oficcejo
499 lines
14 KiB
Python
499 lines
14 KiB
Python
"""
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智瞰龙虎数据库模块
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用于存储龙虎榜历史数据和分析报告
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"""
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import sqlite3
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from datetime import datetime
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import json
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import pandas as pd
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class LonghubangDatabase:
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"""龙虎榜数据库管理类"""
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def __init__(self, db_path='longhubang.db'):
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"""
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初始化数据库
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Args:
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db_path: 数据库文件路径
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"""
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self.db_path = db_path
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self.init_database()
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def get_connection(self):
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"""获取数据库连接"""
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return sqlite3.connect(self.db_path)
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def init_database(self):
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"""初始化数据库表"""
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conn = self.get_connection()
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cursor = conn.cursor()
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# 龙虎榜原始数据表
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS longhubang_records (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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date TEXT NOT NULL,
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stock_code TEXT NOT NULL,
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stock_name TEXT,
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youzi_name TEXT,
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yingye_bu TEXT,
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list_type TEXT,
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buy_amount REAL,
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sell_amount REAL,
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net_inflow REAL,
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concepts TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(date, stock_code, youzi_name, yingye_bu)
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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_date ON longhubang_records(date)
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''')
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cursor.execute('''
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CREATE INDEX IF NOT EXISTS idx_stock_code ON longhubang_records(stock_code)
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''')
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cursor.execute('''
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CREATE INDEX IF NOT EXISTS idx_youzi_name ON longhubang_records(youzi_name)
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''')
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cursor.execute('''
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CREATE INDEX IF NOT EXISTS idx_net_inflow ON longhubang_records(net_inflow)
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''')
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# AI分析报告表
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS longhubang_analysis (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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analysis_date TEXT NOT NULL,
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data_date_range TEXT,
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analysis_content TEXT,
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recommended_stocks TEXT,
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summary TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP
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)
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''')
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# 股票追踪表（记录推荐股票的后续表现）
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS stock_tracking (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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analysis_id INTEGER,
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stock_code TEXT NOT NULL,
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stock_name TEXT,
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recommended_date TEXT,
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recommended_price REAL,
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target_price REAL,
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stop_loss_price REAL,
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current_price REAL,
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profit_loss_pct REAL,
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status TEXT,
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notes TEXT,
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updated_at TEXT DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY(analysis_id) REFERENCES longhubang_analysis(id)
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)
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''')
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conn.commit()
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conn.close()
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print("[智瞰龙虎] 数据库初始化完成")
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def save_longhubang_data(self, data_list):
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"""
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保存龙虎榜数据
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Args:
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data_list: 龙虎榜数据列表
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Returns:
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int: 成功保存的记录数
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"""
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if not data_list:
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return 0
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conn = self.get_connection()
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cursor = conn.cursor()
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saved_count = 0
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for record in data_list:
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try:
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cursor.execute('''
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INSERT OR REPLACE INTO longhubang_records
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(date, stock_code, stock_name, youzi_name, yingye_bu, list_type,
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buy_amount, sell_amount, net_inflow, concepts)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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record.get('rq') or record.get('日期'),
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record.get('gpdm') or record.get('股票代码'),
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record.get('gpmc') or record.get('股票名称'),
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record.get('yzmc') or record.get('游资名称'),
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record.get('yyb') or record.get('营业部'),
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record.get('sblx') or record.get('榜单类型'),
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float(record.get('mrje') or record.get('买入金额') or 0),
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float(record.get('mcje') or record.get('卖出金额') or 0),
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float(record.get('jlrje') or record.get('净流入金额') or 0),
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record.get('gl') or record.get('概念')
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))
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saved_count += 1
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except Exception as e:
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print(f"保存记录失败: {e}")
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continue
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conn.commit()
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conn.close()
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print(f"[智瞰龙虎] 成功保存 {saved_count} 条龙虎榜记录")
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return saved_count
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def get_longhubang_data(self, start_date=None, end_date=None, stock_code=None):
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"""
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查询龙虎榜数据
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Args:
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start_date: 开始日期
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end_date: 结束日期
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stock_code: 股票代码
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Returns:
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pd.DataFrame: 查询结果
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"""
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conn = self.get_connection()
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query = "SELECT * FROM longhubang_records WHERE 1=1"
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params = []
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if start_date:
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query += " AND date >= ?"
