Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.Comment: Submitted to a journa

Forecasting power of neural networks in cryptocurrency domain : Forecasting the prices of Bitcoin, Ethereum and Cardano with Gated Recurrent Unit and Long Short-Term Memory

Machine learning has developed substantially during the past decades and more emphasis has gone to deeper machine learning methods, i.e., artificial neural networks, computer-based networks seeking to mimic how the human brain functions. The groundwork for ANN research was established already in the 1940s and the advancement of ANNs has been ex-tensive. Price prediction of different financial assets is a broadly studied field, as researchers have been trying to create models to predict the volatile and noisy environment of financial markets. Also, ANNs have been placed for these hard prediction tasks, as their advantage is the ability to find non-linear patterns in uncertain and volatile setting. Cryptocurrencies have made their way to the common audience in the past years. After Nakamoto (2008) presented the first proposal for an electronic cash system, Bitcoin, the number of different cryptocurrencies has exceeded over 8 000. Also, the market capitaliza-tion of all cryptocurrencies has grown rapidly, in November 2021 the aggregate market capi-talization topped 3 000 billion U.S. dollars. Cryptocurrencies are not a small concept for closed groups of tech-people, but a phenomenon that concerns also in the governmental level. This study utilizes recurrent neural networks, GRU and LSTM, in the prediction task regarding cryptocurrencies. In addition to trading data, this study uses Google trend-based popularity score to try to better the ANNs accuracy. In addition to the sole prediction task, the study compares the two used RNN architectures and presents the performance and accuracy with selected performance measures. The results show that recurrent neural networks have potential in prediction tasks in the cryptocurrency domain. The constructed models were able to find coherent trends in the price fluctuations but the average differences on actual and predicted prices were compara-tively high, with the introduced simple RNN models. On average, the LSTM model was able to predict the cryptocurrency prices more accurately, but the GRU model showed also great evidence of prediction accuracy in the domain. All in all, the cryptocurrency prediction task is a hard task due to its volatile nature, but his study shows great evidence for ANNs ability to predict cryptocurrency prices. Considering the findings, further research could be applied to more optimized and complex ANN models as the models used in the study were relatively simple one-layer models.Koneoppiminen on kehittynyt erittäin paljon viimeisten vuosikymmenten aikana, painottuen