While generating and testing hypotheses, we jump between machine-driven data analysis and human thought, all to understand every step of a sophisticated trading idea. To build and test our models, we convert the complicated algorithms in our minds to computer code, using Python, Matlab, C#, or C++. We share a passion for problem-solving and applying our work to real-time opportunities within the capital markets.

Are the assumptions accurate, or have they introduced a bias? Can they be relaxed, or are they maybe not needed at all? We talk with traders or other team members regularly to make sure the important effects are being captured without degrading performance.

Once we’re convinced the strategy idea is indeed a good trade, we ensure that the final release behaves as expected and work with software developers about how to further improve the footprint of the strategy.

After developing a trading strategy, we often need to recalibrate or reassess the strategy based on changes in market conditions or differences in its real world behavior from that in backtesting.