Cryptocurrency price prediction using LSTM neural networks

The interest in cryptocurrencies is increasing among individuals and investors. Bitcoin is the leading existing cryptocurrency with the highest market capitalization. However, its high volatility aligns with political uncertainty making it very difficult to predict its value. Therefore, there is a need to create advanced models that use mathematical and statistical methods to reduce investment risk. This research aims to verify if long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM) neural networks, can be used with Savitzky–Golay filter to predict next-day bitcoin closing prices. We found evidence both networks can be used effectively to predict bitcoin prices. LSTM performed 4.49 mean absolute percentage error (MAPE) and BiLSTM 4.44 MAPE. We also found that using Savitzky– Golay filter and dropout regularization significantly improved the model’s prediction performance.O interesse em moedas digitais tem aumentado por parte de indivíduos e investidores. A bitcoin é a moeda digital com maior capitalização de mercado, no entanto, a sua alta volatilidade alinhada à incerteza política, torna muito difícil prever seu valor. Portanto, existe a necessidade de criar modelos avançados que utilizem métodos matemáticos e estatísticos para reduzir o risco de investimento. Este estudo tem como objetivo verificar se as redes neurais artificiais de memória longo curto prazo (LSTM) e redes bidirecionais de memória longo curto prazo (BiLSTM) podem ser usadas juntamente com o filtro Savitzky-Golay para prever os preços de fecho do dia seguinte da bitcoin. Os resultados mostraram que existe evidência que ambas as redes podem ser usadas de forma efetiva. LSTM obteve um erro percentual absoluto médio (MAPE) de 4.49 e BiLSTM um MAPE de 4,44. Também o uso do filtro Savitzky-Golay e regularização, melhora significativamente o desempenho de previsão dos modelos

Data analysis in deep learning classification models, a financial application for bitcoin

Uses for machine learning methods have dramatically increased over the last decade. With a diverse array of industries making use of it, it is no surprise that the financial industry has been one of its first adopters and pioneer in its development. However, precise measurements must be considered when dealing with financial data extracted from the market. This work project is an execution of Professor Marcos López de Prado (Cornell University)data analysis techniques for financial machine learning algorithms. The prepared data was then used as an input in a deep neural network for multi class classification, with the objective of making price direction predictions. Bitcoin was the selected financial instrument for this study, given its high volatility and its virtually global accessibility

Predicting Cryptocurrency Price Change Direction from Supply-Side Factors via Machine Learning Methods

Cryptocurrency prices are highly variable. Predicting changes in cryptocurrency price is a hugely important topic to investors and researchers, with much existing research on demand-side factors. The goal of this research project is to design and implement machine learning models to predict future cryptocurrency price change direction based primarily on supply-side factors. Different unsupervised machine learning techniques are used to build the predictive models. These techniques include K Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naïve Bayesian Classifier, and Random Forest Classifier. A dataset of 10 daily supply-side metrics for three prominent cryptocurrencies (Bitcoin, Ethereum, and Litecoin) at four different time horizons (ranging from one day to 30 days) are used to build and test the machine learning models. The outputs of these models indicate the predicted direction of the price movement over the time horizon (i.e., whether the price would go up or down), not the magnitude of the movement. Experimental results show that predictions were very unreliable for the shorter time spans but very reliable for the longest time spans. The Artificial Neural Network and Random Forest classifiers consistently outperformed the other techniques and achieved a prediction accuracy of over 90% in most models and over 95% in the best models. Experimental results show also that there is no significant difference in predictability between the three prominent cryptocurrencies.https://scholarworks.moreheadstate.edu/celebration_posters_2022/1038/thumbnail.jp

Investigating the Predictability of a Chaotic Time-Series Data using Reservoir Computing, Deep-Learning and Machine- Learning on the Short-, Medium- and Long-Term Pricing of Bitcoin and Ethereum.

This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series is then compared to evaluate the models to find the best fit model. The models are fine-tuned, with hyperparameters, design of the network within the LSTM and the reservoir size within the Echo State Network being adjusted to improve accuracy and speed. This research highlights the effect of the trends within the cryptocurrency and its effect on predictive models, these models will then be optimized with hyperparameter tuning, and be evaluated to compare the models across the two currencies. It is found that the datasets for each cryptocurrency are different, due to the different permutation importance, which does not affect the overall predictability of the models with the short and medium-term predictions having the same models being the top performers. This research confirms that the chaotic data although can have positive results for shortand medium-term prediction, for long-term prediction, technical analysis basedprediction is not sufficient

Algorithmic trading with cryptocurrencies - does twitter sentiment impact short-term price fluctuations in bitcoin

Since its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. This work is part of a work project, which performs a holistic approach to build an intra day Bitcoin trading algorithm based on predictive analysis of Machine Learning models. This part performs a Sentiment Analysis on Twitter data, showing a Granger causal relationship between the extracted Sentiment and the Bitcoin price

Influencers, are they responsible for Bitcoin's volatility? Transfer entropy and Granger causality in prol of an answer

