Trading simulator with real market data

This dissertation investigates cryptocurrency trading simulator enriched with historical market data as a testing tool, covering foundational financial principles, blockchain’s impact, trading intricacies, and the role of trading systems and market data. It includes a thorough review of existing literature, emphasizing real market data. The simulator, featuring three core modules, allows rigorous testing with trusted market data and efficient code structure. It outlines data collection and processing methods, addressing sources, sample sizes, reliability, and ethics. This implemented simulator offers a realistic evaluation environment, interacting with a database module and supporting parameter customization. Rigorous testing validates its accuracy and reliability for system assessment. The dissertation concludes by outlining the future of cryptocurrency trading simulator research, focusing on improving accuracy and functionality, with the potential for significant contributions to the field; SUMÁRIO: Simulador de negociação com dados de mercado reais - Esta dissertação investiga um simulador de negociação de criptomoedas desenvolvido com dados históricos de mercado como ferramenta de teste, baseando-se em princípios financeiros fundamentais, impacto da blockchain, complexidades da negociação, o papel dos sistemas de negociação e dos dados de mercado. Inclui uma revisão completa da literatura existente, com ênfase nos dados do mercado. O simulador, apresenta três módulos principais, permite testes rigorosos com dados de mercado confiáveis e estrutura de código eficiente. O mesmo delineia métodos de recolha e processamento de dados, abordando fontes, tamanhos de amostra, confiabilidade e ética. O simulador implementado oferece um ambiente de avaliação realista, interagindo com uma base de dados e suporta a personalização de parâmetros. Testes rigorosos validam a sua precisão e confiabilidade para a avaliação do sistema. A dissertação conclui sumarizando o futuro de cripto simuladores, focando-se na melhoria de precisão e funcionalidade, com o potencial de contribuir significativamente para o campo

Rockstar Effect in Distributed Project Management on GitHub Social Networks

The internet has become increasingly social, opening up new space for online collaboration and distributed project management. Decentralized management techniques such as open-source software, distributed development, and software-as-a-service allow software developers to easily connect online and to solve complex problems collaboratively. Online rockstars, who are well-respected in a community and are followed by numerous other users, often influence the decisions of project managers and clients in software development. Understanding the effects of these rockstars can greatly facilitate technology development and adoption in distributed project management. This paper presents a study of the GitHub social network to understand rockstar effect in distributed project management. In GitHub, developers often collaborate in distributed teams and interact in their online social networks, which evolve with the popularity of software repositories and actions of rockstars. To understand how rockstars influence the popularity of software repositories, this research constructed temporal social networks from 2015 to 2017 between 13.5 million software repositories and 2.6 million GitHub users and examined the evolvement of the behavior of 245,501 rockstar followers. The results show that the more followers a rockstar has, the more triadic events there are in his/her participated repository. And the difference of a number of events between top rockstar and other rockstars is much higher in participative events than in contributive events, indicating higher triadic influence from top rockstar in those events for technology development in distributed project management

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

Stochastic Sampling and Machine Learning Techniques for Social Media State Production

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries\u27 use of the global information environment. Our hope is that this type of simulation can be used to predict and understand the spread of misinformation, false narratives, fraudulent financial pump and dump schemes, and cybersecurity threats. To do this, our research team has created an agent-based model that can handle a variety of prediction tasks. This dissertation introduces a set of sampling and deep learning techniques that we developed to predict specific aspects of the evolution of online social networks that have proven to be challenging to accurately predict with the agent-based model. First, we compare different strategies for predicting network evolution with sampled historical data based on community features. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over different datasets. Second, we introduce a deep learning model for burst prediction. Bursts may serve as a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross-platform social media data is valuable for predicting bursts within a single social media platform. An LSTM model is proposed in order to capture the temporal dependencies and associations based upon activity information. These volume predictions can also serve as a valuable input for our agent-based model. Finally, we conduct an exploration of Graph Convolutional Networks to investigate the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of targeted graph convolutional networks. Graph Convolutional Networks are important in the social network context as the sociological and anthropological concept of \u27homophily\u27 allows for the method to use network associations in assisting the attribute predictions in a social network

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Self-induced consensus of Reddit users to characterise the GameStop short squeeze

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: Reddit conversation data used in this study can be retrieved from the Pushshift API at https://www.reddit.com/r/pushshift/. Stock price and traded volumes data are instead obtained by the Polygon API at https://polygon.io.The short squeeze of GameStop (GME) shares in mid-January 2021 has been primarily orchestrated by retail investors of the Reddit r/wallstreetbets community. As such, it represents a paramount example of collective coordination action on social media, resulting in large-scale consensus formation and significant market impact. In this work we characterise the structure and time evolution of Reddit conversation data, showing that the occurrence and sentiment of GME-related comments (representing how much users are engaged with GME) increased significantly much before the short squeeze actually took place. Taking inspiration from these early warnings as well as evidence from previous literature, we introduce a model of opinion dynamics where user engagement can trigger a self-reinforcing mechanism leading to the emergence of consensus, which in this particular case is associated to the success of the short squeeze operation. Analytical solutions and model simulations on interaction networks of Reddit users feature a phase transition from heterogeneous to homogeneous opinions as engagement grows, which we qualitatively compare to the sudden hike of GME stock price. Although the model cannot be validated with available data, it offers a possible and minimal interpretation for the increasingly important phenomenon of self-organized collective actions taking place on social networks.Tor Vergata University of Rom

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Walk this Way! Incentive Structures of Different Token Designs for Blockchain-Based Applications

Cryptoeconomics is an emerging research area in the field of blockchain technology aiming at understanding token design mechanisms intended to incentivize certain behaviors. Whereas several blockchain ecosystems have been emerging in recent years, little is known about incentive design in blockchain protocols other than Bitcoin. To address this gap, we use agent-based modeling (ABM) to simulate the effects of different token designs on usage in the context of prediction markets. We find that network tokens (i.e., tokens providing services within a system) provide the largest incentive for individuals to join and become long-term active users. Moreover, we find that investment tokens (i.e., tokens used to passively invest in the issuing entity) provide the smallest incentive compared to network tokens and cryptocurrencies (i.e., means of payment in a blockchain ecosystem). We advance the literature by testing the boundary conditions of different token designs for blockchain-based ecosystems using a novel ABM approach