592 lines
24 KiB
Python
592 lines
24 KiB
Python
"""推荐引擎
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管理推荐状态，提供推荐查询接口。
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将筛选结果持久化并管理历史推荐。
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"""
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import logging
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import json
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import asyncio
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from datetime import datetime, timedelta
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from app.engine.screener import run_screening
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from app.data.models import Recommendation, MarketTemperature, SectorInfo
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from app.db.database import get_db
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from app.db import tables
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logger = logging.getLogger(__name__)
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# 内存中的最新推荐结果
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_latest_result: dict | None = None
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# 扫描锁：防止同时触发两次扫描
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_scan_lock = asyncio.Lock()
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_scan_running = False
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async def refresh_recommendations(trade_date: str = None, scan_session: str = "manual") -> dict:
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"""刷新推荐列表（带扫描锁防止并发）"""
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global _latest_result, _scan_running
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if _scan_lock.locked():
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logger.warning("扫描已在执行中，跳过本次触发")
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return _latest_result or {"market_temp": None, "hot_sectors": [], "recommendations": []}
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async with _scan_lock:
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_scan_running = True
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try:
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result = await run_screening(trade_date)
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# 给每条推荐添加 scan_session
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for rec in result.get("recommendations", []):
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rec.scan_session = scan_session
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rec.created_at = datetime.now()
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_latest_result = result
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# 持久化到数据库
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await _save_to_db(result)
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# 更新历史推荐跟踪（检查之前推荐的后续表现）
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await _update_tracking()
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return result
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finally:
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_scan_running = False
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async def _update_tracking():
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"""更新历史推荐的跟踪数据"""
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try:
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from sqlalchemy import text
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from app.data.tushare_client import tushare_client
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trade_date = tushare_client.get_latest_trade_date()
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async with get_db() as db:
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# 查找所有活跃的推荐（有 entry_price 且未被标记为 closed）
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result = await db.execute(
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text(
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"SELECT id, ts_code, entry_price, target_price, stop_loss "
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"FROM recommendations "
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"WHERE entry_price IS NOT NULL "
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"AND entry_price > 0 "
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"AND id NOT IN (SELECT DISTINCT recommendation_id FROM recommendation_tracking WHERE status = 'closed') "
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"AND date(created_at) <= date(:today) "
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"ORDER BY created_at DESC LIMIT 50"
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),
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{"today": datetime.now().strftime("%Y-%m-%d")},
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)
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rows = result.fetchall()
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if not rows:
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return
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# 获取这些股票的今日收盘价
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codes = [r[1] for r in rows]
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daily_all = tushare_client.get_daily_all(trade_date)
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price_map = {}
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if not daily_all.empty:
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for _, row in daily_all.iterrows():
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if row["ts_code"] in codes:
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price_map[row["ts_code"]] = row["close"]
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tracked = 0
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for r in rows:
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rec_id, ts_code, entry_price, target_price, stop_loss = r
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current_price = price_map.get(ts_code)
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if current_price is None or entry_price is None or entry_price <= 0:
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continue
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pct = round((current_price - entry_price) / entry_price * 100, 2)
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hit_target = target_price and current_price >= target_price
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hit_stop = stop_loss and current_price <= stop_loss
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status = "closed" if (hit_target or hit_stop) else "active"
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# 检查今天是否已经跟踪过
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exists = await db.execute(
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text(
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"SELECT id FROM recommendation_tracking "
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"WHERE recommendation_id = :rid AND track_date = :td"
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),
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{"rid": rec_id, "td": trade_date},
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)
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if exists.fetchone():
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continue
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await db.execute(
