"""Step 22：执行假设压力测试。

Step 20/21 的结论建立在「信号K线收盘价成交、只扣 0.08% 手续费」上。
实盘拿不到这个价。本步逐层加码，看结论在哪一层塌掉：
  A 收盘入场（基准）
  B 次根开盘入场 —— 真实下单节奏
  C 次根开盘 + 5bp 滑点
  D 次根开盘 + 10bp 滑点
"""
from __future__ import annotations

import argparse
import os
import sys
import warnings
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path

import numpy as np
import pandas as pd

warnings.filterwarnings("ignore")
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
    os.environ.setdefault(v, "1")

HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(HERE))
sys.path.insert(0, str(HERE.parent))
pd.set_option("display.width", 280)

SL, TP, MAXB = 1.5, 3.0, 48
VARIANTS = [("A 收盘入场", 0, 0.0), ("B 次根开盘", 1, 0.0),
            ("C 次根+5bp", 1, 0.0005), ("D 次根+10bp", 1, 0.0010)]


def run_one(task: tuple) -> dict | None:
    import warnings as _w
    _w.filterwarnings("ignore")
    sys.path.insert(0, str(HERE))
    sys.path.insert(0, str(HERE.parent))

    from chanlun import TF_DF
    from lib.breakout import run_trades
    from lib.data import fetch_ohlcv
    from lib.fast_bsp3 import find_fast_bsp3
    from lib.fx_signal import extract_fx_signals, signals_to_frame
    from lib.nested_bsp import attach_htf_context, htf_fx_timeline
    from lib.nested_level import build_htf_zones

    sym, ltf, h1, h2 = task
    try:
        df_l = fetch_ohlcv(f"{sym}/USDT:USDT", ltf, 10**9)
        if df_l is None or len(df_l) < 3000:
            return None
        chan_l = TF_DF(df_l, 1, ltf)
        cdf = chan_l.dataframe
        sig = find_fast_bsp3(cdf, build_htf_zones(cdf, ltf, chan=chan_l))
        if sig.empty or len(sig) < 10:
            return None

        for tf, pref in ((h1, "h1"), (h2, "h2")):
            df_h = fetch_ohlcv(f"{sym}/USDT:USDT", tf, 10**9)
            if df_h is None or len(df_h) < 300:
                continue
            chan_h = TF_DF(df_h, 1, tf)
            s = signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe))
            sig = attach_htf_context(sig, cdf, htf_fx_timeline(s, chan_h.dataframe), pref)

        entries = list(zip(sig["entry_idx"].astype(int), sig["direction"].astype(int)))
        m = sig.set_index("entry_idx")
        frames = []
        for name, delay, slip in VARIANTS:
            tr = run_trades(cdf, entries, SL, TP, MAXB, entry_delay=delay, slippage=slip)
            if tr.empty:
                continue
            tr["variant"] = name
            tr["symbol"] = sym
            tr["ltf"] = ltf
            tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()]
            for c in ("h1_agree", "h2_agree"):
                tr[c] = tr["entry_idx"].map(m[c]) if c in m.columns else np.nan
            frames.append(tr)
        return {"task": f"{sym} {ltf}", "trades": pd.concat(frames, ignore_index=True)}
    except Exception as e:
        return {"task": f"{sym} {ltf}", "error": repr(e)[:200]}


def desc(r: np.ndarray, label: str) -> dict:
    if len(r) < 5:
        return {}
    win, loss = r[r > 0], r[r <= 0]
    sd = r.std(ddof=1)
    return {
        "口径": label, "笔数": len(r),
        "胜率": f"{(r > 0).mean() * 100:.1f}%",
        "均收益": f"{r.mean() * 100:+.3f}%",
        "中位": f"{np.median(r) * 100:+.3f}%",
        "PF": f"{win.sum() / abs(loss.sum()):.2f}" if len(loss) else "inf",
        "t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}",
    }


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--pairs", default="15m:1h:4h,30m:2h:4h,1h:4h:1d")
    ap.add_argument("--symbols", default="BTC,ETH,SOL")
    ap.add_argument("--workers", type=int, default=6)
    args = ap.parse_args()

    pairs = [tuple(p.split(":")) for p in args.pairs.split(",") if p]
    syms = [s.strip() for s in args.symbols.split(",") if s.strip()]
    tasks = [(s, *p) for p in pairs for s in syms]
    print(f"[压测] {len(tasks)} 个任务 × {len(VARIANTS)} 种执行口径\n", flush=True)

    res = []
    with ProcessPoolExecutor(max_workers=args.workers) as ex:
        futs = {ex.submit(run_one, t): t for t in tasks}
        for i, f in enumerate(as_completed(futs), 1):
            r = f.result()
            if r is None or "error" in (r or {}):
                print(f"  [{i}/{len(tasks)}] 跳过 {r['task'] if r else futs[f]}", flush=True)
                continue
            res.append(r)
            print(f"  [{i}/{len(tasks)}] {r['task']} ok", flush=True)

    if not res:
        print("无结果")
        return
    allt = pd.concat([r["trades"] for r in res], ignore_index=True)
    allt.to_csv(HERE / "out" / "step22_execution.csv", index=False)
    a1 = allt["h1_agree"] == 1
    both = a1 & (allt["h2_agree"] == 1)

    print("\n########## 1. 大级别同向，逐层加码执行成本 ##########")
    rows = [desc(allt[a1 & (allt.variant == v)]["ret"].to_numpy(), v)
            for v, _, _ in VARIANTS]
    print(pd.DataFrame([r for r in rows if r]).to_string(index=False))

    print("\n########## 2. 双大级别同向 ##########")
    rows = [desc(allt[both & (allt.variant == v)]["ret"].to_numpy(), v)
            for v, _, _ in VARIANTS]
    print(pd.DataFrame([r for r in rows if r]).to_string(index=False))

    print("\n########## 3. 最保守口径(D)下的分级别 ##########")
    d = allt[(allt.variant == VARIANTS[-1][0]) & a1]
    rows = [desc(g["ret"].to_numpy(), f"{tf} 同向") for tf, g in d.groupby("ltf")]
    print(pd.DataFrame([r for r in rows if r]).to_string(index=False))

    print("\n########## 4. 最保守口径(D)下的分年（同向）##########")
    d = d.copy()
    d["year"] = pd.to_datetime(d["date"]).dt.year
    rows = [desc(g["ret"].to_numpy(), str(y)) for y, g in d.groupby("year") if len(g) >= 25]
    print(pd.DataFrame([r for r in rows if r]).to_string(index=False))

    print("\n########## 5. 最保守口径(D)下的尾部依赖（同向）##########")
    r = d["ret"].to_numpy()
    for k in (0, 5, 10, 20):
        v = r if k == 0 else r[r <= np.quantile(r, 1 - k / 100)]
        win, loss = v[v > 0], v[v <= 0]
        sd = v.std(ddof=1)
        print(f"  剔除最赚{k:>2}%: n={len(v):>4}  PF={win.sum() / abs(loss.sum()):.2f}  "
              f"t={v.mean() / (sd / np.sqrt(len(v))):+.2f}  中位={np.median(v) * 100:+.3f}%")


if __name__ == "__main__":
    main()