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>
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
"""XM07 — Sharpe-rank momentum cross-sectional strategy.
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Score = roll_mean(ret, L) / roll_std(ret, L) (realized Sharpe ratio over L days)
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Rank assets cross-sectionally each H days, long top-k / short bottom-k.
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Grid: L in {30, 60, 90}, then vary universe/H/k around the best L.
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<=5 study_xs calls total.
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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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def sharpe_score(P, L):
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"""Causal realized Sharpe = roll_mean(ret, L) / roll_std(ret, L).
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Uses daily returns (P.ret). Higher = stronger risk-adjusted momentum -> long.
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"""
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mu = xs.roll_mean(P.ret, L)
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sigma = xs.roll_std(P.ret, L)
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# avoid division by near-zero vol; set to NaN if sigma too small
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score = mu / np.where(sigma > 1e-8, sigma, np.nan)
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return score # (n_days x n_assets), higher = long
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# ---- Grid (5 calls) --------------------------------------------------------
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# Step 1: sweep L on "majors" universe with fixed H=10, k=5, long_short=True
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print("=" * 60)
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print("XM07 Sharpe-rank momentum — grid search")
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print("=" * 60)
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results = {}
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# Call 1: L=30, majors
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r1 = xs.study_xs("XM07_L30_majors", lambda P: sharpe_score(P, 30),
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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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results["L30_majors"] = r1
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# Call 2: L=60, majors
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r2 = xs.study_xs("XM07_L60_majors", lambda P: sharpe_score(P, 60),
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universe="majors", H=10, k=5, long_short=True)
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print(xs.fmt(r2))
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results["L60_majors"] = r2
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# Call 3: L=90, majors
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r3 = xs.study_xs("XM07_L90_majors", lambda P: sharpe_score(P, 90),
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universe="majors", H=10, k=5, long_short=True)
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print(xs.fmt(r3))
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results["L90_majors"] = r3
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# Pick best L by hold-out Sharpe among the 3
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best_L_key = max(["L30_majors", "L60_majors", "L90_majors"],
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key=lambda k: results[k]["holdout"]["sharpe"])
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best_L = int(best_L_key.split("_")[0][1:]) # extract integer
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print(f"\nBest L = {best_L} (by hold-out Sharpe)")
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# Call 4: best L on "all" universe (49 alts) to test breadth
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r4 = xs.study_xs(f"XM07_L{best_L}_all", lambda P: sharpe_score(P, best_L),
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universe="all", H=10, k=5, long_short=True)
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print(xs.fmt(r4))
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results[f"L{best_L}_all"] = r4
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# Call 5: best L on majors, try H=20 (less frequent rebalance, lower fee drag)
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r5 = xs.study_xs(f"XM07_L{best_L}_H20", lambda P: sharpe_score(P, best_L),
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universe="majors", H=20, k=5, long_short=True)
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print(xs.fmt(r5))
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results[f"L{best_L}_H20"] = r5
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# ---- Pick overall best config -----------------------------------------------
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print("\n" + "=" * 60)
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print("SUMMARY — picking best config")
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print("=" * 60)
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def score_config(r):
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"""Prefer: earns_slot, then hold-out, then full Sharpe, then distinctness."""
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earns = int(r.get("earns_slot", False))
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ho = r["holdout"]["sharpe"]
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full = r["full"]["sharpe"]
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dist = 1.0 - abs(r.get("corr_xs01", 1.0)) # higher = more distinct
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return (earns, ho, full, dist)
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best_key = max(results.keys(), key=lambda k: score_config(results[k]))
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best = results[best_key]
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print(f"Best config: {best_key}")
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print(xs.fmt(best))
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print("JSON:", xs.as_json(best))
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