"""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()