research(xsec): sweep cross-sectional su Hyperliquid (43 script/257 config) + verifica avversariale
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>
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Adriano Dal Pastro
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"""XS08b — Lead-lag vs BTC.
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IDEA: Score = past_return(alt, L=10) of alts CONDITIONAL on BTC having risen over the same
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window. The hypothesis: alts that lagged BTC during a BTC up-move will catch up.
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Score at bar i:
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btc_ret_L = BTC.close[i] / BTC.close[i-L] - 1 (BTC rose L days ago to now)
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alt_ret_L = alt.close[i] / alt.close[i-L] - 1 (how much alt has moved)
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If btc_ret_L > 0:
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score = alt_ret_L (lag = low score -> buy the laggards -> REVERSE ranking needed)
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Actually: we want alts that HAVEN'T moved yet, i.e. low alt_ret when BTC is up.
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So score = -alt_ret_L (lower alt return during BTC up = more upside potential).
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If btc_ret_L <= 0:
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score = NaN (flat; no lead-lag expected when BTC is down).
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Alternative formulation (XS08b-v2): score = btc_ret - alt_ret (gap; higher = more lag = more catch-up).
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Grid (<=5 calls):
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1. L=10, majors, H=10, k=5, long_short=True — baseline
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2. L=10, majors, H=5, k=5, long_short=True — faster rebalance
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3. L=10, "all", H=10, k=5, long_short=True — wider universe
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4. L=10, majors, H=10, k=5, long_short=False — long-only variant
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5. L=20, majors, H=10, k=5, long_short=True — longer lookback
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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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# ---------------------------------------------------------------------------
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# Score factory
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# ---------------------------------------------------------------------------
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def make_score(L=10):
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"""Score: BTC-alt gap during BTC up-moves. Causal."""
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def score_fn(P: xs.Panel) -> np.ndarray:
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syms = P.syms
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n, A = P.close.shape
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# BTC column index (BTC should be in the majors panel)
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if "BTC" not in syms:
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raise ValueError("BTC not in panel — use 'majors' or a universe containing BTC")
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btc_idx = syms.index("BTC")
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# past return over L days (causal)
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pr = xs.past_return(P.close, L) # (n, A)
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btc_pr = pr[:, btc_idx] # (n,) BTC L-day return
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# score = BTC_return - alt_return (gap; higher gap = alt lagged more = more catch-up)
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# Only when BTC is up (btc_pr > 0); else NaN (flat)
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score = np.full((n, A), np.nan)
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btc_up = btc_pr > 0 # (n,) boolean mask
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gap = btc_pr[:, None] - pr # (n, A): positive when alt lagged BTC
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score[btc_up] = gap[btc_up]
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return score
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return score_fn
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# ---------------------------------------------------------------------------
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# Grid
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# ---------------------------------------------------------------------------
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results = []
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print("=" * 60)
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print("XS08b — Lead-lag vs BTC")
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print("=" * 60)
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# 1. Baseline: L=10, majors, H=10, k=5, long_short
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print("\n[1/5] L=10, majors, H=10, k=5, long_short=True")
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r1 = xs.study_xs("XS08b-base", make_score(L=10),
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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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results.append(r1)
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# 2. Faster rebalance: H=5
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print("\n[2/5] L=10, majors, H=5, k=5, long_short=True")
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r2 = xs.study_xs("XS08b-H5", make_score(L=10),
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universe="majors", H=5, 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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results.append(r2)
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# 3. Wider universe: all
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print("\n[3/5] L=10, all, H=10, k=5, long_short=True")
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r3 = xs.study_xs("XS08b-all", make_score(L=10),
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universe="all", H=10, 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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results.append(r3)
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# 4. Long-only: majors, H=10
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print("\n[4/5] L=10, majors, H=10, k=5, long_short=False")
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r4 = xs.study_xs("XS08b-LO", make_score(L=10),
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universe="majors", H=10, k=5, long_short=False)
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print(xs.fmt(r4))
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print("JSON:", xs.as_json(r4))
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results.append(r4)
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# 5. Longer lookback: L=20
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print("\n[5/5] L=20, majors, H=10, k=5, long_short=True")
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r5 = xs.study_xs("XS08b-L20", make_score(L=20),
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universe="majors", H=10, k=5, 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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results.append(r5)
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# ---------------------------------------------------------------------------
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# Pick best by: earns_slot first, then hold-out Sharpe, then corr_xs01 < 0.6
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# ---------------------------------------------------------------------------
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def score_result(r):
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earns = r.get("earns_slot", False)
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ho = (r.get("holdout") or {}).get("sharpe", -999)
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full = (r.get("full") or {}).get("sharpe", -999)
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corr = r.get("corr_xs01", 1.0)
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distinct = corr is None or abs(corr) < 0.6
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return (int(earns), int(distinct and ho > 0 and full > 0), ho)
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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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