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COVID-19 affects significant human activity around the globe, including Bitcoin prices. The Bitcoin price is well known for its volatility, so it is not a big shocker when the panic-selling occurs during the pandemic. However, the mechanism to cope with these breakouts, especially the bearish one, is contentious. The experts give numerous pieces of advice with different conclusions in the end. It is also the same with Machine Learning. Various kernels show different results regarding how the price will move. It depends on the window size, how the data is being preprocessed, and the algorithm used. This paper inspects the best combination that various machine learning can offer with a linear approach to navigate the price prediction based on its depth interval, window size until the algorithms themselves. This paper also proposed a new approach to seeing the prediction range called s-steps ahead prediction using a linear model. The result shows that simple machine learning can herd 99.715% profit even during the bearish breakout.
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 Lahmiri, Salim, Stelios Bekiros, and Antonio Salvi. "Long-range memory, distributional variation and randomness of bitcoin volatility." Chaos, Solitons & Fractals 107 (2018): 43-48.
 Brandvold, Morten, et al. "Price discovery on Bitcoin exchanges." Journal of International Financial Markets, Institutions and Money 36 (2015): 18- 35.
 Brauneis, Alexander, et al. "A High-Frequency Analysis of Bitcoin Markets." Available at SSRN 3478514 (2019).
 Wu, Liang, and Shujuan Chen. "Long memory and efficiency of Bitcoin under heavy tails." Applied Economics (2020): 1-12.
 De Filippi, Primavera, and Benjamin Loveluck. "The invisible politics of bitcoin: governance crisis of a decentralized infrastructure." Internet Policy Review 5.4 (2016).
 Baqer, Khaled, et al. "Stressing out: Bitcoin "stress testing"." International Conference on Financial Cryptography and Data Security. Springer, Berlin, Heidelberg, 2016.
 Johannsen, Kolja. "Dead Cat Bounce-Demand Reversal Following the Bursting of a Bubble." Available at SSRN 2961304 (2016).
 Baker, Scott R., et al. "The unprecedented stock market reaction to COVID-19." The Review of Asset Pricing Studies (2020).
 Al-Awadhi, Abdullah M., et al. "Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns." Journal of Behavioral and Experimental Finance (2020): 100326.
 Koehn, Maximilian-Benedikt, and Andrejs Cekuls. "A BEHAVIOURAL FINANCE EXPLANATION OF SPECULATIVE BUBBLES: EVIDENCE FROM THE BITCOIN PRICE DEVELOPMENT." Scientific Programme Committee (2019): 410.
 Pavlyshenko, Bohdan. "Using stacking approaches for machine learning models." 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018.
 Hover, Franz S., and Michael S. Triantafyllou. "System Design for Uncertainty." Cambridge, MA: MIT Center for Ocean Engineering, 2010.
 Bakar, Nashirah Abu, and Sofian Rosbi. "Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction." International Journal of Advanced Engineering Research and Science 4.11 (2017): 237311.
 McNally, Sean, Jason Roche, and Simon Caton. "Predicting the price of bitcoin using machine learning." 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, 2018.
 Shen, Dehua, Andrew Urquhart, and Pengfei Wang. "Does twitter predict Bitcoin?" Economics Letters 174 (2019): 118-122.
 Claessen, Koen, Jonas Duregård, and Michał H. Pałka. "Generating constrained random data with uniform distribution." Journal of functional programming 25 (2015).
 W. Feller, "An introduction to probability theory and its applications", 2, Wiley (1971).
 Johnson, N. L.; Kotz, S.; N., Balakrishnan. "Continuous Univariate Distributions." Vol. 2 (2nd ed.), (1995).
 Haight, Frank A., Handbook of the Poisson Distribution, New York, NY, USA: John Wiley & Sons, (1967).
 Varberg, Dale E., Edwin Joseph Purcell, and Steven E. Rigdon. Calculus with differential equations. Pearson/Prentice Hall, 2007.
 Shao, Jun. "Linear model selection by cross-validation." Journal of the American statistical Association 88.422 (1993): 486-494.
 Godin, Benoît. "The linear model of innovation: The historical construction of an analytical framework." Science, Technology, & Human Values 31.6 (2006): 639-667.
 Lee, Seunghak, Jun Zhu, and Eric P. Xing. "Adaptive multi-task lasso: with application to eqtl detection." Advances in neural information processing systems. 2010.
 Januaviani, Trisha Magdalena Adelheid, et al. "The best model of LASSO with the LARS (least angle regression and shrinkage) algorithm using Mallow’s Cp." World Scientific News 116 (2019): 245-252.
 Chand, Sohail, Sarah Ahmad, and Madeeha Batool. "Solution path efficiency and oracle variable selection by Lasso-type methods." Chemometrics and Intelligent Laboratory Systems 183 (2018): 140-146.
 Zhang, Long, and Kang Li. "Forward and backward least angle regression for nonlinear system identification." Automatica 53 (2015): 94-102.
 Madigan, David, and Greg Ridgeway. "Discussion of" Least angle regression" by Efron et al." arXiv preprint math/0406469 (2004).
 P. J. Huber, Robust Regression: Asymptotics, Conjectures, and Monte Carlo. Ann. Statist. 1 (1973), 799–821.
 R. W. Hill, Robust Regression When There Are Outliers in the Carriers. Ph.D. Dissertation, Harvard University, Boston, MA, 1977.
 Rahmanl, Raziur, et al. "Adaptive Multi-task Elastic Net based feature selection from Pharmacogenomics Databases." 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018.
 Kibria, B. M., and Shipra Banik. "Some ridge regression estimators and their performances." Journal of Modern Applied Statistical Methods 15.1 (2016): 12.
 Exterkate, Peter, et al. "Nonlinear forecasting with many predictors using kernel ridge regression." International Journal of Forecasting 32.3 (2016): 736-753.
 Garrido, Mario, et al. "Hardware architectures for the fast Fourier transform." Handbook of Signal Processing Systems. Springer, Cham, 2019. 613-647.
 Kennedy, James, and Russell Eberhart. "Particle swarm optimization." Proceedings of ICNN’95-International Conference on Neural Networks. Vol. 4. IEEE, 1995.
 Du, Ke-Lin, and M. N. S. Swamy. "Particle swarm optimization." Search and optimization by metaheuristics. Birkhäuser, Cham, 2016. 153-173.