Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins among owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. The extreme volatility of such trading prices across all different sets of crypto-assets remains undisputed. However, the relations between the trading prices across different cryptocoins remains largely unexplored. Major coin exchanges indicate trend correlation to advise for sells or buys. However, price correlations remain largely unexplored. We shed some light on the trend correlations across a large variety of cryptocoins, by investigating their coin/price correlation trends over the past two years. We study the causality between the trends, and exploit the derived correlations to understand the accuracy of state-of-the-art forecasting techniques for time series modeling (e.g., GBMs, LSTM and GRU) of correlated cryptocoins. Our evaluation shows (i) strong correlation patterns between the most traded coins (e.g., Bitcoin and Ether) and other types of cryptocurrencies, and (ii) state-of-the-art time series forecasting algorithms can be used to forecast cryptocoins price trends. We released datasets and code to reproduce our analysis to the research community.

Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This study aims to address these challenges by developing a reinforcement learning-based automated trading system for five popular altcoins~(cryptocurrencies other than Bitcoin): Binance Coin, Ethereum, Litecoin, Ripple, and Tether. To this end, we present CausalReinforceNet, a framework framed as a decision support system. Designed as the foundational architecture of the trading system, the CausalReinforceNet framework enhances the capabilities of the reinforcement learning agent through causal analysis. Within this framework, we use Bayesian networks in the feature engineering process to identify the most relevant features with causal relationships that influence cryptocurrency price movements. Additionally, we incorporate probabilistic price direction signals from dynamic Bayesian networks to enhance our reinforcement learning agent's decision-making. Due to the high volatility of the cryptocurrency market, we design our framework to adopt a conservative approach that limits sell and buy position sizes to manage risk. We develop two agents using the CausalReinforceNet framework, each based on distinct reinforcement learning algorithms. The results indicate that our framework substantially surpasses the Buy-and-Hold benchmark strategy in profitability. Additionally, both agents generated notable returns on investment for Binance Coin and Ethereum.

Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. The popularity is partly due to their unique specifications originating from blockchain-related characteristics such as privacy, decentralisation, and untraceability. Despite their growing popularity, cryptocurrencies remain a high-risk investment due to their price volatility and uncertainty. The inherent volatility in cryptocurrency prices, coupled with internal cryptocurrency-related factors and external influential global economic factors makes predicting their prices and price movement directions challenging. Nevertheless, the knowledge obtained from predicting the direction of cryptocurrency prices can provide valuable guidance for investors in making informed investment decisions. To address this issue, this paper proposes a dynamic Bayesian network (DBN) approach, which can model complex systems in multivariate settings, to predict the price movement direction of five popular altcoins (cryptocurrencies other than Bitcoin) in the next trading day. The efficacy of the proposed model in predicting cryptocurrency price directions is evaluated from two perspectives. Firstly, our proposed approach is compared to two baseline models, namely an auto-regressive integrated moving average and support vector regression. Secondly, from a feature engineering point of view, the impact of twenty-three different features, grouped into four categories, on the DBN's prediction performance is investigated. The experimental results demonstrate that the DBN significantly outperforms the baseline models. In addition, among the groups of features, technical indicators are found to be the most effective predictors of cryptocurrency price directions.

The growth of market capitalisation and the number of altcoins (cryptocurrencies other than Bitcoin) provide investment opportunities and complicate the prediction of their price movements. A significant challenge in this volatile and relatively immature market is the problem of predicting cryptocurrency prices which needs to identify the factors influencing these prices. The focus of this study is to investigate the factors influencing altcoin prices, and these factors have been investigated from a causal analysis perspective using Bayesian networks. In particular, studying the nature of interactions between five leading altcoins, traditional financial assets including gold, oil, and S\&P 500, and social media is the research question. To provide an answer to the question, we create causal networks which are built from the historic price data of five traditional financial assets, social media data, and price data of altcoins. The ensuing networks are used for causal reasoning and diagnosis, and the results indicate that social media (in particular Twitter data in this study) is the most significant influencing factor of the prices of altcoins. Furthermore, it is not possible to generalise the coins' reactions against the changes in the factors. Consequently, the coins need to be studied separately for a particular price movement investigation.

As artificial intelligence (AI) rapidly works its complex magic on one sector of the economy after another, there is an increasingly pressing need for compute resources to power all this machine intelligence. Training a model like ChatGPT costs more than $5 million, and running the early ChatGPT demo, even before usage increased to its current level, costs OpenAI around $100,000 per day. And AI is more than just text generation; applying AI to practical problems across multiple industries requires similar large neural models trained on a diversity of data types -- medical, financial, customer information, geospatial and so forth. Moving beyond the limitations of current neural net AI toward systems with higher levels of artificial general intelligence will almost surely be even more compute intensive. It's only natural that a small but increasing number of crypto miners are now looking at how to leverage their own compute infrastructures to help push forward the AI revolution.

Audiences seemed unprepared for the progress made by artificial intelligence while they weren't looking. Federico Pistono, however, foresaw this moment, and the many similar moments we will undoubtedly witness in the near future, as far as six years ago, in his bestselling book "Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and be Happy." In addition to researching the societal implications of artificial intelligence on the evolution of society, Mr. Pistono is an entrepreneur, investor, startup founder, public speaker and most recently, Head of Blockchain at Hyperloop Transportation Technologies- a company that is working on the first supersonic land transport. He took a few minutes out of his busy schedule to speak with CryptoComes about Bitcoin's prospects, surveillance states and why he might take some time before writing another book. Katya Michaels: Among all your diverse interests and achievements, what is the thread that ties it all together?