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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+"""XVa2 — Cross-sectional RSI reversal.
+
+Idea: compute RSI(14) per asset; score = -RSI so oversold assets go long (low RSI = long).
+This is a mean-reversion signal: buy the most oversold, short the most overbought.
+
+Grid (<=5 calls):
+  1. RSI(14) reversal, majors, H=10, k=5, LS
+  2. RSI(14) reversal, all, H=10, k=5, LS
+  3. RSI(14) reversal, all, H=5, k=5, LS  (faster rebalance)
+  4. RSI(7) reversal, all, H=5, k=5, LS   (shorter RSI period)
+  5. RSI(14) reversal, all, H=10, k=7, LS  (wider basket)
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
+import xslib as xs
+import numpy as np
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+
+
+def rsi_score(close: np.ndarray, win: int = 14) -> np.ndarray:
+    """Compute -RSI(win) per asset column causally. Returns (n_days, n_assets) score matrix."""
+    n, A = close.shape
+    out = np.full((n, A), np.nan)
+    for a in range(A):
+        out[:, a] = -al.rsi(close[:, a], win)
+    return out
+
+
+results = []
+
+# 1. RSI(14) on majors, H=10, k=5, LS
+rep1 = xs.study_xs(
+    "XVa2-RSI14-majors-H10-k5",
+    lambda P: rsi_score(P.close, 14),
+    universe="majors", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep1))
+results.append(rep1)
+
+# 2. RSI(14) on all, H=10, k=5, LS
+rep2 = xs.study_xs(
+    "XVa2-RSI14-all-H10-k5",
+    lambda P: rsi_score(P.close, 14),
+    universe="all", H=10, k=5, long_short=True
+)
+print(xs.fmt(rep2))
+results.append(rep2)
+
+# 3. RSI(14) on all, H=5, k=5, LS (faster rebalance)
+rep3 = xs.study_xs(
+    "XVa2-RSI14-all-H5-k5",
+    lambda P: rsi_score(P.close, 14),
+    universe="all", H=5, k=5, long_short=True
+)
+print(xs.fmt(rep3))
+results.append(rep3)
+
+# 4. RSI(7) on all, H=5, k=5, LS (shorter RSI)
+rep4 = xs.study_xs(
+    "XVa2-RSI7-all-H5-k5",
+    lambda P: rsi_score(P.close, 7),
+    universe="all", H=5, k=5, long_short=True
+)
+print(xs.fmt(rep4))
+results.append(rep4)
+
+# 5. RSI(14) on all, H=10, k=7, LS (wider basket)
+rep5 = xs.study_xs(
+    "XVa2-RSI14-all-H10-k7",
+    lambda P: rsi_score(P.close, 14),
+    universe="all", H=10, k=7, long_short=True
+)
+print(xs.fmt(rep5))
+results.append(rep5)
+
+# Pick best by: earns_slot first, then hold-out Sharpe, then distinctness from XS01
+def score_rep(r):
+    earns = 1 if r["earns_slot"] else 0
+    hold_sh = r["holdout"].get("sharpe", -999) or -999
+    xs01_corr = abs(r["corr_xs01"] or 1.0)
+    full_sh = r["full"].get("sharpe", -999) or -999
+    return (earns, hold_sh, full_sh, -xs01_corr)
+
+best = max(results, key=score_rep)
+
+print("\n" + "=" * 60)
+print("BEST CONFIG:")
+print(xs.fmt(best))
+print("JSON:", xs.as_json(best))