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feat(xsec): dispersion-gate XS01 live (disp_min=0.0313) — Sharpe 3.46, PORT06 OOS 10.07->10.37; FC01 funding-carry scartata

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Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Adriano Dal Pastro
2026-06-10 21:42:12 +00:00
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scripts/analysis/funding_carry_research.py
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"""FC01 — Funding-carry market-neutral (ricerca, 2026-06-10).
Idea: su Deribit i long pagano gli short quando il funding e' positivo (e
viceversa). W12 (scartata) shortava il perp su funding alto = direzionale.
Qui il meccanismo NUOVO e' il CARRY NEUTRALE: short della gamba con funding
alto / long della gamba con funding basso (BTC vs ETH, dollar-neutral),
incassando il DIFFERENZIALE di funding con esposizione residua = solo lo
spread ETH/BTC (correlazione ~0.95).
Dati REALI: data/regime/{btc,eth}_funding.parquet (orario, 2019-12 -> 2026-06,
interest_1h effettivo + index_price). Causale: decisione al close t con
funding noto fino a t; accrual dal bar t+1; fee 0.10% RT per GAMBA.
Varianti:
FC-A spread-carry 2 gambe (il candidato): entra quando lo spread di funding
smussato supera la soglia, esce quando rientra / max_bars.
FC-B single-asset carry direzionale (confronto onesto con W12): short se
funding smussato > thr, long se < -thr.
Protocollo: TRAIN fino a OOS_DATE (2023-11-01) per scegliere la config,
OOS dopo; griglia robustezza; sweep fee; breakdown annuale.
uv run python scripts/analysis/funding_carry_research.py
"""
from __future__ import annotations
import sys
from pathlib import Path
import numpy as np
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(PROJECT_ROOT))
FEE_RT = 0.001 # 0.10% RT per gamba (taker, baseline progetto)
OOS_DATE = "2023-11-01"
HRS_YEAR = 24 * 365
def load_panel():
btc = pd.read_parquet("data/regime/btc_funding.parquet")
eth = pd.read_parquet("data/regime/eth_funding.parquet")
for d in (btc, eth):
d["dt"] = pd.to_datetime(d["timestamp"], unit="ms")
m = btc.set_index("dt")[["interest_1h", "index_price"]].rename(
columns={"interest_1h": "f_btc", "index_price": "p_btc"}).join(
eth.set_index("dt")[["interest_1h", "index_price"]].rename(
columns={"interest_1h": "f_eth", "index_price": "p_eth"}),
how="inner").sort_index()
m = m.dropna()
return m
def explore(m):
print("=" * 96)
print(" [0] ESPLORAZIONE — funding orario reale Deribit, "
f"{m.index[0].date()} -> {m.index[-1].date()} ({len(m)} ore)")
print("=" * 96)
for a in ("btc", "eth"):
f = m[f"f_{a}"] * HRS_YEAR * 100 # annualizzato %
print(f" {a.upper()}: funding annualizzato mean {f.mean():+6.2f}% "
f"med {f.median():+6.2f}% p10 {f.quantile(.1):+7.2f}% "
f"p90 {f.quantile(.9):+7.2f}% %ore>0 {100*(f>0).mean():.0f}%")
sp = (m["f_eth"] - m["f_btc"]) * HRS_YEAR * 100
print(f" SPREAD ETH-BTC annualizzato: mean {sp.mean():+6.2f}% "
f"p10 {sp.quantile(.1):+7.2f}% p90 {sp.quantile(.9):+7.2f}%")
# persistenza: autocorr dello spread smussato 24h a vari lag
s24 = (m["f_eth"] - m["f_btc"]).rolling(24).mean()
for lag in (24, 72, 168):
c = s24.autocorr(lag)
print(f" autocorr spread(24h-smooth) lag {lag:>4}h: {c:+.3f}")
# quanto duramo sopra soglia? episodi |spread ann| > 10%
thr = 0.10 / HRS_YEAR
above = (s24.abs() > thr).astype(int)
runs = (above.groupby((above != above.shift()).cumsum()).sum())
runs = runs[runs > 0]
if len(runs):
print(f" episodi |spread|>10% ann: {len(runs)} durata mediana "
f"{runs.median():.0f}h p90 {runs.quantile(.9):.0f}h")
# ---------------------------------------------------------------------------
# Backtest FC-A: spread-carry 2 gambe
# ---------------------------------------------------------------------------
def carry_pair(m, smooth=72, thr_ann=10.0, exit_frac=0.0, max_bars=24 * 30,
fee_rt=FEE_RT, sl=None):
"""Entra quando |spread smussato| > thr (annualizzato %); short la gamba
col funding alto, long l'altra, 1x notional per gamba. Esce quando lo
spread smussato scende sotto exit_frac*thr (o cambia segno) o max_bars.