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tables.recommendation_tracking_table.insert().values(
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recommendation_id=rec_id,
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track_date=trade_date,
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current_price=current_price,
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pct_from_entry=pct,
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hit_target=hit_target,
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hit_stop_loss=hit_stop,
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status=status,
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)
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)
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tracked += 1
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await db.commit()
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if tracked > 0:
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logger.info(f"已更新 {tracked} 条推荐跟踪记录")
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except Exception as e:
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logger.error(f"更新推荐跟踪失败: {e}")
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async def get_performance_stats() -> dict:
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"""获取推荐胜率统计"""
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try:
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from sqlalchemy import text
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async with get_db() as db:
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# 总推荐数
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result = await db.execute(
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text("SELECT COUNT(DISTINCT id) FROM recommendations")
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)
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total = result.scalar() or 0
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# 有跟踪记录的推荐
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result = await db.execute(
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text(
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"SELECT COUNT(DISTINCT r.id) FROM recommendations r "
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"INNER JOIN recommendation_tracking t ON t.recommendation_id = r.id"
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)
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)
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tracked = result.scalar() or 0
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# 胜率基于最新跟踪日的最终 pct（正值=盈利，负值=亏损）
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result = await db.execute(
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text(
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"SELECT COUNT(*) FROM ("
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" SELECT t.recommendation_id, t.pct_from_entry as latest_pct "
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" FROM recommendation_tracking t "
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" INNER JOIN ("
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" SELECT recommendation_id, MAX(id) as max_id "
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" FROM recommendation_tracking GROUP BY recommendation_id"
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" ) latest ON t.id = latest.max_id"
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") WHERE latest_pct > 0"
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)
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)
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winning = result.scalar() or 0
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# 平均收益（基于最新跟踪日的 pct）
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result = await db.execute(
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text(
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"SELECT AVG(latest_pct) FROM ("
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" SELECT t.pct_from_entry as latest_pct "
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" FROM recommendation_tracking t "
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" INNER JOIN ("
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" SELECT recommendation_id, MAX(id) as max_id "
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" FROM recommendation_tracking GROUP BY recommendation_id"
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" ) latest ON t.id = latest.max_id"
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")"
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)
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)
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avg_return = result.scalar()
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avg_return = round(float(avg_return), 2) if avg_return else 0
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# 达到目标价的推荐
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result = await db.execute(
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text(
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"SELECT COUNT(DISTINCT recommendation_id) FROM recommendation_tracking "
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"WHERE hit_target = 1"
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)
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)
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hit_target_count = result.scalar() or 0
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# 触发止损的推荐
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result = await db.execute(
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text(
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"SELECT COUNT(DISTINCT recommendation_id) FROM recommendation_tracking "
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"WHERE hit_stop_loss = 1"
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)
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)
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hit_stop_count = result.scalar() or 0
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# 最近跟踪的推荐详情
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result = await db.execute(
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text(
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"SELECT r.ts_code, r.name, r.signal, r.entry_price, "
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" r.target_price, r.stop_loss, r.entry_signal_type, r.score, "
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" t.pct_from_entry, t.current_price, t.track_date, t.hit_target, t.hit_stop_loss, "
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" r.created_at "
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"FROM recommendations r "
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"INNER JOIN recommendation_tracking t ON t.recommendation_id = r.id "
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"INNER JOIN ("
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" SELECT recommendation_id, MAX(id) as max_id "
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" FROM recommendation_tracking GROUP BY recommendation_id"
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") latest ON t.id = latest.max_id "
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"ORDER BY r.created_at DESC LIMIT 20"
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)
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)
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details = []
