"""Pydantic DTO —— 这就是 OpenAPI 契约（前端据此生成类型化客户端）。

契约先于业务锁定：字段一旦定下，前端可并行开发，后端实现改动不影响前端。
"""
from __future__ import annotations

from datetime import datetime
from typing import Literal

from pydantic import BaseModel, Field


# ---------- Candle ----------
class CandleOut(BaseModel):
    ts: datetime
    open: float
    high: float
    low: float
    close: float
    volume: float
    amount: float | None = None    # 成交额（元）；无数据为 null
    turnover: float | None = None  # 换手率 %；无数据为 null

    model_config = {"from_attributes": True}


# ---------- Backtest ----------
class BacktestRequest(BaseModel):
    symbol: str = "000001"
    timeframe: str = "1d"
    strategy: str = "macd_cross"        # macd_cross | ma_cross | single_ma
    params: dict[str, float] = Field(default_factory=dict)  # 各策略参数
    initial_cash: float = 1000000.0
    fast_mode: bool = False  # True => 关闭 T+1/费用，交互试探
    start: datetime | None = None
    end: datetime | None = None


class SignalOut(BaseModel):
    ts: datetime
    side: str   # "buy" | "sell"
    price: float
    qty: float


class EquityPoint(BaseModel):
    ts: datetime
    value: float


class IndicatorOut(BaseModel):
    strategy: str
    data: dict[str, list[float | None]] = {}  # 列名 -> 序列（MACD: macd/signal/hist；均线: fast/slow 或 ma）


class MetricsOut(BaseModel):
    total_return: float
    max_drawdown: float
    sharpe: float
    volatility: float
    num_trades: int = 0
    win_rate: float = 0.0


class BacktestResponse(BaseModel):
    symbol: str
    timeframe: str
    strategy: str
    candles: list[CandleOut]
    indicators: IndicatorOut
    signals: list[SignalOut]
    equity: list[EquityPoint]
    metrics: MetricsOut
    final_cash: float
    final_position: float
    initial_cash: float


class SyncRequest(BaseModel):
    symbol: str
    start: str | None = None       # YYYYMMDD
    end: str | None = None
    source: str = "auto"           # auto | tushare | akshare
    force: bool = False            # True => 忽略缓存重新拉取


# ---------- Event Backtest（自然语言事件回测） ----------
class EventBacktestSpec(BaseModel):
    """事件回测参数：entry 条件在信号日 D 收盘确认 -> D+1 买入 -> 持有 N 日卖出。"""
    entry: ScreenConditions
    entry_timing: Literal["next_open", "next_close"] = "next_open"  # 次日开盘/收盘买入
    holding_days: int = Field(default=3, ge=1, le=250)               # 买入后再持有 N 个交易日
    exit_timing: Literal["close", "open"] = "close"                  # 到期按收盘/开盘卖出


class EventBacktestRequest(BaseModel):
    text: str = Field(min_length=2, max_length=500)
    spec: EventBacktestSpec | None = None   # 直传则跳过 LLM 解析（调参重跑）
    ts_code: str | None = None              # 指定则只回测该股；空则全市场
    start: datetime | None = None
    end: datetime | None = None


class EventTradeOut(BaseModel):
    ts_code: str
    name: str | None = None
    entry_date: datetime
    entry_price: float
    exit_date: datetime
    exit_price: float
    ret_pct: float                          # 区间收益率 %（复权校正）


class EventYearStatOut(BaseModel):
    year: int
    samples: int
    mean_pct: float
    median_pct: float
    win_rate: float


class EventStatsOut(BaseModel):
    samples: int
    stocks: int
    mean_pct: float
    median_pct: float
    win_rate: float                         # %
    std_pct: float = 0.0
    p10_pct: float = 0.0
    p25_pct: float = 0.0
    p75_pct: float = 0.0
    p90_pct: float = 0.0
    max_pct: float = 0.0
    min_pct: float = 0.0
    by_year: list[EventYearStatOut] = Field(default_factory=list)


class EventBacktestResponse(BaseModel):
    text: str
    spec: EventBacktestSpec
    universe: str                            # "all" 或 ts_code
    start: datetime
    end: datetime
    stats: EventStatsOut
    trades: list[EventTradeOut] = Field(default_factory=list)  # 最好+最差样本（各 100）
    total: int


class SyncResponse(BaseModel):
    symbol: str
    bars: int
    source: str


# ---------- Screener（智能选股） ----------
Op = Literal["gt", "ge", "lt", "le", "between"]


class IndicatorCondition(BaseModel):
    """技术指标条件（在最近 lookback 个交易日窗口内判定）。

    indicator 白名单见 screener/llm.py 的 SYSTEM_PROMPT（kdj_j / rsi / macd_dif…）。
    设置 value_indicator 时为指标间比较（如 DIF > DEA、close < boll_lower），value 填 0 占位。
    """
    indicator: str
    params: dict[str, float] = Field(default_factory=dict)  # 如 {"n": 9, "m1": 3, "m2": 3}
    op: Op
    value: float
    value2: float | None = None       # between 上界
    value_indicator: str | None = None  # 比较对象为另一指标（同白名单）时使用
    value_params: dict[str, float] = Field(default_factory=dict)  # 比较对象指标参数（默认沿用 params/默认值）
    lookback: int = 1                 # 检查最近 N 个交易日
    match: Literal["all", "any"] = "all"  # all=连续满足；any=任一满足


class SnapshotCondition(BaseModel):
    """每日快照条件（最新交易日截面）。市值单位亿元；换手率为百分数（5 表示 5%）。"""
    field: str                        # total_mv|circ_mv|pe_ttm|pb|turnover_rate|close
    op: Op
    value: float
    value2: float | None = None


