TECHNIQUES USED TO EXTRACT FEATURES FROM CANDLESTICK CHARTS IN THE STOCK MARKET A SYSTEMATIC REVIEW

Abstract

In the financial sector, accurately forecasting stock market trends is essential for guiding the investment and trading decisions of investors and traders. These professionals often rely on candlestick charts to analyze and predict stock price fluctuations. In recent times, various methods and algorithms have been applied to leverage candlestick charts for prediction purposes. This systematic review aims to examine the application of Japanese candlesticks and machine learning techniques, including artificial neural networks, in predicting stock market trends. It also delves into the effective feature engineering strategies for extracting pertinent information from raw data, encompassing technical indicators and candlestick charts. The review encompasses 30 studies published between 2019 and 2023, selected based on criteria that include the utilization of candlestick charts in stock market analysis. The findings reveal that numerous studies employing automatic encoders, convolutional neural networks, and Gramian Angular Field (GAF) for feature geometry extraction from candlestick charts also identify common patterns.

Keywords

• Japanese Candlestick
• Stock Market
• Machine Learning.

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