107 lines
5.4 KiB
Python
107 lines
5.4 KiB
Python
#!/usr/bin/env python3
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"""实盘数据累积系统（MoFin）：每日收盘后累积重点行业/个股的实盘数据。
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目标：从当前开始累积实盘数据（资金流、行业热度、龙虎榜代理等），定时回测调整策略。
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数据源（MoFin已有）：stock_daily(日线)、stock_capital_flow(资金流)、stock_news(新闻)、sector_index_daily(行业指数)
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增量：每日收盘后计算并累积：
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- 重点行业（近期热点）的资金流/新闻热度/行业趋势
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- 重点个股的龙虎榜代理（大单/主力资金）
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- 市场状态（大盘RSI/ADX/行业强弱分布）
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输出：/home/hmo/MoFin/data/live_data/ 目录下按日期累积
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"""
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import sqlite3, json, os, pandas as pd, numpy as np
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from datetime import datetime, timedelta
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DB = "/home/hmo/MoFin/data/mofin.db"
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LIVE_DIR = "/home/hmo/MoFin/data/live_data"
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os.makedirs(LIVE_DIR, exist_ok=True)
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def collect_daily_snapshot(date_str=None):
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"""累积某日的实盘数据快照"""
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conn = sqlite3.connect(f"file:{DB}?mode=ro", uri=True)
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conn.execute("PRAGMA query_only=ON")
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if date_str is None:
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date_str = datetime.now().strftime('%Y-%m-%d')
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print(f"Collecting live data for {date_str}")
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# 1. 大盘状态（上证）
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mkt = pd.read_sql("SELECT date, close FROM sector_index_daily WHERE sector='上证指数' ORDER BY date DESC LIMIT 60", conn)
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mkt['date'] = mkt['date'].astype(str)
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mkt = mkt.sort_values('date').reset_index(drop=True)
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mkt['ret'] = mkt['close'].pct_change()
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mkt['gain'] = mkt['ret'].clip(lower=0)
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mkt['loss'] = (-mkt['ret']).clip(lower=0)
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mkt['ag'] = mkt['gain'].rolling(14).mean()
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mkt['al'] = mkt['loss'].rolling(14).mean()
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mkt['rsi'] = 100 - 100 / (1 + mkt['ag'] / mkt['al'].replace(0, np.nan))
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mkt['rsi'] = mkt['rsi'].fillna(50)
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mkt['ma20'] = mkt['close'].rolling(20).mean()
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latest = mkt.iloc[-1] if len(mkt) else None
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# 2. 行业热度（当日新闻数）
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news = pd.read_sql("SELECT code, date FROM stock_news WHERE date >= ? AND date <= ?", conn,
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params=[(pd.Timestamp(date_str) - pd.Timedelta(days=5)).strftime('%Y-%m-%d'), date_str])
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news['date'] = news['date'].astype(str).str[:10]
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secmap = dict(conn.execute("SELECT code, sector FROM stock_sectors_em").fetchall())
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news['sector'] = news['code'].map(secmap)
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sector_news = news.groupby('sector').size().reset_index(name='news_count').sort_values('news_count', ascending=False)
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# 3. 行业资金流（当日主力资金净流入）
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flow = pd.read_sql("SELECT code, date, main_net FROM stock_capital_flow WHERE date = ?", conn, params=[date_str])
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flow['sector'] = flow['code'].map(secmap)
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sector_flow = flow.groupby('sector')['main_net'].sum().reset_index().sort_values('main_net', ascending=False)
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# 4. 行业指数（当日涨跌）
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sec_idx = pd.read_sql("SELECT sector, date, close, change_pct FROM sector_index_daily WHERE date = ?", conn, params=[date_str])
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sec_idx['sector'] = sec_idx['sector'].astype(str)
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# 5. 重点行业识别（新闻热度+资金流+行业指数强势）
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merged = sector_news.merge(sector_flow, on='sector', how='outer').merge(
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sec_idx[['sector', 'change_pct']], on='sector', how='outer').fillna(0)
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merged['heat_score'] = merged['news_count'] * 0.5 + merged['main_net'].clip(lower=0) / 1e8 * 0.3 + merged['change_pct'] * 10
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hot_sectors = merged.sort_values('heat_score', ascending=False).head(20)
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# 6. 重点个股（热点行业内的强势股）
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# 用当日涨幅 + 资金流筛选
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stocks = pd.read_sql(f"""
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SELECT sd.code, sd.date, sd.close, sd.open, sd.high, sd.low, sd.volume,
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scf.main_net, scf.main_pct
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FROM stock_daily sd
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LEFT JOIN stock_capital_flow scf ON sd.code = scf.code AND sd.date = scf.date
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WHERE sd.date = ?
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""", conn, params=[date_str])
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stocks['sector'] = stocks['code'].map(secmap)
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# 只保留热点行业的股票
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hot_sector_names = set(hot_sectors['sector'].head(10))
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hot_stocks = stocks[stocks['sector'].isin(hot_sector_names)].copy()
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hot_stocks['ret'] = hot_stocks.groupby('code')['close'].transform(lambda x: x.pct_change())
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hot_stocks = hot_stocks.sort_values(['sector', 'ret'], ascending=[True, False])
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top_stocks = hot_stocks.groupby('sector').head(5)
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snapshot = {
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"date": date_str,
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"market": {
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"close": float(latest['close']) if latest is not None else None,
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"rsi": float(latest['rsi']) if latest is not None and not np.isnan(latest['rsi']) else None,
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"ma20": float(latest['ma20']) if latest is not None and not np.isnan(latest['ma20']) else None,
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},
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"hot_sectors": hot_sectors.to_dict('records'),
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"top_stocks": top_stocks.to_dict('records'),
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"sector_news": sector_news.head(20).to_dict('records'),
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"sector_flow": sector_flow.head(20).to_dict('records'),
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}
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# 保存
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out_path = os.path.join(LIVE_DIR, f"{date_str}.json")
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with open(out_path, 'w', encoding='utf-8') as f:
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json.dump(snapshot, f, ensure_ascii=False, indent=1, default=str)
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print(f" Saved: {out_path} ({os.path.getsize(out_path)//1024}KB)")
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print(f" Hot sectors: {hot_sectors['sector'].head(5).tolist()}")
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conn.close()
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return snapshot
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if __name__ == '__main__':
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import sys
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date_str = sys.argv[1] if len(sys.argv) > 1 else None
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collect_daily_snapshot(date_str)
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