TY  - JOUR
T1  - TECHNIQUES USED TO EXTRACT FEATURES FROM CANDLESTICK CHARTS IN THE STOCK MARKET A SYSTEMATIC REVIEW
AU  - Kutucu, Hakan
AU  - Mersal, Edrees Ramadan
PY  - 2024
DA  - February
Y2  - 2023
JF  - Current Trends in Computing
JO  - CTC
PB  - Karabuk University
WT  - DergiPark
SN  - 2980-3152
SP  - 104
EP  - 121
VL  - 1
IS  - 2
LA  - en
AB  - 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.
KW  - Japanese Candlestick
KW  - Stock Market
KW  - Machine Learning.
CR  - [1] Ahmad, M., Soeparno, H., & Napitupulu, T. A. (2020). Stock trading alert: fuzzy knowledge-based systems and technical analysis. 2020 International Conference on Information Technology Systems and Innovation, ICITSI 2020 - Proceedings, 155–160. https://doi.org/10.1109/ICITSI50517.2020.9264914
CR  - [2] Andriyanto, A. (2020). Sectoral stock prediction using Convolutional Neural Networks with candlestick patterns as input images. International Journal of Emerging Trends in Engineering Research, 8(6), 2249–2252. https://doi.org/10.30534/ijeter/2020/07862020
CR  - [3] Barra, S., Carta, S. M., Corriga, A., Podda, A. S., Recupero, D.R. (2020). Deep learning and time series-to-image encoding for financial forecasting. IEEE/CAA Journal of Automatica Sinica, 7(3), 683–692. https://doi.org/10.1109/JAS.2020.1003132
CR  - [4] Birogul, S., Temur, G., & Kose, U. (2020). YOLO Object Recognition Algorithm and “Buy-Sell Decision” Model over 2D Candlestick Charts. IEEE Access, 8, 91894–91915. https://doi.org/10.1109/ACCESS.2020.2994282
CR  - [5] Brim, A., & Flann, N. S. (2022). Deep reinforcement learning stock market trading utilizing a CNN with candlestick images. PLoS ONE, 17(2 February). https://doi.org/10.1371/journal.pone.0263181
CR  - [6] Chen, J. H., & Tsai, Y. C. (2020). Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovation, 6(1). https://doi.org/10.1186/s40854-020-00187-0
CR  - [7] Chen, J.-H., & Tsai, Y.-C. (2022). Dynamic Deep Convolutional Candlestick Learner. http://arxiv.org/abs/2201.08669
CR  - [8] Du, B., Fernandez-Reyes, D., & Barucca, P. (2020). Image Processing Tools for Financial Time Series Classification. http://arxiv.org/abs/2008.06042
CR  - [9] Fengqian, Di., & Chao, L. (2020). An Adaptive Financial Trading System Using Deep Reinforcement Learning with Candlestick Decomposing Features. IEEE Access, 8, 63666–63678. https://doi.org/10.1109/ACCESS.2020.2982662
CR  - [10] Hassen, O. A., Darwish, S. M., Abu, N. A., & Abidin, Z. Z. (2020). Application of cloud model in qualitative forecasting for stock market trends. Entropy, 22(9). https://doi.org/10.3390/e22090991
CR  - [11] Hung, C. C., & Chen, Y. J. (2021). DPP: Deep predictor for price movement from candlestick charts. PLoS ONE, 16(6 June 2021). https://doi.org/10.1371/journal.pone.0252404
CR  - [12] Jearanaitanakij, K., & Passaya, B. (2019). Predicting Short Trend of Stocks by Using Convolutional Neural Network and Candlestick Patterns. 2019 4th International Conference on Information Technology (InCIT2019).
