Tag: trading

  • Is Binarycent Legit? A Comprehensive Review

    Binarycent is a trading platform that has garnered attention from both novice and experienced traders. In this comprehensive review, we explore the legitimacy of Binarycent, its features, user experiences, and overall reliability.

    What is Binarycent?

    Binarycent is an online trading platform that offers various financial instruments, including binary options, forex, and cryptocurrency trading. The platform is designed to be user-friendly, catering to traders at different skill levels. However, the question of its legitimacy remains a critical concern for potential users.

    Features and Offerings

    Binarycent boasts several features that appeal to traders:

    1. Low Minimum Deposit: Binarycent allows a minimum deposit of just $10, making it accessible for beginners.
    2. Wide Range of Assets: Users can trade in forex, commodities, indices, and cryptocurrencies.
    3. High Payouts: The platform claims to offer payouts as high as 95%.
    4. 24/7 Trading: Traders can access the platform and trade at any time, day or night.
    5. Customer Support: Binarycent provides 24/7 customer support via live chat, email, and phone.

    User Experience and Reviews

    User reviews are mixed, with some traders praising the platform for its ease of use and variety of trading options, while others express concerns about withdrawal processes and customer service responses. Common points highlighted in user reviews include:

    • Ease of Use: The platform is user-friendly with a straightforward interface.
    • Customer Support: While some users report positive experiences, others have encountered delays in response times.
    • Withdrawal Issues: Several users have reported difficulties in withdrawing funds, citing lengthy processing times and verification hurdles.

    Legitimacy and Regulation

    One of the primary concerns for any trading platform is its regulatory status. As of now, Binarycent is not regulated by any major financial authority. This lack of regulation raises red flags for many potential users, as regulatory oversight is a crucial aspect of ensuring transparency and fairness in trading practices.

    Security Measures

    Binarycent claims to implement robust security measures to protect user data and funds. These include SSL encryption and secure payment gateways. However, the lack of regulatory oversight means users must rely on the platform’s internal policies and assurances.

    Pros and Cons

    Pros:

    • Low minimum deposit requirement
    • Variety of trading instruments
    • High payout potential
    • 24/7 customer support

    Cons:

    • Not regulated by major financial authorities
    • Mixed user reviews, particularly regarding withdrawals
    • Potential delays in customer support responses

    Conclusion

    Is Binarycent legit? The platform offers several appealing features and has a user-friendly interface that caters to a wide range of traders. However, the lack of regulatory oversight and mixed user reviews, particularly concerning withdrawal processes, are significant drawbacks. Potential users should approach Binarycent with caution, conduct thorough research, and consider other reputable, regulated trading platforms before making any financial commitments.

    Disclaimer: Trading in binary options, forex, and cryptocurrencies involves significant risk and may not be suitable for all investors. Always conduct your own research and consult with a financial advisor before engaging in trading activities.

  • 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, 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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 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.

  • Longitudinal perspective on cryptocurrency trading and increased gambling problems: a 3 wave national survey study.

    DOI: 10.1016/j.puhe.2022.10.002

    ABSTRACT: Cryptocurrency trading has gained popularity over the last few years. Trading is facilitated by online platforms that enable 24/7 trading. Cryptocurrency trading is potentially attractive to gamblers, and it may increase their gambling problems. Furthermore, cryptocurrency trading might be a particularly harmful activity for those gambling offshore. We investigated whether cryptocurrency trading predicts excessive gambling over time. We also analyzed how cryptocurrency trading combined with offshore gambling is associated with excessive gambling.This was a population-based longitudinal survey study.We surveyed a sample of Finnish people aged 18-75 years (N = 1022, 51.27% male) at three time points in 6-month intervals: April 2021 (T1), October to November 2021 (T2), and April to May 2022 (T3). Of the original T1 respondents, 66.80% took part in T2 and T3. Outcome measure was excessive gambling using the Problem Gambling Severity Index, and the predictor was cryptocurrency trading. We adjusted models for onshore and offshore gambling online, excessive gaming (Internet Gaming Disorder Test), excessive internet use (Compulsive Internet Use Scale), excessive alcohol use (Alcohol Use Disorders Identification Test), and sociodemographic background factors. We used multilevel regression models to investigate within-person and between-person effects.Cryptocurrency trading has increased in popularity over time. Within-person changes in cryptocurrency trading predicted increased excessive gambling. Excessive gambling was also generally more common among cryptocurrency traders. The full model that was adjusted for the number of confounding factors showed that cryptocurrency trading had a within-person effect on excessive gambling. Of the confounding factors, offshore online gambling, excessive gaming, and excessive internet use had within-person effects on excessive gambling. Offshore and onshore online gamblers and excessive gamers showed more excessive gambling than others. Those participants who were both cryptocurrency traders and offshore gamblers showed significantly higher rate of excessive gambling than others.Cryptocurrency trading is a risky activity and associated with a higher rate of excessive gambling over time. Such activity is especially risky among offshore online gamblers, who could view cryptocurrency trading as another form of gambling or as a way to make money for gambling. Policymakers and counselors should be aware of the risks of cryptocurrency trading.

