Electronica AI’s team of data scientists work with big streams of data, transforming complex relationships into meaningful structures with high predictive accuracy.
WHAT WE DO
Using our foundation in machine learning and finance, we develop systems to validate, train and deploy efficient analytics tools.
We have developed a state of the art data science pipeline for analyzing dense time series data.
Our algorithms get smarter everyday, learning to stay ahead of the curve and to operate on noisy data.
Aris founded Electronica AI in 2013 building on his success developing quantitative strategies at some of Canada's most sophisticated hedge funds. Aris studied finance at the University of Concordia with a background in quantitative trading. He was an early adopter in applying machine learning techniques to large data sets on equities and derivatives. His work at Electronica AI continues on this path, which positions him to see opportunities in artificial intelligence and prediction advancements.
Applies 25 years of deep domain expertise to deploy robust and scalable solutions to the most complex technology problems. Tests alpha models utilizing ML approaches on option, equity and FX data and alternative datasets. Paul designed, built and managed a high-performance, cluster based fault-tolerant equity trading system that is part of core NYSE infrastructure, supporting 5% of US stock market volume.
Responsible for designing, optimizing and implementing predictive algorithms. Jon maintains and builds out the Electronica backtesting engine which facilitates estimation of strategy performance on historical data. He also implements algorithms for optimization of trading strategies and estimation of future performance with Machine Learning based techniques.
Alex designed and maintains the Electronica website, utilizing Heroku and NodeJS for a custom server experience. Alex is also responsible for the deployment of data mining bots that collect historical market price and sentiment data.
Consults with Electronica's quantitative analysis team on integrating key insights and techniques from cutting-edge machine learning research into predictive sequence analysis for financial markets. Geoff is pursuing his Ph.D. in Machine Learning at U Toronto and is joining the Vector Institute for Artificial Intelligence in the fall of 2017 as a junior researcher in the founding cohort. Geoff previously completed his BSc at the University of British Columbia in Computer Science and Statistics, working on Machine Learning engineering and research in Prof. Mark Schmidt's Machine Learning Lab.
Moeen is a University of Toronto graduate, persuing a degree in Applied Computing. He provides scripted analytics for thousands of equity markets every day, analyzing short term expected performance.