diff --git a/scripts/surfer/multistrat_paper.py b/scripts/surfer/multistrat_paper.py
new file mode 100644
index 000000000..e613abb11
--- /dev/null
+++ b/scripts/surfer/multistrat_paper.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""multistrat — local paper-forward of the adaptive multi-strat book (deployable spec, Phase 1).
+
+6 streams via free Yahoo daily adj-close: SPY/IEF/GLD/PDBC/DBMF + BTC-USD. Pipeline (deterministic
+from history each run): vol-normalize each stream -> edge-decay trust theta (down-weight decaying /
+resurrect recovered) -> trust-weighted combination -> adaptive risk layer (EMA vol, Kelly-floor,
+continuous self-recovering drawdown de-lever, z-score correlation de-risk, leverage-floor),
+UNLEVERED (MAXLEV 1.0). Forward-start, idempotent per UTC day, catch-up. No capital, no agents.
+
+  python3 multistrat_paper.py status      cum return + annualized + current target weights & leverage
+  python3 multistrat_paper.py run          book new trading days, persist (cron)
+  python3 multistrat_paper.py weights [USD] today's $ allocation per ETF
+  (alias paper == run)
+"""
+import datetime
+import json
+import math
+import os
+import sys
+import urllib.request
+
+import numpy as np
+
+_REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+STATE = os.path.join(_REPO, "data/surfer/multistrat_state.json")
+INSTR = [("SPY", "equity"), ("IEF", "bond"), ("GLD", "gold"), ("PDBC", "commod"), ("DBMF", "trend"), ("BTC-USD", "crypto")]
+TARGET_VOL, MAXLEV, KELLY_FLOOR, LEV_FLOOR = 0.10, 1.0, 0.5, 0.3
+DD_DEADBAND, DD_SENS, DD_FLOOR = 0.05, 3.0, 0.40
+
+
+def get(url, tries=4):
+    import time
+    for a in range(tries):
+        try:
+            return json.loads(urllib.request.urlopen(urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"}), timeout=30).read())
+        except Exception:
+            if a == tries - 1:
+                raise
+            time.sleep(2 * (a + 1))
+
+
+def yhist(sym):
+    res = get(f"https://query1.finance.yahoo.com/v8/finance/chart/{sym}?interval=1d&range=2y")["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 build():
+    data = {nm: yhist(sym) for sym, nm in INSTR}
+    dates = sorted(set.intersection(*[set(d) for d in data.values()]))
+    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
+    return dates, R
+
+
+def volnorm(col, tv=TARGET_VOL, win=63):
+    out = np.zeros_like(col)
+    for t in range(win, len(col)):
+        rv = col[t - win:t].std() * math.sqrt(252)
+        out[t] = col[t] * min(5.0, tv / (rv + 1e-9))
+    return out
+
+
+def trust(col, win=126, a=0.06):
+    th = 1.0; out = np.ones_like(col)
+    for t in range(win, len(col)):
+        seg = col[t - win:t]; sr = seg.mean() / (seg.std() + 1e-9) * math.sqrt(252)
+        th = (1 - a) * th + a * float(np.clip((sr + 0.5) / 1.0, 0.1, 1.0))
+        out[t] = th
+    return out
+
+
+def book_series(R):
+    S = R.shape[1]
+    M = np.column_stack([volnorm(R[:, j]) for j in range(S)])
+    TH = np.column_stack([trust(M[:, j]) for j in range(S)])
+    tw = TH / np.maximum(TH.sum(1, keepdims=True), 1e-9)
+    combo = np.nansum(tw * M, axis=1)
+    T = len(combo); book = np.zeros(T); eq = 1.0; peak = 1.0
+    emu = float(np.nanmean(combo[:63])); evar = float(np.nanvar(combo[:63])) + 1e-12; chist = []
