MathAware Nude AI Generators & NSFW

Author: Achim Wanders

  • Germany’s Metaverse Market: An Overview

    Germany’s foray into the Metaverse has been marked by robust growth and significant investments. The country’s strong technical expertise, coupled with its established digital infrastructure, has laid a strong foundation for Metaverse development. Companies like Meta (formerly known as Facebook), Decentraland, and various German startups are spearheading efforts to integrate virtual reality (VR) and augmented reality (AR) technologies into everyday life.

    The German government’s proactive stance on digital transformation further bolsters this growth. With initiatives like the Digital Strategy 2025, the government aims to support technological advancements and digital innovation, which in turn aids the Metaverse market.

    Comparative Analysis: Germany vs. Other Leading Countries

    1. United States
      • Market Size: The U.S. leads the Metaverse market with substantial investments from tech giants like Meta, Google, and Microsoft.
      • Innovation: Home to Silicon Valley, the U.S. boasts a high concentration of technological innovation and startup culture.
      • Government Support: Federal and state governments offer considerable support through funding and favorable policies for tech development.
    2. China
      • Market Size: China is rapidly closing the gap with heavy investments in VR/AR technologies by companies like Tencent and Alibaba.
      • Innovation: The Chinese tech ecosystem is characterized by rapid innovation and adaptation of new technologies.
      • Government Support: The Chinese government’s significant investments in digital infrastructure and tech startups provide a solid backbone for Metaverse advancements.
    3. South Korea
      • Market Size: South Korea is another major player with strong investments from companies like Samsung and Naver.
      • Innovation: Known for its advanced tech landscape and high internet penetration rates, South Korea is a leader in tech innovation.
      • Government Support: The South Korean government has launched initiatives like the “New Deal” to support digital and green technologies, including the Metaverse.
    4. Japan
      • Market Size: Japan’s Metaverse market is growing with investments from Sony and various gaming companies.
      • Innovation: Japan is renowned for its innovations in gaming and VR technologies.
      • Government Support: The Japanese government supports digital transformation through policies like the Basic Plan for the Advancement of Utilizing Public and Private Sector Data.

    Strengths and Challenges for Germany

    Strengths

    • Engineering Excellence: Germany’s reputation for engineering excellence translates into high-quality Metaverse technologies.
    • Strong Digital Infrastructure: Well-developed digital infrastructure supports seamless integration of Metaverse applications.
    • Government Support: Policies and initiatives aimed at digital innovation bolster Metaverse market growth.

    Challenges

    • Regulatory Hurdles: Stringent data protection laws, while crucial for privacy, can pose challenges for rapid innovation.
    • Competition: Fierce competition from U.S., China, and South Korea necessitates continuous innovation and investment.

    Future Prospects

    Germany’s Metaverse market holds immense potential. Continued investment in digital infrastructure, coupled with favorable government policies, can position Germany as a leader in the global Metaverse landscape. By addressing challenges and fostering innovation, Germany can leverage its strengths to achieve significant advancements in this burgeoning field.

    In conclusion, while Germany’s Metaverse market is burgeoning, it faces stiff competition from other leading countries. However, with its strong technical foundation and supportive policies, Germany is well-positioned to make substantial strides in the Metaverse arena.

  • Brightedge vs. Competitors: Which SEO Tool Offers the Best Features?

    Brightedge’s Core Features

    Brightedge is renowned for its robust capabilities in the realm of search engine optimization. Some of its standout features include:

    1. Content Performance Marketing
      • Brightedge offers a comprehensive content performance dashboard that helps users track how their content performs across various channels. This feature provides insights into what’s working and what needs improvement, facilitating data-driven decisions.
    2. Keyword Reporting and Recommendations
      • The platform provides detailed keyword reports and suggests actionable keyword recommendations. This allows users to identify high-opportunity keywords and optimize their content accordingly.
    3. Competitive Analysis
      • With Brightedge, businesses can benchmark their performance against competitors. This includes insights into competitor keywords, backlinks, and overall SEO strategies, enabling users to stay ahead of the competition.
    4. AI and Machine Learning
      • Brightedge employs artificial intelligence to enhance its SEO capabilities. The platform’s AI can predict trends, automate complex tasks, and offer strategic insights, making SEO efforts more efficient and effective.

    Competitor Analysis

    While Brightedge is a formidable tool, it faces stiff competition from several other SEO platforms. Here’s a look at a few notable competitors and their features:

    1. SEMrush

    • Keyword Magic Tool
      • SEMrush’s Keyword Magic Tool is highly regarded for its extensive database. It helps users find profitable keywords and offers various filters to narrow down search results.
    • Site Audit
      • The comprehensive site audit feature identifies technical SEO issues, offering detailed recommendations for improvements. This feature is crucial for maintaining a healthy and optimized website.
    • Backlink Analytics
      • SEMrush provides in-depth backlink analysis, allowing users to understand their link-building efforts and identify opportunities for acquiring high-quality backlinks.
    • Competitor Analysis
      • Like Brightedge, SEMrush offers competitor analysis, but with an added emphasis on paid traffic insights, giving users a holistic view of their competitor’s online strategies.

    2. Ahrefs

    • Site Explorer
      • Ahrefs’ Site Explorer is renowned for its ability to analyze a website’s organic search traffic and backlink profile. It offers insights into the top-performing pages and keywords.
    • Content Explorer
      • This feature allows users to discover popular content within their niche, helping to identify content gaps and opportunities for creating engaging and high-performing content.
    • Rank Tracker
      • Ahrefs’ Rank Tracker feature allows users to monitor their search rankings over time and compare their performance against competitors.
    • Backlink Checker
      • Known for its extensive backlink database, Ahrefs’ Backlink Checker provides detailed insights into a website’s backlink profile, helping users understand their link-building potential.

    Comparative Analysis

    When comparing Brightedge to its competitors, several factors come into play:

    1. Data Breadth and Depth
      • SEMrush and Ahrefs are often praised for the extensive breadth and depth of their data, especially in keyword and backlink analysis. Brightedge, however, leverages AI to provide more predictive insights.
    2. User Interface and Usability
      • Brightedge’s interface is designed for enterprise-level users, which might present a steeper learning curve for beginners. SEMrush and Ahrefs offer more user-friendly interfaces that can be easier for newcomers to navigate.
    3. Pricing
      • Pricing can be a decisive factor. Brightedge’s enterprise-level solutions come at a premium, which might be prohibitive for smaller businesses. SEMrush and Ahrefs offer more tiered pricing structures that cater to a range of business sizes and budgets.
    4. Unique Features
      • Brightedge’s AI-driven insights and content performance marketing capabilities are unique selling points. SEMrush excels in keyword research and competitive analysis, while Ahrefs provides unparalleled backlink analysis.

