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Cryptocurrency trading: a comprehensive survey,lds seminary 2022 assessment

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It's a huge opportunity and a huge problem. A lot of people are drowning in their data and don't know how to use it to make decisions. Other organizations have figured out how to use these very powerful technologies to really gain insights rapidly from their data. What we're really trying to do is to look at that end-to-end journey of data and to build really compelling, powerful capabilities and services at each stop in that data journey and then…knit all that together with strong concepts like governance.

By putting good governance in place about who has access to what data and where you want to be careful within those guardrails that you set up, you can then set people free to be creative and to explore all the data that's available to them.

AWS has more than services now. Have you hit the peak for that or can you sustain that growth? We're not done building yet, and I don't know when we ever will be. We continue to both release new services because customers need them and they ask us for them and, at the same time, we've put tremendous effort into adding new capabilities inside of the existing services that we've already built.

We don't just build a service and move on. Inside of each of our services — you can pick any example — we're just adding new capabilities all the time. One of our focuses now is to make sure that we're really helping customers to connect and integrate between our different services. So those kinds of capabilities — both building new services, deepening our feature set within existing services, and integrating across our services — are all really important areas that we'll continue to invest in.

Do customers still want those fundamental building blocks and to piece them together themselves, or do they just want AWS to take care of all that? There's no one-size-fits-all solution to what customers want. It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers.

But it's absolutely vital. Part of that is because of the size of datasets and because of the machine learning capabilities which are now being created. They require vast amounts of compute, but nobody will be able to do that compute unless we keep dramatically improving the price performance. We also absolutely have more and more customers who want to interact with AWS at a higher level of abstraction…more at the application layer or broader solutions, and we're putting a lot of energy, a lot of resources, into a number of higher-level solutions.

One of the biggest of those … is Amazon Connect, which is our contact center solution. In minutes or hours or days, you can be up and running with a contact center in the cloud. At the beginning of the pandemic, Barclays … sent all their agents home. In something like 10 days, they got 6, agents up and running on Amazon Connect so they could continue servicing their end customers with customer service.

We've built a lot of sophisticated capabilities that are machine learning-based inside of Connect. We can do call transcription, so that supervisors can help with training agents and services that extract meaning and themes out of those calls. We don't talk about the primitive capabilities that power that, we just talk about the capabilities to transcribe calls and to extract meaning from the calls. It's really important that we provide solutions for customers at all levels of the stack.

Given the economic challenges that customers are facing, how is AWS ensuring that enterprises are getting better returns on their cloud investments? Now's the time to lean into the cloud more than ever, precisely because of the uncertainty.

We saw it during the pandemic in early , and we're seeing it again now, which is, the benefits of the cloud only magnify in times of uncertainty. For example, the one thing which many companies do in challenging economic times is to cut capital expense. For most companies, the cloud represents operating expense, not capital expense. You're not buying servers, you're basically paying per unit of time or unit of storage. That provides tremendous flexibility for many companies who just don't have the CapEx in their budgets to still be able to get important, innovation-driving projects done.

Another huge benefit of the cloud is the flexibility that it provides — the elasticity, the ability to dramatically raise or dramatically shrink the amount of resources that are consumed.

You can only imagine if a company was in their own data centers, how hard that would have been to grow that quickly. The ability to dramatically grow or dramatically shrink your IT spend essentially is a unique feature of the cloud. These kinds of challenging times are exactly when you want to prepare yourself to be the innovators … to reinvigorate and reinvest and drive growth forward again.

We've seen so many customers who have prepared themselves, are using AWS, and then when a challenge hits, are actually able to accelerate because they've got competitors who are not as prepared, or there's a new opportunity that they spot. We see a lot of customers actually leaning into their cloud journeys during these uncertain economic times.

Do you still push multi-year contracts, and when there's times like this, do customers have the ability to renegotiate? Many are rapidly accelerating their journey to the cloud. Some customers are doing some belt-tightening. What we see a lot of is folks just being really focused on optimizing their resources, making sure that they're shutting down resources which they're not consuming.

You do see some discretionary projects which are being not canceled, but pushed out. Every customer is free to make that choice. But of course, many of our larger customers want to make longer-term commitments, want to have a deeper relationship with us, want the economics that come with that commitment.

We're signing more long-term commitments than ever these days. We provide incredible value for our customers, which is what they care about. That kind of analysis would not be feasible, you wouldn't even be able to do that for most companies, on their own premises. So some of these workloads just become better, become very powerful cost-savings mechanisms, really only possible with advanced analytics that you can run in the cloud. In other cases, just the fact that we have things like our Graviton processors and … run such large capabilities across multiple customers, our use of resources is so much more efficient than others.

We are of significant enough scale that we, of course, have good purchasing economics of things like bandwidth and energy and so forth. So, in general, there's significant cost savings by running on AWS, and that's what our customers are focused on. The margins of our business are going to … fluctuate up and down quarter to quarter. It will depend on what capital projects we've spent on that quarter.

Obviously, energy prices are high at the moment, and so there are some quarters that are puts, other quarters there are takes. The important thing for our customers is the value we provide them compared to what they're used to. And those benefits have been dramatic for years, as evidenced by the customers' adoption of AWS and the fact that we're still growing at the rate we are given the size business that we are.

That adoption speaks louder than any other voice. Do you anticipate a higher percentage of customer workloads moving back on premises than you maybe would have three years ago? Absolutely not. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud. Moving internal enterprise IT workloads like SAP to the cloud, that's a big trend.

Creating new analytics capabilities that many times didn't even exist before and running those in the cloud. More startups than ever are building innovative new businesses in AWS. Our public-sector business continues to grow, serving both federal as well as state and local and educational institutions around the world. It really is still day one.

The opportunity is still very much in front of us, very much in front of our customers, and they continue to see that opportunity and to move rapidly to the cloud. In general, when we look across our worldwide customer base, we see time after time that the most innovation and the most efficient cost structure happens when customers choose one provider, when they're running predominantly on AWS.

A lot of benefits of scale for our customers, including the expertise that they develop on learning one stack and really getting expert, rather than dividing up their expertise and having to go back to basics on the next parallel stack.

That being said, many customers are in a hybrid state, where they run IT in different environments. In some cases, that's by choice; in other cases, it's due to acquisitions, like buying companies and inherited technology. We understand and embrace the fact that it's a messy world in IT, and that many of our customers for years are going to have some of their resources on premises, some on AWS.

Some may have resources that run in other clouds. We want to make that entire hybrid environment as easy and as powerful for customers as possible, so we've actually invested and continue to invest very heavily in these hybrid capabilities.

A lot of customers are using containerized workloads now, and one of the big container technologies is Kubernetes.

We have a managed Kubernetes service, Elastic Kubernetes Service, and we have a … distribution of Kubernetes Amazon EKS Distro that customers can take and run on their own premises and even use to boot up resources in another public cloud and have all that be done in a consistent fashion and be able to observe and manage across all those environments.

So we're very committed to providing hybrid capabilities, including running on premises, including running in other clouds, and making the world as easy and as cost-efficient as possible for customers.

Can you talk about why you brought Dilip Kumar, who was Amazon's vice president of physical retail and tech, into AWS as vice president applications and how that will play out? He's a longtime, tenured Amazonian with many, many different roles — important roles — in the company over a many-year period. Dilip has come over to AWS to report directly to me, running an applications group. We do have more and more customers who want to interact with the cloud at a higher level — higher up the stack or more on the application layer.

