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Macroeconomic Event Base Expert Advisor for Forex Trades: Through Algo Trading | IEEE Conference Publication | IEEE Xplore

Macroeconomic Event Base Expert Advisor for Forex Trades: Through Algo Trading


Abstract:

An implicit foreign exchange trading decision is generally at the mercy of effective forex forecasting. Forex trade has been scrutinized using two sequestered approaches,...Show More

Abstract:

An implicit foreign exchange trading decision is generally at the mercy of effective forex forecasting. Forex trade has been scrutinized using two sequestered approaches, technical and fundamental analysis. The fundamental data of the economy, correspondingly have a decisive influence on the forex. The idea of seeing the macroeconomic announcements turns progressively challenging due to the direction of the gigantic and variant figure of prices' determining factor and the rapid fluctuations in market dynamics. The main aim of this study is to develop a macroeconomic event base expert advisor to identify the patterns and provide a signal and automate trades based fundamental analysis to forex day traders. Moreover, the paper emphasizes the association between forex high-frequency dynamic forces and macroeconomic announcements that can be determined as events. The sensitive nature of the macroeconomic factors in the forex market must be considered in any system that efforts to deliver users with reasonable and effective insights into the market. The expected implication is to provide a novel methodology for the interested parties and provide a more accurate algo trading system for Forex Day traders. This expert advisor helps to address the gaps in fundamental analysis-based studies and helps to make successful and proper trading decisions as well. This study applies the Machine learning methodologies like linear regression, support vector machine to Random Forest optimizes indicators parameters in order to react straightway to slight variation in the marketplace status by producing swap signals driven fundamental analyses. Further main algorithm develops using mathematical modeling and Backpropagation Weight Decay Neural Network.
Date of Conference: 02-03 December 2021
Date Added to IEEE Xplore: 16 May 2022
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ISSN Information:

Conference Location: Colombo, Sri Lanka

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I. Introduction

The foreign exchange market (Forex) is a global currency trading marketplace that involves with decentralist or Over the Counter (OTC) trading [1]. Forex trading is not carried out in one central location; also, not controlled by any single entity or institution. In terms of exchange capacity, it is by far the leading and the most liquid financial market in the world followed by the credit market [2]. International banks, hedge funds, commercial firms, numerous central banks and, of course, retail Forex brokers and investors are main participants in this trade. The market is open 24 hours a day in various parts of the world, from 5 pm EST on Sunday till 4 pm EST on Friday. There is at least one market open at any point in time, So, a couple of hours of overlap between the market closure of one country and the opening of another [3]. Investment in forex market has high risk because of this market is so enormous, it is also extremely liquid. Further in the primary stage, short term investors are discouraged because they are failed in market analysis. Therefore, analysis on the price moments should be conduct. Most forex trading platforms have the appropriate set of technical analytical indicators along with data visual aid to encourage visualization of data embedded trends [4]. The decisions of most traders are essentially based on technical analysis, while fundamental analysis is typically handled based on the experience of traders separately. The technical indicators can only indicate adequately the future price course, it would be dangerous to overlook the impact of external factors. Thus, domain experts suggest that by addressing the fundamentals and interrelated factors, it is necessary to realize the overall market and economy situation during decision taking phase [5].

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