CuPy from pip had never compiled a kernel on the dev box (no CUDA_PATH for the
splayed CUDA-12 wheel layout); the probe swallowed the error and every book to
date was built on the NumPy path under a "GPU backtester" name.
- backtest/gpu_kernels.py (new): lazily probed, never-silent BACKEND state
(CUDA_PATH pointed at the nvidia-cuda-runtime-cu12 header wheel; the probe
compiles a kernel and keeps the failure reason), --backend auto|gpu|cpu and
FB2_BACKEND, and three CUDA kernels compiled with --fmad=false: ewm_batch
(the _gpu_ewm_alpha recurrence, one thread per series), roll_mean_var
(pandas 3.0.2 roll_mean/roll_var replica, Kahan + Welford + unstable
recompute), resolve_trades (trailing and static exits, one thread per trade).
- gpu_backtest.py: precompute_indicators and simulate_trades dispatch to the
GPU twins or to the untouched NumPy paths; caches are host NumPy and device
memory is released before the replay workers fork; the Backend: line names
the device or the fallback reason.
- test_all.py Module 36 (7 tests): a visible CUDA device must never fall back
silently; kernels vs their CPU references on synthetic edge cases; backend
selection; import guards (cupy banned from the live roots). Suite 536.
- CLAUDE.md, instructions_for_backtest.txt, research note with the evidence.
Verification: all five reference MD5s reproduced on the GPU and with
--backend cpu (ten runs, GPU cache fallbacks 0); precompute GPU == CPU on all
42 cached windows (2.34 M bars); suite green with GPU, with
CUDA_VISIBLE_DEVICES="" and with FB2_BACKEND=cpu; --workers 1 and --record
pairs equal. The per-bar replay stays the live Python signal code on CPU
workers by design; wall time is roughly unchanged (precompute ~3x faster).
Harwinder Sekhon
02589cbb88