    Conclusion

    Ultimately, the best SEO tool depends on the specific needs and objectives of a business. Brightedge stands out with its AI-driven insights and comprehensive content performance tracking, making it ideal for enterprise-level users seeking advanced SEO solutions. SEMrush offers a balanced mix of features suitable for both beginners and advanced users, with strong capabilities in keyword research and site audits. Ahrefs, with its extensive backlink database and user-friendly interface, is an excellent choice for those focused on link-building and competitive analysis.

    By carefully evaluating these tools against your unique requirements, you can make an informed decision that will bolster your SEO efforts and drive your business toward greater digital success.

  • PromptPerfect vs. Traditional Prompt Engineering: Enhancing AI Performance

    Understanding Traditional Prompt Engineering

    Traditional prompt engineering involves manually crafting and tuning prompts to elicit desired responses from AI models. This method relies heavily on the expertise and intuition of the engineer, who must understand both the AI model’s architecture and the specific application requirements. The process often includes several iterations of trial and error, where prompts are adjusted based on the model’s responses until satisfactory performance is achieved.

    While traditional prompt engineering has been effective, it is labor-intensive and time-consuming. Engineers must constantly stay abreast of updates in AI technology to refine their prompts. Moreover, the effectiveness of this method can vary significantly depending on the engineer’s skill and experience.

    Introducing PromptPerfect

    PromptPerfect is an advanced tool designed to streamline and enhance the prompt engineering process. It leverages machine learning algorithms to automate the generation and optimization of prompts. By analyzing a vast array of data and previous interactions, PromptPerfect can craft prompts that are more likely to yield accurate and relevant responses from AI models.

    The key advantage of PromptPerfect lies in its ability to reduce the time and effort required for prompt engineering. Instead of manually crafting each prompt, engineers can rely on the tool to generate multiple high-quality prompts rapidly. This not only accelerates the development process but also ensures a higher degree of consistency and precision in AI interactions.

    Enhancing AI Performance

    The comparative effectiveness of PromptPerfect and traditional prompt engineering can be assessed through several metrics: response accuracy, development time, and scalability.

    1. Response Accuracy:
      • Traditional methods depend on the depth of the engineer’s knowledge and experience. While they can achieve high accuracy, it often takes multiple iterations.
      • PromptPerfect, through its data-driven approach, can swiftly generate prompts that align closely with the desired outcomes, improving initial response accuracy.
    2. Development Time:
      • Manual prompt engineering is inherently slow, requiring significant human intervention.
      • PromptPerfect reduces development time by automating prompt generation and refinement, allowing engineers to focus on higher-level architecture and strategy.
    3. Scalability:
      • Traditional methods are less scalable, as the prompt generation process does not significantly benefit from increased data or interactions.
      • PromptPerfect excels in scalability, as its algorithms improve with more data and interactions, continuously enhancing prompt quality over time.

    Case Studies and Real-World Applications

    Several organizations have reported notable improvements in AI performance after integrating PromptPerfect into their workflow. For instance, a leading tech company reduced their prompt design time by 60% and observed a 20% increase in response accuracy for their customer service AI. Another enterprise in the finance sector reported that using PromptPerfect led to more precise data extraction from financial reports, enhancing their decision-making processes.

    Conclusion

    While traditional prompt engineering has served as a robust foundation for AI development, the advent of tools like PromptPerfect marks a significant leap forward. By automating and optimizing the prompt generation process, PromptPerfect not only enhances the efficiency and accuracy of AI interactions but also empowers engineers to tackle more complex challenges. As AI continues to integrate deeper into various industries, leveraging advanced tools like PromptPerfect will be crucial in driving forward the next wave of innovation and performance.

  • Cryptocurrency trading: a comprehensive survey

    DOI: 10.1186/s40854-021-00321-6

    ABSTRACT: Abstract In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading ( e . g ., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

    – Comprehensive survey of cryptocurrency trading research
    – Promising opportunities remain open in cryptocurrency trading

    – Comprehensive survey of cryptocurrency trading research
    – Promising opportunities remain open in cryptocurrency trading

    – Comprehensive survey of cryptocurrency trading research
    – Promising opportunities remain open in cryptocurrency trading

    – The behavior of cryptocurrencies as an asset is still not fully understood.
    – Some aspects of cryptocurrency trading research remain open for exploration.

    Methods used: – The behavior of cryptocurrencies as an asset is still not fully understood.
    – Some aspects of cryptocurrency trading research remain open for exploration.

    – Summarizes existing research on cryptocurrency trading
    – Identifies promising opportunities in cryptocurrency trading

    – Comprehensive survey of 146 research papers on cryptocurrency trading
    – Analysis of datasets, research trends, and distribution among research objects and technologies

    – The paper provides a comprehensive survey of cryptocurrency trading research.
    – It covers 146 research papers on various aspects of cryptocurrency trading.

    – Paper provides a comprehensive survey of cryptocurrency trading research.
    – Covers 146 research papers on various aspects of cryptocurrency trading.

    A comprehensive survey of cryptocurrency trading research can be found in this paper , with a focus on cryptocurrency trading systems, cryptocurrency trading platforms, trading signals, trading strategy research and risk management.

  • The rapid growth of cryptocurrencies: How profitable is trading in digital money?

    DOI: 10.1002/ijfe.2778

    ABSTRACT: There has been a tremendous growth in cryptocurrencies, which has challenged policy makers around the globe. We obtain millisecond data of some of the most frequently traded cryptocurrencies – bitcoin, ethereum, ripple, litecoin and dash – and two cryptocurrency indices – CRIX and CCI30 – to examine their profitability. Our profitability findings suggest that cryptocurrency traders generate significant profits after considering reasonable transaction costs. We also observe that cryptocurrency market participants can expand and sustain the levels of profitability levels in the subsequent trading activity. Our robustness checks with more recent post-Covid data are consistent with the initial profitability findings, although we observe lower levels of profits for the two indices and weaker profit persistency for all digital assets.

    – Cryptocurrency traders generate significant profits after considering transaction costs.
    – Profitability levels can be sustained in subsequent trading activity.

    – Cryptocurrency traders generate significant profits after considering transaction costs.
    – Profitability levels can be sustained in subsequent trading activity.