    – Cryptocurrency trading predicts increased excessive gambling over time.
    – Offshore online gamblers who also trade cryptocurrency have a higher rate of excessive gambling.

    – Cryptocurrency trading predicts increased excessive gambling over time.
    – Offshore online gamblers who also trade cryptocurrency have a higher rate of excessive gambling.

    – Cryptocurrency trading predicts increased excessive gambling over time.
    – Offshore online gamblers who also trade cryptocurrency have a higher rate of excessive gambling.

    – Limited to a sample of Finnish people aged 18-75 years.
    – Only surveyed at three time points in 6-month intervals.

    Methods used: – Limited to a sample of Finnish people aged 18-75 years.
    – Only surveyed at three time points in 6-month intervals.

    – Cryptocurrency trading is associated with increased gambling problems over time.
    – Offshore online gamblers who also engage in cryptocurrency trading are at higher risk of excessive gambling.

    – Cryptocurrency trading predicts increased excessive gambling over time.
    – Offshore online gamblers who also trade cryptocurrency have higher rates of excessive gambling.

    – Cryptocurrency trading predicts increased excessive gambling over time.
    – Offshore online gamblers who also trade cryptocurrency have higher rates of excessive gambling.

    – The paper investigates the relationship between cryptocurrency trading and excessive gambling.
    – It explores how cryptocurrency trading combined with offshore gambling affects excessive gambling.

    In this paper , the authors investigated whether cryptocurrency trading predicts excessive gambling over time, and they also analyzed how cryptocurrency trading combined with offshore gambling is associated with excessive gambling, finding that within-person changes in cryptocurrency trading predicted increased excessive gambling.

  • 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.

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

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

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

    Methods used:

    – Proposed approach addresses backtest overfitting in cryptocurrency trading.
    – Less overfitted agents have higher returns, offering confidence for real market deployment.

    – 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.

    This paper proposes a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning, and shows that the less overfittedDeep 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 ideal legal regulation for decentralized finance as the development of indonesia crypto asset trading

    DOI: 10.26532/jph.v9i3.21245

    ABSTRACT: Decentralized Finance (DeFI) has positively impacted the development of crypto asset trading and has been adopted by various countries except for Indonesia. This study aims to identify the urgency of regulating DeFi as the development of crypto asset trading in Indonesia and construct the ideal regulation. This research is normative legal research with a statutory and conceptual approach. The research results stated the urgency of regulating DeFi as the development of crypto asset trading is: a) other countries have used DeFi because it can develop crypto asset trading for the better by creating value stability in crypto assets, having a function for lending and borrowing crypto assets, having transparency in transactions and lower crypto asset trading fees; b) DeFi technology adopted in the development of the Digital Rupiah project by BI and adopted by physical traders of crypto assets in Indonesia; c) as a form of legal protection from risks arising from technical or non-technical negligence or intention to protection from crime. Ideal legal regulation for decentralized finance as the development of Indonesia crypto asset trading is the formation of a regulation by CoFTRA in the form of technical guidelines and implementation mechanisms, in the form of a new written CoFTRA Regulation, in the form of a decree from the Head of CoFTRA whose focus is to regulate DeFi as development Crypto asset trading.

