diff --git a/scripts/surfer/crypto_funding_liveness.py b/scripts/surfer/crypto_funding_liveness.py
new file mode 100644
index 000000000..d1212e8fc
--- /dev/null
+++ b/scripts/surfer/crypto_funding_liveness.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""Decay/liveness: is the crypto funding carry still ALIVE now, or competed to death?
+
+The harvest was +12 Sharpe 2020-24 but negative 2022, 2025, 2026. Decisive question: is that
+(a) STRUCTURAL COMPRESSION (funding yield shrinking year-over-year = arbitraged away = dead) or
+(b) REGIME (funding flips negative in deleverage, recovers = dormant)? And does a regime filter
+(only harvest reliably-positive funding, else sit in cash) rescue the recent period?
+
+Diagnostics: (1) funding-yield decay table by year; (2) quarterly net-harvest liveness timeline;
+(3) regime-filtered vs unfiltered in the recent window.
+"""
+import datetime
+import math
+import os
+import sys
+
+import numpy as np
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+import pit_sweep  # noqa: E402
+
+
+def sharpe(r):
+    r = r[np.isfinite(r)]
+    return float("nan") if len(r) < 20 or r.std() == 0 else r.mean() / r.std() * math.sqrt(365)
+
+
+def harvest(fund, elig, hurdle, c_round, trail=None):
+    T, N = fund.shape
+    cond = fund > hurdle
+    if trail:                                                   # regime filter: trailing-mean funding > hurdle
+        tf = np.full_like(fund, np.nan)
+        for t in range(trail, T):
+            tf[t] = np.nanmean(fund[t - trail:t], axis=0)
+        cond = tf > hurdle
+    sig = np.zeros((T, N))
+    sig[1:] = np.where(elig[1:] & cond[:-1], 1.0, 0.0)         # causal
+    w = sig / np.maximum(sig.sum(1, keepdims=True), 1)
+    f = np.nan_to_num(fund)
+    gross = np.sum(w[:-1] * f[1:], axis=1)
+    turn = np.sum(np.abs(w[1:] - w[:-1]), axis=1)
+    net = gross - turn * (c_round / 2)
+    inmkt = sig.sum(1)[1:] > 0                                  # days actually positioned
+    return net, inmkt
+
+
+def main():
+    syms, days, close, qv, fund = pit_sweep.load()
+    T, N = fund.shape
+    year = (1970 + days / 365.25).astype(int)
+    qv30 = np.full_like(qv, np.nan)
+    for t in range(30, T):
+        qv30[t] = np.nanmean(qv[t - 30:t], axis=0)
+    elig = np.isfinite(qv30) & (qv30 > 5e6) & np.isfinite(fund)
+
+    print("\n===== (1) FUNDING-YIELD DECAY by year (compression vs regime) =====")
+    print(f"{'year':>6} {'#coins':>7} {'frac_pos':>9} {'mean_all(bp)':>13} {'mean_pos(bp)':>13}")
+    for y in range(2019, 2027):
+        m = year == y
+        e = elig[m]
+        fy = fund[m][e]
+        if len(fy) < 100:
+            continue
+        ncoin = int(e.sum(1).mean()) if e.ndim == 2 else int(e.mean() * N)
+        fpos = float((fy > 0).mean())
+        print(f"{y:>6} {ncoin:>7} {fpos:>9.2f} {1e4*fy.mean():>13.2f} {1e4*fy[fy > 0].mean():>13.2f}")
+
+    print("\n===== (2) QUARTERLY net-harvest liveness (broad, thr=0, 10bp) =====")
+    net, _ = harvest(fund, elig, 0.0, 0.0010)
+    q = ((days[1:] - days[1]) / 91.3).astype(int)
+    dd = days[1:]
+    print(f"{'quarter':>10} {'netAPR%':>8} {'Sharpe':>7}")
+    for qq in sorted(set(q)):
+        seg = net[q == qq]
+        d0 = datetime.date.fromordinal(int(dd[q == qq][0]) + 719163)
+        if d0.year >= 2024:                                    # focus recent
+            print(f"{str(d0):>10} {100 * seg.sum() * (365 / max(len(seg), 1)) / 1:>8.1f} {sharpe(seg):>7.1f}")
+
+    print("\n===== (3) REGIME FILTER rescue test (recent: 2025-2026) =====")
+    recent = year[1:] >= 2025
+    variants = [("unfiltered thr=0 10bp", harvest(fund, elig, 0.0, 0.0010)[0]),
+                ("filtered tf14>3bp 10bp", harvest(fund, elig, 0.0003, 0.0010, trail=14)[0]),
+                ("filtered tf14>5bp 10bp", harvest(fund, elig, 0.0005, 0.0010, trail=14)[0]),
+                ("filtered tf30>5bp 10bp", harvest(fund, elig, 0.0005, 0.0010, trail=30)[0])]
+    print(f"{'variant':>24} {'2025-26 APR%':>13} {'2025-26 Sharpe':>15} {'full Sharpe':>12}")
+    for nm, r in variants:
+        rr = r[recent]
+        apr = 100 * np.nansum(rr) * 365 / max(np.isfinite(rr).sum(), 1)
+        print(f"{nm:>24} {apr:>13.1f} {sharpe(rr):>15.1f} {sharpe(r):>12.1f}")
+
+    print("\nVERDICT: if mean_pos(bp) stable across years but frac_pos dipped 2025-26 = REGIME (dormant, recoverable).")
+    print("If mean_pos shrinks monotonically = COMPRESSION (competed to death). If a filter turns 2025-26 positive")
+    print("= alive-but-needs-regime-timing. If even filtered 2025-26 is negative/zero = the edge is GONE now.")
+
+
+if __name__ == "__main__":
+    main()