    – Cryptocurrency traders generate significant profits after considering transaction costs.
    – Profitability levels can be sustained in subsequent trading activity.

    – Lower levels of profits observed for the two indices.
    – Weaker profit persistency observed for all digital assets.

    Methods used: – Lower levels of profits observed for the two indices.
    – Weaker profit persistency observed for all digital assets.

    – Cryptocurrency traders can generate significant profits after considering transaction costs.
    – Profitability levels in cryptocurrency trading can be sustained and expanded.

    – Cryptocurrency traders generate significant profits after considering transaction costs.
    – Profitability levels in subsequent trading activity can be expanded and sustained.

    – Cryptocurrency traders generate significant profits after considering transaction costs.
    – Profitability levels in subsequent trading activity can be expanded and sustained.

    – The paper examines the profitability of trading in cryptocurrencies.
    – Traders generate significant profits after considering transaction costs.

    In this article , the authors obtain millisecond data of some of the most frequently traded cryptocurrencies (e.g., bitcoin, ethereum, ripple, litecoin and dash) and two cryptocurrency indices (CRIX and CCI30) to examine their profitability.

  • Cryptocurrency investment: A safe venture or a new type of gambling?

    DOI: 10.4309/JGI.2021.47.8

    ABSTRACT: Investment behaviour and gambling overlap from time to time. It is stated that there is a spectrum between gambling and investment behaviour, and there are “speculative” investment tools in the middle of the spectrum. Considering that it presents a higher risk because of its high volatility compared to traditional investment instruments, trading cryptocurrencies can become pathological and gambling-like. This study aims to investigate the pathological trading behaviour and frequency among cryptocurrency investors, to investigate additional gambling disorders, and to investigate the relationship between cryptocurrency investment behaviour and impulsivity. An online questionnaire was created to investigate these issues. In the questionnaire, the Pathological Trading Scale, the South Oaks Gambling Screen Test and the Barratt Impulsivity Scale were all used. A total of three hundred persons were evaluated. We found that total pathological traders were 48.7% of all traders, impulsivity in 18–25 age group was higher, high-frequency traders were more pathological, and their impulsivity was higher; also margin traders and day traders show more pathological behaviour. It seems that an important part of cryptocurrency traders may be pathological, and certain of them may have cryptocurrency addiction, which can be evaluated as a subtype of gambling disorder.Resume Le comportement de l’investisseur et celui du joueur se chevauchent de temps a autre. On dit qu’il existe un spectre entre ces deux comportements, au milieu duquel se trouvent des outils d’investissement « speculatif ». Compte tenu de leur risque plus eleve du a leur plus grande volatilite par rapport aux instruments d’investissement traditionnels, les echanges de cryptomonnaies peuvent devenir pathologiques et s’apparenter aux jeux de hasard. Cette etude vise a analyser le comportement des investisseurs de cryptomonnaies et la frequence de leurs operations afin d’examiner d’autres troubles lies a la pratique des jeux de hasard et la relation entre le comportement des investisseurs de cryptomonnaies et l’impulsivite. Un questionnaire en ligne a ete cree a cette fin et la Pathological Trading Scale, le South Oaks Gambling Screen Test et la Barratt Impulsivity Scale y etaient utilises. En tout, 300 personnes ont ete evaluees. Nous avons constate que les joueurs pathologiques representaient 48,7% de tous les speculateurs, que l’impulsivite dans le groupe des personnes de 18 a 25 ans etait plus elevee, et que les speculateurs qui effectuaient des transactions plus souvent etaient plus pathologiques et faisaient preuve d’une plus grande impulsivite; de plus, les speculateurs sur marge et les speculateurs sur seance affichaient un comportement plus pathologique. Il semble qu’une proportion importante des speculateurs de cryptomonnaies peuvent etre pathologiques, et que certains d’entre eux peuvent etre dependants a l’egard des cryptomonnaies, ce qui peut etre evalue comme un sous-type de jeu compulsif.

    – 48.7% of cryptocurrency traders were pathological
    – Impulsivity was higher in the 18-25 age group

    – 48.7% of cryptocurrency traders were pathological
    – Impulsivity was higher in the 18-25 age group

    – 48.7% of cryptocurrency traders were pathological
    – Impulsivity was higher in the 18-25 age group

    – No mention of sample size limitations
    – No mention of potential biases in the study

    Methods used: – No mention of sample size limitations
    – No mention of potential biases in the study

    – A significant proportion of cryptocurrency traders may exhibit pathological behavior.
    – Certain individuals may have cryptocurrency addiction, similar to gambling disorder.

    – 48.7% of cryptocurrency traders were found to be pathological.
    – Impulsivity was higher in the 18-25 age group and among high-frequency traders.

    – Study investigates pathological trading behavior and frequency among cryptocurrency investors.
    – Findings suggest a significant proportion of cryptocurrency traders may be pathological.

    – The study investigates the overlap between investment behavior and gambling.
    – It examines the pathological trading behavior and frequency among cryptocurrency investors.

    In this article, the authors investigated the relationship between cryptocurrency investment behavior and impulsivity, and found that an important part of cryptocurrency traders may be pathological, and certain of them may have cryptocurrency addiction, which can be evaluated as a subtype of gambling disorder.

  • Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting

    DOI: 10.48550/arxiv.2209.05559

    ABSTRACT: Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits in backtesting, which may suffer from the false positive issue due to overfitting. In this paper, we propose a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning. First, we formulate the detection of backtest overfitting as a hypothesis test. Then, we train the DRL agents, estimate the probability of overfitting, and reject the overfitted agents, increasing the chance of good trading performance. Finally, on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022 (during which the crypto market crashed two times), we show that the less overfitted deep reinforcement learning agents have a higher return than that of more overfitted agents, an equal weight strategy, and the S&P DBM Index (market benchmark), offering confidence in possible deployment to a real market.

    – The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading using deep reinforcement learning.
    – The less overfitted deep reinforcement learning agents have a higher return than more overfitted agents, equal weight strategy, and market benchmark.

    – The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading using deep reinforcement learning.
    – The less overfitted deep reinforcement learning agents have a higher return than more overfitted agents, equal weight strategy, and market benchmark.

    – The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading using deep reinforcement learning.
    – The less overfitted deep reinforcement learning agents have a higher return than more overfitted agents, equal weight strategy, and market benchmark.

    – False positive issue due to overfitting in backtesting
    – The crypto market crashed two times during the testing period

    Methods used: – False positive issue due to overfitting in backtesting
    – The crypto market crashed two times during the testing period

    – The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading.
    – The less overfitted deep reinforcement learning agents have a higher return.

