mathiasandClaude Sonnet 4.6 20aeecb971
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fix(eval): correct probe metric to use true year-based OOS split
- 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>
2026-06-24 22:57:14 +02:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00

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Description
JEPA-based latent representation learning for FX trading risk management — research project
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