feat(crypto): delta-neutral funding harvest — the one edge that breaks 0.7

Investigated crypto funding carry (researchers' flagged retail path to >1 Sharpe). DELTA-NEUTRAL
cash-and-carry (short perp+long spot, collect funding, price cancels) -- different from the
directional carry trap. Funding positive 75pct of time. Backtest net of cost: APR 9.9pct, raw
Sharpe +5.68, maxDD -7.4pct, survives to 20bp cost, worst month -1.5pct (delta-neutral held).
Discipline finds: (1) real; (2) 5.68 inflated (funding-only model, 1.8pct vol; real basis vol
-> honest ~2-3 Sharpe = literature's 1.5-2.5); (3) regime-dependent + DECAYING (negative 2022,
2025, 2026). Caveats: counterparty/exchange tail (FTX -100pct, not in backtest = real killer),
decay, operationally real. The ONE genuine path past the 0.7 ceiling -- but it's crypto +
counterparty tail is the price of admission. The journey converges: higher Sharpe exists,
reachable, but lives where the user didn't want to go with a tail the Sharpe doesn't show.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/surfer/crypto_funding_harvest.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""Delta-neutral crypto funding HARVEST (cash-and-carry) — the one retail path to higher Sharpe.
+
+When perp funding > 0 (longs pay shorts), go short-perp + long-spot (DELTA-NEUTRAL: price cancels)
+and collect the funding rate as carry. No price prediction. Causal: position[t] from funding[t-1]
+(funding is persistent), collect funding[t]. Net of realistic round-trip cost. Stress: worst months
+(crashes flip funding negative). Variants: broad vs majors, threshold, cost sensitivity.
+Treats `fund` as per-day funding (conservative; if per-8h the real APR/Sharpe is higher).
+"""
+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 metrics(r):
+    r = np.asarray(r); r = r[np.isfinite(r)]
+    if len(r) < 50 or r.std() == 0:
+        return (float("nan"),) * 4
+    ann = r.mean() * 365; vol = r.std() * math.sqrt(365)
+    eq = np.cumprod(1 + r); dd = float((eq / np.maximum.accumulate(eq) - 1).min())
+    return ann, vol, ann / vol, dd
+
+
+def harvest(fund, liq, thr, c_round, year, label, topk=None):
+    T, N = fund.shape
+    elig = liq & np.isfinite(fund)
+    sig = np.zeros((T, N))
+    sig[1:] = np.where(elig[1:] & (fund[:-1] > thr), 1.0, 0.0)      # causal: position from yesterday's funding
+    if topk:                                                        # restrict to top-K best-funded eligible
+        for t in range(1, T):
+            idx = np.where(sig[t] > 0)[0]
+            if len(idx) > topk:
+                keep = idx[np.argsort(-fund[t - 1, idx])[:topk]]
+                m = np.zeros(N); m[keep] = 1.0; sig[t] = m
+    w = sig / np.maximum(sig.sum(1, keepdims=True), 1)             # equal-weight positioned
+    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)                  # fraction of book traded
+    net = gross - turn * (c_round / 2)                             # one-way cost each side
+    ann, vol, sr, dd = metrics(net)
+    npos = sig.sum(1); avgpos = npos[npos > 0].mean()
+    print(f"{label:>26} {100*ann:>6.1f} {100*vol:>6.1f} {sr:>+7.2f} {100*dd:>+7.1f} {avgpos:>6.0f} {100*turn.mean():>6.1f}")
+    return net
+
+
+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)
+    liq = np.isfinite(qv30) & (qv30 > 5e6)                          # >$5M/day quote volume
+    majors = np.array([s in ("BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT", "XRPUSDT", "ADAUSDT",
+                              "DOGEUSDT", "AVAXUSDT", "LINKUSDT", "MATICUSDT") for s in syms])
+    liq_maj = liq & majors[None, :]
+
+    print(f"\n===== CRYPTO FUNDING HARVEST (delta-neutral, {N} coins, {T} days) =====")
+    print(f"{'variant':>26} {'APR%':>6} {'vol%':>6} {'Sharpe':>7} {'maxDD%':>7} {'#pos':>6} {'turn%':>6}")
+    base = harvest(fund, liq, 0.0, 0.0010, year, "broad, thr=0, 10bp")
+    harvest(fund, liq, 0.0003, 0.0010, year, "broad, thr=3bp, 10bp")
+    harvest(fund, liq, 0.0, 0.0010, year, "broad top-20, 10bp", topk=20)
+    harvest(fund, liq_maj, 0.0, 0.0010, year, "majors only, 10bp")
+    print("  -- cost sensitivity (broad, thr=0) --")
+    harvest(fund, liq, 0.0, 0.0005, year, "broad, 5bp")
+    harvest(fund, liq, 0.0, 0.0020, year, "broad, 20bp")
+    harvest(fund, liq, 0.0, 0.0040, year, "broad, 40bp (harsh)")
+
+    # per-year + worst-month stress on the base case
+    print("\n  per-year Sharpe (broad, thr=0, 10bp):")
+    print("    " + " ".join(f"{y}:{metrics(base[year[1:]==y])[2]:+.1f}" for y in range(2019, 2027) if (year[1:] == y).sum() > 60))
+    # monthly buckets
+    mo = (days[1:] / 30.4).astype(int)
+    worst = sorted(set(mo), key=lambda m: base[mo == m].sum())[:5]
+    print("  worst 5 ~monthly buckets (crash stress — does delta-neutral hold?):")
+    for m in worst:
+        seg = base[mo == m]
+        d0 = int(days[1:][mo == m][0])
+        import datetime
+        print(f"    ~{datetime.date.fromordinal(d0+719163)}: {100*seg.sum():+.1f}% over {len(seg)}d")
+    print("\nVERDICT: if net Sharpe >> 1 AND survives crash months (delta-neutral so price-neutral) = the genuine")
+    print("retail higher-Sharpe edge. Watch: turnover cost (funding flips), and whether tail months go deeply negative.")
+
+
+if __name__ == "__main__":
+    main()