DocumentCode
2224774
Title
Better trade exits for foreign exchange currency trading using FXGP
Author
Loginov, Alexander ; Wilson, Garnett ; Heywood, Malcolm
Author_Institution
Dalhousie University, Faculty of Computer Science, 6050 University Avenue Halifax, NS, Canada
fYear
2015
fDate
25-28 May 2015
Firstpage
2510
Lastpage
2517
Abstract
Retracement is the tendency of markets to move between upper ‘resistance’ and lower ‘support’ price levels. Human traders frequently make use of visual tools to help identify these resistance and support levels so that they can by used in their trading decisions. These decision can be put into trading strategies composed of rules designed to mitigate losses after a trade is started, often called ‘stop loss’ orders, or to take profit at a near optimal time, often called ‘take profit’ orders. However, identifying such resistance and support levels is notoriously difficult given market volatility. Indeed, the levels need recalculating on a continuous basis, and only hold to an approximate degree. In this work we describe an approach for evolving buy-stay-sell currency trading rules using genetic programming. These rules are explicitly linked to technical indicators that incorporate features characterizing retracement. Benchmarking is then performed using the most recent three years of data from the EURUSD foreign exchange market with three different methods of identifying retracement based on moving average, pivot points and Fibonacci ratios. Investment strategies employing Fibonacci ratios and found to provide superior performance among the strategies examined.
Keywords
Decision trees; Immune system; Market research; Resistance; Sociology; Statistics; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2015 IEEE Congress on
Conference_Location
Sendai, Japan
Type
conf
DOI
10.1109/CEC.2015.7257197
Filename
7257197
Link To Document