Real-time head-to-head: Adaptive modeling of financial market data using XGBoost and CatBoost
Which gradient boosted decision tree algorithm is superior for adaptive modeling of financial market data, XGBoost or CatBoost? There are plenty of articles comparing these algorithms on arbitrary static datasets, but how do they perform in a live, chaotic environment? How about resource usage like average training times, average inference times, CPU utilization, and RAM consumption? And finally, how well do these predictions translate into profit? To answer these questions, we designed a benchmark experiment, ran it live, and collected the results. Spoiler alert — XGBoost was way faster and won by almost 4x in terms of profitability.
Financial markets are inherently chaotic with price action reacting to unforeseen news events, market manipulation, and heard mentality. Traditional modeling techniques often struggle to keep up with such unpredictability. This is where adaptive modeling comes into play by providing a dynamic framework that can adjust and adapt to changing market conditions on the fly.
0 Comments