FactorB

FactorB is a modular, open-source framework for factor-based modeling and optimization designed to support data science workflows that rely on factor decomposition and factor-aware experimentation. It provides components for data ingestion, feature engineering, factor extraction, modeling, and evaluation, enabling researchers and engineers to build reproducible experiments with interchangeable parts.

The core runtime orchestrates pipelines as directed graphs and supports a factor engine that implements common decomposition techniques, interaction terms, and factor calibration. A plugin system allows adapters to data sources, modeling backends, and visualization tools, while an experiment tracker records runs, configurations, and results to promote reproducibility.

FactorB was released as an open-source project in 2021 by the Factor Foundation, with ongoing major releases that added scalability features, distributed execution, and improved interoperability with existing data platforms. The project adopts a permissive license and a governance model based on community maintainers and contributor guidelines.

In practice, FactorB is used in finance for factor-based risk and return modeling, in marketing for customer segmentation and response modeling, and in biology for integrative analyses that combine multiple data modalities. It supports parallel execution, scalable data handling, and compatibility with common data stores and visualization systems.

Related concepts include factor analysis, factor models, and feature engineering.