    – Urgency of regulating DeFi in Indonesia
    – Ideal legal regulation for decentralized finance

    – Urgency of regulating DeFi in Indonesia
    – Ideal legal regulation for decentralized finance

    – Urgency of regulating DeFi in Indonesia
    – Ideal legal regulation for decentralized finance

    Methods used:

    – Urgency to regulate DeFi for better crypto asset trading.
    – Ideal regulation formation by CoFTRA to regulate DeFi.

    – Urgency of regulating DeFi in Indonesia
    – Ideal legal regulation for decentralized finance

    – DeFi positively impacts crypto asset trading in various countries.
    – Urgency to regulate DeFi in Indonesia for legal protection and development.

    – The paper discusses the impact of decentralized finance (DeFi) on crypto asset trading.
    – It aims to identify the urgency of regulating DeFi in Indonesia.

    In this paper , the authors identify the urgency of regulating decentralized finance as the development of crypto asset trading in Indonesia and construct the ideal regulation for decentralized finance in Indonesia, which is normative legal research with a statutory and conceptual approach.

  • Cryptocurrency Trading Bot with Sentimental Analysis and Backtracking Using Predictive ML

    DOI: 10.1007/978-981-19-7455-7_37

    ABSTRACT: Algorithmic trading is a process of converting a trading strategy into computer code which buys and sells the shares or performs trades in an automated, fast, and accurate way. Sentiment analysis is a powerful social media tool that enables us to understand its users. It is an important factor because emotions and attitudes toward a topic can become actionable pieces of information useful in understanding market trends, saving time and effort by the means of automation. Bringing together the art of sentimental analysis of social media and backtracking of historical price data, the paper, coupled with state-of-the-art APIs from leading crypto exchanges, is set to predict the best options and place trade orders, taking into account variables set by the user such as STOP LOSS and risk profiles. Combining the historical data from Binance APIs, coupled with sentimental analysis of Twitter tweets, our work aims at delivering highly accurate trade orders and executing them in real time without any human intervention.

    – Algorithmic trading with sentiment analysis and backtracking can predict and execute accurate trade orders.
    – The paper aims to automate cryptocurrency trading using historical data and Twitter sentiment analysis.

    – Algorithmic trading with sentiment analysis and backtracking can predict and execute accurate trade orders.
    – The paper aims to automate cryptocurrency trading using historical data and Twitter sentiment analysis.

    – Algorithmic trading with sentiment analysis and backtracking can predict and execute accurate trade orders.
    – The paper aims to automate cryptocurrency trading using historical data and Twitter sentiment analysis.

    Methods used:

    – Automated trading strategy using sentiment analysis and historical price data.
    – Highly accurate trade orders executed in real time without human intervention.

    – Highly accurate trade orders and real-time execution without human intervention.
    – Integration of sentimental analysis of social media and backtracking of historical price data.

    – Algorithmic trading using sentiment analysis and historical price data for cryptocurrency.
    – Predicts trade options and executes them in real-time without human intervention.

    – Algorithmic trading converts trading strategies into automated computer code.
    – Sentiment analysis and backtracking are used to predict and execute trade orders.

    In this article , the authors combine the art of sentimental analysis of social media and backtracking of historical price data to predict the best options and place trade orders, taking into account variables set by the user such as STOP LOSS and risk profiles.

  • Cryptocurrency Analysis and Forecasting

    DOI: 10.1109/ASIANCON55314.2022.9909168

    ABSTRACT: Cryptocurrencies are becoming a well-known and commonly acknowledged kind of substitute trade money. Most monetary businesses now include cryptocurrency. Accordingly, cryptocurrency trading is widely regarded as the most of prevalent and capable types of lucrative investments. However, because this financial sector is already known for its extreme volatility and quick price changes, over brief periods of time. For such constantly changing nature of crypto trends and price, it has become a necessary part for traders and crypto enthusiast to get a detailed analysis before investing. Also, the construction of a precise and dependable forecasting model is regarded vital for portfolio management and optimization. In this paper we propose a web system, which will help to understand cryptocurrency in a more statistical way. Proposed system focuses mainly on four coins : Bitcoin, Ethereum, Dogecoin and Shiba Inu performing analysis and forecasting on all the four coins. System will also do statistical comparison between the coins. Analysis and comparison is carried out using python libraries and modules whereas LSTM and ARIMA are used for forecasting. Extensive research was conducted using real-time and historical information, on four key cryptocurrencies, two of which had the greatest market capitalization, notably Bitcoin and Ethereum, while the other, Dogecoin and Shiba Inu, that had a significant growth in market capitalization over the previous year. In comparison to old fully-connected deep neural networks, the suggested model may employ mixed crypto data more proficiently, minimizing overfitting and computing costs.

