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EVENT DETAILS |
Predicting the movements of price action instruments such as stocks, ForEx, commodities, etc., has been a demanding problem for quantitative strategists for years. Simply applying machine learning to raw price movements has proven to yield disappointing results.
New tools from deep learning can substantially enhance the quality of results when applied to traditional technical indicators rather than prices including their corresponding entry & exit signals. In this session Kris Skrinak explores analysis tools combined with deep learning training accessible through Amazon's SageMaker to enhance the quality of predictive capabilities of two technical indicators: MACD & Slow Stochastics. We use a custom portfolio as a baseline for prediction. Then we explore the capabilities of optimizing the statistical parameters of these indicators first, followed by hyper parameter optimization of the deep learning model deepAR. The session will illustrate how to build such indicators in SageMaker notebooks. No prior experience is required.
Every month the deep learning community of New York gathers at the AWS loft to share discoveries & achievements & describe new techniques.
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