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params.append(start_date)
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if end_date:
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query += " AND date <= ?"
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params.append(end_date)
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if stock_code:
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query += " AND stock_code = ?"
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params.append(stock_code)
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query += " ORDER BY date DESC, net_inflow DESC"
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df = pd.read_sql_query(query, conn, params=params)
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conn.close()
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return df
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def get_top_youzi(self, start_date=None, end_date=None, limit=20):
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"""
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获取活跃游资排名
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Args:
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start_date: 开始日期
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end_date: 结束日期
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limit: 返回数量
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Returns:
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pd.DataFrame: 游资排名
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"""
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conn = self.get_connection()
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query = '''
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SELECT
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youzi_name,
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COUNT(*) as trade_count,
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SUM(buy_amount) as total_buy,
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SUM(sell_amount) as total_sell,
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SUM(net_inflow) as total_net_inflow
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FROM longhubang_records
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WHERE 1=1
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'''
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params = []
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if start_date:
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query += " AND date >= ?"
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params.append(start_date)
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if end_date:
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query += " AND date <= ?"
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params.append(end_date)
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query += '''
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GROUP BY youzi_name
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ORDER BY total_net_inflow DESC
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LIMIT ?
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'''
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params.append(limit)
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df = pd.read_sql_query(query, conn, params=params)
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conn.close()
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return df
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def get_top_stocks(self, start_date=None, end_date=None, limit=20):
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"""
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获取热门股票排名
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Args:
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start_date: 开始日期
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end_date: 结束日期
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limit: 返回数量
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Returns:
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pd.DataFrame: 股票排名
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"""
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conn = self.get_connection()
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query = '''
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SELECT
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stock_code,
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stock_name,
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COUNT(DISTINCT youzi_name) as youzi_count,
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SUM(buy_amount) as total_buy,
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SUM(sell_amount) as total_sell,
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SUM(net_inflow) as total_net_inflow,
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GROUP_CONCAT(DISTINCT concepts) as all_concepts
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FROM longhubang_records
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WHERE 1=1
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'''
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params = []
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if start_date:
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query += " AND date >= ?"
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params.append(start_date)
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if end_date:
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query += " AND date <= ?"
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params.append(end_date)
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query += '''
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GROUP BY stock_code, stock_name
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ORDER BY total_net_inflow DESC
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LIMIT ?
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'''
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params.append(limit)
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df = pd.read_sql_query(query, conn, params=params)
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conn.close()
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return df
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def save_analysis_report(self, data_date_range, analysis_content,
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recommended_stocks, summary, full_result=None):
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"""
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保存AI分析报告（完整版）
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Args:
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data_date_range: 数据日期范围
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analysis_content: 分析内容（JSON字符串或字典）
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recommended_stocks: 推荐股票列表
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summary: 摘要
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full_result: 完整的分析结果字典（可选）
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Returns:
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int: 报告ID
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"""
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conn = self.get_connection()
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cursor = conn.cursor()
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# 如果传入的是字典，转换为JSON字符串
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if isinstance(analysis_content, dict):
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analysis_content = json.dumps(analysis_content, ensure_ascii=False, indent=2)
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cursor.execute('''
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INSERT INTO longhubang_analysis
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(analysis_date, data_date_range, analysis_content, recommended_stocks, summary)
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VALUES (?, ?, ?, ?, ?)
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''', (
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datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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data_date_range,
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analysis_content,
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json.dumps(recommended_stocks, ensure_ascii=False),
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summary
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))
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report_id = cursor.lastrowid
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conn.commit()
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conn.close()
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print(f"[智瞰龙虎] 分析报告已保存 (ID: {report_id})")
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return report_id
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def get_analysis_reports(self, limit=10):
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"""
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获取历史分析报告
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Args:
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limit: 返回数量
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Returns:
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pd.DataFrame: 报告列表
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"""
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conn = self.get_connection()
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query = '''
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SELECT * FROM longhubang_analysis
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ORDER BY created_at DESC
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LIMIT ?
