去掉所有 AI 变量和逻辑，然后只加一个新的，在 confirm_trade_entry 里确认入场是否可信

config_examples/freqaiprimer.json

@@ -112,13 +112,7 @@
                 "main_plot": {},
                 "subplots": {
                     "&-predictions": {
-                        "&-exit_return": {"color": "red"}
+                        "&-entry_confidence": {"color": "green"}
-                    },
-                    "&-risk": {
-                        "&-max_drawdown": {"color": "orange"}
-                    },
-                    "&-timing": {
-                        "&-hold_duration": {"color": "blue"}
                     }
                 }
             }

freqtrade/templates/freqaiprimer.py

@@ -372,38 +372,19 @@ class FreqaiPrimer(IStrategy):
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame:
         """
-        设置 FreqAI 训练标签 (3个变量，简化版)
+        设置 FreqAI 训练标签 (1个变量，简化版)
-        - &-exit_return: 出场回归标签 (未来收益率)
+        - &-entry_confidence: 入场置信度评分 (0-1)，仅用于 confirm_trade_entry 最终确认
-        - &-max_drawdown: 最大回撤预测 (入场风险过滤)
-        - &-hold_duration: 最佳持仓时长 (0-1，出场ROI优化)
         """
         label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
         
-        # ========== 1. 出场回归标签 ==========
+        # ========== 入场置信度标签 ==========
-        # 直接使用未来收益率
+        # 基于未来收益率估算入场好坏，使用 S 型函数映射到 0-1
         future_return = dataframe["close"].shift(-label_period) / dataframe["close"] - 1
-        dataframe["&-exit_return"] = future_return
+        dataframe["&-entry_confidence"] = 1 / (1 + np.exp(-future_return * 50))
-        
-        # ========== 2. 最大回撤标签 (入场风险过滤) ==========
-        # 使用未来最低价计算最大回撤，使用 min_periods=1 避免过多 NaN
-        future_low = dataframe["low"].shift(-1).rolling(window=label_period, min_periods=1).min()
-        dataframe["&-max_drawdown"] = (future_low / dataframe["close"]) - 1
-        
-        # ========== 3. 最佳持仓时长标签 (出场ROI优化) ==========
-        # 基于未来收益率估算持仓价值，归一化到 0-1
-        # 收益率高 → hold_duration 高 (应该持仓久)
-        # 收益率低/负 → hold_duration 低 (应该尽快出场)
-        future_return_clipped = future_return.clip(-0.1, 0.1)  # 限制在 -10% ~ +10%
-        dataframe["&-hold_duration"] = (future_return_clipped + 0.1) / 0.2  # 归一化到 0-1
         
         # ========== 统一处理 NaN，避免训练数据被全部过滤 ==========
-        # 所有标签列末尾会因为 shift(-label_period) 产生 NaN，用前向填充避免丢失样本
+        if "&-entry_confidence" in dataframe.columns:
-        label_columns = [
+            dataframe["&-entry_confidence"] = dataframe["&-entry_confidence"].fillna(method='ffill').fillna(0)
-            "&-exit_return", "&-max_drawdown", "&-hold_duration"
-        ]
-        for col in label_columns:
-            if col in dataframe.columns:
-                dataframe[col] = dataframe[col].fillna(method='ffill').fillna(0)
         
         return dataframe
 
@@ -669,54 +650,11 @@ class FreqaiPrimer(IStrategy):
         # 合并所有条件
         traditional_condition = breakout_condition | volume_spike | macd_downward | rsi_overbought
         
