generated from mathias/template-go-web
chore: fixture backlog for Phase A toy run + gitignore cleanup
- 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>
This commit is contained in:
+17
@@ -34,3 +34,20 @@ bin/
|
|||||||
|
|
||||||
# downloaded + processed market data (track via DVC/MinIO, #10 — not git)
|
# downloaded + processed market data (track via DVC/MinIO, #10 — not git)
|
||||||
data/
|
data/
|
||||||
|
|
||||||
|
# autoresearch run dirs (ephemeral; each scaffold rebuilds from fixtures/)
|
||||||
|
runs/
|
||||||
|
|
||||||
|
# ephemeral experiment outputs (generated by train.py / loop.py)
|
||||||
|
metrics.json
|
||||||
|
embeddings.json
|
||||||
|
HEARTBEAT
|
||||||
|
STATUS.md
|
||||||
|
|
||||||
|
# python caches
|
||||||
|
__pycache__/
|
||||||
|
*.pyc
|
||||||
|
.pytest_cache/
|
||||||
|
|
||||||
|
# built Go binaries
|
||||||
|
eval
|
||||||
|
|||||||
@@ -0,0 +1,11 @@
|
|||||||
|
{
|
||||||
|
"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?",
|
||||||
|
"nodes": [
|
||||||
|
{
|
||||||
|
"id": "phase-a-toy",
|
||||||
|
"status": "autoresearch-ready",
|
||||||
|
"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.",
|
||||||
|
"candidate_metric": "val_vol_r2"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
@@ -1,14 +0,0 @@
|
|||||||
{
|
|
||||||
"val_vol_r2": 0.3641397896593044,
|
|
||||||
"phase1_r2": 0.3908407688140869,
|
|
||||||
"n_test": 11641,
|
|
||||||
"knobs": {
|
|
||||||
"WINDOW": 120,
|
|
||||||
"PATCH_LEN": 24,
|
|
||||||
"D_MODEL": 128,
|
|
||||||
"DEPTH": 2,
|
|
||||||
"ALPHA": 0.1,
|
|
||||||
"DELTA_T_MAX": 3,
|
|
||||||
"EPOCHS": 300
|
|
||||||
}
|
|
||||||
}
|
|
||||||
Reference in New Issue
Block a user