"""Step 13：中枢突破的三关稳健性验证。

Step 12 发现中枢突破跨周期单调为正（PF 1.18/1.52/2.17），但样本偏小、
且切过多个维度，必须过三关才能当真：

  关一  多品种   BTC / ETH / SOL —— 信号是否只存在于 BTC
  关二  分时段   按年切 —— 是否只靠某一段行情
  关三  参数面   sl/tp 网格 —— 是否只在某个参数点成立

任何一关塌掉，都说明 Step 12 是数据挖掘的产物。
"""
from __future__ import annotations

import argparse
import sys
import warnings
from pathlib import Path

import numpy as np
import pandas as pd

warnings.filterwarnings("ignore")
sys.path.insert(0, str(Path(__file__).resolve().parent))

from lib.breakout import run_trades, summarize_trades
from lib.data import fetch_ohlcv
from lib.nested_level import build_htf_zones
from step12_zs_breakout import collect_breakouts

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from chanlun import TF_DF

pd.set_option("display.width", 260)

SYMBOLS = ["BTC/USDT:USDT", "ETH/USDT:USDT", "SOL/USDT:USDT"]
TFS = ["15m", "1h", "4h"]


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--sl", type=float, default=1.5)
    ap.add_argument("--tp", type=float, default=3.0)
    ap.add_argument("--max-bars", type=int, default=48)
    args = ap.parse_args()

    pool: list[pd.DataFrame] = []

    print("########## 关一：多品种 × 多周期（突破即入，sl1.5 tp3）##########")
    rows = []
    for sym in SYMBOLS:
        for tf in TFS:
            try:
                df = fetch_ohlcv(sym, tf, 10**9)
            except Exception:
                continue
            if df is None or len(df) < 2000:
                continue
            chan = TF_DF(df, 1, tf)
            cdf = chan.dataframe
            zones = build_htf_zones(df, tf)
            bo = collect_breakouts(cdf, zones)
            if bo.empty or len(bo) < 15:
                continue
            entries = list(zip(bo["bo_idx"].astype(int), bo["dir"].astype(int)))
            tr = run_trades(cdf, entries, args.sl, args.tp, args.max_bars)
            if tr.empty:
                continue
            s = summarize_trades(tr, f"{sym.split('/')[0]:>4} {tf:>3}")
            s["假突破率"] = f"{bo['is_fake'].mean() * 100:.0f}%"
            rows.append(s)

            tr = tr.copy()
            tr["symbol"] = sym.split("/")[0]
            tr["tf"] = tf
            tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()]
            pool.append(tr)
    print(pd.DataFrame(rows).to_string(index=False))

    if not pool:
        print("无足够样本")
        return
    allt = pd.concat(pool, ignore_index=True)

    print(f"\n  合并 {len(allt)} 笔：", end="")
    r = allt["ret"].to_numpy()
    sd = r.std(ddof=1)
    win, loss = r[r > 0], r[r <= 0]
    print(f"胜率 {(r > 0).mean() * 100:.1f}%  均收益 {r.mean() * 100:+.3f}%  "
          f"PF {win.sum() / abs(loss.sum()):.2f}  t值 {r.mean() / (sd / np.sqrt(len(r))):+.2f}")

    print("\n########## 关二：按年分段（合并全部品种周期）##########")
    allt["year"] = pd.to_datetime(allt["date"]).dt.year
    rows = []
    for y, g in allt.groupby("year"):
        if len(g) < 25:
            continue
        s = summarize_trades(g, str(y))
        rows.append(s)
    print(pd.DataFrame(rows).to_string(index=False))
    pos_years = sum(1 for _, g in allt.groupby("year")
                    if len(g) >= 25 and g["ret"].mean() > 0)
    tot_years = sum(1 for _, g in allt.groupby("year") if len(g) >= 25)
    print(f"\n  盈利年份 {pos_years}/{tot_years}")

    print("\n########## 关三：参数网格（合并全部品种周期，1h 为主）##########")
    rows = []
    for sl in (1.0, 1.5, 2.0, 2.5):
        for tp in (2.0, 3.0, 4.0):
            sub = []
            for sym in SYMBOLS:
                for tf in TFS:
                    try:
                        df = fetch_ohlcv(sym, tf, 10**9)
                    except Exception:
                        continue
                    if df is None or len(df) < 2000:
                        continue
                    chan = TF_DF(df, 1, tf)
                    cdf = chan.dataframe
                    bo = collect_breakouts(cdf, build_htf_zones(df, tf))
                    if bo.empty:
                        continue
                    e = list(zip(bo["bo_idx"].astype(int), bo["dir"].astype(int)))
                    t = run_trades(cdf, e, sl, tp, args.max_bars)
                    if not t.empty:
                        sub.append(t)
            if sub:
                rows.append(summarize_trades(pd.concat(sub, ignore_index=True),
                                             f"sl{sl} tp{tp}"))
    grid = pd.DataFrame(rows)
    print(grid.to_string(index=False))
    pos = sum(1 for v in grid["均收益"] if v.startswith("+"))
    print(f"\n  正收益参数点 {pos}/{len(grid)}")

    print("\n########## 补充：合并样本的方向与周期拆分 ##########")
    rows = []
    for d, nm in [(1, "向上突破"), (-1, "向下突破")]:
        g = allt[allt["direction"] == d]
        if len(g) >= 25:
            rows.append(summarize_trades(g, nm))
    for tf in TFS:
        g = allt[allt["tf"] == tf]
        if len(g) >= 25:
            rows.append(summarize_trades(g, f"周期 {tf}"))
    print(pd.DataFrame(rows).to_string(index=False))

    out = Path(__file__).parent / "out" / "step13_all_trades.csv"
    out.parent.mkdir(exist_ok=True)
    allt.to_csv(out, index=False)
    print(f"\n明细已写入 {out}")


if __name__ == "__main__":
    main()