arxiv:2003.11352v2 [q-fin.tr] 24 apr 2020 · peer version of electronic cash that can be sent...

Cryptocurrency Trading: A Comprehensive SurveyFan Fanga,∗, Carmine Ventrea, Michail Basiosb, Hoiliong Kongb, Leslie Kanthanb,David Martinez-Regob, Fan Wub and Lingbo Lib,∗

aKing’s College London, UKbTuring Intelligence Technology Limited, UK

A R T I C L E I N F O

Keywords:trading, cryptocurrency, machine learn-ing, econometrics

A B S T R A C T

In the recent years, the tendency of the number of financial institutions including cryptocurrencies intheir portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included byasset managers. Even though they share some commonalities with more traditional assets, they have aseparate nature of its own and their behaviour as an asset is still under 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 118research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems,bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio constructionand crypto-assets, technical trading and others). This paper also analyses datasets, research trends anddistribution among research objects (contents/properties) and technologies, concluding with somepromising opportunities that remain open in cryptocurrency trading.

1. IntroductionCryptocurrencies have experienced broad market accep-

tance and fast development despite their recent conception.Many hedge funds and asset managers have began to includecryptocurrency-related assets into their portfolios and trad-ing strategies. The academic community has similarly spentconsiderable efforts in researching cryptocurrency trading.This paper seeks to provide a comprehensive survey of theresearch on cryptocurrency trading, by which we mean anystudy aimed at facilitating and building strategies to tradecryptocurrencies.

As an emerging market and research direction, cryp-tocurrencies and cryptocurrency trading have seen consid-erable progress and a notable upturn in interest and activ-ity [97]. From Figure 1, we observe over 85% of papershave appeared since 2018, demonstrating the emergence ofcryptocurrency trading as new research area in financial trad-ing.

The literature is organised according into six distinct as-pects of cryptocurrency trading:

• Cryptocurrency trading software systems (i.e., real-time trading systems, turtle trading systems, arbitragetrading systems);

• Systematic trading including technical analysis, pairstrading and other systematic trading methods;

∗Corresponding [email protected] ( Fan Fang);

[email protected] ( Carmine Ventre);[email protected] ( Michail Basios); [email protected]( Hoiliong Kong); [email protected] ( Leslie Kanthan);[email protected] ( David Martinez-Rego);[email protected] ( Fan Wu); [email protected] (Lingbo Li)

ORCID(s):

Figure 1: Cryptocurrency Trading Publications (cumulative)during 2013-2019

• Emergent trading technologies including economet-ric methods, machine learning technology and otheremergent trading methods;

• Portfolio and cryptocurrency assets including researchamong cryptocurrency co-movements and crypto-assetportfolio research;

• Market condition research including bubbles [100] orcrash analysis and extreme conditions;

• Other Miscellaneous cryptocurrency trading research.

In this survey we aim at compiling the most relevant re-search in these areas and extract a set of descriptive indica-tors that can give an idea of the level of maturity research inthis are has achieved.

We also summarise research distribution (among researchproperties, categories and research technologies). The dis-tribution among properties and categories identifies classifi-cations of research objectives and contents. The distribution

Fan Fang et al.: Preprint submitted to Elsevier of 29

arX

iv:2

003.

1135

2v2

[q-

fin.

TR

] 2

4 A

pr 2

020

Cryptocurrency Trading: A Comprehensive Survey

among technologies identifies classifications of methodol-ogy or technical methods in researching cryptocurrency trad-ing. Specifically, we subdivide research distribution amongcategories and technologies into statistical methods and ma-chine learning technologies. Moreover, We identify datasetsand opportunities (potential research directions) that haveappeared in the cryptocurrency trading area. To ensure thatour survey is self-contained, we aim to provide sufficientmaterial to adequately guide financial trading researcherswho are interested in cryptocurrency trading.

There has been related work that discussed or partiallysurveyed the literature related to cryptocurrency trading. Kyr-iazis et al. [158] surveyed efficiency and profitable tradingopportunities in cryptocurrency markets. Ahamad et al. [4]and Sharma et al. [210] gave a brief survey on cryptocurren-cies. Ujan et al. [180] gave a brief survey of cryptocurrencysystems. To the best of our knowledge, no previous workhas provided a comprehensive survey particularly focusedon cryptocurrency trading.

In summary, the paper makes the following contribu-tions:

Definition. This paper defines cryptocurrency trading andcategorises it into: cryptocurrency markets, cryptocur-rency trading models and cryptocurrency trading strate-gies. The core content of this survey is trading strate-gies for cryptocurrencies while we cover all aspectsof it.

Multidisciplinary Survey. The paper provides a compre-hensive survey of 118 cryptocurrency trading papers,across different academic disciplines such as financeand economics, artificial intelligence and computerscience. Some papers may cover multiple aspects andwill be surveyed for each category.

Analysis. The paper analyses the research distribution, datasetsand trends that characterise the cryptocurrency trad-ing literature.

Horizons. The paper identifies challenges, promising re-search directions in cryptocurrency trading, aimed topromote and facilitate further research.

Figure 2 depicts the paper structure, which is informedby the review schema adopted. More details about this canbe found in Section 4.

2. Cryptocurrency TradingThis section provides an introduction to cryptocurrency

trading. We will discuss Blockchain, as the enabling tech-nology, cryptocurrency markets and cryptocurrency trad-ing strategies.

2.1. Blockchain2.1.1. Blockchain Technology Introduction

Blockchain is a digital ledger of economic transactionsthat can be used to record not just financial transactions, but

Figure 2: Tree structure of the contents in this paper

Figure 3: Workflow of Blockchain transaction

any object with an intrinsic value. [221]. In its simplestform, a Blockchain is a series of immutable data recordswith time stamps, which are managed by a cluster of ma-chines that do not belong to any single entity. Each of thesedata blocks is protected by cryptographic principle and boundto each other in a chain (cf. Figure 3 for the workflow).

Cryptocurrencies like Bitcoin are made on a peer-to-peer network structure. Each peer has a complete historyof all transactions, thus recording the balance of each ac-count. For example, a transaction is a file that says “A paysX Bitcoins to B” that is signed by A using its private key.This is basic public key cryptography, but also the build-ing block on which cryptocurrencies are based. After beingsigned, the transaction is broadcast on the network. When apeer discovers a new transaction, it checks to make sure thatthe signature is valid (this amounts to use the signer’s publickey, denoted as algorithm in Figure 3). If the verification isvalid then the block is added to the chain; all other blocksadded after it will “confirm” that transaction. For example,if a transaction is contained in block 502 and the length ofthe blockchain is 507 blocks, it means that the transactionhas 5 confirmations (507-502) [207].

2.1.2. From Blockchain to CryptocurrenciesConfirmation is a critical concept in cryptocurrencies;

only miners can confirm transactions. Miners add blocksto the Blockchain; they retrieve transactions in the previ-ous block and combine it with the hash of the precedingblock to obtain its hash, and then store the derived hash intothe current block. Miners in Blockchain accept transactions,mark them as legitimate and broadcast them across the net-work. After the miner confirms the transaction, each nodemust add it to its database. In layman terms, it has become



part of the Blockchain and miners undertake this work toobtain cryptocurrency tokens, such as Bitcoin. In contrastto Blockchain, cryptocurrencies are related to the use of to-kens based on distributed ledger technology. Any transac-tion involving purchase, sale, investment, etc. involves aBlockchain native token or sub-token. Blockchain is a plat-form that drives cryptocurrency and is a technology that actsas a distributed ledger for the network. The network createsthe means of transaction and enable the transfer of valueand information. Cryptocurrencies are the tokens used inthese networks to send value and pay for these transactions.They can be thought of as tools on the Blockchain, and insome cases can also function as resources or utilities. Otherinstances, they are used to digitize the value of assets. Insummary, Cryptocurrencies are part of an ecosystem basedon Blockchain technology.

2.2. Introduction of cryptocurrency market2.2.1. What is cryptocurrency?

Cryptocurrency is a decentralised medium of exchangewhich uses cryptographic functions to conduct financial trans-actions [84]. Cryptocurrencies leverage the Blockchain tech-nology to gain decentralisation, transparency, and immutabil-ity [176]. In the above, we have discussed how Blockchaintechnology is implemented for cryptocurrencies.

In general, the security of cryptocurrencies is built oncryptography, neither by people nor on trust [183]. For ex-ample, Bitcoin uses a method called ”Elliptic Curve Cryp-tography” to ensure that transactions involving Bitcoin aresecure [235]. Elliptic curve cryptography is a type of pub-lic key cryptography that relies on mathematics to ensurethe security of transactions. When someone attempts to cir-cumvent the aforesaid encryption scheme by brute force,it takes them one tenth the age of the universe to find avalue match when trying 250 billion possibilities every sec-ond [112]. Regarding its use as a currency, cryptocurrencyhas the same properties as money. It has controlled supply.Most cryptocurrencies limit the supply of tokens. E.g. forBitcoin, the supply will decrease over time and will reach itsfinal quantity sometime around 2,140. All cryptocurrenciescontrol the supply of tokens through a timetable encoded inthe Blockchain.

One of the most important features of cryptocurrenciesis the exclusion of financial institution intermediaries [119].The absence of a “middleman” lowers transaction costs fortraders. For comparison, if a bank’s database is hacked ordamaged, the bank will rely entirely on its backup to recoverany information that is lost or compromised. With cryp-tocurrencies, even if part of the network is compromised,the rest will continue to be able to verify transactions cor-rectly. Cryptocurrencies also have the important feature ofnot being controlled by any central authority [206]: the de-centralised nature of the Blockchain ensures cryptocurren-cies are theoretically immune to government control and in-terference.

As of December 20, 2019, there exist 4,950 cryptocur-rencies and 20,325 cryptocurrency markets; the market cap

Figure 4: Total Market Capitalization and Volume of cryp-tocurrency market, USD

is around 190 billion dollars [72]. Figure 4 shows histori-cal data on global market capitalisation and 24-hour tradingvolume [227]. The total market cap is calculated by aggre-gating the dollar market cap of all cryptocurrencies. Fromthe figure, we can observe how cryptocurrencies experienceexponential growth in 2017 and a large bubble burst in early2018. But in recent years, cryptocurrencies have show signsof stabilising.

There are three mainstream cryptocurrencies: Bitcoin(BTC), Ethereum (ETH), and Litecoin (LTC). Bitcoin wascreated in 2009 and garnered massive popularity. On Oc-tober 31, 2008, an individual or group of individuals oper-ating under the pseudonym Satoshi Nakamoto released theBitcoin white paper and described it as: ”A pure peer-to-peer version of electronic cash that can be sent online forpayment from one party to another without going througha counter party, ie. a financial institution.” [182] Launchedby Vitalik Buterin in 2015, Ethereum is a special Blockchainwith a special token called Ether (ETH symbol in exchanges).A very important feature of Ethereum is the ability to createnew tokens on the Ethereum Blockchain. The Ethereum net-work went live on July 30, 2015, and pre-mined 72 millionEthereum. Litecoin is a peer-to-peer cryptocurrency createdby Charlie Lee. It was created according to the Bitcoin pro-tocol, but it uses a different hashing algorithm. Litecoinuses a memory-intensive proof-of-work algorithm, Scrypt.Scrypt allows consumer hardware (such as GPUs) to minethose coins.

Figure 5 shows percentages of total cryptocurrency mar-ket capitalisation; Bitcoin and Ethereum occupy the vastmajority of the total market capitalisation (data collected on8 Jan 2020).

2.2.2. Cryptocurrency ExchangesA cryptocurrency exchange or digital currency exchange

(DCE) is a business that allows customers to trade cryp-tocurrencies. Cryptocurrency exchanges can be market mak-ers, usually using the bid-ask spread as a commission forservices, or as a matching platform, by simply charging fees.

Table 1 shows the top or classical cryptocurrency ex-changes according to the rank list, by volume, compiledon “nomics” website [188]. Chicago Mercantile Exchange(CME), Chicago Board Options Exchange (CBOE) and BAKKT



Table 1Cryptocurrency exchanges Lists

Exchanges Category Supported currencies Fiat Currency Registration country Regulatory authorityCME Derivatives BTC and Ethereum [65] USD USA [67] CFTC [66]CBOE Derivatives BTC [53] USD USA [52] CFTC [54]BAKKT (NYSE) Derivatives BTC [14] USD USA [15] CFTC [14]BitMex Derivatives 12 cryptocurrencies [30] USD Seychelles [31] -Binance Spot 98 cryptocurrencies [26] EUR, NGN, RUB, TRY Malta [171] FATF [25]Coinbase Spot 28 cryptocurrencies [70] EUR, GBP, USD USA [36] SEC [71]Bitfinex Spot > 100 cryptocurrencies [27] EUR, GBP, JPY, USD British Virgin Islands [28] NYAG [29]Bitstamp Spot 5 cryptocurrencies [32] EUR, USD Luxembourg [33] CSSF [34]Poloniex Spot 23 cryptocurrencies [202] USD USA [202] -

Figure 5: Percentage of Total Market Capitalisation [73]

(backed by New York Stock Exchange) are regulated cryp-tocurrency exchanges. Fiat currency data also comes from“nomics” website [188]. Regulatory authority and supportedcurrencies of listed exchanges are collected from officialwebsites or blogs.

2.3. Cryptocurrency Trading2.3.1. Definition

Firstly we give a definition of cryptocurrency trading.

Definition 1. Cryptocurrency trading is the act of buyingand selling of cryptocurrencies with the intention of makinga profit.

The definition of cryptocurrency trading can be broken downinto three aspects: object, operation mode and trading strat-egy. The object of cryptocurrency trading is the asset be-ing traded, which is “cryptocurrency”. The operation modeof cryptocurrency trading depends on the means of transac-tion in cryptocurrency market, which can be classified into“trading of cryptocurrency Contract for Differences (CFD)”(The contract between the two parties, often referred to asthe “buyer” and “seller”, stipulates that the buyer will paythe seller the difference between themselves when the po-sition closes [10]) and “buying and selling cryptocurrenciesvia an exchange”. A trading strategy in cryptocurrency trad-ing, formulated by an investor, is an algorithm that definesa set of predefined rules to buy and sell on cryptocurrencymarkets.

2.3.2. Advantages of Trading CryptocurrencyThe benefits of cryptocurrency trading include:

Drastic fluctuations. The volatility of cryptocurrencies areoften likely to attract speculative interest and investors.The rapid fluctuations of intraday prices can providetraders with great money-earning opportunities, but italso includes more risk.

24-hour market. The cryptocurrency market is available24 hours a day, 7 days a week because it is a de-centralised market. Unlike buying and selling stocksand commodities, the cryptocurrency market is nottraded physically from a single location. Cryptocur-rency transactions can take place between individuals,in different venues across the world.

Near Anonymity. Buying goods and services using cryp-tocurrencies is done online and does not require tomake one’s own identity public. With increasing con-cerns over identity theft and privacy, cryptocurren-cies can thus provide users with some advantages re-garding privacy. Different exchanges have specificKnow-Your-Customer (KYC) measures used to iden-tify users or customers [3]. The KYC undertook inthe exchanges allows financial institutions to reducethe financial risk while maximising the wallet owner’sanonymity.

Peer-to-peer transactions. One of the biggest benefits ofcryptocurrencies is that they do not involve financialinstitution intermediaries. As mentioned above, thiscan reduce transaction costs. Moreover, this featuremight appeal users who distrust traditional systems.Over-the-counter (OTC) cryptocurrency markets of-fer, in this context, peer-to-peer transactions on theBlockchain. The most famous cryptocurrency OTCmarket is “LocalBitcoin [166]”.

Programmable “smart” capabilities. Some cryptocurren-cies can bring other benefits to holders, including lim-ited ownership and voting rights. Cryptocurrenciesmay also include partial ownership interest in physi-cal assets such as artwork or real estate.



3. Cryptocurrency Trading StrategyCryptocurrency trading strategy is the main focus of this

survey. There are many trading strategies, which can bebroadly divided into two main categories: technical and fun-damental. They are similar in the sense that they both relyon quantifiable information that can be backtested againsthistorical data to verify their performance. In recent years,a third kind of trading strategy, that we call quantitative, hasreceived increasing attention. Such a trading strategy is sim-ilar to a technical trading strategy because it uses trading ac-tivity information on the exchange to make buying or sell-ing decisions. Quantitative traders build trading strategieswith quantitative data, which is mainly derived from price,volume, technical indicators or ratios to take advantage ofinefficiencies in the market and are executed automaticallyby a trading software. Cryptocurrency market is differentfrom traditional markets as there are more arbitrage oppor-tunities, higher fluctuation and transparency. Due to thesecharacteristics, most traders and analysts prefer using quan-titative trading strategies in cryptocurrency markets.

3.1. Cryptocurrency Trading Software SystemSoftware trading systems allow international transactions,

process customer accounts and information, and accept andexecute transaction orders [44]. A cryptocurrency tradingsystem is a set of principles and procedures that are pre-programmed to allow trade between cryptocurrencies andbetween fiat currencies and cryptocurrencies. Cryptocur-rency trading systems are built to overcome price manip-ulation, cybercriminal activities and transaction delays [20].When developing a cryptocurrency trading system, we mustconsider capital market, base asset, investment plan and strate-gies [179]. Strategies are the most important part of an ef-fective cryptocurrency trading system and they will be in-troduced below. There exist several cryptocurrency tradingsystems that are available commercially, for example Cap-folio, 3Commas, CCXT, Freqtrade and Ctubio. From thesecryptocurrency trading systems, investors can obtain pro-fessional trading strategy support, fairness and transparencyfrom professional third-party consulting company and fastcustomer services.

3.2. Systematic TradingSystematic Trading is a way to define trading goals,

risk controls and rules. In general, systematic trading in-cludes high frequency trading and slower investment typeslike systematic trend tracking. In this survey, we dividesystematic cryptocurrency trading into technical analysis,pairs trading and others. Technical analysis in cryptocur-rency trading is the act of using historical patterns of trans-action data to assist a trader in assessing current and pro-jecting future market conditions for the purpose of makingprofitable trades. Price and volume charts summarise alltrading activity made by market participants in an exchangeand affect their decisions. Some experiments showed thatthe use of specific technical trading rules allows generat-ing excess returns, which is useful to cryptocurrency traders

and investors in making optimal trading and investment de-cisions [110]. Pairs trading is a systematic trading strat-egy which consider two similar assets with slightly differentspreads. If the spread widens, short the high stocks and buythe low stocks. When the spread narrows again to a certainequilibrium value, a profit is generated [88]. Papers shownin this section involve the analysis and comparison of tech-nical indicator, pairs and informed trading, amongst otherstrategies.

3.3. Emergent Trading TechnologiesEmergent trading strategies for cryptocurrency include

strategies that are based on econometrics and machine learn-ing technologies.

3.3.1. Econometrics on CryptocurrencyEconometric methods apply a combination of statistical

and economic theories to estimate economic variables andpredict their values [233]. Statistical models use mathe-matical equations to encode information extracted from thedata [145]. In some cases, statistical modeling techniquescan quickly provide sufficiently accurate models [23]. Othermethods might be used, such as sentiment-based predictionand long-and-short-term volatility classification based pre-diction [58]. The prediction of volatility can be used tojudge the price fluctuation of cryptocurrencies, which is alsovaluable for the pricing of cryptocurrency related deriva-tives [140].

When studying cryptocurrency trading using economet-rics, researchers apply statistical models on time-series datalike generalised autoregressive conditional heteroskedastic-ity (GARCH) and BEKK (named after Baba, Engle, Kraftand Kroner, 1995 [90]) models to evaluate the fluctuationof cryptocurrencies [49]. A linear statistical model is amethod to evaluate the linear relationship between pricesand an explanatory variable [185]. When there exist morethan one explanatory variable, we can model the linear re-lationship between explanatory (independent) and response(dependent) variables with multiple linear models. The com-mon linear statistical model used in time-series analysis isautoregressive moving average (ARMA) model [63].