enemmän syvempien koneoppimisen metodien, kuten keinotekoisten neuroverkkojen (ANN), kehitykseen. Keinotekoiset neuroverkot ovat tietokoneeseen perustuvia verkkoja, jotka pyrkivät jäljittelemään ihmisaivojen toimintaa. Keinotekoisten neuroverkkojen tutki-mus on alkanut jo 1940-luvulla, josta lähtien kyseisten verkkojen kehitys on ollut nopeaa. Eri omaisuuslajien hintakehityksen ennustaminen on laajasti tutkittu alue, kun tutkijat ovat yrit-täneet luoda malleja, joilla he ovat pyrkineet ennustamaan epävarmaa rahoitusmarkkinaym-päristöä. Keinotekoiset neuroverot on valjastettu tähän vaikeaan tehtävän, koska niiden selkeänä etuna on kyky löytää epälineaarisia yhteyksiä epävarmassa ja epävakaassa ympäris-tössä. Viime vuosien aikana kryptovaluutat ovat yleistyneet huomattavasti, niin yksityissijoittajien kun institutionaalisten sijoittajien joukossa. Sen jälkeen, kun Nakamoto (2008) esitteli en-simmäisen ehdotuksen käteisen ja valuutan sähköisestä järjestelmästä, kryptovaluuttojen lukumäärä on kasvanut yli 8 000 yksittäiseen valuuttaan. Samaan aikaan kryptovaluuttojen yhteenlaskettu markkina-arvo on kasvanut räjähdysmäisesti, marraskuussa 2021 kokonais-markkina-arvo kasvoi yli 3 000 miljardiin Yhdysvaltojen dollariin. Nykyään kryptovaluutat eivät ole vain konsepti suljetuille teknologiasta kiinnostuneille ryhmille, vaan ilmiö, joka vaikuttaa myös valtiollisella tasolla. Tämä tutkimus hyödyntää toistuvia neuroverkkoja (recurrent neural networks), GRU ja LSTM, kryptovaluuttojen hintakehityksen ennustamisessa. Kaupankäyntitietojen lisäksi, tut-kimuksessa käytetään Googlen hakutiedusteluihn perustuvaa Google Trend suosiomittaria, neuroverkkojen tarkkuuden parantamiseksi. Kryptovaluuttojen hintakehityksen ennustami-sen lisäksi, tutkimuksessa verrataan kahta RNN-rakennetta ja esitellään molempien verkko-jen tarkkuutta sekä verrataan sitä valituilla tarkkuusmittareilla. Tutkimuksen tulokset osoittavat, että yksinkertaisilla RNN-rakenteilla on selkeää potentiaalia kryptovaluuttojen hintakehityksen ennustamisessa. Tutkimuksessa luodut mallit löytävät johdonmukaisia ja selkeitä trendejä, mutta keskimääräiset erotukset todellisilla ja ennuste-tuilla hinnoilla oli suhteellisesti korkeat. Tutkituista malleista LSTM-malli tuottaa keskimäärin tarkempia ennusteita kuin GRU-malli, mutta erot mallien tarkkuuksissa ovat pienet. Kokonai-suudessaan kryptovaluuttojen hintojen ennustaminen on vaikea tehtävä kryptovaluut-tamarkkinan epävakaan luonteen johdosta, tämä tutkimus kuitenkin osoittaa näyttöä keino-tekoisten neuroverkkojen kyvystä ennustaa kryptovaluuttojen hintoja. Ottaen huomioon tutkimuksen löydökset, lisätutkimusta voisi soveltaa tarkemmin optimoituihin ja kompleksi-simpiin keinotekoisiin neuroverkkoihin, sillä tässä tutkimuksessa käytetyt mallit olivat suh-teellisen yksinkertaisia