We talked about Connect, our contact center solution, and we've also built services specifically for the healthcare industry like a data lake for healthcare records called Amazon HealthLake. We've built a lot of industrial services like IoT services for industrial settings, for example, to monitor industrial equipment to understand when it needs preventive maintenance.

We have a lot of capabilities we're building that are either for … horizontal use cases like Amazon Connect or industry verticals like automotive, healthcare, financial services. We see more and more demand for those, and Dilip has come in to really coalesce a lot of teams' capabilities, who will be focusing on those areas.

You can expect to see us invest significantly in those areas and to come out with some really exciting innovations. Would that include going into CRM or ERP or other higher-level, run-your-business applications?

I don't think we have immediate plans in those particular areas, but as we've always said, we're going to be completely guided by our customers, and we'll go where our customers tell us it's most important to go next. It's always been our north star. Correction: This story was updated Nov. Bennett Richardson bennettrich is the president of Protocol. Prior to joining Protocol in , Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company.

Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB.

Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University. Prior to joining Protocol in , he worked on the business desk at The New York Times, where he edited the DealBook newsletter and wrote Bits, the weekly tech newsletter.

He has previously worked at MIT Technology Review, Gizmodo, and New Scientist, and has held lectureships at the University of Oxford and Imperial College London. He also holds a doctorate in engineering from the University of Oxford. We launched Protocol in February to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication. As of today, we will not publish any more stories.

All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.

Building this publication has not been easy; as with any small startup organization, it has often been chaotic.

But it has also been hugely fulfilling for those involved. We could not be prouder of, or more grateful to, the team we have assembled here over the last three years to build the publication. They are an inspirational group of people who have gone above and beyond, week after week.

Today, we thank them deeply for all the work they have done. We also thank you, our readers, for subscribing to our newsletters and reading our stories. We hope you have enjoyed our work. As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems. Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.

org and is the author of "Campaign ' A Turning Point for Digital Media," a book about how the presidential campaigns used digital media and data. On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. And he said that while some MLops systems can manage a larger number of models, they might not have desired features such as robust data visualization capabilities or the ability to work on premises rather than in cloud environments.

As companies expand their use of AI beyond running just a few ML models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, many machine learning practitioners Protocol interviewed for this story say that they have yet to find what they need from prepackaged MLops systems.

Companies hawking MLops platforms for building and managing machine learning models include tech giants like Amazon, Google, Microsoft, and IBM and lesser-known vendors such as Comet, Cloudera, DataRobot, and Domino Data Lab.

It's actually a complex problem. Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers. The model must recognize those distinctions. For instance, Hollman said the company built an ML feature management platform from the ground up.

For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials. DBS has incorporated open-source tools for coding and application security purposes such as Nexus, Jenkins, Bitbucket, and Confluence to ensure the smooth integration and delivery of ML models, Gupta said. Intuit has also used open-source tools or components sold by vendors to improve existing in-house systems or solve a particular problem, Hollman said.

However, he emphasized the need to be selective about which route to take. I think that the best AI will be a build plus buy. However, creating consistency through the ML lifecycle from model training to deployment to monitoring becomes increasingly difficult as companies cobble together open-source or vendor-built machine learning components, said John Thomas, vice president and distinguished engineer at IBM.

The reality is most people are not there, so you have a whole bunch of different tools. Companies struggling to find suitable off-the-shelf MLops platforms are up against another major challenge, too: finding engineering talent. Many companies do not have software engineers on staff with the level of expertise necessary to architect systems that can handle large numbers of models or accommodate millions of split-second decision requests, said Abhishek Gupta, founder and principal researcher at Montreal AI Ethics Institute and senior responsible AI leader and expert at Boston Consulting Group.

For one thing, smaller companies are competing for talent against big tech firms that offer higher salaries and better resources. For companies with less-advanced AI operations, shopping at the existing MLops platform marketplace may be good enough, Hollman said. To give you the best possible experience, this site uses cookies.

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cfpb law lending rohit chopra regulation. November 21, Keep Reading Show less. cryptocurrency blockchain bitcoin lawsuit. sponsored content. November 14, The Financial Technology Association FTA represents industry leaders shaping the future of finance.

We champion the power of technology-centered financial services and advocate for the modernization of financial regulation to support inclusion and responsible innovation. Penny Lee, Chief Executive Officer, Financial Technology Association.

sponsored sponsored content. amazon web services. aws amazon web services cloud computing analytics data analytics adam selipsky. Farewell from Protocol. November 15, Cybersecurity issues As a digital technology, cryptocurrencies are subject to cyber security breaches and can fall into the hands of hackers.

Mitigating this situation requires ongoing maintenance of the security infrastructure and the use of enhanced cyber security measures that go beyond those used in traditional banking Kou et al.

Regulations Authorities around the world face challenging questions about the nature and regulation of cryptocurrency as some parts of the system and its associated risks are largely unknown. There are currently three types of regulatory systems used to control digital currencies, they include: closed system for the Chinese market, open and liberal for the Swiss market,and open and strict system for the US market UKTN At the same time, we notice that some countries such as India is not at par in using the cryptocurrency.

This thing is not regulated. Cryptocurrency trading strategy is the main focus of this survey. There are many trading strategies, which can be broadly divided into two main categories: technical and fundamental. Technical and fundamental trading are two main trading analysis thoughts when it comes to analyzing the financial markets. Most traders use these two analysis methods or both Oberlechner Cryptocurrency trading can draw on the experience of stock market trading in most scenarios.

So we divide trading strategies into two main categories: technical and fundamental trading. They are similar in the sense that they both rely on quantifiable information that can be backtested against historical data to verify their performance.

In recent years, a third kind of trading strategy, which we call programmatic trading, has received increasing attention. Such a trading strategy is similar to a technical trading strategy because it uses trading activity information on the exchange to make buying or selling decisions. programmatic traders build trading strategies with quantitative data, which is mainly derived from price, volume, technical indicators or ratios to take advantage of inefficiencies in the market and are executed automatically by trading software.

Cryptocurrency market is different from traditional markets as there are more arbitrage opportunities, higher fluctuation and transparency. Due to these characteristics, most traders and analysts prefer using programmatic trading in cryptocurrency markets. Software trading systems allow international transactions, process customer accounts and information, and accept and execute transaction orders Calo and Johnson A cryptocurrency trading system is a set of principles and procedures that are pre-programmed to allow trade between cryptocurrencies and between fiat currencies and cryptocurrencies.

Cryptocurrency trading systems are built to overcome price manipulation, cybercriminal activities and transaction delays Bauriya et al. When developing a cryptocurrency trading system, we must consider the capital market, base asset, investment plan and strategies Molina Strategies are the most important part of an effective cryptocurrency trading system and they will be introduced below.

There exist several cryptocurrency trading systems that are available commercially, for example, Capfolio, 3Commas, CCXT, Freqtrade and Ctubio. From these cryptocurrency trading systems, investors can obtain professional trading strategy support, fairness and transparency from the professional third-party consulting companies and fast customer services. Systematic trading is a way to define trading goals, risk controls and rules. In general, systematic trading includes high frequency trading and slower investment types like systematic trend tracking.

