Paper Info
Title
FOREX Trading Strategy Optimization.
Abstract
Developing robust trading rules for forex trading remains a significant challenge for both academics and practitioners. We employ a genetic algorithm to evolve a diverse set of profitable trading rules based on weighted moving average method. We use the daily closing rates between four pairs of currencies – EUR/USD, GBP/USD, USD/JPY, USD/CHF – to develop and evaluate our method. Results are presented for all four currency pairs over the 16 years from 2000 to 2015. Developed approach yields acceptably high returns on out-of-sample data. The rules obtained using our genetic algorithm result in significantly higher returns than those produced by rules identified through exhaustive search.
Year
Venue
Field
2017
Decision Economics@DCAI
Econometrics,Trading strategy,Evolutionary algorithm,Brute-force search,Computer science,Foreign exchange market,Trading rules,Moving average,Genetic algorithm,Distributed computing,Currency
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Svitlana Galeshchuk1304.36
Sumitra Mukherjee231131.75