Human Intelligence

We use supervised learning with more than 100 variables derived from price and alternative data (macro data such as non-farm pay-roll, volatility and market volume, sentiment index, FED meetings, etc.). The algorithm analyses the impact of these data to retain only the most explanatory variables. We apply gradient boosting methods in random forest algorithms to give a probability on the prediction.