3.3.2. Machine Learning TechnologyMachine learning is an efficient tool for developing Bit-

coin and other cryptocurrency trading strategies [175] be-cause it can infer data relationships that are often not di-rectly observable by humans. From the most basic perspec-tive, Machine Learning relies on the definition of two maincomponents: input features and objective function. The def-inition of Input Features (data sources) is where knowledgeof fundamental and technical analysis comes into play. Wemay divide the input into several groups of features, for ex-ample, those based on Economic indicators (such as, grossdomestic product indicator, interest rates,etc.), Social in-dicators (Google Trends, Twitter, etc.), Technical indica-tors (price, volume,etc.) and other Seasonal indicators (timeof day, day of week, etc.). The objective function definesthe fitness criteria one uses to judge if the Machine Learn-



Figure 6: Process of machine learning in predicting cryp-tocurrency

ing model has learnt the task at hand. Typical predictivemodels try to anticipate numeric (e.g., price) or categorical(e.g., trend) unseen outcomes. The machine learning modelis trained by using historic input data (sometimes calledin-sample) to generalise patterns therein to unseen (out-of-sample) data to (approximately) achieve the goal defined bythe objective function. Clearly, in the case of trading, thegoal is to infer trading signals from market indicators whichhelp to anticipate asset future returns.

Generalisation error is a pervasive concern in the ap-plication of Machine Learning to real applications, and ofutmost importance in Financial applications. We need touse statistical approaches, such as, cross validation, to val-idate the model before we actually use it to make predic-tions. In machine learning, this is typically called “valida-tion”. The process of using machine learning technology topredict cryptocurrency is shown in Figure 6.

Depending on the formulation of the main learning loop,we can classify Machine Learning approaches into threecategories: Supervised learning, Unsupervised learning andReinforcement learning. Supervised learning is used to de-rive a predictive function from labelled training data. La-belled training data means that each training instance in-cludes inputs and expected outputs. Usually these expectedoutput are produced by a supervisor and represent the ex-pected behaviour of the model. The most used labels intrading are derived from in sample future returns of assets.Unsupervised learning tries to infer structure from unla-belled training data and it can be used during exploratorydata analysis to discover hidden patterns or to group dataaccording to any pre-defined similarity metrics. Reinforce-ment learning depart from an utility function and the soft-ware agent is trained to maximize this utility under currentand estimated future environment states. The objective isimplicitly defined by the utility function, and agents canchoose to exchange short term returns for future ones. Infinancial sector, some trading challenges can be expressedas a game in which an agent aims at maximising end of pe-riod return.

The use of machine learning in cryptocurrency tradingresearch encompasses the connection between data sourcesunderstanding and machine learning model research. Fur-ther concrete examples are shown in later section.

3.4. Portfolio ResearchPortfolio theory advocates a diversification of invest-

ments to maximize returns for a given level of risk by al-

locating assets strategically. The celebrated mean-varianceoptimisation is a prominent example of this approach [172].Generally, crypto asset denotes a digital asset (i.e., cryp-tocurrencies and derivatives). There are some common waysto build a diversified portfolio in crypto assets. The firstmethod is to diversify across markets, which is to mix awide variety of investments within a portfolio of cryptocur-rency market. The second method is to consider the in-dustry sector, which is to avoid investing too much moneyin any one category. Diversified investment of portfolio incryptocurrency market includes portfolio across cryptocur-rencies [165] and portfolio across global market includingstocks and futures [133].

3.5. Market Condition ResearchMarket condition research appears especially important

for cryptocurrencies. A financial bubble is a significant in-crease in the price of an asset without changes in its intrinsicvalue [42]. Many experts pinpoint a cryptocurrency bubblein 2017 when the prices of cryptocurrencies grew by 900%.In 2018, Bitcoin faced a collapse in its value. This signif-icant fluctuation inspired researchers to study bubbles andextreme conditions in cryptocurrency trading.

4. Paper Collection and Review SchemaThe section introduces the scope and approach of our

paper collection, a basic analysis, and the structure of oursurvey.

4.1. Survey ScopeWe adopt a bottom up approach to the research in cryp-

tocurrency trading, starting from the systems up to risk man-agement techniques. For the underlying trading system, thefocus is at the optimisation of trading platforms structureand improvements of computer science technologies.

At a higher level, researchers focus on the design ofmodels to predict return or volatility in cryptocurrency mar-kets. These techniques become useful to the generation oftrading signals. on the next level above predictive mod-els, researchers discuss technical trading methods to tradein real cryptocurrency markets. Bubbles and extreme condi-tions are hot topics in cryptocurrency trading because, asdiscussed above, these markets have shown to be highlyvolatile (whilst volatility went down after crashes). Portfo-lio and cryptocurrency asset management are effective meth-ods to control risk. We group these two areas in risk man-agement research. Other papers included in this survey in-clude topics like pricing rules, dynamic market analysis,regulatory implications, and so on. Table 2 shows the gen-eral scope of cryptocurrency trading included in this survey.

Since many trading strategies and methods in cryptocur-rency trading are closely related to stock trading, some re-searchers migrate or use the research results for the latter tothe former. When conducting this research, we only con-sider those papers whose research focus on cryptocurrencymarkets or a comparison of trading in those and other finan-cial markets.



Table 2Survey scope table

Trading (bottom up)

Trading SystemPrediction (return)Prediction (volatility)Technical trading methods

Risk management Bubble and extreme conditionPorfolio and Cryptocurrency asset

Others

Specifically, we apply the following criteria when col-lecting papers related to cryptocurrency trading:

1. The paper introduces or discusses the general idea ofcryptocurrency trading or one of the related aspects ofcryptocurrency trading.

2. The paper proposes an approach, study or frameworkthat targets optimised efficiency or accuracy of cryp-tocurrency trading.

3. The paper compares different approaches or perspec-tives in trading cryptocurrency.

By “cryptocurrency trading” here, we mean one of the termslisted in Table 2 and discussed above.

Some researchers gave a brief survey of cryptocurrency [4,210], cryptocurrency systems [180] and cryptocurrency trad-ing opportunities [158]. These surveys are rather limited inscope as compared to ours, which also includes a discussionon the latest papers in the area; we want to remark that thisis a fast moving research field.

4.2. Paper Collection MethodologyTo collect the papers in different areas or platforms, we

used keyword searches on Google Scholar and arXiv, twoof the most popular scientific databases. The keywords usedfor searching and collecting are listed below. [Crypto] meanscryptocurrency market, which is our research interest be-cause methods might be different among different markets.We conducted 6 searches across the two repositories just be-fore October 15, 2019.

- [Crypto] + Trading- [Crypto] + Trading system- [Crypto] + Prediction- [Crypto] + Trading strategy- [Crypto] + Risk Management- [Crypto] + Portfolio

To ensure a high coverage, we adopted the so-calledsnowballing [239] method on each paper found throughthese keywords. We checked papers added from snowballingmethods that satisfy the criteria introduced above, until wereached closure.

4.3. Collection ResultsTable 3 shows the details of the results from our paper

collection. Keyword searches and snowballing resulted in118 papers across the six research areas of interest in Sec-tion 4.1.

Table 3Paper query results. #Hits, #Title, and #Body denote thenumber of papers returned by the search, left after title filter-ing, and left after body filtering, respectively.

Key Words #Hits #Title #Body[Crypto] + Trading 555 32 29[Crypto] + Trading System 4 3 2[Crypto] + Prediction 26 14 13[Crypto] + Trading Strategy 22 9 8[Crypto] + Risk Management /[Crypto] + Portfolio 120 14 14

Query - - 66Snowball - - 52Overall - - 118

Figure 7: Publication Venue Distribution

Figure 7 shows distribution of papers published at dif-ferent research sites. Among all the papers, 42.37% pa-pers are published in Finance and Economics venues suchas Journal of Financial Economics (JFE), Cambridge Centrefor Alternative Finance (CCAF), Finance Research Letters,Centre for Economic Policy Research (CEPR) and Journalof Risk and Financial Management (JRFM); 5.08% papersare published in Science venues such as Public Library OfScience one (PLOS one), Royal Society open science andSAGE; 16.95% papers are published in Intelligent Engi-neering and Data Mining venues such as Symposium Se-ries on Computational Intelligence (SSCI), Intelligent Sys-tems Conference (IntelliSys), Intelligent Data Engineeringand Automated Learning (IDEAL) and International Con-ference on Data Mining (ICDM); 4.24% papers are pub-lished in Physics venues such as Physica A and Interna-tional Cosmetic Physicians Society (ICPS); 11.02% papersare published in AI and complex system venues such asComplexity and International Federation for Information Pro-cessing (IFIP); 18.64% papers are published in Others venueswhich contains independently published papers and disser-tations; 1.70% papers are published on arXiv. The distribu-tion of different venues shows that cryptocurrency tradingis mostly published in Finance and Economics venues, butwith a wide diversity otherwise.



Table 4Review Schema

Classification Sec Topic

Cryptocurrency Trading Software System

5.1 Trading Infrastructure System5.2 Real-time Cryptocurrency Trading System5.3 Turtle trading system in Cryptocurrency market5.4 Arbitrage Trading Systems for Cryptocurrencies5.5 Comparison of three cryptocurrency trading systems

Systematic Trading6.1 Technical Analysis6.2 Pairs Trading6.3 Others

Emergent Trading Technologies7.1 Econometrics on cryptocurrency7.2 Machine learning technology7.3 Others

Portfolio and Cryptocurrency Assets 8.1 Research among cryptocurrency pairs and related factors8.2 Crypto-asset portfolio research

Market condition research 9.1 Bubbles and crash analysis9.2 Extreme condition

Others 10 Others related to Cryptocurrency Trading

Summary Analysis of Literature Review

11.1 Timeline11.2 Research distribution among properties11.3 Research distribution among categories and technologies11.4 Datasets used in cryptocurrency trading

4.4. Survey OrganisationWe discuss the contributions of the collected papers and

a statistical analysis of these papers in the remainder of thepaper, according to Table 4.

The papers in our collection are organised and presentedfrom six angles. We introduce the work about several dif-ferent cryptocurrency trading software systems in Section5. Section 6 introduces systematic trading applied to cryp-tocurrency trading. In Section 7, we introduce some emer-gent trading technologies including econometrics on cryp-tocurrencies, machine learning technologies and other emer-gent trading technologies in cryptocurrency market. Section8 introduces research on cryptocurrency pairs and relatedfactors and crypto-asset portfolios research. In Section 9 wediscuss cryptocurrency market condition research, includingbubbles, crash analysis, and extreme conditions. Section 10introduces other research included in cryptocurrency tradingnot covered above.

We would like to emphasize that the six headings abovefocus on a particular aspect of cryptocurrency trading; wegive a complete organisation of the papers collected undereach heading. This implies that those papers covering morethan one aspect will be discussed in different sections, oncefrom each angle.

We analyse and compare the number of research paperson different cryptocurrency trading properties and technolo-gies in Section 11, where we also summarise the datasetsand the timeline of research in cryptocurrency trading.

We build upon this review to conclude in Section 12 withsome opportunities for future research.

5. Cryptocurrency Trading Software Systems5.1. Trading Infrastructure Systems

Following the development of computer science and cryp-tocurrency trading, many cryptocurrency trading systems/botshave been developed. Table 5 compares the cryptocurrencytrading systems existing in the market. The table is sortedbased on URL types (GitHub or Official website) and GitHubstars (if appropriate).

Capfolio is a proprietary payable cryptocurrency tradingsystem which is a professional analysis platform and has aadvanced backtesting engine [45]. It supports five differentcryptocurrency exchanges.

3 Commas is a proprietary payable cryptocurrency trad-ing system platform which can take profit and stop loss or-ders at the same time [1]. Twelve different cryptocurrencyexchanges are compatible with this system.

CCXT is a cryptocurrency trading system with a unifiedAPI out of the box and optional normalized data, and sup-ports many Bitcoin / Ether / Altcoin exchange markets andmerchant APIs. Any trader or developer can create a tradingstrategy based on this data and access public transactionsthrough the APIs [55]. The CCXT library is used to con-nect and trade with cryptocurrency exchanges and paymentprocessing services worldwide. It provides quick access tomarket data for storage, analysis, visualisation, indicator de-velopment, algorithmic trading, strategy backtesting, auto-mated code generation and related software engineering. Itis designed for coders, skilled traders, data scientists and fi-nancial analysts to build trading algorithms. Current CCXTfeatures include:

I. Support for many cryptocurrency exchanges;II. Fully implemented public and private APIs;

III. Optional normalized data for cross-exchange analysisand arbitrage;



IV. Out-of-the-box unified API, very easy to integrate.

Blackbird Bitcoin Arbitrage is a C++ trading systemthat automatically executes long / short arbitrage betweenBitcoin exchanges. It can generate market-neural strategieswhich do not transfer funds between exchanges [35]. Themotivation behind Blackbird is to naturally profit from thesetemporary price differences between different exchanges whilebeing market neutral. Unlike other Bitcoin arbitrage sys-tems, Blackbird does not sell but actually short sells Bitcoinon the short exchange. This feature offers two importantadvantages. Firstly, the strategy is always market agnostic:fluctuations (rising or falling) in the Bitcoin market will notaffect the strategy returns. This eliminates the huge risks ofthis strategy. Secondly, this strategy does not require trans-ferring funds (USD or BTC) between Bitcoin exchanges.Buy and sell transactions are conducted in parallel on twodifferent exchanges. There is no need to deal with transmis-sion delays.

StockSharp is an open source trading platform for trad-ing at any market of the world including 48 cryptocurrencyexchanges [216]. It has a free C# library and free tradingcharting application. Manual or automatic trading (algo-rithmic trading robot, regular or HFT) can be run on thisplatform. StockSharp consists of five components that offerdifferent features:

I. S#.Designer - Free universal algorithm strategy app,easy to create strategies;

II. S#.Data - free software that can automatically load andstore market data;

III. S#.Terminal - free trading chart application (tradingterminal);

IV. S#.Shell - ready-made graphics framework that can bechanged according to needs and has fully open sourcein C#;

V. S#.API - a free C# library for programmers using Vi-sual Studio. Any trading strategies can be created inS#.API.

Freqtrade is a free and open source cryptocurrency trad-ing robot system written in Python. It is designed to supportall major exchanges and is controlled by telegram. It con-tains backtesting, mapping and money management tools,and strategy optimization through machine learning [102].Freqtrade has following features:

I. Persistence: Persistence is achieved through sqlite tech-nology;

II. Strategy optimization through machine learning: Usemachine learning to optimize your trading strategy pa-rameters with real trading data;

III. Marginal Position Size: Calculates winning rate, risk-return ratio, optimal stop loss and adjusts position size,and then trades positions for each specific market;

IV. Telegram management: use telegram to manage therobot.

V. Dry run: Run the robot without spending money;

CryptoSignal is a professional technical analysis cryp-tocurrency trading system [80]. Investors can track over 500coins of Bittrex, Bitfinex, GDAX, Gemini and more. Auto-mated technical analysis include momentum, RSI, IchimokuCloud, 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 implementa-tion of trading strategies; Secondly, it is easy to install withDocker.

Ctubio is a C++ based low latency (high frequency)cryptocurrency trading system [81]. This trading system canplace or cancel orders through supported cryptocurrency ex-changes in less than a few milliseconds. Moreover, it pro-vides a charting system that can visualise the trading ac-count status including trades completed, target position forfiat currency, etc.

Catalyst is a analysis and visualization of cryptocur-rency trading system [51]. It makes trading strategies easyto express and trace back historical data (daily and minuteresolution), providing analysis and insights into the perfor-mance of specific strategies. Catalyst allows users to shareand organize data and build profitable, data-driven invest-ment strategies. Catalyst not only supports the trading exe-cution but also offers historical price data of all crypto assets(from minute to daily resolution). Catalyst also has back-testing and real-time trading capabilities, which enables userto seamlessly transit between the two different trading modes.Lastly, Catalyst integrates statistics and machine learning li-braries (such as matplotlib, scipy, statsmodels and sklearn)to support the development, analysis and visualization of thelatest trading systems.

Golang Crypto Trading Bot is a Go based cryptocur-rency trading system [111]. Users can test the strategy insandbox environment simulation. If simulation mode is en-abled, a fake balance for each coin must be specified foreach exchange.

5.2. Real-time Cryptocurrency Trading SystemsAmit et al. [20] developed a real-time Cryptocurrency

Trading System. A real-time cryptocurrency trading sys-tem is composed of clients, server and database. Tradersuse a web-application to login the server to buy/sell cryptoassets. The server collects cryptocurrency market data bycreating a script which uses the Coinmarket API. Finally,the database collects balances, trades and order book infor-mation from server. Data in database is the copy of masterdata. Authors used login authentication, IP address vali-dation, session-hashing-salt and user document verificationto protect the security of this trading system. The authorstested the system with an experiment that demonstrates user-friendly experiences for traders in cryptocurrency exchangeplatform.

5.3. Turtle trading system in Cryptocurrencymarket

The original Turtle Trading system is a trend followingtrading system developed in the 1970’s. The idea is to gen-



Table 5Comparison of existing cryptocurrency trading systems. #Exchange, Language, and#Popularity denote the number of the exchanges that are supported by this software,programming language used, and the popularity of the software (number of the stars inGithub).

Name Features #Exchange Language Open-Source URL #PopularityCapfolio Professional analysis platform, 5 Not mentioned No Official website [45]

Advanced backtesting engine3 Commas Simultaneous take profit and 12 Not mentioned No Official website [1]

stop loss ordersCCXT An out of the box unified API, 10 JavaScript / Python / PHP Yes GitHub [55] 13k

optional normalized dataBlackBird Strategy is market-neutral 8 C++ Yes GitHub [35] 4.7k

strategy not transfer funds between exchangesStockSharp Free C# library, 48 C# Yes GitHub [216] 2.6k

free trading charting applicationFreqtrade Strategy Optimization by machine learning, 2 Python Yes GitHub [102] 2.4k

Calculate edge position sizingCryptoSignal Technical analysis trading system 4 Python Yes GitHub [80] 1.9kCtubio Low latency 1 C++ Yes GitHub [81] 1.7kCatalyst Analysis and visualization of system 4 Python Yes GitHub [51] 1.7kseamless transition between live

and back-testingGoLang Sandbox environment simulation 7 Go Yes GitHub [111] 277

erate buy and sell signals on a stock for short-term and long-term breakouts and its cut-loss condition which is measuredby Average true range (ATR) [137]. The trading system willadjust 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 TurtleTrading system is improved with smaller time interval spansand introduces a new rule by using exponential moving av-erage (EMA). Three EMA values are used to trigger “buy”signal: 30EMA (Fast), 60EMA (Slow), 100EMA (Long).The author of [137] performed backtesting and comparingboth trading systems (Original Turtle and Extended Tur-tle) on 8 prominent cryptocurrencies. Through the exper-iment, Original Turtle Trading System achieved 18.59% av-erage net profit margin (percentage of net profit over totalrevenue) and 35.94% average profitability (percentage ofwinning trades over total numbers of trades) in 87 tradesthrough nearly one year. Extended Turtle Trading Systemachieved 114.41% average net profit margin and 52.75%average profitability in 41 trades through the same time in-terval. This research showed how Extended Turtle TradingSystem compared can improve over Original Turtle TradingSystem in trading cryptocurrencies.

5.4. Arbitrage Trading Systems forCryptocurrencies

Christian [194] introduced arbitrage trading systems forcryptocurrencies. Arbitrage trading aims to spot the differ-ences in price that can occur when there are discrepancies inthe levels of supply and demand across multiple exchanges.As a result, a trader could realise a quick and low-risk profitby buying from one exchange and selling at a higher priceon a different exchange. Arbitrage trading signals are caughtby automated trading software. The technical differencesbetween data sources impose a server process to be organ-ised for each data source. Relational databases and SQL area reliable solution due to large amount of relational data.The author used the system to catch arbitrage opportunities

on 25 May 2018 among 787 cryptocurrencies on 7 differentexchanges. The research paper [194] listed the best ten trad-ing signals made by this system from 186 available foundsignals. The results showed that the system caught tradingsignal of “BTG-BTC” to get a profit up to 495.44% whenarbitraging to buy in Cryptopia exchange and sell in Bi-nance exchange. Another three well traded arbitrage signals(profit expectation around 20% mentioned by the author)were found on 25 May 2018. Arbitrage Trading SoftwareSystem introduced in that paper presented general princi-ples and implementation of arbitrage trading system in cryp-tocurrency market.

