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/XS03b.py

@@ -0,0 +1,126 @@
+"""XS03b — Beta-hedged momentum.
+
+IDEA: Instead of plain cross-sectional momentum (XS01), use RESIDUAL momentum:
+  score = cumulative idiosyncratic return over lookback L.
+  The residual is ret - beta*mkt_ret (rolling beta vs equal-weight panel),
+  so each asset's score reflects ONLY its idiosyncratic drift, stripping
+  out shared market moves. The resulting book is already dollar-neutral
+  (long-short) but also implicitly market-beta-neutral because the signal
+  itself filters out mkt co-movement.
+
+WHY DISTINCT FROM XS01: Plain XS01 ranks on raw momentum; the top assets
+in a bull market are often the highest-beta assets (not idiosyncratic winners).
+Beta-hedged momentum ranks on WHAT IS LEFT after removing mkt factor:
+  - In bull: avoids accidental overweight of market beta
+  - In bear: avoids accidental short of low-beta (defensive) assets
+  - Net: the book is more idiosyncratic and less correlated to raw XS momentum.
+
+GRID (5 backtests max):
+  1. majors, L=30, beta_win=90, H=10, k=5, LS
+  2. majors, L=60, beta_win=90, H=10, k=5, LS
+  3. all,    L=30, beta_win=90, H=10, k=5, LS
+  4. all,    L=60, beta_win=90, H=10, k=5, LS
+  5. all,    blend L=[30,60] residuals, beta_win=90, H=10, k=5, LS  (like XS01 blend)
+
+Pick best by: earns_slot > holdout_sharpe > distinctness from XS01.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+
+
+def residual_momentum(P, L, beta_win=90):
+    """Cumulative idiosyncratic return over L days (causal).
+
+    Residual daily ret = ret - rolling_beta * market_ret.
+    Cumulate over L days to get momentum score on idiosyncratic drift.
+    """
+    resid = xs.residual_return(P.ret, beta_win)   # (n_days x n_assets)
+    # Cumulate residuals over L days (causal: sum of past L residual daily rets)
+    n, A = resid.shape
+    cum = np.full((n, A), np.nan)
+    for i in range(L, n):
+        cum[i] = np.nansum(resid[i - L:i], axis=0)
+    return cum
+
+
+def blend_residual_mom(P, Ls=(30, 60), beta_win=90):
+    """Cross-sectional z-score blend of multiple lookback residual momentums."""
+    scores = []
+    for L in Ls:
+        s = residual_momentum(P, L, beta_win)
+        scores.append(xs.xs_zscore(s))
+    return np.nanmean(scores, axis=0)
+
+
+print("=== XS03b: Beta-hedged Momentum ===\n")
+
+results = []
+
+# 1. majors, L=30
+print("Run 1/5: majors, L=30, beta_win=90, H=10, k=5 LS")
+r1 = xs.study_xs(
+    "XS03b-MAJ-L30",
+    lambda P: residual_momentum(P, 30, 90),
+    universe="majors", H=10, k=5, long_short=True
+)
+print(xs.fmt(r1))
+results.append(r1)
+
+# 2. majors, L=60
+print("\nRun 2/5: majors, L=60, beta_win=90, H=10, k=5 LS")
+r2 = xs.study_xs(
+    "XS03b-MAJ-L60",
+    lambda P: residual_momentum(P, 60, 90),
+    universe="majors", H=10, k=5, long_short=True
+)
+print(xs.fmt(r2))
+results.append(r2)
+
+# 3. all, L=30
+print("\nRun 3/5: all, L=30, beta_win=90, H=10, k=5 LS")
+r3 = xs.study_xs(
+    "XS03b-ALL-L30",
+    lambda P: residual_momentum(P, 30, 90),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(r3))
+results.append(r3)
+
+# 4. all, L=60
+print("\nRun 4/5: all, L=60, beta_win=90, H=10, k=5 LS")
+r4 = xs.study_xs(
+    "XS03b-ALL-L60",
+    lambda P: residual_momentum(P, 60, 90),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(r4))
+results.append(r4)
+
+# 5. all, blend [30,60]
+print("\nRun 5/5: all, blend L=[30,60], beta_win=90, H=10, k=5 LS")
+r5 = xs.study_xs(
+    "XS03b-ALL-BLEND",
+    lambda P: blend_residual_mom(P, (30, 60), 90),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(r5))
+results.append(r5)
+
+# --- Pick best ---
+def score_config(r):
+    """Priority: earns_slot > holdout_sharpe > full_sharpe > distinctness."""
+    slot = 1 if r.get("earns_slot") else 0
+    hs = r["holdout"].get("sharpe", -9)
+    fs = r["full"]["sharpe"]
+    corr = r.get("corr_xs01") or 1.0
+    distinct = 1.0 - abs(corr)
+    return (slot, hs, fs, distinct)
+
+best = max(results, key=score_config)
+
+print("\n\n=== BEST CONFIG ===")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))