    – Less overfitted deep reinforcement learning agents have higher returns.
    – The proposed approach offers confidence in possible deployment to a real market.

    – Paper proposes a practical approach to address backtest overfitting in cryptocurrency trading using deep reinforcement learning.
    – Less overfitted agents have higher returns than more overfitted agents.

    – Paper addresses backtest overfitting in cryptocurrency trading using deep reinforcement learning.
    – Proposes a practical approach to detect and reject overfitted agents.

    In this article , the authors propose a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning, and show that less overfitted deep RL agents have a higher return than that of more overfitted agents, an equal weight strategy, and the S&P DBM Index.

  • Automated Cryptocurrency Trading Bot Implementing DRL

    DOI: 10.47836/pjst.30.4.22

    ABSTRACT: A year ago, one thousand USD invested in Bitcoin (BTC) alone would have appreciated to three thousand five hundred USD. Deep reinforcement learning (DRL) recent outstanding performance has opened up the possibilities to predict price fluctuations in changing markets and determine effective trading points, making a significant contribution to the finance sector. Several DRL methods have been tested in the trading domain. However, this research proposes implementing the proximal policy optimisation (PPO) algorithm, which has not been integrated into an automated trading system (ATS). Furthermore, behavioural biases in human decision-making often cloud one’s judgement to perform emotionally. ATS may alleviate these problems by identifying and using the best potential strategy for maximising profit over time. Motivated by the factors mentioned, this research aims to develop a stable, accurate, and robust automated trading system that implements a deep neural network and reinforcement learning to predict price movements to maximise investment returns by performing optimal trading points. Experiments and evaluations illustrated that this research model has outperformed the baseline buy and hold method and exceeded models of other similar works.

    – The research proposes a new automated cryptocurrency trading system integrated with DRL.
    – The experimental analysis of the model showed exceptional results, surpassing similar works.

    – The research proposes a new automated cryptocurrency trading system integrated with DRL.
    – The experimental analysis of the model showed exceptional results, surpassing similar works.

    – The research proposes a new automated cryptocurrency trading system integrated with DRL.
    – The experimental analysis of the model showed exceptional results, surpassing similar works.

    – Humans have limitations in terms of availability and rational thinking.
    – Humans tend to be their own greatest enemy due to emotions.

    Methods used: – Humans have limitations in terms of availability and rational thinking.
    – Humans tend to be their own greatest enemy due to emotions.

    – Resolves human hindrance in automated trading.
    – Demonstrates exceptional results surpassing similar works.

    – The research model outperformed the baseline buy and hold method.
    – The research model exceeded models of other similar works.

    – Proposed implementation of PPO algorithm in an automated trading system.
    – Aims to develop a stable, accurate, and robust trading system using DRL.

    – The paper proposes implementing the PPO algorithm in an automated trading system.
    – The research aims to develop a stable and accurate trading system using DRL.

    In this paper , a stable, accurate, and robust automated trading system that implements a deep neural network and reinforcement learning to predict price movements to maximize investment returns by performing optimal trading points is proposed.

  • The psychology of cryptocurrency trading: Risk and protective factors

    DOI: 10.1556/2006.2021.00037

    ABSTRACT: Background and aims Crypto-currency trading is a rapidly growing form of behaviour characterised by investing in highly volatile digital assets based largely on blockchain technology. In this paper, we review the particular structural characteristics of this activity and its potential to give rise to excessive or harmful behaviour including over-spending and compulsive checking. We note that there are some similarities between online sports betting and day trading, but also several important differences. These include the continuous 24-hour availability of trading, the global nature of the market, and the strong role of social media, social influence and non-balance sheet related events as determinants of price movements. Methods We review the specific psychological mechanisms that we propose to be particular risk factors for excessive crypto trading, including: over-estimations of the role of knowledge or skill, the fear of missing out (FOMO), preoccupation, and anticipated regret. The paper examines potential protective and educational strategies that might be used to prevent harm to inexperienced investors when this new activity expands to attract a greater percentage of retail or community investors. Discussion and conclusions The paper suggests the need for more specific research into the psychological effects of regular trading, individual differences and the nature of decision-making that protects people from harm, while allowing them to benefit from developments in blockchain technology and crypto-currency.

    – Crypto trading is a rapidly growing activity.
    – It has the potential to be riskier for inexperienced traders.

    – Crypto trading is a rapidly growing activity.
    – It has the potential to be riskier for inexperienced traders.

    – Crypto trading is a rapidly growing activity.
    – It has the potential to be riskier for inexperienced traders.

    Methods used:

    – Crypto trading may be riskier for inexperienced traders influenced by media attention or FOMO sentiments.
    – Research can inform consumer protections and potential regulation of trading platforms.

    – The paper reviews the structural characteristics of cryptocurrency trading and its potential for excessive or harmful behavior.
    – The paper suggests the need for more research into the psychological effects of trading and protective strategies for inexperienced investors.

    – Examines risk factors and protective strategies in cryptocurrency trading
    – Calls for more research on psychological effects and decision-making

    – Paper reviews characteristics and risks of cryptocurrency trading.
    – Explores psychological factors and potential protective strategies.

    In this paper, the authors review the particular structural characteristics of crypto-currency trading and its potential to give rise to excessive or harmful behaviour including over-spending and compulsive checking.

  • Patterns of financial crimes using cryptocurrencies

    DOI: 10.55643/ser.2.44.2022.454

    ABSTRACT: The cryptocurrency market is rapidly gaining momentum and is becoming an alternative financial platform to the traditional financial trading market. Currently, cryptocurrency is of particular interest to criminals to make illegal profits, such as money laundering, terrorist financing, financing the proliferation of weapons of mass destruction, corruption. The main purpose of the study is to identify information signs that indicate the implementation of illegal financial transactions using cryptocurrencies. Empirical (observation, description) and theoretical (grouping, synthesis, abstraction) research methods were used for this research. According to the results of the study, it is established that the signs of illegal transactions with cryptocurrency are: non-transparent cryptocurrency contracts; encrypted cryptocurrency transactions; impersonal transactions; fragmented systematic transactions into marginal, limited amounts to avoid identification; transactions that do not comply with the approved transaction protocols; currency exchange transactions by unidentified traders; confusing cryptocurrency to other forms of electronic funds in order to withdraw such funds in cash. The authors of the article identify the main agents in the cryptocurrency economy (centralized and decentralized cryptocurrency exchanges, token issuers, distribution services, gaming services, cryptocurrency wallets). The paper describes software products for the identification of illegal cryptocurrency transactions. The results of the study are of practical value to national regulators in strengthening financial stability and combating illegal financial transactions. management of financial institutions to improve the system of counteraction to illegal financial transactions using payment cards, namely the creation of separate bodies for analysis and regulation of fraud in the banking sector, strengthening responsibility for fraud at the legislative level, establishing a single authentication standard for customers, development of open banking.

