QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics.
The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statistical capabilities for market regime detection using Hidden Markov Models and Bayesian inference tools for parameter estimation via Markov Chain Monte Carlo sampling.
The framework covers a broad surface of systematic investment capabilities, including statistical arbitrage implementation with cointegration testing and mean-reversion strategies. It further includes tools for portfolio risk optimization, market risk analysis, and financial time-series modeling using ARIMA and GARCH models.
The repository is primarily implemented as a collection of Jupyter Notebooks.