5.5. Comparison of three cryptocurrency tradingsystems

Real-time trading system has real-time function in col-lecting data and generating trading algorithms. Turtle trad-ing system and arbitrage trading system have shown a sharpcontrast in their profit and risk behavior. Using Turtle trad-ing system in cryptocurrency markets got high return withhigh risk. Arbitrage trading system is inferior in terms ofrevenue but also has a lower risk. One feature that turtletrading system and arbitrage trading system have in com-mon is they performed well in capturing alpha.

6. Systematic Trading6.1. Technical Analysis

Many researchers have focused on technical indicators(patterns) analysis for trading on cryptocurrency markets.Examples of studies with this approach include “Turtle Souppattern strategy” [222], “Nem (XEM) strategy” [225], “Amaz-ing Gann Box strategy” [223], “Busted Double Top Pat-tern strategy” [224], and “Bottom Rotation Trading strat-egy” [226]. Table 6 shows the comparison among thesefive classical technical trading strategies using technical in-dicators. “Turtle soup pattern strategy” [222] used 2-daybreakout of price in predicting price trends of cryptocurren-



Table 6Comparison among five classical technical trading strategies

Technical trading strategy Core Methods Tecchnical tools/patternsTurtle Soup pattern [222] 2-daybreakout of price Chart trading patterns

Nem (XEM) [225] Price trends combined ROC & RSI Rate of Change indictor (ROC)Relative strength index (RSI)

Amazing Gann Box [223] Predict exact points of rises and fallsin Gann Box (catch explosive trends)

Candlestick, boxcharts withFibonacci Retracement

Busted Double Top Pattern [224] Bearish reversal trading pattern thatgenerates a sell signal Price chart pattern

Bottom Rotation Trading [226] Pick the bottom before the reversalhappens Price chart pattern, box chart

cies. This strategy is a kind of chart trading pattern. “Nem(XEM) strategy” combined Rate of Change (ROC) indica-tor and Relative Strength Index (RSI) in predicting pricetrends [225]. “Amazing Gann Box” predicted exact pointsof increase and decrease in Gann Box which are used tocatch explosive trends of cryptocurrency price [223]. Tech-nical analysis tools such as candlestick and boxcharts withFibonacci Retracement based on golden ratio are used in thistechnical analysis. Fibonacci Retracement uses horizontallines to indicate where possible support and resistance levelsare in the market. “Busted Double Top Pattern” used Bear-ish reversal trading pattern which generates a sell signal topredict price trends [224]. “Bottom Rotation Trading” is atechnical analysis method which picks the bottom before thereversal happens. This strategy used price chart pattern andbox chart as technical analysis tools.

Sungjoo et al. [116] investigated using genetic program-ming (GP) to find attractive technical patterns in the cryp-tocurrency market. Over 12 technical indicators includingMoving Average (MA) and Stochastic oscillator were usedin experiments; adjusted gain, match count, relative marketpressure and diversity measures have been used to quantifythe attractiveness of technical patterns. With extended ex-periments, the GP system is shown to find successfully at-tractive technical patterns, which are useful for portfolio op-timization. Hudson et al. [124] applied almost 15, 000 tech-nical trading rules (classified into MA rules, filter rules, sup-port resistance rules, oscillator rules and channel breakoutrules). This comprehensive study found that technical trad-ing rules provide investors with significant predictive powerand profitability. Shaen et al. [76] analysed various technicaltrading rules in the form of the moving average-oscillatorand trading range break-out strategies to generate higherreturns in cryptocurrency markets. By using one-minutedollar denominated Bitcoin close-price data, the backtestshowed variable-length moving average (VMA) rule per-forms best considering it generates most useful signals inhigh frequency trading.

6.2. Pairs TradingPairs trading is a trading strategy that attempts to ex-

ploit mean-reversion between the prices of certain securi-ties. Miroslav [99] investigated applicability of standardpairs trading approaches on cryptocurrency data with thebenchmarks of Gatev et al. [109]. The pairs trading strategy

is constructed in two steps. Firstly, suitable pairs with a sta-ble long-run relationship are identified. Secondly, the long-run equilibrium is calculated and pairs trading strategy isdefined by the spread based on the values. The research alsoextended intra-day pairs trading using high frequency data.Overall, the model was able to achieve 3% monthly profitin Miroslav’s experiments [99]. Broek [41] applied pairstrading based on cointegration in cryptocurrency trading and31 pairs were found to be significantly cointegrated (withinsector and cross-sector). By selecting four pairs and testingover a 60-day trading period, the pairs trading strategy gotits profitability from arbitrage opportunities, which rejectedthe Efficient-market hypothesis (EMH) for the cryptocur-rency market.

6.3. OthersOther systematic trading methods in cryptocurrency trad-

ing mainly include informed trading. Using USD / BTCexchange rate trading data, Feng et al. [98] found evidenceof informed trading in the Bitcoin market in that quantilesof the order sizes of buyer-initiated (seller-initiated) ordersare abnormally high before large positive (negative) events,compared to the quantiles of seller-initiated (buyer-initiated)orders; this study adopts a new indicator inspired by the vol-ume imbalance indicator [87]. The evidence of informedtrading in Bitcoin market suggests that investors profit ontheir private information when they get information beforeit is widely available.

7. Emergent Trading Technologies7.1. Econometrics on cryptocurrency

Copula-quantile causality analysis and Granger-causalityanalysis are methods to investigate causality in cryptocur-rency trading analysis. Bouri et al. [38] applied a copula-quantile causality approach on volatility in the cryptocur-rency market. The approach of the experiment extendedCopula-Granger-causality in distribution (CGCD) methodof Lee and Yang [162] in 2014. The experiment constructedtwo tests of CGCD using copula functions. The paramet-ric test employed six parametric copula functions to dis-cover dependency density between variables. The perfor-mance matrix of these functions varies with independentcopula density. Three distribution regions are the focus ofthis research: left tail (1%, 5%, 10% quantile), central re-



gion (40%, 60% quantile and median) and right tail (90%,95%, 99% quantile). The study provided significant evi-dence of Granger causality from trading volume to the re-turns of seven large cryptocurrencies on both left and righttails. Badenhorst [12] attempted to reveal whether spot andderivative market volumes affect Bitcoin price volatility withGranger-causality method and ARCH (1,1). The result showsspot trading volumes have a significant positive effect onprice volatility while relationship between cryptocurrencyvolatility and derivative market is uncertain.

Several econometrics methods in time series research,such as GARCH and BEKK, have been used in the liter-ature on cryptocurrency trading. Conrad et al. [75] usedthe GARCH-MIDAS model to extract long and short-termvolatility components of Bitcoin market. The technical de-tails of this model decomposed the conditional variance intolow-frequency and high-frequency component. The resultsidentified that S&P 500 realized volatility has a negative andhighly significant effect on long-term Bitcoin volatility andS&P 500 volatility risk premium has a significantly positiveeffect on long-term Bitcoin volatility. Ardia et al. [7] usedthe Markov Switching GARCH (MSGARCH) model to testthe existence of institutional changes in the GARCH volatil-ity dynamics of Bitcoin’s logarithmic returns. A Bayesianmethod was used for estimating model parameters and cal-culating VaR prediction. The results showed that MSGARCHmodels clearly outperform single-regime GARCH for Value-at-Risk forecasting. Troster et al. [228] performed generalGARCH and GAS (Generalized Auto-regressive Score) anal-ysis to model and predict Bitcoin’s returns and risks. Theexperiment found that the GAS model with heavy-tailed dis-tribution can provide the best out-of-sample prediction andgoodness-of-fit attributes for Bitcoin’s return and risk mod-eling. The results also illustrated the importance of mod-elling excess kurtosis for Bitcoin returns. Charles et al. [59]studied four cryptocurrency markets including Bitcoin, Dash,Litecoin and Ripple. Results showed cryptocurrency returnsare strongly characterised by the presence of jumps as wellas structural breaks except Dash market. Four GARCH-typemodels (i.e., GARCH, APARCH, IGARCH and FIGARCH)and three return types with structural breaks (original re-turns, jump-filtered returns, and jump-filtered returns) areconsidered. The research indicated the importance of jumpsin cryptocurrency volatility and structural breakthroughs.

Some researchers focused on long memory methods forvolatility in cryptocurrency markets. Long memory meth-ods focused on long-range dependence and significant long-term correlations among fluctuations on markets. Chaim etal. [57] estimated a multivariate stochastic volatility modelwith discontinuous jumps in cryptocurrency markets. Theresults showed that permanent volatility appears to be drivenby major market developments and popular interest level.Caporale et al. [46] examined persistence in cryptocurrencymarket by Rescaled range (R/S) analysis and fractional in-tegration. The results of the study indicated that the mar-ket is persistent (there is a positive correlation between itspast and future values) and that its level changes over time.

Khuntin et al. [147] applied the adaptive market hypothesis(AMH) in the predictability of Bitcoin evolving returns. Theconsistent test of Dominguez and Lobato [83], generalizedspectral (GS) of Escanciano and Velasco [92] are applied incapturing time-varying linear and nonlinear dependence inbitcoin returns. The results verified Evolving Efficiency inBitcoin price changes and evidence of dynamic efficiency inline with AMH’s claims.

Katsiampa et al. [143] applied three pair-wise bivariateBEKK models to examine the conditional volatility dynam-ics along with interlinkages and conditional correlations be-tween three pairs of cryptocurrencies in 2018. More specifi-cally, the BEKK-MGARCH methodology also captured cross-market effects of shocks and volatility, which are also knownas shock transmission effects and volatility spillover effects.The experiment found evidence of bi-directional shock trans-mission effects between Bitcoin and both Ether and Lit-coin. In particular, bi-directional shock spillover effects areidentified between three pairs (Bitcoin, Ether and Litcoin)and time-varying conditional correlations exist with positivecorrelations mostly prevailing. In 2019, Katsiampa [142]further researched an asymmetric diagonal BEKK model toexamine conditional variances of five cryptocurrencies thatare significantly affected by both previous squared errorsand past conditional volatility. The experiment tested thenull hypothesis of the unit root against stationarity hypothe-sis. Once stationarity is ensured, ARCH LM is tested forARCH effects to examine requirement of volatility mod-elling in return series. Volatility co-movements in amongcryptocurrency pairs are also tested by multivariate GARCHmodel. The results confirmed the non-normality and het-eroskedasticity of price returns in cryptocurrency markets.The finding also identified the effects of cryptocurrencies’volatility dynamics due to major news. Hultman [125] setout to examine GARCH (1,1), bivariate-BEKK (1,1) and astandard stochastic model to forecast the volatility of Bit-coin. A rolling window approach is used in these exper-iments. Mean absolute error (MAE), Mean squared error(MSE) and Root-mean-square deviation (RMSE) are threeloss criteria adopted to evaluate the degree of error betweenpredicted and true values. The result shows the followingrank of loss functions: GARCH (1,1) > bivariate-BEKK(1,1) > Standard stochastic for all the three different loss cri-teria; in other words, GARCH(1,1) appeared best in predict-ing the volatility of Bitcoin. Wavelet time-scale persistenceanalysis is also applied in prediction and research of volatil-ity in cryptocurrency markets [191]. The results showed thatinformation efficiency (efficiency) and volatility persistencein cryptocurrency market are highly sensitive to time scales,measures of returns and volatility, and institutional changes.Adjepong et al. [191] connected with a similar research ofCorbet et al. [79] and showed that GARCH is quicker thanBEKK to absorb new information regarding the data.

7.2. Machine Learning TechnologyAs we have previously stated, Machine learning technol-

ogy constructs computer algorithms that automatically im-



prove themselves by finding patterns in existing data with-out explicit instructions [122]. The rapid development ofmachine learning in recent years has promoted its applica-tion to cryptocurrency trading, especially in prediction ofcryptocurrency returns.

7.2.1. Common Machine Learning Technology in thissurvey

Several machine learning technologies are applied in cryp-tocurrency trading. We distinguish these by the objective setto the algorithm: classification, clustering, regression, rein-forcement learning. We have separated a section specificallyon deep learning due to its instrinsic variation of techniquesand wide adoption.

Classification Algorithms. Classification in machinelearning mean the objective of categorising incoming ob-jects into different categories as needed, where we can as-sign labels to each category (e.g., up and down). NaiveBayes (NB) [205], Support Vector Machine (SVM) [236],K-Nearest Neighbours (KNN) [236], Decision Tree (DT) [103],Random Forest (RF) [164] and Gradient Boosting (GB) [105]algorithms habe been used in cryptocurrency trading basedon papers we collected. NB is a probabilistic classifier basedon Bayes’ theorem with strong (naive) conditional indepen-dence assumptions between features [205]. SVM is super-vised learning model that aims at achieving high marginclassifiers connecting to learning bounds theory [245]. SVMsassign new examples to one category or another, making ita non-probabilistic binary linear classifier [236], althoughsome corrections can make a probabilistic interpretation oftheir output [146]. KNN is a memory-based or lazy learn-ing algorithm, where the function is only approximated lo-cally, and all calculations are be postponed to inference time[236]. DT is a decision support tool algorithm that usesa tree-like decision graph or model to segment input pat-terns into regions to then asign an associated label to eachregion [103]. RF is an ensemble learning method. The al-gorithm operates by constructing a large number of decisiontrees during training and outputting the average consensusas predicted class in the case of classification or mean pre-diction value in the case of regression [164]. GB producesa prediction model in the form of an ensemble of weak pre-diction models [105].

Clustering Algorithms. Clustering is a machine learn-ing technique that involves grouping data points in a waythat each group shows some regularity [130]. K-Means is avector quantization used for clustering analysis in data min-ing. K-means stores the k-centroids used to define the clus-ters; a point is considered to be in a particular cluster if it iscloser to the cluster’s centroid than any other centroid [234].K-Means is one of the most used clustering algorithms usedin cryptocurrency trading according to the papers we col-lected.

Regression Algorithms. We have defined regressionas any statistical technique that aims at estimating a con-tinuous value [156]. Linear Regression (LR) and Scatter-plot Smoothing are common techniques used in solving re-

gression problems in cryptocurrency trading. LR is a lin-ear method used to model the relationship between a scalarresponse (or dependent variable) and one or more explana-tory variables (or independent variables) [156]. ScatterplotSmoothing is a technology to fit functions through scatterplots to best represent relationships between variables [104].

Deep Learning Algorithms. Deep learning is a moderntake on artificial neural networks (ANNs) [246], made pos-sible by the advances in computational power. An ANN isa computational system inspired by the natural neural net-works that make up the animal’s brain. The system “learns”to perform tasks including prediction by considering exam-ples. Deep learning’s superior accuracy comes from highcomputational complexity cost. Deep learning algorithmsare currently the basis for many modern artificial intelli-gence applications [220]. Convolutional neural networks(CNNs) [160], Recurrent neural networks (RNNs) [177],Gated recurrent units (GRUs) [64], Multilayer perceptron(MLP) and Long short-term memory (LSTM) [61] networksare most common deep learning technologies used in cryp-tocurrency trading. A CNN is a specific type of neural net-work layer commonly used for supervised learning. CNNshave found their best success in image processing and natu-ral language processing problems. An attempt to use CNNsin cryptocurrency can be shown in [136]. An RNN is atype of artificial neural network in which connections be-tween nodes form a directed graph with possible loops. Thisstructure of RNNs makes them suitable for processing time-series data [177] due to the introduction of memory in the re-current connections. They face nevertheless for the vanish-ing gradients problem [192] and so different variations havebeen recently proposed. LSTM [61] is a particular RNN ar-chitecture widely used. LSTMs have shown to be superiorto non gated RNNs on financial time-series problems be-cause they have the ability to selectively remember patternsfor a long time. A GRU [64] is another gated version of thestandard RNN which has been used in crypto trading [85].Another deep learning technology used in cryptocurrencytrading is Seq2seq, which is a specific implementatio ofthe Encoder–Decoder architecture [240]. Seq2seq was firstaimed at solving natural language processing problems, buthas been also applied it in cryptocurrency trend predictionsin [215].

Reinforcement Learning Algorithms. Reinforcementlearning (RL) is an area of machine learning leveraging theidea that software agents act in the environment to maximizea cumulative reward [219]. Deep Q-Learning (DQN) [114]and Deep Boltzmann Machine (DBM) [208] are commontechnologies used in cryptocurreny trading using RL. DeepQ learning uses neural networks to approximate Q-valuefunctions. A state is given as input, and Q values for all pos-sible actions are generated as outputs [114]. DBM is a typeof binary paired Markov random field (undirected probabil-ity graphical model) with multiple layers of hidden randomvariables [208]. It is a network of randomly coupled randombinary units.



7.2.2. Research on Machine Learning ModelsIn the development of machine learning trading signal,

technical indicators have usually been used as input fea-tures. Nakano et al. [182] explored Bitcoin intraday tech-nical trading based on ANNs for return prediction. The ex-periment obtained medium frequency price and volume data(time interval of data is 15min) of Bitcoin from a cryptocur-rency exchange. An ANN predicts the price trends (up anddown) in next period from the input data. Data is prepro-cessed to construct a training dataset which contains a ma-trix of technical patterns including EMA, Emerging MarketsSmall Cap (EMSD), relative strength index (RSI), etc. Theirnumerical experiments contain different research aspects in-cluding base ANN research, effects of different layers, ef-fects of different activation functions, different outputs, dif-ferent inputs and effects of additional technical indicators.The results have shown that the use of various technical in-dicators possibly prevents over-fitting in the classificationof non-stationary financial time-series data, which enhancestrading performance compared to primitive technical trad-ing strategy. (Buy-and-Hold is the benchmark strategy inthis experiment.)

Some classification and regression machine learning mod-els are applied in cryptocurrency trading by predicting pricestrends. Most researchers have focused on the comparisonof different classification and regression machine learningmethods. Sun et al. [218] used random forests (RFs) withfactors in Alpha01 [134] (capturing features from historyof cryptocurrency market) to build a prediction model. Theexperiment collected data from API in cryptocurrency ex-changes and selected 5-minute frequency data for backtest-ing. The results showed that the performances are propor-tional to the amount of data (more data, more accurate) andthe factors used in the RF model appear to have differentimportance. For example, “Alpha024” and “Alpha032” fea-tures appeared as the most important in the model adopted.(The alpha features come from paper “101 Formulaic Al-phas" [134].) Vo et al. [232] applied RFs in High-Frequencycryptocurrency Trading (HFT) and compared it with deeplearning models. Minute-level data is collected when utilis-ing a forward fill imputation methods to replace the NULLvalue (i.e., a missing value). Different periods and RF treesare tested in the experiments. The authors also compared F-1 precision and recall metrics between RF and Deep Learn-ing (DL). The results showed that RF is effective despitemulticollinearity occurring in ML features, the lack of modelidentification also potentially leading to model identifica-tion issues; this research also attempted to create an HFTstrategy for Bitcoin using RF. Maryna et al. [249] inves-tigated the profitability of an algorithmic trading strategybased on training a SVM model to identify cryptocurrencieswith high or low predicted returns. The results showed thatthe performance of the SVM strategy was the fourth beingbetter only than S&P B&H strategy, which simply buys-and-hold the S&P index. (There are other 4 benchmarkstrategies in this research.)The authors observed that SVMneeds a large number of parameters and so is very prone

to overfitting, which caused its bad performance. Barnwalet al. [17] used generative and discriminative classifiers tocreate a stacking model, particularly 3 generative and 6 dis-criminative classifiers combined by a one-layer Neural Net-work, to predict the direction of cryptocurrency price. Adiscriminative classifier directly model the relationship be-tween unknown and known data, while generative classifiersmodel the prediction indirectly through the data generationdistribution [187]. Technical indicators including trend, mo-mentum, volume and volatility, are collected as features ofthe model. The authors discussed how different classifiersand features affect the prediction. Attanasio et al. [9] com-pared a variety of classification algorithms including SVM,NB and RF in predicting next-day price trends of a givencryptocurrency. The results showed that due to the hetero-geneity and volatility of cryptocurrencies financial instru-ments, forecasting models based on a series of forecastsappeared better than a single classification technology intrading cryptocurrencies. Madan et al. [169] modelled Bit-coin price prediction problem as a binomial classificationtask, experimenting with a custom algorithm that leveragesboth random forests and generalized linear models. Dailydata, 10-minute data and 10-second data are used in theexperiments. The experiments showed that 10-minute datagave a better sensitivity and specificity ratio than 10-seconddata (10-second prediction achieved around 10% accuracy).Considering predictive trading, 10-minute data helped showclearer trends in the experiment compared to 10-second back-testing. Similarly, Virk [231] compared RF, SVM, GB andLR to predict price of Bitcoin. The results showed thatSVM achieved the highest accuracy of 62.31% and preci-sion value 0.77 among binomial classification machine learn-ing algorithms.

