"""基于中台日线的简易回测引擎（均线交叉策略）。

读取 quotes_daily，金叉满仓 / 死叉空仓，输出资金曲线与核心指标。
"""
from __future__ import annotations

from sqlalchemy import select

from db import get_session
from models import DailyQuote


def _ma(arr, n):
    out = [None] * len(arr)
    s = 0.0
    for i, v in enumerate(arr):
        s += v
        if i >= n:
            s -= arr[i - n]
        if i >= n - 1:
            out[i] = s / n
    return out


def run_backtest(symbol: str, fast: int = 5, slow: int = 20, fee: float = 0.0005):
    with get_session() as s:
        rows = s.execute(
            select(DailyQuote.date, DailyQuote.close)
            .where(DailyQuote.code == symbol)
            .order_by(DailyQuote.date)
        ).all()

    if len(rows) < slow + 5:
        return {"ok": False, "msg": "该股票库内日线不足，请先在数据中台入库", "have": len(rows)}

    dates = [r[0].isoformat() for r in rows]
    close = [float(r[1]) for r in rows]
    maf, mas = _ma(close, fast), _ma(close, slow)

    equity, bench = [], []
    cash, pos, shares = 1.0, 0, 0.0
    trades, wins, entry = 0, 0, 0.0
    peak, max_dd = 1.0, 0.0
    base = close[0]

    for i in range(len(close)):
        if maf[i] is not None and mas[i] is not None:
            if pos == 0 and maf[i] > mas[i]:
                shares = cash * (1 - fee) / close[i]
                cash, pos, entry = 0.0, 1, close[i]
                trades += 1
            elif pos == 1 and maf[i] < mas[i]:
                cash = shares * close[i] * (1 - fee)
                shares, pos = 0.0, 0
                if close[i] > entry:
                    wins += 1
        nav = cash + shares * close[i]
        equity.append(round(nav, 4))
        bench.append(round(close[i] / base, 4))
        peak = max(peak, nav)
        max_dd = max(max_dd, (peak - nav) / peak)

    total_ret = equity[-1] - 1
    bench_ret = bench[-1] - 1
    closed = trades - pos
    win_rate = (wins / closed) if closed > 0 else 0.0
    return {
        "ok": True, "symbol": symbol, "fast": fast, "slow": slow,
        "dates": dates, "equity": equity, "bench": bench,
        "metrics": {
            "total_return": round(total_ret * 100, 2),
            "bench_return": round(bench_ret * 100, 2),
            "excess": round((total_ret - bench_ret) * 100, 2),
            "max_drawdown": round(max_dd * 100, 2),
            "trades": trades,
            "win_rate": round(win_rate * 100, 1),
        },
    }