df5c591441
Clean OOS test (filter chosen on 2019-24, applied blind to 2025-26): IS-best filter holds OOS Sharpe +5.1; 9/12 strong-IS filters also OOS-positive; naive no-filter FAILS OOS (-5.8) = validated, not curve-fit. Wrote deployable spec: delta-neutral long-spot/short-perp harvest, tf30>5bp regime filter, equal-weight, daily rebalance, 10bp cost budget. Counterparty/exchange tail (-100pct) is THE risk (Sharpe-blind) -> venue selection + collateral spreading + withdrawal discipline + deleverage kill-switch. Realistic ~2-3.6 Sharpe market-neutral (after basis-vol haircut). Phased rollout: paper-forward -> micro-live -> small-live -> scale, gate each. The one validated edge past the 0.7 ceiling; price of admission is crypto + counterparty risk. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
93 lines
3.9 KiB
Python
93 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""Clean OOS validation of the funding-harvest regime filter.
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Protocol (no peeking): choose the regime-filter params on IN-SAMPLE 2019-2024 by IS Sharpe, then
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apply that EXACT filter BLIND to OUT-OF-SAMPLE 2025-2026. If the IS-best filter still works OOS,
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the edge is validated, not curve-fit. Also reports the full grid's OOS so we can see if it's
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robust across reasonable filters (real) or only the lucky one (suspicious). Strategy structure
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fixed; only the regime filter (trailing window, hurdle) is the tuned degree of freedom.
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Raw Sharpe is funding-only (vol understated ~2-3x); realistic ~ raw / 2.5.
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"""
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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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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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import pit_sweep # noqa: E402
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def sharpe(r):
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r = r[np.isfinite(r)]
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return float("nan") if len(r) < 20 or r.std() == 0 else r.mean() / r.std() * math.sqrt(365)
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def apr(r):
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r = r[np.isfinite(r)]
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return 100 * r.sum() * 365 / max(len(r), 1)
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def main():
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syms, days, close, qv, fund = pit_sweep.load()
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T, N = fund.shape
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year = (1970 + days / 365.25).astype(int)
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qv30 = np.full_like(qv, np.nan)
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for t in range(30, T):
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qv30[t] = np.nanmean(qv[t - 30:t], axis=0)
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elig = np.isfinite(qv30) & (qv30 > 5e6) & np.isfinite(fund)
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f = np.nan_to_num(fund)
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# precompute trailing means
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trailmean = {}
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for tr in (14, 30):
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tm = np.full_like(fund, np.nan)
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for t in range(tr, T):
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tm[t] = np.nanmean(fund[t - tr:t], axis=0)
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trailmean[tr] = tm
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def harvest(trail, hurdle, c=0.0010):
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cond = (fund > hurdle) if trail == 0 else (trailmean[trail] > hurdle)
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sig = np.zeros((T, N))
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sig[1:] = np.where(elig[1:] & cond[:-1], 1.0, 0.0) # causal
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w = sig / np.maximum(sig.sum(1, keepdims=True), 1)
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gross = np.sum(w[:-1] * f[1:], axis=1)
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turn = np.sum(np.abs(w[1:] - w[:-1]), axis=1)
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net = gross - turn * (c / 2)
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inmkt = (sig.sum(1)[1:] > 0)
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return net, inmkt
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yr = year[1:]
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IS = yr <= 2024
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OOS = yr >= 2025
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grid = [(tr, h) for tr in (0, 14, 30) for h in (0.0, 0.0002, 0.0003, 0.0005)]
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rows = []
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for tr, h in grid:
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net, inmkt = harvest(tr, h)
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rows.append(dict(tr=tr, h=h, net=net, inmkt=inmkt,
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is_sr=sharpe(net[IS]), oos_sr=sharpe(net[OOS]),
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oos_apr=apr(net[OOS]), oos_inmkt=float(inmkt[OOS].mean())))
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print(f"\n===== CLEAN OOS VALIDATION — IS 2019-2024 / OOS 2025-2026 =====")
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print(f"{'filter':>18} {'IS Sharpe':>10} {'OOS Sharpe':>11} {'OOS APR%':>9} {'OOS in-mkt%':>11}")
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for r in rows:
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nm = "naive thr=0" if (r["tr"] == 0 and r["h"] == 0) else (f"inst>{r['h']*1e4:.0f}bp" if r["tr"] == 0 else f"tf{r['tr']}>{r['h']*1e4:.0f}bp")
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print(f"{nm:>18} {r['is_sr']:>+10.1f} {r['oos_sr']:>+11.1f} {r['oos_apr']:>+9.1f} {100*r['oos_inmkt']:>10.0f}")
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best = max(rows, key=lambda r: r["is_sr"])
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bnm = f"tf{best['tr']}>{best['h']*1e4:.0f}bp" if best["tr"] else f"inst>{best['h']*1e4:.0f}bp"
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print(f"\n IS-BEST filter (chosen blind to OOS): {bnm} -> IS Sharpe {best['is_sr']:+.1f}")
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print(f" ITS OOS RESULT: Sharpe {best['oos_sr']:+.1f} APR {best['oos_apr']:+.1f}% in-market {100*best['oos_inmkt']:.0f}% of days")
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print(f" (realistic Sharpe after basis vol ~ raw / 2.5 = {best['oos_sr']/2.5:+.1f})")
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# robustness: how many filters with IS Sharpe>2 also have OOS Sharpe>1
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good = [r for r in rows if r["is_sr"] > 2]
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robust = [r for r in good if r["oos_sr"] > 1]
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print(f"\n robustness: of {len(good)} filters with IS Sharpe>2, {len(robust)} also have OOS Sharpe>1")
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print("\nVERDICT: IS-best filter OOS Sharpe>1 (realistic) + broad robustness = VALIDATED, deployable.")
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print("If IS-best collapses OOS or only 1 filter works = curve-fit, not real.")
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if __name__ == "__main__":
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main()
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