9a90720500
Backtested the EXACT live pipeline on REAL ETFs (SPY/IEF/GLD/PDBC/DBMF+BTC), 2019-2026 multi-regime. Adaptive unlevered: Sharpe +1.20, ann +6.1%, maxDD -5.4%, positive EVERY year incl 2022. Beats 60/40 (+0.87, -21%) and equity (+0.85, -34%) with ~1/4 the drawdown. Better than the ~0.7 framing because real ETFs (DBMF/PDBC) >> roll-zeroed futures proxies + adaptive risk layer. Caveats: Sharpe partly low-vol-flattered (maxDD -5.4% + every-year-positive are the robust signals); 2019-26 favorable for premia; mild param-contamination -> realistic fwd ~0.8-1.0. Modest absolute return (~.1k/yr on 5k, very smooth); lever for $ is capital. The genuine deliverable of the search, validated on real instruments across regimes. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
66 lines
3.2 KiB
Python
66 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""Backtest the EXACT deployable ETF book (the multistrat_paper live pipeline) on ~6y Yahoo history.
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Real instruments (SPY/IEF/GLD/PDBC/DBMF + BTC-USD), same adaptive pipeline as the live harness
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(vol-norm -> edge-decay trust -> trust-weighted combo -> adaptive risk layer, unlevered). Window is
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limited by DBMF inception (~2019), so it spans COVID-2020, bear-2022, bulls-2021/2024 = multiple
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regimes. Per-year Sharpe = the regime/robustness check; compared to plain 60/40 (SPY/IEF).
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"""
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import datetime
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import json
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import math
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import os
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import sys
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import urllib.request
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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 multistrat_paper import INSTR, book_series # exact live pipeline # noqa: E402
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def yhist(sym, rng="10y"):
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res = json.loads(urllib.request.urlopen(urllib.request.Request(
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f"https://query1.finance.yahoo.com/v8/finance/chart/{sym}?interval=1d&range={rng}",
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headers={"User-Agent": "Mozilla/5.0"}), timeout=30).read())["chart"]["result"][0]
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ts = res["timestamp"]; ind = res["indicators"]
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adj = ind.get("adjclose", [{}])[0].get("adjclose") or ind["quote"][0]["close"]
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return {datetime.datetime.utcfromtimestamp(t).strftime("%Y-%m-%d"): float(c) for t, c in zip(ts, adj) if c is not None}
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def stats(r):
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r = r[np.isfinite(r) & (r != 0)]
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ann = r.mean() * 252; vol = r.std() * math.sqrt(252)
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eq = np.cumprod(1 + r); dd = float((eq / np.maximum.accumulate(eq) - 1).min())
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return ann, vol, (ann / vol if vol > 0 else float("nan")), dd
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def main():
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data = {nm: yhist(sym) for sym, nm in INSTR}
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dates = sorted(set.intersection(*[set(d) for d in data.values()]))
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R = np.zeros((len(dates), len(INSTR)))
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for j, (_, nm) in enumerate(INSTR):
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s = np.array([data[nm][d] for d in dates]); R[1:, j] = s[1:] / s[:-1] - 1
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print(f"DEPLOYABLE ETF BOOK backtest — {len(dates)} days ({dates[0]}..{dates[-1]})")
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book, w, L = book_series(R)
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a, v, sh, dd = stats(book)
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print(f" adaptive multi-strat (unlevered): Sharpe {sh:+.2f} ann {100*a:+.1f}% vol {100*v:.1f}% maxDD {100*dd:+.1f}%")
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# 60/40 benchmark over same window
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j_eq = [nm for _, nm in INSTR].index("equity"); j_bd = [nm for _, nm in INSTR].index("bond")
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r6040 = np.zeros(len(dates)); r6040[1:] = 0.6 * R[1:, j_eq] + 0.4 * R[1:, j_bd]
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a2, v2, sh2, dd2 = stats(r6040)
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print(f" 60/40 (SPY/IEF) benchmark: Sharpe {sh2:+.2f} ann {100*a2:+.1f}% vol {100*v2:.1f}% maxDD {100*dd2:+.1f}%")
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# equity buy-hold
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aeq, veq, sheq, ddeq = stats(np.concatenate([[0], R[1:, j_eq]]))
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print(f" equity buy-hold (SPY): Sharpe {sheq:+.2f} ann {100*aeq:+.1f}% maxDD {100*ddeq:+.1f}%")
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yr = np.array([int(d[:4]) for d in dates])
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print(" per-year Sharpe (adaptive book): " + " ".join(f"{y}:{stats(book[yr == y])[2]:+.1f}" for y in sorted(set(yr)) if (yr == y).sum() > 100))
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print(f" current target weights: " + " ".join(f"{nm}:{100*w[j]*L:.0f}%" for j, (_, nm) in enumerate(INSTR)))
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print("\n VERDICT: adaptive book Sharpe >= 60/40 AND lower maxDD across most years = the deployable book")
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print(" delivers on real ETFs. If ~60/40, the diversification+adaptive layer earns its keep modestly.")
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if __name__ == "__main__":
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main()
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