mathiasandClaude Sonnet 4.6 e31905dc43
CD / Lint / Test / Vet (push) Successful in 4s
CD / Build & Import (push) Failing after 8s
CD / Deploy via GitOps (push) Has been skipped
feat(data): EUR/USD hourly pipeline + 2008-2023 M1 dataset (#2)
- scripts/prepare_hourly.py: M1→hourly aggregation (realized_vol = sqrt(Σr²),
  MIN_BARS=30 threshold, no weekend rows, year-based split preserved)
- tests/test_prepare_hourly.py: 5 TDD tests, all green
- train.py: USE_HOURLY=True, WINDOW=240 (10-day), PATCH_LEN=24 (1-day patches);
  build() prefers eurusd_hourly.parquet, falls back to daily; EXPORT BLOCK updated
- Taskfile.yml: data:fetch:historical, data:prepare:hourly, data:prepare:all, data:test
- 98,591 hourly rows (2008-2023) covering GFC, Euro crisis, Brexit, COVID, Fed cycle

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-25 13:12:48 +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

hostexecutor

Generated from mathias/template-go-web.

Bootstrap

After creating from template, run:

go mod tidy       # regenerate go.sum with real module path
task generate     # generate templ files
task build        # build the binary
S
Description
JEPA-based latent representation learning for FX trading risk management — research project
Readme
370 KiB
Languages
Python 76.4%
Go 18.4%
Shell 4.3%
templ 0.6%
Dockerfile 0.3%