+    Lc = np.zeros(T)
+    for t in range(63, T - 1):
+        x = combo[t]; emu = 0.97 * emu + 0.03 * x; evar = 0.97 * evar + 0.03 * (x - emu) ** 2
+        L = min(MAXLEV, TARGET_VOL / (math.sqrt(max(evar, 1e-12) * 252) + 1e-9))
+        L = min(L, max(KELLY_FLOOR, (emu * 252) / (evar * 252 + 1e-9)))
+        dd = eq / peak - 1.0
+        L *= float(np.clip(1 - DD_SENS * max(0.0, -dd - DD_DEADBAND), DD_FLOOR, 1.0))
+        cm = np.corrcoef(np.nan_to_num(M[t - 63:t]).T); ac = (cm.sum() - S) / (S * S - S); chist.append(ac)
+        if len(chist) > 60:
+            h = np.array(chist[-120:]); z = (ac - h.mean()) / (h.std() + 1e-9)
+            L *= float(np.clip(1 - 0.2 * max(0.0, z), 0.5, 1.0))
+        L = float(np.clip(L, LEV_FLOOR, MAXLEV)); Lc[t] = L
+        book[t + 1] = L * combo[t + 1]; eq *= (1 + book[t + 1]); peak = max(peak, eq)
+    return book, tw[-1], Lc[-2] if T > 1 else 0.0
+
+
+def load_state():
+    if os.path.exists(STATE):
+        return json.load(open(STATE))
+    return {"last_date": "", "days": 0, "sum_r": 0.0, "sumsq_r": 0.0, "equity": 1.0, "peak": 1.0, "max_dd": 0.0}
+
+
+def main():
+    cmd = sys.argv[1] if len(sys.argv) > 1 else "status"
+    if cmd in ("run", "paper"):
+        st = load_state(); dates, R = build(); book, w, L = book_series(R)
+        os.makedirs(os.path.dirname(STATE), exist_ok=True)
+        if not st["last_date"]:
+            st["last_date"] = dates[-1]; json.dump(st, open(STATE, "w"))
+            print(f"initialized forward tracking from {dates[-1]}"); return
+        booked = 0
+        for i in range(1, len(dates)):
+            if dates[i] <= st["last_date"]:
+                continue
+            r = float(book[i])
+            st["days"] += 1; st["sum_r"] += r; st["sumsq_r"] += r * r
+            st["equity"] *= (1 + r); st["peak"] = max(st["peak"], st["equity"])
+            st["max_dd"] = min(st["max_dd"], st["equity"] / st["peak"] - 1); st["last_date"] = dates[i]; booked += 1
+        json.dump(st, open(STATE, "w"))
+        print(f"{datetime.date.today()} booked {booked} day(s) -> day {st['days']} (through {st['last_date']}) "
+              f"cum {100*(st['equity']-1):+.2f}% leverage {L:.2f}")
+    elif cmd == "weights":
+        cap = float(sys.argv[2]) if len(sys.argv) > 2 else 35000.0
+        dates, R = build(); _, w, L = book_series(R)
+        print(f"target allocation for ${cap:,.0f} (leverage {L:.2f}x, unlevered book):")
+        for j, (sym, nm) in enumerate(INSTR):
+            print(f"  {nm:>7} ({sym:>8}): {100*w[j]*L:>5.1f}%  ${cap*w[j]*L:>8,.0f}")
+    else:
+        st = load_state()
+        n = st["days"]; m = (st["sum_r"] / n) if n else 0; var = (st["sumsq_r"] / n - m * m) if n > 1 else 0
+        sh = (m * 252) / (math.sqrt(max(var, 1e-12)) * math.sqrt(252)) if n > 1 and var > 0 else float("nan")
+        print(f"adaptive multi-strat paper — day {n} (through {st['last_date'] or 'n/a'})")
+        print(f"  cum {100*(st['equity']-1):+.2f}%  ann-Sharpe {sh:+.2f}  maxDD {100*st['max_dd']:+.1f}%")
+        dates, R = build(); _, w, L = book_series(R)
+        print(f"  current leverage {L:.2f}x | target weights:")
+        for j, (sym, nm) in enumerate(INSTR):
+            print(f"     {nm:>7} ({sym:>8}): {100*w[j]*L:>5.1f}%")
+
+
+if __name__ == "__main__":
+    main()