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>
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
"""XM03 — Vol-Scaled (Risk-Adjusted) Momentum
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MECHANISM: Score = past_return(close, L) / roll_std(ret, L)
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This is a Sharpe-like signal: normalises raw momentum by the volatility of that asset
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over the same window. Should favour assets that moved up *smoothly* (high Sharpe trend)
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over those that had large one-off jumps (noisy high return).
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Grid: L in {30, 60, 90}; universe in {all, majors}; long_short True/False.
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Goal: test if risk-adjusted scoring is DISTINCT from plain XS01 momentum and ADDS to the
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live TP01+XS01+VRP01 portfolio.
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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 vol_adj_momentum(P, L: int) -> np.ndarray:
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"""Causal Sharpe-like score: past_return / roll_std(ret, L).
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Higher = long. Returns (n_days x n_assets).
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Avoid divide-by-zero by replacing 0-vol rows with NaN -> harness treats NaN as neutral.
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"""
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pr = xs.past_return(P.close, L) # causal past return over L days
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rv = xs.roll_std(P.ret, L) # causal rolling std of daily returns
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# Replace zeros/near-zeros with NaN to avoid Inf
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rv_safe = np.where(rv < 1e-8, np.nan, rv)
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score = pr / rv_safe
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return score
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print("XM03 — Vol-Scaled (Risk-Adjusted) Momentum")
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print("=" * 60)
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# 1) All universe, L=30 (short horizon vol-adj)
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rep1 = xs.study_xs(
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"XM03_ALL_L30",
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lambda P: vol_adj_momentum(P, 30),
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universe="all", H=10, k=5, long_short=True
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)
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print(xs.fmt(rep1))
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print("JSON:", xs.as_json(rep1))
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print()
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# 2) All universe, L=60 (medium horizon)
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rep2 = xs.study_xs(
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"XM03_ALL_L60",
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lambda P: vol_adj_momentum(P, 60),
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universe="all", H=10, k=5, long_short=True
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)
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print(xs.fmt(rep2))
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print("JSON:", xs.as_json(rep2))
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print()
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# 3) All universe, L=90 (long horizon)
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rep3 = xs.study_xs(
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"XM03_ALL_L90",
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lambda P: vol_adj_momentum(P, 90),
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universe="all", H=10, k=5, long_short=True
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)
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print(xs.fmt(rep3))
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print("JSON:", xs.as_json(rep3))
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print()
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# 4) Majors only (same universe as XS01), L=60 — can vol-adj beat plain MOM on XS01 turf?
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rep4 = xs.study_xs(
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"XM03_MAJORS_L60",
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lambda P: vol_adj_momentum(P, 60),
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universe="majors", H=10, k=5, long_short=True
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)
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print(xs.fmt(rep4))
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print("JSON:", xs.as_json(rep4))
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print()
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# 5) All universe, L=60, long-only — does vol-adj work as selection filter?
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rep5 = xs.study_xs(
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"XM03_ALL_L60_LO",
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lambda P: vol_adj_momentum(P, 60),
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universe="all", H=10, k=5, long_short=False
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)
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print(xs.fmt(rep5))
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print("JSON:", xs.as_json(rep5))
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print()
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def score_rep(r):
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earns = int(r["earns_slot"])
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hold_sh = r["holdout"].get("sharpe", -9)
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full_sh = r["full"]["sharpe"]
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corr_xs01 = r.get("corr_xs01") or 1.0
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distinctness = 1 - abs(corr_xs01)
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return (earns, hold_sh, full_sh, distinctness)
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all_reps = [rep1, rep2, rep3, rep4, rep5]
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best = max(all_reps, key=score_rep)
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print("=" * 60)
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print(f"BEST CONFIG: {best['name']}")
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print(xs.fmt(best))
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print("JSON:", xs.as_json(best))
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