merge hyperopted

freqtrade/templates/freqaiprimer.py

@@ -35,6 +35,15 @@ class FreqaiPrimer(IStrategy):
         super().__init__(config)  # 调用父类的初始化方法并传递config
         # 存储从配置文件加载的默认值
         self._trailing_stop_positive_default = 0.004  # 降低默认值以更容易触发跟踪止盈
+        
+        # 验证 FreqAI 配置
+        if self.config and "freqai" in self.config:
+            logger.info(f"FreqAI 已启用: {self.config['freqai'].get('model', 'Unknown')}")
+        else:
+            logger.warning("FreqAI 未在配置文件中启用或配置缺失")
+        
+        # 验证 h1_max_candles 参数
+        assert self.h1_max_candles.value <= 50, f"h1_max_candles={self.h1_max_candles.value} 超出安全范围！"
 
     @property
     def protections(self):
@@ -84,6 +93,47 @@ class FreqaiPrimer(IStrategy):
     can_short = False  # 禁用做空
     freqai_predicted_add_step = 0.057  # 默认0.5%
 
+    # FreqAI 配置
+    freqai_info = {
+        "enabled": True,
+        "identifier": "freqai_primer_mixed",
+        "model": "LightGBMRegressor",
+        "feature_parameters": {
+            "include_timeframes": ["3m", "15m", "1h"],
+            "include_shifted_candles": 2,
+            "label_period_candles": 12
+        },
+        "data_split_parameters": {
+            "test_size": 0.2,
+            "shuffle": False
+        },
+        "model_training_parameters": {
+            "price_value_divergence": {
+                "model": "LightGBMRegressor",
+                "model_params": {
+                    "n_estimators": 200,
+                    "learning_rate": 0.05,
+                    "num_leaves": 31,
+                    "verbose": -1
+                }
+            },
+            "optimal_first_length": {
+                "model": "LightGBMClassifier",
+                "model_params": {
+                    "n_estimators": 150,
+                    "learning_rate": 0.1,
+                    "num_leaves": 15,
+                    "max_depth": 8,
+                    "min_child_samples": 10,
+                    "class_weight": "balanced",
+                    "verbose": -1
+                }
+            }
+        },
+        "fit_live_predictions_candles": 100,
+        "live_retrain_candles": 100
+    }
+
     #  [propertiesGrp_List]--------------------------------------------------------------------------------------------------------------------------------------
     #  [propertiesGrp step="1" name="第一轮优化" epochs="300" space="buy " description="入场基础条件优化,入场确认条件优化"]
     bb_std                     = DecimalParameter(2.0, 5.0, decimals=1, default=3.5, optimize=True, load=True, space='buy')      # 安全：2.0-5.0
@@ -408,6 +458,8 @@ class FreqaiPrimer(IStrategy):
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 🔧 调用FreqAI进行预测
+        dataframe = self.freqai.start(dataframe, metadata, self)
         
         # 计算 3m 周期的指标
         bb_length_value = self.bb_length.value
@@ -525,6 +577,11 @@ class FreqaiPrimer(IStrategy):
 
         # 合并 1h 数据
         dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
+        
+        # 🔧 验证 FreqAI 预测列是否存在
+        if '&-add_position_step' not in dataframe.columns:
+            logger.debug(f"[{metadata['pair']}] FreqAI预测列不存在，将在训练后自动生成")
+            dataframe['&-add_position_step'] = self.freqai_predicted_add_step  # 使用默认值
 
         # 验证合并后的列
         #logger.info(f"[{metadata['pair']}] 合并后的数据框列名: {list(dataframe.columns)}")