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Year 2023, Volume: 1 Issue: 2, 104 - 121, 02.02.2024

Abstract

References

  • [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
  • [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
  • [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
  • [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
  • [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
  • [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
  • [7] Chen, J.-H., & Tsai, Y.-C. (2022). Dynamic Deep Convolutional Candlestick Learner. http://arxiv.org/abs/2201.08669
  • [8] Du, B., Fernandez-Reyes, D., & Barucca, P. (2020). Image Processing Tools for Financial Time Series Classification. http://arxiv.org/abs/2008.06042
  • [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
  • [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
  • [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
  • [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).
  • [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
  • [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
  • [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
  • [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
  • [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
  • [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
  • [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).
  • [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
  • [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
  • [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
  • [23] Poženel, M., & Lavbič, D. (2019). Discovering the Language of Stocks. https://doi.org/10.3233/978-1-61499-941-6-243
  • [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
  • [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
  • [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
  • [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
  • [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
  • [29] Wang, H., Huang, W., & Wang, S. (2021). Forecasting open-high-low-close data contained in candlestick chart. http://arxiv.org/abs/2104.00581
  • [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

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

Year 2023, Volume: 1 Issue: 2, 104 - 121, 02.02.2024

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.

References

  • [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
  • [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
  • [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
  • [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
  • [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
  • [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
  • [7] Chen, J.-H., & Tsai, Y.-C. (2022). Dynamic Deep Convolutional Candlestick Learner. http://arxiv.org/abs/2201.08669
  • [8] Du, B., Fernandez-Reyes, D., & Barucca, P. (2020). Image Processing Tools for Financial Time Series Classification. http://arxiv.org/abs/2008.06042
  • [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
  • [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
  • [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
  • [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).
  • [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
  • [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
  • [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
  • [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
  • [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
  • [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
  • [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).
  • [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
  • [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
  • [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
  • [23] Poženel, M., & Lavbič, D. (2019). Discovering the Language of Stocks. https://doi.org/10.3233/978-1-61499-941-6-243
  • [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
  • [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
  • [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
  • [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
  • [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
  • [29] Wang, H., Huang, W., & Wang, S. (2021). Forecasting open-high-low-close data contained in candlestick chart. http://arxiv.org/abs/2104.00581
  • [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
There are 30 citations in total.

Details

Primary Language English
Subjects Deep Learning
Journal Section Reviews
Authors

Edrees Ramadan Mersal This is me 0000-0001-7833-421X

Hakan Kutucu 0000-0001-7144-7246

Publication Date February 2, 2024
Submission Date October 20, 2023
Acceptance Date December 11, 2023
Published in Issue Year 2023 Volume: 1 Issue: 2

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