925 lines
40 KiB
Python
925 lines
40 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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import traceback
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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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# 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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def _has_valid_market_breadth(market_temp: MarketTemperature | None) -> bool:
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if not market_temp:
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return False
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return (market_temp.up_count or 0) + (market_temp.down_count or 0) > 0
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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 _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 await _load_today_from_db()
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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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# 持久化到数据库（这是 async 操作，需要在主线程中执行）
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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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"review_after_days, lifecycle_status, created_at "
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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 COALESCE(lifecycle_status, 'candidate') NOT IN ('closed_win', 'closed_loss', 'invalidated', 'expired') "
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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, review_after_days, lifecycle_status, created_at = 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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track_metrics = _calculate_tracking_metrics(
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ts_code=ts_code,
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entry_price=float(entry_price),
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current_price=float(current_price),
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created_at=created_at,
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latest_trade_date=trade_date,
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)
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pct = track_metrics["pct_from_entry"]
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max_price = track_metrics["max_price"]
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min_price = track_metrics["min_price"]
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max_return_pct = track_metrics["max_return_pct"]
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max_drawdown_pct = track_metrics["max_drawdown_pct"]
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days_since = track_metrics["days_since_recommendation"]
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hit_target = bool(target_price and max_price >= target_price)
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hit_stop = bool(stop_loss and min_price <= stop_loss)
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review_days = int(review_after_days or 3)
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expired = days_since >= review_days and not hit_target and not hit_stop
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status, new_lifecycle, close_reason = _derive_lifecycle_status(
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hit_target=hit_target,
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hit_stop=hit_stop,
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expired=expired,
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pct=pct,
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previous_status=lifecycle_status or "candidate",
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)
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review_note = _build_review_note(
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pct=pct,
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max_return_pct=max_return_pct,
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max_drawdown_pct=max_drawdown_pct,
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days_since=days_since,
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close_reason=close_reason,
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)
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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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max_price=max_price,
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min_price=min_price,
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max_return_pct=max_return_pct,
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max_drawdown_pct=max_drawdown_pct,
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days_since_recommendation=days_since,
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hit_target=hit_target,
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hit_stop_loss=hit_stop,
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close_reason=close_reason,
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review_note=review_note,
