feat: 添加预测结果保存功能，用于分析预测质量问题

webui/app.py

@@ -8,6 +8,7 @@ from flask import Flask, render_template, request, jsonify
 from flask_cors import CORS
 import sys
 import warnings
+import datetime
 warnings.filterwarnings('ignore')
 
 # 添加项目根目录到路径
@@ -121,6 +122,90 @@ def load_data_file(file_path):
     except Exception as e:
         return None, f"加载文件失败: {str(e)}"
 
+def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params):
+    """保存预测结果到文件"""
+    try:
+        # 创建预测结果目录
+        results_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'prediction_results')
+        os.makedirs(results_dir, exist_ok=True)
+        
+        # 生成文件名
+        timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+        filename = f'prediction_{timestamp}.json'
+        filepath = os.path.join(results_dir, filename)
+        
+        # 准备保存的数据
+        save_data = {
+            'timestamp': datetime.datetime.now().isoformat(),
+            'file_path': file_path,
+            'prediction_type': prediction_type,
+            'prediction_params': prediction_params,
+            'input_data_summary': {
+                'rows': len(input_data),
+                'columns': list(input_data.columns),
+                'price_range': {
+                    'open': {'min': float(input_data['open'].min()), 'max': float(input_data['open'].max())},
+                    'high': {'min': float(input_data['high'].min()), 'max': float(input_data['high'].max())},
+                    'low': {'min': float(input_data['low'].min()), 'max': float(input_data['low'].max())},
+                    'close': {'min': float(input_data['close'].min()), 'max': float(input_data['close'].max())}
+                },
+                'last_values': {
+                    'open': float(input_data['open'].iloc[-1]),
+                    'high': float(input_data['high'].iloc[-1]),
+                    'low': float(input_data['low'].iloc[-1]),
+                    'close': float(input_data['close'].iloc[-1])
+                }
+            },
+            'prediction_results': prediction_results,
+            'actual_data': actual_data,
+            'analysis': {}
+        }
+        
+        # 如果有实际数据，进行对比分析
+        if actual_data and len(actual_data) > 0:
+            # 计算连续性分析
+            if len(prediction_results) > 0 and len(actual_data) > 0:
+                last_pred = prediction_results[0]  # 第一个预测点
+                first_actual = actual_data[0]      # 第一个实际点
+                
+                save_data['analysis']['continuity'] = {
+                    'last_prediction': {
+                        'open': last_pred['open'],
+                        'high': last_pred['high'],
+                        'low': last_pred['low'],
+                        'close': last_pred['close']
+                    },
+                    'first_actual': {
+                        'open': first_actual['open'],
+                        'high': first_actual['high'],
+                        'low': first_actual['low'],
+                        'close': first_actual['close']
+                    },
+                    'gaps': {
+                        'open_gap': abs(last_pred['open'] - first_actual['open']),
+                        'high_gap': abs(last_pred['high'] - first_actual['high']),
+                        'low_gap': abs(last_pred['low'] - first_actual['low']),
+                        'close_gap': abs(last_pred['close'] - first_actual['close'])
+                    },
+                    'gap_percentages': {
+                        'open_gap_pct': (abs(last_pred['open'] - first_actual['open']) / first_actual['open']) * 100,
+                        'high_gap_pct': (abs(last_pred['high'] - first_actual['high']) / first_actual['high']) * 100,
+                        'low_gap_pct': (abs(last_pred['low'] - first_actual['low']) / first_actual['low']) * 100,
+                        'close_gap_pct': (abs(last_pred['close'] - first_actual['close']) / first_actual['close']) * 100
+                    }
+                }
+        
+        # 保存到文件
+        with open(filepath, 'w', encoding='utf-8') as f:
+            json.dump(save_data, f, indent=2, ensure_ascii=False)
+        
+        print(f"预测结果已保存到: {filepath}")
+        return filepath
+        
+    except Exception as e:
+        print(f"保存预测结果失败: {e}")
+        return None
+
 def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0):
     """创建预测图表"""
     # 使用指定的历史数据起始位置，而不是总是从df的开头开始
@@ -505,6 +590,26 @@ def predict():
                 'amount': float(row['amount']) if 'amount' in row else 0
             })
         
+        # 保存预测结果到文件
+        try:
+            save_prediction_results(
+                file_path=file_path,
+                prediction_type=prediction_type,
+                prediction_results=prediction_results,
+                actual_data=actual_data,
+                input_data=x_df,
+                prediction_params={
+                    'lookback': lookback,
+                    'pred_len': pred_len,
+                    'temperature': temperature,
+                    'top_p': top_p,
+                    'sample_count': sample_count,
+                    'start_date': start_date if start_date else 'latest'
+                }
+            )
+        except Exception as e:
+            print(f"保存预测结果失败: {e}")
+        
         return jsonify({
             'success': True,
             'prediction_type': prediction_type,