    – Signs of illegal transactions with cryptocurrency include non-transparent contracts and encrypted transactions.
    – The study identifies main agents in the cryptocurrency economy and software products for identifying illegal transactions.

    – Signs of illegal transactions with cryptocurrency include non-transparent contracts and encrypted transactions.
    – The study identifies main agents in the cryptocurrency economy and software products for identifying illegal transactions.

    – Signs of illegal transactions with cryptocurrency include non-transparent contracts and encrypted transactions.
    – The study identifies main agents in the cryptocurrency economy and software products for identifying illegal transactions.

    Methods used:

    – Strengthening financial stability and combating illegal financial transactions
    – Improving the system of counteraction to illegal financial transactions using payment cards

    – Signs of illegal transactions with cryptocurrency identified
    – Software products for identification of illegal cryptocurrency transactions described

    – Cryptocurrency is being used for illegal financial transactions.
    – Study identifies signs of illegal transactions and agents in cryptocurrency economy.

    – Cryptocurrency market gaining momentum as alternative financial platform
    – Study identifies signs of illegal financial transactions using cryptocurrencies

    In this paper , the authors identify information signs that indicate the implementation of illegal financial transactions using cryptocurrencies and describe software products for the identification of illegal cryptocurrency transactions, such as non-transparent cryptocurrency contracts, encrypted cryptocurrency transactions; impersonal transactions; fragmented systematic transactions into marginal, limited amounts to avoid identification; transactions that do not comply with the approved transaction protocols; currency exchange transactions by unidentified traders; confusing cryptocurrency to other forms of electronic funds in order to withdraw such funds in cash.

  • Cryptocurrency trading, mental health and addiction: a qualitative analysis of reddit discussions

    DOI: 10.1080/16066359.2023.2174259

    ABSTRACT: Background: The volatility and 24/7 nature of the cryptocurrency market allows traders to engage in highly speculative trading patterns that closely resemble gambling. Considering its potential for addiction and economic loss, it is important to investigate the impact that cryptocurrency trading has on mental health. Therefore, we analyzed Reddit discussions regarding mental health, gambling, and addiction from members of the discussion board, r/cryptocurrency, during a recent downturn in the market.Method: We collected 1315 threads submitted to the subreddit r/cryptocurrency from January 3rd to February 4th 2022. A thematic analysis was employed, which included threads that discussed psychological wellbeing, mental health or gambling.Results: We thematically analyzed the content threads that discussed psychological wellbeing, mental health or gambling. Our analysis identified three main themes present in user discussion. Theme 1 (emotional state and mental health) captured users’ discussion on their wellbeing, mental health and emotional responses to the market downturn. Theme 2 (strategies for coping) examined coping strategies recommended by users to combat distress or trading urges. Theme 3 (likeness to gambling) captured a discussion on the relationship between cryptocurrency and gambling based on its fixating properties and risk profile.Conclusions: Reddit is a valuable resource for examining the experiences and attitudes of the cryptocurrency community. Discussion from users provided insight into the mental distress market downturns cause and strategies to help combat these. Our findings offer qualitative insights into the problems experienced by individuals who cryptocurrency trade and encourage further investigation into its relationship with mental health and addiction.

    – Reddit discussions provide insights into mental distress caused by market downturns.
    – Further investigation needed on the relationship between cryptocurrency trading and mental health/addiction.

    – Reddit discussions provide insights into mental distress caused by market downturns.
    – Further investigation needed on the relationship between cryptocurrency trading and mental health/addiction.

    – Reddit discussions provide insights into mental distress caused by market downturns.
    – Further investigation needed on the relationship between cryptocurrency trading and mental health/addiction.

    Methods used:

    – Understanding the impact of cryptocurrency trading on mental health.
    – Identifying coping strategies to combat distress or trading urges.

    – Three main themes identified in user discussions: emotional state and mental health, coping strategies, and likeness to gambling.
    – Findings provide qualitative insights into mental distress and strategies to combat it.

    – Investigated impact of cryptocurrency trading on mental health and addiction
    – Analyzed Reddit discussions on mental health, gambling, and coping strategies

    – Study analyzes impact of cryptocurrency trading on mental health and addiction.
    – Reddit discussions provide insights into distress and coping strategies of traders.

    For example, the authors analyzed Reddit discussions regarding mental health, gambling, and addiction from members of the discussion board, r/cryptocurrency, during a recent downturn in the market.

  • “Buy High, Sell Low”: A Qualitative Study of Cryptocurrency Traders Who Experience Harm

    DOI: 10.3390/ijerph20105833

    ABSTRACT: The constant, substantial price fluctuations of cryptocurrency allow traders to engage in highly speculative trading that closely resembles gambling. With significant financial loss associated with adverse mental health outcomes, it is important to investigate the impact that market participation has on mental health. Therefore, we conducted interviews with 17 participants who self-reported problems due to trading. Thematic analysis was conducted revealing themes: (1) factors in engagement, (2) impacts of trading and (3) harm reduction. Factors in engagement captured factors that motivated and sustained cryptocurrency trading. Impacts of trading outlined how cryptocurrency trading positively and negatively impacted participants. Harm reduction described methods participants employed to reduce mental distress from trading. Our study provides novel insights into the adverse impacts of cryptocurrency trading across multiple domains, especially mental health, relationships and finances. They also indicate the importance of further research on effective coping strategies for distress caused by financial loss from trading. Additionally, our study reveals the significant role social environments play on participants’ expectations and intentions regarding cryptocurrency trading. These social networks extend beyond real-life relationship to include celebrity and influencer endorsement. This encourages investigation into the content of cryptocurrency promotions and the influence they have on individuals’ decision to trade.

    – Cryptocurrency trading has adverse impacts on mental health, relationships, and finances.
    – Further research is needed on coping strategies for distress caused by financial loss.

    – Cryptocurrency trading has adverse impacts on mental health, relationships, and finances.
    – Further research is needed on coping strategies for distress caused by financial loss.

    – Cryptocurrency trading has adverse impacts on mental health, relationships, and finances.
    – Further research is needed on coping strategies for distress caused by financial loss.