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status=status,
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)
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)
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await db.execute(
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text(
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"UPDATE recommendations SET lifecycle_status = :status "
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"WHERE id = :rid"
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),
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{"status": new_lifecycle, "rid": rec_id},
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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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from app.db.error_logger import log_error
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await log_error("recommender", f"更新推荐跟踪失败: {e}", detail=traceback.format_exc())
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def _calculate_tracking_metrics(
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ts_code: str,
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entry_price: float,
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current_price: float,
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created_at,
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latest_trade_date: str,
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) -> dict:
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"""计算推荐后的收益、最大收益和最大回撤。
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使用 Tushare 日线高低价回放推荐后的表现；失败时退化为当前价。
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"""
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from app.data.tushare_client import tushare_client
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created_date = str(created_at)[:10] if created_at else ""
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created_yyyymmdd = created_date.replace("-", "") if created_date else latest_trade_date
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max_price = current_price
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min_price = current_price
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days_since = 0
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try:
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df = tushare_client.get_stock_daily(ts_code, days=60)
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if not df.empty:
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df = df[df["trade_date"] >= created_yyyymmdd].sort_values("trade_date")
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if not df.empty:
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max_price = float(df["high"].max())
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min_price = float(df["low"].min())
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days_since = len(df["trade_date"].unique()) - 1
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except Exception as e:
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logger.debug(f"计算跟踪指标失败 {ts_code}: {e}")
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pct = round((current_price - entry_price) / entry_price * 100, 2)
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max_return_pct = round((max_price - entry_price) / entry_price * 100, 2)
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max_drawdown_pct = round((min_price - entry_price) / entry_price * 100, 2)
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return {
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"pct_from_entry": pct,
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"max_price": round(max_price, 2),
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"min_price": round(min_price, 2),
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"max_return_pct": max_return_pct,
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"max_drawdown_pct": max_drawdown_pct,
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"days_since_recommendation": max(days_since, 0),
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}
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def _derive_lifecycle_status(
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hit_target: bool,
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hit_stop: bool,
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expired: bool,
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pct: float,
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previous_status: str,
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) -> tuple[str, str, str]:
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if hit_target:
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return "closed", "closed_win", "hit_target"
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if hit_stop:
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return "closed", "closed_loss", "hit_stop_loss"
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if expired:
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if pct > 0:
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return "closed", "closed_win", "review_expired_profit"
