"""回测域路由：策略回测（旧 API，K线+指标+买卖点+净值）+ 自然语言事件回测。"""
from __future__ import annotations

import json

import pandas as pd
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.ext.asyncio import AsyncSession

from ..backtest.engine import BacktestConfig, run_backtest
from ..backtest.events import EventEngineError, run_event_backtest
from ..backtest.strategies import build_strategy
from ..data import fetcher, repository
from ..data.aggregation import bars_per_year, resample_bars
from ..data.symbols import is_etf_symbol
from ..db import get_session
from ..models import BacktestRun
from ..schemas import (
    BacktestRequest,
    BacktestResponse,
    CandleOut,
    EquityPoint,
    EventBacktestRequest,
    EventBacktestResponse,
    IndicatorOut,
    MetricsOut,
    SignalOut,
)
from ..screener.llm import parse_event_spec, ScreenerError
from ._deps import rows_to_bars, series_to_jsonable

router = APIRouter()


@router.post("/backtest", response_model=BacktestResponse)
async def backtest(
    req: BacktestRequest,
    session: AsyncSession = Depends(get_session),
) -> BacktestResponse:
    # 真实数据：本地无缓存则先拉取
    if not await fetcher.is_cached(session, req.symbol):
        try:
            await fetcher.sync_symbol(session, req.symbol, source="auto")
        except Exception as e:  # noqa: BLE001
            raise HTTPException(status_code=502, detail=f"数据拉取失败: {e}")

    # 日线为基底，聚合到请求周期
    rows = await repository.get_candles(
        session, req.symbol, "1d", start=req.start, end=req.end, limit=100000
    )
    if not rows:
        raise HTTPException(status_code=404, detail=f"无数据: symbol={req.symbol}")

    bars = resample_bars(rows_to_bars(rows), req.timeframe)
    if len(bars) < 2:
        raise HTTPException(status_code=400, detail=f"周期 {req.timeframe} 下数据不足，无法回测")

    try:
        strategy = build_strategy(req.strategy, req.params)
    except Exception as e:  # noqa: BLE001
        raise HTTPException(status_code=400, detail=f"策略构建失败: {e}")
    cfg = BacktestConfig(
        initial_cash=req.initial_cash,
        fast_mode=req.fast_mode,
        bars_per_year=bars_per_year(req.timeframe),
        is_fund=is_etf_symbol(req.symbol),  # ETF 免印花税/过户费
    )
    result = run_backtest(bars, strategy, cfg)

    df: pd.DataFrame = result["df"]
    m = result["metrics"]

    # 记录到回测运行注册表（可复现/可审计的基础）
    session.add(
        BacktestRun(
            symbol=req.symbol,
            strategy=req.strategy,
            timeframe=req.timeframe,
            params_json=json.dumps(req.params, ensure_ascii=False),
            initial_cash=req.initial_cash,
            total_return=m["total_return"],
            max_drawdown=m["max_drawdown"],
            sharpe=m["sharpe"],
            num_trades=m["num_trades"],
        )
    )
    await session.commit()

    candles = [
        CandleOut(ts=r["ts"], open=r["open"], high=r["high"], low=r["low"],
                  close=r["close"], volume=r["volume"],
                  amount=r["amount"] if "amount" in df.columns else None,
                  turnover=r["turnover"] if "turnover" in df.columns else None)
        for _, r in df.iterrows()
    ]
    signals = [
        SignalOut(ts=f.ts, side=f.side.value, price=f.price, qty=f.qty)
        for f in result["fills"]
    ]
    indicators = IndicatorOut(
        strategy=req.strategy,
        data={col: series_to_jsonable(df[col]) for col in result["indicator_cols"]},
    )
    equity = [EquityPoint(ts=t.to_pydatetime(), value=float(v))
              for t, v in result["equity"].items()]

    return BacktestResponse(
        symbol=req.symbol,
        timeframe=req.timeframe,
        strategy=req.strategy,
        candles=candles,
        indicators=indicators,
        signals=signals,
        equity=equity,
        metrics=MetricsOut(**m),
        final_cash=result["final_cash"],
        final_position=result["final_position"],
        initial_cash=req.initial_cash,
    )


@router.post("/backtest/event", response_model=EventBacktestResponse)
async def backtest_event(
    req: EventBacktestRequest,
    session: AsyncSession = Depends(get_session),
) -> EventBacktestResponse:
    """自然语言事件回测：入场条件命中 -> 次日买入 -> 持有 N 日，单股或全市场汇总统计。
    直传 spec 则跳过 LLM（前端调参重跑）。"""
    try:
        spec = req.spec or await parse_event_spec(req.text)
        result = await run_event_backtest(
            session, spec,
            ts_code=req.ts_code,
            start=req.start.date() if req.start else None,
            end=req.end.date() if req.end else None,
        )
    except ScreenerError as e:
        raise HTTPException(status_code=502, detail=str(e))
    except EventEngineError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:  # noqa: BLE001
        raise HTTPException(status_code=500, detail=f"事件回测失败: {e}")
    return EventBacktestResponse(
        text=req.text,
        spec=result["spec"],
        universe=result["universe"],
        start=result["start"],
        end=result["end"],
        stats=result["stats"],
        trades=result["trades"],
        total=result["total"],
    )