Investment Selection By Combining Chaos Theory with Artificial Intelligence
The I Know First forecasting algorithm models the markets as non-stationary chaotic systems with fractal properties. Features: Multi-representation – wide and deep supervised learning, data munging to expose important features. Generalization through explicit regularization. Plurality of forecasts. Principal Component Analysis results in near orthogonal inputs. Genetic adaptable algorithm. Can Reinforcement Learning system learn to trade the market? We will report here our results of using Reinforcement Learning to create a trading system based on the I Know First forecasts.
With over 35 years of research in artificial intelligence and machine learning, Dr. Lipa Roitman has developed algorithms to predict chaotic events in financial markets. He has a M.Sc. in chemistry and organic chemistry from Novosibirsk University, a Ph.D. in organic and physical organic chemistry, and photochemistry from the Weizmann Institute of Science, a post doctorate in polymer chemistry from the University of Akron, and has a background in mathematics and statistics. His past work includes: R&D Chemist for Rohm and Haas Corp. USA.
As Co-Founder and CTO of I Know First Ltd., the company uses his algorithm to predict markets and supply big data solutions for institutional and large private investors. Lipa is responsible for the design, development, characterization, specification, and implementation of algorithmic trading solutions. In addition, his other responsibilities include development of quantitative trading strategies, predictive analytics, and development of quantitative trading strategies, algorithmic trading applications, and Big-Data solutions for hedge funds and institutional clients.