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.