research(xsec): sweep cross-sectional su Hyperliquid (43 script/257 config) + verifica avversariale

Nuova harness condivisa xslib.py (panel HL certificato, score per-asset causale, book long-k/short-k vol-targeted leak-free) + 43 script in runs/ su 11 famiglie (MOM/REV/VOL/ DIST/LIQ/VAL/STRUCT/UNIV). Scoring = earns_slot (full>0 AND hold-out>0 AND marginal ADDS al portafoglio live AND corr XS01<0.6, con jackknife drop-one-month). Find: 42/257 config earns_slot=True, ma TUTTE con corr TP01 -0.2..-0.4 e PnL ~solo 2025. Verify (verify_survivors.py, 3 scettici deterministici): - S1 redundancy: cluster low-vol = UNA scommessa (XV01=XU02=1.00, XV02/XV03 r 0.44-0.67); XM09/XL02/XS06b/XR02 distinti (corr media off-diag +0.20). - S2 short-beta: cluster low-vol carica 0.44-0.70 su short-market -> NON market-neutral, e' un tilt short-alt-beta di regime. XM09(0.08)/XR02(-0.21) NON short-beta. - S3 per-anno: cluster low-vol decade (XV01/XU02 2026 -0.09); XL02 morto (2025 -0.14, 2026 -0.43); XM09 (0.82/0.50/0.74) e XR02 (0.84/0.40/2.68) positivi in tutti e 3 gli anni. Esito: nessuna sleeve nuova. Cluster low-vol RIGETTATO (regime-bet), XL02 RIGETTATO (overfit). 2 LEAD genuini (XM09 trend-gated x-sec momentum, XR02 reversal vol-gated) -> forward-monitor, non deployabili (panel 2.5y regime unico + STAT-MODE esecuzione). Portafoglio live invariato. Incluso anche options_vrp_managed.py (A/B VRP01 hold-to-expiry vs gestione attiva del doc credit-spread): la gestione attiva DISTRUGGE l'edge (combo FULL managed Sh -1.29 vs HtE +0.96, il delta-exit taglia i vincenti) -> scartata, VRP01 resta hold-to-expiry. Diari: 2026-06-20-xsec-strategies-sweep.md, 2026-06-20-vrp-active-management.md. gitignore: data/paper_portfolio/ (stato runtime paper) + scripts/research/xsec/runs/out/ (output rigenerabile). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-20 21:36:57 +00:00
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+"""XR01 — Short-term Reversal on the Hyperliquid certified alt panel.
+
+Score = -past_return(close, L)   (long losers / short winners)
+Grid: L in {1, 3, 5, 7}
+Known prior: REV5 negative — confirm / diagnose.
+
+We try <=5 study_xs calls:
+  1. L=1  majors   H=5  k=5  L/S
+  2. L=3  majors   H=5  k=5  L/S
+  3. L=5  majors   H=5  k=5  L/S  (baseline to confirm negative prior)
+  4. L=7  majors   H=5  k=5  L/S
+  5. best L (by hold-out) on universe="all"  (same H/k)
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+UNIVERSE_BASE = "majors"
+H = 5
+K = 5
+
+results = []
+
+for L in [1, 3, 5, 7]:
+    def score_fn(P, L=L):
+        return -xs.past_return(P.close, L)
+
+    rep = xs.study_xs(
+        f"XR01_L{L}_maj",
+        score_fn,
+        universe=UNIVERSE_BASE,
+        H=H,
+        k=K,
+        long_short=True,
+        target_vol=0.20,
+    )
+    print(xs.fmt(rep))
+    print("JSON:", xs.as_json(rep))
+    results.append((L, rep))
+
+# Pick best L by hold-out Sharpe
+best_L, best_rep = max(results, key=lambda x: x[1]["holdout"]["sharpe"])
+print(f"\n=== Best L on majors: L={best_L} (hold-out Sharpe={best_rep['holdout']['sharpe']:.3f}) ===\n")
+
+# Run the best L on "all" universe
+def score_best(P, L=best_L):
+    return -xs.past_return(P.close, L)
+
+rep_all = xs.study_xs(
+    f"XR01_L{best_L}_all",
+    score_best,
+    universe="all",
+    H=H,
+    k=K,
+    long_short=True,
+    target_vol=0.20,
+)
+print(xs.fmt(rep_all))
+print("JSON:", xs.as_json(rep_all))
+
+# Final summary: pick the overall best (by earns_slot, then hold-out)
+all_reps = [r for _, r in results] + [rep_all]
+all_reps_sorted = sorted(all_reps, key=lambda r: (r.get("earns_slot", False), r["holdout"]["sharpe"]), reverse=True)
+final = all_reps_sorted[0]
+print("\n=== FINAL BEST ===")
+print(xs.fmt(final))
+print("JSON:", xs.as_json(final))