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
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+"""Step 21：最优配置的尽职调查。
+
+Step 20 在完整数据（BTC/ETH/SOL，2172~2543 天）上给出两个候选：
+  双大级别同向   2055 笔  PF 1.95  中位 +0.301%  t 11.43
+  30m/2h+4h     269 笔  PF 2.24  中位 +0.915%  t  5.51
+
+本步只做一件事：判断它们是靠尾部还是靠主体，以及扛不扛得住成本。
+"""
+from __future__ import annotations
+
+import sys
+import warnings
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+warnings.filterwarnings("ignore")
+pd.set_option("display.width", 280)
+
+HERE = Path(__file__).resolve().parent
+FEE = 0.0008
+
+
+def desc(r: np.ndarray, label: str) -> dict:
+    if len(r) < 5:
+        return {}
+    win, loss = r[r > 0], r[r <= 0]
+    sd = r.std(ddof=1)
+    return {
+        "配置": label, "笔数": len(r),
+        "胜率": f"{(r > 0).mean() * 100:.1f}%",
+        "均收益": f"{r.mean() * 100:+.3f}%",
+        "中位": f"{np.median(r) * 100:+.3f}%",
+        "赔率": f"{win.mean() / abs(loss.mean()):.2f}" if len(win) and len(loss) else "—",
+        "PF": f"{win.sum() / abs(loss.sum()):.2f}" if len(loss) else "inf",
+        "偏度": f"{pd.Series(r).skew():.2f}",
+        "t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}",
+    }
+
+
+def trim(r: np.ndarray, label: str) -> list[dict]:
+    rows = []
+    for k in (0, 2, 5, 10, 20):
+        v = r if k == 0 else r[r <= np.quantile(r, 1 - k / 100)]
+        if len(v) < 10:
+            continue
+        win, loss = v[v > 0], v[v <= 0]
+        sd = v.std(ddof=1)
+        rows.append({
+            "配置": label, "剔除最赚": f"{k}%", "笔数": len(v),
+            "PF": f"{win.sum() / abs(loss.sum()):.2f}" if len(loss) else "inf",
+            "中位": f"{np.median(v) * 100:+.3f}%",
+            "t值": f"{v.mean() / (sd / np.sqrt(len(v))):+.2f}",
+        })
+    return rows
+
+
+def main() -> None:
+    t = pd.read_csv(HERE / "out" / "step20_main_trades.csv", parse_dates=["date"])
+    a1 = t["h1_agree"] == 1
+    a2 = t["h2_agree"] == 1
+    both = a1 & a2
+
+    cfgs = {
+        "全部信号": t,
+        "双大级别同向": t[both],
+        "30m/2h+4h 全部": t[t.ltf == "30m"],
+        "30m/2h+4h 双同向": t[(t.ltf == "30m") & both],
+        "15m+30m+1h 双同向": t[t.ltf.isin(["15m", "30m", "1h"]) & both],
+    }
+
+    print("########## 1. 候选配置画像 ##########")
+    print(pd.DataFrame([desc(g["ret"].to_numpy(), k) for k, g in cfgs.items()
+                        if len(g) >= 5]).to_string(index=False))
+    print("\n  中位为正 = 靠主体赚钱；偏度低 = 不依赖极端值。")
+
+    print("\n########## 2. 尾部依赖 ##########")
+    rows = []
+    for k in ("双大级别同向", "30m/2h+4h 双同向", "15m+30m+1h 双同向"):
+        rows += trim(cfgs[k]["ret"].to_numpy(), k)
+    print(pd.DataFrame(rows).pivot(index="剔除最赚", columns="配置",
+                                   values=["PF", "t值"]).to_string())
+
+    print("\n########## 3. 成本敏感性 ##########")
+    rows = []
+    for mult, name in ((1, "0.08%"), (2, "0.16%"), (3, "0.24%"), (5, "0.40%")):
+        for k in ("双大级别同向", "30m/2h+4h 双同向", "15m+30m+1h 双同向"):
+            r = cfgs[k]["ret"].to_numpy() - (mult - 1) * FEE
+            win, loss = r[r > 0], r[r <= 0]
+            sd = r.std(ddof=1)
+            rows.append({
+                "成本": name, "配置": k,
+                "PF": f"{win.sum() / abs(loss.sum()):.2f}",
+                "t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}",
+            })
+    print(pd.DataFrame(rows).pivot(index="成本", columns="配置",
+                                   values=["PF", "t值"]).to_string())
+
+    print("\n########## 4. 30m 双同向：分年与分品种 ##########")
+    g = cfgs["30m/2h+4h 双同向"].copy()
+    g["year"] = pd.to_datetime(g["date"]).dt.year
+    rows = [desc(x["ret"].to_numpy(), str(y)) for y, x in g.groupby("year") if len(x) >= 8]
+    print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
+    rows = [desc(x["ret"].to_numpy(), s) for s, x in g.groupby("symbol") if len(x) >= 8]
+    print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
+
+    print("\n########## 5. 推荐组合：15m/30m/1h 双同向，分年 ##########")
+    g = cfgs["15m+30m+1h 双同向"].copy()
+    g["year"] = pd.to_datetime(g["date"]).dt.year
+    rows = [desc(x["ret"].to_numpy(), str(y)) for y, x in g.groupby("year") if len(x) >= 15]
+    print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
+    pos = sum(1 for _, x in g.groupby("year") if len(x) >= 15 and x["ret"].mean() > 0)
+    tot = sum(1 for _, x in g.groupby("year") if len(x) >= 15)
+    print(f"\n  盈利年份 {pos}/{tot}")
+
+    print("\n########## 6. 出场结构与持有时长 ##########")
+    g = cfgs["15m+30m+1h 双同向"]
+    print(g.groupby("reason").agg(
+        笔数=("ret", "size"),
+        占比=("ret", lambda x: f"{len(x) / len(g) * 100:.0f}%"),
+        均收益=("ret", lambda x: f"{x.mean() * 100:+.3f}%"),
+        均持有=("bars_held", lambda x: f"{x.mean():.0f}"),
+    ).to_string())
+
+    print("\n########## 7. 权益曲线（固定 1% 风险，按时间序）##########")
+    for k in ("双大级别同向", "15m+30m+1h 双同向", "30m/2h+4h 双同向"):
+        g = cfgs[k].sort_values("date")
+        r = g["ret"].to_numpy()
+        stop = abs(np.quantile(r, 0.05))
+        sc = np.clip(r / max(stop, 1e-6) * 0.01, -0.1, 0.5)
+        eq = np.cumprod(1 + sc)
+        dd = (1 - eq / np.maximum.accumulate(eq)).max()
+        yrs = (g["date"].max() - g["date"].min()).days / 365.25
+        cagr = eq[-1] ** (1 / yrs) - 1 if yrs > 0 else np.nan
+        shp = sc.mean() / sc.std(ddof=1) * np.sqrt(len(sc) / yrs) if yrs > 0 else np.nan
+        print(f"  {k:>18}: 年化 {cagr * 100:+6.1f}%  最大回撤 {dd * 100:5.1f}%  "
+              f"Sharpe {shp:5.2f}  频率 {len(r) / yrs:.0f} 笔/年")
+
+
+if __name__ == "__main__":
+    main()