From b664673df345b65818570ea69e24b1c9cc366f11 Mon Sep 17 00:00:00 2001
From: jgrusewski <jgrusewski@localhost>
Date: Sat, 6 Jun 2026 19:35:37 +0200
Subject: [PATCH] =?UTF-8?q?feat(surfer):=20Fear&Greed=20gate=20=E2=80=94?=
 =?UTF-8?q?=20sentiment=20regime-dependent,=20not=20robust?=
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F&G market-timing: full-sample IC negative (fear->continuation), recent OOS positive,
IS/OOS sign-flip (-0.65->+0.75), 2026 flipped -0.8 = regime-dependent, not robust.
Free non-price is mostly market-wide timing (low-breadth, regime-prone); cross-sectional
on-chain (the valuable kind) needs paid data. Non-price free frontier mostly dead.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/surfer/fng_gate.py | 80 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 scripts/surfer/fng_gate.py

diff --git a/scripts/surfer/fng_gate.py b/scripts/surfer/fng_gate.py
new file mode 100644
index 000000000..c350974cc
--- /dev/null
+++ b/scripts/surfer/fng_gate.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+"""Phase A (non-price) — Fear & Greed sentiment gate (market-timing signal).
+
+Does crypto sentiment (alternative.me Fear & Greed, daily since 2018) predict forward
+crypto-market returns? Contrarian hypothesis: extreme fear -> buy, extreme greed -> sell.
+Market = equal-weight return over the crypto_pit universe. Tests contrarian-level,
+extreme-only, and sentiment-change signals -> forward 1/7/30d returns, IC + timing-strategy
+Sharpe (net 5bp), per-year, OOS. A market-timing sleeve is directional -> diversifies the
+market-neutral momentum book if it works.
+"""
+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__)))
+import pit_sweep  # noqa: E402
+from signal_sweep import sharpe_t  # noqa: E402
+import torch  # noqa: E402
+
+DEV = "cuda" if torch.cuda.is_available() else "cpu"
+DAY = 86_400
+
+
+def fng():
+    cache = "data/surfer/fng.json"
+    if os.path.exists(cache):
+        return json.load(open(cache))
+    d = json.load(urllib.request.urlopen(urllib.request.Request(
+        "https://api.alternative.me/fng/?limit=0&format=json", headers={"User-Agent": "curl/8"}), timeout=30))["data"]
+    out = {int(r["timestamp"]) // DAY: float(r["value"]) for r in d}
+    os.makedirs("data/surfer", exist_ok=True); json.dump(out, open(cache, "w"))
+    return {int(k): v for k, v in out.items()}
+
+
+def main():
+    fg = fng()
+    syms, days, close, qv, fund = pit_sweep.load()
+    R = np.zeros_like(close); R[1:] = np.log(close)[1:] - np.log(close)[:-1]; R = np.where(np.isfinite(R), R, 0.0)
+    mkt = {int(days[t]): float(np.nanmean(R[t][np.isfinite(close[t])])) for t in range(len(days))}  # EW market ret day t
+    common = sorted(set(fg) & set(mkt))
+    f = np.array([fg[d] for d in common]); r = np.array([mkt[d] for d in common])
+    cum = np.concatenate([[0], np.cumsum(r)])
+    year = (1970 + np.array(common) / 365.25).astype(int)
+    n = len(common); split = int(0.7 * n)
+
+    def fwd(h):
+        out = np.full(n, np.nan)
+        out[:n - h] = cum[h + 1:n + 1] - cum[1:n - h + 1]  # sum of mkt returns t+1..t+h
+        return out
+
+    sigs = {
+        "contrarian_level": -(f - 50),
+        "extreme_only": np.where(f < 25, 1.0, np.where(f > 75, -1.0, 0.0)),
+        "sentiment_chg": np.concatenate([[np.nan] * 7, f[7:] - f[:-7]]),  # rising sentiment (momentum)
+    }
+    print(f"\n===== FEAR&GREED GATE — market-timing, {n} days ({common[0]}..{common[-1]}), cost 5bp =====")
+    print(f"{'signal':>16} " + " ".join(f"h={h}:IC" for h in [1, 7, 30]) + "   timing(h=7): full/IS/OOS/peryr")
+    for nm, s in sigs.items():
+        ics = []
+        for h in [1, 7, 30]:
+            fw = fwd(h); m = np.isfinite(s) & np.isfinite(fw)
+            ics.append(float(np.corrcoef(s[m], fw[m])[0, 1]) if m.sum() > 100 else float("nan"))
+        # timing strategy at h=7 (weekly hold): position = sign(s) lagged, daily mkt return, ~weekly turnover
+        pos = np.sign(s); pnl = np.full(n, np.nan)
+        pnl[1:] = pos[:-1] * r[1:]
+        turn = np.abs(np.diff(np.nan_to_num(pos)))
+        pnl[1:] -= turn * 5e-4
+        T = lambda x: torch.tensor(x[np.isfinite(x)], device=DEV, dtype=torch.float64)
+        full = sharpe_t(T(pnl)); isr = sharpe_t(T(pnl[:split])); oos = sharpe_t(T(pnl[split:]))
+        py = " ".join(f"{y}:{sharpe_t(T(pnl[year==y])):+.1f}" for y in range(2019, 2027) if (year == y).sum() > 60)
+        print(f"{nm:>16} " + " ".join(f"{ic:+.3f}" for ic in ics) + f"   {full:+.2f}/{isr:+.2f}/{oos:+.2f}  {py}")
+    print("\nVERDICT: contrarian IC>0 (fear->up) + timing OOS Sharpe>0 net = real sentiment signal (directional sleeve).")
+
+
+if __name__ == "__main__":
+    main()