diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py index 4a5b6d02..bd2dcee3 100644 --- a/freqtrade/templates/freqaiprimer.py +++ b/freqtrade/templates/freqaiprimer.py @@ -108,12 +108,14 @@ class FreqaiPrimer(IStrategy): for col in columns_to_clean: dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0) - logger.debug(f"[{metadata['pair']}] 特征工程完成，列：{list(dataframe.columns)}") + pair = metadata.get('pair', 'Unknown') + logger.debug(f"[{pair}] 特征工程完成，列：{list(dataframe.columns)}") return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: + pair = metadata.get('pair', 'Unknown') if len(dataframe) < 200: - 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") + logger.warning(f"[{pair}] 数据量不足（{len(dataframe)}根K线），需要至少200根K线进行训练") return dataframe dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) @@ -126,11 +128,12 @@ class FreqaiPrimer(IStrategy): dataframe["&-price_value_divergence"] = dataframe["&-price_value_divergence"].replace([np.inf, -np.inf], 0).ffill().fillna(0) dataframe["volume_z_score"] = dataframe["volume_z_score"].replace([np.inf, -np.inf], 0).ffill().fillna(0) - logger.debug(f"[{metadata['pair']}] 目标列生成完成，列：{list(dataframe.columns)}") + logger.debug(f"[{pair}] 目标列生成完成，列：{list(dataframe.columns)}") return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - logger.info(f"[{metadata['pair']}] 当前可用列（调用FreqAI前）：{list(dataframe.columns)}") + pair = metadata.get('pair', 'Unknown') + logger.info(f"[{pair}] 当前可用列（调用FreqAI前）：{list(dataframe.columns)}") # 计算200周期EMA和历史价值背离 dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) @@ -138,14 +141,14 @@ class FreqaiPrimer(IStrategy): # 调用FreqAI预测价值背离 if not hasattr(self, 'freqai') or self.freqai is None: - logger.error(f"[{metadata['pair']}] FreqAI 未初始化，请确保回测命令中启用了 --freqai") + logger.error(f"[{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: - logger.warning(f"[{metadata['pair']}] 回归模型未生成 &-price_value_divergence，回退到规则计算") + logger.warning(f"[{pair}] 回归模型未生成 &-price_value_divergence，回退到规则计算") dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"] # 计算其他指标 @@ -163,7 +166,6 @@ class FreqaiPrimer(IStrategy): dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0) # 获取 labels_mean 和 labels_std - pair = metadata["pair"] labels_mean = None labels_std = None @@ -174,7 +176,7 @@ class FreqaiPrimer(IStrategy): # 从最新的子目录读取 metadata.json try: model_base_dir = os.path.join(self.config["user_data_dir"], "models", self.freqai_info["identifier"]) - pair_base = pair.split('/')[0] # 取币对基础部分，例如 "TRUMP/USDT" -> "TRUMP" + pair_base = pair.split('/')[0] if '/' in pair else pair # 取币对基础部分，例如 "TRUMP/USDT" -> "TRUMP" sub_dirs = glob.glob(os.path.join(model_base_dir, f"sub-train-{pair_base}_*")) if not sub_dirs: @@ -247,42 +249,45 @@ class FreqaiPrimer(IStrategy): logger.info("==============================================") self.stats_logged = True - logger.info(f"[{metadata['pair']}] 指标计算完成，列：{list(dataframe.columns)}") + # 移除问题日志行，避免 KeyError + # 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: + def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + pair = metadata.get('pair', 'Unknown') 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"]) + if "&-price_value_divergence" in dataframe.columns: + buy_condition = (dataframe["&-price_value_divergence"] < self.buy_threshold) + buy_condition &= (dataframe["volume_z_score"] > 1.5) + buy_condition &= (dataframe["rsi"] < 40) + buy_condition &= (dataframe["close"] <= dataframe["bb_lowerband"]) conditions.append(buy_condition) else: - logger.warning("⚠️ &-price_value_divergence 列缺失，跳过该条件") + logger.warning(f"[{pair}] ⚠️ &-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") + dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 + logger.info(f"[{pair}] 入场信号触发，条件满足") - return df + return dataframe - def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: + def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + pair = metadata.get('pair', 'Unknown') conditions = [] - if "&-price_value_divergence" in df.columns: - sell_condition = (df["&-price_value_divergence"] > self.sell_threshold) - sell_condition |= (df["rsi"] > 75) + if "&-price_value_divergence" in dataframe.columns: + sell_condition = (dataframe["&-price_value_divergence"] > self.sell_threshold) + sell_condition |= (dataframe["rsi"] > 75) conditions.append(sell_condition) else: - logger.warning("⚠️ &-price_value_divergence 列缺失，跳过该条件") + logger.warning(f"[{pair}] ⚠️ &-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") + dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 + logger.info(f"[{pair}] 出场信号触发，条件满足") - return df + return dataframe def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float,