There are two major projects I am actively involved in: OLPS and LIBOL. 

OLPS - Online Portfolio Selection via Machine Learning

OLPS is a research project for studying online portfolio selection using state-of-the-art machine learning algorithms. In this task, a portfolio manager is a decision maker, whose goal is to produce a portfolio strategy, aiming to maximize the cumulative wealth. He/she computes the portfolios sequentially. In each period, the manager has access to the sequence of previous price relative vectors. Then, he/she computes a new portfolio for next unknown price relative vector, where the decision criterion varies among different managers. The portfolio is scored based on portfolio period return. This procedure is repeated until the end, and the portfolio strategy is finally scored according to portfolio cumulative wealth.

For more details on the toolbox - including downloads, and documentation, click here. Alternately, OLPS toolbox is also available on GitHub.

LIBOL - A Library of Online Learning Algorithms

LIBOL is an open-source machine learning library that consists of a family of classical and state-of-the-art online learning algorithms for large-scale machine learning and data mining research. It includes two categories of online learning methods: regular linear online learning algorithms and kernel-based online learning algorithms.

For more details on the toolbox - including downloads, and documentation, click here. Alternately, LIBOL toolbox is also available on GitHub.