d43fb5e61f
Bought XSP (mini-SPX) options ohlcv-1d 2013-2026 = $66.86 (hard-capped $70, get_cost-gated, year-chunked vs 504, 2025 backfilled) + SPY $0.01. CORRECTION: index VRP is NOT $900 (that was SPY root); clean instrument XSP $66.86/13y or SPX $143/13y. THREE measurement attempts all gave spurious NEGATIVE VRP (RV 28-33% vs implied ~16%) = MEASUREMENT ERROR not finding (contradicts decades of SPX VRP evidence). Bugs: noisy parity underlying; ~469/2800 days survive -> multi-day gaps inflate RV; SPY!=XSP divergence corrupts ATM. Root: XSP EOD ohlcv too sparse to reconstruct clean underlying+ATM-IV. Implied (16.7%) reads right. Frontier real+affordable+in-hand but proper extraction is a real options-quant build (denser SPX +$76 or IV-surface w/ quotes), not a gate. Did NOT record -2.7 as a result (artifact). Crypto momentum+VRP remains only deploy-grade edge. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
79 lines
3.9 KiB
Python
79 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""Equity-index VRP, robust internal-parity version (self-consistent, no external underlying).
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Per day: implied forward F = MEDIAN over the 5 near-ATM strikes of (K + C - P) [put-call parity,
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median-smoothed to kill per-strike noise]. Use F consistently for ATM, implied vol, AND the
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realized-vol series. Internally consistent -> avoids both the single-strike-parity noise and the
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SPY!=XSP divergence that produced spurious negative VRP. XSP 2013-2026.
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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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from equity_vrp import load_xsp, to_epoch_day # noqa: E402
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from signal_sweep import 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 main():
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d = load_xsp()
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d["exp_day"] = d["expiry"].map(to_epoch_day)
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d["ttm"] = d["exp_day"] - d["day"]
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d = d[(d["ttm"] >= 20) & (d["ttm"] <= 45)]
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F, IVm = {}, {}
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for day, g in d.groupby("day"):
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exp = g.iloc[(g["ttm"] - 30).abs().argsort()].iloc[0]["exp_day"]
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ge = g[g["exp_day"] == exp]
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calls = ge[ge["right"] == "C"].groupby("strike")["close"].mean()
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puts = ge[ge["right"] == "P"].groupby("strike")["close"].mean()
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common = np.array(sorted(set(calls.index) & set(puts.index)))
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if len(common) < 5:
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continue
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rough = common[np.abs(calls[common].values - puts[common].values).argmin()] # min|C-P| ~ ATM
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near = common[np.argsort(np.abs(common - rough))[:5]] # 5 nearest strikes
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f = float(np.median([k + float(calls[k]) - float(puts[k]) for k in near])) # parity forward (median)
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katm = common[np.abs(common - f).argmin()]
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straddle = float(calls[katm] + puts[katm])
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T = float(ge["ttm"].iloc[0]) / 365.0
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if f <= 0 or straddle <= 0 or T <= 0:
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continue
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F[int(day)] = f; IVm[int(day)] = straddle / (0.8 * f * math.sqrt(T))
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days = np.array(sorted(F))
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S = np.array([F[x] for x in days]); IV = np.array([IVm[x] for x in days])
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r = np.zeros(len(days)); r[1:] = np.log(S[1:] / S[:-1])
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r = np.clip(r, -0.25, 0.25) # guard residual parity glitches
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H = 21
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rv = np.full(len(days), np.nan)
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for t in range(len(days) - H):
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rv[t] = np.std(r[t + 1:t + 1 + H]) * math.sqrt(252)
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vrp = IV - rv; fin = np.isfinite(vrp)
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iv_lag = np.concatenate([[np.nan], IV[:-1]])
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svol = (iv_lag ** 2) / 252.0 - r ** 2
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year = np.array([1970 + x / 365.25 for x in days]).astype(int)
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T_ = lambda x: torch.tensor(np.asarray(x)[np.isfinite(np.asarray(x))], device=DEV, dtype=torch.float64)
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print(f"\n===== EQUITY-INDEX VRP (robust internal parity) — {len(days)} days ({days.min()}..{days.max()}) =====")
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print(f"implied-forward level {S[0]:.0f}->{S[-1]:.0f} (should track SPX/10 ~ 400->650)")
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print(f"mean implied {np.nanmean(IV):.1%} mean fwd-realized {np.nanmean(rv):.1%} "
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f"mean VRP {np.nanmean(vrp[fin]):+.1%} frac>0 {np.mean(vrp[fin]>0):.2f}")
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sk = float(((svol[np.isfinite(svol)] - np.nanmean(svol)) ** 3).mean() / np.nanstd(svol) ** 3)
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print(f"short-vol daily P&L: Sharpe {sharpe_t(T_(svol)):+.2f} skew {sk:+.2f} worst {np.nanmin(svol)/np.nanstd(svol):+.1f}σ")
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print("per-year: " + " ".join(f"{y}:{sharpe_t(T_(svol[year==y])):+.1f}" for y in range(2013, 2027) if (year == y).sum() > 60))
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rising = np.zeros(len(days))
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for t in range(6, len(days)):
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rising[t] = 0.0 if IV[t - 1] > IV[t - 6] else 1.0
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tm = rising * svol
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print(f"tail-managed: Sharpe {sharpe_t(T_(tm)):+.2f} worst {np.nanmin(tm)/np.nanstd(tm):+.1f}σ")
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print("per-year TM: " + " ".join(f"{y}:{sharpe_t(T_(tm[year==y])):+.1f}" for y in range(2013, 2027) if (year == y).sum() > 60))
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print("\nVERDICT: implied-forward tracking ~SPX/10 + mean RV ~13-16% + VRP>0 => measurement clean & premium real.")
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if __name__ == "__main__":
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main()
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