From 55c25baf510107189c00bac3e165dd968bcf294e Mon Sep 17 00:00:00 2001 From: "zhangkun9038@dingtalk.com" Date: Mon, 18 Aug 2025 01:33:39 +0800 Subject: [PATCH] =?UTF-8?q?=E7=BB=BF=E8=89=B2=E9=80=9A=E9=81=93=E5=85=A5?= =?UTF-8?q?=E5=9C=BA=E6=9C=BA=E5=88=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- freqtrade/templates/freqaiprimer.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py index d8b23f20..eb40ed2b 100644 --- a/freqtrade/templates/freqaiprimer.py +++ b/freqtrade/templates/freqaiprimer.py @@ -643,13 +643,18 @@ class FreqaiPrimer(IStrategy): # 绿色通道和趋势状态的条件已经设置好buy_condition conditions.append(buy_condition) - # 调试日志 + # 调试日志 - 添加更多调试信息 divergence_value = dataframe["&-price_value_divergence"].iloc[-1] volume_z_score_value = dataframe["volume_z_score"].iloc[-1] rsi_value = dataframe["rsi"].iloc[-1] stochrsi_value = dataframe["stochrsi_k"].iloc[-1] bb_close_value = dataframe["close"].iloc[-1] bb_lower_value = dataframe["bb_lowerband"].iloc[-1] + bb_upper_value = dataframe["bb_upperband"].iloc[-1] + + # 输出关键指标以便调试 + logger.info(f"[{pair}] 📊 关键指标 - 偏离度: {divergence_value:.6f}, RSI: {rsi_value:.2f}, STOCHRSI: {stochrsi_value:.2f}, 价格: {bb_close_value:.4f}, 下轨: {bb_lower_value:.4f}, 上轨: {bb_upper_value:.4f}") + logger.info(f"[{pair}] 🔍 买入阈值 - 偏离度: {self.buy_threshold:.6f}, RSI: {rsi_threshold:.2f}, STOCHRSI: {stochrsi_threshold:.2f}") # 定义条件名称和状态 conditions_summary = [ ("&-price_value_divergence", divergence_value, "<", self.buy_threshold, cond1.iloc[-1]), @@ -732,7 +737,7 @@ class FreqaiPrimer(IStrategy): dataframe["stochrsi_k"] = stochrsi["fastk"] dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) # 计算短期价格涨幅（最近 5 根 K 线，约 15 分钟） - dataframe["short_term_return"] = dataframe["close"].pct_change(5) * 100 # 百分比回报 + dataframe["short_term_return"] = dataframe["close"].pct_change(5, fill_method=None) * 100 # 百分比回报 # 获取市场趋势得分 trend_score = self.get_market_trend(dataframe=dataframe, metadata={'pair': pair}) @@ -1174,7 +1179,7 @@ class FreqaiPrimer(IStrategy): kline_score += 15 elif pair_df["bearish_engulfing"].iloc[-1]: kline_score -= 15 - volatility = pair_df["close"].pct_change(10).std() * 100 + volatility = pair_df["close"].pct_change(10, fill_method=None).std() * 100 if volatility > 0.5: kline_score += 10 if price_score > 0 else -10