#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 主力选股批量分析历史记录数据库模块 """ import sqlite3 import json from datetime import datetime from typing import List, Dict, Optional, Tuple import pandas as pd class MainForceBatchDatabase: """主力选股批量分析历史数据库管理类""" def __init__(self, db_path: str = "main_force_batch.db"): """初始化数据库连接""" self.db_path = db_path self._init_database() def _init_database(self): """初始化数据库表结构""" conn = sqlite3.connect(self.db_path) cursor = conn.cursor() # 批量分析历史记录表 cursor.execute(''' CREATE TABLE IF NOT EXISTS batch_analysis_history ( id INTEGER PRIMARY KEY AUTOINCREMENT, analysis_date TEXT NOT NULL, batch_count INTEGER NOT NULL, analysis_mode TEXT NOT NULL, success_count INTEGER NOT NULL, failed_count INTEGER NOT NULL, total_time REAL NOT NULL, results_json TEXT NOT NULL, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ) ''') # 创建索引 cursor.execute(''' CREATE INDEX IF NOT EXISTS idx_analysis_date ON batch_analysis_history(analysis_date) ''') conn.commit() conn.close() def _clean_results_for_json(self, results: List[Dict]) -> List[Dict]: """ 清理结果数据，确保可以JSON序列化 Args: results: 原始结果列表 Returns: 清理后的结果列表 """ def clean_value(value): """递归清理值""" # 处理None if value is None: return None # 处理DataFrame - 只保留前100行避免数据过大 elif isinstance(value, pd.DataFrame): if len(value) > 100: return value.head(100).to_dict('records') return value.to_dict('records') # 处理Series elif isinstance(value, pd.Series): return value.to_dict() # 处理字典 - 递归清理 elif isinstance(value, dict): return {k: clean_value(v) for k, v in value.items()} # 处理列表 - 递归清理 elif isinstance(value, (list, tuple)): return [clean_value(v) for v in value] # 处理基本类型 elif isinstance(value, (str, int, float, bool)): return value # 其他对象转为字符串 else: try: return str(value) except: return "无法序列化" cleaned = [] for result in results: try: cleaned_result = {} for key, value in result.items(): cleaned_result[key] = clean_value(value) cleaned.append(cleaned_result) except Exception as e: # 如果单个结果清理失败，记录错误 cleaned.append({ "error": f"清理失败: {str(e)}", "original_keys": list(result.keys()) if isinstance(result, dict) else [] }) return cleaned def save_batch_analysis( self, batch_count: int, analysis_mode: str, success_count: int, failed_count: int, total_time: float, results: List[Dict] ) -> int: """ 保存批量分析结果 Args: batch_count: 分析股票数量 analysis_mode: 分析模式（sequential/parallel） success_count: 成功数量 failed_count: 失败数量 total_time: 总耗时（秒） results: 分析结果列表 Returns: 记录ID """ conn = sqlite3.connect(self.db_path) cursor = conn.cursor() analysis_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S") # 清理结果数据，确保可以JSON序列化 cleaned_results = self._clean_results_for_json(results) results_json = json.dumps(cleaned_results, ensure_ascii=False, default=str) cursor.execute(''' INSERT INTO batch_analysis_history (analysis_date, batch_count, analysis_mode, success_count, failed_count, total_time, results_json) VALUES (?, ?, ?, ?, ?, ?, ?) ''', (analysis_date, batch_count, analysis_mode, success_count, failed_count, total_time, results_json)) record_id = cursor.lastrowid conn.commit() conn.close() return record_id def get_all_history(self, limit: int = 50) -> List[Dict]: """ 获取所有历史记录 Args: limit: 返回记录数量限制 Returns: 历史记录列表 """ conn = sqlite3.connect(self.db_path) cursor = conn.cursor() cursor.execute(''' SELECT id, analysis_date, batch_count, analysis_mode, success_count, failed_count, total_time, results_json, created_at FROM batch_analysis_history ORDER BY created_at DESC LIMIT ? ''', (limit,)) rows = cursor.fetchall() conn.close() history = [] for row in rows: try: results = json.loads(row[7]) except: results = [] history.append({ 'id': row[0], 'analysis_date': row[1], 'batch_count': row[2], 'analysis_mode': row[3], 'success_count': row[4], 'failed_count': row[5], 'total_time': row[6], 'results': results, 'created_at': row[8] }) return history def get_record_by_id(self, record_id: int) -> Optional[Dict]: """ 根据ID获取单条记录 Args: record_id: 记录ID Returns: 记录详情 """ conn = sqlite3.connect(self.db_path) cursor = conn.cursor() cursor.execute(''' SELECT id, analysis_date, batch_count, analysis_mode, success_count, failed_count, total_time, results_json, created_at FROM batch_analysis_history WHERE id = ? ''', (record_id,)) row = cursor.fetchone() conn.close() if not row: return None try: results = json.loads(row[7]) except: results = [] return { 'id': row[0], 'analysis_date': row[1], 'batch_count': row[2], 'analysis_mode': row[3], 'success_count': row[4], 'failed_count': row[5], 'total_time': row[6], 'results': results, 'created_at': row[8] } def delete_record(self, record_id: int) -> bool: """ 删除记录 Args: record_id: 记录ID Returns: 是否删除成功 """ conn = sqlite3.connect(self.db_path) cursor = conn.cursor() cursor.execute('DELETE FROM batch_analysis_history WHERE id = ?', (record_id,)) affected_rows = cursor.rowcount conn.commit() conn.close() return affected_rows > 0 def get_statistics(self) -> Dict: """ 获取统计信息 Returns: 统计数据 """ conn = sqlite3.connect(self.db_path) cursor = conn.cursor() # 总记录数 cursor.execute('SELECT COUNT(*) FROM batch_analysis_history') total_records = cursor.fetchone()[0] # 总分析股票数 cursor.execute('SELECT SUM(batch_count) FROM batch_analysis_history') total_stocks = cursor.fetchone()[0] or 0 # 总成功数 cursor.execute('SELECT SUM(success_count) FROM batch_analysis_history') total_success = cursor.fetchone()[0] or 0 # 总失败数 cursor.execute('SELECT SUM(failed_count) FROM batch_analysis_history') total_failed = cursor.fetchone()[0] or 0 # 平均耗时 cursor.execute('SELECT AVG(total_time) FROM batch_analysis_history') avg_time = cursor.fetchone()[0] or 0 conn.close() return { 'total_records': total_records, 'total_stocks_analyzed': total_stocks, 'total_success': total_success, 'total_failed': total_failed, 'average_time': round(avg_time, 2), 'success_rate': round(total_success / total_stocks * 100, 2) if total_stocks > 0 else 0 } # 全局数据库实例 batch_db = MainForceBatchDatabase()