An efficient implementation of the backtesting of trading strategies

Authors:
Jiarui Ni;Chengqi Zhang
Affiliations:
Faculty of Information Technology, University of Technology, Sydney, Broadway, Australia;Faculty of Information Technology, University of Technology, Sydney, Broadway, Australia
Venue:
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
Year:
2005

Citing 3
Cited 0

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Abstract

Some trading strategies are becoming more and more complicated and utilize a large amount of data, which makes the backtesting of these strategies very time consuming. This paper presents an efficient implementation of the backtesting of such a trading strategy using a parallel genetic algorithm (PGA) which is fine tuned based on thorough analysis of the trading strategy. The reuse of intermediate results is very important for such backtesting problems. Our implementation can perform the backtesting within a reasonable time range so that the tested trading strategy can be properly deployed in time.