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 22：执行假设压力测试。
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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。
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实盘拿不到这个价。本步逐层加码，看结论在哪一层塌掉：
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A 收盘入场（基准）
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B 次根开盘入场 —— 真实下单节奏
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C 次根开盘 + 5bp 滑点
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D 次根开盘 + 10bp 滑点
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"""
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from __future__ import annotations
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import argparse
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import os
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import sys
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import warnings
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from concurrent.futures import ProcessPoolExecutor, as_completed
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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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for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
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os.environ.setdefault(v, "1")
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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sys.path.insert(0, str(HERE.parent))
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pd.set_option("display.width", 280)
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SL, TP, MAXB = 1.5, 3.0, 48
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VARIANTS = [("A 收盘入场", 0, 0.0), ("B 次根开盘", 1, 0.0),
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("C 次根+5bp", 1, 0.0005), ("D 次根+10bp", 1, 0.0010)]
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def run_one(task: tuple) -> dict | None:
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import warnings as _w
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_w.filterwarnings("ignore")
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sys.path.insert(0, str(HERE))
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sys.path.insert(0, str(HERE.parent))
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from chanlun import TF_DF
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from lib.breakout import run_trades
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from lib.data import fetch_ohlcv
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from lib.fast_bsp3 import find_fast_bsp3
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from lib.fx_signal import extract_fx_signals, signals_to_frame
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from lib.nested_bsp import attach_htf_context, htf_fx_timeline
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from lib.nested_level import build_htf_zones
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sym, ltf, h1, h2 = task
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try:
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df_l = fetch_ohlcv(f"{sym}/USDT:USDT", ltf, 10**9)
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if df_l is None or len(df_l) < 3000:
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return None
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chan_l = TF_DF(df_l, 1, ltf)
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cdf = chan_l.dataframe
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sig = find_fast_bsp3(cdf, build_htf_zones(cdf, ltf, chan=chan_l))
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if sig.empty or len(sig) < 10:
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return None
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for tf, pref in ((h1, "h1"), (h2, "h2")):
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df_h = fetch_ohlcv(f"{sym}/USDT:USDT", tf, 10**9)
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if df_h is None or len(df_h) < 300:
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continue
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chan_h = TF_DF(df_h, 1, tf)
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s = signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe))
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sig = attach_htf_context(sig, cdf, htf_fx_timeline(s, chan_h.dataframe), pref)
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entries = list(zip(sig["entry_idx"].astype(int), sig["direction"].astype(int)))
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m = sig.set_index("entry_idx")
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frames = []
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for name, delay, slip in VARIANTS:
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tr = run_trades(cdf, entries, SL, TP, MAXB, entry_delay=delay, slippage=slip)
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if tr.empty:
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continue
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tr["variant"] = name
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tr["symbol"] = sym
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tr["ltf"] = ltf
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tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()]
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for c in ("h1_agree", "h2_agree"):
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tr[c] = tr["entry_idx"].map(m[c]) if c in m.columns else np.nan
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frames.append(tr)
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return {"task": f"{sym} {ltf}", "trades": pd.concat(frames, ignore_index=True)}
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except Exception as e:
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return {"task": f"{sym} {ltf}", "error": repr(e)[:200]}
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def desc(r: np.ndarray, label: str) -> dict:
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if len(r) < 5:
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return {}
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win, loss = r[r > 0], r[r <= 0]
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sd = r.std(ddof=1)
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return {
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"口径": label, "笔数": len(r),
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"胜率": f"{(r > 0).mean() * 100:.1f}%",
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"均收益": f"{r.mean() * 100:+.3f}%",
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"中位": f"{np.median(r) * 100:+.3f}%",
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"PF": f"{win.sum() / abs(loss.sum()):.2f}" if len(loss) else "inf",
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"t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}",
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}
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--pairs", default="15m:1h:4h,30m:2h:4h,1h:4h:1d")
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ap.add_argument("--symbols", default="BTC,ETH,SOL")
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ap.add_argument("--workers", type=int, default=6)
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args = ap.parse_args()
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pairs = [tuple(p.split(":")) for p in args.pairs.split(",") if p]
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syms = [s.strip() for s in args.symbols.split(",") if s.strip()]
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tasks = [(s, *p) for p in pairs for s in syms]
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print(f"[压测] {len(tasks)} 个任务 × {len(VARIANTS)} 种执行口径\n", flush=True)
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res = []
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with ProcessPoolExecutor(max_workers=args.workers) as ex:
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futs = {ex.submit(run_one, t): t for t in tasks}
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for i, f in enumerate(as_completed(futs), 1):
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r = f.result()
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if r is None or "error" in (r or {}):
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print(f" [{i}/{len(tasks)}] 跳过 {r['task'] if r else futs[f]}", flush=True)
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continue
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res.append(r)
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print(f" [{i}/{len(tasks)}] {r['task']} ok", flush=True)
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if not res:
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print("无结果")
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return
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allt = pd.concat([r["trades"] for r in res], ignore_index=True)
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allt.to_csv(HERE / "out" / "step22_execution.csv", index=False)
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a1 = allt["h1_agree"] == 1
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both = a1 & (allt["h2_agree"] == 1)
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print("\n########## 1. 大级别同向，逐层加码执行成本 ##########")
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rows = [desc(allt[a1 & (allt.variant == v)]["ret"].to_numpy(), v)
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for v, _, _ in VARIANTS]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 2. 双大级别同向 ##########")
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rows = [desc(allt[both & (allt.variant == v)]["ret"].to_numpy(), v)
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for v, _, _ in VARIANTS]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 3. 最保守口径(D)下的分级别 ##########")
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d = allt[(allt.variant == VARIANTS[-1][0]) & a1]
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rows = [desc(g["ret"].to_numpy(), f"{tf} 同向") for tf, g in d.groupby("ltf")]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 4. 最保守口径(D)下的分年（同向）##########")
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d = d.copy()
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d["year"] = pd.to_datetime(d["date"]).dt.year
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rows = [desc(g["ret"].to_numpy(), str(y)) for y, g in d.groupby("year") if len(g) >= 25]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 5. 最保守口径(D)下的尾部依赖（同向）##########")
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r = d["ret"].to_numpy()
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for k in (0, 5, 10, 20):
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v = r if k == 0 else r[r <= np.quantile(r, 1 - k / 100)]
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win, loss = v[v > 0], v[v <= 0]
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sd = v.std(ddof=1)
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print(f" 剔除最赚{k:>2}%: n={len(v):>4} PF={win.sum() / abs(loss.sum()):.2f} "
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f"t={v.mean() / (sd / np.sqrt(len(v))):+.2f} 中位={np.median(v) * 100:+.3f}%")
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if __name__ == "__main__":
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main()
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