去掉iloc[-1]
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zhangkun9038@dingtalk.com
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@ -371,18 +371,15 @@ class FreqaiPrimer(IStrategy):
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logger.info(f"[{pair}] 熊市信号 - 条件c (看跌形态): {cond_c.iloc[-1]}")
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logger.info(f"[{pair}] 熊市信号 - 条件d (成交量下降): {cond_d.iloc[-1]}")
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# 汇总所有条件的得分
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bearish_score = 0
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if cond_a.iloc[-1]:
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bearish_score += 25
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if cond_b.iloc[-1]:
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bearish_score += 25
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if cond_c.iloc[-1]:
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bearish_score += 25
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if cond_d.iloc[-1]:
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bearish_score += 25
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logger.info(f"[{pair}] 熊市信号总得分: {bearish_score}/100")
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# 使用向量化操作计算得分，避免使用iloc[-1]进行业务逻辑判断
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bearish_score_vector = (cond_a.astype(int) + cond_b.astype(int) + cond_c.astype(int) + cond_d.astype(int)) * 25
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# 仅在日志中使用最后一行的值（这是允许的用途）
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if len(dataframe) > 0:
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bearish_score = bearish_score_vector.iloc[-1]
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logger.info(f"[{pair}] 熊市信号总得分: {bearish_score}/100")
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else:
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bearish_score = 0
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return bearish_signal
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@ -648,9 +645,14 @@ class FreqaiPrimer(IStrategy):
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cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold * 1.4) # 超宽松STOCHRSI
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core_conditions = [cond1, cond2, cond3, cond4, cond5]
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satisfied_count = sum([c.iloc[-1] for c in core_conditions])
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buy_condition = satisfied_count >= 4
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logger.info(f"[{pair}] 🟢 牛市绿色通道：持仓{open_trades}≤2个，25USDT入场，5条件需要满足{satisfied_count}/4个")
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# 使用向量化操作计算满足条件的数量
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satisfied_count_vector = cond1.astype(int) + cond2.astype(int) + cond3.astype(int) + cond4.astype(int) + cond5.astype(int)
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buy_condition = satisfied_count_vector >= 4
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# 仅在日志中使用最后一行的值
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if len(dataframe) > 0:
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satisfied_count = satisfied_count_vector.iloc[-1]
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logger.info(f"[{pair}] 🟢 牛市绿色通道：持仓{open_trades}≤2个，25USDT入场，5条件需要满足{satisfied_count}/4个")
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elif trend_status == "bullish":
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# 牛市正常通道：持仓>2个，75USDT入场，必须满足全部7个条件
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@ -691,37 +693,44 @@ class FreqaiPrimer(IStrategy):
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# 绿色通道和趋势状态的条件已经设置好buy_condition
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conditions.append(buy_condition)
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# 调试日志 - 添加更多调试信息
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divergence_value = dataframe["&-price_value_divergence"].iloc[-1]
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volume_z_score_value = dataframe["volume_z_score"].iloc[-1]
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rsi_value = dataframe["rsi"].iloc[-1]
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stochrsi_value = dataframe["stochrsi_k"].iloc[-1]
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bb_close_value = dataframe["close"].iloc[-1]
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bb_lower_value = dataframe["bb_lowerband"].iloc[-1]
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bb_upper_value = dataframe["bb_upperband"].iloc[-1]
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# 输出关键指标以便调试
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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}")
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logger.info(f"[{pair}] 🔍 买入阈值 - 偏离度: {self.buy_threshold:.6f}, RSI: {rsi_threshold:.2f}, STOCHRSI: {stochrsi_threshold:.2f}")
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# 定义条件名称和状态
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conditions_summary = [
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("&-price_value_divergence", divergence_value, "<", self.buy_threshold, cond1.iloc[-1]),
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("volume_z_score", volume_z_score_value, ">", volume_z_score_threshold, cond2.iloc[-1]),
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("rsi", rsi_value, "<", rsi_threshold, cond3.iloc[-1]),
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("close <= bb_lowerband", bb_close_value, "<=", bb_lower_value, cond4.iloc[-1]),
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("stochrsi_k", stochrsi_value, "<", stochrsi_threshold, cond5.iloc[-1]),
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]
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# 根据趋势状态添加对应的条件6和7
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if trend_status == "bullish" and open_trades <= 2:
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# 绿色通道只有5个条件
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pass
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else:
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# 其他通道有7个条件
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conditions_summary.extend([
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("非熊市", None, None, None, cond6.iloc[-1]),
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("STOCHRSI未持续超买", None, None, None, cond7.iloc[-1]),
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])
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# 调试日志 - 仅在日志中使用最后一行的值
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if len(dataframe) > 0:
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divergence_value = dataframe["&-price_value_divergence"].iloc[-1]
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volume_z_score_value = dataframe["volume_z_score"].iloc[-1]
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rsi_value = dataframe["rsi"].iloc[-1]
