Development of an intelligent method based on fuzzy technical indicators for predicting and trading the euro-dollar exchange rate

Subject Areas : Financial engineering

alireza sadeghi ¹ , amir Daneshvar ² , Mahdi Madanchi Zaj ³

1 - Department of financial management , science and research branch, Islamic Azad university, tehran, iran
2 - Department of information technology, Electronic Campus, Islamic Azad university, tehran, iran
3 - Department of Financial Management, Electronic Campus, Islamic Azad University, Tehran, Iran

Received: 2020-10-08 Accepted : 2020-11-13 Published : 2020-12-21

Keywords: Genetic Algorithm, Support vector machine, Forex, fuzzy technical indicators,

Abstract :

Today, the Forex market is the largest financial market in the world. Determining the right strategy for buying or selling in the Forex market is based on predicting the price trend. Therefore, to choose a suitable strategy in Forex, complex meta-heuristic models are used. In this research, by predicting the market trend and based on trading rules based on fuzzy technical indicators, a new method for investing in the Forex market is presented. For forecasting, a combination of hyper support vector machine (HSVM) algorithm is used and for market classification in three different classes (uptrend, downtrend, sideway) and a dynamic genetic algorithm is used to optimize trading rules. Five fuzzy technical indicators have been used to determine the trading rules. Euro-dollar pair data is used as daily training and test data for a daily period between 2010 and 2019. The results obtained compared to traditional methods have had promising results.

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