ML-based automated issue management, defect classification and bug triaging.
Deep learning-based semi-automatic trading system for US stock market. The system automatically extracts a sensible information (in form of features for a deep learning model) from both publicly-available and subscription-based sources of daily and high-frequency market data. After a second stage of dimensionality reduction and features re-combination (via back-testing & cross-validation) the most relevant features are then used to train a complex non-linear model on GPU. The portfolio optimization-driven trading strategy uses probabilistic forecasts made by the model and current positions on the market to generate specific instructions for the traders.
ML-based semi-automatic market making and position trading system utilizing statistical arbitrage opportunities in volatility index – equity index future spreads on US market.
Simulation platform for trading finance. It can simulate exchange functionality, exchange behavior, smart order routers, client behavior, market data sources and all interactions between them with a high degree of realism and consistency. A high-frequency exchange simulator uses a set of ML-based models to replicate a realistic behavior of the market. An integrated order-matching engine allows for tested trading strategies be surrounded by a realistic trading environment which can simulate various stress-testing scenarios (including exotic ones) while still keeping things realistic.