class ScreenConditions(BaseModel):
    indicator: list[IndicatorCondition] = Field(default_factory=list)
    snapshot: list[SnapshotCondition] = Field(default_factory=list)
    exclude_st: bool = True
    exclude_delisted: bool = True
    exclude_bj: bool = True           # 排除北交所


class ScreenerRunRequest(BaseModel):
    text: str = Field(min_length=2, max_length=500)
    # 直传条件则跳过 LLM 解析（预留给"微调再跑"）
    conditions: ScreenConditions | None = None


class ScreenerItemOut(BaseModel):
    ts_code: str
    name: str
    close: float | None = None        # 最新收盘价（元）
    pct_chg: float | None = None      # 日涨跌幅 %
    total_mv: float | None = None     # 总市值（亿元）
    circ_mv: float | None = None      # 流通市值（亿元）
    pe_ttm: float | None = None
    pb: float | None = None
    turnover_rate: float | None = None
    indicators: dict[str, float | None] = Field(default_factory=dict)  # 引用到的指标最新值


class ScreenerRunResponse(BaseModel):
    conditions: ScreenConditions
    trade_date: datetime | None       # 数据基准交易日
    total: int                        # 命中总数（items 可能被截断）
    items: list[ScreenerItemOut]
    indicator_labels: dict[str, str] = Field(default_factory=dict)  # "kdj_j" -> "KDJ J(9,3,3)"


class ScreenerSyncRequest(BaseModel):
    days: int = Field(default=90, ge=10, le=250)  # 同步最近 N 个交易日
    force: bool = False                # True => 全量重拉（幂等）


class ScreenerSyncStatus(BaseModel):
    running: bool
    step: str | None = None            # 进行中步骤文案
    total_days: int = 0
    done_days: int = 0
    error: str | None = None
    ready: bool = False                # 至少 1 个交易日数据可用于选股
    last_trade_date: datetime | None = None
    last_synced_at: datetime | None = None
    stats: dict[str, int] = Field(default_factory=dict)  # stocks/daily_rows/snapshot_rows/dates


# ---------- 个股详情预览（选股结果点入，全屏同花顺/通达信式） ----------
class PreviewInfoOut(BaseModel):
    ts_code: str
    symbol: str
    name: str
    industry: str | None = None
    area: str | None = None
    market: str | None = None          # 主板/创业板/科创板/北交所
    list_date: str | None = None
    trade_date: datetime | None = None  # 行情/信息卡数据基准交易日
    open: float | None = None
    high: float | None = None
    low: float | None = None
    close: float | None = None
    pre_close: float | None = None
    pct_chg: float | None = None       # 日涨跌幅 %
    volume_hand: float | None = None   # 成交量（手）
    amount_yi: float | None = None     # 成交额（亿元）
    turnover_rate: float | None = None # 换手率 %
    pe_ttm: float | None = None
    pb: float | None = None
    total_mv: float | None = None      # 总市值（亿元）
    circ_mv: float | None = None       # 流通市值（亿元）


class PreviewResponse(BaseModel):
    ts_code: str
    symbol: str
    source: str                        # bfq|qfq|hfq=实际复权口径（本地 adj_factor 换算） | market=近段未复权兜底
    info: PreviewInfoOut
    candles: list[CandleOut]
    indicators: dict[str, dict[str, list[float | None]]] = Field(default_factory=dict)
    # indicators 形如 {"ma": {"ma5": [...], ...}, "macd": {"dif": ...}, "kdj": {...}, "rsi": {...}, "boll": {...}}
    has_more: bool = False             # 返回窗口之前是否还有更早历史（前端向左滚动翻页用）


# ---------- Auth ----------
class LoginRequest(BaseModel):
    username: str = Field(min_length=1, max_length=64)
    password: str = Field(min_length=1, max_length=1024)


class CurrentUserOut(BaseModel):
    id: int
    username: str

    model_config = {"from_attributes": True}


class LoginResponse(BaseModel):
    user: CurrentUserOut
    expires_at: datetime


# ---------- 股票列表（全市场浏览） ----------
class StockListItemOut(BaseModel):
    ts_code: str
    symbol: str
    name: str
    industry: str | None = None
    market: str | None = None
    close: float | None = None       # 最新收盘（candles 未复权）
    prev_close: float | None = None
    pct_chg: float | None = None     # 最新两根日线计算
    last_ts: datetime | None = None
    bar_count: int | None = None     # 本地缓存日线条数
    watched: bool = False            # 是否自选（当前用户）


class StockListResponse(BaseModel):
    total: int
    items: list[StockListItemOut]


# ---------- 看股页筛选项 ----------
class FacetItemOut(BaseModel):
    name: str
    count: int


class StockFacetsResponse(BaseModel):
    industries: list[FacetItemOut] = Field(default_factory=list)
    areas: list[FacetItemOut] = Field(default_factory=list)


# ---------- 用户偏好 / 自选股 / 提问历史 ----------
class PreferencesOut(BaseModel):
    prefs: dict[str, object] = Field(default_factory=dict)  # key -> JSON 值


class PreferencesUpdate(BaseModel):
    prefs: dict[str, object]  # 部分更新：只覆盖出现的 key（值为 null 表示删除）


class WatchlistOp(BaseModel):
    ts_code: str = Field(min_length=6, max_length=12)


class ScreenerQueryOut(BaseModel):
    id: int
    text: str
    conditions: ScreenConditions | None = None
    hit_count: int | None = None
    created_at: datetime

    model_config = {"from_attributes": True}


class ScreenerQueryListResponse(BaseModel):
    items: list[ScreenerQueryOut]