8df1c7eea2
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, ~$32 credits). Verdict: Sharpe +0.32, CPCV median +0.30, IS +0.36/OOS +0.08 (sign-consistent), Deflated Sharpe ~0.92 at honest n_trials. Real but weak edge; fails deploy-grade gates (correct), passes edge-exists. Includes roll-neutralization fix (max-vol outright + zero roll-day returns) that eliminated 988%/day continuous-contract artifacts (RB vol 550%->31%). Plus install_torch_gpu.sh. Data gitignored. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
42 lines
1.5 KiB
Bash
42 lines
1.5 KiB
Bash
#!/usr/bin/env bash
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# Install GPU PyTorch for the surfer experiments (local RTX 3050 Ti, driver 580 → CUDA 12.x).
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#
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# RECOMMENDED (no sudo — installs to ~/.local, matching your existing numpy/scipy/databento):
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# bash scripts/install_torch_gpu.sh
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#
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# System-wide (only if you really want it):
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# sudo bash scripts/install_torch_gpu.sh
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#
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# Pick a different CUDA wheel tag if cu124 ever 404s (cu126 / cu128 also work on driver 580):
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# bash scripts/install_torch_gpu.sh cu126
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set -euo pipefail
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CUDA_TAG="${1:-cu124}"
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TORCH_INDEX="https://download.pytorch.org/whl/${CUDA_TAG}"
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if [ "$(id -u)" -eq 0 ]; then
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echo "[install] running as ROOT → system-wide site-packages (--break-system-packages)"
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FLAGS="--break-system-packages"
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else
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echo "[install] running as USER → ~/.local (matches existing packages; no sudo needed)"
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FLAGS="--user --break-system-packages"
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fi
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echo "[install] torch from ${TORCH_INDEX}"
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python3 -m pip install ${FLAGS} torch --index-url "${TORCH_INDEX}"
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echo "[verify] importing torch + checking CUDA on the local GPU"
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python3 - <<'PY'
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import torch
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print(" torch:", torch.__version__)
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print(" cuda available:", torch.cuda.is_available())
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if torch.cuda.is_available():
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print(" device:", torch.cuda.get_device_name(0),
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"| capability:", torch.cuda.get_device_capability(0))
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x = torch.randn(1_000_000, device="cuda")
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print(" GPU tensor op OK, sum =", float(x.sum()))
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else:
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raise SystemExit("CUDA NOT available to torch — check driver / try a different CUDA_TAG (cu126/cu128)")
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print(" ✅ GPU PyTorch ready")
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PY
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