refactor: 精简仓库为 chanlun 核心与 web 分析，移除威科夫与遗留模块
删除根目录旧 Chan 模块、策略、配置、文档及 wyckoff 相关代码；更新缠论 pipeline 与笔中枢计算；补充 research 研究与 web 测试。 Co-authored-by: Cursor <cursoragent@cursor.com>
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jackyu66git
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"""Step 13：中枢突破的三关稳健性验证。
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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、
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且切过多个维度，必须过三关才能当真：
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关一 多品种 BTC / ETH / SOL —— 信号是否只存在于 BTC
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关二 分时段 按年切 —— 是否只靠某一段行情
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关三 参数面 sl/tp 网格 —— 是否只在某个参数点成立
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任何一关塌掉，都说明 Step 12 是数据挖掘的产物。
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"""
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from __future__ import annotations
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import argparse
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import sys
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import warnings
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from pathlib import Path
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import numpy as np
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import pandas as pd
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warnings.filterwarnings("ignore")
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from lib.breakout import run_trades, summarize_trades
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from lib.data import fetch_ohlcv
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from lib.nested_level import build_htf_zones
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from step12_zs_breakout import collect_breakouts
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from chanlun import TF_DF
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pd.set_option("display.width", 260)
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SYMBOLS = ["BTC/USDT:USDT", "ETH/USDT:USDT", "SOL/USDT:USDT"]
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TFS = ["15m", "1h", "4h"]
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--sl", type=float, default=1.5)
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ap.add_argument("--tp", type=float, default=3.0)
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ap.add_argument("--max-bars", type=int, default=48)
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args = ap.parse_args()
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pool: list[pd.DataFrame] = []
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print("########## 关一：多品种 × 多周期（突破即入，sl1.5 tp3）##########")
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rows = []
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for sym in SYMBOLS:
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for tf in TFS:
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try:
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df = fetch_ohlcv(sym, tf, 10**9)
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except Exception:
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continue
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if df is None or len(df) < 2000:
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continue
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chan = TF_DF(df, 1, tf)
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cdf = chan.dataframe
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zones = build_htf_zones(df, tf)
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bo = collect_breakouts(cdf, zones)
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if bo.empty or len(bo) < 15:
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continue
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entries = list(zip(bo["bo_idx"].astype(int), bo["dir"].astype(int)))
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tr = run_trades(cdf, entries, args.sl, args.tp, args.max_bars)
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if tr.empty:
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continue
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s = summarize_trades(tr, f"{sym.split('/')[0]:>4} {tf:>3}")
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s["假突破率"] = f"{bo['is_fake'].mean() * 100:.0f}%"
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rows.append(s)
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tr = tr.copy()
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tr["symbol"] = sym.split("/")[0]
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tr["tf"] = tf
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tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()]
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pool.append(tr)
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print(pd.DataFrame(rows).to_string(index=False))
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if not pool:
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print("无足够样本")
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return
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allt = pd.concat(pool, ignore_index=True)
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print(f"\n 合并 {len(allt)} 笔：", end="")
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r = allt["ret"].to_numpy()
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sd = r.std(ddof=1)
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win, loss = r[r > 0], r[r <= 0]
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print(f"胜率 {(r > 0).mean() * 100:.1f}% 均收益 {r.mean() * 100:+.3f}% "
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f"PF {win.sum() / abs(loss.sum()):.2f} t值 {r.mean() / (sd / np.sqrt(len(r))):+.2f}")
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print("\n########## 关二：按年分段（合并全部品种周期）##########")
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allt["year"] = pd.to_datetime(allt["date"]).dt.year
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rows = []
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for y, g in allt.groupby("year"):
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if len(g) < 25:
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continue
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s = summarize_trades(g, str(y))
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rows.append(s)
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print(pd.DataFrame(rows).to_string(index=False))
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pos_years = sum(1 for _, g in allt.groupby("year")
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if len(g) >= 25 and g["ret"].mean() > 0)
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tot_years = sum(1 for _, g in allt.groupby("year") if len(g) >= 25)
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print(f"\n 盈利年份 {pos_years}/{tot_years}")
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print("\n########## 关三：参数网格（合并全部品种周期，1h 为主）##########")
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rows = []
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for sl in (1.0, 1.5, 2.0, 2.5):
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for tp in (2.0, 3.0, 4.0):
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sub = []
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for sym in SYMBOLS:
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for tf in TFS:
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try:
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df = fetch_ohlcv(sym, tf, 10**9)
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except Exception:
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continue
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if df is None or len(df) < 2000:
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continue
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chan = TF_DF(df, 1, tf)
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cdf = chan.dataframe
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bo = collect_breakouts(cdf, build_htf_zones(df, tf))
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if bo.empty:
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continue
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e = list(zip(bo["bo_idx"].astype(int), bo["dir"].astype(int)))
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t = run_trades(cdf, e, sl, tp, args.max_bars)
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if not t.empty:
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sub.append(t)
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if sub:
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rows.append(summarize_trades(pd.concat(sub, ignore_index=True),
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f"sl{sl} tp{tp}"))
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grid = pd.DataFrame(rows)
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print(grid.to_string(index=False))
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pos = sum(1 for v in grid["均收益"] if v.startswith("+"))
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print(f"\n 正收益参数点 {pos}/{len(grid)}")
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print("\n########## 补充：合并样本的方向与周期拆分 ##########")
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rows = []
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for d, nm in [(1, "向上突破"), (-1, "向下突破")]:
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g = allt[allt["direction"] == d]
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if len(g) >= 25:
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rows.append(summarize_trades(g, nm))
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for tf in TFS:
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g = allt[allt["tf"] == tf]
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if len(g) >= 25:
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rows.append(summarize_trades(g, f"周期 {tf}"))
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print(pd.DataFrame(rows).to_string(index=False))
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out = Path(__file__).parent / "out" / "step13_all_trades.csv"
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out.parent.mkdir(exist_ok=True)
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allt.to_csv(out, index=False)
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print(f"\n明细已写入 {out}")
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if __name__ == "__main__":
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main()
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