Bitcoin, like any other cryptocurrency, is subject to fluctuations in price. The volatility of this market can be a reflection of several reasons, such as public opinion, social networks and news. Social networks, in particular Twitter, are increasingly used as an important source of value extraction because through this network, it is possible to find out about news in real-time, follow the repercussions, know what experts in the financial world are commenting or thinking and even decide based on influencer's opinion whether to invest or not. This study investigates the influence that a specific group of people exert on Bitcoin volatility. A selection of influencers from the “crypto world” was made, and through the Twitter API, it was possible to select the tweets of the object of study. To choose the classification model for sentiment analysis, two techniques were compared, one being very popular with a focus on the domain of social networks and the other recently created and focused on finance. From the selected technique, only positive and negative sentiments were considered, and then the daily series of the Sentiment Score was calculated. Next, the causal relationship between Bitcoin and sentiment was investigated using Granger causality and Transfer Entropy tests. Transfer Entropy showed encouraging results, suggesting that there is a transfer of information from Sentiment to Returns and that it is possible for an influencer to contribute to Bitcoin’s volatilityO Bitcoin, assim como qualquer outra criptomoeda, está sujeito a flutuações no preço. A volatilidade desse mercado pode ser reflexo de vários motivos, tais como, opinião pública, redes sociais e notícias. As redes sociais, em particular o Twitter, cada vez mais é utilizado como uma fonte importante de extração de valor, isto porque através desta rede é possível saber das novidades em tempo real, acompanhar as repercussões, saber o que entendedores do mundo financeiro estão a comentar e decidir até mesmo com base na opinião de um influenciador se irá investir ou não. Este estudo investiga a influência que determinadas pessoas exercem sobre a volatilidade do Bitcoin. Foi feita uma seleção de influenciadores do “mundo crypto” e através da API do Twitter foi possível selecionar os tweets de objeto de estudo. Para a escolha do modelo de classificação para análise de sentimento foram comparadas duas técnicas, sendo uma muito popular com foco no domínio de redes sociais e a outra recém-criada e focada em finanças. A partir da técnica selecionada, apenas os sentimentos positivos e negativos foram considerados e então calculada a série diária do Sentiment Score. A seguir foi investigada a relação causal entre o Bitcoin e o sentiment utilizando os testes de causalidade de Granger e Entropia de Transferência. A Entropia de Transferência mostrou resultados animadores que sugerem existir transferência de informação de Sentiment para Returns e que, portanto, é possível que um influencer contribua para a volatilidade do Bitcoin

Machine Learning Methods to Exploit the Predictive Power of Open, High, Low, Close (OHLC) Data

Novel machine learning techniques are developed for the prediction of financial markets, with a combination of supervised, unsupervised and Bayesian optimisation machine learning methods shown able to give a predictive power rarely previously observed. A new data mining technique named Deep Candlestick Mining (DCM) is proposed that is able to discover highly predictive dataset specific candlestick patterns (arrangements of open, high, low, close (OHLC) aggregated price data structures) which significantly outperform traditional candlestick patterns. The power that OHLC features can provide is further investigated, using LSTM RNNs and XGBoost trees, in the prediction of a mid-price directional change, defined here as the mid-point between either the open and close or high and low of an OHLC bar. This target variable has been overlooked in the literature, which is surprising given the relative ease of predicting it, significantly in excess of noisier financial quantities. However, the true value of this quantity is only known upon the period's ending – i.e. it is an after-the-fact observation. To make use of and enhance the remarkable predictability of the mid-price directional change, multi-period predictions are investigated by training many LSTM RNNs (XGBoost trees being used to identify powerful OHLC input feature combinations), over different time horizons, to construct a Bayesian optimised trend prediction ensemble. This fusion of long-, medium- and short-term information results in a model capable of predicting market trend direction to greater than 70% better than random. A trading strategy is constructed to demonstrate how this predictive power can be used by exploiting an artefact of the LSTM RNN training process which allows the trading system to size and place trades in accordance with the ensemble's predictive certainty

Applied Data Science Approaches in FinTech: Innovative Models for Bitcoin Price Dynamics

Living in a data-intensive environment is a natural consequence to the continuous innovations and technological advancements, that created countless opportunities for addressing domain-specific challenges following the Data Science approach. The main objective of this thesis is to present applied Data Science approaches in FinTech, focusing on proposing innovative descriptive and predictive models for studying and exploring Bitcoin Price Dynamics and Bitcoin Price Prediction. With reference to the research area of Bitcoin Price Dynamics, two models are proposed. The first model is a Network Vector Autoregressive model that explains the dynamics of Bitcoin prices, based on a correlation network Vector Autoregressive process that models interconnections between Bitcoin prices from different exchange markets and classical assets prices. The empirical findings show that Bitcoin prices from different markets are highly interrelated, as in an efficiently integrated market, with prices from larger and/or more connected exchange markets driving other prices. The results confirm that Bitcoin prices are unrelated with classical market prices, thus, supporting the diversification benefit property of Bitcoin. The proposed model can predict Bitcoin prices with an error rate of about 11% of the average price. The second proposed model is a Hidden Markov Model that explains the observed time dynamics of Bitcoin prices from different exchange markets, by means of the latent time dynamics of a predefined number of hidden states, to model regime switches between different price vectors, going from "bear'' to "stable'' and "bear'' times. Structured with three hidden states and a diagonal variance-covariance matrix, the model proves that the first hidden state is concentrated in the initial time period where Bitcoin was relatively new and its prices were barely increasing, the second hidden state is mostly concentrated in a period where Bitcoin prices were steadily increasing, while the third hidden state is mostly concentrated in the last period where Bitcoin prices witnessed a high rate of volatility. Moreover, the model shows a good predictive performance when implemented on an out of sample dataset, compared to the same model structured with a full variance-covariance matrix. The third and final proposed model, falls within the area of Bitcoin Price Prediction. A Hybrid Hidden Markov Model and Genetic Algorithm Optimized Long Short Term Memory Network is proposed, aiming at predicting Bitcoin prices accurately, by introducing new features that are not usually considered in the literature. Moreover, to compare the performance of the proposed model to other models, a more traditional ARIMA model has been implemented, as well as a conventional Genetic Algorithm-optimized Long Short Term Memory Network. With a mean squared error of 33.888, a root mean squared error of 5.821 and a mean absolute error of 2.510, the proposed model achieves the lowest errors among all the implemented models, which proves its effectiveness in predicting Bitcoin prices