Evolving Hierarchical Temporal Memory-Based Trading Models

We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.