Using Bitcoin Pricing Data to Create a Profitable Algorithmic Trading Strategy

Prince Nathan  S; Onkar  Saudagar

doi:10.31149/ijhcs.v4i4.2413

Prince Nathan S Pg Student, Department of Data science and Analytics, National Institute of Electronics and Information Technology, Chennai, India
Onkar Saudagar Student, Pune Institute of Computer Technology, Department of Information Technology Engineering, Pune, India

DOI: https://doi.org/10.31149/ijhcs.v4i4.2413

Keywords: Data science, Machine Learning, Regression, Pca, Neural Network, Data Analysis

Abstract

Crypto currency has drastically increased its growth in recent years and Bitcoin(BTC)is a very popular type of currency among all the other types of crypto currencies which is been used in most of the sectors nowadays for trading, transactions, bookings etc. In this paper, we aim to predict the change in bitcoin prices by using machine learning techniques on data from Investing.com. We interpret the output and accuracy rate using various machine learning models. To see whether to buy or sell the bitcoin we created exploratory data analysis from a year of data set and predict the next 5 days change using machine learning models like logistic Regression, Logistic Regression with PCA (Principal Component Analysis) and Neural network.

References

D. Kondor, I. Csabai, J. Szäle, M. Pósfai, and G. Vattay, ‘‘Inferring the interplay between network structure and market effects in bitcoin,’’ New J. Phys., vol. 16, no. 12, 2014, Art. no. 125003.

S. Hochreiter and J. Schmidhuber, ‘‘Long short-term memory,’’ Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.

W. Yiying and Z. Yeze, ‘‘Cryptocurrency price analysis with artificial intelligence,’’ in Proc. 5th Int. Conf. Inf. Manage. (ICIM), Mar. 2019, pp. 97–101.

J. Rebane, ‘‘Seq2Seq RNNs and ARIMA models for cryptocurrency prediction : A comparative study,’’

C. J. Hutto and E. Gilbert, ‘‘Vader: A parsimonious rule-based model for sentiment analysis of social media text,’’ in Proc. ICWSM, 2014, pp. 1–16.

Y. Peng, P. H. M. Albuquerque, J. M. Camboim de Sá, A. J. A. Padula, and M. R. Montenegro, ‘‘The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression,’’ Expert Syst. Appl., vol. 97, pp. 177–192, May 2018.

G. T. Friedlob and F. J. Plewa, Jr., Understanding Return on Investment. Hoboken, NJ, USA: Wiley, 1996.

E. F. Fama, ‘‘Random walks in stock market prices,’’ Financial Anal. J., vol. 21, no. 5, pp. 55–59, Sep. 1965.

B. G. Malkiel, A Random Walk Down Wall Street: Including a Life-Cycle Guide to Personal Investing. New York, NY, USA: W. W. Norton & Company, 1973.

D. Vasan, M. Alazab, S. Wassan, B. Safaei, and Q. Zheng, ‘‘Image-based malware classification using ensemble of CNN architectures (IMCEC),’’ Comput. Secur., vol. 92, May 2020, Art. no. 101748.

D. Mungra, A. Agrawal, P. Sharma, S. Tanwar, and M. S. Obaidat, ‘‘PRATIT: A CNN-based emotion recognition system using histogram equalization and data augmentation,’’ Multimedia Tools Appl., vol. 79, nos. 3–4, pp. 2285–2307, Jan. 2020.

A. C. Logue, ‘‘The efficient market hypothesis and its critics,’’ CFA Dig., vol. 33, no. 4, pp. 40–41, Nov. 2003.

W. Brock, J. Lakonishok, and B. LeBARON, ‘‘Simple technical trading rules and the stochastic properties of stock returns,’’ J. Finance, vol. 47, no. 5, pp. 1731–1764, Dec. 1992.

R. U. Khan, X. Zhang, M. Alazab, and R. Kumar, ‘‘An improved convolutional neural network model for intrusion detection in networks,’’ in Proc. Cybersecurity Cyberforensics Conf. (CCC), May 2019, pp. 74–77.

Bitinfocharts. Accessed: May 1, 2020. [Online]. Available: https://bitinfocharts.com/

D. Garcia, C. J. Tessone, P. Mavrodiev, and N. Perony, ‘‘The digital traces of bubbles: Feedback cycles between socio-economic signals in the bitcoin economy,’’ J. Roy. Soc. Interface, vol. 11, no. 99, Oct. 2014, Art. no. 20140623.

S. Rahman, J. N. Hemel, S. Junayed Ahmed Anta, H. A. Muhee, and J. Uddin, ‘‘Sentiment analysis using R: An approach to correlate cryptocurrency price fluctuations with change in user sentiment using machine learning,’’ in Proc. Joint 7th Int. Conf. Informat., Electron. Vis. (ICIEV) 2nd Int. Conf. Imag., Vis. Pattern Recognit. (icIVPR), Jun. 2018, pp. 492–497.

L. Kristoufek, ‘‘BitCoin meets Google trends and wikipedia: Quantifying the relationship between phenomena of the Internet era,’’ Sci. Rep., vol. 3, no. 1, p. 3415, Dec. 2013.

J. Patel, S. Shah, P. Thakkar, and K. Kotecha, ‘‘Predicting stock market index using fusion of machine learning techniques,’’ Expert Syst. Appl., vol. 42, no. 4, pp. 2162–2172, Mar. 2015.

D. Vasan, M. Alazab, S. Wassan, H. Naeem, B. Safaei, and Q. Zheng, ‘‘IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture,’’ Comput. Netw., vol. 171, Apr. 2020, Art. no. 107138.