feat(surfer): equity low-vol gauntlet — real structure, marginal after realistic cost

Full gauntlet on the lottery-aversion lead (liquid high-vol cohort). BASE L/S +0.74/OOS +0.72 but: name-bootstrap frac>0 only 0.77 (fragile vs momentum's 1.00); edge lives at $5-20M floor (smaller names, real spreads > the 10bp assumed), dies at $50M; the L/S edge is mostly shorting hard-to-borrow hype names (30%/yr borrow -> +0.04 gone); long-only realistic = +0.33, DSR 0.07 (not significant). corr to crypto book -0.05 (uncorrelated -> would diversify IF real). DSR 0.22/0.07 -- neither clears 0.5. Closest non-crypto market, theoretically sound, uncorrelated -- but marginal after realistic borrow+small-name cost, NOT deploy-grade. A lead on the shelf, not a strategy. Crypto momentum+VRP remains the only deploy-grade edge. Re-confirms the boundary. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-06 20:55:17 +02:00
parent 05a81c5e5f
commit 30f944f015
1 changed files with 129 additions and 0 deletions
--- a/scripts/surfer/equity_lowvol_gauntlet.py
+++ b/scripts/surfer/equity_lowvol_gauntlet.py
@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+"""Full gauntlet on the equity low-vol/lottery-aversion lead (liquid high-vol cohort).
+
+The make-or-break for the first non-crypto edge: (1) name-bootstrap (robust to WHICH names? —
+the test that killed carry), (2) cohort-param robustness (did $10M/60pct manufacture it?),
+(3) realistic short-borrow + long-only (is the edge trapped in the un-cheap short?),
+(4) honest deflation + per-year, (5) correlation to the crypto momentum book (diversifier?).
+Free — DBEQ on disk.
+"""
+import math
+import os
+import sys
+
+import numpy as np
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from equity_factor_gate import load, roll  # noqa: E402
+from signal_sweep import xs_weights, validate, sharpe_t  # noqa: E402
+import pit_sweep  # noqa: E402
+from surfer_poc import compute_weights, CFG  # noqa: E402
+import torch  # noqa: E402
+
+DEV = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def main():
+    insts, days, close, dvol = load()
+    T, N = close.shape
+    lc = np.log(close)
+    R = np.zeros((T, N)); R[1:] = lc[1:] - lc[:-1]; R = np.where(np.isfinite(R), R, 0.0)
+    dv30 = roll(np.mean, np.nan_to_num(dvol), 30)
+    vol63 = roll(np.std, R, 63)
+    year = (1970 + days / 365.25).astype(int)
+    T_ = lambda x: torch.tensor(x[np.isfinite(x)], device=DEV, dtype=torch.float64)
+
+    def cohort(dv_floor, vol_pct):
+        u = np.zeros((T, N), bool)
+        for t in range(T):
+            liq = (dv30[t] > dv_floor) & np.isfinite(close[t]) & np.isfinite(vol63[t])
+            e = np.where(liq)[0]
+            if len(e) > 50:
+                u[t, e[vol63[t, e] >= np.quantile(vol63[t, e], vol_pct)]] = True
+        return u
+
+    def smooth_weekly(w, K=5):
+        a = 2.0 / 6
+        for t in range(1, T):
+            w[t] = a * w[t] + (1 - a) * w[t - 1]
+        wh = w.copy(); last = 0
+        for t in range(T):
+            if t % K == 0:
+                last = t
+            wh[t] = w[last]
+        return wh
+
+    def lowvol_pnl(univ, cols=None, long_only=False, borrow_ann=0.0):
+        sig = (-vol63).copy(); sig[~univ] = np.nan
+        if cols is not None:
+            mask = np.zeros(N, bool); mask[cols] = True; sig[:, ~mask] = np.nan
+        if long_only:
+            w = np.zeros((T, N))
+            for t in range(T):
