Files
foxhunt/scripts/surfer/pit_sweep.py
jgrusewski f986510099 feat(surfer): wire tail-managed VRP into PoC as second sleeve (two-sleeve book)
backtest now reports momentum + VRP (tail-managed, corr -0.04) + COMBINED book
(momentum 70% / VRP 30% risk) Sharpe +1.56, all years positive. paper marks BOTH
sleeves + combined equity each run; status shows all three + live Sharpes. VRP gate
('don't sell vol when DVOL rising/elevated') runs live -- currently 'flat (gated)'.
cwd-independence fixes: pit_sweep.load + _vrp_calib now use abs paths so the cron paper
run calibrates VRP correctly from $HOME (was silently falling back to zero-VRP). Live
panel depth bumped to cover the 60d VRP gate window. Honest: VRP +2.85 is the no-Greeks
proxy (real ~1-1.5) so combined is optimistic (~0.8-1.0 realistic) -- still > momentum alone.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-06 17:29:40 +02:00

90 lines
4.0 KiB
Python

#!/usr/bin/env python3
"""Point-in-time survivorship test for crypto cross-sectional momentum.
Universe is rebuilt EACH DAY: among coins with active trailing-30d dollar-volume (>0),
take the top-K by that volume. Dead coins (LUNA, SRM, ...) are IN the cross-section while
they trade and DROP OUT (after carrying their crash) when volume dies. No lookahead:
volume & momentum use only past data; weights apply to next-day return. If momentum's
edge survives here — especially 2022 with LUNA included — it is real beyond the bootstrap proxy.
"""
import glob
import math
import os
import sys
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from signal_sweep import xs_weights, pnl_w, validate, sharpe_t # noqa: E402
import torch # noqa: E402
DEV = "cuda" if torch.cuda.is_available() else "cpu"
def load():
syms, data = [], {}
_base = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) # cwd-independent
for p in sorted(glob.glob(os.path.join(_base, "data/surfer/crypto_pit/*.npz"))):
d = np.load(p); s = p.split("/")[-1][:-4]
data[s] = (d["day"].astype(np.int64), d["close"].astype(float), d["qvol"].astype(float), d["funding"].astype(float))
syms.append(s)
syms = sorted(syms)
days = np.array(sorted(set().union(*[set(data[s][0].tolist()) for s in syms])))
di = {int(v): i for i, v in enumerate(days.tolist())}
T, N = len(days), len(syms)
close = np.full((T, N), np.nan); qv = np.full((T, N), np.nan); fund = np.full((T, N), np.nan)
for j, s in enumerate(syms):
dd, cc, vv, ff = data[s]
for k in range(len(dd)):
r = di[int(dd[k])]; close[r, j] = cc[k]; qv[r, j] = vv[k]; fund[r, j] = ff[k]
return syms, days, close, qv, fund
def trailing(lc, L):
out = np.full_like(lc, np.nan); out[L:] = lc[L:] - lc[:-L]; return out
def main():
syms, days, close, qv, fund = load()
T, N = close.shape
lc = np.log(close)
R = np.zeros((T, N)); R[1:] = lc[1:] - lc[:-1]; R = np.where(np.isfinite(R), R, 0.0)
Reff = R - np.where(np.isfinite(fund), fund, 0.0)
qv = np.nan_to_num(qv)
dv30 = np.full((T, N), 0.0) # trailing 30d mean dollar-volume
for t in range(30, T):
dv30[t] = qv[t - 30:t].mean(axis=0)
dead = sum(1 for j in range(N) if qv[-30:, j].mean() == 0)
year = (1970 + days / 365.25).astype(int)
print(f"\n===== POINT-IN-TIME MOMENTUM — {N} coins ({dead} dead/delisted), {T}d =====")
for TOPK in [30, 50]:
# daily universe = top-K by trailing dollar-vol among actively-trading coins
univ = np.zeros((T, N), bool)
for t in range(T):
elig = np.where((dv30[t] > 0) & np.isfinite(close[t]))[0]
if len(elig) == 0:
continue
k = min(TOPK, len(elig))
top = elig[np.argsort(-dv30[t, elig])[:k]]
univ[t, top] = True
print(f"\n--- TOPK={TOPK} (avg coins/day in-universe = {univ.sum(1).mean():.0f}) ---")
print(f"{'lookback':>9} {'full':>6} {'IS':>6} {'OOS':>6} {'CPCVmed':>8} {'DSR':>5} | per-year (2020..2026)")
for L in [10, 20, 30]:
sig = trailing(lc, L).copy()
sig[~univ] = np.nan # restrict signal to PIT universe
w = xs_weights(sig)
pnl = pnl_w(w, Reff, cost_bp=10)
v = validate(pnl, days, 6)
py = []
for y in range(2020, 2027):
m = year[1:] == y
py.append(sharpe_t(torch.tensor(pnl[m], device=DEV, dtype=torch.float64)) if m.sum() > 30 else float("nan"))
pys = " ".join(f"{p:>+5.2f}" if not math.isnan(p) else " n/a" for p in py)
print(f"{L:>9} {v['full']:>+6.2f} {v['is_']:>+6.2f} {v['oos']:>+6.2f} {v['med']:>+8.2f} {v['dsr']:>5.2f} | {pys}")
print("\nVERDICT: if mom_20-30 stays positive (esp. 2022 with LUNA in-universe) => survivorship-robust REAL edge.")
if __name__ == "__main__":
main()