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for row in result.fetchall():
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r = row._mapping
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details.append({
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"ts_code": r["ts_code"],
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"name": r["name"],
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"signal": r["signal"],
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"entry_signal_type": r["entry_signal_type"],
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"score": r["score"],
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"entry_price": r["entry_price"],
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"target_price": r["target_price"],
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"stop_loss": r["stop_loss"],
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"current_price": r["current_price"],
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"pct_from_entry": r["pct_from_entry"],
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"track_date": r["track_date"],
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"hit_target": bool(r["hit_target"]),
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"hit_stop_loss": bool(r["hit_stop_loss"]),
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"created_at": str(r["created_at"])[:10] if r["created_at"] else "",
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})
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win_rate = round(winning / tracked * 100, 1) if tracked > 0 else 0
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return {
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"total_recommendations": total,
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"tracked": tracked,
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"winning": winning,
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"win_rate": win_rate,
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"avg_return": avg_return,
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"hit_target_count": hit_target_count,
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"hit_stop_count": hit_stop_count,
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"details": details,
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}
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except Exception as e:
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logger.error(f"获取胜率统计失败: {e}")
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return {
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"total_recommendations": 0, "tracked": 0, "winning": 0,
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"win_rate": 0, "avg_return": 0, "hit_target_count": 0,
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"hit_stop_count": 0, "details": [],
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}
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async def get_latest_recommendations() -> dict:
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"""获取最新推荐结果"""
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if _latest_result:
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return _latest_result
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# 如果内存中没有，从数据库加载今日的
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return await _load_today_from_db()
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async def get_latest_sectors() -> list[SectorInfo]:
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"""获取最新的板块热度数据（只读缓存，不触发扫描）"""
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if _latest_result and _latest_result.get("hot_sectors"):
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return _latest_result["hot_sectors"]
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# 内存中没有，从数据库加载
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return await _load_sectors_from_db()
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async def get_recommendation_history(days: int = 7) -> list[dict]:
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"""获取历史推荐记录，按日期分组返回"""
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import json
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start = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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async with get_db() as db:
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from sqlalchemy import text
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# 查询所有历史推荐，按 ts_code 去重（每天取最新一条）
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stmt = text(
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"SELECT * FROM recommendations "
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"WHERE created_at >= :start "
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"AND score >= 60 "
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"AND id IN ("
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" SELECT MAX(id) FROM recommendations "
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" WHERE created_at >= :start "
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" GROUP BY date(created_at), ts_code"
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") "
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"ORDER BY created_at DESC, score DESC"
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)
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result = await db.execute(stmt, {"start": start})
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rows = result.fetchall()
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# 按日期分组
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grouped: dict[str, list[dict]] = {}
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for row in rows:
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r = row._mapping
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# SQLite created_at 是字符串 "YYYY-MM-DD HH:MM:SS"
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ca = r["created_at"]
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if ca:
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date_str = str(ca)[:10] # 取前10字符即日期部分
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created_at_str = str(ca)
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else:
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date_str = "unknown"
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created_at_str = None
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rec_dict = {
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"ts_code": r["ts_code"],
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"name": r["name"],
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"sector": r["sector"] or "",
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"score": r["score"] or 0,
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"level": _score_to_level_static(r["score"] or 0),
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"signal": r["signal"] or "HOLD",
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"market_temp_score": r["market_temp_score"] or 0,
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"sector_score": r["sector_score"] or 0,
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"capital_score": r["capital_score"] or 0,
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"technical_score": r["technical_score"] or 0,
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"supply_demand_score": r.get("supply_demand_score") or 0,