+                e = np.where(np.isfinite(sig[t]))[0]
+                if len(e) > 20:
+                    k = max(int(0.10 * len(e)), 5); w[t, e[np.argsort(-sig[t, e])[:k]]] = 1.0 / k
+        else:
+            w = xs_weights(sig)
+        w = smooth_weekly(w)
+        g = np.sum(w[:-1] * R[1:], axis=1)
+        g -= np.sum(np.abs(w[1:] - w[:-1]), axis=1) * 10.0 / 1e4              # 10bp trade cost
+        if borrow_ann > 0:                                                    # borrow on short notional
+            short_notional = np.sum(np.clip(-w[:-1], 0, None), axis=1)
+            g -= short_notional * borrow_ann / 252.0
+        return g
+
+    base = cohort(1e7, 0.60)
+    base_pnl = lowvol_pnl(base)
+    v = validate(base_pnl, days, 30)
+    print(f"\n===== EQUITY LOW-VOL GAUNTLET (liquid high-vol cohort, deflate N=30) =====")
+    print(f"BASE L/S: full {v['full']:+.2f} OOS {v['oos']:+.2f} CPCVmed {v['med']:+.2f} DSR {v['dsr']:.2f}")
+
+    # (1) name-bootstrap
+    cohort_cols = np.where(base.any(0))[0]
+    rng = np.random.default_rng(5); sh = []
+    for _ in range(200):
+        c = rng.choice(cohort_cols, size=max(len(cohort_cols) // 2, 20), replace=False)
+        s = sharpe_t(T_(lowvol_pnl(base, cols=c)))
+        if not math.isnan(s):
+            sh.append(s)
+    sh = np.array(sh)
+    print(f"(1) name-bootstrap(200): frac>0 {np.mean(sh>0):.2f}  median {np.median(sh):+.2f}  5th {np.percentile(sh,5):+.2f}")
+
+    # (2) cohort-param robustness
+    print("(2) cohort-param grid (L/S full Sharpe):")
+    for fl in [5e6, 1e7, 2e7, 5e7]:
+        row = []
+        for vp in [0.50, 0.60, 0.70]:
+            row.append(sharpe_t(T_(lowvol_pnl(cohort(fl, vp)))))
+        print(f"     ${fl/1e6:>3.0f}M floor: " + "  ".join(f"vp{int(vp*100)}:{r:+.2f}" for vp, r in zip([0.50,0.60,0.70], row)))
+
+    # (3) short-borrow + long-only
+    print("(3) short-borrow & long-only (net):")
+    for ba in [0.0, 0.10, 0.30]:
+        print(f"     L/S borrow {int(ba*100)}%/yr: {sharpe_t(T_(lowvol_pnl(base, borrow_ann=ba))):+.2f}")
+    lo = lowvol_pnl(base, long_only=True)
+    vlo = validate(lo, days, 30)
+    print(f"     LONG-ONLY (no borrow): full {vlo['full']:+.2f} OOS {vlo['oos']:+.2f} CPCVmed {vlo['med']:+.2f} DSR {vlo['dsr']:.2f}")
+
+    # (4) per-year (base L/S)
+    print("(4) per-year (base L/S): " + " ".join(f"{y}:{sharpe_t(T_(base_pnl[year[1:]==y])):+.2f}" for y in range(2023,2027) if (year[1:]==y).sum()>40))
+
+    # (5) correlation to crypto momentum book
+    syms, cdays, cc, cqv, cf = pit_sweep.load()
+    cw, _ = compute_weights(cc, cqv, cdays, CFG)
+    cR = np.zeros_like(cc); cR[1:] = np.log(cc)[1:] - np.log(cc)[:-1]; cR = np.where(np.isfinite(cR), cR, 0.0)
+    cff = np.where(np.isfinite(cf), cf, 0.0)
+    cmom = np.sum(cw[:-1] * (cR - cff)[1:], axis=1)
+    cmd = {int(cdays[1:][i]): cmom[i] for i in range(len(cmom))}
+    emd = {int(days[1:][i]): base_pnl[i] for i in range(len(base_pnl))}
+    common = sorted(set(cmd) & set(emd))
+    a = np.array([cmd[d] for d in common]); b = np.array([emd[d] for d in common])
+    m = np.isfinite(a) & np.isfinite(b)
+    corr = float(np.corrcoef(a[m], b[m])[0, 1]) if m.sum() > 50 else float("nan")
+    print(f"(5) corr to crypto-momentum book: {corr:+.2f}  (low => diversifier in a preferred market)")
+    print("\nVERDICT: bootstrap frac>0~1 + grid broadly + long-only positive + DSR>0.5 + low crypto-corr = real 2nd sleeve.")
+
+
+if __name__ == "__main__":
+    main()