In this survey, we divide systematic cryptocurrency trading into technical analysis, pairs trading and others. Technical analysis in cryptocurrency trading is the act of using historical patterns of transaction data to assist a trader in assessing current and projecting future market conditions for the purpose of making profitable trades.

Price and volume charts summarise all trading activity made by market participants in an exchange and affect their decisions. Some experiments showed that the use of specific technical trading rules allows generating excess returns, which is useful to cryptocurrency traders and investors in making optimal trading and investment decisions Gerritsen et al. Pairs trading is a systematic trading strategy that considers two similar assets with slightly different spreads. If the spread widens, short the high cryptocurrencies and buy the low cryptocurrencies.

When the spread narrows again to a certain equilibrium value, a profit is generated Elliott et al. Papers shown in this section involve the analysis and comparison of technical indicators, pairs and informed trading, amongst other strategies. Tools for building automated trading systems in cryptocurrency market are those emergent trading strategies for cryptocurrency. These include strategies that are based on econometrics and machine learning technologies.

Econometric methods apply a combination of statistical and economic theories to estimate economic variables and predict their values Vogelvang Statistical models use mathematical equations to encode information extracted from the data Kaufman In some cases, statistical modeling techniques can quickly provide sufficiently accurate models Ben-Akiva et al.

Other methods might be used, such as sentiment-based prediction and long-and-short-term volatility classification based prediction Chang et al. The prediction of volatility can be used to judge the price fluctuation of cryptocurrencies, which is also valuable for the pricing of cryptocurrency-related derivatives Kat and Heynen When studying cryptocurrency trading using econometrics, researchers apply statistical models on time-series data like generalised autoregressive conditional heteroskedasticity GARCH and BEKK named after Baba, Engle, Kraft and Kroner, Engle and Kroner models to evaluate the fluctuation of cryptocurrencies Caporin and McAleer A linear statistical model is a method to evaluate the linear relationship between prices and an explanatory variable Neter et al.

When there exists more than one explanatory variable, we can model the linear relationship between explanatory independent and response dependent variables with multiple linear models.

The common linear statistical model used in the time-series analysis is the autoregressive moving average ARMA model Choi Machine learning is an efficient tool for developing Bitcoin and other cryptocurrency trading strategies McNally et al. From the most basic perspective, Machine Learning relies on the definition of two main components: input features and objective function. The definition of Input Features data sources is where knowledge of fundamental and technical analysis comes into play.

We may divide the input into several groups of features, for example, those based on Economic indicators such as, gross domestic product indicator, interest rates, etc. and other Seasonal indicators time of day, day of the week, etc. The objective function defines the fitness criteria one uses to judge if the Machine Learning model has learnt the task at hand.

Typical predictive models try to anticipate numeric e. The machine learning model is trained by using historic input data sometimes called in-sample to generalise patterns therein to unseen out-of-sample data to approximately achieve the goal defined by the objective function. Clearly, in the case of trading, the goal is to infer trading signals from market indicators which help to anticipate asset future returns. Generalisation error is a pervasive concern in the application of Machine Learning to real applications, and of utmost importance in Financial applications.

We need to use statistical approaches, such as cross validation, to validate the model before we actually use it to make predictions. The process of using machine learning technology to predict cryptocurrency is shown in Fig. Depending on the formulation of the main learning loop, we can classify Machine Learning approaches into three categories: Supervised learning, Unsupervised learning and Reinforcement learning.

We list a general comparison IntelliPaat among these three machine learning methods in Table 2. Supervised learning is used to derive a predictive function from labeled training data. Labeled training data means that each training instance includes inputs and expected outputs.

Usually, these expected outputs are produced by a supervisor and represent the expected behaviour of the model. The most used labels in trading are derived from in sample future returns of assets. Unsupervised learning tries to infer structure from unlabeled training data and it can be used during exploratory data analysis to discover hidden patterns or to group data according to any pre-defined similarity metrics. Reinforcement learning utilises software agents trained to maximise a utility function, which defines their objective; this is flexible enough to allow agents to exchange short term returns for future ones.

In the financial sector, some trading challenges can be expressed as a game in which an agent aims at maximising the return at the end of the period. Further concrete examples are shown in a later section. Portfolio theory advocates diversification of investments to maximize returns for a given level of risk by allocating assets strategically. The celebrated mean-variance optimisation is a prominent example of this approach Markowitz Generally, crypto asset denotes a digital asset i.

There are some common ways to build a diversified portfolio in crypto assets. The first method is to diversify across markets, which is to mix a wide variety of investments within a portfolio of the cryptocurrency market. The second method is to consider the industry sector, which is to avoid investing too much money in any one category. Diversified investment of portfolio in the cryptocurrency market includes portfolio across cryptocurrencies Liu and portfolio across the global market including stocks and futures Kajtazi and Moro Market condition research appears especially important for cryptocurrencies.

A financial bubble is a significant increase in the price of an asset without changes in its intrinsic value Brunnermeier and Oehmke ; Kou et al. In , Bitcoin faced a collapse in its value. This significant fluctuation inspired researchers to study bubbles and extreme conditions in cryptocurrency trading. Some experts believe that the extreme volatility of exchange rates means that cryptocurrency exposure should be kept at a low percentage of your portfolio.

In any case, bubbles and crash analysis is an important researching area in cryptocurrency trading. The section introduces the scope and approach of our paper collection, a basic analysis, and the structure of our survey.

We adopt a bottom-up approach to the research in cryptocurrency trading, starting from the systems up to risk management techniques. For the underlying trading system, the focus is on the optimisation of trading platforms structure and improvements of computer science technologies.

At a higher level, researchers focus on the design of models to predict return or volatility in cryptocurrency markets. These techniques become useful to the generation of trading signals. on the next level above predictive models, researchers discuss technical trading methods to trade in real cryptocurrency markets. Bubbles and extreme conditions are hot topics in cryptocurrency trading because, as discussed above, these markets have shown to be highly volatile whilst volatility went down after crashes.

Portfolio and cryptocurrency asset management are effective methods to control risk. We group these two areas in risk management research. Other papers included in this survey include topics like pricing rules, dynamic market analysis, regulatory implications, and so on. Table 3 shows the general scope of cryptocurrency trading included in this survey. Since many trading strategies and methods in cryptocurrency trading are closely related to stock trading, some researchers migrate or use the research results for the latter to the former.

When conducting this research, we only consider those papers whose research focuses on cryptocurrency markets or a comparison of trading in those and other financial markets. Specifically, we apply the following criteria when collecting papers related to cryptocurrency trading:. The paper introduces or discusses the general idea of cryptocurrency trading or one of the related aspects of cryptocurrency trading.

The paper proposes an approach, study or framework that targets optimised efficiency or accuracy of cryptocurrency trading. Some researchers gave a brief survey of cryptocurrency Ahamad et al. These surveys are rather limited in scope as compared to ours, which also includes a discussion on the latest papers in the area; we want to remark that this is a fast-moving research field.

To collect the papers in different areas or platforms, we used keyword searches on Google Scholar and arXiv, two of the most popular scientific databases.

We also choose other public repositories like SSRN but we find that almost all academic papers in these platforms can also be retrieved via Google Scholar; consequently, in our statistical analysis, we count those as Google Scholar hits. We choose arXiv as another source since it allows this survey to be contemporary with all the most recent findings in the area.

