ca3877c328
Realism layers on PIT mom_20 TOPK=50: robust to 20bp (+0.59) and liquidity floor (top-50 already deep, no effect); death-spiral exclusion +0.60. Winsorize +/-20%/day cuts to +0.50 and weakens 2021/22 -> edge leans partly on large moves. COMBINED realistic stress +0.18 (CPCV~0); HARSH negative. winsor likely over-conservative -> true deployable Sharpe ~0.4-0.6, not the 0.76 headline. Real but modest edge; recent years strongest. Deploy needs signal-robustness refinement + diversifying 2nd edge. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
88 lines
3.8 KiB
Python
88 lines
3.8 KiB
Python
#!/usr/bin/env python3
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"""Execution-realism stress for point-in-time crypto momentum — is the magnitude believable?
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Layers on the realistic frictions that could inflate the paper Sharpe:
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- cost 10→20→30 bp on turnover (smaller coins cost more)
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- WINSORIZE per-coin daily returns to ±cap (you cannot fill a short into an 80%/day collapse)
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- EXCLUDE each coin's final K days (delisting death-spiral: untradeable → zero PnL there)
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- liquidity FLOOR: only trade coins with trailing dollar-vol > floor
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If mom_20/30 stays positive EVERY year under the combined stress, the edge magnitude is real.
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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 as ps # noqa: E402
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from signal_sweep import xs_weights, pnl_w, validate, sharpe_t # noqa: E402
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import torch # noqa: E402
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DEV = "cuda" if torch.cuda.is_available() else "cpu"
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def trailing(lc, L):
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out = np.full_like(lc, np.nan); out[L:] = lc[L:] - lc[:-L]; return out
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def main():
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syms, days, close, qv, fund = ps.load()
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T, N = close.shape
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lc = np.log(close)
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R = np.zeros((T, N)); R[1:] = lc[1:] - lc[:-1]; R = np.where(np.isfinite(R), R, 0.0)
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fund = np.where(np.isfinite(fund), fund, 0.0)
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qv = np.nan_to_num(qv)
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dv30 = np.zeros((T, N))
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for t in range(30, T):
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dv30[t] = qv[t - 30:t].mean(axis=0)
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year = (1970 + days / 365.25).astype(int)
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# death-spiral mask: each coin's final 5 valid days = untradeable
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tradeable = np.ones((T, N), bool)
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for j in range(N):
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idx = np.where(np.isfinite(close[:, j]))[0]
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if len(idx):
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tradeable[max(0, idx[-1] - 4):idx[-1] + 1, j] = False
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def variant(cost_bp, winsor, excl_death, dv_floor, L=20, TOPK=50):
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Reff = (R - fund)
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if winsor is not None:
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Reff = np.clip(Reff, -winsor, winsor)
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if excl_death:
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Reff = np.where(tradeable, Reff, 0.0)
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univ = np.zeros((T, N), bool)
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for t in range(T):
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elig = np.where((dv30[t] > dv_floor) & np.isfinite(close[t]))[0]
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if len(elig) == 0:
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continue
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k = min(TOPK, len(elig))
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univ[t, elig[np.argsort(-dv30[t, elig])[:k]]] = True
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sig = trailing(lc, L).copy(); sig[~univ] = np.nan
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pnl = pnl_w(xs_weights(sig), Reff, cost_bp=cost_bp)
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v = validate(pnl, days, 6)
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py = [sharpe_t(torch.tensor(pnl[year[1:] == y], device=DEV, dtype=torch.float64))
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if (year[1:] == y).sum() > 30 else float("nan") for y in range(2020, 2027)]
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return v, py
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print(f"\n===== EXECUTION-REALISM STRESS — PIT momentum (mom_20, TOPK=50) — {N} coins =====")
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print(f"{'variant':>34} {'full':>6} {'IS':>6} {'OOS':>6} {'CPCVmed':>8} | per-year 2020..2026")
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cfgs = [
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("baseline 10bp", dict(cost_bp=10, winsor=None, excl_death=False, dv_floor=0)),
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("20bp cost", dict(cost_bp=20, winsor=None, excl_death=False, dv_floor=0)),
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("winsor ±20%", dict(cost_bp=10, winsor=0.20, excl_death=False, dv_floor=0)),
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("excl death-spiral (last 5d)", dict(cost_bp=10, winsor=None, excl_death=True, dv_floor=0)),
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("dv-floor $5M/day", dict(cost_bp=10, winsor=None, excl_death=False, dv_floor=5e6)),
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("COMBINED 20bp+wins20+death+$5M", dict(cost_bp=20, winsor=0.20, excl_death=True, dv_floor=5e6)),
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("HARSH 30bp+wins10+death+$10M", dict(cost_bp=30, winsor=0.10, excl_death=True, dv_floor=1e7)),
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]
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for nm, cfg in cfgs:
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v, py = variant(**cfg)
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pys = " ".join(f"{p:>+5.2f}" if not math.isnan(p) else " n/a" for p in py)
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print(f"{nm:>34} {v['full']:>+6.2f} {v['is_']:>+6.2f} {v['oos']:>+6.2f} {v['med']:>+8.2f} | {pys}")
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print("\nVERDICT: positive every year under COMBINED/HARSH => believable, sizable edge (not paper).")
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if __name__ == "__main__":
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main()
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