    Methods used:

    – Further research needed on coping strategies for distress caused by financial loss from trading.
    – Investigation into the content of cryptocurrency promotions and their influence on trading decisions.

    – Adverse impacts of cryptocurrency trading on mental health, relationships, and finances.
    – Importance of researching effective coping strategies for distress caused by financial loss.

    – Study investigates impact of cryptocurrency trading on mental health and relationships.
    – Participants employ harm reduction strategies to cope with financial loss.

    – Study investigates impact of cryptocurrency trading on mental health and well-being.
    – Interviews conducted with 17 participants who experienced harm from trading.

    In this paper , the authors conducted interviews with 17 participants who self-reported problems due to cryptocurrency trading and revealed the significant role social environments play on participants’ expectations and intentions regarding cryptocurrency trading.

  • Cryptocurrency Trading based on Heuristic Guided Approach with Feature Engineering

    DOI: 10.1109/icodsa55874.2022.9862934

    ABSTRACT: In recent years, machine learning and deep learning techniques have been frequently used in Algorithmic Trading. Algorithmic Trading means trading Forex, stock market, commodities, and many markets with the help of computers using systems created with various technical analysis indicators. The BTC/USD market is a market that allows buying and selling of products. People aim to profit by buying and selling in the Bitcoin market. Reinforcement Learning (RL) was also helpful in achieving those kinds of goals. Reinforcement learning is a sub-topic of machine learning. RL addresses the problem of a computational agent learning to make decisions by trial and error. For our application, it is aimed to make as much profit as possible. This study focuses on developing a novel tool to automate currency trading like a BTC/USD in a simulated market with maximum profit and minimum loss. RL technique with a modified version of the Collective Decision Optimization Algorithm is used to implement the proposed model. Feature engineering is also performed to create features that improve the result.

    – The paper proposes a novel tool for automated cryptocurrency trading.
    – Reinforcement learning and feature engineering are used to improve trading performance.

    – The paper proposes a novel tool for automated cryptocurrency trading.
    – Reinforcement learning and feature engineering are used to improve trading performance.

    – The paper proposes a novel tool for automated cryptocurrency trading.
    – Reinforcement learning and feature engineering are used to improve trading performance.

    Methods used:

    – Development of a novel tool for automated currency trading.
    – Use of reinforcement learning and feature engineering to maximize profit.

    – The paper develops a novel tool for automated currency trading.
    – Reinforcement learning and feature engineering are used to improve results.

    – Machine learning and deep learning techniques used in Algorithmic Trading.
    – Reinforcement Learning (RL) and feature engineering used for cryptocurrency trading.

    – The paper focuses on using machine learning and deep learning techniques in cryptocurrency trading.
    – It aims to develop a tool for automated currency trading with maximum profit and minimum loss.

    In this article , the authors used reinforcement learning (RL) to automate currency trading like a BTC/USD in a simulated market with maximum profit and minimum loss, where RL technique with a modified version of the Collective Decision Optimization Algorithm is used to implement the proposed model.

    “Success is not final, failure is not fatal: It is the courage to continue that counts.” – Winston Churchill

  • Cryptocurrency Trading: A Comprehensive Survey.

    DOI:

    ABSTRACT: In recent years, the tendency of the number of financial institutions including cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects(contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

    • Comprehensive survey of cryptocurrency trading research
    • Promising opportunities remain open in cryptocurrency trading

    • Comprehensive survey of cryptocurrency trading research

    • Promising opportunities remain open in cryptocurrency trading

    • Comprehensive survey of cryptocurrency trading research

    • Promising opportunities remain open in cryptocurrency trading

    • Cryptocurrencies have their own separate nature and their behavior is still being understood.

    • Some limitations in understanding cryptocurrency trading and risk management.

    Methods used: – Cryptocurrencies have their own separate nature and their behavior is still being understood.
    – Some limitations in understanding cryptocurrency trading and risk management.

    • Summarizes existing research on cryptocurrency trading
    • Identifies promising opportunities in cryptocurrency trading

    • The paper provides a comprehensive survey of cryptocurrency trading research.

    • It covers 146 research papers on various aspects of cryptocurrency trading.

    • Survey of 146 research papers on cryptocurrency trading

    • Covers various aspects including trading systems, risk management, and portfolio construction

    • Paper provides a comprehensive survey of cryptocurrency trading research.

    • Covers 146 research papers on various aspects of cryptocurrency trading.

    This paper provides a comprehensive survey of cryptocurrency Trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto- assets, technical trading and others).

  • Coupling of cryptocurrency trading with the sustainable environmental goals: is it on the cards?

    DOI: 10.1002/BSE.2947

    ABSTRACT: Following the systematic review and bibliometric analysis of current literature, this paper attempts to investigate whether the wealth generated through cryptocurrency trading can assist in attaining United Nation’s (UN) Sustainable Development Goal (SDG) 7, affordable and clean energy and UN SDG 13 related to climate action. The critical analysis of literature indicates a growing interest in cryptocurrency, the UN’s SDGs and the negative effect of crypto mining on the use of enormous energy. However, there is a clear gap in the literature focusing on the possibility of using the wealth generated through cryptocurrency trading in financing environmentally friendly projects and attaining the UN’s SDG 7 and SDG 13. The findings and the future research direction of this study aim to expand the academic literature related to SDG 7 and SDG 13 and the relationship between cryptocurrency and sustainability even during the uncertain period. This study provides evidence about the theoretical models that can be applied in the discussion of the complex relationship between cryptocurrency, clean energy and climate action. Our findings will provide policymakers in identifying ways to convert the cryptocurrency generated wealth in attaining sustainable socio-economic goals in the future.

    – Growing interest in cryptocurrency and UN’s SDGs
    – Gap in literature regarding using cryptocurrency wealth for sustainable goals

    – Growing interest in cryptocurrency and UN’s SDGs
    – Gap in literature regarding using cryptocurrency wealth for sustainable goals

    – Growing interest in cryptocurrency and UN’s SDGs
    – Gap in literature regarding using cryptocurrency wealth for sustainable goals

    – Gap in literature on using cryptocurrency wealth for sustainable projects
    – Lack of focus on relationship between cryptocurrency, clean energy, and climate action

    Methods used: – Gap in literature on using cryptocurrency wealth for sustainable projects
    – Lack of focus on relationship between cryptocurrency, clean energy, and climate action

    • Identifying ways to convert cryptocurrency wealth for sustainability.
    • Providing evidence for theoretical models on cryptocurrency and sustainability.

