Adriano Dal Pastro
8426d05f12
Esteso fetch_hyperliquid a 52 alt certificati (cross-venue vs Binance, flat 0%, 2024+; +gate delistato per MKR/FXS). Ma il cross-sectional momentum sui 52 e' NEGATIVO (FULL -0.1..-0.6, k grande non aiuta) vs +0.67/OOS0.91 sui 19 major (stessa finestra): i ~33 small-cap (WIF/JUP/ORDI/PYTH/TAO..) sono idiosincratici/mean-reverting e rovesciano il momentum relativo. "Piu' asset = piu' robusto" e' FALSO per l'XS momentum: la breadth utile e' quella dei major liquidi. Fix: lo sleeve _xsec_returns usa XS_UNIVERSE esplicito (19 major), non glob-all (aggiungere parquet certificati non lo rompe piu'). I 52 parquet restano su disco per ricerca futura, non per XS01. Portafoglio ripristinato e invariato: TP01 70% + XS01 30%, FULL Sh 1.41 / HOLD 1.15. 12 test ok. Diario 2026-06-19-xsec-universe-expansion.md. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
97 lines
4.6 KiB
Python
97 lines
4.6 KiB
Python
"""FETCH + CERTIFY universo Hyperliquid (Cerbero MCP MAINNET) — espansione cross-sectional.
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Hyperliquid (via cerbero-mcp mainnet) offre ~230 perp liquidi, ma storia nativa REALE solo dal
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2024 (pre-2024 = backfill, volume 0). Qui scarico un set liquido a 1d (2024+), e CERTIFICO ogni
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asset come BTC/ETH: cross-venue vs Binance (realismo) + flat-bar (liquidita'). Scrivo SOLO i puliti
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in data/raw/hl_<sym>_1d.parquet (namespace dedicato, NON mischiato col Deribit BTC/ETH).
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Disciplina: Cerbero ci ha gia' bruciato (testnet) -> niente fiducia, solo certificazione.
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uv run python scripts/analysis/fetch_hyperliquid.py
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"""
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from __future__ import annotations
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import sys, time
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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import numpy as np, pandas as pd, requests, ccxt
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RAW = PROJECT_ROOT / "data" / "raw"
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START = "2024-01-01"; END = "2026-06-17"
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# UNIVERSO ESTESO: alt liquidi noti su Hyperliquid (mappa Binance auto = SYM/USDT). Il gate di
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# certificazione (cross-venue + liquidita' + flat) scarta i non-conformi. k-prefissi esclusi
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# (scaling 1000x complica il cross-venue). MATIC morto escluso.
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SYMS = ["BTC","ETH","SOL","BNB","XRP","DOGE","AVAX","LINK","LTC","ADA","ARB","OP","SUI","APT",
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"INJ","TIA","SEI","NEAR","AAVE","ATOM","DYDX","APE","CRV","LDO","STX","GMX","SNX","BCH",
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"COMP","MKR","WLD","UNI","TRX","FIL","RUNE","ENA","ORDI","JUP","WIF","PYTH","FET","AR",
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"ETC","ALGO","GALA","SAND","AXS","DOT","FXS","BLUR","JTO","PENDLE","ONDO","TAO"]
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BINANCE = {s: f"{s}/USDT" for s in SYMS}
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def _h():
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env={}
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for ln in open(PROJECT_ROOT/".env.mainnet"):
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ln=ln.strip()
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if ln and not ln.startswith("#") and "=" in ln: k,v=ln.split("=",1); env[k]=v.strip()
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return {"Authorization":f"Bearer {env['CERBERO_TOKEN']}","X-Bot-Tag":env.get('CERBERO_BOT_TAG','fetch'),"Content-Type":"application/json"}
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def fetch_hl(sym, H, interval="1d"):
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r=requests.post("https://cerbero-mcp.tielogic.xyz/mcp/tools/get_historical",
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headers=H, json={"exchange":"hyperliquid","instrument":sym,"interval":interval,
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"start_date":START,"end_date":END}, timeout=60)
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c=r.json().get("candles",[])
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if not c: return pd.DataFrame()
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df=pd.DataFrame(c)[["timestamp","open","high","low","close","volume"]]
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return df.drop_duplicates("timestamp").sort_values("timestamp").reset_index(drop=True)
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def binance_daily(sym_b, start_ms, end_ms):
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ex=ccxt.binance({"enableRateLimit":True})
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out={}; since=start_ms
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while since<=end_ms:
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try: r=ex.fetch_ohlcv(sym_b,"1d",since=since,limit=500)
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except Exception: break
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r=[x for x in r if x[0]>=since]
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if not r: break
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for x in r:
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if start_ms<=x[0]<=end_ms and x[4]: out[int(x[0])]=float(x[4])
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nxt=int(r[-1][0])+86400000
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if nxt<=since: break
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since=nxt
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return pd.Series(out)
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def main():
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H=_h()
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print("="*92); print(" FETCH + CERTIFY Hyperliquid 1d (Cerbero mainnet) — cross-venue vs Binance + liquidita'"); print("="*92)
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print(f" {'sym':<6}{'barre':>7}{'start':>12}{'flat%':>7}{'med_bps':>9}{'>1%':>7}{'verdetto':>12}")
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certified=[]
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for s in SYMS:
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df=fetch_hl(s,H)
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if df.empty: print(f" {s:<6} vuoto"); continue
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ts=pd.to_datetime(df["timestamp"],unit="ms",utc=True)
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flat=((df.open==df.high)&(df.high==df.low)&(df.low==df.close)).mean()*100
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# cross-venue vs Binance USDT (daily close)
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ref=binance_daily(BINANCE[s], int(df["timestamp"].iloc[0]), int(df["timestamp"].iloc[-1]))
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a=df.set_index("timestamp")["close"]
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m=pd.concat([a.rename("a"),ref.rename("b")],axis=1,join="inner").dropna()
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if len(m)>5:
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bps=(m["a"]-m["b"]).abs()/m["b"]*1e4
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med=bps.median(); g1=(bps>100).mean()*100
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else: med=g1=float("nan")
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# gate "delistato/migrato": l'ultima barra dev'essere recente (entro ~21g da END),
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# altrimenti l'asset tronca l'universo cross-sectional (es. MKR fermo a 2025-09, FXS 2026-01).
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recent = (pd.Timestamp(END, tz="UTC") - ts.iloc[-1]) <= pd.Timedelta("21D")
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clean = (not np.isnan(med)) and med<60 and g1<3 and flat<5 and recent
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v = "PULITO" if clean else "scarta"
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print(f" {s:<6}{len(df):>7}{str(ts.iloc[0].date()):>12}{flat:>6.1f}%{med:>9.1f}{g1:>6.1f}%{v:>12}")
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if clean:
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df.to_parquet(RAW/f"hl_{s.lower()}_1d.parquet", index=False); certified.append(s)
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print(f"\n CERTIFICATI ({len(certified)}): {certified}")
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print(" Scritti in data/raw/hl_<sym>_1d.parquet (namespace dedicato). Universo per cross-sectional.")
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if __name__=="__main__":
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main()
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