Ritorna array di net-return per trade + serie equity oraria (additiva)."""
f_sp = (m["f_eth"] - m["f_btc"]).rolling(smooth).mean().to_numpy()
fe = m["f_eth"].to_numpy()
fb = m["f_btc"].to_numpy()
pe = m["p_eth"].to_numpy()
pb = m["p_btc"].to_numpy()
n = len(m)
thr = thr_ann / 100 / HRS_YEAR
ex = exit_frac * thr
sli = m.index[:n] if sl is None else None
rets, lens, accs = [], [], []
eq = np.zeros(n)
i = smooth
while i < n - 1:
s = f_sp[i]
if not np.isfinite(s) or abs(s) <= thr:
i += 1
continue
d = -1 if s > 0 else 1 # s>0: ETH paga di piu' -> short ETH/long BTC
e_eth, e_btc = pe[i], pb[i]
acc = 0.0
j = i + 1
end = min(n - 1, i + max_bars)
while j <= end:
# accrual del funding sull'ora j: short riceve +f, long paga f
acc += (-d) * fe[j] + d * fb[j]
if abs(f_sp[j]) <= ex or np.sign(f_sp[j]) != np.sign(s):
break
j += 1
j = min(j, end)
price_leg = d * (pe[j] - e_eth) / e_eth - d * (pb[j] - e_btc) / e_btc
net = price_leg + acc - 2 * fee_rt
rets.append(net)
lens.append(j - i)
accs.append(acc)
eq[j] += net
i = j + 1
rets = np.array(rets)
eqs = pd.Series(eq, index=m.index).cumsum()
return rets, np.array(lens), np.array(accs), eqs
# ---------------------------------------------------------------------------
# Backtest FC-B: carry direzionale single-asset (confronto/W12 onesto)
# ---------------------------------------------------------------------------
def carry_single(m, asset="eth", smooth=72, thr_ann=20.0, exit_frac=0.0,
max_bars=24 * 30, fee_rt=FEE_RT):
f = m[f"f_{asset}"].rolling(smooth).mean().to_numpy()
fr = m[f"f_{asset}"].to_numpy()
p = m[f"p_{asset}"].to_numpy()
n = len(m)
thr = thr_ann / 100 / HRS_YEAR
ex = exit_frac * thr
rets = []
i = smooth
while i < n - 1:
s = f[i]
if not np.isfinite(s) or abs(s) <= thr:
i += 1
continue
d = -1 if s > 0 else 1 # funding alto -> short (incassa)
e = p[i]
acc = 0.0
j = i + 1
end = min(n - 1, i + max_bars)
while j <= end:
acc += (-d) * fr[j]
if abs(f[j]) <= ex or np.sign(f[j]) != np.sign(s):
break
j += 1
j = min(j, end)
net = d * (p[j] - e) / e + acc - fee_rt
rets.append(net)
i = j + 1
return np.array(rets)
def stats(rets, idx_len_hours, label="", lens=None, accs=None):
if len(rets) == 0:
return f" {label:<28s} 0 trade"
yrs = idx_len_hours / HRS_YEAR
pnl = rets.sum() * 100
win = (rets > 0).mean() * 100
tpy = len(rets) / yrs
sh = rets.mean() / (rets.std() + 1e-12) * np.sqrt(max(tpy, 1e-9))
extra = ""
if lens is not None and len(lens):
extra = f" | hold med {np.median(lens):.0f}h"
if accs is not None and len(accs):
extra += f" | carry quota {100*np.sum(accs)/max(np.sum(rets),1e-9):.0f}%"
return (f" {label:<28s} {len(rets):>4d} tr | win {win:>4.0f}% | "
f"PnL {pnl:>+7.1f}% | {tpy:>5.1f} tr/anno | Sh {sh:>5.2f}{extra}")
def main():
m = load_panel()
explore(m)
cut = m.index.searchsorted(pd.Timestamp(OOS_DATE))
mtr, moo = m.iloc[:cut], m.iloc[cut:]
print(f"\n TRAIN {m.index[0].date()} -> {OOS_DATE} | OOS -> {m.index[-1].date()}")
print("\n" + "=" * 96)
print(" [1] FC-A spread-carry 2 gambe (fee 0.10% RT x2 gambe) — griglia su TRAIN")
print("=" * 96)
grid = []
for smooth in (24, 72, 168):
for thr in (5.0, 10.0, 20.0):
r, ln, ac, _ = carry_pair(mtr, smooth=smooth, thr_ann=thr)
grid.append((smooth, thr, r))
print(stats(r, len(mtr), f"TRAIN s{smooth} thr{thr:.0f}%", ln, ac))
print("\n Le stesse config in OOS (mai usate per scegliere):")
for smooth in (24, 72, 168):
for thr in (5.0, 10.0, 20.0):
r, ln, ac, _ = carry_pair(moo, smooth=smooth, thr_ann=thr)
print(stats(r, len(moo), f"OOS s{smooth} thr{thr:.0f}%", ln, ac))
print("\n" + "=" * 96)
print(" [2] FC-B carry direzionale single-asset (confronto, fee 0.10% RT)")
print("=" * 96)
for a in ("btc", "eth"):
for thr in (10.0, 30.0):
rtr = carry_single(mtr, a, thr_ann=thr)
roo = carry_single(moo, a, thr_ann=thr)
print(stats(rtr, len(mtr), f"TRAIN {a} thr{thr:.0f}%"))
print(stats(roo, len(moo), f"OOS {a} thr{thr:.0f}%"))
print("\n" + "=" * 96)
print(" [3] FC-A: sweep fee (config mediana s72 thr10) e breakdown annuale")
print("=" * 96)
for fee in (0.0005, 0.001, 0.002):
r, ln, ac, _ = carry_pair(m, smooth=72, thr_ann=10.0, fee_rt=fee)
print(stats(r, len(m), f"FULL fee {fee*100:.2f}% RT/gamba", ln, ac))
_, _, _, eq = carry_pair(m, smooth=72, thr_ann=10.0)
yr = eq.groupby(eq.index.year).apply(lambda s: (s.iloc[-1] - s.iloc[0]) * 100)
print(" annuale (PnL additivo %):",
{int(k): round(float(v), 1) for k, v in yr.items()})
if __name__ == "__main__":
main()