Installation¶

Requirements¶

Python 3.12+
Polars 0.20+

Basic Installation¶

pip install ml4t-diagnostic

Optional Dependencies¶

ML4T Diagnostic has optional dependency groups for different use cases:

Visualization¶

For Plotly charts, tearsheets, and PDF export:

pip install ml4t-diagnostic[viz]

Includes: plotly, matplotlib, seaborn, kaleido, pypdf

Machine Learning Backends¶

For LightGBM and XGBoost model analysis:

pip install ml4t-diagnostic[ml]

Includes: lightgbm, xgboost

Backtest Bridge¶

For ml4t-backtest integration and result-to-tearsheet bridges:

pip install ml4t-diagnostic[backtest]

Includes: ml4t-backtest

Dashboard¶

For the optional Streamlit trade diagnostics dashboard:

pip install ml4t-diagnostic[dashboard]

Includes: streamlit

Full Installation¶

Install all optional dependencies:

pip install ml4t-diagnostic[all]

Development Installation¶

For contributing to ML4T Diagnostic:

git clone https://github.com/ml4t/diagnostic.git
cd ml4t-diagnostic
pip install -e ".[all,dev]"

Using The Book Code Locally¶

If you are running the third-edition notebooks or case studies against a local checkout, install the library in editable mode so the book code sees your current branch:

uv pip install -e /path/to/ml4t-diagnostic

See the Book Guide for the chapter and case-study map. For the new reporting bridge, see the Backtest Tearsheets guide.

Verify Installation¶

import ml4t.diagnostic as diag
print(diag.__version__)

Dependencies¶

Core¶

Package	Version	Purpose
polars	≥0.20.0	Primary data processing
pandas	≥2.0.0	Compatibility layer
numpy	≥1.24.0	Numerical computing
scipy	≥1.10.0	Scientific computing
scikit-learn	≥1.3.0	ML utilities
statsmodels	≥0.14.0	Statistical tests
numba	≥0.57.0	JIT compilation

Optional¶

Package	Group	Purpose
lightgbm	ml	Gradient boosting
xgboost	ml	Gradient boosting
shap	ml	SHAP explanations
plotly	viz	Interactive charts
matplotlib	viz	Static charts
arch	advanced	GARCH models