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>
2026-08-27 01:05:12 +08:00
parent 5c10e35b76
commit 7f393b93ed
360 changed files with 140008 additions and 41167 deletions
@@ -0,0 +1,95 @@
+"""Step 3：滞后成本曲线——量化「若能更早识别信号，收益能回来多少」。
+
+做法：把入场点设在分型后第 k 根K线，k 从 0 扫到 30，看收益随 k 的衰减。
+k 是反事实的（实时并不能在分型后第 k 根就确认信号），但它给出了
+「压缩确认滞后」这条优化路线的收益天花板。
+"""
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from lib.bsp_eval import run_pipeline
+from lib.data import fetch_ohlcv
+from lib.walkforward import analyze_stability, replay
+
+pd.set_option("display.width", 240)
+
+from chanlun.core.ChanEnum import Chan_BSP_DIR
+
+SYMBOL, TF, WINDOW, STEP = "BTC/USDT:USDT", "1h", 3000, 4
+HOLD = 20  # 固定持有期
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--zs", default="seg", choices=["seg", "pure"])
+    ap.add_argument("--hold", type=int, default=HOLD)
+    args = ap.parse_args()
+    hold = args.hold
+
+    df = fetch_ohlcv(SYMBOL, TF, 50000)
+    _, final_bsp = run_pipeline(df, TF, zs_source=args.zs)
+    final_keys = {
+        (str(b.type).replace("Chan_BSP_TYPE.", ""),
+         1 if b.dir == Chan_BSP_DIR.BUY else -1, str(b.end_time))
+        for b in final_bsp if b.is_sure and b.sure_time is not None
+    }
+    result = replay(df, TF, window=WINDOW, step=STEP, zs_source=args.zs,
+                    cache_key=f"{SYMBOL.replace('/', '_').replace(':', '-')}_{TF}")
+    life = analyze_stability(result, final_keys, df, window=WINDOW)
+    valid = life[~life["truncated"]].copy()
+    valid = valid.dropna(subset=["fx_idx"])
+    valid["fx_idx"] = valid["fx_idx"].astype(int)
+
+    closes = df["close"].to_numpy(dtype=float)
+    n = len(df)
+
+    print(f"[中枢来源] {args.zs}")
+    print(f"[样本] 有效信号 = {len(valid)}   持有期 = {hold} 根")
+    print(f"[实际] 实时首见滞后中位数 = {valid['observed_lag'].median():.0f} 根\n")
+
+    def curve(sub: pd.DataFrame, label: str) -> None:
+        out = []
+        for k in (0, 2, 4, 6, 8, 10, 12, 16, 20, 25, 30):
+            rets = []
+            for _, r in sub.iterrows():
+                i = int(r["fx_idx"]) + k
+                j = i + hold
+                if i >= n or j >= n:
+                    continue
+                d = int(r["direction"])
+                rets.append(d * (closes[j] - closes[i]) / closes[i])
+            if not rets:
+                continue
+            a = np.array(rets)
+            sd = a.std(ddof=1)
+            out.append({
+                "k": k, "n": len(a), "mean": a.mean(), "winrate": (a > 0).mean(),
+                "tstat": a.mean() / (sd / np.sqrt(len(a))) if sd else np.nan,
+            })
+        o = pd.DataFrame(out)
+        o["mean"] = o["mean"].map(lambda v: f"{v * 100:+.2f}%")
+        o["winrate"] = o["winrate"].map(lambda v: f"{v * 100:.0f}%")
+        o["tstat"] = o["tstat"].map(lambda v: f"{v:+.2f}")
+        print(f"--- {label} ---")
+        print(o.to_string(index=False))
+        print()
+
+    curve(valid, "全部信号")
+    curve(valid[valid.direction == 1], "仅做多信号 (B1/B2/B3)")
+    curve(valid[valid.direction == -1], "仅做空信号 (S1/S2/S3)")
+    for t in sorted(valid["bsp_type"].unique()):
+        sub = valid[valid.bsp_type == t]
+        if len(sub) >= 25:
+            curve(sub, f"{t}  (n={len(sub)})")
+
+
+if __name__ == "__main__":
+    main()