-        # ==================== FreqAI 出场信号融合 (市场自适应) ====================
+        # ==================== 出场信号：仅使用传统条件 ====================
-        # 检查 FreqAI 预测结果是否可用
+        final_condition = traditional_condition
-        if "&-exit_return" in dataframe.columns and "do_predict" in dataframe.columns:
+
-            # 为每一行根据市场状态计算自适应退出阈值，避免在 populate_ 中使用 iloc[-1]
-            if 'market_state' in dataframe.columns:
-                exit_th_series = dataframe['market_state'].apply(
-                    lambda s: self._get_adaptive_thresholds(s)['exit_threshold']
-                )
-            else:
-                default_th = self._get_adaptive_thresholds('neutral')['exit_threshold']
-                exit_th_series = np.full(len(dataframe), default_th)
-            
-            # 回归器预测: 未来收益率
-            # 熊市时 exit_th 为负值(如-0.02)，更容易触发出场
-            # 牛市时 exit_th 为正值(如+0.02)，需要更大下跌才出场
-            ml_exit_signal = (dataframe["&-exit_return"] < exit_th_series)
-            
-            # 置信度过滤: do_predict >= 1 表示预测可信
-            ml_confidence = (dataframe["do_predict"] >= 1)
-            
-            # 融合策略: 传统条件 OR (ML出场信号 AND ML置信度)
-            # 出场使用 OR 逻辑，更激进地保护利润
-            final_condition = traditional_condition | (ml_exit_signal & ml_confidence)
-            
-            # 记录融合信息（只统计数量，不再依赖最后一行）
-            logger.info(
-                f"[{metadata['pair']}] FreqAI出场: "
-                f"传统={int(traditional_condition.sum())}, "
-                f"ML出场={int(ml_exit_signal.sum())}, "
-                f"最终={int(final_condition.sum())}"
-            )
-        else:
-            # FreqAI 未启用，使用传统条件
-            final_condition = traditional_condition
-        
-        # 设置出场信号
         dataframe.loc[final_condition, 'exit_long'] = 1
-        
+
-        # 增强调试信息
-        #logger.info(f"[{metadata['pair']}] 出场条件检查:")
-        #logger.info(f"  - 价格突破布林带上轨: {breakout_condition.sum()} 次")
-        #logger.info(f"  - 成交量显著放大: {volume_spike.sum()} 次")
-        #logger.info(f"  - MACD 下降趋势: {macd_downward.sum()} 次")
-        #logger.info(f"  - RSI 超买: {rsi_overbought.sum()} 次")
-        #logger.info(f"  - 最终条件: {final_condition.sum()} 次")
-        #logger.info(f"  - 使用参数: exit_bb_upper_deviation={self.exit_bb_upper_deviation.value}, exit_volume_multiplier={self.exit_volume_multiplier.value}, rsi_overbought={self.rsi_overbought.value}")
-        
-        # 日志记录
         if dataframe['exit_long'].sum() > 0:
             logger.info(f"[{metadata['pair']}] 触发出场信号数量: {dataframe['exit_long'].sum()}")
         
@@ -767,55 +705,13 @@ class FreqaiPrimer(IStrategy):
         )
         traditional_condition = condition_count >= self.min_condition_count.value
         
-        # ==================== FreqAI 入场信号融合 (市场自适应) ====================
+        # ==================== 入场信号：仅使用传统条件 ====================
-        # 检查 FreqAI 预测结果是否可用
+        final_condition = traditional_condition
-        if "&-max_drawdown" in dataframe.columns and "do_predict" in dataframe.columns:
-            # 行级动态阈值，避免在 populate_ 中使用 iloc[-1]
-            if 'market_state' in dataframe.columns:
-                drawdown_th_series = dataframe['market_state'].apply(
-                    lambda s: self._get_adaptive_thresholds(s)['max_drawdown']
-                )
-            else:
-                default_th = self._get_adaptive_thresholds('neutral')
-                drawdown_th_series = np.full(len(dataframe), default_th['max_drawdown'])
-            
-            # 置信度过滤: do_predict >= 1 表示预测可信
-            ml_confidence = (dataframe["do_predict"] >= 1)
-            
-            # 最大回撤过滤: 使用动态阈值
-            ml_drawdown_safe = (dataframe["&-max_drawdown"] >= drawdown_th_series)
-            
-            # === 极简融合策略：FreqAI 仅做风险否决 ===
-            bad_risk_mask = (dataframe["&-max_drawdown"] < drawdown_th_series) & ml_confidence
-            
-            final_condition = traditional_condition & ~bad_risk_mask
-            
-            # 记录融合信息（仅统计数量）
-            logger.info(
-                f"[{metadata['pair']}] FreqAI入场: "
-                f"传统={int(traditional_condition.sum())}, "
-                f"回撤安全={int(ml_drawdown_safe.sum())}, "
-                f"风险否决={int(bad_risk_mask.sum())}, "
-                f"最终={int(final_condition.sum())}"
-            )
-        else:
-            # FreqAI 未启用，使用传统条件
-            final_condition = traditional_condition
         