    – Proposed web system for cryptocurrency analysis and forecasting
    – Use of LSTM and ARIMA for forecasting

    – Proposed web system for cryptocurrency analysis and forecasting
    – Use of LSTM and ARIMA for forecasting

    – Proposed web system for cryptocurrency analysis and forecasting
    – Use of LSTM and ARIMA for forecasting

    – Extreme volatility and quick price changes in the cryptocurrency market.
    – Overfitting and computing costs in fully-connected deep neural networks.

    Methods used: – Extreme volatility and quick price changes in the cryptocurrency market.
    – Overfitting and computing costs in fully-connected deep neural networks.

    – Provides a web system for statistical analysis and forecasting of cryptocurrencies.
    – Focuses on Bitcoin, Ethereum, Dogecoin, and Shiba Inu for analysis and comparison.

    – Proposed web system for cryptocurrency analysis and forecasting
    – Focus on Bitcoin, Ethereum, Dogecoin, and Shiba Inu

    – Cryptocurrencies are widely used and considered lucrative investments.
    – The paper proposes a web system for cryptocurrency analysis and forecasting.

    – Cryptocurrencies are widely used and considered as lucrative investments.
    – The paper proposes a web system for statistical analysis and forecasting of cryptocurrencies.

    A web system, which will help to understand cryptocurrency in a more statistical way, focuses mainly on four coins : Bitcoin, Ethereum, Dogecoin and Shiba Inu performing analysis and forecasting on all the four coins.

  • Gambling and online trading: emerging risks of real-time stock and cryptocurrency trading platforms.

    DOI: 10.1016/j.puhe.2022.01.027

    ABSTRACT: Online platforms enable real-time trading activities that are similar to those of gambling. This study aimed to investigate the associations of traditional investing, real-time stock trading, and cryptocurrency trading with excessive behavior and mental health problems.This was a cross-sectional population-based survey.The participants were Finnish people aged 18-75 years (N = 1530, 50.33% male). Survey asked about monthly regular investing, real-time stock-trading platform use, and cryptocurrency trading. The study had measures for excessive behavior: gambling (Problem Gambling Severity Index), gaming (Internet Gaming Disorder Test), internet use (Compulsive Internet Use Scale), and alcohol use (Alcohol Use Disorders Identification Test). Psychological distress (Mental Health Inventory), perceived stress (Perceived Stress Scale), COVID-19 anxiety, and perceived loneliness were also measured. Background factors included sociodemographic variables, instant loan taking, and involvement in social media identity bubbles (Identity Bubble Reinforcement Scale). Multivariate analyses were conducted with regression analysis.Within the sample, 22.29% were categorized into monthly regular investors only, 3.01% were investors using real-time stock-trading platforms, and 3.59% were cryptomarket traders. Real-time stock-trading platform use and cryptocurrency trading were associated with younger age and male gender. Cryptomarket traders were more likely to have an immigrant background and have taken instant loans. Both real-time stock-trading platform use and cryptomarket trading were associated with higher excessive behavior. Cryptomarket traders especially reported higher excessive gambling, gaming, and internet use than others. Cryptomarket traders reported also higher psychological distress, perceived stress, and loneliness.Regular investing is not a risk factor for excessive behavior. However, rapid online trading platforms and applications were significantly more commonly used by participants reporting excessive behavior and mental health problems. The strong association between cryptomarket trading and excessive behavior in particular underlines the need to acknowledge the potential risks related to real-time trading platforms.

    – Real-time stock-trading platforms and cryptocurrency trading are associated with excessive behavior and mental health problems.
    – Cryptomarket traders have higher levels of excessive gambling, gaming, internet use, psychological distress, perceived stress, and loneliness.

