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>
This commit is contained in:
Adriano Dal Pastro
@@ -0,0 +1,132 @@
|
||||
"""XS05b — Risk-parity momentum (inverse-vol weighted legs).
|
||||
|
||||
MECHANISM: Select top-k / bottom-k by plain 60-day momentum (same as XS01),
|
||||
but instead of equal-weighting within long/short legs, weight each asset by
|
||||
INVERSE of its own recent volatility (60-day rolling std of daily returns).
|
||||
This approximates risk-parity within the cross-sectional book: lower-vol
|
||||
assets get larger weight, so each leg contributes roughly equal risk.
|
||||
|
||||
LIMITATION / CAVEAT:
|
||||
- xslib.study_xs always equal-weights within legs (the score only determines
|
||||
SELECTION, not position sizing). We cannot pass per-asset weights directly
|
||||
through the study_xs interface.
|
||||
- Workaround: encode the inverse-vol signal INTO the score. After selecting
|
||||
the top-k / bottom-k by momentum rank, the harness will still equal-weight
|
||||
— but by blending the momentum z-score with the inverse-vol z-score we bias
|
||||
the SELECTION toward low-vol winners (i.e., the most risk-efficient longs
|
||||
rank higher). This is a partial approximation: true risk-parity would rescale
|
||||
weights post-selection; here we rescale the ranking pre-selection.
|
||||
- The blend is: score = z(mom60) + alpha * z(1/vol60), where alpha=1 gives
|
||||
equal weight to momentum rank and inverse-vol rank.
|
||||
|
||||
GRID (<=5 calls):
|
||||
1. XS05b-base : majors, H=10, k=5, L=60, alpha=1 (blend)
|
||||
2. XS05b-all : all (49 alts), H=10, k=5, L=60, alpha=1
|
||||
3. XS05b-a05 : majors, H=10, k=5, L=60, alpha=0.5 (lighter inv-vol)
|
||||
4. XS05b-a2 : majors, H=10, k=5, L=60, alpha=2.0 (heavier inv-vol)
|
||||
5. XS05b-H5 : majors, H=5, k=5, L=60, alpha=1 (faster rebalance)
|
||||
"""
|
||||
import sys
|
||||
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/xsec")
|
||||
import xslib as xs
|
||||
import numpy as np
|
||||
|
||||
|
||||
def score_xs05b(P, L=60, alpha=1.0):
|
||||
"""Risk-parity momentum score (causal).
|
||||
|
||||
score = z_cross(mom_L) + alpha * z_cross(inv_vol_L)
|
||||
Higher score -> more risk-efficient momentum winner -> long.
|
||||
Lower score -> more risk-efficient momentum loser -> short.
|
||||
"""
|
||||
# 1. momentum signal (L-day return, causal)
|
||||
mom = xs.past_return(P.close, L) # (n_days, n_assets), uses close[i-L:i]
|
||||
z_mom = xs.xs_zscore(mom)
|
||||
|
||||
# 2. inverse-vol signal (rolling std of daily returns, causal)
|
||||
vol = xs.roll_std(P.ret, L) # (n_days, n_assets)
|
||||
inv_vol = np.where(vol > 0, 1.0 / vol, np.nan)
|
||||
z_inv_vol = xs.xs_zscore(inv_vol)
|
||||
|
||||
# 3. blend
|
||||
score = z_mom + alpha * z_inv_vol
|
||||
return score
|
||||
|
||||
|
||||
results = {}
|
||||
|
||||
# --- Config 1: majors, H=10, k=5, alpha=1 (baseline blend) ---
|
||||
rep1 = xs.study_xs(
|
||||
"XS05b-base",
|
||||
lambda P: score_xs05b(P, L=60, alpha=1.0),
|
||||
universe="majors",
|
||||
H=10, k=5, long_short=True
|
||||
)
|
||||
results["XS05b-base"] = rep1
|
||||
print(xs.fmt(rep1))
|
||||
print("JSON:", xs.as_json(rep1))
|
||||
print()
|
||||
|
||||
# --- Config 2: all alts, H=10, k=5, alpha=1 ---
|
||||
rep2 = xs.study_xs(
|
||||
"XS05b-all",
|
||||
lambda P: score_xs05b(P, L=60, alpha=1.0),
|
||||
universe="all",
|
||||
H=10, k=5, long_short=True
|
||||
)
|
||||
results["XS05b-all"] = rep2
|
||||
print(xs.fmt(rep2))
|
||||
print("JSON:", xs.as_json(rep2))
|
||||
print()
|
||||
|
||||
# --- Config 3: majors, H=10, k=5, alpha=0.5 (lighter inv-vol) ---
|
||||
rep3 = xs.study_xs(
|
||||
"XS05b-a05",
|
||||
lambda P: score_xs05b(P, L=60, alpha=0.5),
|
||||
universe="majors",
|
||||
H=10, k=5, long_short=True
|
||||
)
|
||||
results["XS05b-a05"] = rep3
|
||||
print(xs.fmt(rep3))
|
||||
print("JSON:", xs.as_json(rep3))
|
||||
print()
|
||||
|
||||
# --- Config 4: majors, H=10, k=5, alpha=2.0 (heavier inv-vol) ---
|
||||
rep4 = xs.study_xs(
|
||||
"XS05b-a2",
|
||||
lambda P: score_xs05b(P, L=60, alpha=2.0),
|
||||
universe="majors",
|
||||
H=10, k=5, long_short=True
|
||||
)
|
||||
results["XS05b-a2"] = rep4
|
||||
print(xs.fmt(rep4))
|
||||
print("JSON:", xs.as_json(rep4))
|
||||
print()
|
||||
|
||||
# --- Config 5: majors, H=5, k=5, alpha=1 (faster rebalance) ---
|
||||
rep5 = xs.study_xs(
|
||||
"XS05b-H5",
|
||||
lambda P: score_xs05b(P, L=60, alpha=1.0),
|
||||
universe="majors",
|
||||
H=5, k=5, long_short=True
|
||||
)
|
||||
results["XS05b-H5"] = rep5
|
||||
print(xs.fmt(rep5))
|
||||
print("JSON:", xs.as_json(rep5))
|
||||
print()
|
||||
|
||||
# --- Summary ---
|
||||
print("=" * 60)
|
||||
print("SUMMARY — XS05b grid")
|
||||
print("=" * 60)
|
||||
fmt_h = f"{'Config':<16} {'FullSh':>7} {'HoldSh':>7} {'MaxDD':>7} {'CorrXS01':>9} {'EarnsSlot':>10} {'Verdict':>10}"
|
||||
print(fmt_h)
|
||||
print("-" * 70)
|
||||
for name, r in results.items():
|
||||
fs = r["full"]["sharpe"]
|
||||
hs = r["holdout"]["sharpe"]
|
||||
dd = r["full"]["maxdd"]
|
||||
cxs = r.get("corr_xs01", float("nan"))
|
||||
es = r.get("earns_slot", False)
|
||||
vd = r.get("marginal", {}).get("verdict", "N/A")
|
||||
print(f"{name:<16} {fs:>7.2f} {hs:>7.2f} {dd:>7.2f} {cxs:>9.3f} {str(es):>10} {vd:>10}")
|
||||
Reference in New Issue
Block a user