Open AccessForex exchange using big data analytics
Author(s) -
Jayakumar Sadhasivam,
M. Arun,
R Deepa,
V. Muthukumaran,
R. Lokesh Kumar,
Rajiv B. Kumar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/4/042060
Subject(s) - python (programming language) , computer science , stock exchange , big data , foreign exchange , foreign exchange market , stock price , analytics , artificial intelligence , econometrics , data mining , data science , machine learning , finance , economics , monetary economics , paleontology , series (stratigraphy) , biology , operating system
Analysis and Prediction of forex has gained immense value in today’s economy. The stock price prediction is a difficult process owing to the irregularities in stock prices. Every trader wants to know if the pattern has been repeated in past to know what the possible output of the current situation will be. The primary objective is to propose a methodology that will use a historical dataset and provide a more accurate prediction on stock price. In this paper, we will be using machine learning pattern recognition algorithm on forex tick dataset. The learned model then will produce pattern from the given dataset and on the pattern of increasing or decreasing, the buyer will initiate a buy or sell the stock respectively. We will use python coding to execute the algorithm in jupyter notebook. Matplot library will help us to perform graphing in the process and Numpy will be helpful in doing statistical analysis of data.