Performance and functional enhancement of artificial market psychology simulator
Artificial Market Psychology Simulator (AMPS) is an adaptive and programmable simulation system designed to assist researchers in the study of psychology of securities traders. It forms a part of the larger system that includes the MIT Web Market-a double-auction engine for securities trading and RStudio-a physiology data acquisition software. The AMPS system controls price patterns and induces market events in an adaptive fashion. This research improves the performance and functioning of AMPS by generating realistic market scenarios in response to the trading subject's physiological feedback during the simulation process. Despite the subject's knowledge of the simulated system, AMPS generates market scenarios such that the trading subject can attach credibility to these events and respond accordingly. The adaptive nature of AMPS can be attributed to its ability to adjust market response based on observed physiological characteristics of the research subject. Another improvement is the integration of AMPS with RStudio. The simulation system uses real-time market and physiology data provided by the MIT Web Market and RStudio respectively. A prototypical scenario that represents a research subject's typical emotional states in response to market volatility during the simulation process has also been modeled. One specific software challenge is the integration of RStudio-written in C++, and AMPS-written in JAVA. Another challenge is that AMPS system needs to be modified to reflect different rule sets for different class of users based on their physiological response. The logging system for errors and events has been improved while the error and exception handling has been structured and streamlined as well. Acknowledgements I dedicate this thesis to my parents for their love, inspiration and guidance during my entire academic career; my brother for always being supportive and encouraging and finally my fiancee for being very patient and understanding with me during my graduate study while enduring many months of separation. This thesis is a culmination of my efforts and the guidance and support of several people at MIT. I extend my sincere gratitude to: Prof. Andrew Lo for providing me the wonderful opportunity to work on this research project and agreeing to supervise my thesis. Dr. Dmitry Repin for constantly guiding me through all the critical phases of the project through his insightful feedback. His invaluable guidance and support in understanding the functional requirements and design of the system were critical to the success of the project. Ms. Svetlana Sussman at the Lab for Financial Engineering for crucial …