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>
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
"""XD02 — High-skew momentum (POSITIVE sign).
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Mechanism: Score = +roll_skew(ret, 60).
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Idea: positive skew = right-tailed distribution = asset had big up-moves.
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Does positive skew predict cross-sectional outperformance in crypto alts?
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(XD01 tested negative skew; this tests the opposite hypothesis.)
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Grid (<= 5 runs):
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1. majors, H=10, k=5, LS (baseline)
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2. all, H=10, k=5, LS (wider universe)
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3. majors, H=5, k=5, LS (faster rebalance)
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4. majors, H=10, k=5, LS, win=30 (shorter lookback)
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5. majors, H=10, k=3, LS (concentrated book)
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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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# Score: positive rolling skewness of daily returns
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# Higher skew -> more right-tailed -> long this asset
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def score_skew(P, win=60):
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return xs.roll_skew(P.ret, win)
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print("=" * 60)
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print("XD02 — HIGH-SKEW MOMENTUM (positive sign, does positive skew pay?)")
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print("=" * 60)
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# Run 1: majors, H=10, k=5, LS, win=60
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r1 = xs.study_xs("XD02-MJ-H10-k5-w60", lambda P: score_skew(P, 60),
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universe="majors", H=10, k=5, long_short=True)
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print(xs.fmt(r1))
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print("JSON:", xs.as_json(r1))
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print()
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# Run 2: all universe, H=10, k=5, LS, win=60
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r2 = xs.study_xs("XD02-ALL-H10-k5-w60", lambda P: score_skew(P, 60),
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universe="all", H=10, k=5, long_short=True)
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print(xs.fmt(r2))
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print("JSON:", xs.as_json(r2))
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print()
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# Run 3: majors, H=5, k=5, LS, win=60 (faster rebalance)
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r3 = xs.study_xs("XD02-MJ-H5-k5-w60", lambda P: score_skew(P, 60),
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universe="majors", H=5, k=5, long_short=True)
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print(xs.fmt(r3))
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print("JSON:", xs.as_json(r3))
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print()
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# Run 4: majors, H=10, k=5, LS, win=30 (shorter lookback)
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r4 = xs.study_xs("XD02-MJ-H10-k5-w30", lambda P: score_skew(P, 30),
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universe="majors", H=10, k=5, long_short=True)
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print(xs.fmt(r4))
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print("JSON:", xs.as_json(r4))
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print()
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# Run 5: majors, H=10, k=3, LS, win=60 (concentrated)
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r5 = xs.study_xs("XD02-MJ-H10-k3-w60", lambda P: score_skew(P, 60),
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universe="majors", H=10, k=3, long_short=True)
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print(xs.fmt(r5))
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print("JSON:", xs.as_json(r5))
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print()
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# Select best by: earns_slot > holdout sharpe > corr_xs01 (lower is better)
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results = [r1, r2, r3, r4, r5]
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earners = [r for r in results if r["earns_slot"]]
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if earners:
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best = max(earners, key=lambda r: r["holdout"].get("sharpe", 0))
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else:
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# fallback: highest holdout + positive full, then lowest xs01 corr
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pos = [r for r in results if r["full"]["sharpe"] > 0 and r["holdout"].get("sharpe", 0) > 0]
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if pos:
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best = max(pos, key=lambda r: r["holdout"].get("sharpe", 0) - abs(r.get("corr_xs01") or 0))
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else:
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best = max(results, key=lambda r: r["holdout"].get("sharpe", -99))
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print("=" * 60)
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print("BEST CONFIG:")
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print(xs.fmt(best))
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print("JSON:", xs.as_json(best))
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