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 20：全级别对 × 全品种的区间套批量回测（多进程）。
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数据已补全为 BTC/ETH/SOL 全周期 2172~2543 天，此前所有结论都建立在残缺样本上，
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本步用完整数据一次性重跑，并行执行避免逐个等待。
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任务单元 = (品种, 小级别, 大级别1, 大级别2)。
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每个 worker 独立跑：小级别中枢 -> 快速三买 -> 挂大级别分型 -> 事件驱动回测。
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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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os.environ.setdefault("OMP_NUM_THREADS", "1")
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os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
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os.environ.setdefault("MKL_NUM_THREADS", "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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DEFAULT_PAIRS = "5m:15m:1h,15m:1h:4h,30m:2h:4h,1h:4h:1d,4h:1d:1w"
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SYMBOLS = ["BTC", "ETH", "SOL"]
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# 1m 有 300 万+ 根，全量跑内存吃紧，默认只取最近这么多根
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MAX_ROWS = {"1m": 1_200_000}
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def run_one(task: tuple) -> dict | None:
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"""单个 (品种, 级别对) 的完整回测。运行在子进程里。"""
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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_short, ltf, h1, h2 = task
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symbol = f"{sym_short}/USDT:USDT"
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try:
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df_l = fetch_ohlcv(symbol, ltf, MAX_ROWS.get(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_l = chan_l.dataframe
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zones = build_htf_zones(cdf_l, ltf, chan=chan_l)
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sig = find_fast_bsp3(cdf_l, zones)
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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(symbol, 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_l, 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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tr = run_trades(cdf_l, entries, SL, TP, MAXB)
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if tr.empty:
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return None
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m = sig.set_index("entry_idx")
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tr = tr.copy()
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tr["symbol"] = sym_short
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tr["ltf"] = ltf
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tr["pair"] = f"{ltf}/{h1}+{h2}"
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tr["date"] = cdf_l["date"].to_numpy()[tr["entry_idx"].to_numpy()]
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for c in ("h1_agree", "h2_agree", "depth", "lag", "width_pct"):
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tr[c] = tr["entry_idx"].map(m[c]) if c in m.columns else np.nan
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return {
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"task": f"{sym_short} {ltf}/{h1}+{h2}",
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"n_zones": len(zones),
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"n_sig": len(sig),
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"trades": tr,
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}
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except Exception as e: # 单个任务失败不应拖垮整批
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return {"task": f"{sym_short} {ltf}/{h1}+{h2}", "error": repr(e)[:200]}
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def stats(g: pd.DataFrame, label: str) -> dict:
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r = g["ret"].to_numpy()
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if len(r) == 0:
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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) if len(r) > 1 else 0.0
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return {
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"分组": 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) and loss.sum() else "inf",
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"偏度": f"{pd.Series(r).skew():.2f}" if len(r) > 2 else "—",
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"t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}" if sd else "—",
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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=DEFAULT_PAIRS)
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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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ap.add_argument("--tag", default="main")
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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"[批量] {len(tasks)} 个任务，{args.workers} 进程并行\n", flush=True)
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results, errors = [], []
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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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t = futs[f]
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if r is None:
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print(f" [{i}/{len(tasks)}] {t[0]} {t[1]}/{t[2]} — 样本不足", flush=True)
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continue
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if "error" in r:
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errors.append(r)
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print(f" [{i}/{len(tasks)}] {r['task']} — 失败 {r['error']}", flush=True)
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continue
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results.append(r)
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print(f" [{i}/{len(tasks)}] {r['task']} — 中枢 {r['n_zones']} "
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f"信号 {r['n_sig']} 交易 {len(r['trades'])}", flush=True)
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if not results:
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print("\n无有效结果")
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return
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allt = pd.concat([r["trades"] for r in results], ignore_index=True)
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out = HERE / "out" / f"step20_{args.tag}_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{'=' * 110}")
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print("########## 一、各级别对（全部信号 vs 大级别同向）##########")
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rows = []
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for pair, g in allt.groupby("pair"):
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rows.append(stats(g, f"{pair} 全部"))
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a1 = g["h1_agree"] == 1
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if a1.sum() >= 20:
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rows.append(stats(g[a1], f"{pair} +大级别同向"))
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if (~a1).sum() >= 20:
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rows.append(stats(g[~a1], f"{pair} 反向(对照)"))
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 二、各品种（大级别同向）##########")
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rows = []
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for (sym, pair), g in allt.groupby(["symbol", "pair"]):
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gg = g[g["h1_agree"] == 1]
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if len(gg) >= 20:
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rows.append(stats(gg, f"{sym} {pair}"))
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 三、合并总览 ##########")
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a1 = allt["h1_agree"] == 1
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a2 = allt["h2_agree"] == 1
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rows = [stats(allt, "全部"), stats(allt[a1], "+大级别1同向"),
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stats(allt[a1 & a2], "+双大级别同向"), stats(allt[~a1], "反向(对照)")]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 四、分年（大级别同向）##########")
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sub = allt[a1].copy()
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sub["year"] = pd.to_datetime(sub["date"]).dt.year
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rows = [stats(g, str(y)) for y, g in sub.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########## 五、尾部依赖（大级别同向）##########")
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r = sub["ret"].to_numpy()
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for k in (0, 2, 5, 10):
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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):>5} PF={win.sum() / abs(loss.sum()):.2f} "
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f"t={v.mean() / (sd / np.sqrt(len(v))):+.2f} "
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f"中位={np.median(v) * 100:+.3f}%")
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if errors:
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print(f"\n失败任务 {len(errors)} 个")
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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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