模型，评分，修复网格策略市场状态监听

2026-06-22 10:48:40 +08:00
parent 2e3202968d
commit f938c453e1
42 changed files with 3803 additions and 108 deletions
@@ -0,0 +1,197 @@
+"""
+候选股过滤与数据加载
+"""
+import pandas as pd
+from datetime import date, timedelta
+from core.scoring.models import (
+    KlineStock, StockInfo, IndustryMapping,
+    KlineIndex, MarketRegimeDaily, SectorFeaturesDaily,
+)
+from core.scoring.config import (
+    FILTER_MIN_CLOSE, FILTER_MAX_CLOSE, REQUIRE_DAYS,
+    WINDOW_180D, HS300_CODE,
+)
+from core.logger import LogLevel, PrintLog
+
+
+class DataContext:
+    """特征计算所需的全部数据上下文"""
+
+    def __init__(self, trade_date: date):
+        self.trade_date = trade_date
+        self.require_days = REQUIRE_DAYS
+        # 原始数据
+        self.kline: pd.DataFrame = None        # 候选股 K线 (180d)
+        self.stock_info: dict = {}             # code → StockInfo dict
+        self.industry_map: dict = {}           # code → industry_name
+        self.hs300_kline: pd.DataFrame = None  # HS300 K线 (180d)
+        self.market_regime: pd.DataFrame = None  # 市场状态 (180d)
+        self.sector_features: pd.DataFrame = None  # 行业指数 (180d)
+        # 候选股列表
+        self.candidates: list[str] = []
+        self.excluded: dict[str, str] = {}     # code → reason
+
+
+def _get_stock_codes_for_date(trade_date: date) -> list:
+    """获取评分日所有符合条件的股票代码（非ST/退市）"""
+    rows = (StockInfo
+            .select(StockInfo.code, StockInfo.listing_status)
+            .where(StockInfo.listing_status.not_in(['delisted', 'ST']))
+            .dicts())
+    return [row['code'] for row in rows]
+
+
+def _check_kline_sufficiency(stock_code: str, trade_date: date) -> tuple[bool, str, float]:
+    """检查单只股票的K线数据是否满足评分条件"""
+    # 查询最近 REQUIRE_DAYS + 30 (留缓冲) 个交易日
+    start = trade_date - timedelta(days=(REQUIRE_DAYS + 60) * 2)
+    rows = (KlineStock
+            .select(KlineStock.trade_date, KlineStock.close)
+            .where(
+                (KlineStock.stock_code == stock_code) &
+                (KlineStock.trade_date >= start) &
+                (KlineStock.trade_date <= trade_date)
+            )
+            .order_by(KlineStock.trade_date.desc())
+            .dicts())
+
+    if not rows:
+        return False, '无K线数据', 0
+
+    # 过滤有效收盘价 (close > 0)
+    valid_rows = [r for r in rows if r['close'] and r['close'] > 0]
+    if len(valid_rows) < REQUIRE_DAYS:
+        return False, f'交易天数不足({len(valid_rows)}<{REQUIRE_DAYS})', 0
+
+    latest_close = valid_rows[0]['close']
+
+    # 价格区间检查
+    if latest_close < FILTER_MIN_CLOSE:
+        return False, f'价格过低({latest_close:.2f}<{FILTER_MIN_CLOSE})', latest_close
+    if latest_close > FILTER_MAX_CLOSE:
+        return False, f'价格过高({latest_close:.2f}>{FILTER_MAX_CLOSE})', latest_close
+
+    return True, '', latest_close
+
+
+def load_candidates(trade_date: date) -> DataContext:
+    """
+    加载评分日候选股及全部所需数据。
+    返回 DataContext，包含候选股列表和预加载的原始数据。
+    """
+    ctx = DataContext(trade_date)
+    start_180 = trade_date - timedelta(days=365)  # 取约1年数据覆盖180个交易日
+
+    # 1. 获取非ST/退市的全部股票
+    all_codes = [r.split('.')[0] for r in _get_stock_codes_for_date(trade_date)]
+    PrintLog(LogLevel.INFO, f'[validator] 全市场有效股票: {len(all_codes)} 只')
+
+    # 2. 批量加载 KlineStock (180d 窗口)
+    raw_codes = all_codes  # 使用纯数字代码查询
+    kline_rows = (KlineStock
+                  .select()
+                  .where(
+                      (KlineStock.stock_code.in_(raw_codes)) &
+                      (KlineStock.trade_date >= start_180) &
+                      (KlineStock.trade_date <= trade_date)
+                  )
+                  .order_by(KlineStock.stock_code, KlineStock.trade_date)
+                  .dicts())
+
+    kline_df = pd.DataFrame(kline_rows)
+    if kline_df.empty:
+        PrintLog(LogLevel.WARNING, '[validator] KlineStock 无数据')