    – Growing interest in cryptocurrency, UN’s SDGs, and negative effects of crypto mining.
    – Gap in literature regarding using cryptocurrency wealth for sustainable projects.

    – Investigates if cryptocurrency trading can support sustainable goals
    – Identifies gap in literature on using cryptocurrency wealth for sustainability

    – Investigates if wealth from cryptocurrency trading can support sustainable development goals.
    – Identifies a gap in literature regarding using cryptocurrency wealth for environmental projects.

    In this paper, the authors investigate whether the wealth generated through cryptocurrency trading can assist in attaining United Nation’s (UN) Sustainable Development Goal (SDG) 7, affordable and clean energy and UN SDG 13 related to climate action.

  • Trading Strategies for Cryptocurrencies Based on Machine Learning Scenarios

    DOI: 10.54691/bcpbm.v38i.4234

    ABSTRACT: A Cryptocurrency is a peer-to-peer digital exchange system in which cryptography is used to generate and distribute currency units. Bitcoin as the foremost digital currency, using asymmetric cryptographic algorithms, blockchain technology, was conceptualized by Satoshi Nakamoto in 2008 and born in 2009. In 14 years, digital currency has gone from being initially controversial and worthless to rapid increase in value. The huge fluctuations in its price have attracted worldwide attention, and more people have begun to pay attention to the investment strategy of digital currency. Starting from the attributes of Bitcoin, this paper objectively compares the application effect of arbitrage strategy and trend strategy in machine learning on Bitcoin, analyzes and summarizes and predicts the future of Bitcoin’s investment. To be specific, the arbitrage strategy involves three methods, i. e. , cash arbitrage, cross-exchange arbitrage and related variety arbitrage; trend strategy involves two methods, i. e. , the timing method and the multi-factor method. These results shed light on guiding further exploration of potential of investing digital currencies, which provides an in-depth summary analysis of risk-free arbitrage and digital currency value forecasts.

    – Comparison of arbitrage and trend strategies in machine learning on Bitcoin
    – Analysis and prediction of Bitcoin’s investment future

    – Comparison of arbitrage and trend strategies in machine learning on Bitcoin
    – Analysis and prediction of Bitcoin’s investment future

    – Comparison of arbitrage and trend strategies in machine learning on Bitcoin
    – Analysis and prediction of Bitcoin’s investment future

    Methods used:

    – Comparison of arbitrage and trend strategies in machine learning on Bitcoin
    – Analysis and prediction of Bitcoin’s investment future

    – Comparison of arbitrage and trend strategies in machine learning on Bitcoin
    – Analysis, summary, and prediction of Bitcoin’s investment and future

    – Paper compares arbitrage and trend strategies in machine learning for Bitcoin.
    – Analyzes investment strategies and predicts future of Bitcoin’s investment.

    – Paper analyzes trading strategies for cryptocurrencies based on machine learning scenarios.
    – Compares arbitrage strategy and trend strategy in machine learning on Bitcoin.

    In this article , the authors compared the application effect of arbitrage strategy and trend strategy in machine learning on Bitcoin, analyzes and summarizes and predicts the future of Bitcoin’s investment, and provides an in-depth summary analysis of risk-free arbitrage and digital currency value forecasts.

  • Cryptocurrency Trading BoT Using Python

    DOI: 10.22214/ijraset.2023.52125

    ABSTRACT: Abstract: Our daily life has been merged online and they become more flexible and more effective. A huge growth in number of online users has activated virtual word concepts and created a new business phenomenon which is cryptocurrency to facilitate the financial activities such as buying, selling and trading. Cryptocurrency represent valuable and intangible objects which are used electronically in different applications and networks such as online social networks, online social games, virtual worlds and peer to peer networks. The use of virtual currency has become widespread in many different systems in recent years. At present, trading is done by human which is a hectic work. The first disadvantage is that we analyze the chosen cryptocurrency will rise or fall in value and buy or sell as per our strategies which is not accurate and efficient always, the second one is that we need to keep a watch every second to cope with loss, which is not possible for a human. We can use a bot that can trade by itself to maximize the profit. The bot function by taking the relative strength index (RSI) of the coin and calculates the selling price and buying price for the existing price. This can help the user to gain profit and be relaxed in the fluctuation period of the coin. These bots help capitalize on market opportunities and cut down time spent on monitoring.

    – The paper proposes a trading bot coded in Python that uses the relative strength index (RSI) to determine when to buy and sell cryptocurrencies.
    – The bot is trained using a random forest regression model and historical data.

    – The paper proposes a trading bot coded in Python that uses the relative strength index (RSI) to determine when to buy and sell cryptocurrencies.
    – The bot is trained using a random forest regression model and historical data.

    – The paper proposes a trading bot coded in Python that uses the relative strength index (RSI) to determine when to buy and sell cryptocurrencies.
    – The bot is trained using a random forest regression model and historical data.

    Methods used:

    – The paper proposes a trading bot coded in Python that uses the relative strength index (RSI) to automate cryptocurrency trading.
    – The bot helps maximize profit and reduce the time spent on monitoring market opportunities.

    – Digital behavior metrics have a significant effect on cryptocurrency trade websites’ traffic.
    – Organic traffic increases with higher bounce rate and unique visitors.

    – The paper proposes a Python trading bot for cryptocurrency trading.
    – The bot uses the relative strength index (RSI) to determine buying and selling prices.

    – The paper aims to build an efficient cryptocurrency trading bot.
    – The bot uses the Relative Strength Index (RSI) to make trading decisions.

    In this paper , the authors proposed a bot that can trade by itself to maximize the profit of the user in order to relax the user during the fluctuation period of the coin, which can help the user to gain profit and be relaxed in the fluctuating period of coin.

  • Relationship of Cryptocurrencies with Gambling and Addiction

    DOI: 10.18863/pgy.1127924

    ABSTRACT: Cryptocurrencies has been considered as both an investment tool and a great invention that will replace money and change the world order. Although crypto currency trading has been investigated in many aspects, the psychological dimension that directly affects investors has often been ignored. Control of cryptocurrency trading is in the hands of investors rather than a central authority or institution. Thus, the value of cryptocurrencies changes with the reactions of investors. This situation suggests that psychological factors may be more prominent in cryptocurrency trading. Cryptocurrency trading has many similarities with gambling and betting, such as risk taking, getting quick returns, extreme gains or losses. Some significant components of behavioral addiction are also seen in individuals who spend so much time with cryptocurrency trading. The purpose of this article is to provide a better understanding of the psychological effects of cryptocurrency trading, which has entered our lives over a relatively brief period of time and reached millions of investors.

