diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py index c050dad3..b02be075 100644 --- a/freqtrade/templates/freqaiprimer.py +++ b/freqtrade/templates/freqaiprimer.py @@ -1,6 +1,9 @@ import logging import numpy as np import datetime +import os +import json +import glob from functools import reduce from freqtrade.persistence import Trade import talib.abstract as ta @@ -10,7 +13,7 @@ from freqtrade.strategy import IStrategy logger = logging.getLogger(__name__) -class FreqaiPrimer(IStrategy): +class ValueDivergencePrimerRegression(IStrategy): minimal_roi = { "0": 0.02, "30": 0.01, @@ -48,7 +51,7 @@ class FreqaiPrimer(IStrategy): } freqai_info = { - "identifier": "divergence_model", + "identifier": "divergence_model", # 注意：实际目录是 test58，可能需要调整 "model": "LightGBMRegressor", "feature_parameters": { "include_timeframes": ["3m", "15m", "1h"], @@ -138,6 +141,7 @@ class FreqaiPrimer(IStrategy): logger.error(f"[{metadata['pair']}] FreqAI 未初始化，请确保回测命令中启用了 --freqai") dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"] else: + logger.debug(f"self.freqai 类型：{type(self.freqai)}") dataframe = self.freqai.start(dataframe, metadata, self) if "&-price_value_divergence" not in dataframe.columns: @@ -158,27 +162,47 @@ class FreqaiPrimer(IStrategy): for col in ["ema200", "bb_upperband", "bb_middleband", "bb_lowerband", "rsi", "volume_z_score", "&-price_value_divergence", "price_value_divergence"]: dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0) - # 直接从 FreqAI 获取 labels_mean 和 labels_std + # 获取 labels_mean 和 labels_std pair = metadata["pair"] - if hasattr(self, 'freqai') and self.freqai is not None and pair in self.freqai.data: - # 从 self.freqai.data 中获取 labels_mean 和 labels_std - if "labels_mean" in self.freqai.data and "&-price_value_divergence" in self.freqai.data["labels_mean"]: - labels_mean = self.freqai.data["labels_mean"]["&-price_value_divergence"] - labels_std = self.freqai.data["labels_std"]["&-price_value_divergence"] + labels_mean = None + labels_std = None + + # 调试：打印 freqai_info 和 user_data_dir + logger.debug(f"freqai_info identifier：{self.freqai_info['identifier']}") + logger.debug(f"user_data_dir：{self.config['user_data_dir']}") + + # 从最新的子目录读取 metadata.json + try: + # 优先使用实际目录名 test58（硬编码，临时解决方案） + # model_base_dir = "/freqtrade/user_data/models/test58" + + # 如果需要动态获取（推荐），取消注释以下代码： + model_base_dir = os.path.join(self.config["user_data_dir"], "models", self.freqai_info["identifier"]) + + pair_clean = pair.replace('/', '_') # 将 "KAITO/USDT" 转换为 "KAITO_USDT" + sub_dirs = glob.glob(os.path.join(model_base_dir, f"sub-train-{pair_clean}_*")) + + if not sub_dirs: + logger.warning(f"[{pair}] 未找到任何子目录：{model_base_dir}/sub-train-{pair_clean}_*") else: - logger.warning(f"[{pair}] 无法从 FreqAI 获取 labels_mean 和 labels_std，重新计算") - # 回退：重新计算真实目标值 - dataframe["&-price_value_divergence_actual"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"] - dataframe["&-price_value_divergence_actual"] = dataframe["&-price_value_divergence_actual"].replace([np.inf, -np.inf], 0).ffill().fillna(0) - recent_data = dataframe["&-price_value_divergence_actual"].tail(self.freqai_info["fit_live_predictions_candles"]) - labels_mean = recent_data.mean() - labels_std = recent_data.std() - if np.isnan(labels_std) or labels_std == 0: - labels_std = 0.01 - logger.warning(f"[{pair}] labels_std 计算异常，使用默认值 0.01") - else: - logger.warning(f"[{pair}] FreqAI 未初始化或数据不可用，重新计算") - # 回退：重新计算真实目标值 + # 按时间戳排序，获取最新的子目录 + latest_sub_dir = max(sub_dirs, key=lambda x: int(x.split('_')[-1])) + metadata_file = os.path.join(latest_sub_dir, f"cb_{pair_clean.lower()}_{latest_sub_dir.split('_')[-1]}_metadata.json") + + if os.path.exists(metadata_file): + with open(metadata_file, "r") as f: + metadata = json.load(f) + labels_mean = metadata["labels_mean"]["&-price_value_divergence"] + labels_std = metadata["labels_std"]["&-price_value_divergence"] + logger.info(f"[{pair}] 从最新子目录 {latest_sub_dir} 读取 labels_mean：{labels_mean}, labels_std：{labels_std}") + else: + logger.warning(f"[{pair}] 最新的 metadata.json 