Different deep learning models have been used in find-ing patterns of price movements in cryptocurrency markets.Zhengy et al. [247] implemented two machine learning mod-els, fully-connected ANN and LSTM to predict cryptocur-rency price dynamics. The results showed that ANN ingeneral outperforms LSTM although theoretically LSTM ismore suitable than ANN in terms of modeling time seriesdynamics; the performance measures considered are MAEand RMSE in joint prediction (five cryptocurrencies dailyprices prediction). The findings show that future state of atime series for cryptocurrencies is highly dependent on itshistoric evolution. Kwon et al. [157] used an LSTM model,with a three-dimensional price tensor representing the pastprice changes of cryptocurrencies as input. This model out-performs the GB model in terms of F1-score. Specifically,it has a performance improvement of about 7% over the GBmodel in 10-minute prices prediction. In particular, the ex-periments showed that LSTM is more suitable when classi-fying cryptocurrency data with high volatility. Alessandrettiet al. [5] tested Gradient boosting decision trees (includingsingle regression and XGBoost-augmented regression) andLSTM model on forecasting daily cryptocurrency prices.They found methods based on gradient boosting decisiontrees worked best when predictions were based on short-



term windows of 5/10 days while LSTM worked best whenpredictions were based on 50 days of data. The relative im-portance of the features in both models are compared and anoptimised portfolio composition (based on geometric meanreturn and Sharpe ratio) is discussed in this paper. Phal-adisailoed et al. [196] chose regression models (Theil-SenRegression and Huber Regression) and deep learning basedmodels (LSTM and GRU) to compare the performance ofpredicting the rise and fall of Bitcoin price. In terms oftwo common measure metrics, MSE and R-Square (R2),GRU shows the best accuracy. Fan et al. [94] applied anautoencoder-augmented LSTM structure in predicting mid-price of 8 cryptocurrency pairs. Level-2 limit order booklive data is collected and the experiment achieved 78% accu-racy of price movements prediction in high frequency trad-ing (tick level). This research improved and verified theview of Sirignano et al. [213] that universal models havebetter performance than currency-pair specific models forcryptocurrency markets. Moreover, “Walkthrough” (i.e., re-train the original deep learning model itself when it appearsto no longer be valid) is proposed as a method to optimisethe training of a deep learning model and shown to signifi-cantly improve the prediction accuracy.

Researchers have also focused on comparing of clas-sical statistical models and machine/deep learning models.Rane et al. [203] described classical time series predictionmethods and machine learning algorithms used for predict-ing Bitcoin price. Statistical models such as Autoregres-sive Integrated Moving Average models (ARIMA), Bino-mial Generalized Linear Model and GARCH are comparedwith machine learning models such as SVM, LSTM andNon-linear Auto-Regressive with Exogenous Input Model(NARX). The observation and results showed that NARXmodel is the best model with nearly 52% predicting accu-racy based on 10 seconds interval. Rebane et al. [204] com-pared traditional models like ARIMA with modern popu-lar model like seq2seq in predicting cryptocurrency returns.The result showed that the seq2seq model exhibited demon-strable improvement over the ARIMA model for Bitcoin-USD prediction but the seq2seq model showed very poorperformance in extreme cases. The authors proposed per-forming additional investigations, such as the use of LSTMinstead of GRU units to improve the performance. Similarmodels were also compared by Stuerner et al. [217] whoexplored the superiority of automated investment approachin trend following and technical analysis in cryptocurrencytrading. Samuel et al. [195] explored vector auto-regressivemodel (VAR model), a more complex RNN, and a hybridof the two in residual recurrent neural networks (R2N2) inpredicting cryptocurrency returns. The RNN with ten hid-den layers is optimised for the setting and the neural net-work augmented by VAR allows the network to be shal-lower, quicker and to have a better prediction than a RNN.RNN, VAR and R2N2 models are compared. The resultsshowed that the VAR model has phenomenal test periodperformance and thus props up the R2N2 model, while theRNN performs poorly. This research is an attempt on op-

timisation of model design and applying to prediction oncryptocurrency returns.

7.2.3. Sentiment AnalysisSentiment analysis, a popular research topic in the age of

social media, has also been adopted to improve predictionsfor cryptocurrency trading. This data source typically has tobe combined with Machine Learning for the generation oftrading signals.

Lamon et al. [159] used daily news and social mediadata labelled on actual price changes, rather than on positiveand negative sentiment. By this approach, the predictionon price is replaced with positive and negative sentiment.The experiment acquired cryptocurrency related news arti-cle headlines from website like “cryptocoinsnews” and twit-ter API. Weights are taken in positive and negative wordsin cryptocurrency market. Authors compared Logistic Re-gression (LR), Linear Support Vector Machine (LSVM) andNB as classifiers and concluded that LR is the best classifierin daily price prediction with 43.9% of price increases cor-rectly predicted and 61.9% of price decreases correctly fore-casted. Smuts [214] conducted similar binary sentiment-based price prediction method with a LSTM model usingGoogle Trends and Telegram sentiment. In detail, the senti-ment was extracted from Telegram by using a novel measurecalled VADER [126]. The backtesting reached 76% accu-racy on the test set during the first half of 2018 in predictinghourly prices. Nasir et al. [184] researched relationship be-tween cryptocurrency returns and search engines. The ex-periment employed a rich set of established empirical ap-proaches including VAR framework, copulas approach andnon-parametric drawings of time series. The results foundthat Google searches exert significant influence on Bitcoinreturns, especially in short-term interval. Kristoufek [154]discussed positive and negative feedback of Google trendsor daily views on Wikipedia. The author mentioned dif-ferent methods including Cointegration, Vector autoregres-sion and Vector error-correction model to find causal rela-tionships between prices and searched terms in cryptocur-rency market. The results indicated that the search trendsand cryptocurrency prices are connected. There is also aclear asymmetry between the effects of increased interest incurrencies above or below their trend values from the ex-periment. Young et al. [149] analysed user comments andreplies in online communities and its connection with cryp-tocurrency volatility. After crawling comments and repliesin online communities, authors tagged the extent of positiveand negative topics. Then relationship between price andnumber of transaction of cryptocurrency is tested accordingto comments and replies to selected data. At last, a predic-tion model using machine learning based on selected datais created to predict fluctuations in cryptocurrency market.The results show the amount of accumulated data and ani-mated community activities exerted a direct effect on fluc-tuation in the price and volume of cryptocurrency.

Phillips et al. [201] applied dynamic topic modellingand Hawkes model to decipher relationships between top-



ics and cryptocurrency price movements. Authors used La-tent Dirichlet allocation (LDA) model for topic modelling,which assumes each document contains multiple topics todifferent extents. The experiment showed that particulartopics tend to precede certain types of price movements incryptocurrency market and authors proposed the relation-ships could be built into a real-time cryptocurrency trad-ing. Li et al. [163] analysed Twitter sentiment and tradingvolume and an Extreme Gradient Boosting Regression TreeModel in prediction of ZClassic (ZCL) cryptocurrency mar-ket. Sentiment analysis using natural language processingfrom Python package “Textblob” assigns impactful wordsa polarity value. Values of weighted and unweighted sen-timent indices are calculated on hourly basis by summingweights of coinciding tweets, which makes us compare thisindex to ZCL price data. The model achieved a Pearsoncorrelation of 0.806 when applied to test data, yielding astatistical significance at the p<0.0001 level. Flori [101] re-lied on a Bayesian framework that combines market-neutralinformation with subjective beliefs to construct diversifiedinvestment strategies in Bitcoin market. The result showsthat news and media attention seem to contribute to influ-ence the demand for Bitcoin and enlarge the perimeter of thepotential investors, probably stimulating price euphoria andupwards-downwards market dynamics. Author’s researchhighlighted the importance of news in guiding portfolio re-balancing.

Similarly, Colianni et al. [74], Garcia et al. [107], Za-muda et al. [243] et al. used sentiment analysis technol-ogy applying it in the cryptocurrency trading area and hadsimilar results. Colianni et al. [74] cleaned data and ap-plied supervised machine learning algorithms such as logis-tic regression, Naive Bayes and support vector machines,etc. in Twitter Sentiment Analysis for cryptocurrency trad-ing. Garcia et al. [107] applied multidimensional analysisand impulse analysis in social signals of sentiment effectsand algorithmic trading of Bitcoin. The results verified thelong-standing assumption that transaction-based social me-dia sentiment has the potential to generate a positive returnon investment. Zamuda et al. [243] adopted new sentimentanalysis indicators and used multi-target portfolio selectionto avoid risks in cryptocurrency trading. The perspective isrationalized based on the elastic demand for computing re-sources of the cloud infrastructure. An general model eval-uating influence between user’s network Action-Reaction-Influence-Model (ARIM) is mentioned in this research. Bar-tolucci et al. [18] researched cryptocurrency prices with the“Butterfly effect”, which means “issues” of open-source projectprovides insights to improve prediction of cryptocurrencyprices. Sentiment, politeness, emotions analysis of GitHubcomments are applied in Ethereum and Bitcoin markets. Theresults showed that these metrics have predictive power oncryptocurrency prices.

7.2.4. Reinforcement LearningDeep reinforcement algorithms bypass prediction and

go straight to market management actions to achieve high

cumulated profit [120]. Bu et al. [43] proposed a combina-tion of double Q-network and unsupervised pre-training us-ing DBM to generate and enhance the optimal Q-function incryptocurrency trading. The trading model contains agentsin series in the form of two neural networks, unsupervisedlearning modules and environments. The input market stateconnects a encoding network which includes spectral fea-ture extraction (convolution-pooling module) and temporalfeature extraction (LSTM module). A double-Q networkfollows the encoding network and actions are generated fromthis network. Compared to existing deep learning mod-els (LSTM, CNN, MLP, etc.), this model achieved high-est profit even facing an extreme market situation (recorded24% of profit while cryptocurrency market price drops by-64%). Juchli [131] applied two implementations of rein-forcement learning agents, a Q-Learning agent, which servesas the learner when no market variables are provided, anda DQN agent which was developed to handle the featurespreviously mentioned. The DQN agent was backtested un-der the application of two different neural network architec-tures. The results showed that the DQN-CNN agent (convo-lutional neural network) is superior to the DQN-MLP agent(multilayer perceptron) in backtesting prediction. Lucarelliet al. [167] focused on improving automated cryptocurrencytrading with a deep reinforcement learning approach. Dou-ble and Dueling double deep Q-learning networks are com-pared for 4 years. By setting rewards functions as Sharperatio and profit, the double Q-learning method demonstratedto be the most profitable approach in trading cryptocurrency.

7.3. OthersAtsalakis et al. [8] proposes a computational intelligence

technique that uses a hybrid Neuro-Fuzzy controller, namelyPATSOS, to forecast the direction in the change of the dailyprice of Bitcoin. The proposed methodology outperformstwo other computational intelligence models, the first be-ing developed with a simpler neuro-fuzzy approach, and thesecond being developed with artificial neural networks. Ac-cording to the signals of the proposed model, the invest-ment return obtained through trading simulation is 71.21%higher than the investment return obtained through a simplebuy and hold strategy. This application is proposed for thefirst time in forecasting of Bitcoin price movements. Topo-logical data analysis is applied to forecasting price trends ofcryptocurrency markets in [148]. The approach is to harnesstopological features of attractors of dynamical systems forarbitrary temporal data. The results showed that the methodcan effectively separate important topological patterns andsampling noise (like bid–ask bounces, discreteness of pricechanges, differences in trade sizes or informational contentof price changes etc.) by providing theoretical results. Kur-bucz [155] designed a complex method consisting of single-hidden layer feedforward neural networks (SLFNs) to (i) de-termine the predictive power of the most frequent edges ofthe transaction network (a public ledger that records all Bit-coin transactions) on the future price of Bitcoin; and, (ii)to provide an efficient technique for applying this untapped



dataset in day trading. The research found a significantlyhigh accuracy (60.05%) for the price movement classifica-tions base on information can be obtained using a small sub-set of edges (approximately 0.45% of all unique edges). Itis worth noting that, Kondor et al. [150, 152] firstly pub-lished some papers giving analysis on transaction networkson cryptocurrency markets and applied related research inidentifying Bitcoin users [132]. Abay et al. [2] attemptedto understand the network dynamics behind the Blockchaingraphs using topological features. The results showed thatstandard graph features such as the degree distribution oftransaction graphs may not be sufficient to capture networkdynamics and their potential impact on Bitcoin price fluc-tuations. Maurice et al [191] applied wavelet time-scalepersistence in analysing returns and volatility in cryptocur-rency markets. The experiment examined long-memory andmarket efficiency characteristics in cryptocurrency marketsusing daily data for more than two years. The authors em-ployed a log-periodogram regression method in researchingstationarity in cryptocurrency market and used ARFIMA-FIGARCH class of models in examining long-memory be-haviour of cryptocurrencies across time and scale. In gen-eral, experiments indicated that heterogeneous memory be-haviour existed in eight cryptocurrency markets using dailydata over the full-time period and across scales (August 25,2015 to March 13, 2018).

8. Portfolio and Cryptocurrency Assets8.1. Research among cryptocurrency pairs and

related factorsJi et al. [128] examined connectedness via return and

volatility spillovers across six large cryptocurrencies (col-lected from coinmarketcap lists from August 7 2015 to Febru-ary 22 2018) and found Litecoin and Bitcoin to have themost effect on other cryptocurrencies. The authors followedmethods of Diebold et al. [82] and built positive/negative re-turns and volatility connectedness networks. Furthermore,the regression model is used to identify drivers of variouscryptocurrency integration levels. Further analysis revealedthat the relationship between each cryptocurrency in termsof return and volatility is not necessarily due to its mar-ket size. Adjepong et al. [190] explored market coherenceand volatility causal linkages of seven leading cryptocurren-cies. Wavelet-based methods are used to examine marketconnectedness. Parametric and nonparametric tests are em-ployed to investigate directions of volatility spillovers of theassets. Experiments revealed from diversification benefits tolinkages of connectedness and volatility in cryptocurrencymarkets.

Some researchers explored relationship between cryp-tocurrency and different factors, including futures, gold etc.Hale et al. [117] suggested that Bitcoin prices rise and fallrapidly after CME issues futures consistent with pricing dy-namics. Specifically, the authors pointed out that the rapidrise and subsequent decline in prices after the introductionof futures is consistent with trading behaviour in cryptocur-

rency market. Bai et al. [13] studied a trading algorithm forforeign exchange on a cryptocurrency Market using Auto-mated Triangular Arbitrage method. Implementing pricingstrategy, implementing trading algorithms and developing agiven trading simulation are three problems solved by thisresearch. Kang et al. [139] examined the hedging and diver-sification properties of gold futures versus Bitcoin prices byusing dynamic conditional correlations (DCCs) and waveletcoherence. DCC-GARCH model [89] is used to estimate thetime-varying correlation between Bitcoin and gold futuresby modelling the variance and the co-variance but also thistwo flexibility. Wavelet coherence method focused more onco-movement between Bitcoin and gold futures. From ex-periments, the wavelet coherence results indicated volatilitypersistence, causality and phase difference between Bitcoinand gold. Dyhrberg et al [86] applied GARCH model andthe exponential GARCH model in analysing similarities be-tween Bitcoin, gold and the US dollar. The experimentsshowed that Bitcoin, gold and the US dollar have similar-ity with the variables of the GARCH model, have similarhedging capabilities and react symmetrically to good andbad news. The authors observed that Bitcoin can combinesome advantages of commodities and currencies in finan-cial markets to be a tool for portfolio management. Bauret al. [19] extended the research of Dyhrberg et al.; samedata and sample periods are tested [86] with GARCH andEGARCH-(1,1) models but the experiments reached differ-ent conclusions. Baur et al. found that Bitcoin has uniquerisk-return characteristics compared with other assets. Theynoticed that Bitcoin excess returns and volatility resemble arather highly speculative asset with respect to gold or the USdollar. Bouri et al. [37] studied relationship between Bitcoinand energy commodities by applying DCCs and GARCH(1,1) model. In particular, the results showed that Bitcoinis a strong hedge and safe haven for energy commodities.Kakushadze [135] proposed factor models for the cross-sectionof daily cryptoasset returns and provided source code fordata downloads, computing risk factors and backtesting forall cryptocurrencies and a host of various other digital as-sets. The results showed that cross-sectional statistical ar-bitrage trading may be possible for cryptoassets subject toefficient executions and shorting. Beneki et al. [24] testedhedging abilities between Bitcoin and Ethereum by a multi-variate BEKK-GARCH methodology and impulse responseanalysis within VAR model. The results indicated a volatil-ity transaction from Ethereum to Bitcoin, which impliedpossible profitable trading strategies on the cryptocurrencyderivative market. Guglielmo et al. [48] examined week ef-fect in cryptocurrency markets and explored the feasibilityof this indicator in trading practice. Student t-test, ANOVA,Kruskal–Wallis and Mann–Whitney tests were carried outfor cryptocurrency data in order to compare time periodsthat may be characterized by anomalies with other time pe-riods. When anomaly is detected, an algorithm was estab-lished to exploit profit opportunities (MetaTrader terminalin MQL4 is mentioned in this research). The results showedevidence of anomaly (abnormal positive returns on Mon-



days) in Bitcoin market by backtesting in 2013-2016.

8.2. Crypto-asset Portfolio ResearchSome researchers applied portfolio theory for crypto as-

sets. Corbet et al. [77] gave a systematic analysis of cryp-tocurrencies as financial assets. Brauneis et al. [40] ap-plied the Markowitz mean-variance framework in order toassess risk-return benefits of cryptocurrency portfolios. Inan out-of-sample analysis accounting for transaction cost,they found that combining cryptocurrencies enriches the setof ‘low’-risk cryptocurrency investment opportunities. Interms of the Sharpe ratio and certainty equivalent returns,the 1∕N-portfolio (i.e., “naive” strategies, such as equallydividing amongst asset classes) outperformed single cryp-tocurrencies and more than 75% in terms of the Sharpe ratioand certainty equivalent returns of mean-variance optimalportfolios. Castro et al. [50] developed a portfolio optimi-sation model based on the Omega measure which is morecomprehensive than the Markowitz model, and applied thisto four crypto-asset investment portfolios by means of a nu-merical application. Experiments showed crypto-assets im-proves the return of the portfolios, but on the other hand,also increase the risk exposure.

