"""研究服务：把 Research Specification 编排为数据获取 + 引擎执行。

本层是业务入口：API / Agent 只能调用这里的用例（AGENT.md §16/§17），
禁止直接拼接引擎配置。数据一律经 Repository 获取（防未来函数由查询层保证）。
"""

from __future__ import annotations

from datetime import date, timedelta

import pandas as pd

from app.domain.entities.market import Stock
from app.domain.entities.research import (
    BacktestResult,
    FactorTestReport,
    ResearchSpec,
    UniverseSpec,
)
from app.domain.repositories.market import (
    DailyBarRepository,
    StockRepository,
)
from app.quant.engine import QuantEngine


def filter_stocks(stocks: list[Stock], universe: UniverseSpec, as_of: date) -> list[Stock]:
    """按股票池口径过滤（名称含 ST 判定 —— 名称快照为当日口径，属历史可追溯数据）。"""
    out: list[Stock] = []
    for s in stocks:
        if s.delist_date is not None and s.delist_date < as_of:
            continue
        if universe.exclude_st and s.name and "ST" in s.name.upper():
            continue
        if (
            universe.min_listing_days
            and s.list_date
            and (as_of - s.list_date).days < universe.min_listing_days
        ):
            continue
        out.append(s)
    return out


def bars_to_daily_df(bars) -> pd.DataFrame:
    """DailyBar 列表 → 引擎长表 DataFrame（symbol/trade_date/ohlc/volume/amount）。

    领域实体中的 Decimal 在此转 float，供 pandas 数值运算（保持 DataFrame 全数值列）。
    """
    df = pd.DataFrame([b.model_dump() for b in bars])
    if not df.empty:
        for col in ("open", "high", "low", "close", "volume", "amount"):
            if col in df.columns:
                df[col] = df[col].astype(float)
    return df


class ResearchService:
    """研究用例入口（因子测试 / 回测）。依赖注入 Repository 与引擎。"""

    def __init__(
        self,
        stock_repo: StockRepository,
        daily_repo: DailyBarRepository,
        engine: QuantEngine,
    ) -> None:
        self._stock_repo = stock_repo
        self._daily_repo = daily_repo
        self._engine = engine

    def run_factor_test(self, spec: ResearchSpec, horizon_days: int = 21) -> FactorTestReport:
        if spec.type != "factor_test":
            raise ValueError("factor_test 用例需要 spec.type=factor_test")
        daily = self._load_daily(spec)
        return self._engine.run_factor_test(daily, spec, horizon_days=horizon_days)

    def run_backtest(self, spec: ResearchSpec) -> BacktestResult:
        if spec.type != "backtest":
            raise ValueError("backtest 用例需要 spec.type=backtest")
        daily = self._load_daily(spec)
        return self._engine.run_backtest(daily, spec)

    # ---- 数据装配 ----

    def _load_daily(self, spec: ResearchSpec) -> pd.DataFrame:
        start, end = spec.period
        # 回测前预留因子 warmup（lookback≤120 交易日，取 300 自然日余量）
        data_start = start - timedelta(days=300)
        stocks = filter_stocks(self._stock_repo.list(), spec.universe, as_of=start)
        if not stocks:
            return pd.DataFrame()
        get_many = getattr(self._daily_repo, "get_range_many", None)
        if get_many is not None:
            bars = list(get_many([s.symbol for s in stocks], data_start, end))
        else:  # 兜底：逐只查询
            bars = []
            for s in stocks:
                bars.extend(self._daily_repo.get_range(s.symbol, data_start, end))
        return bars_to_daily_df(bars)