文件 {metadata_file} 不存在") + except Exception as e: + logger.warning(f"[{pair}] 无法从子目录读取 labels_mean 和 labels_std：{e}，重新计算") + + # 回退：如果无法从文件读取，则重新计算 + if labels_mean is None or labels_std is None: + logger.warning(f"[{pair}] 无法获取 labels_mean 和 labels_std，重新计算") dataframe["&-price_value_divergence_actual"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"] dataframe["&-price_value_divergence_actual"] = dataframe["&-price_value_divergence_actual"].replace([np.inf, -np.inf], 0).ffill().fillna(0) recent_data = dataframe["&-price_value_divergence_actual"].tail(self.freqai_info["fit_live_predictions_candles"]) @@ -220,92 +244,6 @@ class FreqaiPrimer(IStrategy): logger.info(f"[{pair}] 动态卖出阈值：{self.sell_threshold:.4f}") if not self.stats_logged: - logger.info("===== 所有币对的 labels_mean 和 labels_std 汇总 =====") - for p, stats in self.pair_stats.items(): - logger.info(f"[{p}] labels_mean：{stats['labels_mean']:.4f}, labels_std：{stats['labels_std']:.4f}") - logger.info("==============================================") - self.stats_logged = True + logger.info("===== 所有币对的 labels_mean 和 labels_std 汇总... - logger.info(f"[{metadata['pair']}] 指标计算完成，列：{list(dataframe.columns)}") - return dataframe - - def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: - conditions = [] - - if "&-price_value_divergence" in df.columns: - buy_condition = (df["&-price_value_divergence"] < self.buy_threshold) - buy_condition &= (df["volume_z_score"] > 1.5) - buy_condition &= (df["rsi"] < 40) - buy_condition &= (df["close"] <= df["bb_lowerband"]) - conditions.append(buy_condition) - else: - logger.warning("⚠️ &-price_value_divergence 列缺失，跳过该条件") - - if len(conditions) > 0: - df.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 - logger.info(f"[{metadata['pair']}] 入场信号触发，条件满足") - - return df - - def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: - conditions = [] - - if "&-price_value_divergence" in df.columns: - sell_condition = (df["&-price_value_divergence"] > self.sell_threshold) - sell_condition |= (df["rsi"] > 75) - conditions.append(sell_condition) - else: - logger.warning("⚠️ &-price_value_divergence 列缺失，跳过该条件") - - if len(conditions) > 0: - df.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 - logger.info(f"[{metadata['pair']}] 出场信号触发，条件满足") - - return df - - def adjust_trade_position(self, trade: Trade, current_time: datetime, - current_rate: float, current_profit: float, - min_roi: Dict[float, float], max_profit: float): - hold_time = (current_time - trade.open_date_utc).total_seconds() / 60 - - if hold_time < 15: - 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") - return None - - profit_ratio = (current_rate - trade.open_rate) / trade.open_rate - if profit_ratio >= self.trailing_stop_start and not self.trailing_stop_enabled: - self.trailing_stop_enabled = True - trade.adjust_max_rate(current_rate) - logger.info(f"[{trade.pair}] 价格上涨超过 {self.trailing_stop_start*100:.1f}%，启动 Trailing Stop") - - if self.trailing_stop_enabled: - max_rate = trade.max_rate - trailing_stop_price = max_rate * (1 - self.trailing_stop_distance) - if current_rate < trailing_stop_price: - logger.info(f"[{trade.pair}] 价格回落至 Trailing Stop 点 {trailing_stop_price:.6f}，触发卖出") - return -1 - trade.adjust_max_rate(current_rate) - - if hold_time > 30: - logger.info(f"[{trade.pair}] 持仓时间超过30分钟，强制平仓") - return -1 - return None - - def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, - time_in_force: str, current_time: datetime, **kwargs) -> bool: - recent_trades = Trade.get_trades( - pair=pair, - is_open=False, - close_date=current_time - datetime.timedelta(minutes=5) - ).all() - if len(recent_trades) > 0: - logger.info(f"[{pair}] 5分钟内有近期交易，跳过本次入场") - return False - self.trailing_stop_enabled = False - return True - - def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, - rate: float, time_in_force: str, exit_reason: str, - current_time: datetime, **kwargs) -> bool: - logger.info(f"[{pair}] 退出交易，原因：{exit_reason}, 利润：{trade.calc_profit_ratio(rate):.2%}") - return True +出错了，请刷新以重新连接或重试。