diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
index 431ca8d..f508f0a 100644
--- a/freqtrade/templates/FreqaiExampleStrategy.py
+++ b/freqtrade/templates/FreqaiExampleStrategy.py
@@ -71,31 +71,27 @@ class FreqaiExampleStrategy(IStrategy):
         },
     }
 
-    def featcaure_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
-        # 计算关键指标
+    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
+        # 确保基础价格数据存在
+        if "close" not in dataframe.columns:
+            raise ValueError("Dataframe must contain 'close' column")
+            
+        # 计算技术指标
         dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
-        
-        # 计算短期和长期SMA
         dataframe["sma_short"] = ta.SMA(dataframe, timeperiod=12)
         dataframe["sma_long"] = ta.SMA(dataframe, timeperiod=26)
+        dataframe["sma_cross"] = np.where(dataframe["sma_short"] > dataframe["sma_long"], 1, -1)
         
-        # 计算SMA交叉信号
-        dataframe["sma_cross"] = np.where(
-            dataframe["sma_short"] > dataframe["sma_long"], 1, -1
-        )
-        
-        # 计算布林带
+        # 布林带
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
         dataframe["bb_lowerband"] = bollinger["lower"]
         dataframe["bb_middleband"] = bollinger["mid"]
         dataframe["bb_upperband"] = bollinger["upper"]
-        
-        # 计算价格与布林带的关系
         dataframe["bb_pct"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
             dataframe["bb_upperband"] - dataframe["bb_lowerband"]
         )
         
-        # 成交量相关特征
+        # 成交量特征
         dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
         dataframe["volume_pct"] = dataframe["volume"] / dataframe["volume_ma"]
         
@@ -104,13 +100,25 @@ class FreqaiExampleStrategy(IStrategy):
         dataframe["pct_change_5"] = dataframe["close"].pct_change(5)
         dataframe["pct_change_10"] = dataframe["close"].pct_change(10)
         
+        # 动量指标
+        dataframe["momentum"] = dataframe["close"] / dataframe["close"].shift(4) - 1
+        dataframe["roc"] = ta.ROC(dataframe, timeperiod=10)
+        
+        # 波动率
+        dataframe["volatility"] = dataframe["close"].pct_change().rolling(window=20).std()
+        
         # 数据清理
         for col in dataframe.columns:
             if dataframe[col].dtype in ["float64", "int64"]:
                 dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
                 dataframe[col] = dataframe[col].ffill().fillna(0)
         
-        logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}")
+        # 确保至少有一个特征
+        if len(dataframe.columns) == 0:
+            raise ValueError("No features generated in feature engineering")
+            
+        logger.info(f"特征工程完成，生成特征数量：{len(dataframe.columns)}")
+        logger.debug(f"特征列表：{list(dataframe.columns)}")
         return dataframe
 
     def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: