feat(surfer): minute-horizon gate — deep ML (Mamba2/CfC/TLOB) has no fuel

Before building deep sequence models, tested whether cost-surviving signal exists at
the minute horizon for them to extract. 12 perps, 60d free 1m klines (OFI proxy from
takerBuyQuote/quoteVol), features -> forward 1/5/15min returns net of 5bp taker. ALL
features negative net (best gross ~0.2bp vs 5bp cost = 25x gap); reversal IC -0.027 and
vol-continuation +0.009 are real but ~25x too small; OFI/taker-imbalance ~zero predictive
power. -> No minute-horizon signal -> Mamba2/CfC/TLOB have nothing to extract -> don't
build (no model lifts IC 25x). Crossing-cost wall holds in crypto (sec-ES 100x, min-crypto
25x); maker=adverse-selection wall, full-LOB=colocation territory. Gate saved a multi-week
GPU build for $0/10min. Product remains the daily two-sleeve book.

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

scripts/surfer/micro_gate.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""Phase A — minute-horizon crypto signal gate (does deep ML have anything to extract?).
+
+Before training Mamba2/CfC/TLOB, prove there's cost-surviving predictability at the minute
+horizon. Free Binance 1m klines carry an OFI proxy (takerBuyQuote/quote). Test per-coin
+short-horizon features -> forward returns, pooled, net of taker cost. If even simple features
+show net edge, deep sequence models can plausibly extract more; if not, same wall as ES.
+
+Features (info up to t): r1,r5,r15 (momentum/reversal), ofi (taker imbalance), ofi5 (smoothed),
+vol_z. Targets: forward log-return over h in {1,5,15} min. Cost: 5bp round-trip taker.
+Reports per-feature IC + best simple strategy per-trade net edge (bps) vs cost. GPU compute.
+"""
+import json
+import math
+import os
+import sys
+import time
+import urllib.request
+
+import numpy as np
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+import torch  # noqa: E402
+
+DEV = "cuda" if torch.cuda.is_available() else "cpu"
+OUT = "data/surfer/crypto1m"
+SYMS = ["BTCUSDT", "ETHUSDT", "SOLUSDT", "XRPUSDT", "BNBUSDT", "DOGEUSDT",
+        "ADAUSDT", "AVAXUSDT", "LINKUSDT", "LTCUSDT", "SUIUSDT", "NEARUSDT"]
+DAYS = 60
+COST_BP = 5.0
+
+
+def _get(u):
+    return json.load(urllib.request.urlopen(urllib.request.Request(u, headers={"User-Agent": "curl/8"}), timeout=30))
+
+
+def fetch(sym):
+    cache = f"{OUT}/{sym}.npz"
+    if os.path.exists(cache):
+        d = np.load(cache); return d["close"], d["qv"], d["tbq"]
+    end = int(time.time() * 1000); start = end - DAYS * 86_400_000
+    rows = []
+    cur = start
+    for _ in range(200):
+        k = _get(f"https://fapi.binance.com/fapi/v1/klines?symbol={sym}&interval=1m&startTime={cur}&limit=1500")
+        if not k:
+            break
+        rows += k
+        cur = k[-1][0] + 1
+        if len(k) < 1500 or cur >= end:
+            break
+        time.sleep(0.05)
+    d = {int(r[0]): (float(r[4]), float(r[7]), float(r[10])) for r in rows}  # close, quoteVol, takerBuyQuote
+    ts = sorted(d)
+    close = np.array([d[t][0] for t in ts]); qv = np.array([d[t][1] for t in ts]); tbq = np.array([d[t][2] for t in ts])
+    os.makedirs(OUT, exist_ok=True)
+    np.savez(cache, close=close, qv=qv, tbq=tbq)
+    return close, qv, tbq
+
+
+def roll_mean(x, w):
+    c = np.concatenate([[0], np.cumsum(x)])
+    out = np.full_like(x, np.nan); out[w - 1:] = (c[w:] - c[:-w]) / w; return out
+
+
+def main():
+    feats_all = {k: [] for k in ["r1", "r5", "r15", "ofi", "ofi5", "vol_z"]}
+    fwd_all = {h: [] for h in [1, 5, 15]}
+    print(f"fetching 1m klines ({len(SYMS)} perps, {DAYS}d)...")
+    for s in SYMS:
+        close, qv, tbq = fetch(s)
+        lc = np.log(close)
+        r1 = np.full_like(lc, np.nan); r1[1:] = lc[1:] - lc[:-1]
+        r5 = np.full_like(lc, np.nan); r5[5:] = lc[5:] - lc[:-5]
+        r15 = np.full_like(lc, np.nan); r15[15:] = lc[15:] - lc[:-15]
+        ofi = np.where(qv > 0, 2 * tbq / qv - 1.0, 0.0)           # taker buy imbalance in [-1,1]
+        ofi5 = roll_mean(ofi, 5)
+        vz = (qv - roll_mean(qv, 30)) / (roll_mean(qv, 30) + 1e-9)
+        feats = {"r1": r1, "r5": r5, "r15": r15, "ofi": ofi, "ofi5": ofi5, "vol_z": vz}
+        for k in feats_all:
+            feats_all[k].append(feats[k])
+        for h in fwd_all:
+            fwd = np.full_like(lc, np.nan); fwd[:-h] = lc[h:] - lc[:-h]
+            fwd_all[h].append(fwd)
+    F = {k: np.concatenate(v) for k, v in feats_all.items()}
+    FW = {h: np.concatenate(v) for h, v in fwd_all.items()}
+
+    print(f"\n===== MINUTE-HORIZON GATE — {len(SYMS)} perps, {DAYS}d, cost={COST_BP}bp round-trip =====")
+    print("IC = corr(feature_t, forward-return); per-trade NET = mean(sign(feat)*fwd) - cost, in bps")
+    print(f"{'feature':>8} " + " ".join(f"h={h}:IC/netbp" for h in [1, 5, 15]))
+    for fk, fv in F.items():
+        cells = []
+        for h, fw in FW.items():
+            m = np.isfinite(fv) & np.isfinite(fw)
+            a, b = fv[m], fw[m]
+            ic = float(np.corrcoef(a, b)[0, 1]) if len(a) > 1000 else float("nan")
+            net_bp = (float(np.mean(np.sign(a) * b)) - COST_BP / 1e4) * 1e4
+            cells.append(f"{ic:+.3f}/{net_bp:+.1f}")
+        print(f"{fk:>8} " + "   ".join(cells))
+    print("\nVERDICT: any feature with positive per-trade NET bps (edge > cost) => minute-horizon signal")
+    print("exists for Mamba2/CfC/TLOB to extract -> build GPU model POC. All negative => same wall as ES.")
+
+
+if __name__ == "__main__":
+    main()