Introduction

Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolutional Network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. After that, these candlestick charts will be feed as input for training a Convolution neural network model. This Convolution neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of stock market. Using Taiwan 50 and Indonesian 10 stock market historical time series data we can achieve a promising results- 92.2 % and 92.1 % accuracy for Taiwan and Indonesia stock market respectively. Our performance results significantly outperform the existing methods.

DeepCandle is a web server to predict the stock market price movements. We are using Deep Learning Neural Networks and Candlestick chart to predict the stock market. Here you can try predict the price movement of specific stock market using our model.

Methodology

The flowchart of the study is show as follows.

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