The interested reader is warned that these papers have not undergone formal peer review. The keywords used for searching and collecting are listed below. We conducted 6 searches across the two repositories until July 1, To ensure high coverage, we adopted the so-called snowballing Wohlin method on each paper found through these keywords. We checked papers added from snowballing methods that satisfy the criteria introduced above until we reached closure.

Table 4 shows the details of the results from our paper collection. Keyword searches and snowballing resulted in papers across the six research areas of interest in " Survey scope " section. Figure 7 shows the distribution of papers published at different research sites.

Among all the papers, The distribution of different venues shows that cryptocurrency trading is mostly published in Finance and Economics venues, but with a wide diversity otherwise. We discuss the contributions of the collected papers and a statistical analysis of these papers in the remainder of the paper, according to Table 5. The papers in our collection are organised and presented from six angles. We introduce the work about several different cryptocurrency trading software systems in " Cryptocurrency trading software systems " section.

In " Emergent trading technologies " section, we introduce some emergent trading technologies including econometrics on cryptocurrencies, machine learning technologies and other emergent trading technologies in the cryptocurrency market.

Section 8 introduces research on cryptocurrency pairs and related factors and crypto-asset portfolios research. In " Bubbles and crash analysis " and " Extreme condition " sections we discuss cryptocurrency market condition research, including bubbles, crash analysis, and extreme conditions.

We would like to emphasize that the six headings above focus on a particular aspect of cryptocurrency trading; we give a complete organisation of the papers collected under each heading. This implies that those papers covering more than one aspect will be discussed in different sections, once from each angle.

We analyse and compare the number of research papers on different cryptocurrency trading properties and technologies in " Summary analysis of literature review " section, where we also summarise the datasets and the timeline of research in cryptocurrency trading. We build upon this review to conclude in " Opportunities in cryptocurrency trading " section with some opportunities for future research.

Table 6 compares the cryptocurrency trading systems existing in the market. The table is sorted based on URL types GitHub or Official website and GitHub stars if appropriate. Capfolio is a proprietary payable cryptocurrency trading system which is a professional analysis platform and has an advanced backtesting engine Capfolio It supports five different cryptocurrency exchanges.

Twelve different cryptocurrency exchanges are compatible with this system. Any trader or developer can create a trading strategy based on this data and access public transactions through the APIs Ccxt The CCXT library is used to connect and trade with cryptocurrency exchanges and payment processing services worldwide.

It provides quick access to market data for storage, analysis, visualisation, indicator development, algorithmic trading, strategy backtesting, automated code generation and related software engineering. It is designed for coders, skilled traders, data scientists and financial analysts to build trading algorithms.

Current CCXT features include:. It can generate market-neutral strategies that do not transfer funds between exchanges Blackbird The motivation behind Blackbird is to naturally profit from these temporary price differences between different exchanges while being market neutral. Unlike other Bitcoin arbitrage systems, Blackbird does not sell but actually short sells Bitcoin on the short exchange. This feature offers two important advantages. Firstly, the strategy is always market agnostic: fluctuations rising or falling in the Bitcoin market will not affect the strategy returns.

This eliminates the huge risks of this strategy. Secondly, this strategy does not require transferring funds USD or BTC between Bitcoin exchanges. Buy and sell transactions are conducted in parallel on two different exchanges. There is no need to deal with transmission delays. StockSharp is an open-source trading platform for trading at any market of the world including 48 cryptocurrency exchanges Stocksharp It has a free C library and free trading charting application.

Manual or automatic trading algorithmic trading robot, regular or HFT can be run on this platform. StockSharp consists of five components that offer different features:. Shell - ready-made graphics framework that can be changed according to needs and has a fully open source in C ;.

API - a free C library for programmers using Visual Studio. Any trading strategies can be created in S. Freqtrade is a free and open-source cryptocurrency trading robot system written in Python. It is designed to support all major exchanges and is controlled by telegram. It contains backtesting, mapping and money management tools, and strategy optimization through machine learning Fretrade Freqtrade has the following features:. Strategy optimization through machine learning: Use machine learning to optimize your trading strategy parameters with real trading data;.

Marginal Position Size: Calculates winning rate, risk-return ratio, optimal stop loss and adjusts position size, and then trades positions for each specific market;. CryptoSignal is a professional technical analysis cryptocurrency trading system Cryptosignal Investors can track over coins of Bittrex, Bitfinex, GDAX, Gemini and more.

Automated technical analysis includes momentum, RSI, Ichimoku Cloud, MACD, etc. The system gives alerts including Email, Slack, Telegram, etc. CryptoSignal has two primary features. First of all, it offers modular code for easy implementation of trading strategies; Secondly, it is easy to install with Docker.

This trading system can place or cancel orders through supported cryptocurrency exchanges in less than a few milliseconds. Moreover, it provides a charting system that can visualise the trading account status including trades completed, target position for fiat currency, etc. Catalyst is an analysis and visualization of the cryptocurrency trading system Catalyst It makes trading strategies easy to express and backtest them on historical data daily and minute resolution , providing analysis and insights into the performance of specific strategies.

Catalyst allows users to share and organise data and build profitable, data-driven investment strategies. Catalyst not only supports the trading execution but also offers historical price data of all crypto assets from minute to daily resolution. Catalyst also has backtesting and real-time trading capabilities, which enables users to seamlessly transit between the two different trading modes.

Lastly, Catalyst integrates statistics and machine learning libraries such as matplotlib, scipy, statsmodels and sklearn to support the development, analysis and visualization of the latest trading systems.

Golang Crypto Trading Bot is a Go based cryptocurrency trading system Golang Users can test the strategy in sandbox environment simulation. If simulation mode is enabled, a fake balance for each coin must be specified for each exchange. Bauriya et al. A real-time cryptocurrency trading system is composed of clients, servers and databases. The server collects cryptocurrency market data by creating a script that uses the Coinmarket API.

Finally, the database collects balances, trades and order book information from the server. The authors tested the system with an experiment that demonstrates user-friendly and secure experiences for traders in the cryptocurrency exchange platform. The original Turtle Trading system is a trend following trading system developed in the s.

The idea is to generate buy and sell signals on stock for short-term and long-term breakouts and its cut-loss condition which is measured by Average true range ATR Kamrat et al.

The trading system will adjust the size of assets based on their volatility. Essentially, if a turtle accumulates a position in a highly volatile market, it will be offset by a low volatility position. Extended Turtle Trading system is improved with smaller time interval spans and introduces a new rule by using exponential moving average EMA.

The author of Kamrat et al. Through the experiment, Original Turtle Trading System achieved an Extended Turtle Trading System achieved This research showed how Extended Turtle Trading System compared can improve over Original Turtle Trading System in trading cryptocurrencies.

Christian Păuna introduced arbitrage trading systems for cryptocurrencies. Arbitrage trading aims to spot the differences in price that can occur when there are discrepancies in the levels of supply and demand across multiple exchanges. As a result, a trader could realise a quick and low-risk profit by buying from one exchange and selling at a higher price on a different exchange.

Arbitrage trading signals are caught by automated trading software. The technical differences between data sources impose a server process to be organised for each data source. Relational databases and SQL are reliable solution due to the large amounts of relational data. The author used the system to catch arbitrage opportunities on 25 May among cryptocurrencies on 7 different exchanges.

