generated from mathias/template-go-web
caccd1aa7b3b84738be1171e863e74235261e25f
- 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>
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mathias/template-go-web.
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Description
JEPA-based latent representation learning for FX trading risk management — research project
370 KiB
Languages
Python
76.4%
Go
18.4%
Shell
4.3%
templ
0.6%
Dockerfile
0.3%