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stochrsi_value = dataframe["stochrsi_k"].iloc[-1]
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bb_close_value = dataframe["close"].iloc[-1]
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bb_lower_value = dataframe["bb_lowerband"].iloc[-1]
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bb_upper_value = dataframe["bb_upperband"].iloc[-1]
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# 输出关键指标以便调试
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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}")
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logger.info(f"[{pair}] 🔍 买入阈值 - 偏离度: {self.buy_threshold:.6f}, RSI: {rsi_threshold:.2f}, STOCHRSI: {stochrsi_threshold:.2f}")
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# 获取条件结果的最后一行值（仅用于日志）
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cond1_last = cond1.iloc[-1]
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cond2_last = cond2.iloc[-1]
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cond3_last = cond3.iloc[-1]
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cond4_last = cond4.iloc[-1]
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cond5_last = cond5.iloc[-1]
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cond6_last = cond6.iloc[-1] if trend_status != "bullish" or open_trades > 2 else False
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cond7_last = cond7.iloc[-1] if trend_status != "bullish" or open_trades > 2 else False
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# 定义条件名称和状态（仅用于日志）
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conditions_summary = [
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("&-price_value_divergence", divergence_value, "<", self.buy_threshold, cond1_last),
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("volume_z_score", volume_z_score_value, ">", volume_z_score_threshold, cond2_last),
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("rsi", rsi_value, "<", rsi_threshold, cond3_last),
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("close <= bb_lowerband", bb_close_value, "<=", bb_lower_value, cond4_last),
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("stochrsi_k", stochrsi_value, "<", stochrsi_threshold, cond5_last),
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]
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# 根据趋势状态添加对应的条件6和7
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if trend_status != "bullish" or open_trades > 2:
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conditions_summary.extend([
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("非熊市", None, None, None, cond6_last),
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("STOCHRSI未持续超买", None, None, None, cond7_last),
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])
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# 输出每个条件的状态
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logger.info(f"[{pair}] === 买入条件检查 ===")
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@ -936,17 +945,24 @@ class FreqaiPrimer(IStrategy):
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conditions.append(sell_condition)
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# 调试日志 - 使用安全的值获取
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divergence_value = dataframe["&-price_value_divergence"].iloc[-1] if len(dataframe) > 0 and not dataframe["&-price_value_divergence"].isna().iloc[-1] else 0.0
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rsi_value = dataframe["rsi"].iloc[-1] if len(dataframe) > 0 and not dataframe["rsi"].isna().iloc[-1] else 0.0
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stochrsi_value = dataframe["stochrsi_k"].iloc[-1] if len(dataframe) > 0 and not dataframe["stochrsi_k"].isna().iloc[-1] else 0.0
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adx_value = dataframe["adx"].iloc[-1] if len(dataframe) > 0 and not dataframe["adx"].isna().iloc[-1] else 0.0
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short_term_return = dataframe["short_term_return"].iloc[-1] if len(dataframe) > 0 and not dataframe["short_term_return"].isna().iloc[-1] else 0.0
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logger.info(f"[{pair}] 卖出条件检查 - "
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f"&-price_value_divergence={divergence_value:.6f} > {self.sell_threshold * float(self.exit_divergence_multiplier.value):.6f}: {cond1.iloc[-1]}, "
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f"rsi={rsi_value:.2f} > {float(self.exit_rsi_threshold.value)} & stochrsi_k={stochrsi_value:.2f} > {float(self.exit_stochrsi_threshold.value)}: {cond2.iloc[-1]}, "
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f"short_term_return={short_term_return:.2f}% > {rapid_rise_threshold:.2f}% & profit > {min_profit*100:.2f}%: {cond3.iloc[-1]}, "
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f"adx={adx_value:.2f}, trend_score={trend_score:.2f}")
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# 调试日志 - 仅在日志中使用最后一行的值（这是允许的用途）
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if len(dataframe) > 0:
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divergence_value = dataframe["&-price_value_divergence"].iloc[-1] if not dataframe["&-price_value_divergence"].isna().iloc[-1] else 0.0
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rsi_value = dataframe["rsi"].iloc[-1] if not dataframe["rsi"].isna().iloc[-1] else 0.0
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stochrsi_value = dataframe["stochrsi_k"].iloc[-1] if not dataframe["stochrsi_k"].isna().iloc[-1] else 0.0
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adx_value = dataframe["adx"].iloc[-1] if not dataframe["adx"].isna().iloc[-1] else 0.0
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short_term_return = dataframe["short_term_return"].iloc[-1] if not dataframe["short_term_return"].isna().iloc[-1] else 0.0
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# 获取条件结果的最后一行值（仅用于日志）
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cond1_last = cond1.iloc[-1]
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cond2_last = cond2.iloc[-1]
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cond3_last = cond3.iloc[-1]
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logger.info(f"[{pair}] 卖出条件检查 - "
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f"&-price_value_divergence={divergence_value:.6f} > {self.sell_threshold * float(self.exit_divergence_multiplier.value):.6f}: {cond1_last}, "
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f"rsi={rsi_value:.2f} > {float(self.exit_rsi_threshold.value)} & stochrsi_k={stochrsi_value:.2f} > {float(self.exit_stochrsi_threshold.value)}: {cond2_last}, "
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f"short_term_return={short_term_return:.2f}% > {rapid_rise_threshold:.2f}% & profit > {min_profit*100:.2f}%: {cond3_last}, "
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f"adx={adx_value:.2f}, trend_score={trend_score:.2f}")
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else:
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logger.warning(f"[{pair}] ⚠️ &-price_value_divergence 列缺失，跳过该条件")
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