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 and exit signals. In this session Kris Skrinak and Igor Alekseev explore the use of Databricks analysis tools combined with deep learning training accessible through Amazon’s SageMaker to enhance the quality of predictive capabilities of two technical indicators: MACD and Slow stochastics. We use the S&P 500 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 Databricks notebooks and extend Databricks’ functionality to train deep learning models in the cloud via PySpark and Amazon SageMaker. No prior experience is required.
Kris Skrinak is the Global Machine Learning Technical Lead for the Amazon Partner Network (APN). He co-founded the Machine Learning group for the APN in 2017. Kris started his career as a Quantitative Strategist at Goldman Sachs, developed predictive maintenance apps as an AI Engineer at ATT, and prior to Amazon was Computer Vision Architect at GoPro. He founded and sold 2 Silicon Valley startups in Finance and Network Monitoring: ClearStation and SiteRock. As the technical lead for horizontal support of AI/ML he's the 1st line of support for AWS Partners looking to develop or expand their use of these.