         # 设置入场信号
         dataframe.loc[final_condition, 'enter_long'] = 1
         
-        # 增强调试信息
+        # 计算条件间的相关性（仅用于分析）
-        #logger.info(f"[{metadata['pair']}] 入场条件检查:")
-        #logger.info(f"  - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
-        #logger.info(f"  - RSI 超卖: {rsi_condition_1h.sum()} 次")
-        #logger.info(f"  - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
-        #logger.info(f"  - MACD 上升趋势: {macd_condition_1h.sum()} 次")
-        #logger.info(f"  - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
-        #logger.info(f"  - 趋势确认: {trend_confirmation.sum()} 次")
-        #logger.info(f"  - 最终条件: {final_condition.sum()} 次")
-        # 在populate_entry_trend方法末尾添加
-        # 计算条件间的相关性
         conditions = DataFrame({
             'close_to_bb': close_to_bb_lower_1h,
             'rsi': rsi_condition_1h,
@@ -826,6 +722,7 @@ class FreqaiPrimer(IStrategy):
         })
         correlation = conditions.corr().mean().mean()
         #logger.info(f"[{metadata['pair']}] 条件平均相关性: {correlation:.2f}") 
+        
         # 日志记录
         if dataframe['enter_long'].sum() > 0:
             logger.info(f"[{metadata['pair']}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
@@ -902,20 +799,35 @@ class FreqaiPrimer(IStrategy):
     ) -> bool:
         """
         交易买入前的确认函数，用于最终决定是否执行交易
-        此处实现剧烈拉升检查逻辑
+        - 步骤1: 剧烈拉升过滤（纯规则）
+        - 步骤2: FreqAI 入场置信度过滤（仅作为最终确认，不影响回测一致性）
         """
-        # 默认允许交易
         allow_trade = True
-        
+
         # 仅对多头交易进行检查
         if side == 'long':
-            # 检查是否处于剧烈拉升的不稳固区域
+            # 步骤1: 检查是否处于剧烈拉升的不稳固区域
             is_unstable_region = self.detect_h1_rapid_rise(pair)
             if is_unstable_region:
                 #logger.info(f"[{pair}] 由于检测到剧烈拉升，取消入场交易")
                 allow_trade = False
-                
+
-        # 如果没有阻止因素，允许交易
+            # 步骤2: 使用 FreqAI 入场置信度进行二次确认
+            try:
+                dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+                if "&-entry_confidence" in dataframe.columns and "do_predict" in dataframe.columns and len(dataframe) > 0:
+                    last_row = dataframe.iloc[-1]
+                    score = last_row["&-entry_confidence"]
+                    do_predict = last_row["do_predict"]
+
+                    # 简单规则：预测有效且置信度足够高才允许入场
+                    min_confidence = 0.52
+                    if do_predict < 1 or np.isnan(score) or score < min_confidence:
+                        #logger.info(f"[{pair}] 由于 FreqAI 入场置信度不足(score={score:.3f}, do_predict={do_predict})，取消入场交易")
+                        allow_trade = False
+            except Exception as e:
+                logger.error(f"[{pair}] confirm_trade_entry 中 FreqAI 置信度检查异常: {str(e)}")
+
         return allow_trade
             
     def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float, 
@@ -964,20 +876,8 @@ class FreqaiPrimer(IStrategy):
             dynamic_roi_threshold = 0.0
 
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        # 优先使用市场状态特征；若不存在则回退为中性市场，避免依赖不存在的sma列
+        # 优先使用市场状态特征；若不存在则回退为中性市场
         current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'neutral'
-        
-        # ==================== FreqAI hold_duration 动态调整 ====================
-        # 使用 ML 预测的最佳持仓时长来调整 ROI 阈值
-        # hold_duration 在 0-1 之间，0 = 应立即出场，1 = 应持有到最后
-        if '&-hold_duration' in dataframe.columns and 'do_predict' in dataframe.columns:
-            ml_hold_duration = dataframe['&-hold_duration'].iloc[-1]
-            ml_predict_valid = dataframe['do_predict'].iloc[-1] >= 1
-            if ml_predict_valid and not np.isnan(ml_hold_duration):
-                # 如果预测应尽快出场(hold_duration < 0.3)，降低 ROI 阈值（更容易触发出场）
-                # 如果预测应持有(hold_duration > 0.7)，提高 ROI 阈值（更难触发出场）
-                hold_adjustment = 1.0 + (ml_hold_duration - 0.5) * 0.6  # 范围: 0.7 ~ 1.3
-                dynamic_roi_threshold = dynamic_roi_threshold * hold_adjustment
 
         entry_tag = trade.enter_tag if hasattr(trade, 'enter_tag') else None
         profit_ratio = current_profit / dynamic_roi_threshold if dynamic_roi_threshold > 0 else 0