Bedi et al. [21] examined diversification capabilities ofBitcoin for a global portfolio spread across six asset classesfrom the standpoint of investors dealing in five major fiatcurrencies, namely US Dollar, Great Britain Pound, Euro,Japanese Yen and Chinese Yuan. They employed modifiedConditional Value-at-Risk and standard deviation as mea-sures of risk to perform portfolio optimisations across threeasset allocation strategies and provided insights into sharpdisparity in Bitcoin trading volumes across national curren-cies from a portfolio theory perspective. Similar researchhas been done by Antipova et al. [6], which explored thepossibility of establishing and optimizing a global portfolioby diversifying investments using one or more cryptocur-rencies, and assessing returns to investors in terms of risksand returns. Fantazzini et al. [96] proposed a set of modelswhich can be used to estimate the market risk for a portfo-lio of crypto-currencies, and simultaneously estimate theircredit risk using the Zero Price Probability (ZPP) model.The results revealed the superiority of the t-copula/skewed-tGARCH model for market risk, and the ZPP-based modelsfor credit risk.

Trucios et al. [229] proposed a methodology based onvine copulas and robust volatility models to estimate theValue-at-Risk (VaR) and Expected Shortfall (ES) of cryp-tocurrency portfolios. The proposed algorithm displayedgood performance in estimating both VaR and ES. Hrytsiuket al. [123] showed that the cryptocurrency returns can bedescribed by the Cauchy distribution and obtained the an-alytical expressions for VaR risk measures and performedcalculations accordingly. As a result of the optimisation,the sets of optimal cryptocurrency portfolios were built intheir experiments.

Jiang et al. [129] proposed a two-hidden-layer CNN thattakes the historical price of a group of cryptocurrency assets

as an input and outputs the weight of the group of cryp-tocurrency assets. This research focused on portfolio re-search in cryptocurrency assets using emerging technolo-gies like CNN. Training is conducted in an intensive man-ner to maximise cumulative returns, which is considered areward function of the CNN network. The performance ofthe CNN strategy is compared with the three benchmarksand the other three portfolio management algorithms (buyand hold strategy, Uniform Constant Rebalanced Portfolioand Universal Portfolio with Online Newton Step and Pas-sive Aggressive Mean Reversion); the results are positivein that the model is only second to the Passive AggressiveMean Reversion algorithm (PAMR). Estalayo et al. [93] re-ported initial findings around the combination of DL mod-els and Multi-Objective Evolutionary Algorithms (MOEAs)for allocating cryptocurrency portfolios. Technical rationaleand details were given on the design of a stacked DL recur-rent neural network, and how its predictive power can be ex-ploited for yielding accurate ex ante estimates of the returnand risk of the portfolio. Results obtained for a set of exper-iments carried out with real cryptocurrency data have veri-fied the superior performance of their designed deep learn-ing model with respect to other regression techniques.

9. Market Condition Research9.1. Bubbles and Crash Analysis

Phillips and Yu proposed a methodology to test for thepresence of cryptocurrency bubble [62], which is extendedby Shaen et al. [78]. The method is based on supremumAugmented Dickey–Fuller (SADF) to test for the bubblethrough the inclusion of a sequence of forward recursiveright-tailed ADF unit root tests. An extended methodol-ogy generalised SADF (GSAFD), is also tested for bubbleswithin cryptocurrency data. The research concluded thatthere is no clear evidence of a persistent bubble in cryp-tocurrency markets including Bitcoin or Ethereum. Bouriet al. [39] date-stamped price explosiveness in seven largecryptocurrencies and revealed evidence of multiple periodsof explosivity in all cases. GSADF is used to identify mul-tiple explosiveness periods and logistic regression is em-ployed to uncover evidence of co-explosivity across cryp-tocurrencies. The results showed that the likelihood of ex-plosive periods in one cryptocurrency generally depends onthe presence of explosivity in other cryptocurrencies andpoints toward a contemporaneous co-explosivity that doesnot necessarily depend on the size of each cryptocurrency.

Extended research by Phillips et al. [197, 198] (who ap-plied a recursive augmented Dickey-Fuller algorithm, whichis called PSY test) and Landsnes et al. [91] studied pos-sible predictors of bubble periods of certain cryptocurren-cies. The evaluation includes multiple bubble periods in allcryptocurrencies. The result shows that higher volatility andtrading volume is positively associated with the presence ofbubbles across cryptocurrencies. In terms of bubble predic-tion, authors found the probit model to perform better thanthe linear models.



Phillips et al. [199] used Hidden Markov Model (HMM)and Superiority and Inferiority Ranking (SIR) method toidentify bubble-like behaviour in cryptocurrency time se-ries. Considering HMM and SIR method, epidemic detec-tion mechanism is used in social media to predict cryptocur-rency price bubbles, which classify bubbles through epi-demic and non-epidemic labels. Experiments have demon-strated a strong relationship between Reddit usage and cryp-tocurrency prices. This work also provides some empiricalevidence that bubbles mirror the social epidemic-like spreadof an investment idea. Guglielmo et al. [47] examined theprice overreactions in the case of cryptocurrency trading.Some parametric and non-parametric tests confirmed pres-ence of price patterns after overreactions, which identifiedthat the next-day price changes in both directions are biggerthan after “normal” days. The results also showed that theoverreaction detected in the cryptocurrency market wouldnot give available profit opportunities (possibly due to trans-action costs) that cannot be considered as evidence of theEMH. Chaim et al. [56] analysed high unconditional volatil-ity of cryptocurrency from a standard log-normal stochasticvolatility model to discontinuous jumps of volatility and re-turns. The experiment indicated the importance of incorpo-rating permanent jumps to volatility in cryptocurrency mar-kets.

9.2. Extreme conditionDifferently from traditional fiat currencies, cryptocur-

rencies are risky and exhibit heavier tail behaviour. Paraskeviet al. [144] found extreme dependence between returns andtrading volumes. Evidence of asymmetric return-volume re-lationship in the cryptocurrency market was also found byexperiment, as a result of discrepancies in the correlationbetween positive and negative return exceedances across allthe cryptocurrencies.

There has been a price crash in late 2017 to early 2018in cryptocurrency [242]. Yaya et al. [242] researched per-sistence and dependence of Bitcoin on other popular alter-native coins before and after 2017/18 crash in cryptocur-rency markets. The result showed that higher persistenceof shocks is expected after the crash due to speculations inthe mind of cryptocurrency traders, and more evidences ofnon-mean reversions, implying chances of further price fallin cryptocurrencies.

10. Others related to Cryptocurrency TradingSome other research papers related to cryptocurrency

trading treat distributed in market behaviour, regulatory mech-anisms and benchmarks.

Krafft et al. [153] and Yang [241] analysed market dy-namics and behavioural anomalies respectively to under-stand effects of market behaviour in cryptocurrency market.Krafft et al. discussed potential ultimate causes, potentialbehavioural mechanisms and potential moderating contex-tual factors to enumerate possible influence of GUI and APIon cryptocurrency markets. Then they highlighted potentialsocial and economic impact of human-computer interaction

in digital agency design. Yang applied behavioural theoriesof asset pricing anomalies in testing 20 market anomaliesusing cryptocurrency trading data. The results showed thatanomaly research focused more on the role of speculators,which gave a new idea to research the momentum and rever-sal in cryptocurrency market. Cocco et al. [69] implementeda mechanism to form a Bitcoin price and specific behaviourfor each type of trader including the initial wealth distri-bution following Pareto’s law, order-based transaction andprice settlement mechanism. Specifically, the model repro-duced the unit root attributes of the price series, the fat tailphenomenon, the volatility clustering of price returns, thegeneration of Bitcoins, hashing power and power consump-tion.

Leclair [161] and Vidal-Thomás et al. [230] analysed theexistence of herding in the cryptocurrency market. Leclairapplied herding methods of Huang and Salmon [127] in esti-mating the market herd dynamics in the CAPM framework.Vidal-Thomás et al. analyse the existence of herds in thecryptocurrency market by returning cross-sectional standard(absolute) deviations. Both their findings showed signifi-cant evidence of market herding in cryptocurrency market.Makarov et al. [170] studied price impact and arbitrage dy-namics in the cryptocurrency market and found that 85% ofthe variations in bitcoin returns and the idiosyncratic com-ponents of order flow play an important role in explainingthe size of the arbitrage spreads between exchanges.

In November 2019, Griffin et al. put forward a paperon the thesis of unsupported digital money inflating cryp-tocurrency prices [113], which caused a great stir in the aca-demic circle and public opinion. Using algorithms to anal-yse Blockchain data, they found that purchases with Tetherare timed following market downturns and result in sizeableincreases in Bitcoin prices. By mapping the blockchains ofBitcoin and Tether, they were able to establish that one largeplayer on Bitfinex uses Tether to purchase large amounts ofBitcoin when prices are falling and following the prod ofTether.

More researches involved benchmark and developmentin cryptocurrency market [121, 248], regulatory frameworkanalysis [209], data mining technology in cryptocurrencytrading [193], application of efficient market hypothesis incryptocurrency market [212] and artificial financial marketsfor studying a cryptocurrency market [68]. Hileman et al. [121]segmented the cryptocurrency industry into four key sec-tors: exchanges, wallets, payments and mining. They gavea benchmarking study of individuals, data, regulation, com-pliance practices, costs of firms and global map of mining incryptocurrency market in 2017. Zhou et al. [248] discussedthe status and future of computer trading in the largest groupof Asia-Pacific economies and then considered algorithmicand high frequency trading in cryptocurrency markets aswell. Shanaev et al. [209] used data on 120 regulatory eventsto study the implications of cryptocurrency regulation andthe results showed that stricter regulation of cryptocurrencyis not desirable. Akhilesh et al. [193] used the average ab-solute error calculated between the actual and predicted val-



ues of the market sentiment of different cryptocurrencies onthat day as a method for quantifying the uncertainty. Theyused the comparison of uncertainty quantification methodsand opinion mining to analyse current market conditions.Sigaki et al. [212] used permutation entropy and statisti-cal complexity on the sliding time window returned by theprice log to quantify the dynamic efficiency of more thanfour hundred cryptocurrencies. As a result, the cryptocur-rency market showed significant compliance with efficientmarket assumptions. Cocco et al. [68] described an agent-based artificial cryptocurrency market in which heteroge-neous agents buy or sell cryptocurrencies. The proposedsimulator is able to reproduce some real statistical prop-erties of price returns observed in the Bitcoin real market.Marko [189] considered the future use of cryptocurrenciesas money based on the long-term value of cryptocurrencies.Neil et al. [106] analysed the influence of network effect onthe competition of new cryptocurrency markets. Aurelio etal. [16] gave a survey based on hybrid analysis, which pro-posed a methodological hybrid method for comprehensiveliterature review and provided the latest technology in thecryptocurrency economics literature.

There also exists some research and papers introducingthe basic process and rules of cryptocurrency trading in-cluding findings of Hansel et al. [118], Kate [141], Garzaet al. [108], Ward et al. [237] and Fantazzini et al. [95].Hansel et al. [118] introduced basics of cryptocurrency, Bit-coin and Blockchain, ways to identify profitable trend inthe market, ways to use Altcoin trading platforms such asGDAX and Coinbase, methods of using a crypto wallet tostore and protect the coins in their book. Kate et al. [141]set six steps to show how to start an investment without anytechnical skills in cryptocurrency market. This book is anentry-level trading manual for starters learning cryptocur-rency trading. Garza et al. [108] simulated automatic cryp-tocurrency trading system, which helps investors limit sys-temic risks and improve market returns. This paper is an ex-ample to start designing an automatic cryptocurrency trad-ing system. Ward st al. [237] discussed algorithmic cryp-tocurrency trading using several general algorithms, and mod-ifications thereof including adjusting the parameters used ineach strategy, as well as mixing multiple strategies or dy-namically changing between strategies. This paper is an ex-ample to start algorithmic trading in cryptocurrency market.Fantazzini et al. [95] introduced the R packages Bitcoin-Finance and bubble, including financial analysis of cryp-tocurrency markets including Bitcoin.

A community resource, that is, a platform for scholarlycommunication, about cryptocurrencies and Blockchains is“Blockchain research network", see [186].

11. Summary Analysis of Literature ReviewThis section analyses the timeline, the research distribu-

tion among technology and methods, the research distribu-tion among properties. It also summarises the datasets thathave been used in cryptocurrency trading research.

Figure 8: Timeline of cryptocurrency trading research

11.1. TimelineFigure 8 shows several major events in cryptocurrency

trading. The timeline contains milestone events in cryp-tocurrency trading and important scientific breakthroughs inthis area.

As early as 2009, Satoshi Nakamoto proposed and in-vented first decentralised cryptocurrency, Bitcoin [181]. Itis considered to be the start of cryptocurrency. In 2010, thefirst cryptocurrency exchange was founded, which meanscryptocurrency would not be an OTC market but traded onexchanges based on auction market system.

In 2013, Kristoufek [154] concluded that there is a strongcorrelation between Bitcoin price and the frequency of “Bit-coin” search queries in Google Trends and Wikipedia. In2014, Lee and Yang [162] firstly proposed to check causal-ity from copula-based causality in the quantile method fromtrading volumes of seven major cryptocurrencies to returnsand volatility.

In 2015, Cheah et al. [60] discussed bubble and specula-tion of Bitcoin and cryptocurrencies. In 2016, Dyhrberg [86]explored Bitcoin volatility using GARCH models combinedwith gold and US dollars.

From late 2016 to 2017, machine learning and deep learn-ing technology were applied in prediction of cryptocurrencyreturn. In 2016, McNally et al. [174] predicted Bitcoin priceusing LSTM algorithm. Bell and Zbikowski et al. [22, 244]applied SVM algorithm to predict trends of cryptocurrencyprice. In 2017, Jiang et al. [129] used double Q-network andpretrained it using DBM for the prediction of cryptocurren-cies portfolio weights.

In recent years, several research directions including crossasset portfolios [21, 50, 40], transaction network applica-tions [155, 39], machine learning optimisation [203, 8, 247]have been considered in the cryptocurrency trading area.

11.2. Research Distribution among PropertiesWe counted the number of papers covering different as-

pects of cryptocurrency trading. Figure 9 shows the result.The attributes in the legend are ranked according to the num-ber of papers that specifically test the attribute.

Over one-third (38.98%) of the papers research predic-tion of returns. Another one-third of papers focuses on re-searching bubbles and extreme conditions and relationshipbetween pairs and portfolios in cryptocurrency trading. Theremaining researching topics (prediction of volatility, trad-ing system, technical trading and others) have roughly onethird share.



Figure 9: Research distribution among cryptocurrency trad-ing properties

11.3. Research Distribution among Categories andTechnologies

This section introduces and compares categories and tech-nologies in cryptocurrency trading. When papers cover mul-tiple technologies or compare different methods, we drawstatistics from the different technical perspectives.

Among all the 118 papers, 79 papers (66.95%) cover sta-tistical methods and machine learning categories. These pa-pers basically research technical-level cryptocurrency trad-ing including mathematical modelling and statistics. Otherpapers related to trading systems on pure technical indica-tors and introducing the industry and its history are not in-cluded in this analysis. Among all 79 papers, 60 papers(77.22%) present statistical methods and technologies in cryp-tocurrency trading research and 25.35% papers research ma-chine learning applied to cryptocurrency trading (cf. Fig-ure 10). It is interesting to mention that, there are 16 pa-pers (25.32%) applying and comparing more than one tech-nique in cryptocurrency trading. More specifically, Bach etal. [11], Alessandretti et al. [5], Vo et al. [232], Phaladis-ailoed et al. [196], Siaminos [211], Rane et al. [203] usedboth statistical methods and machine learning methods incryptocurrency trading.

Table 7 shows the results of search hits in all trading ar-eas (not limited to cryptocurrencies) . From the table, wecan see that most research findings focused on statisticalmethods in trading, which means most of the research ontraditional markets still focused on using statistical methodsfor trading. But we observed that machine learning in trad-ing had a higher degree of attention. It might because thetraditional technical and fundamental have been arbitraged,so the market has moved in recent years to find new anoma-lies to exploit. Meanwhile, the results also showed thereexist many opportunities for research in the widely studiedareas of machine learning applied to trading in cryptocur-rency markets (cf. Section 12).

11.3.1. Research Distribution among Statisticalmethods

As from Figure 10, we further classified the papers us-ing statistical methods into 6 categories: (i) basic regres-

Table 7Search hits of research distribution in all trading areas

Technology Category Google Scholar hits Google hits Arxiv hitsStatistical methods 1.22M 62M 1240Machine learning methods 483K 150M 520

sion methods; (ii) linear classifiers and clustering; (iii) time-series analysis; (iv) decision trees and probabilistic classi-fiers; (v) modern portfolios theory; and, (vi) Others.

Basic regression methods include regression methods(Linear Regression), function estimation and CGCD method.Linear Classifiers and Clustering include SVM and KNNalgorithm. Time-series analysis include GARCH model,BEKK model, ARIMA model, Wavelet time-scale method.Decision Trees and probabilistic classifiers include Boost-ing Tree, RF model. Modern portfolio theory include Value-at-Risk (VaR) theory, expected-shortfall (ES), Markowitzmean-variance framework. Others include industry, marketdata and research analysis in cryptocurrency market.

The figure shows that basic Regression methods and time-series analysis are the most commonly used methods in thisarea.

11.3.2. Research Distribution among MachineLearning Categories

Papers using machine learning account for 22.78% (c.fFigure 10) of the total. We further classified these papersinto three categories: (vii) ANNs, (viii) LSTM/RNN/GRUs,and (ix) DL/RL.

The figure also shows that methods based on LSTM,RNN and GRU are the most popular in this subfield.

ANNs contain papers researching ANN applications incryptocurrency trading such as back propagation (BP) NN.LSTM/RNN/GRUs include papers using neural networkswhich exploit the temporal structure of the data, a technol-ogy especially suitable for time series prediction and finan-cial trading. DL/RL include papers using Multilayer NeuralNetworks and Reinforcement Learning. The difference be-tween ANN and DL is that generally DL refers to an ANNwith multiple hidden layers while ANN refers to simplestructure neural network contained input layer, hidden layer(one or multiple), and an output layer.

11.4. Datasets used in Cryptocurrency TradingTables 8–10 show the details for some representative

datasets used in cryptocurrency trading research. Table 8shows the market datasets. They mostly include price, trad-ing volume, order-level information, collected from cryp-tocurrency exchanges. Table 9 shows the sentiment-baseddata. Most of datasets in this table contain market data andmedia/Internet data with emotional or statistical labels. Ta-ble 10 gives two examples of datasets used in the collectedpapers that are not covered in the first two tables.

The column “Currency” shows the types of cryptocur-rencies included; this shows that Bitcoin is the most com-monly used currency for cryptocurrency researches. Thecolumn “Description” shows general description and types



Table 8Datasets (1/3):Market Data

Research Source Description Currency Data Resolution Time Range Usage Data Sources

Bouri et al. [38] price,volatility,detrended volume data

Bitcoin,Ethereum,5 other cryptocurrencies

daily From: 2013/01/01To: 2017/12/31

Prediction of volatility/return CoinMarketCap

Nakano et al. [182] high frequency price,volume data

Bitcoin 15min From: 2016/07/31To: 2018/01/24

Prediction of return Poloniex

Bu et al. [43] three pieces time slice fordifferent research objectives

Bitcoin and seven altcoins Not mentioned From: 2016/05/14To: 2016/07/03From: 2018/01/01To: 2018/01/31From: 2017/07/01To: 2017/07/31

Maximum profit with DRL Not mentioned

Sun et al. [218] price, volatility ETC-USDT,other 12 cryptocurrencies

1 minute,5 minutes,30 minutes,one hour,one day

From: August 2017To: December 2018

Prediction of return Binance, Bitfinex

Guo et al. [115] volatility,order book data

Bitcoin hourly volatility observations,order book snapshots

From: September 2015To: April 2017

Prediction of volatility Not mentioned

Vo et al. [232] timestamps,the OHLC prices etc.

