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>
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
"""XM04 — Residual / Idiosyncratic Momentum
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IDEA: Instead of raw past return, score = cumulative idiosyncratic (beta-removed) return
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over the last L days. Should be a cleaner momentum signal: strips out the common market
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component and scores assets on their STOCK-SPECIFIC performance.
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Signal: for each day i and asset a, sum the daily residual_returns over [i-L+1 .. i].
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residual_ret[t,a] = ret[t,a] - beta_t_a * market_ret[t]
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score[i,a] = sum(residual_ret[i-L+1:i+1, a]) (causal: uses data <= i)
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Grid (<=5 calls):
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1. XM04-L30-maj : majors universe, L=30, H=10, k=5, LS
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2. XM04-L60-maj : majors universe, L=60, H=10, k=5, LS
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3. XM04-L30-all : all universe, L=30, H=10, k=5, LS
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4. XM04-L30-maj-H5: majors, L=30, H=5, k=5, LS (faster rebal)
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5. XM04-L30-maj-LO: majors, L=30, H=10, k=5, long-only (LO)
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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 resid_mom_score(P, L=30, beta_win=60):
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"""Cumulative residual return over the last L days.
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residual_ret[t,a] = ret[t,a] - beta(win)*market_ret[t]
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score[i,a] = rolling sum of residual_ret over window L.
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All causal: roll_beta uses data <=i, rolling sum uses [i-L+1..i].
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"""
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# daily idiosyncratic returns (n_days x n_assets), causal
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resid = xs.residual_return(P.ret, win=beta_win)
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# rolling sum over L days = cumulative idiosyncratic momentum
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score = xs.roll_mean(resid, L) * L # equiv to rolling sum (roll_mean * win)
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return score
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configs = [
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# name, universe, L, H, k, long_short
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("XM04-L30-maj", "majors", 30, 10, 5, True),
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("XM04-L60-maj", "majors", 60, 10, 5, True),
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("XM04-L30-all", "all", 30, 10, 5, True),
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("XM04-L30-maj-H5", "majors", 30, 5, 5, True),
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("XM04-L30-maj-LO", "majors", 30, 10, 5, False),
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]
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results = []
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for name, univ, L, H, k, ls in configs:
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print(f"\nRunning {name} ...")
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rep = xs.study_xs(
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name,
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lambda P, _L=L: resid_mom_score(P, L=_L, beta_win=60),
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universe=univ,
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H=H,
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k=k,
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long_short=ls,
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)
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print(xs.fmt(rep))
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results.append(rep)
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# Pick best: prefer earns_slot, then highest hold-out Sharpe, then most distinct from XS01
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def score_config(r):
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earns = r["earns_slot"]
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hold_sh = r["holdout"].get("sharpe", -99)
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full_sh = r["full"]["sharpe"]
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corr_xs = r["corr_xs01"] or 1.0
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# primary: earns_slot; secondary: holdout Sharpe; tiebreak: distinctness
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return (earns, hold_sh, full_sh, -abs(corr_xs))
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best = max(results, key=score_config)
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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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