ML-based automated issue management, defect classification and bug triaging.
Peter Gagarinov shares his experience with integrating Apache Ignite with external machine frameworks.
Peter shares his Apache Ignite experience. He will show how one can minimize the number of blocks in a complex, scalable backend for an ML-based, automated issue-management system (Alliedium), as you stay within the Java ecosystem and the microservice paradigm.
PgMex is a high-performance PostgreSQL client library for Matlab that enables a Matlab-based application to communicate with PostgreSQL database in the Matlab native way by passing data in a form of matrices, multi-dimensional arrays and structures. The library is written in pure C which gives a significant performance boost for both small and data-heavy database requests. Both Windows and Linux platforms are supported.
Deep learning-based semi-automatic trading system for US stock market.
ML-based semi-automatic market making and position trading system utilizing statistical arbitrage opportunities in volatility index – equity index future spreads on US market.
Broker-side stress-testing and optimization system for both aggregating and scaling multiple traders/strategies operating with options, futures, ETFs and stocks into a single portfolio for a better profitability/risk ratio for a broker.
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.
Semi-automatic trading system built for US options market making and position trading using a relative value implied volatility modeling based on a statistical forecasting of co-movements of implied volatility surfaces.
Infrastructure built for US proprietary trading firm for storing and analyzing tick data and order entry data in backtesting and real-time modes, providing an environment for defining and operating value-added metrics calculations in real-time and on historical time spans.