Due to the size of data needed, running software to analyze and tuning intraday trading strategies can take large amounts of time away from analysts, who would like to be able to evaluate strategies and optimize strategy parameters very quickly, ideally in the blink of an eye. Fortunately, Big Data technologies are evolving rapidly and can be leveraged for these purposes. These technologies include software systems for distributed computing, parallel hardware, and on demand computing resources in the cloud. This report presents a distributed software system for trading strategy analysis. It also demonstrates the effectiveness of Machine Learning techniques in decreasing parameter optimization workload. The results from tests run on two different commercial cloud service providers show linear scalability when analyzing intraday trading strategies. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/24094 |
| Date | 23 April 2014 |
| Creators | Mauldin, Timothy Allan |
| Source Sets | University of Texas |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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