Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange
DOI:
https://doi.org/10.30812/matrik.v22i2.2676Keywords:
Candlestick Chart, Classification, Foreign Exchange, Machine Learning, Prediction, Support Vector MachineAbstract
Foreign Exchange, commonly called Forex, is a form of investment in the non-real sector in great demand. Forex is a marketplace that specializes in foreign exchange trading. Technology advancements have made it easy to monitor investment conditions in real time and present them in an easyto - understand graphical form. As a result, predictions are closely related to investment, starting from market sentiment and economic conditions to technical matters. One of the Artificial Intelligence methods that can be used in classifying is the Support Vector Machine (SVM). SVM is a machine learning classification method based on the Structural Risk Minimization (SRM) principle to find the best hyperplane that separates two classes in the input space that determines the classification decision function by minimizing empirical risk. This study used candlestick patterns to predict foreign exchange chart movements using the Support Vector Machine (SVM) classification method. The purpose of this study was to measure the accuracy of the Support Vector Machine method in making predictions using candlestick patterns so that it can assist traders in making decisions in forex trading. The accuracy level obtained from the data classification results reached 90.72% with a precision of 87.69%. With a relatively good level of accuracy, the Support Vector Machine (SVM) method can be used to predict chart movements in foreign exchange using candlesticks to indicate the current trend’s direction.
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Regression Analysis Model,†Proceedings - 2020 16th Dahe Fortune China Forum and Chinese High-Educational Management
Annual Academic Conference, DFHMC 2020, pp. 180–183, 2020.
[2] M. Liang, S. Wu, X. Wang, and Q. Chen, “A Stock Time Series Forecasting Approach Incorporating Candlestick Patterns and
Sequence Similarity,†Expert Systems with Applications, vol. 205, p. 117595, nov 2022.
[3] M. D. Stasiak, “CandlestickThe Main Mistake of Economy Research in High Frequency Markets,†International Journal of
Financial Studies, vol. 8, no. 4, pp. 1–15, 2020.
[4] C. C. Hung and Y. J. Chen, “DPP: Deep Predictor for Price Movement from Candlestick Charts,†PLoS ONE, vol. 16, no. 6
June 2021, pp. 1–14, 2021.
[5] N. Nuraeni, P. Astuti, O. Irnawati, I. Darwati, and D. D. Harmoko, “High Accuracy in Forex Predictions Using the Neural
Network Method Based on Particle Swarm Optimization,†Journal of Physics: Conference Series, vol. 1641, no. 1, 2020.
[6] S. Theodoridis, Machine learning, 2nd ed. Academic Press, 2020, vol. 45, no. 13.
[7] L. Mohan, J. Pant, P. Suyal, and A. Kumar, “Support Vector Machine Accuracy Improvement with Classification,†Proceedings
- 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020, pp. 477–481,
2020.
[8] A. D. Achmad, “Metode Moving Average dan Metode Support Vector Machine Untuk Prediksi Variabel Meteorologi,†vol. 4,
no. 1, pp. 45–50, 2017.
[9] S. Saikin, S. Fadli, and M. Ashari, “Optimization of Support Vector Machine Method Using Feature Selection to Improve
Classification Results,†JISA(Jurnal Informatika dan Sains), vol. 4, no. 1, pp. 22–27, 2021.
[10] P. Aggarwal and A. K. Sahani, “Comparison of Neural Networks for Foreign Exchange Rate Prediction,†2020 IEEE 15th
International Conference on Industrial and Information Systems, ICIIS 2020 - Proceedings, no. 978, pp. 415–419, 2020.
[11] R. M. and K. L.G., “Predicting Foreign Exchange using Digital Signal Processing,†British Journal of Computer, Networking
and Information Technology, vol. 4, no. 2, pp. 1–11, 2021.
[12] M. Islam and E. Hossain, “Foreign Exchange Currency Rate Prediction Using a GRU-LSTM Hybrid Network,†Soft Computing
Letters, vol. 3, no. August 2020, p. 100009, 2021.
[13] A. Ramadhan, I. Palupi, and B. A. Wahyudi, “Candlestick Patterns Recognition Using CNN-LSTM Model to Predict Financial
Trading Position in Stock Market,†Journal of Computer System and Informatics (JoSYC), vol. 3, no. 4, pp. 339–347, 2022.
[14] A. N. Puteri, A. Arizal, and A. D. Achmad, “Feature Selection Correlation-Based pada Prediksi Nasabah Bank Telemarketing
untuk Deposito,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 2, pp. 335–342, 2021.
[15] D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan
Algoritma K-NN,†Computer Engineering, Science and System Journal, vol. 4, no. 1, p. 78, 2019.
[16] J. Cervantes, F. Garcia-Lamont, L. Rodr´ıguez-Mazahua, and A. Lopez, “A Comprehensive Survey on Support Vector Machine
Classification: Applications, Challenges and Trends,†Neurocomputing, vol. 408, no. xxxx, pp. 189–215, 2020.
[17] D. A. Pisner and D. M. Schnyer, “Support Vector Machine,†in Machine Learning. Elsevier, 2020, pp. 101–121.
[18] A. Alman, A. Lawi, and Z. Tahir, “Pattern Recognition of Human Activity Based on Smartphone Data Sensors Using SVM
Multiclass,†2019.
[19] A. J. Makrufi,W. Fawwaz, and A. Maki, “Support Vector Machine with Firefly Optimization Algorithm for Apple Fruit Disease
Classification,†vol. 22, no. 1, pp. 179–190, 2022.
[20] R. Satpathy, S. N. Mohanty, S. Satpathy, T. Choudhury, and X. Zhang, Data Analytics in Bioinformatics: A Machine Learning
Perspective. United States of America: Wiley Intercience, 2021.
[21] A. N. Puteri, Dewiani, and Z. Tahir, “Comparison of Potential Telemarketing Customers Predictions with a Data Mining Approach
using the MLPNN and RBFNN Methods,†2019 International Conference on Information and Communications Technology,
ICOIACT 2019, pp. 383–387, 2019.
[22] N. Wayan, S. Saraswati, I. G. Ayu, and A. Diatri, “Recognize the Polarity of Hotel Reviews using Support Vector Machine,â€
vol. 22, no. 1, pp. 25–36, 2022.
[23] [11] H. Hairani, A. Anggrawan, A. I. Wathan, K. A. Latif, K. Marzuki, and M. Zulfikri, “The Abstract of Thesis Classifier by Using
Naive Bayes Method,†in 2021 International Conference on Software Engineering & Computer Systems and 4th International
Conference on Computational Science and Information Management (ICSECS-ICOCSIM), no. August. IEEE, aug 2021, pp.
312–315. [Online]. Available: https://ieeexplore.ieee.org/document/9537006/
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Published
2023-03-31
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How to Cite
[1]
A. N. Puteri, S. Syamsu, T. L. Putra, and A. D. Achmad, “Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange”, MATRIK, vol. 22, no. 2, pp. 249–260, Mar. 2023, doi: 10.30812/matrik.v22i2.2676.
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