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'''
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df = pd.read_sql_query(query, conn, params=[limit])
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conn.close()
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return df
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def get_analysis_report(self, report_id):
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"""
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获取单个分析报告详情
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Args:
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report_id: 报告ID
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Returns:
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dict: 报告详情
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"""
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conn = self.get_connection()
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cursor = conn.cursor()
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cursor.execute('''
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SELECT * FROM longhubang_analysis WHERE id = ?
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''', (report_id,))
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row = cursor.fetchone()
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conn.close()
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if row:
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columns = [desc[0] for desc in cursor.description]
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report = dict(zip(columns, row))
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# 解析JSON字段
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if report.get('recommended_stocks'):
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try:
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report['recommended_stocks'] = json.loads(report['recommended_stocks'])
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except:
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pass
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# 解析analysis_content字段
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if report.get('analysis_content'):
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try:
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report['analysis_content_parsed'] = json.loads(report['analysis_content'])
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except:
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# 如果不是JSON格式，保持原样
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report['analysis_content_parsed'] = None
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return report
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return None
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def update_stock_tracking(self, analysis_id, stock_code, current_price, status, notes=None):
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"""
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更新股票追踪信息
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Args:
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analysis_id: 分析报告ID
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stock_code: 股票代码
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current_price: 当前价格
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status: 状态
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notes: 备注
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"""
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conn = self.get_connection()
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cursor = conn.cursor()
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cursor.execute('''
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UPDATE stock_tracking
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SET current_price = ?, status = ?, notes = ?, updated_at = ?
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WHERE analysis_id = ? AND stock_code = ?
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''', (
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current_price,
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status,
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notes,
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datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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analysis_id,
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stock_code
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))
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conn.commit()
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conn.close()
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def get_statistics(self):
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"""
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获取数据库统计信息
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Returns:
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dict: 统计信息
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"""
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conn = self.get_connection()
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cursor = conn.cursor()
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stats = {}
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# 总记录数
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cursor.execute('SELECT COUNT(*) FROM longhubang_records')
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stats['total_records'] = cursor.fetchone()[0]
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# 涉及股票数
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cursor.execute('SELECT COUNT(DISTINCT stock_code) FROM longhubang_records')
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stats['total_stocks'] = cursor.fetchone()[0]
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# 涉及游资数
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cursor.execute('SELECT COUNT(DISTINCT youzi_name) FROM longhubang_records')
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stats['total_youzi'] = cursor.fetchone()[0]
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# 分析报告数
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cursor.execute('SELECT COUNT(*) FROM longhubang_analysis')
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stats['total_reports'] = cursor.fetchone()[0]
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# 日期范围
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cursor.execute('SELECT MIN(date), MAX(date) FROM longhubang_records')
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date_range = cursor.fetchone()
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stats['date_range'] = {
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'start': date_range[0],
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'end': date_range[1]
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}
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conn.close()
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return stats
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# 测试函数
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if __name__ == "__main__":
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print("=" * 60)
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print("测试智瞰龙虎数据库模块")
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print("=" * 60)
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db = LonghubangDatabase('test_longhubang.db')
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# 测试数据
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test_data = [
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{
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'rq': '2023-03-22',
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'gpdm': '001337',
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'gpmc': '四川黄金',
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'yzmc': '92科比',
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'yyb': '兴业证券股份有限公司南京天元东路证券营业部',
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'sblx': '1',
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'mrje': 14470401,
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'mcje': 15080,
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'jlrje': 14455321,
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'gl': '贵金属,四川板块,昨日连板_含一字,昨日涨停_含一字,黄金概念,次新股'
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}
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]
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# 测试保存
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db.save_longhubang_data(test_data)
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# 测试查询
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df = db.get_longhubang_data()
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print(f"\n查询到 {len(df)} 条记录")
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# 获取统计信息
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stats = db.get_statistics()
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print(f"\n数据库统计: {stats}")
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