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feat(portfolio): XS01 cross-sectional (Hyperliquid) BATTE il portafoglio -> TP01 70% + XS01 30%

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Espansione universo (su input utente "storico da cerbero"): il Cerbero MCP col token MAINNET serve
Hyperliquid (230 perp REALI, storia nativa dal 2024). fetch_hyperliquid.py certifica 19 alt liquidi
a 1d (flat 0%, cross-venue 4-9 bps vs Binance) -> data/raw/hl_*_1d.parquet. Abilita le strategie
CROSS-SECTIONAL (impossibili a 2 asset).

XS01 = cross-sectional momentum market-neutral (long 5 forti / short 5 deboli su ret 30g, ogni 10g,
vol-target 20%). Validato onesto: plateau (config/k/subset), fee-robusto (0.3% RT), scorrelato a TP01
(-0.06), positivo OGNI anno 2024-26, meccanismo complementare (lavora nella dispersione quando TP01
e' in cash). Diverso dal regime-luck RV bocciato (19 asset, plateau, ogni anno+).

Contributo al portafoglio (outer-join + pesi rinormalizzati per sleeve a date diverse):
  TP01-solo FULL 1.30 / HOLD 0.31  ->  TP01 70% + XS01 30%: FULL 1.41 / HOLD 1.15, DD giu', ~ogni anno+.
-> XS01 BATTE il portafoglio esistente: inserito in active_sleeves.

Caveat (documentati): storia XS ~2.5 anni; STAT-MODE (book 19 gambe non eseguibile a 2k -> ~20k),
sleeve diagnostico/forward-monitor. portfolio.combine ora outer-join+renorm. 12 test passano.
Diario 2026-06-19-hyperliquid-xsec.md.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adriano Dal Pastro
2026-06-19 20:05:45 +00:00
parent 18f22160b2
commit a5a61ac7e3
10 changed files with 512 additions and 15 deletions
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scripts/analysis/research_lab.py
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@@ -29,7 +29,23 @@ import pandas as pd
from src.data.downloader import load_data
FEE_RT = 0.001 # 0.10% round-trip taker Deribit (0.05%/lato)
BARS_PER_YEAR = {"5m": 105192.0, "15m": 35064.0, "1h": 8766.0}
BARS_PER_YEAR = {"5m": 105192.0, "15m": 35064.0, "1h": 8766.0,
"4h": 2191.5, "12h": 730.5, "1d": 365.25}
def load_tf(asset: str, tf: str):
"""Carica un TF certificato. 5m/15m/1h diretti; 4h/12h/1d DERIVATI per resample dal 1h
(confini 00:00 UTC). >=12h e' il regime raccomandato (sotto, costi+overfit dominano)."""
if tf in ("5m", "15m", "1h"):
return load_data(asset, tf)
rule = {"4h": "4h", "12h": "12h", "1d": "1D"}[tf]
df = load_data(asset, "1h").copy()
df.index = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
out = df.resample(rule, label="left", closed="left").agg(
{"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}).dropna(subset=["open"])
epoch = pd.Timestamp("1970-01-01", tz="UTC")
out["timestamp"] = ((out.index - epoch) // pd.Timedelta(milliseconds=1)).astype("int64")
return out.reset_index(drop=True)[["timestamp", "open", "high", "low", "close", "volume"]]
# Hold-out FINALE bloccato: NIENTE ricerca/tuning lo tocca finché non è il verdetto (Fase 3).
HOLDOUT_START = "2025-01-01"
# Finestra di validazione OOS usata in ricerca (out-of-sample ma PRE hold-out).