dbbe8f7b22
Expanded futures universe 22->39 liquid CME roots ($16.39 credits, 16y daily). (A) breadth did NOT unlock an edge: XS momentum + TS trend all negative/zero, DSR 0 (efficient market, unlike crypto). (B) diversifier check: futures-trend +0.01, crypto-momentum +0.88, CORRELATION -0.01 (genuinely uncorrelated) but combined +0.63 < +0.88 -> an uncorrelated sleeve with zero standalone edge DILUTES, not diversifies. A diversifier needs low-corr AND positive edge; futures trend has only the former. Caveat: roll-zeroing understates futures trend/carry (floor work ~+0.05-0.14 w/ proper rolls) -> marginal at best. Crypto momentum still the only real edge. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
102 lines
4.7 KiB
Python
102 lines
4.7 KiB
Python
#!/usr/bin/env python3
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"""Multi-asset futures test: does breadth (39 roots) unlock an edge, and is a CTA trend book
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a genuine DIVERSIFIER to the crypto momentum book?
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(A) XS momentum (was null on 22 roots) + TS trend-following (directional CTA) on the 39-asset
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daily futures panel — deflated, per-year, realistic cost.
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(B) The prize: correlation of the futures TS-trend book to the crypto XS-momentum book (overlap
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2019-2026) + the combined two-market book. Low correlation + both positive = real diversification.
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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 signal_sweep import load_panel, build_returns, xs_weights, pnl_w, validate, sharpe_t # noqa: E402
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import pit_sweep # noqa: E402
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from surfer_poc import compute_weights, CFG # 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 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 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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roots, days, close, op, inst, weekday = load_panel()
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lc, R, ov, intr = build_returns(close, op, inst)
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T, N = close.shape
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vol60 = roll(np.std, R, 60)
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invvol = np.where(vol60 > 0, 1.0 / vol60, 0.0)
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year = (1970 + days / 365.25).astype(int)
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yrs = list(range(2011, 2027))
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def wdir(sig): # directional unit-gross (TS trend, net exposure)
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s = np.nan_to_num(sig)
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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, yy):
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return [sharpe_t(torch.tensor(p[yy[1:] == y], device=DEV, dtype=torch.float64))
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if (yy[1:] == y).sum() > 30 else float("nan") for y in yrs]
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print(f"\n===== (A) MULTI-ASSET FUTURES — {N} roots, {T} days =====")
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print(f"{'signal':>16} {'full':>6} {'IS':>6} {'OOS':>6} {'CPCVmed':>8} {'DSR':>5}")
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sigs = {}
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for L in [20, 60, 120]:
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sigs[f"XS_mom_{L}"] = pnl_w(xs_weights(trailing(lc, L)), R, cost_bp=1.5)
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for L in [60, 120, 250]:
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sigs[f"TS_trend_{L}"] = pnl_w(wdir(np.sign(trailing(lc, L)) * invvol), R, cost_bp=1.5)
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NT = 40 # cumulative-session deflation (honest)
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best_ts, best_ts_pnl, best_med = None, None, -9
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for nm, p in sigs.items():
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v = validate(p, days, NT)
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print(f"{nm:>16} {v['full']:>+6.2f} {v['is_']:>+6.2f} {v['oos']:>+6.2f} {v['med']:>+8.2f} {v['dsr']:>5.2f}")
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if nm.startswith("TS_trend") and v["med"] > best_med:
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best_ts, best_ts_pnl, best_med = nm, p, v["med"]
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print(f" futures TS-trend per-year ({best_ts}): " + " ".join(f"{x:+.2f}" if not math.isnan(x) else " n/a" for x in peryear(best_ts_pnl, year)))
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# ---------- (B) DIVERSIFIER: futures trend vs crypto momentum ----------
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syms, cdays, cclose, cqv, cfund = pit_sweep.load()
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cR = np.zeros_like(cclose); cR[1:] = np.log(cclose)[1:] - np.log(cclose)[:-1]
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cR = np.where(np.isfinite(cR), cR, 0.0)
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cfund = np.where(np.isfinite(cfund), cfund, 0.0)
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cw, _ = compute_weights(cclose, cqv, cdays, CFG)
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cReff = cR - cfund
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crypto_book = np.sum(cw[:-1] * cReff[1:], axis=1) # crypto momentum daily PnL (cdays[1:])
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fut_book = best_ts_pnl # futures trend daily PnL (days[1:])
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# align on common epoch-days
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fd, cd = days[1:], cdays[1:]
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fmap = {int(d): fut_book[i] for i, d in enumerate(fd)}
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cmap = {int(d): crypto_book[i] for i, d in enumerate(cd)}
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common = sorted(set(fmap) & set(cmap))
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fa = np.array([fmap[d] for d in common]); ca = np.array([cmap[d] for d in common])
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m = np.isfinite(fa) & np.isfinite(ca)
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corr = float(np.corrcoef(fa[m], ca[m])[0, 1])
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sf, sc = float(np.std(fa[m])), float(np.std(ca[m]))
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comb = ((1 / sf) * fa[m] + (1 / sc) * ca[m]) / (1 / sf + 1 / sc)
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cyear = (1970 + np.array(common) / 365.25).astype(int)[m]
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print(f"\n===== (B) DIVERSIFIER CHECK — futures-trend vs crypto-momentum (overlap {len(fa[m])} days) =====")
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print(f"futures-trend Sharpe {sharpe_t(torch.tensor(fa[m],device=DEV,dtype=torch.float64)):+.2f} "
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f"crypto-momentum Sharpe {sharpe_t(torch.tensor(ca[m],device=DEV,dtype=torch.float64)):+.2f}")
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print(f"CORRELATION = {corr:+.2f} combined-book Sharpe = {sharpe_t(torch.tensor(comb,device=DEV,dtype=torch.float64)):+.2f}")
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print("combined per-year: " + " ".join(f"{y}:{sharpe_t(torch.tensor(comb[cyear==y],device=DEV,dtype=torch.float64)):+.2f}"
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for y in range(2019, 2027) if (cyear == y).sum() > 30))
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print("\nVERDICT: low |corr| + combined Sharpe > each alone = genuine cross-market diversification.")
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if __name__ == "__main__":
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main()
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