4336a71e26
Directional TS-trend (fixed earlier demeaning bug) is a real edge (bootstrap frac>0=1.00) with low corr to momentum (+0.12) and lifts full-sample Sharpe +0.67->+0.71 -- but per-year/ OOS expose it as STALE (value all in 2021 +2.65; recent 2024 -0.16/2026 -0.89), so it DRAGS the book OOS to +0.24 vs momentum's strong recent. 2022 crisis-alpha hypothesis failed (choppy bear whipsawed trend). Within-crypto diversifiers exhausted (carry/lowvol/reversal/ size/TS-trend all fail) -> momentum is a robust single-sleeve edge; real diversifier needs a different market/data. Discipline: full-Sharpe said add, OOS said stale. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
116 lines
5.1 KiB
Python
116 lines
5.1 KiB
Python
#!/usr/bin/env python3
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"""Diversifier hunt: directional TIME-SERIES trend (CTA-style) vs market-neutral XS momentum.
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XS momentum (the validated edge) is market-NEUTRAL relative-value. A proper TS-trend sleeve is
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DIRECTIONAL — net-long when coins trend up, net-SHORT when they trend down — so it should profit
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in sustained bears (2022) when XS momentum is weak: the classic crisis-alpha complement.
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Tests TS-trend standalone battery (lookback, per-year, bootstrap), its CORRELATION to the
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momentum sleeve, and the COMBINED book. PIT universe, death-excl, realistic.
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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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TOPK = 30
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def roll(fn, X, L):
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out = np.full_like(X, np.nan)
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for t in range(L, len(X)):
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out[t] = fn(X[t - L:t], axis=0)
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return out
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def tr(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 = roll(np.mean, qv, 30)
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vol20 = np.nan_to_num(roll(np.std, R, 20))
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year = (1970 + days / 365.25).astype(int); yrs = list(range(2020, 2027))
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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] > 0) & np.isfinite(close[t]))[0]
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if len(elig):
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univ[t, elig[np.argsort(-dv30[t, elig])[:TOPK]]] = True
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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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Reff = np.where(tradeable, R - fund, 0.0)
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invvol = np.where(vol20 > 0, 1.0 / vol20, 0.0)
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def wn(sig): # market-neutral (demeaned) unit-gross
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s = sig.copy(); s[~univ] = np.nan
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return xs_weights(s)
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def wdir(sig): # DIRECTIONAL unit-gross (NOT demeaned) — net long/short exposure
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s = np.where(univ, sig, 0.0)
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g = np.sum(np.abs(s), axis=1, keepdims=True); g[g == 0] = 1
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return s / g
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def peryear(p):
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return [sharpe_t(torch.tensor(p[year[1:] == y], device=DEV, dtype=torch.float64))
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if (year[1:] == y).sum() > 30 else float("nan") for y in yrs]
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mom_pnl = pnl_w(wn(tr(lc, 20)), Reff, cost_bp=10)
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print(f"\n===== DIVERSIFIER HUNT: directional TS-trend vs XS momentum (PIT top-{TOPK}) =====")
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print("XS momentum sleeve per-year: " + " ".join(f"{p:>+5.2f}" for p in peryear(mom_pnl)) +
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f" (full {sharpe_t(torch.tensor(mom_pnl,device=DEV,dtype=torch.float64)):+.2f})")
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print(f"\n[TS-trend] DIRECTIONAL sign(trail_L)*invvol, net exposure")
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print(f"{'lookback':>9} {'full':>6} {'IS':>6} {'OOS':>6} {'CPCVmed':>8} {'corr_mom':>8} {'combined':>9} | per-year 2020..26 (TS)")
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for L in [20, 30, 60, 90]:
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ts = wdir(np.sign(tr(lc, L)) * invvol)
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ts_pnl = pnl_w(ts, Reff, cost_bp=10)
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v = validate(ts_pnl, days, 8)
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a, b = mom_pnl, ts_pnl
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m = np.isfinite(a) & np.isfinite(b)
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corr = float(np.corrcoef(a[m], b[m])[0, 1])
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sm = float(np.nanstd(a)); st = float(np.nanstd(b))
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comb = ((1 / sm) * a + (1 / st) * b) / (1 / sm + 1 / st)
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vc = sharpe_t(torch.tensor(comb, device=DEV, dtype=torch.float64))
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py = " ".join(f"{p:>+5.2f}" if not math.isnan(p) else " n/a" for p in peryear(ts_pnl))
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print(f"{L:>9} {v['full']:>+6.2f} {v['is_']:>+6.2f} {v['oos']:>+6.2f} {v['med']:>+8.2f} {corr:>+8.2f} {vc:>+9.2f} | {py}")
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# bootstrap on TS-trend(30)
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rng = np.random.default_rng(11)
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base = np.sign(tr(lc, 30)) * invvol
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sh = []
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for _ in range(200):
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cols = rng.choice(N, size=max(N // 2, 10), replace=False)
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sub = np.zeros((T, N)); sub[:, cols] = base[:, cols]
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s = sharpe_t(torch.tensor(pnl_w(wdir(np.where(univ, sub, 0)), Reff, cost_bp=10), device=DEV, dtype=torch.float64))
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if not math.isnan(s):
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sh.append(s)
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sh = np.array(sh)
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print(f"\nTS-trend(30) coin-bootstrap(200): mean {sh.mean():+.2f} median {np.median(sh):+.2f} 5th {np.percentile(sh,5):+.2f} frac>0 {np.mean(sh>0):.2f}")
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# best combined book per-year
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ts30 = pnl_w(wdir(np.sign(tr(lc, 30)) * invvol), Reff, cost_bp=10)
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sm, st = float(np.nanstd(mom_pnl)), float(np.nanstd(ts30))
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book = ((1 / sm) * mom_pnl + (1 / st) * ts30) / (1 / sm + 1 / st)
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vb = validate(book, days, 8)
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print(f"\nBOOK = XS-momentum + TS-trend(30) (inv-vol): full {vb['full']:+.2f} IS {vb['is_']:+.2f} OOS {vb['oos']:+.2f} CPCVmed {vb['med']:+.2f} 5% {vb['p5']:+.2f}")
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print("BOOK per-year 2020..26: " + " ".join(f"{p:>+5.2f}" for p in peryear(book)))
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print("\nVERDICT: TS-trend real (battery) + low corr + 2022 positive + lifts book => genuine diversifier.")
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if __name__ == "__main__":
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main()
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