Bitcoin 1minute From: Starting 2015To: End 2016

Prediction of return Bitstamp, Btce, Btcn,Coinbase, Coincheck, and Kraken

Ross et al. [199] price Bitcoin,other 3 cryptocurrencies

daily From: April 2015To: September 2016

Predicting bubbles CryptoCompare

Yaya et al. [242] price Bitcoin,other 12 cryptocurrencies

daily From: 2015/08/07To: 2018/11/28

Bubbles and crashes Coin Metrics

Brauneis et al. [40] individual price,trading volume

500 most capitalizedCryptocurrencies

daily From: 2015/01/01To: 2017/12/31

Portfolios management CoinMarketCap

Feng et al. [98] order-level USD/BTCtrading data

Bitcoin order-level From: 2011/09/13To: 2017/07/17

Trading strategy Bitstamp

Figure 10: Research distribution among cryptocurrencytrading technologies and methods

of datasets. The column “Data Resolution” means latencyof the data (e.g., used in the backtest) – this is useful to dis-tinguish between high frequency trading and low frequencytrading. The column “Time range” shows the time span ofdatasets used in experiments; this is convenient to distin-guish between the current performance in a specific timeinterval and the long-term effect. We also present how thedataset has been used (i.e., the task), cf. column “Usage”.“Data Sources” gives details on where the data is retrievedfrom, including cryptocurrency exchanges, aggregated cryp-tocurrency index and user forums (for sentiment analysis).

12. Opportunities in Cryptocurrency TradingThis section discusses potential opportunities for future

research in cryptocurrency trading.Sentiment-based research. As discussed above, there

is a substantial body of work, which uses natural languageprocessing technology, for sentiment analysis with the ulti-mate goal of using news and media contents to improve theperformance of cryptocurrency trading strategies.

Possible research directions may lie in larger volume ofmedia input (e.g., adding video sources) in sentiment anal-ysis; updating baseline natural language processing modelto perform more robust text preprocessing; applying neu-ral networks in label training; extending samples in termsof holding period; transaction-fees; and, user reputation re-search.

Long-and-short term research. There are significantdifferences between long and short time horizons in cryp-tocurrency trading. In long-term trading, investors mightobtain greater profits but have more possibilities to controlrisk when managing a position for weeks or months. It ismandatory to control for risk on long term strategies due tothe increase in holding period, directly proportional to therisk incurred by the trader. On the other hand, the longerthe horizon, the higher the risk and the most important therisk control. The shorter the horizon, the higher the cost andthe lower the risk, so cost takes over the design of a strat-egy. In short-term trading, automated algorithmic tradingcan be applied when holding periods are less than a week.Researchers can differentiate between long-term and short-term trading in cryptocurrency trading by applying wavelettechnology analysing bubble regimes [200] and consider-



Table 9Datasets (2/3):Sentiment-based data

Research Source Description Currency Time range Usage Data Sources

Kim et al. [149] Online cryptocurrency communities dataand market data

Bitcoin,Ethereum, Ripple From: December 2013To: August, 2016 (Bitcoin)From: August 2015To: August, 2016 (Ethereum)From: CreationTo: August, 2016 (Ripple)

Prediction of fluctuation Each community’s HTML page

Phillips et al. [201] Social media data and orice data Bitcoin and Ethereum From: 2016/08/30To: 2017/08/30

Predict Mutual-Excitation ofCryptocurrency Market Returns

Reddit

Smtus [214] Hourly data on price and trading volumeand search terms from Google Trends

Bitcoin, Ethereumand their respective pricedrivers

From: 2017/12/01To: 2018/06/30

Prediction of price Google Trends, Telegram chat groups

Lamon et al. [159] Daily price data and cryptocurrencyrelated news article headlines

Bitcoin, Ethereum, Litecoin From: 2017/01/01To: 2017/11/30

Prediction of price Kaggle, news headline

Phillips et al. [200] Price and social media factors from Reddit Bitcoin, Ethereum, Monero From: 2010/09/10To: 2017/05/31 (Bitcoin)Others can reference the paper

Waveletcoherence analysis of price BraveNewCoin

Kang et al. [138] Market data and posts and commentsincluding metadata

Bitcoin From: 2009/11/22To: 2018/02/02

Relationships Between BitcoinPrices and User Groups inOnline Community

Bitcoin forum

Table 10Datasets (3/3):Others

Research Source Description Time range Usage Data Sources

Kurbucz [155, 151] Raw and preprocessed data of allBitcoin transactions and daily returns

From: 2016/11/09To: 2018/02/05

Predicting the price of Bitcoinwith transaction network

Bitcoin network dataset [178]

Bedi et al. [21] A diversified portfolio including equity,fixedincome, real estate, alternativeinvestments, commodities and money market

From: July 2010To: December 2018

Cross-currency including cryptocurrencyresearching portfolios

Portfolio sources [21]

ing price explosiveness [39] hypotheses for short-term andlong-term research.

The existing work is mainly about showing the differ-ences between long and short-term in trading cryptocurrency.Long-term in trading means less time would be cost in trendtracing and simple technical indicators in market analysis.Short-term in trading can limit overall risk because smallpositions are used in every transactions. But market noise(interference) and short transaction time might cause somestress in short term trading. It might also be interesting toexplore extraction of trading signals, time series research,application to portfolio management, relationship betweenhuge market crash and small price drop, derivative pricingin cryptocurrency market etc.

Correlation between cryptocurrency and others. Bythe effects of monetary policy and business cycles that arenot controlled by the central bank, cryptocurrency is alwaysnegatively correlated with overall financial market trends.There have been some studies discussing correlations be-tween cryptocurrencies and other financial markets [139,50], which can be used to predict the direction of the cryp-tocurrency market.

Considering the characteristics of cryptocurrency, corre-lation between cryptocurrency and other assets still requiresfurther research. Possible breakthroughs might be achievedwith principal component analysis, relationship between cryp-tocurrency and other currencies in extreme conditions (i.e.,financial collapse).

Bubbles and crash research. To discuss the high volatil-ity and return of cryptocurrencies, current research has fo-

cussed on bubbles of cryptocurrency markets [62], corre-lation between cryptocurrency bubbles and indicators likevolatility index (VIX) [91] (which is a “panic index” to mea-sure the implied volatility of S&P500 Index Options), spillovereffects in cryptocurrency market [168].

Additional research for bubbles and crashes in cryptocur-rency trading could include a connection between the pro-cess of bubble generation and financial collapse and con-ducting a coherent analysis (coherent process analysis fromthe formation of bubbles to aftermath analysis of bubbleburst), analysis of bubble theory by Microeconomics, tryingother physical or industrial models in analysing bubbles incryptocurrency market (i.e., Omori law [238]), discussingthe supply and demand relationship of cryptocurrency inbubble analysis (like using supply and demand graph to sim-ulate the generation of bubbles and simulate the bubble burst).

Game theory and agent-based analysis. Applying gametheory or agent-based modelling in trading is a hot researchdirection in traditional financial market. It might also be in-teresting to apply this method to trading in cryptocurrencymarkets.

Public nature of Blockchain technology. Investiga-tions on the connections between the formation of a givencurrency’s transaction network and its price has increasedrapidly in recent years; the growing attention on user iden-tification [132] also strongly supports this direction. Withan in-depth understanding of these networks, we may iden-tify new features in price prediction and may be closer tounderstanding financial bubbles in cryptocurrency trading.

Balance between the opening of trading research lit-



erature and the fading of alphas. Mclean et al. [173]pointed that investors learn about mispricing in stock mar-kets from academic publications. Similarly, cryptocurrencymarket predictability could also be affected by research pa-pers in the area. A possible attempt is to try new pricingmethods applying real-time market changes. Consideringthe proportion of informed traders increasing in cryptocur-rency market in pricing process is another breaking point(looking for balance between alpha trading and trading re-search literature).

13. ConclusionsWe provided a comprehensive overview and analysis of

research work of cryptocurrency trading. This survey pre-sented a nomenclature of the definitions and current state ofthe art. We further summarised the datasets used for exper-iments, and analysed the research trends and opportunitiesin cryptocurrency trading. We expect this survey to be ben-eficial to academics (e.g., finance researchers) and quantita-tive traders alike. The survey represents a quick way to getfamiliar with the literature on cryptocurrency trading, andcan motivate more researchers to contribute to the pressingproblems in the area, for example along the lines we haveidentified.

AcknowledgementsWe sent the paper to the authors of the papers we cited,

to check our survey for accuracy and/or omissions. Thisalso allowed one final stage in the systematic crawling ofthe literature for relevant work. Many thanks to the mem-bers of the community who kindly provided comments andfeedback on an earlier draft of this paper.

References[1] 3commas, 2020. 3commas smart trading terminal and auto trading

bots. https://3commas.io/. [Online, Accessed: January 26, 2020].[2] Abay, N.C., Akcora, C.G., Gel, Y.R., Kantarcioglu, M., Islambekov,

U.D., Tian, Y., Thuraisingham, B., 2019. Chainnet: Learning onblockchain graphs with topological features, in: 2019 IEEE Interna-tional Conference on Data Mining (ICDM), IEEE. pp. 946–951.

[3] Adeyanju, C., 2019. What crypto exchanges do to comply withkyc, aml and cft regulations. https://cointelegraph.com/news/what-crypto-exchanges-do-to-comply-with-kyc-aml-and-cft-regulations. [Online, Accessed January 11, 2020].

[4] Ahamad, S., Nair, M., Varghese, B., 2013. A survey on crypto cur-rencies, in: 4th International Conference on Advances in ComputerScience, AETACS, Citeseer. pp. 42–48.

[5] Alessandretti, L., ElBahrawy, A., Aiello, L.M., Baronchelli, A.,2018. Anticipating cryptocurrency prices using machine learning.Complexity 2018.

[6] Antipova, V., 2019. Building and testing global investment portfo-lios using alternative asset classes. Master’s thesis. Vytautas Mag-nus University. Lithuania.

[7] Ardia, D., Bluteau, K., Rüede, M., 2019. Regime changes in bitcoingarch volatility dynamics. Finance Research Letters 29, 266–271.

[8] Atsalakis, G.S., Atsalaki, I.G., Pasiouras, F., Zopounidis, C., 2019.Bitcoin price forecasting with neuro-fuzzy techniques. EuropeanJournal of Operational Research 276, 770–780.

[9] Attanasio, G., Cagliero, L., Garza, P., Baralis, E., 2019. Quantita-tive cryptocurrency trading: exploring the use of machine learningtechniques, in: Proceedings of the 5th Workshop on Data Sciencefor Macro-modeling with Financial and Economic Datasets, ACM.p. 1.

[10] Authority, F.C., 2019. Contract for differences. https://www.fca.org.uk/firms/contracts-for-difference. [Online, AccessedJanuary 29, 2020].

[11] Bach, W.G., Kasper, L., 2018. On Machine Learning Based Cryp-tocurrency Trading. Master’s thesis. Aalborg University. Denmark.

[12] Badenhorst, J.J., et al., 2019. Effect of Bitcoin spot and derivativetrading volumes on price volatility. Ph.D. thesis. University of Pre-toria.

[13] Bai, S., Robinson, F., 2019. Automated Triangular Arbitrage:: ATrading Algorithm for Foreign Exchange on a Cryptocurrency Mar-ket. Bachelor’s thesis. KTH Royal Institute of Technology. Sweden.

[14] BAKKT, 2020a. Bakkt markets. https://www.bakkt.com/index.[Online, Accessed February 11, 2020].

[15] BAKKT, 2020b. Bakkt terms of use. https://www.bakkt.com/terms-of-use. [Online, Accessed: February 11, 2020].

[16] Bariviera, A.F., Merediz-Sola, I., 2020. Where do we stand in cryp-tocurrencies economic research? a survey based on hybrid analysis.arXiv preprint arXiv:2003.09723 .

[17] Barnwal, A., Bharti, H., Ali, A., Singh, V., 2019. Stackingwith neural network for cryptocurrency investment. arXiv preprintarXiv:1902.07855 .

[18] Bartolucci, S., Destefanis, G., Ortu, M., Uras, N., Marchesi, M.,Tonelli, R., 2019. The butterfly affect: Impact of development prac-tices on cryptocurrency prices .

[19] Baur, D.G., Dimpfl, T., Kuck, K., 2018. Bitcoin, gold and the usdollar–a replication and extension. Finance Research Letters 25,103–110.

[20] Bauriya, A., Tikone, A., Nandgaonkar, P., Sakure, K.S., 2019. Real-time cryptocurrency trading system. International Research Journalof Engineering and Technology (IRJET) 06.

[21] Bedi, P., Nashier, T., 2020. On the investment credentials of bitcoin:A cross-currency perspective. Research in International Businessand Finance 51, 101087.

[22] Bell, T., 2016. Bitcoin trading agents. University of Southampton .[23] Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bier-

laire, M., Bolduc, D., Boersch-Supan, A., Brownstone, D., Bunch,D.S., et al., 2002. Hybrid choice models: progress and challenges.Marketing Letters 13, 163–175.

[24] Beneki, C., Koulis, A., Kyriazis, N.A., Papadamou, S., 2019. In-vestigating volatility transmission and hedging properties betweenbitcoin and ethereum. Research in International Business and Fi-nance 48, 219–227.

[25] Binance, 2020a. Binance partners with coinfirm to protect the globalcryptocurrency economy and ensure compliance with fatf aml rules.https://www.binance.com/en/blog/386484403820867584/Binance-Partners-with-Coinfirm-to-Protect-the-Global-Cryptocurrency-Economy-and-Ensure-Compliance-with-FATF-AML-Rules.[Online, Accessed February 11, 2020].

[26] Binance, 2020b. Binance review 2020: Pros, cons, fees, features,and safety. https://insidebitcoins.com/cryptocurrency-exchanges/binance-review/. [Online, Accessed: February 11, 2020].

[27] Bitfinex, 2020a. Bitfinex markets. https://www.bitfinex.com/. [On-line, Accessed: February 11, 2020].

[28] Bitfinex, 2020b. Bitfinex terms of service. https://www.bitfinex.com/legal/terms. [Online, Accessed: February11, 2020].

[29] Bitfinex, 2020c. New york court rules that state attorney has ju-risdiction over bitfinex. https://cointelegraph.com/news/new-york-court-rules-that-state-attorney-has-jurisdiction-over-bitfinex. [On-line, Accessed February 11, 2020].

[30] Bitmex, 2020a. Beginner’s guide to bitmex: Complete review.https://blockonomi.com/bitmex-review/. [Online, Accessed: Febru-ary 11, 2020].


https://3commas.io/

https://cointelegraph.com/news/what-crypto-exchanges-do-to-comply-with-kyc-aml-and-cft-regulations



https://www.fca.org.uk/firms/contracts-for-difference

https://www.fca.org.uk/firms/contracts-for-difference

https://www.bakkt.com/index

https://www.bakkt.com/terms-of-use

https://www.bakkt.com/terms-of-use

https://www.binance.com/en/blog/386484403820867584/Binance-Partners-with-Coinfirm-to-Protect-the-Global-Cryptocurrency-Economy-and-Ensure-Compliance-with-FATF-AML-Rules



https://insidebitcoins.com/cryptocurrency-exchanges/binance-review/

https://insidebitcoins.com/cryptocurrency-exchanges/binance-review/

https://www.bitfinex.com/

https://www.bitfinex.com/legal/terms

https://www.bitfinex.com/legal/terms

https://cointelegraph.com/news/new-york-court-rules-that-state-attorney-has-jurisdiction-over-bitfinex

https://cointelegraph.com/news/new-york-court-rules-that-state-attorney-has-jurisdiction-over-bitfinex

https://blockonomi.com/bitmex-review/


[31] Bitmex, 2020b. Bitmex. https://www.bitmex.com/register. [Online,Accessed: February 11, 2020].

[32] Bitstamp, 2020a. Bitstamp review 2020. https://www.fxempire.com/crypto/exchange/bitstamp/review. [On-line, Accessed: February 11, 2020].

[33] Bitstamp, 2020b. Bitstamp who we are. https://www.bitstamp.net/about-us/. [Online, Accessed: February 11, 2020].

[34] Bitstamp, 2020c. Terms of use. https://www.bitstamp.net/terms-of-use/sa. [Online, Accessed February 11, 2020].

[35] Blackbird, 2020. Blackbird bitcoin arbitrage: a long/short market-neutral strategy. https://github.com/butor/blackbird. [Online, Ac-cessed: January 26, 2020].

[36] Bloomberg, 2020. Coinbase inc. https://www.bloomberg.com/profile/company/0776164D:US. [Online, Accessed: February 11,2020].

[37] Bouri, E., Jalkh, N., Molnár, P., Roubaud, D., 2017. Bitcoin forenergy commodities before and after the december 2013 crash: di-versifier, hedge or safe haven? Applied Economics 49, 5063–5073.

[38] Bouri, E., Lau, C.K.M., Lucey, B., Roubaud, D., 2019a. Tradingvolume and the predictability of return and volatility in the cryp-tocurrency market. Finance Research Letters 29, 340–346.

[39] Bouri, E., Shahzad, S.J.H., Roubaud, D., 2019b. Co-explosivity inthe cryptocurrency market. Finance Research Letters 29, 178–183.

[40] Brauneis, A., Mestel, R., 2019. Cryptocurrency-portfolios in amean-variance framework. Finance Research Letters 28, 259–264.

[41] van den Broek, L., Sharif, Z., 2018. Cointegration-based pairs trad-ing framework with application to the cryptocurrency market .

[42] Brunnermeier, M.K., Oehmke, M., 2013. Bubbles, financial crises,and systemic risk, in: Handbook of the Economics of Finance. El-sevier. volume 2, pp. 1221–1288.

[43] Bu, S.J., Cho, S.B., 2018. Learning optimal q-function using deepboltzmann machine for reliable trading of cryptocurrency, in: Inter-national Conference on Intelligent Data Engineering and AutomatedLearning, Springer. pp. 468–480.

[44] Calo, B., Johnson, W., 2002. Global trading system. US Patent App.09/769,036.

[45] Capfolio, 2020. Capfolio cryptocurrency trading platform. https://www.capfol.io/. [Online, Accessed: January 26, 2020].

[46] Caporale, G.M., Gil-Alana, L., Plastun, A., 2018. Persistence inthe cryptocurrency market. Research in International Business andFinance 46, 141–148.

[47] Caporale, G.M., Plastun, A., 2018. Price Overreactions in the Cryp-tocurrency Market. CESifo Working Paper 6861. Munich. URL:http://hdl.handle.net/10419/174984.

[48] Caporale, G.M., Plastun, A., 2019. The day of the week effect inthe cryptocurrency market. Finance Research Letters 31.

[49] Caporin, M., McAleer, M., 2012. Do we really need both bekk anddcc? a tale of two multivariate garch models. Journal of EconomicSurveys 26, 736–751.

[50] Castro, J.G., Tito, E.A.H., Brandão, L.E.T., Gomes, L.L., 2019.Crypto-assets portfolio optimization under the omega measure. TheEngineering Economist , 1–21.

[51] Catalyst, 2020. An algorithmic trading library for crypto-assets inpython. https://github.com/enigmampc/catalyst. [Online, Accessed:January 26, 2020].

[52] CBOE, 2020a. Cboe history. http://www.cboe.com/aboutcboe/history. [Online, Accessed: February 11, 2020].

[53] CBOE, 2020b. Cboe products. https://www.cboe.com. [Online,Accessed: February 11, 2020].

[54] CBOE, 2020c. Cfe regulation. https://www.cboe.com/aboutcboe/legal-regulatory/departmental-overviews/cfe-regulation. [Online,Accessed February 11, 2020].

[55] Ccxt, 2020. Ccxt – cryptocurrency exchange trading library. https://github.com/ccxt/ccxt. [Online, Accessed: January 26, 2020].

[56] Chaim, P., Laurini, M.P., 2018. Volatility and return jumps in bit-coin. Economics Letters 173, 158–163.

[57] Chaim, P., Laurini, M.P., 2019. Nonlinear dependence in cryptocur-rency markets. The North American Journal of Economics and Fi-

nance 48, 32–47.[58] Chang, C.C., Hsieh, P.F., Wang, Y.H., 2015. Sophistication, senti-

ment, and misreaction. Journal of Financial and Quantitative Anal-ysis 50, 903–928.

