Our approach to building trading and investment strategies is based on two steps: first, the building of the system in a traditional framework, and second, the application of machine learning to raise its predictive power.
We are constantly building many strategies with different economic rationales to achieve a more robust diversification: event-based, option-based, relative values, directionals, high-frequency, …
We developed a vast backtesting engine, including over 30 different statistical procedures to minimize the occurrence of false discoveries.
We are constantly updating the procedures included in our backtesting software which we consider one of the primary sources of our competitive advantage.
We dedicated above 6000 hours of work to this Backtesting software: we believe that the time dedicated to building a robust backtesting engine is never enough.
We run our strategies (C++/Python) with the Interactive Brokers API on dedicated Amazon clouds.
Our execution strategies are based on a deep study of the market microstructure and leverage the predictive power of machine learning to minimize the price impact and the other transaction costs.
We use C++, CUDA, and CPU multithreading to perform complex analyses with data streaming.
We employ web scraping to import data from websites into our trading systems directly.