generated from mathias/template-go-web
mathiasandClaude Sonnet 4.6
fa6dcaae0a
- fixtures/phase-a-toy.json — scaffold source for `autoresearch start`
Phase A live run: val_vol_r2 metric, daily EUR/USD, HEPA encoder toy
- .gitignore: add runs/ (ephemeral run dirs), metrics.json, embeddings.json,
HEARTBEAT, STATUS.md, pyc/__pycache__/.pytest_cache, eval binary
- untrack metrics.json (was a stale committed sample, now gitignored)
Launch on koala:
python scripts/autoresearch_start.py fixtures/phase-a-toy.json phase-a-toy
op run -- env LITELLM_KEY="$LITELLM_KEY" \
python loop.py --run-dir runs/phase-a-toy --iters 3
Closes the last gate on jepa-fx-risk#11 Phase A (code shipped v1.4.0;
live run pending).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
12 lines
739 B
JSON
12 lines
739 B
JSON
{
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"strategic_question": "What is the highest-leverage path to a JEPA-based FX tail-risk system that beats a GARCH/EWMA baseline on out-of-sample VaR-breach calibration, given one GPU and a solo researcher?",
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"nodes": [
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{
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"id": "phase-a-toy",
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"status": "autoresearch-ready",
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"question": "Improve the OOS linear-probe R² (val_vol_r2) of the HEPA encoder on EUR/USD daily realized vol. The encoder is a small causal transformer trained with VICReg. Vary one hyperparameter or architectural choice per iteration — model size, learning rate, window, patch length, depth, VICReg loss weights — to push val_vol_r2 as high as possible on the 2022-2023 OOS slice.",
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"candidate_metric": "val_vol_r2"
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}
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]
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}
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