wehub-resource-sync
1.6 KiB
Shared utilities
Library code imported across every chapter and case study. Notebooks run from the
repository root, so import utils and from utils.x import y resolve with no
installation. This is code you import, not run — for command-line tools, see
scripts/.
Configuration and paths. config.py loads and validates the paths and settings
in .env (and sorts out CUDA library paths); paths.py holds the chapter registry
and resolves chapter, case-study, and output directories so notebooks never hard-code
a location.
Figures. style.py defines the ML4T color palette and the matplotlib / Plotly
defaults that give every figure in the book one consistent look.
Data. data_quality.py summarizes coverage, checks OHLC invariants, and subsets
symbols for fast test runs; downloading.py is the shared backbone of the data/
download scripts (argument parsing, path/YAML resolution, atomic writes);
artifact_specs.py loads the per-case-study YAML sidecars that describe market data,
labels, and features.
Modeling and cross-validation. modeling.py is the workhorse — it loads a
modeling dataset, parses model configs, prepares folds, and detects the schema;
cv_splits.py builds the walk-forward splits (calendar-aware, leakage-safe);
predictions_cache.py caches long-form prediction frames so the teaching notebooks
don't recompute them.
Reproducibility. reproducibility.py seeds Python, NumPy, and Torch (CPU + CUDA)
in a single call; storage_benchmarks.py provides the synthetic data and timing
harness behind the Chapter 2 storage benchmarks.