + params['roi_t3'] + params['roi_t2']: params['roi_p1'], + params['roi_t3'] + params['roi_t2'] + params['roi_t1']: 0, + } + + @staticmethod + def roi_space(): + """ROI参数空间""" + return { + 'roi_p1': [0.01, 0.08], + 'roi_p2': [0.01, 0.08], + 'roi_p3': [0.01, 0.08], + 'roi_t1': [10, 120], + 'roi_t2': [10, 60], + 'roi_t3': [10, 60], + } + + @staticmethod + def stoploss_space(): + """止损参数空间""" + return { + 'stoploss': [-0.5, -0.05], + } + + @staticmethod + def trailing_space(): + """追踪止损参数空间""" + return { + 'trailing_stop': [True, False], + 'trailing_stop_positive': [0.005, 0.05], + 'trailing_stop_positive_offset_p1': [0.005, 0.05], + 'trailing_only_offset_is_reached': [True, False], + } + + def buy_params_space(self): + """买入参数空间，包含新的趋势判定阈值""" + return { + # 原有参数 + 'buy_threshold': [-0.05, -0.001], + 'volume_z_score_min': [0.1, 2.0], + 'rsi_max': [20, 60], + + # 新的趋势判定阈值 + 'trend_final_bullish_threshold': [50, 90], # 上涨趋势阈值 + 'trend_final_bearish_threshold': [10, 50], # 下跌趋势阈值 + } + + def sell_params_space(self): + """卖出参数空间""" + return { + 'sell_threshold': [0.001, 0.08], + 'rsi_min': [60, 90], + 'stochrsi_max': [60, 95], + } + + def generate_estimator(self, dimensions: list, **kwargs) -> Any: + """生成优化器""" + from skopt import Optimizer + from skopt.space import Real, Integer + + # 定义参数空间 + space = [ + Real(-0.5, -0.05, name='stoploss'), + Real(0.001, 0.08, name='sell_threshold'), + Real(-0.05, -0.001, name='buy_threshold'), + Real(0.1, 2.0, name='volume_z_score_min'), + Integer(20, 60, name='rsi_max'), + Integer(60, 90, name='rsi_min'), + Integer(60, 95, name='stochrsi_max'), + Integer(50, 90, name='trend_final_bullish_threshold'), + Integer(10, 50, name='trend_final_bearish_threshold'), + ] + + return Optimizer(space, base_estimator='ET', acq_func='EI', n_initial_points=10) + +# 使用示例配置 +if __name__ == "__main__": + print("趋势参数优化配置已加载") + print("支持的优化参数:") + print("- trend_final_bullish_threshold: 50-90") + print("- trend_final_bearish_threshold: 10-50") \ No newline at end of file