cryptocurrency trading: a comprehensive survey

Cryptocurrency Trading A Comprehensive SurveyFan Fang alowast Carmine Ventrea Michail Basiosb Hoiliong Kongb Leslie KanthanbDavid Martinez-Regob Fan Wu b and Lingbo Li blowast

aKingrsquos College London UKbTuring Intelligence Technology Limited UK

A R T I C L E I N F O

Keywordstrading cryptocurrency machine learn-ing econometrics

A B S T R A C T

In 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 understoodIt is therefore important to summarise existing research papers and results on cryptocurrency tradingincluding available trading platforms trading signals trading strategy research and risk managementThis paper provides a comprehensive survey of cryptocurrency trading research by covering 126research papers on various aspects of cryptocurrency trading (eg cryptocurrency trading systemsbubble 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 (contentsproperties) 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 conceptionMany hedge funds and asset managers have begun to in-clude cryptocurrency-related assets into their portfolios andtrading strategies The academic community has similarlyspent considerable efforts in researching cryptocurrency trad-ing This paper seeks to provide a comprehensive survey ofthe research on cryptocurrency trading by which we meanany study aimed at facilitating and building strategies totrade cryptocurrencies

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 [103] From Figure 1 we observe over 85 of papershave appeared since 2018 demonstrating the emergence ofcryptocurrency trading as a new research area in financialtrading

The literature is organised according tosix distinct as-pects of cryptocurrency trading

bull Cryptocurrency trading software systems (ie real-time trading systems turtle trading systems arbitragetrading systems)

bull Systematic trading including technical analysis pairstrading and other systematic trading methods

fanfangkclacuk ( Fan Fang)fanfangkclacuk ( Fan Fang)carmineventrekclacuk ( Carmine Ventre)miketurintechai ( Michail Basios) justinturintechai( Hoiliong Kong) leslieturintechai ( Leslie Kanthan)davidturintechai ( David Martinez-Rego)fanturintechai ( Fan Wu) lingboturintechai (Lingbo Li)

wwwcvrcccvrŽsayahnaorg ( Fan Fang)ORCID(s) 0000-0002-5086-5614 ( Fan Fang)

0000-0002-3073-1352 ( Lingbo Li)

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

bull Emergent trading technologies including economet-ric methods machine learning technology and otheremergent trading methods

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

bull Market condition research including bubbles [106] orcrash analysis and extreme conditions

bull 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 area 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 distributionamong technologies identifies classifications of methodol-

First Author et al of 30

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1135

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Jan

202

1

Cryptocurrency Trading A Comprehensive Survey

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 [166] surveyed efficiency and profitable tradingopportunities in cryptocurrency markets Ahamad et al [4]and Sharma et al [221] gave a brief survey on cryptocur-rencies Ujan et al [191] gave a brief survey of cryptocur-rency systems Ignasi et al [186] performed a bibliometricanalysis of bitcoin literature These relate work outcomesfocused on specific area in cryptocurrency including cryp-tocurrencies and cryptocurrency market introduction cryp-tocurrency systems platforms bitcoin literature review etcTo the best of our knowledge no previous work has pro-vided a comprehensive survey particularly focused on cryp-tocurrency 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 126 cryptocurrency trading papersacross 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

Figure 2 Tree structure of the contents in this paper

Figure 3 Workflow of Blockchain transaction

21 Blockchain211 Blockchain Technology Introduction

Blockchain is a digital ledger of economic transactionsthat can be used to record not just financial transactions butany object with an intrinsic value [232] In its simplestform a Blockchain is a series of immutable data recordswith timestampswhich 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 ldquoA paysX Bitcoins to Brdquo that is signed by A using its private keyThis 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 signerrsquos publickey denoted as the algorithm in Figure 3) If the verificationis valid then the block is added to the chain all other blocksadded after it will ldquoconfirmrdquo that transaction For exampleif a transaction is contained in block 502 and the length ofthe blockchain is 507 blocks it means that the transactionhas 5 confirmations (507-502) [218]

212 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 into

First Author et al Page 2 of 30


the current block Miners in Blockchain accept transactionsmark 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 becomepart 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 createsa means of transaction and enables the transfer of value andinformation Cryptocurrencies are the tokens used in thesenetworks to send value and pay for these transactions Theycan be thought of as tools on the Blockchain and in somecases can also function as resources or utilities In otherinstances they are used to digitise the value of assets Insummary Cryptocurrencies are part of an ecosystem-basedon Blockchain technology