CR  - [13] Kusuma, R. M. I., Ho, T.-T., Kao, W.-C., Ou, Y.-Y., & Hua, K.-L. (2019). Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market. http://arxiv.org/abs/1903.12258
CR  - [14] Lee, J., Kim, R., Koh, Y., & Kang, J. (2019). Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network. https://doi.org/10.1109/ACCESS.2019.2953542
CR  - [15] Liang, M., Wu, S., Wang, X., & Chen, Q. (2022). A stock time series forecasting approach incorporating candlestick patterns and sequence similarity. Expert Systems with Applications, 205. https://doi.org/10.1016/j.eswa.2022.117595
CR  - [16] Lin, Y., Liu, S., Yang, H., & Wu, H. (2021). Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques with a Novelty Feature Engineering Scheme. IEEE Access, 9, 101433–101446. https://doi.org/10.1109/ACCESS.2021.3096825
CR  - [17] Lin, Y., Liu, S., Yang, H., Wu, H., & Jiang, B. (2021). Improving stock trading decisions based on pattern recognition using machine learning technology. PLoS ONE, 16(8 August). https://doi.org/10.1371/journal.pone.0255558
CR  - [18] Naik, N., & Mohan, B. R. (2020). Intraday Stock Prediction Based on Deep Neural Network. National Academy Science Letters, 43(3), 241–246. https://doi.org/10.1007/s40009-019-00859-1
CR  - [19] Nakayama, A., Matsushima, H., Izumi, K., Shimada, T., Sakaji, H., & Yamada, K. (2019). Short-term Stock Price Prediction by Analysis of Order Pattern Images; Short-term Stock Price Prediction by Analysis of Order Pattern Images. In 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).
CR  - [20] Naranjo, R., & Santos, M. (2019). A fuzzy decision system for money investment in stock markets based on fuzzy candlesticks pattern recognition. Expert Systems with Applications, 133, 34–48. https://doi.org/10.1016/j.eswa.2019.05.012
CR  - [21] Orte, F., Mira, J., Sánchez, M. J., Solana, P. (2023). A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction. Research in International Business and Finance, 64. https://doi.org/10.1016/j.ribaf.2022.101829
CR  - [22] Pan, W., Li, J., & Li, X. (2020). Portfolio learning based on deep learning. Future Internet, 12(11), 1–13. https://doi.org/10.3390/fi12110202
CR  - [23] Poženel, M., & Lavbič, D. (2019). Discovering the Language of Stocks. https://doi.org/10.3233/978-1-61499-941-6-243
CR  - [24] Rumpa, L. D., Limbongan, M. E., Biringkanae, A., & Tammu, R. G. (2021). Binary options trading: candlestick prediction using Support Vector Machine (SVM) on M5 time period. IOP Conference Series: Materials Science and Engineering, 1088(1), 012107. https://doi.org/10.1088/1757-899x/1088/1/012107
CR  - [25] Santur, Y. (2022). Candlestick chart-based trading system using ensemble learning for financial assets. Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi. https://doi.org/10.14744/sigma.2022.00039
CR  - [26] Thammakesorn, S., & Sornil, O. (2019). Generating Trading Strategies Based on Candlestick Chart Pattern Characteristics. Journal of Physics: Conference Series, 1195(1). https://doi.org/10.1088/1742-6596/1195/1/012008
CR  - [27] JuHyok U., PengYu L., ChungSong K., UnSok R., KyongSok P. (2020). A new LSTM based reversal point prediction method using upward/downward reversal point feature sets. Chaos, Solitons and Fractals, 132. https://doi.org/10.1016/j.chaos.2019.109559
CR  - [28] Udagawa, Y. (2019, December 2). Mining stock price changes for profitable trade using candlestick chart patterns. ACM International Conference Proceeding Series. https://doi.org/10.1145/3366030.3366053
CR  - [29] Wang, H., Huang, W., & Wang, S. (2021). Forecasting open-high-low-close data contained in candlestick chart. http://arxiv.org/abs/2104.00581
CR  - [30] Wang, M., & Wang, Y. (2019). Evaluating the effectiveness of candlestick analysis in forecasting U.S. stock market. ACM International Conference Proceeding Series, 98–101. https://doi.org/10.1145/3314545.3314555
UR  - https://dergipark.org.tr/en/pub/ctc/issue//1431144
L1  - https://dergipark.org.tr/en/download/article-file/3704724
ER  -