feat(surfer): Phase 0 GPU floor + validation harness — first real (weak) OOS trend edge

PyTorch-GPU diversified-trend floor (TSMOM 1/3/12mo + inverse-vol + 10% vol-target)
+ CPCV/Deflated-Sharpe validation, over 22 CME futures x 19.7y (Databento GLBX
ohlcv-1d via budget-capped fetcher, ~$32 credits). Verdict: Sharpe +0.32, CPCV
median +0.30, IS +0.36/OOS +0.08 (sign-consistent), Deflated Sharpe ~0.92 at honest
n_trials. Real but weak edge; fails deploy-grade gates (correct), passes edge-exists.
Includes roll-neutralization fix (max-vol outright + zero roll-day returns) that
eliminated 988%/day continuous-contract artifacts (RB vol 550%->31%). Plus
install_torch_gpu.sh. Data gitignored.

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

scripts/install_torch_gpu.sh

@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+# Install GPU PyTorch for the surfer experiments (local RTX 3050 Ti, driver 580 → CUDA 12.x).
+#
+# RECOMMENDED (no sudo — installs to ~/.local, matching your existing numpy/scipy/databento):
+#     bash scripts/install_torch_gpu.sh
+#
+# System-wide (only if you really want it):
+#     sudo bash scripts/install_torch_gpu.sh
+#
+# Pick a different CUDA wheel tag if cu124 ever 404s (cu126 / cu128 also work on driver 580):
+#     bash scripts/install_torch_gpu.sh cu126
+set -euo pipefail
+
+CUDA_TAG="${1:-cu124}"
+TORCH_INDEX="https://download.pytorch.org/whl/${CUDA_TAG}"
+
+if [ "$(id -u)" -eq 0 ]; then
+  echo "[install] running as ROOT → system-wide site-packages (--break-system-packages)"
+  FLAGS="--break-system-packages"
+else
+  echo "[install] running as USER → ~/.local (matches existing packages; no sudo needed)"
+  FLAGS="--user --break-system-packages"
+fi
+
+echo "[install] torch from ${TORCH_INDEX}"
+python3 -m pip install ${FLAGS} torch --index-url "${TORCH_INDEX}"
+
+echo "[verify] importing torch + checking CUDA on the local GPU"
+python3 - <<'PY'
+import torch
+print("  torch:", torch.__version__)
+print("  cuda available:", torch.cuda.is_available())
+if torch.cuda.is_available():
+    print("  device:", torch.cuda.get_device_name(0),
+          "| capability:", torch.cuda.get_device_capability(0))
+    x = torch.randn(1_000_000, device="cuda")
+    print("  GPU tensor op OK, sum =", float(x.sum()))
+else:
+    raise SystemExit("CUDA NOT available to torch — check driver / try a different CUDA_TAG (cu126/cu128)")
+print("  ✅ GPU PyTorch ready")
+PY