The research paper Păuna listed the best ten trading signals made by this system from available found signals. Arbitrage Trading Software System introduced in that paper presented general principles and implementation of arbitrage trading system in the cryptocurrency market. Real-time trading systems use real-time functions to collect data and generate trading algorithms.

Turtle trading system and arbitrage trading system have shown a sharp contrast in their profit and risk behaviour. Using Turtle trading system in cryptocurrency markets got high returns with high risk. Arbitrage trading system is inferior in terms of revenue but also has a lower risk. One feature that turtle trading system and arbitrage trading system have in common is they performed well in capturing alpha. Many researchers have focused on technical indicators patterns analysis for trading on cryptocurrency markets.

Table 7 shows the comparison among these five classical technical trading strategies using technical indicators. This strategy is a kind of chart trading pattern. Technical analysis tools such as candlestick and box charts with Fibonacci Retracement based on golden ratio are used in this technical analysis. Fibonacci Retracement uses horizontal lines to indicate where possible support and resistance levels are in the market. This strategy used a price chart pattern and box chart as technical analysis tools.

Ha and Moon investigated using genetic programming GP to find attractive technical patterns in the cryptocurrency market.

Over 12 technical indicators including Moving Average MA and Stochastic oscillator were used in experiments; adjusted gain, match count, relative market pressure and diversity measures have been used to quantify the attractiveness of technical patterns. With extended experiments, the GP system is shown to find successfully attractive technical patterns, which are useful for portfolio optimization. Hudson and Urquhart applied almost 15, to technical trading rules classified into MA rules, filter rules, support resistance rules, oscillator rules and channel breakout rules.

This comprehensive study found that technical trading rules provide investors with significant predictive power and profitability. Corbet et al. By using one-minute dollar-denominated Bitcoin close-price data, the backtest showed variable-length moving average VMA rule performs best considering it generates the most useful signals in high frequency trading.

Grobys et al. The results showed that, excluding Bitcoin, technical trading rules produced an annualised excess return of 8. The analysis also suggests that cryptocurrency markets are inefficient. Al-Yahyaee et al.

The results showed that all markets provide evidence of long-term memory properties and multiple fractals. Furthermore, the inefficiency of cryptocurrency markets is time-varying. The researchers concluded that high liquidity with low volatility facilitates arbitrage opportunities for active traders.

Pairs trading is a trading strategy that attempts to exploit the mean-reversion between the prices of certain securities. Miroslav Fil investigated the applicability of standard pairs trading approaches on cryptocurrency data with the benchmarks of Gatev et al. The pairs trading strategy is constructed in two steps. Firstly, suitable pairs with a stable long-run relationship are identified. Secondly, the long-run equilibrium is calculated and pairs trading strategy is defined by the spread based on the values.

The research also extended intra-day pairs trading using high frequency data. Broek van den Broek and Sharif applied pairs trading based on cointegration in cryptocurrency trading and 31 pairs were found to be significantly cointegrated within sector and cross-sector.

By selecting four pairs and testing over a day trading period, the pairs trading strategy got its profitability from arbitrage opportunities, which rejected the Efficient-market hypothesis EMH for the cryptocurrency market. Lintilhac and Tourin proposed an optimal dynamic pair trading strategy model for a portfolio of assets. The experiment used stochastic control techniques to calculate optimal portfolio weights and correlated the results with several other strategies commonly used by practitioners including static dual-threshold strategies.

Li and Tourin proposed a pairwise trading model incorporating time-varying volatility with constant elasticity of variance type. The experiment calculated the best pair strategy by using a finite difference method and estimated parameters by generalised moment method. Other systematic trading methods in cryptocurrency trading mainly include informed trading. The evidence of informed trading in the Bitcoin market suggests that investors profit on their private information when they get information before it is widely available.

Copula-quantile causality analysis and Granger-causality analysis are methods to investigate causality in cryptocurrency trading analysis. Bouri et al. The approach of the experiment extended the Copula-Granger-causality in distribution CGCD method of Lee and Yang in The experiment constructed two tests of CGCD using copula functions.

The parametric test employed six parametric copula functions to discover dependency density between variables.

The performance matrix of these functions varies with independent copula density. The study provided significant evidence of Granger causality from trading volume to the returns of seven large cryptocurrencies on both left and right tails.

The results showed that permanent shocks are more important in explaining Granger causality whereas transient shocks dominate the causality of smaller cryptocurrencies in the long term. Badenhorst et al. The result shows spot trading volumes have a significant positive effect on price volatility while the relationship between cryptocurrency volatility and the derivative market is uncertain. The results showed increased cryptocurrency market consolidation despite significant price declined in Furthermore, measurement of trading volume and uncertainty are key determinants of integration.

Several econometrics methods in time-series research, such as GARCH and BEKK, have been used in the literature on cryptocurrency trading. Conrad et al. The technical details of this model decomposed the conditional variance into the low-frequency and high-frequency components. Ardia et al. Moreover, a Bayesian method was used for estimating model parameters and calculating VaR prediction. The results showed that MSGARCH models clearly outperform single-regime GARCH for Value-at-Risk forecasting.

Troster et al. The results also illustrated the importance of modeling excess kurtosis for Bitcoin returns. Charles and Darné studied four cryptocurrency markets including Bitcoin, Dash, Litecoin and Ripple. Results showed cryptocurrency returns are strongly characterised by the presence of jumps as well as structural breaks except the Dash market.

Four GARCH-type models i. The research indicated the importance of jumps in cryptocurrency volatility and structural breakthroughs. Autoregressive-moving-average model with exogenous inputs model ARMAX , GARCH, VAR and Granger causality tests are used in the experiments. The results showed that there is no causal relationship between global stock market and gold returns on bitcoin returns, but a causal relationship between ripple returns on bitcoin prices is found.

Some researchers focused on long memory methods for volatility in cryptocurrency markets. Long memory methods focused on long-range dependence and significant long-term correlations among fluctuations on markets. Chaim and Laurini estimated a multivariate stochastic volatility model with discontinuous jumps in cryptocurrency markets. The results showed that permanent volatility appears to be driven by major market developments and popular interest levels.

Caporale et al. The results of the study indicated that the market is persistent there is a positive correlation between its past and future values and that its level changes over time. Khuntia and Pattanayak applied the adaptive market hypothesis AMH in the predictability of Bitcoin evolving returns. The consistent test of Domínguez and Lobato , generalized spectral GS of Escanciano and Velasco are applied in capturing time-varying linear and nonlinear dependence in bitcoin returns.

Gradojevic and Tsiakas examined volatility cascades across multiple trading ranges in the cryptocurrency market. Using a wavelet Hidden Markov Tree model, authors estimated the transition probability of propagating high or low volatility at one time scale range to high or low volatility at the next time scale.

The results showed that the volatility cascade tends to be symmetrical when moving from long to short term. In contrast, when moving from short to long term, the volatility cascade is very asymmetric.

Nikolova et al. The authors used the FD4 method to calculate the Hurst index of a volatility series and describe explicit criteria for determining the existence of fixed size volatility clusters by calculation. Ma et al. The results showed that the proposed new MRS-MIDAS model exhibits statistically significant improvements in predicting the RV of Bitcoin.

