Installation¶

Use this page when you want to install ml4t-engineer, verify the package surface, and then move directly into the first workflow.

Requirements¶

Python 3.12 or higher
Polars 0.20+

Install from PyPI¶

pip install ml4t-engineer

If you standardize on uv, uv pip install ml4t-engineer is equivalent.

Install from Source¶

git clone https://github.com/stefan-jansen/ml4t-engineer.git
cd ml4t-engineer
pip install -e .

Optional Dependencies¶

TA-Lib (for validation)¶

Some indicators can be validated against TA-Lib. Install TA-Lib if you need this:

# macOS
brew install ta-lib
pip install TA-Lib

# Ubuntu/Debian
sudo apt-get install libta-lib-dev
pip install TA-Lib

# Windows
# Download from https://www.ta-lib.org/
pip install TA-Lib

Verify Installation¶

from ml4t.engineer import feature_catalog

# Check available features
all_features = feature_catalog.list()
print(f"Total features: {len(all_features)}")
print(f"Categories: {feature_catalog.categories()}")

Expected output:

Total features: 120
Categories: ['math', 'microstructure', 'ml', 'momentum', 'price_transform', 'regime', 'risk', 'statistics', 'trend', 'volatility', 'volume']

Next Steps¶

Read Quickstart for a first working feature and labeling example.
Read the Book Guide if you are coming from the book or case studies.
Use the API Reference once you need exact object locations.