Concurrent Computing for Accelerating Financial Machine Learning Model Training
Resumo
The financial market generates a large volume of data daily, allowing the increasing use of machine learning algorithms in building predictive models for the stock market. In this environment, time is a crucial factor since stock prices change daily, so the training time of models is a critical factor. This paper proposes a method to optimize the overall training time of 5 reinforcement learning algorithms that predict the weights of each stock in a stock portfolio. Experiments were conducted by varying the number of algorithms executed simultaneously. In addition, the computational characteristics of each algorithm were analyzed concerning the use of memory and processing. From the proposed combination of running the algorithms concurrently, it was possible to reduce the total training time by 33% compared to running the algorithms sequentially. Moreover, this execution led to a commendable 15% decrease in energy consumption.
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