Encoding candlesticks as images for pattern classification using convolutional neural networks

Jun-Hao Chen,Yun-Cheng Tsai

doi:10.1186/s40854-020-00187-0

Abstract

Candlestick charts display the high, low, opening, and closing prices in a specific period. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate. These patterns capture information on the candles. According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts, there are 103 candlestick patterns. Traders use these patterns to determine when to enter and exit. Candlestick pattern classification approaches take the hard work out of visually identifying these patterns. To highlight its capabilities, we propose a two-steps approach to recognize candlestick patterns automatically. The first step uses the Gramian Angular Field (GAF) to encode the time series as different types of images. The second step uses the Convolutional Neural Network (CNN) with the GAF images to learn eight critical kinds of candlestick patterns. In this paper, we call the approach GAF-CNN. In the experiments, our approach can identify the eight types of candlestick patterns with 90.7% average accuracy automatically in real-world data, outperforming the LSTM model.

Highlights

Financial market forecasts are critical research topics in commercial finance and information engineering
It is hard to find the result from other studies to compare the Gramian Angular Field (GAF)-Convolutional Neural Network (CNN) model, so we chose the Long Short-Term Memory model (LSTM) for reliable comparison since it is a standard method to accomplish the time series classification or regression tasks in the current year
Candlestick pattern recognition is an indicator that traders often judge with news, fundamentals, and technical indicators

Summary

Introduction

Financial market forecasts are critical research topics in commercial finance and information engineering. Market prices are susceptible to the expected psychological impact of the overall market These prices are possible to develop predictive models of financial demand through particular pre-processing and complex model architectures. Many tools are existing to help people predict stock price fluctuations and futures indices already (Ding et al 2015). These tools are the neural networks, fuzzy time-series analysis, genetic algorithms, classification trees, statistical regression models, and support vector machines. These machine learning models are generic techniques and used for forecasting. Rather than blindly using machine learning or deep learning architecture to pursue unrealistic low-risk, high-accuracy profit models, it is better to combine these directly with a basic knowledge of transactions to create a reliable, applicable model (Ding et al 2015; Hall 2002)

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