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