At the same time, the occurrence of jumps significantly increases the persistence of high volatility and switches between high and low volatility.

Katsiampa et al. More specifically, the BEKK-MGARCH methodology also captured cross-market effects of shocks and volatility, which are also known as shock transmission effects and volatility spillover effects. The experiment found evidence of bi-directional shock transmission effects between Bitcoin and both Ether and Litcoin. In particular, bi-directional shock spillover effects are identified between three pairs Bitcoin, Ether and Litcoin and time-varying conditional correlations exist with positive correlations mostly prevailing.

In , Katsiampa further researched an asymmetric diagonal BEKK model to examine conditional variances of five cryptocurrencies that are significantly affected by both previous squared errors and past conditional volatility. The experiment tested the null hypothesis of the unit root against the stationarity hypothesis.

Once stationarity is ensured, ARCH LM is tested for ARCH effects to examine the requirement of volatility modeling in return series. Moreover, volatility co-movements among cryptocurrency pairs are also tested by the multivariate GARCH model.

The results confirmed the non-normality and heteroskedasticity of price returns in cryptocurrency markets.

Hultman set out to examine GARCH 1,1 , bivariate-BEKK 1,1 and a standard stochastic model to forecast the volatility of Bitcoin. A rolling window approach is used in these experiments. Mean absolute error MAE , Mean squared error MSE and Root-mean-square deviation RMSE are three loss criteria adopted to evaluate the degree of error between predicted and true values. Wavelet time-scale persistence analysis is also applied in the prediction and research of volatility in cryptocurrency markets Omane-Adjepong et al.

The results showed that information efficiency efficiency and volatility persistence in the cryptocurrency market are highly sensitive to time scales, measures of returns and volatility, and institutional changes. Omane-Adjepong et al. Zhang and Li examined how to price exceptional volatility in a cross-section of cryptocurrency returns. Using portfolio-level analysis and Fama-MacBeth regression analysis, the authors demonstrated that idiosyncratic volatility is positively correlated with expected returns on cryptocurrencies.

As we have previously stated, Machine learning technology constructs computer algorithms that automatically improve themselves by finding patterns in existing data without explicit instructions Holmes et al. The rapid development of machine learning in recent years has promoted its application to cryptocurrency trading, especially in the prediction of cryptocurrency returns.

Some ML algorithms solve both classification and regression problems from a methodological point of view. For clearer classification, we focus on the application of these ML algorithms in cryptocurrency trading. For example, Decision Tree DT can solve both classification and regression problems. But in cryptocurrency trading, researchers focus more on using DT in solving classification problems.

Several machine learning technologies are applied in cryptocurrency trading. We distinguish these by the objective set to the algorithm: classification, clustering, regression, reinforcement learning. We have separated a section specifically on deep learning due to its intrinsic variation of techniques and wide adoption. Classification algorithms Classification in machine learning has the objective of categorising incoming objects into different categories as needed, where we can assign labels to each category e.

Naive Bayes NB Rish et al. SVM is a supervised learning model that aims at achieving high margin classifiers connecting to learning bounds theory Zemmal et al. SVMs assign new examples to one category or another, making it a non-probabilistic binary linear classifier Wang , although some corrections can make a probabilistic interpretation of their output Keerthi et al. KNN is a memory-based or lazy learning algorithm, where the function is only approximated locally, and all calculations are being postponed to inference time Wang DT is a decision support tool algorithm that uses a tree-like decision graph or model to segment input patterns into regions to then assign an associated label to each region Friedl and Brodley ; Fang et al.

RF is an ensemble learning method. The algorithm operates by constructing a large number of decision trees during training and outputting the average consensus as predicted class in the case of classification or mean prediction value in the case of regression Liaw and Wiener GB produces a prediction model in the form of an ensemble of weak prediction models Friedman et al. Clustering algorithms Clustering is a machine learning technique that involves grouping data points in a way that each group shows some regularity Jianliang et al.

K-Means is a vector quantization used for clustering analysis in data mining. K-Means is one of the most used clustering algorithms used in cryptocurrency trading according to the papers we collected. Clustering algorithms have been successfully applied in many financial applications, such as fraud detection, rejection inference and credit assessment. Automated detection clusters are critical as they help to understand sub-patterns of data that can be used to infer user behaviour and identify potential risks Li et al.

Regression algorithms We have defined regression as any statistical technique that aims at estimating a continuous value Kutner et al. Linear Regression LR and Scatterplot Smoothing are common techniques used in solving regression problems in cryptocurrency trading. LR is a linear method used to model the relationship between a scalar response or dependent variable and one or more explanatory variables or independent variables Kutner et al.

Scatterplot Smoothing is a technology to fit functions through scatter plots to best represent relationships between variables Friedman and Tibshirani Deep Learning algorithms Deep learning is a modern take on artificial neural networks ANNs Zhang et al. Deep learning algorithms are currently the basis for many modern artificial intelligence applications Sze et al. Convolutional neural networks CNNs Lawrence et al.

A CNN is a specific type of neural network layer commonly used for supervised learning. CNNs have found their best success in image processing and natural language processing problems. An attempt to use CNNs in cryptocurrency can be shown in Kalchbrenner et al.

An RNN is a type of artificial neural network in which connections between nodes form a directed graph with possible loops. This structure of RNNs makes them suitable for processing time-series data Mikolov et al. They face nevertheless for the vanishing gradients problem Pascanu et al. LSTM Cheng et al.

LSTMs have shown to be superior to nongated RNNs on financial time-series problems because they have the ability to selectively remember patterns for a long time. A GRU Chung et al. Another deep learning technology used in cryptocurrency trading is Seq2seq, which is a specific implementation of the Encoder-Decoder architecture Xu et al. Seq2seq was first aimed at solving natural language processing problems but has been also applied it in cryptocurrency trend predictions in Sriram et al.

Reinforcement learning algorithms Reinforcement learning RL is an area of machine learning leveraging the idea that software agents act in the environment to maximize a cumulative reward Sutton and Barto Deep Q-Learning DQN Gu et al.

Deep Q learning uses neural networks to approximate Q-value functions. A state is given as input, and Q values for all possible actions are generated as outputs Gu et al. DBM is a type of binary paired Markov random field undirected probability graphical model with multiple layers of hidden random variables Salakhutdinov and Hinton It is a network of randomly coupled random binary units.

In the development of machine learning trading signals, technical indicators have usually been used as input features. Nakano et al. The experiment obtained medium frequency price and volume data time interval of data is 15min of Bitcoin from a cryptocurrency exchange. An ANN predicts the price trends up and down in the next period from the input data. Data is preprocessed to construct a training dataset that contains a matrix of technical patterns including EMA, Emerging Markets Small Cap EMSD , relative strength index RSI , etc.

Their numerical experiments contain different research aspects including base ANN research, effects of different layers, effects of different activation functions, different outputs, different inputs and effects of additional technical indicators. The results have shown that the use of various technical indicators possibly prevents over-fitting in the classification of non-stationary financial time-series data, which enhances trading performance compared to the primitive technical trading strategy.

Buy-and-Hold is the benchmark strategy in this experiment. Some classification and regression machine learning models are applied in cryptocurrency trading by predicting price trends.

