Hierarchical Temporal Memory-based algorithmic trading of financial markets
2012 (English)Conference paper (Refereed)
This paper explores the possibility of using the Hierarchical Temporal Memory (HTM) machine learning technology to create a profitable software agent for trading financial markets. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as features 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 divided into a training set, a validation set and three test sets; bearish, bullish and horizontal. The best performing model on the validation set was tested on the three test sets. Artificial Neural Networks (ANNs) were subjected to the same data sets in order to benchmark HTM performance. The results suggest that the HTM technology can be used together with a feature vector of technical indicators to create a profitable trading algorithm for financial markets. Results also suggest that HTM performance is, at the very least, comparable to commonly applied neural network models.
Place, publisher, year, edition, pages
IEEE , 2012.
Machine learning, Data mining, Algorithmic trading
Computer Science Computer and Information Science
Research subject Bussiness and IT
IdentifiersURN: urn:nbn:se:hb:diva-6846DOI: 10.1109/CIFEr.2012.6327784ISI: 000310365100022Local ID: 2320/11579ISBN: 978-1-4673-1802-0OAI: oai:DiVA.org:hb-6846DiVA: diva2:887553
Computational Intelligence for Financial Engineering & Economics (CIFEr), New York, NY, 2012