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"price_action_score": r.get("price_action_score") or 0,
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"position_score": r.get("position_score") or 50,
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"valuation_score": r.get("valuation_score") or 50,
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"entry_price": r["entry_price"],
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"target_price": r["target_price"],
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"stop_loss": r["stop_loss"],
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"reasons": json.loads(r["reasons"]) if r["reasons"] else [],
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"risk_note": "",
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"strategy": r.get("strategy") or "trend_breakout",
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"entry_signal_type": r.get("entry_signal_type") or "none",
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"llm_analysis": r.get("llm_analysis") or "",
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"llm_score": r.get("llm_score"),
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"scan_session": r["scan_session"] or "",
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"created_at": created_at_str,
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}
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if date_str not in grouped:
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grouped[date_str] = []
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grouped[date_str].append(rec_dict)
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# 转为列表，按日期降序
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result_list = []
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for date_str in sorted(grouped.keys(), reverse=True):
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recs = grouped[date_str]
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buy_count = sum(1 for r in recs if r["signal"] == "BUY")
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avg_score = round(sum(r["score"] for r in recs) / len(recs), 1) if recs else 0
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result_list.append({
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"date": date_str,
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"count": len(recs),
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"buy_count": buy_count,
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"avg_score": avg_score,
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"recommendations": recs,
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})
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return result_list
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def _score_to_level_static(score: float) -> str:
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"""根据评分确定推荐等级"""
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if score >= 75:
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return "强烈推荐"
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elif score >= 60:
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return "推荐"
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elif score >= 45:
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return "关注"
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else:
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return "观望"
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async def _save_to_db(result: dict):
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"""将推荐结果保存到数据库"""
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try:
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async with get_db() as db:
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from sqlalchemy import text
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# 保存市场温度
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mt = result.get("market_temp")
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if mt:
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# 使用 INSERT OR REPLACE 确保重复扫描能更新数据
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stmt = text(
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"INSERT OR REPLACE INTO market_temperature "
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"(trade_date, up_count, down_count, limit_up_count, limit_down_count, "
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"max_streak, broken_rate, temperature) "
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"VALUES (:td, :up, :down, :lu, :ld, :ms, :br, :temp)"
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)
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await db.execute(stmt, {
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"td": mt.trade_date,
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"up": mt.up_count,
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"down": mt.down_count,
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"lu": mt.limit_up_count,
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"ld": mt.limit_down_count,
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"ms": mt.max_streak,
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"br": mt.broken_rate,
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"temp": mt.temperature,
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})
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# 保存板块热度（先清除同一 trade_date 的旧数据，避免重复）
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trade_date_val = mt.trade_date if mt else ""
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if trade_date_val:
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await db.execute(
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text("DELETE FROM sector_heat WHERE trade_date = :td"),
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{"td": trade_date_val},
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)
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for sector in result.get("hot_sectors", []):
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stmt = tables.sector_heat_table.insert().values(
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sector_code=sector.sector_code,
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sector_name=sector.sector_name,
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pct_change=sector.pct_change,
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capital_inflow=sector.capital_inflow,
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limit_up_count=sector.limit_up_count,
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heat_score=sector.heat_score,
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stage=sector.stage,
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days_continuous=sector.days_continuous,
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member_count=sector.member_count,
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leading_stocks=json.dumps(sector.leading_stocks, ensure_ascii=False),
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pct_trend=json.dumps(sector.pct_trend, ensure_ascii=False),
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turnover_avg=sector.turnover_avg,
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main_force_ratio=sector.main_force_ratio,
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trade_date=trade_date_val,
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)
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await db.execute(stmt)