    – Real-time stock-trading platforms and cryptocurrency trading are associated with excessive behavior and mental health problems.
    – Cryptomarket traders have higher levels of excessive gambling, gaming, internet use, psychological distress, perceived stress, and loneliness.

    – Real-time stock-trading platforms and cryptocurrency trading are associated with excessive behavior and mental health problems.
    – Cryptomarket traders have higher levels of excessive gambling, gaming, internet use, psychological distress, perceived stress, and loneliness.

    – No information provided about the limitations of the study.

    Methods used: – No information provided about the limitations of the study.

    – Real-time trading platforms may contribute to excessive behavior and mental health problems.
    – Cryptomarket trading is associated with higher excessive behavior and psychological distress.

    – Real-time stock-trading and cryptocurrency trading associated with excessive behavior and mental health problems.
    – Cryptomarket traders reported higher excessive gambling, gaming, and internet use.

    – Study investigates associations of investing and trading with excessive behavior and mental health problems.
    – Real-time stock trading and cryptocurrency trading associated with higher excessive behavior and mental health issues.

    – Study investigates associations of traditional investing, real-time stock trading, and cryptocurrency trading with excessive behavior and mental health problems.
    – Real-time trading platforms and applications are commonly used by participants reporting excessive behavior and mental health problems.

    In this paper , the associations of traditional investing, real-time stock trading, and cryptocurrency trading with excessive behavior and mental health problems were investigated, and the strong association between cryptomarket trading and excessive behavior in particular underlines the need to acknowledge the potential risks related to realtime trading platforms.

  • Combining deep reinforcement learning with technical analysis and trend monitoring on cryptocurrency markets

    DOI: 10.1007/s00521-023-08516-x

    ABSTRACT: Abstract Cryptocurrency markets experienced a significant increase in the popularity, which motivated many financial traders to seek high profits in cryptocurrency trading. The predominant tool that traders use to identify profitable opportunities is technical analysis. Some investors and researchers also combined technical analysis with machine learning, in order to forecast upcoming trends in the market. However, even with the use of these methods, developing successful trading strategies is still regarded as an extremely challenging task. Recently, deep reinforcement learning (DRL) algorithms demonstrated satisfying performance in solving complicated problems, including the formulation of profitable trading strategies. While some DRL techniques have been successful in increasing profit and loss (PNL) measures, these techniques are not much risk-aware and present difficulty in maximizing PNL and lowering trading risks simultaneously. This research proposes the combination of DRL approaches with rule-based safety mechanisms to both maximize PNL returns and minimize trading risk. First, a DRL agent is trained to maximize PNL returns, using a novel reward function. Then, during the exploitation phase, a rule-based mechanism is deployed to prevent uncertain actions from being executed. Finally, another novel safety mechanism is proposed, which considers the actions of a more conservatively trained agent, in order to identify high-risk trading periods and avoid trading. Our experiments on 5 popular cryptocurrencies show that the integration of these three methods achieves very promising results.

    – Combination of DRL approaches with rule-based safety mechanisms achieves promising results.
    – Integration of three methods maximizes PNL returns and minimizes trading risk.

    – Combination of DRL approaches with rule-based safety mechanisms achieves promising results.
    – Integration of three methods maximizes PNL returns and minimizes trading risk.

    – Combination of DRL approaches with rule-based safety mechanisms achieves promising results.
    – Integration of three methods maximizes PNL returns and minimizes trading risk.

    – DRL techniques are not much risk-aware and have difficulty in maximizing PNL and lowering trading risks simultaneously.
    – The integration of DRL approaches with rule-based safety mechanisms is proposed to address this limitation.

    Methods used: – DRL techniques are not much risk-aware and have difficulty in maximizing PNL and lowering trading risks simultaneously.
    – The integration of DRL approaches with rule-based safety mechanisms is proposed to address this limitation.

    – Combination of DRL and technical analysis can lead to profitable trading strategies.
    – Integration of DRL with rule-based safety mechanisms can maximize PNL returns and minimize trading risk.

    – The integration of DRL approaches with rule-based safety mechanisms achieves promising results.
    – The performance of the Integrated TraderNet-CR architecture is evaluated on five cryptocurrency markets.

