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Okechukwu Cornelius C.1 , Aru Okereke Eze2
Section:Research Paper, Product Type: Isroset-Journal
Vol.7 ,
Issue.3 , pp.15-21, Jun-2019
CrossRef-DOI: https://doi.org/10.26438/ijsrcse/v7i3.1521
Online published on Jun 30, 2019
Copyright © Okechukwu Cornelius C., Aru Okereke Eze . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Okechukwu Cornelius C., Aru Okereke Eze , “Development of an Optimized Intelligent Machine Learning Approach in Forex Trading Using Moving Average indicators,” International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.3, pp.15-21, 2019.
MLA Style Citation: Okechukwu Cornelius C., Aru Okereke Eze "Development of an Optimized Intelligent Machine Learning Approach in Forex Trading Using Moving Average indicators." International Journal of Scientific Research in Computer Science and Engineering 7.3 (2019): 15-21.
APA Style Citation: Okechukwu Cornelius C., Aru Okereke Eze , (2019). Development of an Optimized Intelligent Machine Learning Approach in Forex Trading Using Moving Average indicators. International Journal of Scientific Research in Computer Science and Engineering, 7(3), 15-21.
BibTex Style Citation:
@article{C._2019,
author = { Okechukwu Cornelius C., Aru Okereke Eze },
title = {Development of an Optimized Intelligent Machine Learning Approach in Forex Trading Using Moving Average indicators},
journal = {International Journal of Scientific Research in Computer Science and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {3},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {15-21},
url = {https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=1388},
doi = {https://doi.org/10.26438/ijcse/v7i3.1521}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.1521}
UR - https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=1388
TI - Development of an Optimized Intelligent Machine Learning Approach in Forex Trading Using Moving Average indicators
T2 - International Journal of Scientific Research in Computer Science and Engineering
AU - Okechukwu Cornelius C., Aru Okereke Eze
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 15-21
IS - 3
VL - 7
SN - 2347-2693
ER -
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Abstract :
This paper presents the development of an optimized intelligent machine learning approach in Forex trading using two variants of Moving Average indicators. The main aim of the Expert Advisor (EA) development is to introduce a new intelligent model for automated execution of trades in the Forex market, reducing potential losses due to human errors and sentimental factors in trading Forex. In developing this trading model, Momentum strategy was used since it takes advantage of market swings, along with Machine Learning - Genetic algorithm, being a type of supervised learning used in training the past historical data based on selected trading parameters in a Meta Trader 4 (MT4) platform. The new Expert Advisor –Exponential Moving Average (ESMA) was built using the MQL4 language which is based on C++ for programming specific trading strategies and easily facilitates automated trading. The result is an optimized intelligent trading system that implements the intersection of the two moving averages at various periods, to execute trades autonomously with a profit pass rate of 75% visible from the Optimization chart of the MetaTrader 4 (MT4) platform.
Key-Words / Index Term :
Expert Advisor, Forex Trading, Genetic algorithm ,Machine Learning, Moving Averages, Meta Trader
References :
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