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if pct < -2:
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return "closed", "closed_loss", "review_expired_loss"
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return "closed", "expired", "review_expired_flat"
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if previous_status == "actionable":
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return "active", "tracking", ""
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return "active", "tracking", ""
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def _build_review_note(
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pct: float,
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max_return_pct: float,
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max_drawdown_pct: float,
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days_since: int,
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close_reason: str,
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) -> str:
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if close_reason == "hit_target":
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return f"{days_since}个交易日内命中目标，最大浮盈{max_return_pct}%"
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if close_reason == "hit_stop_loss":
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return f"{days_since}个交易日内触发止损，最大回撤{max_drawdown_pct}%"
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if close_reason == "review_expired_profit":
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return f"复盘窗口到期，当前收益{pct}%，最大浮盈{max_return_pct}%"
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if close_reason == "review_expired_loss":
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return f"复盘窗口到期，当前亏损{pct}%，最大回撤{max_drawdown_pct}%"
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if close_reason == "review_expired_flat":
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return f"复盘窗口到期，收益{pct}%，未形成有效进攻"
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return f"跟踪中，当前收益{pct}%，最大浮盈{max_return_pct}%，最大回撤{max_drawdown_pct}%"
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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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# 生命周期分布
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result = await db.execute(
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text(
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"SELECT COALESCE(lifecycle_status, 'candidate') AS status, COUNT(*) AS cnt "
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"FROM recommendations GROUP BY COALESCE(lifecycle_status, 'candidate')"
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)
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)
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lifecycle_counts = {
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row._mapping["status"]: row._mapping["cnt"]
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for row in result.fetchall()
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}
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# 最大浮盈/最大回撤统计
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result = await db.execute(
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text(
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"SELECT AVG(max_return_pct), AVG(max_drawdown_pct) FROM ("
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" SELECT t.recommendation_id, t.max_return_pct, t.max_drawdown_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_extremes = result.fetchone()
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avg_max_return = round(float(avg_extremes[0]), 2) if avg_extremes and avg_extremes[0] is not None else 0
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avg_max_drawdown = round(float(avg_extremes[1]), 2) if avg_extremes and avg_extremes[1] is not None else 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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" r.action_plan, r.lifecycle_status, r.recall_tags, r.prefilter_decision, "
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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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" t.max_return_pct, t.max_drawdown_pct, t.days_since_recommendation, "
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" t.close_reason, t.review_note, 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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"action_plan": r["action_plan"],
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"lifecycle_status": r["lifecycle_status"],
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"recall_tags": json.loads(r["recall_tags"]) if r["recall_tags"] else [],
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"prefilter_decision": r["prefilter_decision"] or "",
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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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"max_return_pct": r["max_return_pct"],
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"max_drawdown_pct": r["max_drawdown_pct"],