    – Cryptocurrency trading may be associated with gambling disorder and addiction.
    – Cryptocurrency trading has similar characteristics to gambling due to sudden price changes.

    – Cryptocurrency trading may be associated with gambling disorder and addiction.
    – Cryptocurrency trading has similar characteristics to gambling due to sudden price changes.

    – Cryptocurrency trading may be associated with gambling disorder and addiction.
    – Cryptocurrency trading has similar characteristics to gambling due to sudden price changes.

    Methods used:

    – Cryptocurrency trading may be associated with gambling disorder and addiction.
    – Psychological factors play a significant role in cryptocurrency trading.

    – The paper aims to understand the psychological effects of cryptocurrency trading.
    – It explores the similarities between cryptocurrency trading and gambling and addiction.

    – Cryptocurrency trading has similarities with gambling and betting.
    – Psychological effects of cryptocurrency trading and its relationship to addiction.

    – The paper explores the psychological effects of cryptocurrency trading.
    – It discusses the similarities between cryptocurrency trading and gambling addiction.

    In this paper , the authors provide a better understanding of the psychological effects of cryptocurrency trading, which has entered our lives over a relatively brief period of time and reached millions of investors.

  • The Role of Crypto Trading in the Economy, Renewable Energy Consumption and Ecological Degradation

    DOI: 10.3390/en15103805

    ABSTRACT: The rapid growth of information technology and industrial revolutions provoked digital transformation of all sectors, from the government to households. Moreover, digital transformations led to the development of cryptocurrency. However, crypto trading provokes a dilemma loop. On the one hand, crypto trading led to economic development, which allowed attracting additional resources to extending smart and green technologies for de-carbonising the economic growth. On the other hand, crypto trading led to intensifying energy sources, which provoked an increase in greenhouse gas emissions and environmental degradation. The paper aims to analyse the connections between crypto trading, economic development of the country, renewable energy consumption, and environmental degradation. The data for analysis were obtained from: Our World in Data, World Data Bank, Eurostat, Ukrstat, Crystal Blockchain, and KOF Globalisation Index. To check the hypothesis, the paper applied the Pedroni and Kao panel cointegration tests, FMOLS and DOLS panel cointegration models, and Vector Error Correction Models. The findings concluded that the increasing crypto trading led to enhanced GDP, real gross fixed capital formation, and globalisation. However, in the long run, the relationship between crypto trading and the share of renewable energies in total energy consumption was not confirmed by the empirical results. For further directions, it is necessary to analyse the impact of crypto trading on land and water pollution.

    – Crypto trading led to economic development and globalisation.
    – Relationship between crypto trading and renewable energy consumption not confirmed.

    – Crypto trading led to economic development and globalisation.
    – Relationship between crypto trading and renewable energy consumption not confirmed.

    – Crypto trading led to economic development and globalisation.
    – Relationship between crypto trading and renewable energy consumption not confirmed.

    – Cryptocurrency is not yet powerful enough to compete with fiat currency.
    – Cryptocurrency has faced challenges in its development in the financial market.

    Methods used: – Cryptocurrency is not yet powerful enough to compete with fiat currency.
    – Cryptocurrency has faced challenges in its development in the financial market.

    – Examines competition among different currencies and exchanges in cryptocurrency market.
    – Explores the current circumstance and future prospects of cryptocurrency in financial market.

    – Crypto trading led to economic development, enhanced GDP, and globalisation.
    – Relationship between crypto trading and renewable energy consumption not confirmed.

    – Crypto trading impacts economic development and globalisation.
    – Relationship between crypto trading and renewable energy consumption inconclusive.

    – The paper analyzes the connections between crypto trading, economic development, renewable energy consumption, and environmental degradation.
    – It explores the dilemma of crypto trading’s impact on economic growth and environmental sustainability.

    The findings concluded that the increasing crypto trading led to enhanced GDP, real gross fixed capital formation, and globalisation, however, in the long run, the relationship between crypto trading and the share of renewable energies in total energy consumption was not confirmed by the empirical results.

  • An automated cryptocurrency trading system based on the detection of unusual price movements with a Time-Series Clustering-Based approach

    DOI: 10.1016/j.eswa.2022.117017

    ABSTRACT: The cryptocurrency market, which has a rapidly growing market size, attracts the increasing attention of individual and institutional investors. While this highly volatile market offers great profit opportunities to investors, it also brings risks due to its sensitivity to speculative news and the unpredictable behaviour of major investors that can cause unsual price movements. In this paper, we argue that rapid and high price fluctuations or unusual patterns that occur in this way may negatively affect the functionality of technical signals that constitute a basis for feature extraction in a machine learning (ML)-based trading system and this may cause the generalization of the model to deteriorate. To address this problem, we propose an end-to-end ML-based trading system including a time series outlier detection module that detects the periods in which unusual price formations are observed. The training of the classification algorithms for the price direction prediction task was performed on the remaining data. We present the results related to the accuracy of the classification models as well as the simulation results obtained using the proposed system for real time trading on the historical data. The findings showed that the outlier detection step significantly increases return on investment for the machine learning-based trading strategies. Besides, the results showed that during the highly volatile periods the trading system becomes more profitable compared to the baseline model and buy&hold strategy.

    – Outlier detection significantly increases return on investment for trading strategies.
    – Trading system is more profitable during highly volatile periods.

    – Outlier detection significantly increases return on investment for trading strategies.
    – Trading system is more profitable during highly volatile periods.

    – Outlier detection significantly increases return on investment for trading strategies.
    – Trading system is more profitable during highly volatile periods.

    – Unusual price movements can negatively affect the functionality of technical signals.
    – The generalization of the model may deteriorate due to unusual price formations.

    Methods used: – Unusual price movements can negatively affect the functionality of technical signals.
    – The generalization of the model may deteriorate due to unusual price formations.

    – The proposed system increases return on investment for machine learning-based trading strategies.
    – The trading system becomes more profitable during highly volatile periods.

    – Outlier detection significantly increases return on investment for trading strategies.
    – Trading system is more profitable during highly volatile periods compared to baseline model.

    – ML-based trading system with outlier detection improves profitability
    – Unusual price movements negatively affect technical signals

    – The paper proposes an ML-based trading system for cryptocurrency markets.
    – It includes a time series outlier detection module to improve profitability.

    In this paper , an end-to-end ML-based trading system including a time series outlier detection module was proposed to detect the periods in which unusual price formations are observed.