Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Sebastião, Hélder; Godinho, Pedro

doi:10.1186/s40854-020-00217-x

Cited by 127 publications

(72 citation statements)

References 87 publications

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“…In [33] ensemble Learning assumes the best output of five Comparable Signal (assemblies 5) models with an annualized ratio of 80,17%, and 91,35% for Sharpe with an annualized return of 9,62% and 5,73%, respectively (around 0,5%). The positive findings support the argument that machine learning offers robust methods for the predictability of cryptocurrencies and the creation of effective trading strategies even under adverse conditions in these markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

Iqbal¹,

Iqbal²,

Jaskani³

et al. 2021

EAI Endorsed Transactions on Creative Technologies

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In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance. To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied. Time-series analysis can predict the future ups and downs in the price of Bitcoin. For this purpose we have used ARIMA, FBProphet, XG Boosting for time series analysis as a machine learning techniques. The parameters on the basis of which we have evaluated these models are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R 2 . We conduct experiments on these three techniques but after conducting time series analysis, ARIMA considered as the best model for forecasting Bitcoin price in the crypto-market with RMSE score of 322.4 and MAE score of 227.3. Additionally, this research can be helpful for investors of crypto-market.

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Section: Literature Reviewmentioning

confidence: 99%

Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

Iqbal¹,

Iqbal²,

Jaskani³

et al. 2021

EAI Endorsed Transactions on Creative Technologies

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“…Since the introduction of this crypto currency in the year 2009, no hacker has been able to infiltrate it due to block chain technology, where each electronic coin is encrypted with a unique digital signature which makes it easier to track and can be trusted. Each owner signs a digital hash from the previous transaction and adding the public key of the next owner before passing it on (4,5) .…”

Section: Introductionmentioning

confidence: 99%

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Ho¹,

Vatambeti²,

Ravichandran³

2021

IJST

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Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model's accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.

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“…These features bring confidence and security to market participants, mainly by solving the double-spending problem, i.e., by avoiding the possibility of a digital entity to be used by the same address in different transactions. The supply of bitcoin is capped at 21 million units; hence, due to its high attractiveness, the demand pressure is prone to long-term price appreciation, and bitcoin is subjected to a long-run deflating process [3].…”

Section: Introductionmentioning

confidence: 99%

Price Appreciation and Roughness Duality in Bitcoin: A Multifractal Analysis

2021

Self Cite

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Since its launch in 2009, bitcoin has thrived, attracting the attention of investors, regulators, academia, and the public in general. Its price dynamics, characterized by extreme volatility, severe jumps, and impressive long-term appreciation, suggest that bitcoin is a new digital asset. This study presents a comprehensive overview of the fractality of bitcoin in a high-frequency framework, namely by applying Multifractal Detrended Fluctuation Analysis (MF-DFA) and a Multifractal Regime Detecting Method (MRDM) to Bitstamp 1 min bitcoin returns from January 2013 to July 2020. The results suggest that bitcoin is multifractal, with smaller and larger fluctuations being persistent and anti-persistent, respectively. Multifractality comes from significant long-range correlations, which cast some doubts on the informational efficiency at this frequency, but mainly comes from fat-tails, which highlights the significant risks undertaken by investors in this market. Our most important result is that the degree and richness of multifractality is time-varying and increased after 2017, when volumes and prices experienced an explosive behaviour. This complexity puts into perspective the duality of bitcoin: while it is characterized by long-run attractiveness and increasing valuation, it also has a high short-run instability. Hence, this study provides some empirical evidence supporting the relationship between these two observable features.

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Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Cited by 127 publications

References 87 publications

Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Price Appreciation and Roughness Duality in Bitcoin: A Multifractal Analysis

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