Thor: Bayesian Optimization

Implements an API and user interface that facilitates Bayesian optimization of machine learning systems. Includes state-of-the-art advances in hyperparameter search such as portfolios of acquisition functions, distributed optimization, and low-discrepancy pseudo-random restarts for kernel parameter tuning. Thor possesses client-side interfaces for Python, R, and MATLAB.

Sif: Gaussian Processes

Implements Bayesian non-parametric inference via Gaussian processes. Sif includes acquisition functions for Bayesian optimization, kernels and approximate kernel inference, and support for fully Bayesian inference with elliptical slice sampling. Sif's modules give low-level access to Bayesian linear and logistic regression models and develops this machinery into Gaussian regression and classification processes.

Odin: Algorithmic Trading Infrastructure

Implements an event-driven live trading (via Interactive Brokers) and backtesting infrastructure in Python. Supports a modular design that allows retail traders to leverage low-level control over signal generation, portfolio management, equity rebalancing, and data streams. Odin integrates closely with a dedicated Postgres equities database and incorporates modern portfolio performance metrics.