- prepare_hourly.py: keep O/H/L columns from M1 zips; compute per-hour
hl_range=log(H/L) and ret_intrabar=log(close/open); backward-compat
(falls back to 4-col output only when O/H/L present in input)
- train.py build(): auto-detect extra features from parquet columns
(FEAT_COLS = [ret, realized_vol] + [hl_range, ret_intrabar] if present)
- 5 new tests (9 total in test_prepare_hourly); 24/24 pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- train.py knobs all readable from JEPA_* env vars (JEPA_WINDOW, JEPA_D_MODEL,
JEPA_DEPTH, etc.) so hpo_sweep.py can override without touching source
- scripts/hpo_sweep.py: 3×2×3 grid over D_MODEL × DEPTH × WINDOW,
logs to results/hpo/hpo_results.jsonl with leaderboard at end
- 3 new tests: env override correctness, configs() schema validation
- 19/19 tests pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Go harness reported 0.42 vs Python 0.36 because export used realized_vol[t]
(current) while Python probe used realized_vol[t+1] (next-period). Fix adds
t+1 < len(df2) guard and uses iloc[t+1] as target. Go now matches Python: 0.3585.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
BATCH_SIZE=512 per step; batched embed() at eval + export time.
78k hourly windows can't fit in GPU in one shot (was fine at 877 daily).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- train.py build(): year-based split (train≤2021, OOS≥2022) replaces
misleading 70/30 mixed-period split; true OOS val_vol_r2 now ~-0.36
vs previously reported +0.18 (artefact of cross-period data leakage)
- train.py: EXPORT_EMBEDDINGS block now exports both train+OOS embeddings
with dates and HV labels for Go eval harness
- cmd/eval: LinearProbeTrainTest uses train stats for standardisation of
both sets (no leakage); standardiseCompute/applyStandardise helpers
- internal/eval: add LinearProbeTrainTest (fit-on-train, eval-on-OOS)
alongside LinearProbe (same-set); 8/8 tests still green
Phase-0 gate result: val_vol_r2=-0.36, silhouette=0.043, erank=58.9/64.
Backbone produces high-rank embeddings (SIGReg working) but does NOT
generalize across 2021→2022 regime boundary. Gate: INCONCLUSIVE/FAIL.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
All 5 kept: val_vol_r2 -0.1543 → +0.0599 (+0.214 total). Backbone learning.
Agent tuning: LR, depth, SIGREG_LAM, EPOCHS. Still well below toy ceiling
(0.37) — real backbone room to grow via #3/#4/#5.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
PatchTST-style transformer encoder with JEPA predictive loss + SIGReg
regularization (Balestriero & LeCun arXiv:2511.08544; time-series placement
from ChronoJEPA). Token-level SIGReg (dual placement) to avoid time-axis
collapse (confirmed real by ChronoJEPA). Baseline val_vol_r2=-0.1543 on first
run — expected for fresh weights with new architecture. Agent will iterate.
SIGReg source: Epps-Pulley statistic, identical math to LeJEPA MINIMAL.md.
Refs: #3 (TS-JEPA reproduce), ChronoJEPA github.com/MrRobotop/ChronoJEPA
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Toy encoder near ceiling. 1 kept (val_vol_r2 0.3032→0.3442), 5 reverts.
Consistent plateau = time to swap in TS-JEPA backbone (#3/#5).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
mathiasandClaude Sonnet 4.6