    – Combination of deep reinforcement learning (DRL) with technical analysis and trend monitoring on cryptocurrency markets.
    – Integration of DRL approaches with rule-based safety mechanisms to maximize PNL returns and minimize trading risk.

    – Cryptocurrency markets have gained popularity, attracting traders and investors.
    – Technical analysis and machine learning are used to forecast market trends.

    In this article , a combination of deep reinforcement learning (DRL) and rule-based safety mechanisms is proposed to both maximize profit and loss (PNL) returns and minimize trading risk.

  • Cryptocurrency Trading and Downside Risk

    DOI: 10.3390/risks11070122

    ABSTRACT: Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.

    – Quantile-based models have the best ability to anticipate negative risks of cryptocurrencies.
    – Weighted average method is used to create the best VaR forecast model.

    – Quantile-based models have the best ability to anticipate negative risks of cryptocurrencies.
    – Weighted average method is used to create the best VaR forecast model.

    – Quantile-based models have the best ability to anticipate negative risks of cryptocurrencies.
    – Weighted average method is used to create the best VaR forecast model.

    Methods used:

    – The paper investigates value-at-risk (VaR) forecasts for cryptocurrencies.
    – Quantile-based models using a weighted average method have the best ability to anticipate negative risks.

    – Quantile models outperform GARCH, EGARCH, GJR, and GAS models.
    – GARCH, EGARCH, and GJR models pass the LR uc and LR cc tests.

    – Cryptocurrency trading has grown in popularity among investors.
    – Volatility models help reduce downside risk.

    – Cryptocurrency trading has grown in popularity among investors.
    – Cryptocurrencies exhibit high volatility and downside risk.

    In this paper , the authors investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregression score (GAS) models.

  • Innovative Cryptocurrency Trade Websites’ Marketing Strategy Refinement, via Digital Behavior

    DOI: 10.1109/access.2022.3182396

    ABSTRACT: Nowadays, the cryptocurrency market is thriving, through the rise in cryptocurrency trading, opening the way for cryptocurrency trading websites’ optimization. Optimization of customer satisfaction is a vital part of cryptocurrency trade organizations’ digital marketing problems. It is vital to keep digital advertisement costs low while driving more traffic to a website. This study aims to define a digital marketing strategy for cryptocurrency trading websites by utilizing digital behavior metrics. Web analytics data were gathered from 10 world-leading cryptocurrency trade websites over 80 days. Statistical analysis of cryptocurrency trade web analytics, Fuzzy Cognitive Mapping modeling, and Agent-Based Model development have been deployed. Enhancement of cryptocurrency trade digital engagement levels can boost organizations’ SEO and SEM strategy campaigns. Outputs of the study provide a handful of insights regarding cryptocurrency trading websites’ digital promotion strategy optimization and the parameters of digital behavior mostly connected with websites’ digital marketing costs and traffic. Cryptocurrency trade organizations should utilize both organic and paid campaigns, observe regularly their website KPIs connected with visitors’ behavior and enhance their website users’ experience, by increasing their engagement.

    – Enhancement of digital behavior metrics should be performed to increase traffic and keywords while keeping costs low.
    – Web analytics’ contribution is substantial in the digital marketing sector.

    – Enhancement of digital behavior metrics should be performed to increase traffic and keywords while keeping costs low.
    – Web analytics’ contribution is substantial in the digital marketing sector.

    – Enhancement of digital behavior metrics should be performed to increase traffic and keywords while keeping costs low.
    – Web analytics’ contribution is substantial in the digital marketing sector.

    Methods used:

    – Optimization of digital behavior metrics can enhance website traffic and reduce costs.
    – Cryptocurrency trade organizations should utilize both organic and paid campaigns.

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

    – Study aims to define digital marketing strategy for cryptocurrency trading websites.
    – Utilizes digital behavior metrics to optimize customer satisfaction and drive more traffic.

    – Cryptocurrency trading websites’ optimization is crucial for customer satisfaction.
    – Digital behavior metrics can refine digital marketing strategies for cryptocurrency trade websites.

    In this article , the authors defined a digital marketing strategy for cryptocurrency trading websites by utilizing digital behavior metrics, which is vital to keep digital advertisement costs low while driving more traffic to a website.

  • 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.