027d73a504
Backtested the cross-venue funding arb on historical funding (Binance/OKX/Hyperliquid). Gross +21%/yr, spreads persist (capture 0.71), but NAIVE daily rebalance is cost-killed (net Sharpe -4.5, negative every month). HYSTERESIS (hold winners until spread decays, entry>10bp/exit>5bp) flips net to +10-14%/yr market-neutral (Sharpe +11-15, but inflated by ~1% vol + idealized fills; realistic ~3-6 / return ~10-14%). Turnover is the swing factor. First edge of the whole search to survive the net-of-cost horde -- market-neutral, persistent, no spot leg (solves hedgeability), operational not predictive. Caveats: 94d/one period, idealized fills, counterparty. Next: switch live harness to hysteresis, deepen history to full year, micro-live. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
68 lines
3.0 KiB
Python
68 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""Backtest the cross-venue funding arb on historical funding (Binance vs Bybit).
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The persistence test, on history: each day pick the top-K coins by |binance-bybit funding spread|,
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position to collect it (short higher-funding venue, long lower), and book the REALIZED next-day
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funding difference (not the snapshot). If spreads persist -> positive; if they mean-revert before
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you collect -> ~0 net. Net of round-trip cost on turnover. Reports gross/net, Sharpe, by top-K.
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"""
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import json
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import math
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import os
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import sys
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import numpy as np
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PANEL = "data/surfer/xvenue/panel.json"
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COST_RT = 0.0010
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HURDLE = 0.0005
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def main():
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panel = json.load(open(PANEL))
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dates = sorted(set().union(*[set(v) for v in panel.values()]))
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di = {d: i for i, d in enumerate(dates)}
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coins = list(panel)
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T, N = len(dates), len(coins)
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bn = np.full((T, N), np.nan); by = np.full((T, N), np.nan)
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for j, c in enumerate(coins):
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for d, (b, y) in panel[c].items():
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bn[di[d], j] = b; by[di[d], j] = y
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spread = bn - by # signed: + means binance funding higher
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print(f"cross-venue backtest: {N} coins, {T} days ({dates[0]}..{dates[-1]})")
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def sim(K, cost):
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rets = []
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prev = set()
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for t in range(T - 1):
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s_t = spread[t]; s_n = spread[t + 1]
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ok = np.isfinite(s_t) & np.isfinite(s_n) & (np.abs(s_t) > HURDLE)
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idx = np.where(ok)[0]
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if len(idx) == 0:
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rets.append(0.0); continue
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top = idx[np.argsort(-np.abs(s_t[idx]))[:K]]
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p = np.sign(s_t[top])
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realized = float(np.mean(p * s_n[top])) # collect next-day actual difference
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cur = set(coins[j] for j in top)
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turn = len(cur ^ prev) / max(len(cur), 1)
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rets.append(realized - turn * (cost / 2)); prev = cur
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r = np.array(rets)
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ann = r.mean() * 365; vol = r.std() * math.sqrt(365)
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eq = np.cumprod(1 + r); dd = float((eq / np.maximum.accumulate(eq) - 1).min())
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return ann, vol, (ann / vol if vol > 0 else float("nan")), dd, eq[-1] - 1
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print(f"\n{'topK':>5} {'annNET%':>8} {'vol%':>6} {'Sharpe':>7} {'maxDD%':>7} {'totalNET%':>9}")
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for K in [5, 10, 20]:
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a, v, sh, dd, tot = sim(K, COST_RT)
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g = sim(K, 0.0)[0]
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print(f"{K:>5} {100*a:>+8.1f} {100*v:>6.1f} {sh:>+7.2f} {100*dd:>+7.1f} {100*tot:>+9.1f} (gross ann {100*g:+.0f}%)")
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# persistence diagnostic: sign(spread_t) == sign(spread_t+1) fraction
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fin = np.isfinite(spread[:-1]) & np.isfinite(spread[1:]) & (np.abs(spread[:-1]) > HURDLE)
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persist = np.mean(np.sign(spread[:-1][fin]) == np.sign(spread[1:][fin]))
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print(f"\n spread-sign persistence (1 day): {100*persist:.0f}% (>>50% = spreads persist = real; ~50% = noise/revert)")
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print(" VERDICT: net Sharpe>1 + persistence>>50% = real capturable edge; net~0/persist~50% = mean-reverts before you collect.")
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if __name__ == "__main__":
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main()
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