Adriano Dal Pastro
9612560479
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>
69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
"""XR01 — Short-term Reversal on the Hyperliquid certified alt panel.
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Score = -past_return(close, L) (long losers / short winners)
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Grid: L in {1, 3, 5, 7}
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Known prior: REV5 negative — confirm / diagnose.
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We try <=5 study_xs calls:
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1. L=1 majors H=5 k=5 L/S
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2. L=3 majors H=5 k=5 L/S
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3. L=5 majors H=5 k=5 L/S (baseline to confirm negative prior)
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4. L=7 majors H=5 k=5 L/S
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5. best L (by hold-out) on universe="all" (same H/k)
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
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import xslib as xs
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import numpy as np
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UNIVERSE_BASE = "majors"
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H = 5
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K = 5
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results = []
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for L in [1, 3, 5, 7]:
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def score_fn(P, L=L):
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return -xs.past_return(P.close, L)
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rep = xs.study_xs(
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f"XR01_L{L}_maj",
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score_fn,
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universe=UNIVERSE_BASE,
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H=H,
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k=K,
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long_short=True,
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target_vol=0.20,
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)
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print(xs.fmt(rep))
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print("JSON:", xs.as_json(rep))
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results.append((L, rep))
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# Pick best L by hold-out Sharpe
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best_L, best_rep = max(results, key=lambda x: x[1]["holdout"]["sharpe"])
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print(f"\n=== Best L on majors: L={best_L} (hold-out Sharpe={best_rep['holdout']['sharpe']:.3f}) ===\n")
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# Run the best L on "all" universe
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def score_best(P, L=best_L):
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return -xs.past_return(P.close, L)
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rep_all = xs.study_xs(
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f"XR01_L{best_L}_all",
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score_best,
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universe="all",
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H=H,
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k=K,
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long_short=True,
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target_vol=0.20,
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)
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print(xs.fmt(rep_all))
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print("JSON:", xs.as_json(rep_all))
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# Final summary: pick the overall best (by earns_slot, then hold-out)
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all_reps = [r for _, r in results] + [rep_all]
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all_reps_sorted = sorted(all_reps, key=lambda r: (r.get("earns_slot", False), r["holdout"]["sharpe"]), reverse=True)
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final = all_reps_sorted[0]
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print("\n=== FINAL BEST ===")
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print(xs.fmt(final))
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print("JSON:", xs.as_json(final))
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