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