修复 1012 模型的股票和指数预测逻辑，优化数据提取方式，修正 impact_2_day 错误

blkeras.py

@@ -218,40 +218,52 @@ def predict(text: str, stock_codes: list):
         index_ndx_predictions = predictions[3].tolist()
         stock_predictions = predictions[4].tolist()
 
-        print(f"Original predictions: {predictions}")
-
         # 打印预测结果，便于调试
-        print("Index INX Predictions:", index_inx_predictions)
-        print("Index DJ Predictions:", index_dj_predictions)
-        print("Index IXIC Predictions:", index_ixic_predictions)
-        print("Index NDX Predictions:", index_ndx_predictions)
-        print("Stock Predictions:", stock_predictions)
+        #print("Index INX Predictions:", index_inx_predictions)
+        #print("Index DJ Predictions:", index_dj_predictions)
+        #print("Index IXIC Predictions:", index_ixic_predictions)
+        #print("Index NDX Predictions:", index_ndx_predictions)
+        #print("Stock Predictions:", stock_predictions)
+
 
 
+        # 针对 1012 模型的修复
+        stock_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), stock_predictions[0], previous_stock_history[0][-1][0])
+        index_inx_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_inx_predictions[0], last_index_inx_value) 
+        index_dj_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_dj_predictions[0], last_index_dj_value) 
+        index_ixic_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_ixic_predictions[0], last_index_ixic_value) 
+        index_ndx_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_ndx_predictions[0], last_index_ndx_value) 
+
+        #print("Stock Predictions after fix:", stock_predictions)
+        #print("Index INX Predictions after fix:", index_inx_predictions)    
+        #print("Index DJ Predictions after fix:", index_dj_predictions)    
+        #print("Index IXIC Predictions after fix:", index_ixic_predictions)    
+        #print("Index NDX Predictions after fix:", index_ndx_predictions)    
+
 
 
         # 获取 index_feature 中最后一天的第一个值
-        last_index_inx_value = previous_stock_inx_index_history[0][-1][0]
-        last_index_dj_value = previous_stock_dj_index_history[0][-1][0]
-        last_index_ixic_value = previous_stock_ixic_index_history[0][-1][0]
-        last_index_ndx_value = previous_stock_ndx_index_history[0][-1][0]
+        last_index_inx_value = previous_stock_inx_index_history[-1][0]
+        last_index_dj_value = previous_stock_dj_index_history[-1][0]
+        last_index_ixic_value = previous_stock_ixic_index_history[-1][0]
+        last_index_ndx_value = previous_stock_ndx_index_history[-1][0]
 
         # 提取 Index Predictions 中每一天的第一个值
-        index_inx_day_1 = index_inx_predictions[0][0][0]
-        index_inx_day_2 = index_inx_predictions[0][1][0]
-        index_inx_day_3 = index_inx_predictions[0][2][0]
+        index_inx_day_1 = index_inx_predictions[0][0]
+        index_inx_day_2 = index_inx_predictions[1][0]
+        index_inx_day_3 = index_inx_predictions[2][0]
 
-        index_dj_day_1 = index_dj_predictions[0][0][0]
-        index_dj_day_2 = index_dj_predictions[0][1][0]
-        index_dj_day_3 = index_dj_predictions[0][2][0]
+        index_dj_day_1 = index_dj_predictions[0][0]
+        index_dj_day_2 = index_dj_predictions[1][0]
+        index_dj_day_3 = index_dj_predictions[2][0]
 
-        index_ixic_day_1 = index_ixic_predictions[0][0][0]
-        index_ixic_day_2 = index_ixic_predictions[0][1][0]
-        index_ixic_day_3 = index_ixic_predictions[0][2][0]
+        index_ixic_day_1 = index_ixic_predictions[0][0]
+        index_ixic_day_2 = index_ixic_predictions[1][0]
+        index_ixic_day_3 = index_ixic_predictions[2][0]
 
-        index_ndx_day_1 = index_ndx_predictions[0][0][0]
-        index_ndx_day_2 = index_ndx_predictions[0][1][0]
-        index_ndx_day_3 = index_ndx_predictions[0][2][0]
+        index_ndx_day_1 = index_ndx_predictions[0][0]
+        index_ndx_day_2 = index_ndx_predictions[1][0]
+        index_ndx_day_3 = index_ndx_predictions[2][0]
 
         # 计算 impact_1_day, impact_2_day, impact_3_day
         impact_inx_1_day = (index_inx_day_1 - last_index_inx_value) / last_index_inx_value
@@ -296,18 +308,6 @@ def predict(text: str, stock_codes: list):
 
 
 
-        # 针对 926 模型的修复
-        stock_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), stock_predictions[0], previous_stock_history[0][-1][0])
-        index_inx_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_inx_predictions[0], last_index_inx_value) 
-        index_dj_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_dj_predictions[0], last_index_dj_value) 
-        index_ixic_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_ixic_predictions[0], last_index_ixic_value) 
-        index_ndx_predictions = stock_fix_for_1012_model(float(X_sentiment[0][0]), index_ndx_predictions[0], last_index_ndx_value) 
-
-        print("Stock Predictions after fix:", stock_predictions)
-        print("Index INX Predictions after fix:", index_inx_predictions)    
-        print("Index DJ Predictions after fix:", index_dj_predictions)    
-        print("Index IXIC Predictions after fix:", index_ixic_predictions)    
-        print("Index NDX Predictions after fix:", index_ndx_predictions)    
 
         # 扩展股票预测数据到分钟级别
         stock_predictions = extend_stock_days_to_mins(stock_predictions)
@@ -323,7 +323,7 @@ def predict(text: str, stock_codes: list):
             "news_title": input_text,
             "ai_prediction_score": float(X_sentiment[0][0]),  # 假设第一个预测值是 AI 预测得分
             "impact_inx_1_day": impact_inx_1_day_str,                # 计算并格式化 impact_1_day
-            "impac_inx_2_day": impact_inx_2_day_str,                # 计算并格式化 impact_2_day
+            "impact_inx_2_day": impact_inx_2_day_str,                # 计算并格式化 impact_2_day
             "impact_inx_3_day": impact_inx_3_day_str,  
             "impact_dj_1_day": impact_dj_1_day_str,                # 计算并格式化 impact_1_day
             "impact_dj_2_day": impact_dj_2_day_str,                # 计算并格式化 impact_2_day
@@ -351,7 +351,7 @@ def predict(text: str, stock_codes: list):
         if len(prediction_cache) > CACHE_MAX_SIZE:
             prediction_cache.popitem(last=False)
 
-        print(f"predict() result: {result}")
+        #print(f"predict() result: {result}")
 
         # 返回预测结果
         return result