Most researchers have focused on the comparison of different classification and regression machine learning methods. Sun et al. The experiment collected data from API in cryptocurrency exchanges and selected 5-min frequency data for backtesting. The results showed that the performances are proportional to the amount of data more data, more accurate and the factors used in the RF model appear to have different importance. Vo and Yost-Bremm applied RFs in High-Frequency cryptocurrency Trading HFT and compared it with deep learning models.

Minute-level data is collected when utilising a forward fill imputation method to replace the NULL value i. Different periods and RF trees are tested in the experiments. The authors also compared F-1 precision and recall metrics between RF and Deep Learning DL. The results showed that RF is effective despite multicollinearity occurring in ML features, the lack of model identification also potentially leading to model identification issues; this research also attempted to create an HFT strategy for Bitcoin using RF.

Slepaczuk and Zenkova investigated the profitability of an algorithmic trading strategy based on training an SVM model to identify cryptocurrencies with high or low predicted returns. There are other 4 benchmark strategies in this research. The authors observed that SVM needs a large number of parameters and so is very prone to overfitting, which caused its bad performance.

Barnwal et al. A discriminative classifier directly models the relationship between unknown and known data, while generative classifiers model the prediction indirectly through the data generation distribution Ng and Jordan Technical indicators including trend, momentum, volume and volatility, are collected as features of the model.

The authors discussed how different classifiers and features affect the prediction. Attanasio et al. Madan et al. Daily data, min data and s data are used in the experiments.

Considering predictive trading, min data helped show clearer trends in the experiment compared to second backtesting. Similarly, Virk compared RF, SVM, GB and LR to predict the price of Bitcoin. The results showed that SVM achieved the highest accuracy of Different deep learning models have been used in finding patterns of price movements in cryptocurrency markets. Zhengyang et al.

The results showed that ANN, in general, outperforms LSTM although theoretically, LSTM is more suitable than ANN in terms of modeling time series dynamics; the performance measures considered are MAE and RMSE in joint prediction five cryptocurrencies daily prices prediction. The findings show that the future state of a time series for cryptocurrencies is highly dependent on its historic evolution.

Kwon et al. This model outperforms the GB model in terms of F1-score. In particular, the experiments showed that LSTM is more suitable when classifying cryptocurrency data with high volatility.

Alessandretti et al. The relative importance of the features in both models are compared and an optimised portfolio composition based on geometric mean return and Sharpe ratio is discussed in this paper. Phaladisailoed and Numnonda chose regression models Theil-Sen Regression and Huber Regression and deep learning-based models LSTM and GRU to compare the performance of predicting the rise and fall of Bitcoin price.

Rane and Dhage described classical time series prediction methods and machine learning algorithms used for predicting Bitcoin price. Statistical models such as Autoregressive Integrated Moving Average models ARIMA , Binomial Generalized Linear Model and GARCH are compared with machine learning models such as SVM, LSTM and Non-linear Auto-Regressive with Exogenous Input Model NARX. Rebane et al. The result showed that the seq2seq model exhibited demonstrable improvement over the ARIMA model for Bitcoin-USD prediction but the seq2seq model showed very poor performance in extreme cases.

The authors proposed performing additional investigations, such as the use of LSTM instead of GRU units to improve the performance. Similar models were also compared by Stuerner who explored the superiority of automated investment approach in trend following and technical analysis in cryptocurrency trading.

Persson et al. The RNN with ten hidden layers is optimised for the setting and the neural network augmented by VAR allows the network to be shallower, quicker and to have a better prediction than an RNN. RNN, VAR and R2N2 models are compared. The results showed that the VAR model has phenomenal test period performance and thus props up the R2N2 model, while the RNN performs poorly. This research is an attempt at optimisation of model design and applying to the prediction on cryptocurrency returns.

Deep Neural Network architectures play important roles in forecasting. In this subsection, we describe the cutting edge Deep Neural Network researches in cryptocurrency trading. Recent studies show the productivity of using models based on such architectures for modeling and forecasting financial time series, including cryptocurrencies. Livieris et al. The first component of the model consists of a convolutional layer and a pooling layer, where complex mathematical operations are performed to develop the features of the input data.

The second component uses the generated LSTM and the features of the dense layer. The results show that due to the sensitivity of the various hyperparameters of the proposed CNN-LSTM and its high complexity, additional optimisation configurations and major feature engineering have the potential to further improve the predictive power.

More Intelligent Evolutionary Optimisation IEO for hyperparameter optimisation is core problem when tuning the overall optimization process of machine learning models Huan et al. Lu et al. Fang et al. This research improved and verified the view of Sirignano and Cont that universal models have better performance than currency-pair specific models for cryptocurrency markets. Yao et al. The experimental results showed that the model performs well for a certain size of dataset.

The proposed integrated model is evaluated using a state-of-the-art deep learning model as a component learner, which consists of a combination of LSTM, bidirectional LSTM and convolutional layers. Kumar and Rath analyzed how deep learning techniques such as MLP and LSTM can help predict the price trend of Ethereum.

Sentiment analysis, a popular research topic in the age of social media, has also been adopted to improve predictions for cryptocurrency trading. This data source typically has to be combined with Machine Learning for the generation of trading signals.

Lamon et al. By this approach, the prediction on price is replaced with positive and negative sentiment. Weights are taken in positive and negative words in the cryptocurrency market. Authors compared Logistic Regression LR , Linear Support Vector Machine LSVM and NB as classifiers and concluded that LR is the best classifier in daily price prediction with Smuts conducted a similar binary sentiment-based price prediction method with an LSTM model using Google Trends and Telegram sentiment.

In detail, the sentiment was extracted from Telegram by using a novel measure called VADER Hutto and Gilbert Nasir et al. The experiment employed a rich set of established empirical approaches including VAR framework, copulas approach and non-parametric drawings of time series.

The results found that Google searches exert significant influence on Bitcoin returns, especially in the short-term intervals. Kristoufek discussed positive and negative feedback on Google trends or daily views on Wikipedia. The author mentioned different methods including Cointegration, Vector autoregression and Vector error-correction model to find causal relationships between prices and searched terms in the cryptocurrency market.

The results indicated that search trends and cryptocurrency prices are connected. There is also a clear asymmetry between the effects of increased interest in currencies above or below their trend values from the experiment. Kim et al.

A federal appeals court struck a major blow against the Consumer Financial Protection Bureau with a finding that its funding mechanism is unconstitutional. The decision is likely to be challenged, setting up a major fight for the future of the top U.

consumer-finance watchdog. As set up under the Dodd-Frank Act, the CFPB is funded by the Federal Reserve rather than congressional appropriations. But Republicans have chafed at what they view as anti-business practices and a lack of oversight.

The structure has been the target of legal challenges before. Democratic Sen. Elizabeth Warren, who oversaw the CFPB's creation , responded to the ruling on Twitter, writing that "extreme right-wing judges are throwing into question every rule the CFPB enforces to protect consumers and businesses alike.

Republican Sen. Cynthia Lummis, meanwhile, said the CFPB "needs the same Congressional oversight as every other government agency. The CFPB is expected to challenge the ruling, though it has yet to confirm that.

To that point, the CFPB issued new guidance to credit-reporting agencies Thursday about omitting what it called "junk data" from credit reports. The CFPB has faced several challenges to its existence over its 11 years in business. In , the Supreme Court ruled that restrictions on when its leader can be removed were unconstitutional, but rejected a plea to strike down the agency as a whole.

