Makine Öğrenmesi İle Borsa Analizi

Year 2021, Issue: 28, 1117 - 1120, 30.11.2021

Mahmut Emir Arslan Pınar Kırcı

https://doi.org/10.31590/ejosat.1012785

Abstract

Borsanın temel mantığı teknik analiz denilen matematiksel işlemlere, grafiklere ve bazı indikatörlere dayanmaktadır ve yatırımcılar işlemlerini bu grafik ve indikatörlerin ürettiği tahmin sonuçlarına göre gerçekleştirmektedirler. Bu projede makine öğrenimi ile geçmiş yıllara dair veriler kullanılarak bir sistem eğitilecek ve bu sistem gelecek günlerdeki bitcoin verilerini görsel hale getirip borsa hareketlerinin momentumuna göre kullanıcıya al ve sat sinyalleri üretecektir. Hedef olarak bugünün ve geleceğin değerli borsalarından birisi olan Bitcoin borsası ele alınacaktır. Doğrusal regresyon yöntemi ile Bitcoinin günlük grafikte en yüksek, en düşük, hacim ve arz-talep verileri üzerinden al-sat sinyalleri üretilecektir. Bu veriler Quandl veritabanı aracılığıyla Bitfinex bitcoin alım satım borsası tarafından elde edilecektir.

Keywords

Doğrusal regresyon, Bitcoin, makine öğrenimi, Quandl veritabanı, Borsa

Stock Market Analysis with Machine Learning

Abstract

The basic logic of the stock market is based on mathematical operations called technical analysis, graphics and some indicators. Investors perform their transactions according to the forecast results produced by these charts and indicators. In this project, a system will be trained using machine learning and data from the past years, and this system will visualize the bitcoin data in the coming days and generate buy and sell signals for the user according to the momentum of the stock market movements. As a target, the Bitcoin stock market, which is one of the valuable stock markets of today and the future, will be discussed. With the linear regression method, buy-sell signals will be generated over the highest, lowest volume and supply-demand data on the daily chart of Bitcoin. These data will be obtained by the Bitfinex bitcoin exchange through the Quandl database.

Keywords

Stock market, Linear regression, Bitcoin, machine learning, Quandl database

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