Title :
Foreign Exchange Trading Rules Using a Single Technical Indicator from Multiple Timeframes
Author :
Shangkun Deng ; Sakurai, Akito
Author_Institution :
Grad. Sch. of Sci. & Technol., Keio Univ., Yokohama, Japan
Abstract :
This study applies a genetic algorithm (GA) to generate trading rules for currency trading based on a single technical indicator named the Relative Strength Index (RSI) as well as multiple timeframes from which we extract the feature. The target trading currency pair is EUR/USD and trading time horizon is one hour. Using more than one timeframe may improve the assessment of the overbought or oversold conditions of the target currency pair, since different traders may have different trading time horizons and thus a trader may consider the overall condition for trading a currency pair from both its longer and shorter timeframes. Therefore, this paper uses a combined signal from a relatively longer timeframe (two hours) and a relatively shorter timeframe (30 minutes), other than the target timeframe (one hour). In addition, since the parameters of the RSI are also crucial for obtaining the best trading rules, we use a GA to search for the best parameters of each RSI. Moreover, we design a GA chromosome to encode trading timing by designating when to buy, sell, and close the position. The experimental results presented in this paper show that the combined signal from multiple timeframes, including that from the target timeframe, improves trading performance.
Keywords :
feature extraction; foreign exchange trading; genetic algorithms; learning (artificial intelligence); search problems; EUR-USD and trading; GA chromosome; RSI best parameter searching; currency trading; feature extraction; foreign exchange trading rules; genetic algorithm; multiple timeframes; oversold condition; relative strength index; single technical indicator; target trading currency pair; trading performance; trading rule generation; trading time horizon; trading timing; Benchmark testing; Biological cells; Genetic algorithms; Support vector machines; Timing; Training; Currency Trading; Genetic Algorithm; Multiple Time Frames; Technical Indicator; Trading Rule;
Conference_Titel :
Advanced Information Networking and Applications Workshops (WAINA), 2013 27th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4673-6239-9
Electronic_ISBN :
978-0-7695-4952-1
DOI :
10.1109/WAINA.2013.7