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>
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
"""XS01b — Double-sort Momentum × Low-Vol
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Score = xs_zscore(past_return(close, 60)) + xs_zscore(-roll_std(ret, 30))
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Combines cross-sectional momentum with low-vol preference (lower realized vol = higher score).
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Grid: universe x H x k variations, <=5 total backtests.
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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 factory ---
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def score_mom_lowvol(mom_L=60, vol_win=30):
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"""Double-sort: momentum z + low-vol z. Both causal (data <= close[i])."""
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def _score(P):
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mom = xs.xs_zscore(xs.past_return(P.close, mom_L))
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# low vol = higher score -> negate std
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lowvol = xs.xs_zscore(-xs.roll_std(P.ret, vol_win))
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return mom + lowvol
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return _score
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# Grid (<=5 calls total):
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# 1. Baseline: majors H10 k5 LS (19 assets, closest to XS01 universe)
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# 2. All universe H10 k5 LS
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# 3. All universe H5 k5 LS (faster rebalance)
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# 4. Majors H10 k5 LS with longer mom window (90d) to differ from XS01
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# 5. All universe H10 k7 LS (wider book)
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configs = [
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dict(name="XS01b-MAJ-H10-k5", universe="majors", H=10, k=5, long_short=True, fn=score_mom_lowvol(60,30)),
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dict(name="XS01b-ALL-H10-k5", universe="all", H=10, k=5, long_short=True, fn=score_mom_lowvol(60,30)),
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dict(name="XS01b-ALL-H5-k5", universe="all", H=5, k=5, long_short=True, fn=score_mom_lowvol(60,30)),
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dict(name="XS01b-MAJ-H10-MOM90", universe="majors", H=10, k=5, long_short=True, fn=score_mom_lowvol(90,30)),
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dict(name="XS01b-ALL-H10-k7", universe="all", H=10, k=7, long_short=True, fn=score_mom_lowvol(60,30)),
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]
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results = []
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for cfg in configs:
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print(f"\nRunning {cfg['name']} ...")
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fn = cfg.pop("fn")
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rep = xs.study_xs(score_fn=fn, **cfg)
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results.append(rep)
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print(xs.fmt(rep))
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print()
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# --- pick best: prefer earns_slot, then hold-out sharpe, then corr_xs01 < 0.6
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def score_result(r):
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earns = 1 if r["earns_slot"] else 0
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hold_sh = r["holdout"].get("sharpe", -99)
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full_sh = r["full"]["sharpe"]
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distinct = 1 if (r["corr_xs01"] or 1.0) < 0.6 else 0
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return (earns, hold_sh, full_sh, distinct)
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best = max(results, key=score_result)
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print("\n" + "="*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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