From a9a1c3c511ef2356c04d20290746bbc3902c1447 Mon Sep 17 00:00:00 2001
From: jgrusewski <jgrusewski@localhost>
Date: Sun, 7 Jun 2026 20:50:27 +0200
Subject: [PATCH] test: diversification maxed in base-6; more streams dilute
 (they're beta-repackaging)

What to do with diversification? Tested sqrt(N) lever: base-6 vs expanded-12 (+REITs/TIPS/HY/intl/
EM/vol-premium). Expanded WORSE (+1.20->+1.04, 2022 +0.1->-1.4): candidates are equity/bond-beta in
disguise -> concentrate not diversify. Trust layer manages edge-health not redundancy. Genuine
uncorrelated net-positive streams are scarce; base-6 already spans the distinct premia. Diversification
is maxed in the deployable book; the lever is capital, not more streams.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/surfer/multistrat_etf_expanded.py | 70 +++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 scripts/surfer/multistrat_etf_expanded.py

diff --git a/scripts/surfer/multistrat_etf_expanded.py b/scripts/surfer/multistrat_etf_expanded.py
new file mode 100644
index 000000000..1666dc95a
--- /dev/null
+++ b/scripts/surfer/multistrat_etf_expanded.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+"""What can we DO with diversification: add more uncorrelated net-positive streams (the sqrt(N) lever).
+
+Base 6 (SPY/IEF/GLD/PDBC/DBMF/BTC) + candidates: VNQ(REITs), TIP(infl-linked), HYG(HY credit),
+EFA(intl eq), EEM(EM eq), PUTW(vol-risk-premium/buy-write). Same adaptive pipeline (edge-decay
+trust auto-down-weights the bad ones). Does the expanded book beat the base-6? The trust layer
+decides which candidates earn their place.
+"""
+import datetime
+import json
+import math
+import os
+import sys
+import urllib.request
+
+import numpy as np
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from multistrat_paper import book_series  # exact adaptive pipeline  # noqa: E402
+
+BASE = [("SPY", "equity"), ("IEF", "bond"), ("GLD", "gold"), ("PDBC", "commod"), ("DBMF", "trend"), ("BTC-USD", "crypto")]
+CAND = [("VNQ", "reit"), ("TIP", "tips"), ("HYG", "hycredit"), ("EFA", "intl"), ("EEM", "em"), ("PUTW", "volprem")]
+
+
+def yhist(sym):
+    res = json.loads(urllib.request.urlopen(urllib.request.Request(
+        f"https://query1.finance.yahoo.com/v8/finance/chart/{sym}?interval=1d&range=10y",
+        headers={"User-Agent": "Mozilla/5.0"}), timeout=30).read())["chart"]["result"][0]
+    ts = res["timestamp"]; ind = res["indicators"]
+    adj = ind.get("adjclose", [{}])[0].get("adjclose") or ind["quote"][0]["close"]
+    return {datetime.datetime.utcfromtimestamp(t).strftime("%Y-%m-%d"): float(c) for t, c in zip(ts, adj) if c is not None}
+
+
+def stats(r):
+    r = r[np.isfinite(r) & (r != 0)]
+    ann = r.mean() * 252; vol = r.std() * math.sqrt(252)
+    eq = np.cumprod(1 + r); dd = float((eq / np.maximum.accumulate(eq) - 1).min())
+    return ann, vol, (ann / vol if vol > 0 else float("nan")), dd
+
+
+def run(instr, data, label):
+    dates = sorted(set.intersection(*[set(data[nm]) for _, nm in instr]))
+    R = np.zeros((len(dates), len(instr)))
+    for j, (_, nm) in enumerate(instr):
+        s = np.array([data[nm][d] for d in dates]); R[1:, j] = s[1:] / s[:-1] - 1
+    book, w, L = book_series(R)
+    a, v, sh, dd = stats(book)
+    yr = np.array([int(d[:4]) for d in dates])
+    py = " ".join(f"{y}:{stats(book[yr==y])[2]:+.1f}" for y in sorted(set(yr)) if (yr == y).sum() > 100)
+    print(f"  {label:>16} ({len(instr)} streams, {len(dates)}d): Sharpe {sh:+.2f}  ann {100*a:+.1f}%  maxDD {100*dd:+.1f}%")
+    print(f"       per-year: {py}")
+    return w, [nm for _, nm in instr]
+
+
+def main():
+    allinstr = BASE + CAND
+    data = {nm: yhist(sym) for sym, nm in allinstr}
+    print(f"EXPANDED BOOK (diversification lever) — base 6 vs +candidates")
+    run(BASE, data, "base-6")
+    w, names = run(allinstr, data, "expanded-12")
+    print(f"\n  trust-layer weights in expanded book (which candidates earned their place):")
+    order = sorted(range(len(names)), key=lambda j: -w[j])
+    for j in order:
+        print(f"     {names[j]:>9}: {100*w[j]:>5.1f}%")
+    print("\n  VERDICT: expanded Sharpe > base-6 = more uncorrelated streams lift the book (diversification works).")
+    print("  If ~equal, the candidates are too correlated / not net-positive -> 6 streams already captures it.")
+
+
+if __name__ == "__main__":
+    main()