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

Development of a dataFrame and a Bot to predict NFT-collection performance

Donat l'enorme creixement de les tecnologies blockchain a finals de 2021. En particular un dels seus actius anomenat NFT, un actiu que és una nova forma d'escassetat digital. Aquest projecte pretén estudiar el seu mercat encara altament volàtil i desenvolupar tant un DataFrame com un bot, capaços de predir el rendiment economic d'una col·lecció de NFTs en el futur proper. El projecte es centra en el mercat principal de NFT Opensea i concretament en les col·leccions de l'ecosistema Ethereum, que té el 80% del mercat a principis de 2022. Degut a la seva novetat, fins ara s'han fet poques aplicacions i estudis sobre aquest nou mercat. Però això no ha impedit que aquest nou actiu augmenti ràpidament en popularitat des de els 10.000 usuaris fins a més d'1,5 milions només en l'últim any. És cert que a mesura que creix l'interès es publicaran més informació i projectes, però cap semblant a aquesta tesi a la data d'inici. Quan s'intenta fer un anàlisi de dades la mida de la mostra és un factor clau, però aquest és un escàs recurs quan es parla de transaccions NFT. Per això per fer tota l'anàlisi i proves d'aquest projecte, s'han seleccionat 36 de les col·leccions més venudes del mercat. Per crear aquest conjunt només s'han triat col·leccions principals, descartant col·leccions secundàries de marques ja consolidades en l'espai. A més, per garantir dades homogènies, tots els DataFrames emmagatzemen informació dels mateixos períodes des de l'1 de desembre de 2021 fins al 31 de març de 2022. Mitjançant l'API d'Opensea, la majoria de les dades necessàries s'han extret directament de la blockchain. Milers de transaccions de cada col·lecció NFT s'han processat i emmagatzemat en dataFrames, produint fitxers .csv facilment llegibles emmagatzemats en un directori per a un ús futur. Finalment, pel que fa al bot, s'ha utilitzat l'algoritme d'aprenentatge automàtic per a la predicció de dades LSTM (Long Short Term Memory). Aquest s'utilitza habitualment per a la predicció de la borsa i ha estat seleccionat com el més prometedor per a aquest projecte. Els models s'han entrenat i provat utilitzant subconjunts dels dataFrames previament construits. Finalment s'ha realitzat una experimentació addicional amb terminis de 4 i 5 mesos de dades i diferents mitjanes mòbils.Given the enormous growth of blockchain technologies at the end of 2021. Particularly one of its assets called NFTs, an asset that is a new form of digital scarcity. This project aims to study their still highly volatile market and develop both a dataFrame and a bot, capable of predicting a NFT collection performance in the near future. Project focuses on the NFT main marketplace Opensea and specifically the collections in the Ethereum ecosystem, which has 80% of the market as of early 2022. Because of its novelty few applications and studies have been done on this field to date. But this hasn't stopped this new asset to rapidly rise in popularity from a small 10k users to more than 1.5M only in the last year. It is true that as interest grows more and more information and projects are being published, but none similar to this thesis as of start date. When trying to do a data analysis sample size is a key factor, but a scarce resource when talking about NFT transactions. Because of this to make all the analysis and testing of this project 36 from the best-selling collections in the market have been selected. To create this set only main collections have been chosen, discarding secondary collections from already established brands. Moreover, to ensure homogeneous data, all dataFrames store information from the same periods from December 1st, 2021 to March 31, 2022. Using the Opensea API, most of the data needed has been taken directly from the blockchain. Thousands of transactions from each NFT collection have then been processed and stored into dataFrames, producing readable .csv files stored into a directory for future use. Lastly regarding the bot, machine learning algorithm for data prediction LSTM (Long Short Term Memory) have been used. Commonly used for stock market prediction, this algorithm has been selected as the most promising one for this project. Models have been trained and tested using subsets of each collection dataFrames. After that, further examination has been made with timeframes of 4 and 5 months of data and different moving averages

Predicting the Price of Cryptocurrency Using Machine Learning Algorithm

It is proposed to conduct a project aimed at forecasting cryptocurrency price values. The concept of cryptocurrencies refers to computerized money that is used for a variety of transactions as well as for long-term investments. The most common cryptocurrency that most of the systems use to conduct their transactions is the Ethereum cryptocurrency. However, it needs to be noted that there are many other well-known crypto currencies other than ethereum as well. We propose to use Machine Learning for this project, which will be trained from the available cryptocurrency price data, to gain intelligence, and then use this knowledge to make accurate predictions. Trading cryptocurrency prices is one of the most popular exchanges right now. It is suggested that both day traders and investors can benefit greatly from using the suggested approach

Detecting Selfish Mining Attacks Against a Blockchain Using Machine Learing

Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. Selfish mining has been a potential issue for blockchains since it was first discovered by Eyal and Sirer. It can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from network users. Several of these attacks were launched in 2018, 2019, and 2020 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but the effectiveness of the fixes is currently unknown. Despite the known vulnerability, there is little researching into detecting these attacks either historically or in real-time. In this research, we build a program to gather data from known selfish mining attacks against the Ethereum Classic blockchain. We then use this data to train a machine-learning algorithm to discover the important features for detecting selfish mining

Analysis of cryptocurrency markets from 2016 to 2019

This thesis explores machine learning techniques in algorithmic trading. We implement a trading computer program that balances a portfolio of cryptocurrencies. We try to outperform an equally weighted strategy. As our machine learning technique, we use deep reinforcement learning. Cryptocurrencies are digital mediums of exchange that use cryptography to secure transactions. The most well-known example is Bitcoin. They are interesting to analyze due to high volatility and lack of previous research. The availability of data is also exceptional. We introduce an algorithmic trading agent – a computer program powered by machine learning. The agent follows some pre-determined instructions and executes market orders. Traditionally a human trader determines these instructions by using some technical indicators. We instead give the trading agent raw price data as input and let it figure out its instructions. The agent uses machine learning to figure out the trading rules. We evaluate the performance of the agent in seven different backtest stories. Each backtest story reflects some unique and remarkable period in cryptocurrency history. One backtest period was from December 2017 when Bitcoin reached its all-time high price. Another one is from April 2017 when Bitcoin almost lost its place as the most valued cryptocurrency. The stories show the market conditions where the agent excels and reveals its risks. The algorithmic trading agent has two goals. First, it chooses initial weights, and then it rebalances these weights periodically. Choosing proper initial weights is crucial since transaction costs make trade action costly. We evaluate the trading agent’s performance in these two tasks by using two agents: a static and a dynamic agent. The static agent only does the weight initialization and does not rebalance. The dynamic agent also rebalances. We find that the agent does a poor job in choosing initial weights. We also want to find out the optimal time-period for rebalancing for the dynamic agent. Therefore, we compare rebalancing periods from 15 minutes to 1 day. To make our results robust, we ran over a thousand simulations. We found that 15 – 30 minutes rebalancing periods tend to work the best. We find that the algorithmic trading agent closely follows an equally weighted strategy. This finding suggests that the agent is unavailable to decipher meaningful signals from the noisy price data. The machine learning approach does not provide an advantage over equally weighted strategy. Nevertheless, the trading agent excels in volatile and mean reverting market conditions. On these periods, the dynamic agent has lower volatility and a higher Sharpe ratio. However, it has a dangerous tendency of following the looser. Our results contribute to the field of algorithmic finance. We show that frequent rebalancing is a useful tool in the risk management of highly volatile asset classes. Further investigation is required to extend these findings beyond cryptocurrencies

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