模型，评分，修复网格策略市场状态监听

core/scoring/models.py

@@ -0,0 +1,132 @@
+"""
+grid_seeker v6.4 数据库模型 — 6 张数据表 + 1 张评分结果表
+所有模型继承 core.database.BaseModel，复用现有 SQLite 连接。
+"""
+from peewee import (
+    CharField, DateField, FloatField, IntegerField,
+    CompositeKey, TextField,
+)
+from core.database import BaseModel, db
+
+
+# ============================================================
+# 1. kline_stock — 个股日K线
+# ============================================================
+class KlineStock(BaseModel):
+    stock_code = CharField(max_length=10)   # 纯数字6位
+    trade_date = DateField()
+    open = FloatField()
+    high = FloatField()
+    low = FloatField()
+    close = FloatField()
+    volume = FloatField()                   # 成交量（股）
+
+    class Meta:
+        primary_key = CompositeKey('stock_code', 'trade_date')
+        indexes = (
+            (('stock_code', 'trade_date'), False),
+        )
+
+
+# ============================================================
+# 2. stocks — 股票基础信息
+# ============================================================
+class StockInfo(BaseModel):
+    code = CharField(max_length=12, primary_key=True)   # 带 SH/SZ/BJ 前缀
+    name = CharField(max_length=32)
+    exchange = CharField(max_length=4)                   # SH / SZ / BJ
+    list_date = DateField(null=True)
+    listing_status = CharField(max_length=16, default='normal')  # normal / delisted / ST
+    total_share = FloatField(null=True)                  # 总股本（股）
+    float_share = FloatField(null=True)                  # 流通股本（股）
+    share_updated_at = DateField(null=True)              # 股本数据同步时间
+
+
+# ============================================================
+# 3. industry — 股票-行业映射
+# ============================================================
+class IndustryMapping(BaseModel):
+    code = CharField(max_length=6, primary_key=True)     # 纯数字6位
+    industry_name = CharField(max_length=64, index=True)
+    industry_classification = CharField(max_length=32)   # 行业分类体系名称
+    update_date = DateField()
+
+
+# ============================================================
+# 4. kline_index — 指数日K线
+# ============================================================
+class KlineIndex(BaseModel):
+    index_code = CharField(max_length=10)   # 指数代码（6位数字）
+    trade_date = DateField()
+    open = FloatField()
+    high = FloatField()
+    low = FloatField()
+    close = FloatField()
+    volume = FloatField()
+
+    class Meta:
+        primary_key = CompositeKey('index_code', 'trade_date')
+        indexes = (
+            (('index_code', 'trade_date'), False),
+        )
+
+
+# ============================================================
+# 5. market_regime_daily — 市场状态（本地计算）
+# ============================================================
+class MarketRegimeDaily(BaseModel):
+    trade_date = DateField(primary_key=True)
+    advancers = FloatField(default=0)         # 当日上涨家数
+    decliners = FloatField(default=0)         # 当日下跌家数
+    advance_ratio = FloatField(default=0)     # 涨跌比 = advancers/(advancers+decliners)
+    turnover = FloatField(default=0)          # 全市场成交额
+    turnover_avg_5d = FloatField(default=0)   # 5日滚动均量
+    turnover_ratio_5d = FloatField(default=0) # 量比 = turnover/turnover_avg_5d
+    source = CharField(max_length=32, default='qmt')
+    is_extreme_panic = IntegerField(default=0)  # advance_ratio<0.2 且 turnover_ratio_5d>1.5
+
+
+# ============================================================
+# 6. sector_features_daily — 行业聚合指数（本地计算）
+# ============================================================
+class SectorFeaturesDaily(BaseModel):
+    trade_date = DateField()
+    sector_name = CharField(max_length=64)    # 与 industry.industry_name 对应
+    sector_ret = FloatField(default=0)        # 行业日收益率（均值）
+    sector_amplitude = FloatField(default=0)  # 行业平均振幅
+    close = FloatField(default=100)           # 行业指数（基值100）
+    ema10 = FloatField(default=0)
+    ema20 = FloatField(default=0)
+    ema200 = FloatField(default=0)
+    score = IntegerField(default=0)           # 趋势评分 0/1/2
+
+    class Meta:
+        primary_key = CompositeKey('trade_date', 'sector_name')
+
+
+# ============================================================
+# 7. ScoringResult — 评分结果（模型输出写入表）
+# ============================================================
+class ScoringResult(BaseModel):
+    stock_code = CharField(max_length=6)       # 纯数字6位
+    trade_date = DateField()                   # 评分日
+    predicted_profit = FloatField(default=0)   # 最终预测利润（=stacking_probability）
+    rank_predicted_rounds = FloatField(null=True)  # Rank 模型输出
+    top_elite_prob = FloatField(null=True)      # Top 模型输出
+    stacking_probability = FloatField(null=True)   # Stacking 模型输出
+    score_rank = IntegerField(default=0)       # 排名
+    candidate_count = IntegerField(default=0)  # 候选股总数
+
+    class Meta:
+        primary_key = CompositeKey('stock_code', 'trade_date')
+
+
+# ============================================================
+# 建表
+# ============================================================
+ALL_SCORING_TABLES = [
+    KlineStock, StockInfo, IndustryMapping, KlineIndex,
+    MarketRegimeDaily, SectorFeaturesDaily, ScoringResult,
+]
+
+db.create_tables(ALL_SCORING_TABLES)