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"days_since_recommendation": r["days_since_recommendation"],
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"close_reason": r["close_reason"],
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"review_note": r["review_note"],
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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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"avg_max_return": avg_max_return,
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"avg_max_drawdown": avg_max_drawdown,
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"hit_target_count": hit_target_count,
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"hit_stop_count": hit_stop_count,
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"lifecycle_counts": lifecycle_counts,
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"route_breakdown": _build_route_breakdown(details),
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"prefilter_breakdown": _build_prefilter_breakdown(details),
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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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from app.db.error_logger import log_error
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await log_error("recommender", f"获取胜率统计失败: {e}", detail=traceback.format_exc())
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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, "avg_max_return": 0,
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"avg_max_drawdown": 0, "hit_target_count": 0,
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"hit_stop_count": 0, "lifecycle_counts": {}, "route_breakdown": [], "prefilter_breakdown": [], "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（直接从数据库读取，不做内存缓存）"""
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return await _load_today_from_db()
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|
||
async def get_latest_sectors() -> list[SectorInfo]:
|
||
"""获取最新的板块热度数据（从数据库读取，不触发扫描）"""
|
||
return await _load_sectors_from_db()
|
||
|
||
|
||
async def get_recommendation_history(days: int = 7) -> list[dict]:
|
||
"""获取历史推荐记录，按日期分组返回"""
|
||
import json
|
||
|
||
start = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
|
||
async with get_db() as db:
|
||
from sqlalchemy import text
|
||
|
||
# 查询所有历史推荐，按 ts_code 去重（每天取最新一条）
|
||
stmt = text(
|
||
"SELECT r.*, "
|
||
"latest_t.current_price AS latest_current_price, "
|
||
"latest_t.pct_from_entry AS latest_pct_from_entry, "
|
||
"latest_t.max_return_pct AS latest_max_return_pct, "
|
||
"latest_t.max_drawdown_pct AS latest_max_drawdown_pct, "
|
||
"latest_t.days_since_recommendation AS latest_days_since_recommendation, "
|
||
"latest_t.close_reason AS latest_close_reason, "
|
||
"latest_t.review_note AS latest_review_note, "
|
||
"latest_t.track_date AS latest_track_date "
|
||
"FROM recommendations r "
|
||
"LEFT JOIN ("
|
||
" SELECT t.* FROM recommendation_tracking t "
|
||
" INNER JOIN ("
|
||
" SELECT recommendation_id, MAX(id) AS max_id "
|
||
" FROM recommendation_tracking GROUP BY recommendation_id"
|
||
" ) lt ON t.id = lt.max_id"
|
||
") latest_t ON latest_t.recommendation_id = r.id "
|
||
"WHERE r.created_at >= :start "
|
||
"AND (r.action_plan IN ('可操作', '重点关注') OR COALESCE(r.llm_score, 0) >= 6 OR r.score >= 56) "
|
||
"AND r.id IN ("
|
||
" SELECT MAX(id) FROM recommendations "
|
||
" WHERE created_at >= :start "
|
||
" GROUP BY date(created_at), ts_code"
|
||
") "
|
||
"ORDER BY r.created_at DESC, r.score DESC"
|
||
)
|
||
result = await db.execute(stmt, {"start": start})
|
||
rows = result.fetchall()
|
||
|
||
# 按日期分组
|
||
grouped: dict[str, list[dict]] = {}
|
||
for row in rows:
|
||
r = row._mapping
|
||
# SQLite created_at 是字符串 "YYYY-MM-DD HH:MM:SS"
|
||
ca = r["created_at"]
|
||
if ca:
|
||
date_str = str(ca)[:10] # 取前10字符即日期部分
|
||
created_at_str = str(ca)
|
||
else:
|
||
date_str = "unknown"
|
||
created_at_str = None
|
||
|
||
rec_dict = {
|
||
"ts_code": r["ts_code"],
|
||
"name": r["name"],
|
||
"sector": r["sector"] or "",
|
||
"score": r["score"] or 0,
|
||
"level": _score_to_level_static(r["score"] or 0),
|
||
"signal": r["signal"] or "HOLD",
|
||
"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,
|
||
"entry_price": r["entry_price"],
|
||
"target_price": r["target_price"],
|
||
"stop_loss": r["stop_loss"],
|
||
"reasons": json.loads(r["reasons"]) if r["reasons"] else [],
|
||
"risk_note": r.get("risk_note") or "",
|
||
"entry_timing": r.get("entry_timing") or "",
|
||
"action_plan": r.get("action_plan") or "观察",
|
||
"trigger_condition": r.get("trigger_condition") or "",
|
||
"invalidation_condition": r.get("invalidation_condition") or "",
|
||
"suggested_position_pct": r.get("suggested_position_pct") or 0,
|
||
"review_after_days": r.get("review_after_days") or 3,
|
||
"lifecycle_status": r.get("lifecycle_status") or "candidate",
|
||
"data_freshness": r.get("data_freshness") or "",
|
||
"strategy": r.get("strategy") or "trend_breakout",
|
||
"entry_signal_type": r.get("entry_signal_type") or "none",
|
||
"llm_analysis": r.get("llm_analysis") or "",
|
||
"llm_score": r.get("llm_score"),
|
||
"recall_tags": json.loads(r.get("recall_tags") or "[]"),
|
||