The most significant fear from progressive lawmakers and consumer groups is that the CFPB could see its resources chopped if left to the whims of Congress. Public Interest Research Group. The new court decision comes as the CFPB, under Biden-appointed director Rohit Chopra , has taken a more aggressive stance toward the financial industry than his Trump administration predecessors.

Chopra has also promised scrutiny over the way large technology companies are expanding into financial services. But the agency is also taking up initiatives with fintech industry support, including finally setting up open-banking rules to guide data-sharing between financial institutions and tech companies.

What the ruling means for the fintech industry remains to be seen. While regulators and companies can occasionally come into conflict, the agencies also serve an important role in providing rules of the road and certainty for business models. His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr. The ways Zia Faruqui right has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Veronica Irwin vronirwin is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle.

Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc. One hundred percent electronic. The author is Magistrate Judge Zia Faruqui. His rulings have made smart references to "The Big Lebowski," "Dr. Strangelove," and "SNL" parodies of the McLaughlin Group. Rather, before taking the judge position Faruqui was one of a group of prosecutors in the U. There, Faruqui prosecuted cases that involved terrorism, child pornography, and weapons proliferation.

But the ways Faruqui has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster. Crypto lawyers have drawn on his prior decisions in the context of the Tornado Cash sanctions, for example. Faruqui spoke with Protocol about the power of his position, and what people in crypto should understand about the law. There was another prosecutor, Christopher Brown — you know, the other Chris Brown — and he had taken an interest in this when we were both working on financial crime in the Washington, D.

Our U. attorney at the time, Jessie Liu, had this idea of using financial investigations in a way that was not limited to just white collar crime, or even narcotics cases, but also for cyber investigations, to national security investigations, and in civil cases.

A lot of what we were investigating was related to following the money and so she wanted us to be this multidisciplinary unit. But I have to say, we started with the goal of wanting to make T-shirts, and we never did that while I was there. Your decisions have also gotten a lot of attention. We're public servants! And in order for the public to have faith and trust us, they need to understand what it is that we're doing and what we're saying.

Humor is one way, not using a lot of legalese is another way. But I think there are many judges who are trying to make the judiciary more accessible, and so people can see the work that we're doing and understand what we're doing and then make their own opinions about if it's right or wrong.

But at least, if it's understandable, then there's still some trust in the framework even if you don't agree with how our decisions are stated. We are ambassadors for the judiciary to the people in our courtroom — it's a very frightening proposition being in court if you've been federally charged, and people have perceptions of what they think can happen there in terms of fairness or unfairness. But then it goes far beyond that.

I do a lot of work with the Administrative Office of the Courts, our central body doing civic education and outreach to high schools, because I want college and high school students and law students to have an experience where they get a chance to talk to a judge.

So my goal is certainly not just getting to one segment of the population, but it's making decisions accessible to whoever's interested in reading them. What has it felt like for you switching from that prosecutor role to magistrate judge? Lawyers are trying to take different frameworks from one topic and apply them to another, and then convince you that that is or is not appropriate.

Being a judge is very different because you're evaluating what the parties present to you as the applicable legal frameworks, and deciding how new, groundbreaking technology fits into legal frameworks that were written 10 or 15 years ago. But that's not really a place where judges get involved in saying how it ought to be regulated. There was, famously, a judge in Florida that said cryptocurrency was not money because you couldn't put it underneath your bed, and that's what money is: something that is tangible.

So different people are going to have different decisions. And that's not just true for crypto, but also other areas of the law. Your best-known crypto decisions strongly assert that crypto is traceable. One way people try to make it less traceable is with mixers, and Tornado Cash was sanctioned by OFAC not too long ago. Do you think the legal reasoning was sound enough for similar sanctions to be applied to other mixers, or decentralized exchanges?

I don't know. I think there's been some discussion that people may litigate some of these things, so I can't comment, because those frequently do come to our courthouse. And I think there are certainly people opining on that, yes and no. So much of what judges do is that we rely on the parties that are before us to tell us what's right and what's wrong. And then, you know, obviously, they'll have different views, and we make a decision based on what people say in front of us.

Are you aware that some legal analysis of the Tornado Cash sanctions references your recent decision in a cryptocurrency sanctions case? That's what good lawyers will always do. Even legislators might look at that as they try to think about where the gaps are. As a prosecutor I had a case where we sued three Chinese banks to give us their bank records, and it had never been done before.

Afterwards, Congress passed a new law, using the decisions from judges in this court and the D. circuit court, the court above us. So I'm sure people look at prior decisions and try to apply them in the ways that they want to.

Are there any misconceptions about how the law applies to crypto, or how your decisions should be interpreted, that you wish you could get across? One misconception is that the judges can't understand this technology — we can. People have these views in two extremes. The lawyer's fundamental job is to take super complex and technical things and boil them down to very easily digestible arguments for a judge, for a jury, or whoever it might be. The financial technology transformation is driving competition, creating consumer choice, and shaping the future of finance.

Hear from seven fintech leaders who are reshaping the future of finance, and join the inaugural Financial Technology Association Fintech Summit to learn more. Financial technology is breaking down barriers to financial services and delivering value to consumers, small businesses, and the economy.

Fintech puts American consumers at the center of their finances and helps them manage their money responsibly. From payment apps to budgeting and investing tools and alternative credit options, fintech makes it easier for consumers to pay for their purchases and build better financial habits. Fintech also arms small businesses with the financial tools for success, including low-cost banking services, digital accounting services, and expanded access to capital.

We advocate for modernized financial policies and regulations that allow fintech innovation to drive competition in the economy and expand consumer choice. Spots are still available for this hybrid event, and you can RSVP here to save your seat. Join us as we discuss how to shape the future of finance. In its broadest sense, Open Banking has created a secure and connected ecosystem that has led to an explosion of new and innovative solutions that benefit the customer, rapidly revolutionizing not just the banking industry but the way all companies do business.

Target benefits are delivered through speed, transparency, and security, and their impact can be seen across a diverse range of use cases. Sharing financial data across providers can enable a customer individual or business to have real-time access to multiple bank accounts across multiple institutions all in one platform, saving time and helping consumers get a more accurate picture of their own finances before taking on debt, providing a more reliable indication than most lending guidelines currently do.

Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need. Open Banking platforms like Klarna Kosma also provide a unique opportunity for businesses to overlay additional tools that add real value for users and deepen their customer relationships. The increased transparency brought about by Open Banking brings a vast array of additional benefits, such as helping fraud detection companies better monitor customer accounts and identify problems much earlier.

The list of new value-add solutions continues to grow. The speed of business has never been faster than it is today. For small business owners, time is at a premium as they are wearing multiple hats every day.

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The CFPB may be facing its most significant legal threat yet,Introduction

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Of course, it's important to remember only certain low-income Americans are eligible for Obama phones. There has been a price crash in late to early in cryptocurrency Yaya et al. He is a leading expert on public opinion and survey methodology, and has directed the PPIC Statewide Survey since Hrytsiuk et al. It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers. Elizabeth Warren, who oversaw the CFPB's creation , responded to the ruling on Twitter, writing that "extreme right-wing judges are throwing into question every rule the CFPB enforces to protect consumers and businesses alike.

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