Big data framework for finding patterns in multi-market trading data


In the United States, multimarket trading is becoming very popular for investors, professionals and high-frequency traders. This research focuses on 13 exchanges and applies data mining algorithm, an unsupervised machine learning technique for discovering the relationships between stock exchanges. In this work, we used an association rule (FP-growth) algorithm for finding trading pattern in exchanges. Thirty days NYSE Trade and Quote (TAQ) data were used for these experiments. We implemented a big data framework of Spark clusters on the top of Hadoop to conduct the experiment. The rules and co-relations found in this work seems promising and can be used by the investors and traders to make a decision.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)