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feat(XS01): affina con blend di lookback [30,90] — FULL 0.80->1.10, portafoglio 1.41->1.48

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Come TP01 fonde gli orizzonti, XS01 ora fonde 30g+90g del momentum cross-sectional (z-score per
lookback, mediato). Sweep: [30,90] e' il sweet spot (fonde i due singoli robusti, anti-overfit):
XS01 standalone FULL 0.80->1.10, DD 21%->14%, corr a TP01 -0.06->-0.12, 100% anni+. Portafoglio
TP01 70 + XS01 30: FULL Sh 1.41->1.48, DD 5.2%->4.6%, ~€/g 1.65->1.78; hold-out 1.15->1.06 (calo
marginale dentro il rumore). Piu' robusto (due orizzonti) + diversifica meglio -> promosso.

sleeves.XS_CFG lookbacks=(30,90), engine _xsec_returns usa lo score blended. 12 test ok.
Diario 2026-06-19-xsec-blend.md.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adriano Dal Pastro
2026-06-19 22:19:12 +00:00
parent bf6ade51af
commit fd5a0bd3cf
3 changed files with 161 additions and 7 deletions
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src/portfolio/sleeves.py
+18 -7
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@@ -52,7 +52,10 @@ def tp01_sleeve(weight: float = 1.0) -> Sleeve:
# trend di TP01: lavora quando TP01 e' in cash). Validato: scripts/portfolio/xsec_research.py.
import glob as _glob
from pathlib import Path as _Path
XS_CFG = dict(L=30, H=10, k=5, mode="mom", target_vol=0.20)
# BLEND di lookback (2026-06-19): fonde 30g+90g del momentum cross-sectional (z-score per
# lookback, mediato) come TP01 fonde gli orizzonti -> piu' robusto del singolo L=30: FULL Sh
# 0.80->1.10, DD 21%->14%, corr a TP01 -0.06->-0.12, 100% anni+. Diario 2026-06-19-xsec-blend.md.
XS_CFG = dict(lookbacks=(30, 90), H=10, k=5, mode="mom", target_vol=0.20)
_HL_DIR = _Path(__file__).resolve().parents[2] / "data" / "raw"
# UNIVERSO ESPLICITO = 19 ALT LIQUIDI MAJOR. NB (2026-06-19): allargare a 52 asset (incluso
# small-cap WIF/JUP/ORDI/PYTH/TAO...) DILUISCE l'edge -> momentum cross-section NEGATIVO sui 52.
@@ -75,15 +78,23 @@ def _xsec_returns() -> pd.Series:
raise FileNotFoundError("universo Hyperliquid XS01 incompleto: gira scripts/analysis/fetch_hyperliquid.py")
C = pd.concat(cols, axis=1, join="inner").sort_index().dropna()
px = C.values; n, A = px.shape
L, H, k, mode, tv = XS_CFG["L"], XS_CFG["H"], XS_CFG["k"], XS_CFG["mode"], XS_CFG["target_vol"]
lookbacks, H, k, mode, tv = XS_CFG["lookbacks"], XS_CFG["H"], XS_CFG["k"], XS_CFG["mode"], XS_CFG["target_vol"]
dret = np.vstack([np.zeros(A), px[1:] / px[:-1] - 1.0])
W = np.zeros((n, A)); w = np.zeros(A)
for i in range(n):
if i >= L and i % H == 0:
order = np.argsort(px[i] / px[i - L] - 1.0)
w = np.zeros(A); lo, hi = order[:k], order[-k:]
if mode == "mom": w[hi] = 0.5 / k; w[lo] = -0.5 / k
else: w[lo] = 0.5 / k; w[hi] = -0.5 / k
if i >= max(lookbacks) and i % H == 0:
score = np.zeros(A); cnt = 0 # blend: media z-score cross-sectional per lookback
for L in lookbacks:
rL = px[i] / px[i - L] - 1.0
sd = rL.std()
if sd > 0:
score += (rL - rL.mean()) / sd; cnt += 1
if cnt:
score /= cnt
order = np.argsort(score)
w = np.zeros(A); lo, hi = order[:k], order[-k:]
if mode == "mom": w[hi] = 0.5 / k; w[lo] = -0.5 / k
else: w[lo] = 0.5 / k; w[hi] = -0.5 / k
W[i] = w
gross = np.zeros(n); gross[1:] = np.sum(W[:-1] * dret[1:], axis=1)
turn = np.zeros(n); turn[0] = np.abs(W[0]).sum(); turn[1:] = np.abs(np.diff(W, axis=0)).sum(axis=1)