"prefilter_decision": r.get("prefilter_decision") or "",
|
||
"prefilter_reason": r.get("prefilter_reason") or "",
|
||
"focus_points": json.loads(r.get("focus_points") or "[]"),
|
||
"tracking": {
|
||
"current_price": r.get("latest_current_price"),
|
||
"pct_from_entry": r.get("latest_pct_from_entry"),
|
||
"max_return_pct": r.get("latest_max_return_pct"),
|
||
"max_drawdown_pct": r.get("latest_max_drawdown_pct"),
|
||
"days_since_recommendation": r.get("latest_days_since_recommendation"),
|
||
"close_reason": r.get("latest_close_reason") or "",
|
||
"review_note": r.get("latest_review_note") or "",
|
||
"track_date": r.get("latest_track_date") or "",
|
||
} if r.get("latest_track_date") else None,
|
||
"scan_session": r["scan_session"] or "",
|
||
"created_at": created_at_str,
|
||
}
|
||
|
||
if date_str not in grouped:
|
||
grouped[date_str] = []
|
||
grouped[date_str].append(rec_dict)
|
||
|
||
# 转为列表，按日期降序
|
||
result_list = []
|
||
for date_str in sorted(grouped.keys(), reverse=True):
|
||
recs = grouped[date_str]
|
||
buy_count = sum(1 for r in recs if r["signal"] == "BUY")
|
||
avg_score = round(sum(r["score"] for r in recs) / len(recs), 1) if recs else 0
|
||
result_list.append({
|
||
"date": date_str,
|
||
"count": len(recs),
|
||
"buy_count": buy_count,
|
||
"avg_score": avg_score,
|
||
"recommendations": recs,
|
||
})
|
||
|
||
return result_list
|
||
|
||
|
||
def _score_to_level_static(score: float) -> str:
|
||
"""根据评分确定推荐等级"""
|
||
if score >= 75:
|
||
return "强烈推荐"
|
||
elif score >= 60:
|
||
return "推荐"
|
||
elif score >= 45:
|
||
return "关注"
|
||
else:
|
||
return "观望"
|
||
|
||
|
||
def _build_route_breakdown(details: list[dict]) -> list[dict]:
|
||
stats: dict[str, dict] = {}
|
||
for item in details:
|
||
for tag in item.get("recall_tags", []) or []:
|
||
bucket = stats.setdefault(tag, {"route": tag, "count": 0, "wins": 0, "avg_return_sum": 0.0})
|
||
bucket["count"] += 1
|
||
pct = float(item.get("pct_from_entry") or 0)
|
||
bucket["avg_return_sum"] += pct
|
||
if pct > 0:
|
||
bucket["wins"] += 1
|
||
result = []
|
||
for bucket in stats.values():
|
||
count = bucket["count"] or 1
|
||
result.append({
|
||
"route": bucket["route"],
|
||
"count": bucket["count"],
|
||
"win_rate": round(bucket["wins"] / count * 100, 1),
|
||
"avg_return": round(bucket["avg_return_sum"] / count, 2),
|
||
})
|
||
return sorted(result, key=lambda item: item["count"], reverse=True)
|
||
|
||
|
||
def _build_prefilter_breakdown(details: list[dict]) -> list[dict]:
|
||
stats: dict[str, dict] = {}
|
||
for item in details:
|
||
key = item.get("prefilter_decision") or "unknown"
|
||
bucket = stats.setdefault(key, {"decision": key, "count": 0, "wins": 0, "avg_return_sum": 0.0})
|
||
bucket["count"] += 1
|
||
pct = float(item.get("pct_from_entry") or 0)
|
||
bucket["avg_return_sum"] += pct
|
||
if pct > 0:
|
||
bucket["wins"] += 1
|
||
result = []
|
||
for bucket in stats.values():
|
||
count = bucket["count"] or 1
|
||
result.append({
|
||
"decision": bucket["decision"],
|
||
"count": bucket["count"],
|
||
"win_rate": round(bucket["wins"] / count * 100, 1),
|
||
"avg_return": round(bucket["avg_return_sum"] / count, 2),
|
||
})
|
||
return sorted(result, key=lambda item: item["count"], reverse=True)
|
||
|
||
|
||
async def _save_to_db(result: dict):
|
||
"""将推荐结果保存到数据库"""
|
||
try:
|
||
async with get_db() as db:
|
||
from sqlalchemy import bindparam, text
|
||
# 保存市场温度
|
||
mt = result.get("market_temp")
|
||
if mt:
|
||
if _has_valid_market_breadth(mt):
|
||
# 使用 INSERT OR REPLACE 确保重复扫描能更新数据
|
||
stmt = text(
|
||
"INSERT OR REPLACE INTO market_temperature "
|
||
"(trade_date, up_count, down_count, limit_up_count, limit_down_count, "
|
||
"max_streak, broken_rate, temperature) "
|
||
"VALUES (:td, :up, :down, :lu, :ld, :ms, :br, :temp)"
|
||
)
|
||
await db.execute(stmt, {
|
||
"td": mt.trade_date,
|
||
"up": mt.up_count,
|
||
"down": mt.down_count,
|
||
"lu": mt.limit_up_count,
|
||
"ld": mt.limit_down_count,
|
||
"ms": mt.max_streak,
|
||
"br": mt.broken_rate,
|
||
"temp": mt.temperature,
|
||
})
|
||
else:
|
||
logger.warning(
|
||
"跳过无效市场温度快照: trade_date=%s temperature=%s up=%s down=%s",
|
||
mt.trade_date,
|
||
mt.temperature,
|
||
mt.up_count,
|
||
mt.down_count,
|
||
)
|
||
|
||
# 保存板块热度（先清除同一 trade_date 的旧数据，再批量插入）
|
||
trade_date_val = mt.trade_date if mt else ""
|
||
if trade_date_val:
|
||
await db.execute(
|
||
text("DELETE FROM sector_heat WHERE trade_date = :td"),
|
||
{"td": trade_date_val},
|
||
)
|
||
sector_values = [
|
||
{
|
||
"sector_code": sector.sector_code,
|
||
"sector_name": sector.sector_name,
|
||
"pct_change": sector.pct_change,
|
||
"capital_inflow": sector.capital_inflow,
|
||
"limit_up_count": sector.limit_up_count,
|
||
"heat_score": sector.heat_score,
|
||
"stage": sector.stage,
|
||
"days_continuous": sector.days_continuous,
|
||
"member_count": sector.member_count,
|
||
"leading_stocks": json.dumps(sector.leading_stocks, ensure_ascii=False),
|
||
"pct_trend": json.dumps(sector.pct_trend, ensure_ascii=False),
|
||
"turnover_avg": sector.turnover_avg,
|
||
"main_force_ratio": sector.main_force_ratio,
|
||
"trade_date": trade_date_val,
|
||
}
|
||
for sector in result.get("hot_sectors", [])
|
||
]
|
||
if sector_values:
|
||