[59] Charles, A., Darné, O., et al., 2019. Volatility estimation for cryp-tocurrencies: Further evidence with jumps and structural breaks.Economics Bulletin 39, 954–968.

[60] Cheah, E.T., Fry, J., 2015. Speculative bubbles in bitcoin markets?an empirical investigation into the fundamental value of bitcoin.Economics Letters 130, 32–36.

[61] Cheng, J., Dong, L., Lapata, M., 2016. Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733 .

[62] Cheung, A., Roca, E., Su, J.J., 2015. Crypto-currency bubbles: anapplication of the phillips–shi–yu (2013) methodology on mt. goxbitcoin prices. Applied Economics 47, 2348–2358.

[63] Choi, B., 2012. ARMA model identification. Springer Science &Business Media.

[64] Chung, J., Gulcehre, C., Cho, K., Bengio, Y., 2014. Empirical eval-uation of gated recurrent neural networks on sequence modeling.arXiv preprint arXiv:1412.3555 .

[65] CME, 2020a. Cme cryptocurrency products. https://www.cmegroup.com/trading/cryptocurrency-indices.html. [Online,Accessed: February 11, 2020].

[66] CME, 2020b. Cme group rules and regulation overview.https://www.cmegroup.com/education/courses/market-regulation/overview/cme-group-rules-and-regulation-overview.html. [Online,Accessed February 11, 2020].

[67] CME, 2020c. Cme groups overview. https://www.cmegroup.com/company/history/. [Online, Accessed: February 11, 2020].

[68] Cocco, L., Concas, G., Marchesi, M., 2017. Using an artificial fi-nancial market for studying a cryptocurrency market. Journal ofEconomic Interaction and Coordination 12, 345–365.

[69] Cocco, L., Marchesi, M., 2016. Modeling and simulation of theeconomics of mining in the bitcoin market. PloS one 11.

[70] Coinbase, 2020a. Coinbase supported cryptocurrencies.https://help.coinbase.com/en/coinbase/getting-started/general-crypto-education/supported-cryptocurrencies.html. [Online,Accessed: February 11, 2020].

[71] Coinbase, 2020b. Our path to listing sec-regulated crypto secu-rities. https://blog.coinbase.com/our-path-to-listing-sec-regulated-crypto-securities-a1724e13bb5a. [Online, Accessed February 11,2020].

[72] CoinMaketCap, 2019. Top 100 cryptocurrencies by market capital-ization URL: https://coinmarketcap.com/.

[73] Coinmarketcap, 2020. Percentage of total market capitalization.https://coinmarketcap.com/charts/}dominance-percentage. [Online,Accessed January 11, 2020].

[74] Colianni, S., Rosales, S., Signorotti, M., 2015. Algorithmic trad-ing of cryptocurrency based on twitter sentiment analysis. CS229Project , 1–5.

[75] Conrad, C., Custovic, A., Ghysels, E., 2018. Long-and short-term cryptocurrency volatility components: A garch-midas analysis.Journal of Risk and Financial Management 11, 23.

[76] Corbet, S., Eraslan, V., Lucey, B., Sensoy, A., 2019a. The effective-ness of technical trading rules in cryptocurrency markets. FinanceResearch Letters 31, 32–37.

[77] Corbet, S., Lucey, B., Urquhart, A., Yarovaya, L., 2019b. Cryp-tocurrencies as a financial asset: A systematic analysis. Interna-tional Review of Financial Analysis 62, 182–199.

[78] Corbet, S., Lucey, B., Yarovaya, L., 2018a. Datestamping the bit-coin and ethereum bubbles. Finance Research Letters 26, 81–88.

[79] Corbet, S., Meegan, A., Larkin, C., Lucey, B., Yarovaya, L., 2018b.Exploring the dynamic relationships between cryptocurrencies andother financial assets. Economics Letters 165, 28–34.

[80] Cryptosignal, 2020. Automated crypto trading and technical analy-sis (ta) bot. https://github.com/CryptoSignal/crypto-signal. [Online,Accessed: January 26, 2020].

[81] Ctubio, 2020. Ctubio - cryptocurrency trading bot. https://


https://www.bitmex.com/register

https://www.fxempire.com/crypto/exchange/bitstamp/review

https://www.fxempire.com/crypto/exchange/bitstamp/review

https://www.bitstamp.net/about-us/

https://www.bitstamp.net/about-us/

https://www.bitstamp.net/terms-of-use/sa

https://www.bitstamp.net/terms-of-use/sa

https://github.com/butor/blackbird

https://www.bloomberg.com/profile/company/0776164D:US

https://www.bloomberg.com/profile/company/0776164D:US

https://www.capfol.io/

https://www.capfol.io/

http://hdl.handle.net/10419/174984

https://github.com/enigmampc/catalyst

http://www.cboe.com/aboutcboe/history

http://www.cboe.com/aboutcboe/history

https://www.cboe.com

https://www.cboe.com/aboutcboe/legal-regulatory/departmental-overviews/cfe-regulation

https://www.cboe.com/aboutcboe/legal-regulatory/departmental-overviews/cfe-regulation

https://github.com/ccxt/ccxt

https://github.com/ccxt/ccxt

https://www.cmegroup.com/trading/cryptocurrency-indices.html

https://www.cmegroup.com/trading/cryptocurrency-indices.html

https://www.cmegroup.com/education/courses/market-regulation/overview/cme-group-rules-and-regulation-overview.html

https://www.cmegroup.com/education/courses/market-regulation/overview/cme-group-rules-and-regulation-overview.html

https://www.cmegroup.com/company/history/

https://www.cmegroup.com/company/history/

https://help.coinbase.com/en/coinbase/getting-started/general-crypto-education/supported-cryptocurrencies.html

https://help.coinbase.com/en/coinbase/getting-started/general-crypto-education/supported-cryptocurrencies.html

https://blog.coinbase.com/our-path-to-listing-sec-regulated-crypto-securities-a1724e13bb5a

https://blog.coinbase.com/our-path-to-listing-sec-regulated-crypto-securities-a1724e13bb5a

https://coinmarketcap.com/

https://coinmarketcap.com/charts/#dominance-percentage

https://github.com/CryptoSignal/crypto-signal

https://github.com/ctubio/Krypto-trading-bot



github.com/ctubio/Krypto-trading-bot. [Online, Accessed: January26, 2020].

[82] Diebold, F.X., Yılmaz, K., 2014. On the network topology ofvariance decompositions: Measuring the connectedness of financialfirms. Journal of Econometrics 182, 119–134.

[83] Domínguez, M.A., Lobato, I.N., 2003. Testing the martingale dif-ference hypothesis. Econometric Reviews 22, 351–377.

[84] Doran, M.D., 2014. A forensic look at bitcoin cryptocurrency. Ph.D.thesis. Utica College.

[85] Dutta, A., Kumar, S., Basu, M., 2020. A gated recurrent unit ap-proach to bitcoin price prediction. Journal of Risk and FinancialManagement 13, 23.

[86] Dyhrberg, A.H., 2016. Bitcoin, gold and the dollar–a garch volatil-ity analysis. Finance Research Letters 16, 85–92.

[87] Easley, D., Engle, R.F., O’Hara, M., Wu, L., 2008. Time-varyingarrival rates of informed and uninformed trades. Journal of FinancialEconometrics 6, 171–207.

[88] Elliott, R.J., Van Der Hoek*, J., Malcolm, W.P., 2005. Pairs trading.Quantitative Finance 5, 271–276.

[89] Engle, R., 2002. Dynamic conditional correlation: A simple class ofmultivariate generalized autoregressive conditional heteroskedastic-ity models. Journal of Business & Economic Statistics 20, 339–350.

[90] Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous general-ized arch. Econometric theory 11, 122–150.

[91] Enoksen, F.A., Landsnes, C.J., 2019. What Can Predict Bubbles inCryptocurrency Prices? Master’s thesis. University of Stavanger,Norway.

[92] Escanciano, J.C., Velasco, C., 2006. Generalized spectral tests forthe martingale difference hypothesis. Journal of Econometrics 134,151–185.

[93] Estalayo, I., Del Ser, J., Osaba, E., Bilbao, M.N., Muhammad, K.,Gálvez, A., Iglesias, A., 2019. Return, diversification and risk incryptocurrency portfolios using deep recurrent neural networks andmulti-objective evolutionary algorithms, in: 2019 IEEE Congresson Evolutionary Computation (CEC), IEEE. pp. 755–761.

[94] Fang, F., Chung, W., Ventre, C., Basios, M., Kanthan, L., Li, L., Wu,F., 2019. Ascertaining price formation in cryptocurrency marketswith deeplearning. arXiv preprint arXiv:2003.00803 .

[95] Fantazzini, D., 2019. Quantitative finance with r and cryptocurren-cies. Amazon KDP, ISBN-13 , 978–1090685315.

[96] Fantazzini, D., Zimin, S., 2020. A multivariate approach for thesimultaneous modelling of market risk and credit risk for cryptocur-rencies. Journal of Industrial and Business Economics 47, 19–69.

[97] Farell, R., 2015. An analysis of the cryptocurrency industry .[98] Feng, W., Wang, Y., Zhang, Z., 2018. Informed trading in the bitcoin

market. Finance Research Letters 26, 63–70.[99] Fil, M., 2019. Pairs trading in cryptocurrency markets .

[100] Flood, R., Hodrick, R.J., Kaplan, P., 1986. An evaluation of recentevidence on stock market bubbles.

[101] Flori, A., 2019. News and subjective beliefs: A bayesian approachto bitcoin investments. Research in International Business and Fi-nance 50, 336–356.

[102] Fretrade, 2020. Freqtrade. https://github.com/freqtrade/freqtrade.[Online, Accessed: January 26, 2020].

[103] Friedl, M.A., Brodley, C.E., 1997. Decision tree classification ofland cover from remotely sensed data. Remote sensing of environ-ment 61, 399–409.

[104] Friedman, J., Tibshirani, R., 1984. The monotone smoothing ofscatterplots. Technometrics 26, 243–250.

[105] Friedman, J.H., 2001. Greedy function approximation: a gradientboosting machine. Annals of statistics , 1189–1232.

[106] Gandal, N., Halaburda, H., 2014. Competition in the cryptocurrencymarket. CEPR Discussion Paper No. DP10157.

[107] Garcia, D., Schweitzer, F., 2015. Social signals and algorithmictrading of bitcoin. Royal Society open science 2, 150288.

[108] Garza, P., 2019. Formal automatic trading in the cryptocurrency era.Ph.D. thesis. Politecnico di Torino.

[109] Gatev, E., Goetzmann, W.N., Rouwenhorst, K.G., 2006. Pairs trad-

ing: Performance of a relative-value arbitrage rule. The Review ofFinancial Studies 19, 797–827.

[110] Gerritsen, D.F., Bouri, E., Ramezanifar, E., Roubaud, D., 2019. Theprofitability of technical trading rules in the bitcoin market. FinanceResearch Letters .

[111] Golang, 2020. A golang implementation of a console-based trad-ing bot for cryptocurrency exchanges. https://github.com/saniales/golang-crypto-trading-bot. [Online, Accessed: January 26, 2020].

[112] Grayblock, 2018. Elliptic-curve cryptography. https://medium.com/coinmonks/elliptic-curve-cryptography-6de8fc748b8b. [Online,Accessed December 29, 2019].

[113] Griffin, J.M., Shams, A., 2019. Is bitcoin really un-tethered? Avail-able at SSRN 3195066 .

[114] Gu, S., Lillicrap, T., Sutskever, I., Levine, S., 2016. Continuousdeep q-learning with model-based acceleration, in: InternationalConference on Machine Learning, pp. 2829–2838.

[115] Guo, T., Bifet, A., Antulov-Fantulin, N., 2018. Bitcoin volatilityforecasting with a glimpse into buy and sell orders, in: 2018 IEEEInternational Conference on Data Mining (ICDM), IEEE. pp. 989–994.

[116] Ha, S., Moon, B.R., 2018. Finding attractive technical patterns incryptocurrency markets. Memetic Computing 10, 301–306.

[117] Hale, G., Krishnamurthy, A., Kudlyak, M., Shultz, P., et al., 2018.How futures trading changed bitcoin prices. FRBSF Economic Let-ter 12.

[118] Hansel, D., 2018. Cryptocurrency trading: How to make money bytrading bitcoin and other cryptocurrency (volume 2) .

[119] Harwick, C., 2016. Cryptocurrency and the problem of intermedia-tion. The Independent Review 20, 569–588.

[120] Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D.,Meger, D., 2018. Deep reinforcement learning that matters, in:Thirty-Second AAAI Conference on Artificial Intelligence.

[121] Hileman, G., Rauchs, M., 2017. Global cryptocurrency benchmark-ing study. Cambridge Centre for Alternative Finance 33.

[122] Holmes, G., Donkin, A., Witten, I.H., 1994. Weka: A machinelearning workbench, in: Proceedings of ANZIIS’94-Australian NewZealnd Intelligent Information Systems Conference, IEEE. pp. 357–361.

[123] Hrytsiuk, P., Babych, T., Bachyshyna, L., 2019. Cryptocur-rency portfolio optimization using value-at-risk measure, in: Strate-gies, Models and Technologies of Economic Systems Management(SMTESM 2019), Atlantis Press.

[124] Hudson, R., Urquhart, A., 2019. Technical analysis and cryptocur-rencies. Available at SSRN 3387950 .

[125] Hultman, H., 2018. Volatility Forecasting An Empirical Study onBitcoin Using Garch and Stochastic Volatility models. Master’s the-sis. Lund University. Sweden.

[126] Hutto, C.J., Gilbert, E., 2014. Vader: A parsimonious rule-basedmodel for sentiment analysis of social media text, in: Eighth inter-national AAAI conference on weblogs and social media.

[127] Hwang, S., Salmon, M., 2004. Market stress and herding. Journalof Empirical Finance 11, 585–616.

[128] Ji, Q., Bouri, E., Lau, C.K.M., Roubaud, D., 2019. Dynamic con-nectedness and integration in cryptocurrency markets. InternationalReview of Financial Analysis 63, 257–272.

[129] Jiang, Z., Liang, J., 2017. Cryptocurrency portfolio managementwith deep reinforcement learning, in: 2017 Intelligent Systems Con-ference (IntelliSys), IEEE. pp. 905–913.

[130] Jianliang, M., Haikun, S., Ling, B., 2009. The application on intru-sion detection based on k-means cluster algorithm, in: 2009 Inter-national Forum on Information Technology and Applications, IEEE.pp. 150–152.

[131] Juchli, M., 2018. Limit order placement optimization with DeepReinforcement Learning: Learning from patterns in cryptocurrencymarket data. Master’s thesis. TU Delft Electrical Engineering.Netherlands.

[132] Juhász, P.L., Stéger, J., Kondor, D., Vattay, G., 2018. A bayesianapproach to identify bitcoin users. PloS one 13.




https://github.com/freqtrade/freqtrade

https://github.com/saniales/golang-crypto-trading-bot

https://github.com/saniales/golang-crypto-trading-bot

https://medium.com/coinmonks/elliptic-curve-cryptography-6de8fc748b8b

https://medium.com/coinmonks/elliptic-curve-cryptography-6de8fc748b8b


[133] Kajtazi, A., Moro, A., 2019. The role of bitcoin in well diversifiedportfolios: A comparative global study. International Review ofFinancial Analysis 61, 143–157.

[134] Kakushadze, Z., 2016. 101 formulaic alphas. Wilmott 2016, 72–81.[135] Kakushadze, Z., 2018. Cryptoasset factor models. Algorithmic Fi-

nance , 1–18.[136] Kalchbrenner, N., Grefenstette, E., Blunsom, P., 2014. A convo-

lutional neural network for modelling sentences. arXiv preprintarXiv:1404.2188 .

[137] Kamrat, S., Suesangiamsakul, N., Marukatat, R., 2018. Technicalanalysis for cryptocurrency trading on mobile phones, in: 2018 3rdTechnology Innovation Management and Engineering Science In-ternational Conference (TIMES-iCON), IEEE. pp. 1–4.

[138] Kang, K., Choo, J., Kim, Y., 2019a. Whose opinion mat-ters? analyzing relationships between bitcoin prices and usergroups in online community. Social Science Computer Review ,0894439319840716.

[139] Kang, S.H., McIver, R.P., Hernandez, J.A., 2019b. Co-movementsbetween bitcoin and gold: A wavelet coherence analysis. PhysicaA: Statistical Mechanics and its Applications , 120888.

[140] Kat, H.M., Heynen, R.C., 1994. Volatility prediction: A comparisonof the stochastic volatility, garch (1, 1) and egarch (1, 1) models.Journal of Derivatives 2.

[141] Kate, C., 2018. Cryptocurrency trading for beginners: 6-steps actionplan to your first investment .

[142] Katsiampa, P., 2019. An empirical investigation of volatility dynam-ics in the cryptocurrency market. Research in International Businessand Finance .

[143] Katsiampa, P., Corbet, S., Lucey, B.M., 2018a. Volatility spillovereffects in leading cryptocurrencies: A bekk-mgarch analysis. Avail-able at SSRN 3232912 .

[144] Katsiampa, P., Gkillas, K., Longin, F., 2018b. Cryptocur-rency market activity during extremely volatile periods URL:https://ssrn.com/abstract=3220781, doi:https://dx.doi.org/10.2139/ssrn.3220781.

[145] Kaufman, P.J., 2013. Trading Systems and Methods,+ Website. vol-ume 591. John Wiley & Sons.

[146] Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.,2001. Improvements to platt’s smo algorithm for svm classifier de-sign. Neural computation 13, 637–649.

[147] Khuntia, S., Pattanayak, J., 2018. Adaptive market hypothesis andevolving predictability of bitcoin. Economics Letters 167, 26–28.

[148] Kim, K., Kim, J., Rinaldo, A., 2018. Time series featurization viatopological data analysis: an application to cryptocurrency trendforecasting. arXiv preprint arXiv:1812.02987 .

[149] Kim, Y.B., Kim, J.G., Kim, W., Im, J.H., Kim, T.H., Kang, S.J.,Kim, C.H., 2016. Predicting fluctuations in cryptocurrency transac-tions based on user comments and replies. PloS one 11, e0161197.

[150] Kondor, D., Csabai, I., Szüle, J., Pósfai, M., Vattay, G., 2014a. In-ferring the interplay between network structure and market effectsin bitcoin. New Journal of Physics 16, 125003.

[151] Kondor, D., Csabai, I., Szule, J., Psfai, M., Vattay, G., 2014b. Infer-ring the interplay between network structure and market effects inbitcoin. New Journal of Physics 16, 125003. URL: https://doi.org/10.1088%2F1367-2630%2F16%2F12%2F125003, doi:10.1088/1367-2630/16/12/125003.

[152] Kondor, D., Pósfai, M., Csabai, I., Vattay, G., 2014c. Do the richget richer? an empirical analysis of the bitcoin transaction network.PloS one 9.

[153] Krafft, P.M., Della Penna, N., Pentland, A.S., 2018. An experimen-tal study of cryptocurrency market dynamics, in: Proceedings of the2018 CHI Conference on Human Factors in Computing Systems,ACM. p. 605.

[154] Kristoufek, L., 2013. Bitcoin meets google trends and wikipedia:Quantifying the relationship between phenomena of the internet era.Scientific reports 3, 3415.

[155] Kurbucz, M.T., 2019. Predicting the price of bitcoin by the mostfrequent edges of its transaction network. Economics Letters 184,

108655.[156] Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W., et al., 2005. Ap-

plied linear statistical models. volume 5. McGraw-Hill Irwin NewYork.

[157] Kwon, D.H., Kim, J.B., Heo, J.S., Kim, C.M., Han, Y.H., 2019.Time series classification of cryptocurrency price trend based on arecurrent lstm neural network. Journal of Information ProcessingSystems 15.