Trend-­following strategies for cryptocurrencies with machine learning

Cryptocurrencies could bring big returns, but they also carry high volatility and big crash sizes. I discovered that trend­following strategies help investors to mitigate cryptocurrency’s risk. I also tested and confirmed that risk managed momentum strategy is applicable to the cryptocurr ency environment and that machine learning implementation further improves volatility reduction.Cryptomoedas poderão levar a retornos elevados, contudo também podem estar expostos a maior volatilidade e quedas excessivas do mercado. Eu descobri que estratégias que seguem tendências ajudam investidores a reduzir o risco das cryptomoedas. Também testei e confirmei que estratégias que gerem o risco de momentum podem ser aplicadas a cryptomoedas e que machine learning contribui para reduzir a exposição a volatilidade

Previsão da tendência da bitcoin utilizando extração de sentimentos do Twitter

Bitcoin is the first decentralized digital currency constituting a successful alternative economic system. As a result, the Bitcoin financial market occupies an important position in society, where it has gained increasing popularity. The correct prediction of this type of market can drastically reduce losses and maximize investor profits. One of the most popular aspects of predicting the cryptocurrency market is the analysis of sentiment in posts shared publicly on social networks. Currently, the Twitter platform generates millions of posts a day, which has attracted several researchers in search of problem solving using sentimental analysis in tweets. With this evolution, it is intended to develop, through Artificial Intelligence (AI) techniques, models capable of predicting the Bitcoin trend based on daily sentimental analysis of posts made on the Twitter platform with Bitcoin’s historical data. Specifically, it is intended to assess whether sentiment positively influences the Bitcoin trend, and whether positive, neutral and negative feelings positively influence the Bitcoin trend in the same way. Finally, it is also objective to assess whether indicators such as market volume and the volume of tweets carried out within the scope of the Bitcoin theme positively influence its trend. To validate the potential of the study, two AI models were developed. The first model was created to classify the sentiments of tweets into three typologies: positive, neutral and negative. This model focused on AI techniques based on Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BI-LSTM) and Convolutional Neural Network (CNN). In turn, the second model was designed to classify Bitcoin’s future trends into strong uptrend, uptrend, downtrend and strong downtrend. In this sense, the model focused on AI techniques based on LSTM and Random Forest Classifier. In general, it was possible to achieve good performance in the development of sentiment classification models, achieving an accuracy value of 87 % in the LSTM and BI-LSTM models and 86% in the model based on CNN technology. Regarding the model focused on predicting the Bitcoin trend, it was possible to validate that sentiment positively influences the Bitcoin trend prediction. More interestingly, neutral sentiment volume has a more significant impact on Bitcoin trend prediction. The Random Forest Classifier technique proved to be the best, recording accuracy of 57.35% in predicting the Bitcoin trend. Removing the sentiment variable made it possible to verify a cadence of 15% to 20% in the Bitcoin trend forecast, which effectively validates that sentiment positively influences the trend forecast.A Bitcoin é considerada a primeira moeda digital descentralizada constituindo um sistema económico alternativo de sucesso. Em resultado, o mercado financeiro da Bitcoin ocupa uma posição importante na sociedade, onde tem vindo a angariar cada vez mais popularidade. Prever acertadamente este tipo de mercado pode reduzir drasticamente as perdas e maximizar os lucros dos investidores. Um dos aspetos mais populares, quando se trata de prever o mercado de cryptomoedas, passa pela análise de sentimentos em posts partilhados publicamente em redes sociais. Atualmente, a plataforma do Twitter, gera milhões de posts todos os dias, o que tem atraído diversos investigadores na procura de resoluções de problemas com recurso à análise sentimental em tweets. Com esta evolução, pretende-se desenvolver através de técnicas de Inteligência Artificial (IA), modelos capazes de prever a trend da Bitcoin com base numa análise sentimental diária dos posts efetuados na plataforma do Twitter com os dados históricos da Bitcoin. Em específico, tenciona-se avaliar se o sentimento influencia positivamente a trend da Bitcoin, bem como avaliar se os sentimentos positivos, neutros e negativos, de forma isolada, influenciam da mesma forma positivamente a trend da Bitcoin. Por fim, é ainda objetivo, avaliar se indicadores como o volume de mercado e o volume de tweets realizado no âmbito do tema da Bitcoin influenciam positivamente a trend da mesma. De forma a validar o potencial do estudo, foram desenvolvidos dois modelos de IA. O primeiro modelo foi criado para efetuar a classificação de sentimentos dos tweets em três tipologias: positivos, neutros e negativos. Este modelo, focou-se em técnicas de IA basedas em LSTM, BI-LSTM e CNN. Por sua vez, o segundo modelo foi elaborado para classificar as trends futuras da Bitcoin em quatro tipologias: strong uptrend, uptrend, downtrend e strong downtrend. Neste sentido, o modelo focou-se em técnicas de IA baseadas em LSTM e Random Forest Classifier. Em geral, foi possível atingir uma boa performance no desenvolvimento dos modelos de classificação de sentimento, atingindo um valor de accuracy de 87% nos modelos LSTM e BI-LSTM, e 86% no modelo baseado na técnica de CNN. Em relação ao modelo focado em prever a trend da Bitcoin, foi possível validar que o sentimento realmente influencia positivamente a previsão da trend da Bitcoin. Mais curiosamente, verificou-se que o volume de sentimento neutro tem um impacto mais significativo na previsão da trend da Bitcoin. A técnica Random Forest Classifier demonstrou ser a melhor, registando uma accuracy de 57,35% na previsão da trend da Bitcoin. Ao remover a variável sentimento foi possível verificar uma cadência de 15% a 20% na previsão da trend da Bitcoin, o que valida efetivamente que o sentimento influencia positivamente a previsão da trend