+        return ctx
+
+    kline_df['trade_date'] = pd.to_datetime(kline_df['trade_date'])
+    PrintLog(LogLevel.INFO, f'[validator] K线原始数据: {len(kline_df)} 行')
+
+    # 3. 逐股过滤
+    candidates = []
+    excluded = {}
+    grouped = kline_df.groupby('stock_code')
+    for code, group in grouped:
+        g_sorted = group.sort_values('trade_date', ascending=False)
+        valid_rows = g_sorted[g_sorted['close'].notna() & (g_sorted['close'] > 0)]
+        if len(valid_rows) < REQUIRE_DAYS:
+            excluded[code] = f'交易天数不足({len(valid_rows)}<{REQUIRE_DAYS})'
+            continue
+        latest_close = valid_rows.iloc[0]['close']
+        if latest_close < FILTER_MIN_CLOSE:
+            excluded[code] = f'价格过低({latest_close:.2f}<{FILTER_MIN_CLOSE})'
+            continue
+        if latest_close > FILTER_MAX_CLOSE:
+            excluded[code] = f'价格过高({latest_close:.2f}>{FILTER_MAX_CLOSE})'
+            continue
+        candidates.append(code)
+
+    PrintLog(LogLevel.INFO,
+             f'[validator] 候选: {len(candidates)} 通过, {len(excluded)} 排除')
+
+    # 4. 裁剪K线到只含候选股 (保留最近180日)
+    kline_df = kline_df[kline_df['stock_code'].isin(candidates)].copy()
+    cut_date = trade_date - timedelta(days=365)
+    kline_df = kline_df[kline_df['trade_date'] >= pd.Timestamp(cut_date)]
+
+    ctx.kline = kline_df
+    ctx.candidates = candidates
+    ctx.excluded = excluded
+
+    # 5. 加载 StockInfo
+    stock_rows = (StockInfo
+                  .select()
+                  .where(StockInfo.code.in_([f'{c}.SH' for c in candidates] +
+                                            [f'{c}.SZ' for c in candidates] +
+                                            [f'{c}.BJ' for c in candidates]))
+                  .dicts())
+    ctx.stock_info = {r['code']: r for r in stock_rows}
+
+    # 6. 加载行业映射: code → industry_name
+    ind_rows = (IndustryMapping
+                .select()
+                .where(IndustryMapping.code.in_(candidates))
+                .dicts())
+    ctx.industry_map = {r['code']: r['industry_name'] for r in ind_rows}
+    PrintLog(LogLevel.INFO, f'[validator] 行业映射: {len(ctx.industry_map)} 只')
+
+    # 7. 加载 HS300 K线
+    hs300_rows = (KlineIndex
+                  .select()
+                  .where(
+                      (KlineIndex.index_code == HS300_CODE) &
+                      (KlineIndex.trade_date >= start_180) &
+                      (KlineIndex.trade_date <= trade_date)
+                  )
+                  .order_by(KlineIndex.trade_date)
+                  .dicts())
+    ctx.hs300_kline = pd.DataFrame(hs300_rows)
+    if not ctx.hs300_kline.empty:
+        ctx.hs300_kline['trade_date'] = pd.to_datetime(ctx.hs300_kline['trade_date'])
+
+    # 8. 加载市场状态 (180d)
+    mkt_rows = (MarketRegimeDaily
+                .select()
+                .where(
+                    (MarketRegimeDaily.trade_date >= start_180) &
+                    (MarketRegimeDaily.trade_date <= trade_date)
+                )
+                .order_by(MarketRegimeDaily.trade_date)
+                .dicts())
+    ctx.market_regime = pd.DataFrame(mkt_rows)
+    if not ctx.market_regime.empty:
+        ctx.market_regime['trade_date'] = pd.to_datetime(ctx.market_regime['trade_date'])
+
+    # 9. 加载行业指数 (180d)
+    sec_rows = (SectorFeaturesDaily
+                .select()
+                .where(
+                    (SectorFeaturesDaily.trade_date >= start_180) &
+                    (SectorFeaturesDaily.trade_date <= trade_date)
+                )
+                .order_by(SectorFeaturesDaily.trade_date)
+                .dicts())
+    ctx.sector_features = pd.DataFrame(sec_rows)
+    if not ctx.sector_features.empty:
+        ctx.sector_features['trade_date'] = pd.to_datetime(ctx.sector_features['trade_date'])
+
+    return ctx