mathiasandClaude Sonnet 4.6 caccd1aa7b feat(features): add hl_range + ret_intrabar OHLCV features (4-channel input)
- 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>
2026-06-26 13:11:59 +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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JEPA-based latent representation learning for FX trading risk management — research project
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