await db.execute(tables.sector_heat_table.insert(), sector_values)
|
||
|
||
# 保存推荐：先批量清除当日旧记录，再批量插入
|
||
today_str = datetime.now().strftime("%Y-%m-%d")
|
||
now_dt = datetime.now()
|
||
qualified_recs = [
|
||
rec for rec in result.get("recommendations", [])
|
||
if (
|
||
rec.action_plan in {"可操作", "重点关注"}
|
||
or (rec.llm_score is not None and rec.llm_score >= 6)
|
||
or rec.score >= 56
|
||
)
|
||
]
|
||
if qualified_recs:
|
||
# 批量删除当日同一 ts_code 的旧记录
|
||
codes = [rec.ts_code for rec in qualified_recs]
|
||
delete_stmt = text(
|
||
"DELETE FROM recommendations "
|
||
"WHERE date(created_at) = :today AND ts_code IN :codes"
|
||
).bindparams(bindparam("codes", expanding=True))
|
||
await db.execute(
|
||
delete_stmt,
|
||
{"today": today_str, "codes": codes},
|
||
)
|
||
# 批量插入新记录
|
||
rec_values = [
|
||
{
|
||
"ts_code": rec.ts_code,
|
||
"name": rec.name,
|
||
"sector": rec.sector,
|
||
"score": rec.score,
|
||
"market_temp_score": rec.market_temp_score,
|
||
"sector_score": rec.sector_score,
|
||
"capital_score": rec.capital_score,
|
||
"technical_score": rec.technical_score,
|
||
"supply_demand_score": rec.supply_demand_score,
|
||
"price_action_score": rec.price_action_score,
|
||
"position_score": rec.position_score,
|
||
"valuation_score": rec.valuation_score,
|
||
"signal": rec.signal,
|
||
"entry_price": rec.entry_price,
|
||
"target_price": rec.target_price,
|
||
"stop_loss": rec.stop_loss,
|
||
"reasons": json.dumps(rec.reasons, ensure_ascii=False),
|
||
"risk_note": rec.risk_note,
|
||
"action_plan": rec.action_plan,
|
||
"trigger_condition": rec.trigger_condition,
|
||
"invalidation_condition": rec.invalidation_condition,
|
||
"suggested_position_pct": rec.suggested_position_pct,
|
||
"review_after_days": rec.review_after_days,
|
||
"lifecycle_status": rec.lifecycle_status,
|
||
"data_freshness": rec.data_freshness,
|
||
"llm_analysis": rec.llm_analysis,
|
||
"strategy": rec.strategy,
|
||
"entry_signal_type": rec.entry_signal_type,
|
||
"entry_timing": rec.entry_timing,
|
||
"llm_score": rec.llm_score,
|
||
"recall_tags": json.dumps(rec.recall_tags, ensure_ascii=False),
|
||
"prefilter_decision": rec.prefilter_decision,
|
||
"prefilter_reason": rec.prefilter_reason,
|
||
"focus_points": json.dumps(rec.focus_points, ensure_ascii=False),
|
||
"scan_session": rec.scan_session,
|
||
"created_at": now_dt,
|
||
}
|
||
for rec in qualified_recs
|
||
]
|
||
await db.execute(tables.recommendations_table.insert(), rec_values)
|
||
|
||
await db.commit()
|
||
logger.info(
|
||
f"已保存 {len(qualified_recs)} 条推荐到数据库"
|
||
f"（共 {len(result.get('recommendations', []))} 条，过滤掉低优先级候选）"
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"保存推荐到数据库失败: {e}")
|
||
from app.db.error_logger import log_error
|
||
await log_error("recommender", f"保存推荐到数据库失败: {e}", detail=traceback.format_exc())
|
||
|
||
|
||
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 CASE WHEN COALESCE(up_count, 0) + COALESCE(down_count, 0) > 0 THEN 0 ELSE 1 END, "
|
||
"REPLACE(trade_date, '-', '') DESC, id 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，优先保留行动级别更高的结果）
|
||
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 (action_plan IN ('可操作', '重点关注') OR COALESCE(llm_score, 0) >= 6 OR score >= 56) "
|
||
"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 [],
|
||
risk_note=r.get("risk_note") or "",
|
||
entry_timing=r.get("entry_timing") or "",
|
||
action_plan=r.get("action_plan") or "观察",
|
||
trigger_condition=r.get("trigger_condition") or "",
|
||
invalidation_condition=r.get("invalidation_condition") or "",
|
||
suggested_position_pct=r.get("suggested_position_pct") or 0,
|
||
review_after_days=r.get("review_after_days") or 3,
|
||
lifecycle_status=r.get("lifecycle_status") or "candidate",
|
||
data_freshness=r.get("data_freshness") or "",
|
||
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"),
|
||
recall_tags=json.loads(r.get("recall_tags") or "[]"),
|
||
prefilter_decision=r.get("prefilter_decision") or "",
|
||
prefilter_reason=r.get("prefilter_reason") or "",
|
||
focus_points=json.loads(r.get("focus_points") or "[]"),
|
||
scan_session=r["scan_session"] or "",
|
||
))
|
||
|
||
return {
|
||
"market_temp": market_temp,
|
||
"hot_sectors": [],
|
||
"capital_filtered": [],
|
||
"recommendations": recommendations,
|
||
"strategy_profile": {
|
||
"strategy_id": recommendations[0].strategy if recommendations else "trend_breakout",
|
||
"name": "当前推荐策略",
|
||
} if recommendations else None,
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"从数据库加载推荐失败: {e}")
|
||
from app.db.error_logger import log_error
|
||
await log_error("recommender", f"从数据库加载推荐失败: {e}", detail=traceback.format_exc())
|
||
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 REPLACE(trade_date, '-', '') = ("
|
||
" SELECT MAX(REPLACE(trade_date, '-', '')) FROM sector_heat"
|
||
") "
|
||
"AND id IN ("
|
||
" SELECT MAX(id) FROM sector_heat "
|
||
" WHERE REPLACE(trade_date, '-', '') = ("
|
||
" SELECT MAX(REPLACE(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"],
|
||
trade_date=r.get("trade_date") or "",
|
||
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}")
|
||
from app.db.error_logger import log_error
|
||
await log_error("recommender", f"从数据库加载板块数据失败: {e}", detail=traceback.format_exc())
|
||
return []
|