We're pleased to announce the integration of the QuantConnect platform with MlFinLab, a leading machine-learning package from the London-based Hudson & Thames research company. Through the integration, quant researchers can easily harness a powerful machine-learning library to improve their investment strategy.

With QuantConnect, MlFinLab researchers can use the package through the entire quant life-cycle; research, backtesting, optimization, and live trading. The library represents more than 30,000 hours of research and development, covering the latest academic research, and is trusted by top quant practitioners globally.

For Hudson & Thames, the integration improves their client onboarding process, allowing researchers to access their package in a preconfigured environment loaded with data. They can demonstrate the capabilities of their technology without releasing the source to the wild. MlFinLab is the first commercial library added by QuantConnect.

To make the integration possible, QuantConnect developed a new fundamental technology - Package Environments. QuantConnect Environments allows the creation of bespoke python virtual environments for individual projects, enabling epic new flexibility in the python libraries we can support. QuantConnect clients constantly request the latest python packages; with Environments, we can quickly deliver them without conflicting with other packages we support.

Learn more about the integration and how to deploy your strategy in our new documentation:

https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/mlfinlab