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>

scripts/research/xsec/runs/XV02.py

@@ -0,0 +1,121 @@
+"""XV02 — Low Idiosyncratic Volatility Anomaly.
+
+Score = -roll_std(residual_return(ret, beta_win=60), 30)
+(negative idiosyncratic volatility: low idio-vol = long, high idio-vol = short).
+
+Distinct from total-vol because we strip the market factor first (beta*mkt),
+keeping only the firm-specific noise. In equities this is the "low idio-vol" anomaly
+(Ang et al. 2006): low idiosyncratic volatility stocks outperform. Testing if the
+same holds cross-sectionally on the HL alt panel.
+
+Grid (<=5 calls total):
+  1. majors H=10 k=5 LS   (baseline)
+  2. all    H=10 k=5 LS   (broader universe)
+  3. majors H=5  k=5 LS   (faster rebalance)
+  4. majors H=10 k=4 LS   (narrower book)
+  5. majors H=10 k=5 LS, shorter beta window (30d)  [to test sensitivity]
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+
+# ---------------------------------------------------------------------------
+# SCORE: negative idiosyncratic vol over last 30 days
+#        (idio ret = ret - rolling_beta_60d * market_ret, then 30d rolling std)
+# ---------------------------------------------------------------------------
+def score_idiovol(P, beta_win=60, vol_win=30):
+    """Low idiosyncratic volatility score (higher = lower idio-vol = long)."""
+    idio = xs.residual_return(P.ret, beta_win)           # n_days x n_assets
+    idio_vol = xs.roll_std(idio, vol_win)                 # rolling std of idio ret
+    # negate: lower vol → higher score → long
+    return -idio_vol
+
+
+results = []
+
+# ---- 1. Baseline: majors, H=10, k=5, LS (beta_win=60, vol_win=30) ----
+rep1 = xs.study_xs(
+    "XV02_majors_H10k5",
+    lambda P: score_idiovol(P, beta_win=60, vol_win=30),
+    universe="majors",
+    H=10, k=5, long_short=True,
+)
+print(xs.fmt(rep1))
+print("JSON:", xs.as_json(rep1))
+results.append(rep1)
+
+# ---- 2. Broader universe: all, H=10, k=5, LS ----
+rep2 = xs.study_xs(
+    "XV02_all_H10k5",
+    lambda P: score_idiovol(P, beta_win=60, vol_win=30),
+    universe="all",
+    H=10, k=5, long_short=True,
+)
+print(xs.fmt(rep2))
+print("JSON:", xs.as_json(rep2))
+results.append(rep2)
+
+# ---- 3. Faster rebalance: majors, H=5, k=5, LS ----
+rep3 = xs.study_xs(
+    "XV02_majors_H5k5",
+    lambda P: score_idiovol(P, beta_win=60, vol_win=30),
+    universe="majors",
+    H=5, k=5, long_short=True,
+)
+print(xs.fmt(rep3))
+print("JSON:", xs.as_json(rep3))
+results.append(rep3)
+
+# ---- 4. Narrower book: majors, H=10, k=4, LS ----
+rep4 = xs.study_xs(
+    "XV02_majors_H10k4",
+    lambda P: score_idiovol(P, beta_win=60, vol_win=30),
+    universe="majors",
+    H=10, k=4, long_short=True,
+)
+print(xs.fmt(rep4))
+print("JSON:", xs.as_json(rep4))
+results.append(rep4)
+
+# ---- 5. Shorter beta window: majors, H=10, k=5, LS, beta_win=30 ----
+rep5 = xs.study_xs(
+    "XV02_majors_H10k5_bw30",
+    lambda P: score_idiovol(P, beta_win=30, vol_win=30),
+    universe="majors",
+    H=10, k=5, long_short=True,
+)
+print(xs.fmt(rep5))
+print("JSON:", xs.as_json(rep5))
+results.append(rep5)
+
+# ---- Summary ----
+print("\n=== XV02 GRID SUMMARY ===")
+for r in results:
+    earns = r["earns_slot"]
+    print(
+        f"  {r['name']:30s}  FULL {r['full']['sharpe']:+.2f}  "
+        f"HOLD {r['holdout'].get('sharpe', 0):+.2f}  "
+        f"corr_xs01 {r['corr_xs01']}  "
+        f"marginal={r['marginal']['verdict']}  "
+        f"earns_slot={earns}"
+    )
+
+# ---- Best config: pick by earns_slot first, then hold-out ----
+earners = [r for r in results if r["earns_slot"]]
+if earners:
+    best = max(earners, key=lambda r: r["holdout"].get("sharpe", -999))
+    print(f"\nBEST (earns_slot): {best['name']}")
+else:
+    # fallback: best hold-out Sharpe with distinct XS01 corr
+    distinct = [r for r in results if (r["corr_xs01"] or 1.0) < 0.6]
+    if distinct:
+        best = max(distinct, key=lambda r: r["holdout"].get("sharpe", -999))
+    else:
+        best = max(results, key=lambda r: r["holdout"].get("sharpe", -999))
+    print(f"\nBEST (hold-out, no earns_slot): {best['name']}")
+
+print("\n--- BEST CONFIG ---")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))