feat(surfer): execution-realism stress — honest momentum magnitude ~0.4-0.6

Realism layers on PIT mom_20 TOPK=50: robust to 20bp (+0.59) and liquidity floor
(top-50 already deep, no effect); death-spiral exclusion +0.60. Winsorize +/-20%/day
cuts to +0.50 and weakens 2021/22 -> edge leans partly on large moves. COMBINED
realistic stress +0.18 (CPCV~0); HARSH negative. winsor likely over-conservative ->
true deployable Sharpe ~0.4-0.6, not the 0.76 headline. Real but modest edge; recent
years strongest. Deploy needs signal-robustness refinement + diversifying 2nd edge.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/surfer/pit_realism.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+"""Execution-realism stress for point-in-time crypto momentum — is the magnitude believable?
+
+Layers on the realistic frictions that could inflate the paper Sharpe:
+  - cost 10→20→30 bp on turnover (smaller coins cost more)
+  - WINSORIZE per-coin daily returns to ±cap (you cannot fill a short into an 80%/day collapse)
+  - EXCLUDE each coin's final K days (delisting death-spiral: untradeable → zero PnL there)
+  - liquidity FLOOR: only trade coins with trailing dollar-vol > floor
+If mom_20/30 stays positive EVERY year under the combined stress, the edge magnitude is real.
+"""
+import math
+import os
+import sys
+
+import numpy as np
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+import pit_sweep as ps  # noqa: E402
+from signal_sweep import xs_weights, pnl_w, validate, sharpe_t  # noqa: E402
+import torch  # noqa: E402
+
+DEV = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def trailing(lc, L):
+    out = np.full_like(lc, np.nan); out[L:] = lc[L:] - lc[:-L]; return out
+
+
+def main():
+    syms, days, close, qv, fund = ps.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)
+    fund = np.where(np.isfinite(fund), fund, 0.0)
+    qv = np.nan_to_num(qv)
+    dv30 = np.zeros((T, N))
+    for t in range(30, T):
+        dv30[t] = qv[t - 30:t].mean(axis=0)
+    year = (1970 + days / 365.25).astype(int)
+
+    # death-spiral mask: each coin's final 5 valid days = untradeable
+    tradeable = np.ones((T, N), bool)
+    for j in range(N):
+        idx = np.where(np.isfinite(close[:, j]))[0]
+        if len(idx):
+            tradeable[max(0, idx[-1] - 4):idx[-1] + 1, j] = False
+
+    def variant(cost_bp, winsor, excl_death, dv_floor, L=20, TOPK=50):
+        Reff = (R - fund)
+        if winsor is not None:
+            Reff = np.clip(Reff, -winsor, winsor)
+        if excl_death:
+            Reff = np.where(tradeable, Reff, 0.0)
+        univ = np.zeros((T, N), bool)
+        for t in range(T):
+            elig = np.where((dv30[t] > dv_floor) & np.isfinite(close[t]))[0]
+            if len(elig) == 0:
+                continue
+            k = min(TOPK, len(elig))
+            univ[t, elig[np.argsort(-dv30[t, elig])[:k]]] = True
+        sig = trailing(lc, L).copy(); sig[~univ] = np.nan
+        pnl = pnl_w(xs_weights(sig), Reff, cost_bp=cost_bp)
+        v = validate(pnl, days, 6)
+        py = [sharpe_t(torch.tensor(pnl[year[1:] == y], device=DEV, dtype=torch.float64))
+              if (year[1:] == y).sum() > 30 else float("nan") for y in range(2020, 2027)]
+        return v, py
+
+    print(f"\n===== EXECUTION-REALISM STRESS — PIT momentum (mom_20, TOPK=50) — {N} coins =====")
+    print(f"{'variant':>34} {'full':>6} {'IS':>6} {'OOS':>6} {'CPCVmed':>8} | per-year 2020..2026")
+    cfgs = [
+        ("baseline 10bp", dict(cost_bp=10, winsor=None, excl_death=False, dv_floor=0)),
+        ("20bp cost", dict(cost_bp=20, winsor=None, excl_death=False, dv_floor=0)),
+        ("winsor ±20%", dict(cost_bp=10, winsor=0.20, excl_death=False, dv_floor=0)),
+        ("excl death-spiral (last 5d)", dict(cost_bp=10, winsor=None, excl_death=True, dv_floor=0)),
+        ("dv-floor $5M/day", dict(cost_bp=10, winsor=None, excl_death=False, dv_floor=5e6)),
+        ("COMBINED 20bp+wins20+death+$5M", dict(cost_bp=20, winsor=0.20, excl_death=True, dv_floor=5e6)),
+        ("HARSH 30bp+wins10+death+$10M", dict(cost_bp=30, winsor=0.10, excl_death=True, dv_floor=1e7)),
+    ]
+    for nm, cfg in cfgs:
+        v, py = variant(**cfg)
+        pys = " ".join(f"{p:>+5.2f}" if not math.isnan(p) else "  n/a" for p in py)
+        print(f"{nm:>34} {v['full']:>+6.2f} {v['is_']:>+6.2f} {v['oos']:>+6.2f} {v['med']:>+8.2f} | {pys}")
+    print("\nVERDICT: positive every year under COMBINED/HARSH => believable, sizable edge (not paper).")
+
+
+if __name__ == "__main__":
+    main()