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# 保存推荐（按 ts_code 清除当日旧记录，避免同一天多次扫描产生重复）
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today_str = datetime.now().strftime("%Y-%m-%d")
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now_dt = datetime.now()
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saved_count = 0
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for rec in result.get("recommendations", []):
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if rec.score < 60:
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continue
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await db.execute(
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text("DELETE FROM recommendations WHERE date(created_at) = :today AND ts_code = :code"),
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{"today": today_str, "code": rec.ts_code},
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)
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stmt = tables.recommendations_table.insert().values(
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ts_code=rec.ts_code,
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name=rec.name,
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sector=rec.sector,
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score=rec.score,
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market_temp_score=rec.market_temp_score,
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sector_score=rec.sector_score,
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capital_score=rec.capital_score,
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technical_score=rec.technical_score,
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supply_demand_score=rec.supply_demand_score,
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price_action_score=rec.price_action_score,
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position_score=rec.position_score,
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valuation_score=rec.valuation_score,
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signal=rec.signal,
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entry_price=rec.entry_price,
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target_price=rec.target_price,
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stop_loss=rec.stop_loss,
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reasons=json.dumps(rec.reasons, ensure_ascii=False),
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llm_analysis=rec.llm_analysis,
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strategy=rec.strategy,
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entry_signal_type=rec.entry_signal_type,
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llm_score=rec.llm_score,
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scan_session=rec.scan_session,
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created_at=now_dt,
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)
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await db.execute(stmt)
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saved_count += 1
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await db.commit()
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logger.info(f"已保存 {saved_count} 条推荐到数据库（共 {len(result.get('recommendations', []))} 条，过滤掉 <60 分）")
|
||
except Exception as e:
|
||
logger.error(f"保存推荐到数据库失败: {e}")
|
||
|
||
|
||
async def _load_today_from_db() -> dict:
|
||
"""从数据库加载今日推荐"""
|
||
today = datetime.now().strftime("%Y-%m-%d")
|
||
try:
|
||
async with get_db() as db:
|
||
from sqlalchemy import text
|
||
import json
|
||
|
||
# 加载市场温度（按 trade_date 取最新交易日）
|
||
result = await db.execute(
|
||
text("SELECT * FROM market_temperature ORDER BY trade_date DESC LIMIT 1")
|
||
)
|
||
mt_row = result.fetchone()
|
||
market_temp = None
|
||
if mt_row:
|
||
m = mt_row._mapping
|
||
market_temp = MarketTemperature(
|
||
trade_date=m["trade_date"],
|
||
up_count=m["up_count"],
|
||
down_count=m["down_count"],
|
||
limit_up_count=m["limit_up_count"],
|
||
limit_down_count=m["limit_down_count"],
|
||
max_streak=m["max_streak"],
|
||
broken_rate=m["broken_rate"],
|
||
temperature=m["temperature"],
|
||
)
|
||
|
||
# 加载推荐（取最近一个有数据的日期，按 ts_code 去重，只取 >= 60 分）
|
||
result = await db.execute(
|
||
text("SELECT * FROM recommendations "
|
||
"WHERE date(created_at) = (SELECT date(created_at) FROM recommendations ORDER BY created_at DESC LIMIT 1) "
|
||
"AND score >= 60 "
|
||
"AND id IN (SELECT MAX(id) FROM recommendations "
|
||
" WHERE date(created_at) = (SELECT date(created_at) FROM recommendations ORDER BY created_at DESC LIMIT 1) "
|
||
" GROUP BY ts_code) "
|
||
"ORDER BY score DESC")
|
||
)
|
||
rows = result.fetchall()
|
||
recommendations = []
|
||
for row in rows:
|
||
r = row._mapping
|
||
recommendations.append(Recommendation(
|
||
ts_code=r["ts_code"],
|
||
name=r["name"],
|
||
sector=r["sector"] or "",
|
||
score=r["score"] or 0,
|
||
market_temp_score=r["market_temp_score"] or 0,
|
||
sector_score=r["sector_score"] or 0,
|
||
capital_score=r["capital_score"] or 0,
|
||
technical_score=r["technical_score"] or 0,
|
||
supply_demand_score=r.get("supply_demand_score") or 0,
|
||
price_action_score=r.get("price_action_score") or 0,
|
||
position_score=r.get("position_score") or 50,
|
||
valuation_score=r.get("valuation_score") or 50,
|
||
signal=r["signal"] or "HOLD",
|
||
entry_price=r["entry_price"],
|
||
target_price=r["target_price"],
|
||
stop_loss=r["stop_loss"],
|
||
reasons=json.loads(r["reasons"]) if r["reasons"] else [],
|
||
llm_analysis=r.get("llm_analysis") or "",
|
||
strategy=r.get("strategy") or "trend_breakout",
|
||
entry_signal_type=r.get("entry_signal_type") or "none",
|
||
llm_score=r.get("llm_score"),
|
||
scan_session=r["scan_session"] or "",
|
||
))
|
||
|
||
return {
|
||
"market_temp": market_temp,
|
||
"hot_sectors": [],
|
||
"capital_filtered": [],
|
||
"recommendations": recommendations,
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"从数据库加载推荐失败: {e}")
|
||
return {"market_temp": None, "hot_sectors": [], "capital_filtered": [], "recommendations": []}
|
||
|
||
|
||
async def _load_sectors_from_db() -> list[SectorInfo]:
|
||
"""从数据库加载最近的板块热度数据"""
|
||
try:
|
||
async with get_db() as db:
|
||
from sqlalchemy import text
|
||
result = await db.execute(
|
||
text(
|
||
"SELECT * FROM sector_heat "
|
||
"WHERE trade_date = (SELECT MAX(trade_date) FROM sector_heat) "
|
||
"AND id IN ("
|
||
" SELECT MAX(id) FROM sector_heat "
|
||
" WHERE trade_date = (SELECT MAX(trade_date) FROM sector_heat) "
|
||
" GROUP BY sector_code"
|
||
") "
|
||
"ORDER BY heat_score DESC"
|
||
)
|
||
)
|
||
rows = result.fetchall()
|
||
sectors = []
|
||
for row in rows:
|
||
r = row._mapping
|
||
# Parse JSON fields with fallback
|
||
leading_stocks = json.loads(r.get("leading_stocks") or "[]")
|
||
pct_trend = json.loads(r.get("pct_trend") or "[]")
|
||
sectors.append(SectorInfo(
|
||
sector_code=r["sector_code"],
|
||
sector_name=r["sector_name"],
|
||
pct_change=r["pct_change"] or 0,
|
||
capital_inflow=r["capital_inflow"] or 0,
|
||
limit_up_count=r["limit_up_count"] or 0,
|
||
days_continuous=r.get("days_continuous") or 0,
|
||
heat_score=r["heat_score"] or 0,
|
||
stage=r.get("stage") or "mid",
|
||
member_count=r.get("member_count") or 0,
|
||
leading_stocks=leading_stocks,
|
||
pct_trend=pct_trend,
|
||
turnover_avg=r.get("turnover_avg") or 0,
|
||
main_force_ratio=r.get("main_force_ratio") or 0,
|
||
))
|
||
return sectors
|
||
except Exception as e:
|
||
logger.error(f"从数据库加载板块数据失败: {e}")
|
||
return []
|