RGBSticks : A New Deep Learning Based Framework for Stock Market Analysis and Prediction

Eren UNLU

Makale detayları

RGBSticks : A New Deep Learning Based Framework for Stock Market Analysis and Prediction

Yazarlar:

Eren UNLU

Yayıncı:

Mahmut DİRİK

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Journal of Soft Computing and Artificial Intelligence 2020 Vol.1 Issue 2, pp.78-85

Belge Tipi:

Makale

Yayın Tarihi:

12/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000497572

Sıklık:

İki Yıllık

pISSN:

2717-8226

Özet:

 

We present a novel intuitive graphical representation for daily stock prices, which we refer as RGBSticks, a variation of classical candle sticks. This representation allows the usage of complex deep learning based techniques, such as deep convolutional autoencoders and deep convolutional generative adversarial networks to produce insightful visualizations for market's past and future states. We believe RGBStick representation has great potential to integrate human decision process and deep learning for stock market analysis and forecasting. The traders who are highly familiar with candlesticks are able to evaluate the results generated by deep learning algorithms by inspecting the varying color shades in a compact, instinctual and rapid fashion.

Makale detayları

RGBSticks : A New Deep Learning Based Framework for Stock Market Analysis and Prediction

Yazarlar: Eren UNLU

Yayıncı: Mahmut DİRİK

Ana Başlıklar: Özel bilgisayar yöntemleri

Dergi: Journal of Soft Computing and Artificial Intelligence 2020 Vol.1 Issue 2, pp.78-85

Belge Tipi: Makale

Yayın Tarihi: 12/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000497572

Sıklık: İki Yıllık

pISSN: 2717-8226

Sayı

Yazarlar:

Eren UNLU

Yayıncı:

Mahmut DİRİK

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Journal of Soft Computing and Artificial Intelligence 2020 Vol.1 Issue 2, pp.78-85

Belge Tipi:

Makale

Yayın Tarihi:

12/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000497572

Sıklık:

İki Yıllık

pISSN:

2717-8226