"""
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')


# ============================================================
# 8. PendingPoolAction — 待执行股票池操作（阶段一标记，阶段二执行）
# ============================================================
class PendingPoolAction(BaseModel):
    """pending_pool_actions 表 — T日标记的操作，待 T+1 执行"""
    action_date = DateField()              # T日日期（标记日期）
    action_type = CharField(max_length=20)  # 'eliminate' | 'liquidate'
    stock_code = CharField(max_length=6)   # 股票代码
    reason = TextField(null=True)          # 淘汰原因描述

    class Meta:
        primary_key = CompositeKey('action_date', 'action_type', 'stock_code')


# ============================================================
# 建表
# ============================================================
ALL_SCORING_TABLES = [
    KlineStock, StockInfo, IndustryMapping, KlineIndex,
    MarketRegimeDaily, SectorFeaturesDaily, ScoringResult, PendingPoolAction,
]

db.create_tables(ALL_SCORING_TABLES)