2b36929b5f
The hysteresis params were chosen by looking at the data = in-sample. Clean OOS protocol (same that killed PEAD): pick best config on first 60% by IS Sharpe, apply EXACT config blind to last 40%. Reports IS-best -> OOS Sharpe, robustness (configs IS>2 that also OOS>1), OOS per-month. Decisive: OOS holds -> real; OOS collapses -> in-sample fit. Monitor armed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
83 lines
3.7 KiB
Python
83 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""CLEAN OOS test of the cross-venue funding arb (hysteresis).
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The hysteresis params (entry/exit/K) were chosen by looking at the data = in-sample. Honest test:
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pick the best config on IN-SAMPLE (first 60% of days) by IS Sharpe, then apply that EXACT config
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BLIND to OUT-OF-SAMPLE (last 40%). If OOS holds -> real; if it collapses -> in-sample fit.
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Reports IS-best config + its OOS result, robustness across configs, and OOS per-month.
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Taker cost 10bp (conservative). Sharpe is inflated by low vol + idealized fills (realistic ~ /2.5).
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"""
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import json
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import math
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import numpy as np
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PANEL = "data/surfer/xvenue2/panel2.json"
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COST = 0.0010
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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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spread = np.full((T, N), np.nan)
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for j, c in enumerate(coins):
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for d, v in panel[c].items():
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vals = list(v.values())
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if len(vals) >= 2:
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spread[di[d], j] = max(vals) - min(vals)
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split = int(0.60 * T)
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print(f"cross-venue OOS: {N} coins, {T} days | IS {dates[0]}..{dates[split-1]} | OOS {dates[split]}..{dates[-1]}")
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def hyst(t0, t1, K, entry, exit_, cost):
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held, rets, prev = {}, [], set()
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for t in range(t0, t1 - 1):
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s_t, s_n = spread[t], spread[t + 1]
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held = {j: 1 for j in held if np.isfinite(s_t[j]) and s_t[j] > exit_}
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if len(held) < K:
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cand = [j for j in np.where(np.isfinite(s_t) & (s_t > entry))[0] if j not in held]
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cand.sort(key=lambda j: -s_t[j])
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for j in cand[:K - len(held)]:
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held[j] = 1
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cur = set(held)
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turn = len(cur ^ prev) / max(len(cur), 1) if cur else 0
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realized = (sum(s_n[j] for j in held if np.isfinite(s_n[j])) / len(held)) if held else 0.0
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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 if len(r) else float("nan")
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vol = r.std() * math.sqrt(365) if len(r) else float("nan")
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return ann, (ann / vol if vol > 0 else float("nan")), r
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grid = [(K, e, x) for K in (10, 20) for e in (0.0005, 0.0010, 0.0020) for x in (0.0003, 0.0005, 0.0010) if x <= e]
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scored = []
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for cfg in grid:
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_, is_sh, _ = hyst(0, split, *cfg, COST)
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_, oos_sh, _ = hyst(split, T, *cfg, COST)
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oa = hyst(split, T, *cfg, COST)[0]
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scored.append((cfg, is_sh, oos_sh, oa))
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best = max(scored, key=lambda s: s[1]) # pick by IS Sharpe ONLY
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cfg, is_sh, oos_sh, oos_ann = best
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print(f"\n IS-BEST config (chosen blind to OOS): K={cfg[0]} entry={cfg[1]*1e4:.0f}bp exit={cfg[2]*1e4:.0f}bp")
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print(f" IS Sharpe {is_sh:+.1f} -> OOS Sharpe {oos_sh:+.1f} | OOS ann {100*oos_ann:+.1f}% (realistic ~/2.5 = {oos_sh/2.5:+.1f})")
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good = [s for s in scored if s[1] > 2]
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robust = [s for s in good if s[2] > 1]
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print(f" robustness: of {len(good)} configs with IS Sharpe>2, {len(robust)} also OOS Sharpe>1")
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# OOS per-month on the IS-best config
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_, _, r = hyst(split, T, *cfg, COST)
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mo = [dates[t][:7] for t in range(split, T - 1)]
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print(" OOS per-month Sharpe (IS-best config):")
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for m in sorted(set(mo)):
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seg = r[np.array(mo) == m]
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if len(seg) > 6 and seg.std() > 0:
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print(f" {m}: {seg.mean()/seg.std()*math.sqrt(365):+.1f} (n={len(seg)})")
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print("\n VERDICT: IS-best OOS Sharpe>1 (realistic, after /2.5) + most months positive = REAL out-of-sample.")
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print(" If OOS collapses to ~0/negative = in-sample fit, joins the dead list.")
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if __name__ == "__main__":
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main()
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