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