[158] Kyriazis, N.A., 2019. A survey on efficiency and profitable trad-ing opportunities in cryptocurrency markets. Journal of Risk andFinancial Management 12, 67.

[159] Lamon, C., Nielsen, E., Redondo, E., 2017. Cryptocurrency priceprediction using news and social media sentiment. SMU Data Sci.Rev 1, 1–22.

[160] Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D., 1997. Facerecognition: A convolutional neural-network approach. IEEE trans-actions on neural networks 8, 98–113.

[161] Leclair, E.M., 2018. Herding in the cryptocurrency market. Econ5029 final research. Carleton University. Canada.

[162] Lee, T.H., Yang, W., 2014. Granger-causality in quantiles betweenfinancial markets: Using copula approach. International Review ofFinancial Analysis 33, 70–78.

[163] Li, T.R., Chamrajnagar, A., Fong, X., Rizik, N., Fu, F., 2019.Sentiment-based prediction of alternative cryptocurrency price fluc-tuations using gradient boosting tree model. Frontiers in Physics 7,98.

[164] Liaw, A., Wiener, M., et al., 2002. Classification and regression byrandomforest. R news 2, 18–22.

[165] Liu, W., 2019. Portfolio diversification across cryptocurrencies. Fi-nance Research Letters 29, 200–205.

[166] Localbtc, 2020. Localbitcoins purchasing online. https://localbitcoins.com. [Online, Accessed: January 11, 2020].

[167] Lucarelli, G., Borrotti, M., 2019. A deep reinforcement learning ap-proach for automated cryptocurrency trading, in: IFIP InternationalConference on Artificial Intelligence Applications and Innovations,Springer. pp. 247–258.

[168] Luu Duc Huynh, T., 2019. Spillover risks on cryptocurrency mar-kets: A look from var-svar granger causality and student copulas.Journal of Risk and Financial Management 12, 52.

[169] Madan, I., Saluja, S., Zhao, A., 2015. Automated bitcoin trad-ing via machine learning algorithms. URL: http://cs229. stanford.edu/proj2014/Isaac% 20Madan 20.

[170] Makarov, I., Schoar, A., 2020. Trading and arbitrage in cryptocur-rency markets. Journal of Financial Economics 135, 293–319.

[171] Maltatoday, 2020. Why world leader cryptoexchange binance moved to malta. https://www.maltatoday.com.mt/business/business_news/93170/why_world_leader_crypto_exchange_binance_moved_to_malta}.XlKZ8Gj7Q2x.[Online, Accessed: February 11, 2020].

[172] Markowitz, H., 1952. Portfolio selection. The Journal of Finance 7,77–91.

[173] McLean, R.D., Pontiff, J., 2016. Does academic research destroystock return predictability? The Journal of Finance 71, 5–32.

[174] McNally, S., 2016. Predicting the price of Bitcoin using MachineLearning. Ph.D. thesis. Dublin, National College of Ireland.

[175] McNally, S., Roche, J., Caton, S., 2018. Predicting the price of bit-coin using machine learning, in: 2018 26th Euromicro InternationalConference on Parallel, Distributed and Network-based Processing(PDP), IEEE. pp. 339–343.

[176] Meunier, S., 2018. Blockchain 101: What is blockchain and howdoes this revolutionary technology work?, in: Transforming ClimateFinance and Green Investment with Blockchains. Elsevier, pp. 23–34.

[177] Mikolov, T., Kombrink, S., Burget, L., Cernocky, J., Khudanpur, S.,2011. Extensions of recurrent neural network language model, in:2011 IEEE international conference on acoustics, speech and signalprocessing (ICASSP), IEEE. pp. 5528–5531.

[178] MIT, 2015. Bitcoin network dataset. https://senseable2015-


https://ssrn.com/abstract=3220781

http://dx.doi.org/https://dx.doi.org/10.2139/ssrn.3220781


https://doi.org/10.1088%2F1367-2630%2F16%2F12%2F125003

https://doi.org/10.1088%2F1367-2630%2F16%2F12%2F125003

http://dx.doi.org/10.1088/1367-2630/16/12/125003

http://dx.doi.org/10.1088/1367-2630/16/12/125003

https://localbitcoins.com

https://localbitcoins.com

https://www.maltatoday.com.mt/business/business_news/93170/why_world_leader_crypto_exchange_binance_moved_to_malta#.XlKZ8Gj7Q2x



https://senseable2015-6.mit.edu/bitcoin



6.mit.edu/bitcoin. [Online, Accessed January 11, 2020].[179] Molina, J., 2019. Develop your crypto-trading system using plain

logic, part 1. URL: https://medium.com/swlh/develop-your-crypto-trading-system-using-plain-logic-part-1-caac02f0a37d.

[180] Mukhopadhyay, U., Skjellum, A., Hambolu, O., Oakley, J., Yu, L.,Brooks, R., 2016. A brief survey of cryptocurrency systems, in:2016 14th annual conference on privacy, security and trust (PST),IEEE. pp. 745–752.

[181] Nakamoto, S., 2009. Bitcoin open source implementation of p2pcurrency. P2P foundation 18.

[182] Nakano, M., Takahashi, A., Takahashi, S., 2018. Bitcoin technicaltrading with artificial neural network. Physica A: Statistical Me-chanics and its Applications 510, 587–609.

[183] Narayanan, A., Bonneau, J., Felten, E., Miller, A., Goldfeder, S.,2016. Bitcoin and cryptocurrency technologies: a comprehensiveintroduction. Princeton University Press.

[184] Nasir, M.A., Huynh, T.L.D., Nguyen, S.P., Duong, D., 2019. Fore-casting cryptocurrency returns and volume using search engines. Fi-nancial Innovation 5, 2.

[185] Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W., 1996.Applied linear statistical models. volume 4. Irwin Chicago.

[186] research network, B., 2020. Platform for scholarly com-munication about cryptocurrencies and blockchains. https://www.blockchainresearchnetwork.org/. [Online, Accessed: April17, 2020].

[187] Ng, A.Y., Jordan, M.I., 2002. On discriminative vs. generative clas-sifiers: A comparison of logistic regression and naive bayes, in: Ad-vances in neural information processing systems, pp. 841–848.

[188] Nomics, 2020. Top cryptocurrency exchanges list. https://nomics.com/exchanges. [Online, Accessed: January 11, 2020].

[189] Ogorevc, M., 2019. Cryptocurrency as money: A trading strategysolution. Available at SSRN 3436041 .

[190] Omane-Adjepong, M., Alagidede, I.P., 2019. Multiresolution anal-ysis and spillovers of major cryptocurrency markets. Research inInternational Business and Finance 49, 191–206.

[191] Omane-Adjepong, M., Alagidede, P., Akosah, N.K., 2019. Wavelettime-scale persistence analysis of cryptocurrency market returns andvolatility. Physica A: Statistical Mechanics and its Applications 514,105–120.

[192] Pascanu, R., Mikolov, T., Bengio, Y., 2013. On the difficulty oftraining recurrent neural networks, in: International conference onmachine learning, pp. 1310–1318.

[193] Patil, A.P., Akarsh, T., Parkavi, A., 2018. A study of opinion miningand data mining techniques to analyse the cryptocurrency market,in: 2018 3rd International Conference on Computational Systemsand Information Technology for Sustainable Solutions (CSITSS),IEEE. pp. 198–203.

[194] Pauna, C., 2018. Arbitrage trading systems for cryptocurrencies.design principles and server architecture. Informatica Economica22, 35–42.

[195] Persson, S., Slottje, A., Shaw, I., 2018. Hybrid Autoregressive-Recurrent Neural Network Architecture for Algorithmic Trading ofCryptocurrencies. Cs230 deep learning thesis. Stanford University.

[196] Phaladisailoed, T., Numnonda, T., 2018. Machine learning modelscomparison for bitcoin price prediction, in: 2018 10th InternationalConference on Information Technology and Electrical Engineering(ICITEE), IEEE. pp. 506–511.

[197] Phillips, P.C., Shi, S., Yu, J., 2015a. Testing for multiple bubbles:Historical episodes of exuberance and collapse in the s&p 500. In-ternational economic review 56, 1043–1078.

[198] Phillips, P.C., Shi, S., Yu, J., 2015b. Testing for multiple bubbles:Limit theory of real-time detectors. International Economic Review56, 1079–1134.

[199] Phillips, R.C., Gorse, D., 2017. Predicting cryptocurrency pricebubbles using social media data and epidemic modelling, in: 2017IEEE Symposium Series on Computational Intelligence (SSCI),IEEE. pp. 1–7.

[200] Phillips, R.C., Gorse, D., 2018a. Cryptocurrency price drivers:

Wavelet coherence analysis revisited. PloS one 13, e0195200.[201] Phillips, R.C., Gorse, D., 2018b. Mutual-excitation of cryptocur-

rency market returns and social media topics, in: Proceedings of the4th International Conference on Frontiers of Educational Technolo-gies, ACM. pp. 80–86.

[202] Poloniex, 2020. Poloniex markets. https://poloniex.com/. [Online,Accessed: February 11, 2020].

[203] Rane, P.V., Dhage, S.N., 2019. Systematic erudition of bitcoin priceprediction using machine learning techniques, in: 2019 5th Interna-tional Conference on Advanced Computing & Communication Sys-tems (ICACCS), IEEE. pp. 594–598.

[204] Rebane, J., Karlsson, I., Denic, S., Papapetrou, P., 2018. Seq2seqrnns and arima models for cryptocurrency prediction: A compara-tive study. SIGKDD Fintech 18.

[205] Rish, I., et al., 2001. An empirical study of the naive bayes clas-sifier, in: IJCAI 2001 workshop on empirical methods in artificialintelligence, pp. 41–46.

[206] Rose, C., 2015. The evolution of digital currencies: Bitcoin, a cryp-tocurrency causing a monetary revolution. International Business &Economics Research Journal (IBER) 14, 617–622.

[207] S., J., 2018. How do blockchain mining and transactions workexplained in 7 simple steps. https://blog.goodaudience.com/how-a-miner-adds-transactions-to-the-blockchain-in-seven-steps-856053271476. [Online, Accessed: January 26, 2020].

[208] Salakhutdinov, R., Hinton, G., 2009. Deep boltzmann machines, in:Artificial intelligence and statistics, pp. 448–455.

[209] Shanaev, S., Sharma, S., Ghimire, B., Shuraeva, A., 2020. Tam-ing the blockchain beast? regulatory implications for the cryptocur-rency market. Research in International Business and Finance 51,101080.

[210] Sharma, S., Krishma, N., Raina, E., 2017. Survey paper on cryp-tocurrency. International Journal of Scientific Research in ComputerScience, Engineering and Information Technology Vol. 2 Issue 3,307–310.

[211] Siaminos, G., 2019. Predicting the value of cryptocurrencies usingmachine learning time series analysis time series analysis time .

[212] Sigaki, H.Y., Perc, M., Ribeiro, H.V., 2019. Clustering patternsin efficiency and the coming-of-age of the cryptocurrency market.Scientific reports 9, 1440.

[213] Sirignano, J., Cont, R., 2019. Universal features of price formationin financial markets: perspectives from deep learning. QuantitativeFinance 19, 1449–1459.

[214] Smuts, N., 2019. What drives cryptocurrency prices?: An investiga-tion of google trends and telegram sentiment. ACM SIGMETRICSPerformance Evaluation Review 46, 131–134.

[215] Sriram, A., Jun, H., Satheesh, S., Coates, A., 2017. Cold fusion:Training seq2seq models together with language models. arXivpreprint arXiv:1708.06426 .

[216] Stocksharp, 2020. Stocksharp - trading platform. https://github.com/StockSharp/StockSharp. [Online, Accessed: January26, 2020].

[217] Stuerner, P., 2019. Algorithmic Cryptocurrency Trading. Ph.D. the-sis. Ulm University.

[218] Sun, J., Zhou, Y., Lin, J., 2019. Using machine learning for cryp-tocurrency trading, in: 2019 IEEE International Conference on In-dustrial Cyber Physical Systems (ICPS), IEEE. pp. 647–652.

[219] Sutton, R.S., Barto, A.G., et al., 1998. Introduction to reinforcementlearning. volume 135. MIT press Cambridge.

[220] Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S., 2017. Efficient process-ing of deep neural networks: A tutorial and survey. Proceedings ofthe IEEE 105, 2295–2329.

[221] Tapscott, D., Tapscott, A., 2016. Blockchain revolution: how thetechnology behind bitcoin is changing money, business, and theworld. Penguin.

[222] TradingstrategyGuides, 2019a. Eos cryptocurrency tradingstrategy–turtle soup pattern. https://tradingstrategyguides.com/eos-cryptocurrency-trading-strategy/. [Online, Accessed January 29,2020].




https://medium.com/swlh/develop-your-crypto-trading-system-using-plain-logic-part-1-caac02f0a37d

https://medium.com/swlh/develop-your-crypto-trading-system-using-plain-logic-part-1-caac02f0a37d

https://www.blockchainresearchnetwork.org/

https://www.blockchainresearchnetwork.org/

https://nomics.com/exchanges

https://nomics.com/exchanges

https://poloniex.com/

https://blog.goodaudience.com/how-a-miner-adds-transactions-to-the-blockchain-in-seven-steps-856053271476



https://github.com/StockSharp/StockSharp

https://github.com/StockSharp/StockSharp

https://tradingstrategyguides.com/eos-cryptocurrency-trading-strategy/

https://tradingstrategyguides.com/eos-cryptocurrency-trading-strategy/


[223] TradingstrategyGuides, 2019b. Free omni cryptocurrency strategy–amazing gann box. https://tradingstrategyguides.com/free-omni-cryptocurrency-strategy/. [Online, Accessed January 29, 2020].

[224] TradingstrategyGuides, 2019c. Iota cryptocurrency strategy–busted double top pattern. https://tradingstrategyguides.com/iota-cryptocurrency-strategy/. [Online, Accessed January 29, 2020].

[225] TradingstrategyGuides, 2019d. Nem (xem) cryp-tocurrency strategy–momentum pinball setup. https://tradingstrategyguides.com/nem-xem-cryptocurrency-strategy/.[Online, Accessed January 29, 2020].

[226] TradingstrategyGuides, 2019e. Tether trading strategy–bottomrotation trading. https://tradingstrategyguides.com/tether-trading-strategy/. [Online, Accessed January 29, 2020].

[227] TradingView, 2020. Total crypto market capitalization andvolume. https://www.tradingview.com/markets/cryptocurrencies/global-charts/. [Online, Accessed February 23, 2020].

[228] Troster, V., Tiwari, A.K., Shahbaz, M., Macedo, D.N., 2019. Bitcoinreturns and risk: A general garch and gas analysis. Finance ResearchLetters 30, 187–193.

[229] Trucíos, C., Tiwari, A.K., Alqahtani, F., 2019. Value-at-risk and ex-pected shortfall in cryptocurrencies’ portfolio: A vine copula-basedapproach. Available at SSRN 3441892 .

[230] Vidal-Tomás, D., Ibáñez, A.M., Farinós, J.E., 2019. Herding in thecryptocurrency market: Cssd and csad approaches. Finance Re-search Letters 30, 181–186.

[231] Virk, D.S., 2017. Prediction of Bitcoin Price using Data Mining.Master’s thesis. National College of Ireland.

[232] Vo, A., Yost-Bremm, C., 2018. A high-frequency algorithmic trad-ing strategy for cryptocurrency. Journal of Computer InformationSystems , 1–14.

[233] Vogelvang, B., 2005. Econometrics: theory and applications withEviews. Pearson Education.

[234] Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S., et al., 2001. Con-strained k-means clustering with background knowledge, in: Icml,pp. 577–584.

[235] Wang, H., He, D., Ji, Y., 2017. Designated-verifier proof of assetsfor bitcoin exchange using elliptic curve cryptography. Future Gen-eration Computer Systems .

[236] Wang, L., 2005. Support vector machines: theory and applications.volume 177. Springer Science & Business Media.

[237] Ward, M., 2018. Algorithmic Trading for Cryptocurrencies. De-partmental honors thesis. UtahState University. United States.

[238] Weber, P., Wang, F., Vodenska-Chitkushev, I., Havlin, S., Stanley,H.E., 2007. Relation between volatility correlations in financialmarkets and omori processes occurring on all scales. Physical Re-view E 76, 016109.

[239] Wohlin, C., 2014. Guidelines for snowballing in systematic litera-ture studies and a replication in software engineering, in: Proceed-ings of the 18th international conference on evaluation and assess-ment in software engineering, pp. 1–10.

[240] Xu, Z., Wang, S., Zhu, F., Huang, J., 2017. Seq2seq fingerprint:An unsupervised deep molecular embedding for drug discovery, in:Proceedings of the 8th ACM International Conference on Bioinfor-matics, Computational Biology, and Health Informatics, pp. 285–294.

[241] Yang, H., 2018. Behavioral anomalies in cryptocur-rency markets. Available at SSRN 3174421 URL:https://ssrn.com/abstract=3174421, doi:https://dx.doi.org/10.2139/ssrn.3174421.

[242] Yaya, O.S., Ogbonna, E.A., Olubusoye, O.E., 2018. How persistentand dependent are pricing of bitcoin to other cryptocurrencies beforeand after 2017/18 crash? .

[243] Zamuda, A., Crescimanna, V., Burguillo, J.C., Dias, J.M., Wegrzyn-Wolska, K., Rached, I., Gonzlez, H., Senkerik, R., Pop, C., Cioara,T., et al., 2019. Forecasting cryptocurrency value by sentiment anal-ysis: An hpc-oriented survey of the state-of-the-art in the cloud era,in: High-Performance Modelling and Simulation for Big Data Ap-plications. Springer, pp. 325–349.

[244] Zbikowski, K., 2016. Application of machine learning algorithmsfor bitcoin automated trading, in: Machine Intelligence and BigData in Industry. Springer, pp. 161–168.

[245] Zemmal, N., Azizi, N., Dey, N., Sellami, M., 2016. Adaptivesemi supervised support vector machine semi supervised learningwith features cooperation for breast cancer classification. Journal ofMedical Imaging and Health Informatics 6, 53–62.

[246] Zhang, S., Yao, L., Sun, A., Tay, Y., 2019. Deep learning based rec-ommender system: A survey and new perspectives. ACM Comput.Surv. 52. URL: https://doi.org/10.1145/3285029, doi:10.1145/3285029.

[247] Zhengyang, W., Xingzhou, L., Jinjin, R., Jiaqing, K., 2019. Predic-tion of cryptocurrency price dynamics with multiple machine learn-ing techniques, in: Proceedings of the 2019 4th International Con-ference on Machine Learning Technologies, ACM. pp. 15–19.

[248] Zhou, H., Kalev, P.S., 2019. Algorithmic and high frequency tradingin asia-pacific, now and the future. Pacific-Basin Finance Journal53, 186–207.

[249] Slepaczuk, R., Zenkova, M., 2018. Robustness of support vectormachines in algorithmic trading on cryptocurrency market. CentralEuropean Economic Journal 5, 186 – 205.


https://tradingstrategyguides.com/free-omni-cryptocurrency-strategy/

https://tradingstrategyguides.com/free-omni-cryptocurrency-strategy/

https://tradingstrategyguides.com/iota-cryptocurrency-strategy/

https://tradingstrategyguides.com/iota-cryptocurrency-strategy/

https://tradingstrategyguides.com/nem-xem-cryptocurrency-strategy/

https://tradingstrategyguides.com/nem-xem-cryptocurrency-strategy/

https://tradingstrategyguides.com/tether-trading-strategy/

https://tradingstrategyguides.com/tether-trading-strategy/

https://www.tradingview.com/markets/cryptocurrencies/global-charts/

https://www.tradingview.com/markets/cryptocurrencies/global-charts/

https://ssrn.com/abstract=3174421



https://doi.org/10.1145/3285029

http://dx.doi.org/10.1145/3285029

http://dx.doi.org/10.1145/3285029

arxiv:2003.11352v2 [q-fin.tr] 24 apr 2020 · peer version of electronic cash that can be sent...

Documents