Tuesday, November 19, 2019

Multi-Layer Machine Learning Approach to FOREX Thesis Proposal

Multi-Layer Machine Learning Approach to FOREX - Thesis Proposal Example According to Gearman and Freund, the ADT approach helps to select the best combination of rules derived from well known technical analysis indicators and we shall be in a position to select the best parameters of the technical indicators. The online learning layer will combine the output of several ADTs incorporated into the system and may eventually suggest a position that will be either short or long. We shall also have a risk management layer that will responsible for the validation of the trading signals at the instance it exceeds a predetermined specific non-zero threshold. The FOREX market is now having most of its transactions being conducted electronically therefore transforming it into a typical electronic market. Many of their customers within the currency exchange market who seek its services are now relying on automated trading systems in order to process large amounts of information and make instantaneous investment decisions regardless of where they are within the global. Performance of technical trading strategies may try to exploit statistical measurable short term market opportunities such as trend spotting and momentum in the foreign currency exchange. Lo, Mamasky, and Wang in their study, used non parametric regressions in order to recognize the technical patterns of large stocks in the trade market. Their findings were that technical indicators usually provide increased information for investors enabling them to compare the unconditional empirical distribution of daily stock returns to the conditional distribution on specific technical indicators. This plays a big role in helping them make informed decisions based on the identified market trends. M. Dempster, T. W. Payne, Y. Romahi, and G. Thompson (2001) in their study did a comparison of some four methods that are applicable in foreign exchange trading which included reinforcement learning, genetic algorithms,

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