22 Introduction of cryptocurrency market221 What is cryptocurrency

Cryptocurrency is a decentralised medium of exchangewhich uses cryptographic functions to conduct financial trans-actions [90] Cryptocurrencies leverage the Blockchain tech-nology to gain decentralisation transparency and immutabil-ity [187] 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 [194] For ex-ample Bitcoin uses a method called rdquoElliptic Curve Cryp-tographyrdquo to ensure that transactions involving Bitcoin aresecure [246] Elliptic curve cryptography is a type of public-key cryptography that relies on mathematics to ensure thesecurity of transactions When someone attempts to circum-vent the aforesaid encryption scheme by brute force it takesthem one-tenth the age of the universe to find a value matchwhen trying 250 billion possibilities every second [118]Regarding its use as a currency cryptocurrency has the sameproperties as money It has a controlled supply Most cryp-tocurrencies limit the supply of tokens Eg for Bitcointhe supply will decrease over time and will reach its finalquantity sometime around 2140 All cryptocurrencies con-trol the supply of tokens through a timetable encoded in theBlockchain

One of the most important features of cryptocurrenciesis the exclusion of financial institution intermediaries [125]The absence of a ldquomiddlemanrdquo lowers transaction costs fortraders For comparison if a bankrsquos 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 compromisedthe rest will continue to be able to verify transactions cor-rectly Cryptocurrencies also have the important feature ofnot being controlled by any central authority [217] the de-centralised nature of the Blockchain ensures cryptocurren-

Figure 4 Total Market Capitalization and Volume of cryp-tocurrency market USD [238]

cies are theoretically immune to government control and in-terference

As of December 20 2019 there exist 4950 cryptocur-rencies and 20325 cryptocurrency markets the market capis around 190 billion dollars [78] Figure 4 shows histor-ical data on global market capitalisation and 24-hour trad-ing volume [238] The total market cap is calculated byaggregating the dollar market cap of all cryptocurrenciesFrom the figure we can observe how cryptocurrencies expe-rience exponential growth in 2017 and a large bubble burstin early 2018 But in recent years cryptocurrencies haveshown signs of stabilisation

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 rdquoA pure peer-to-peer version of electronic cash that can be sent online forpayment from one party to another without going througha counterparty ie a financial institutionrdquo [193] 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 ScryptScrypt

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)

222 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-



Table 1Cryptocurrency exchanges Lists

Exchanges Category Supported currencies Fiat Currency Registration country Regulatory authorityCME Derivatives BTC and Ethereum [71] USD USA [73] CFTC [72]CBOE Derivatives BTC [59] USD USA [58] CFTC [60]BAKKT (NYSE) Derivatives BTC [15] USD USA [16] CFTC [15]BitMex Derivatives 12 cryptocurrencies [31] USD Seychelles [32] -Binance Spot 98 cryptocurrencies [27] EUR NGN RUB TRY Malta [181] FATF [26]Coinbase Spot 28 cryptocurrencies [76] EUR GBP USD USA [37] SEC [77]Bitfinex Spot gt 100 cryptocurrencies [28] EUR GBP JPY USD British Virgin Islands [29] NYAG [30]Bitstamp Spot 5 cryptocurrencies [33] EUR USD Luxembourg [34] CSSF [35]Poloniex Spot 23 cryptocurrencies [213] USD USA [213] -

Figure 5 Percentage of Total Market Capitalisation [79]

changes according to the rank list by volume compiledon ldquonomicsrdquo website [199] Chicago Mercantile Exchange(CME) Chicago Board Options Exchange (CBOE) as wellas BAKKT (backed by New York Stock Exchange) are reg-ulated cryptocurrency exchanges Fiat currency data alsocomes from ldquonomicsrdquo website [199] Regulatory authorityand supported currencies of listed exchanges are collectedfrom official websites or blogs

23 Cryptocurrency Trading231 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 beingtraded which is ldquocryptocurrencyrdquo The operation mode ofcryptocurrency trading depends on the means of transactionin the cryptocurrency market which can be classified intoldquotrading of cryptocurrency Contract for Differences (CFD)rdquo(The contract between the two parties often referred to asthe ldquobuyerrdquo and ldquosellerrdquo stipulates that the buyer will paythe seller the difference between themselves when the po-sition closes [11]) and ldquobuying and selling cryptocurrenciesvia an exchangerdquo A trading strategy in cryptocurrency trad-ing formulated by an investor is an algorithm that defines

a set of predefined rules to buy and sell on cryptocurrencymarkets

232 Advantages of Trading CryptocurrencyThe benefits of cryptocurrency trading include

Drastic fluctuations The volatility of cryptocurrencies areoften likely to attract speculative interest and investorsThe 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 individualsin different venues across the world

Near Anonymity Buying goods and services using cryp-tocurrencies is done online and does not require tomake onersquos 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 ownerrsquosanonymity

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 to users who distrust traditional systemsOver-the-counter (OTC) cryptocurrency markets of-fer in this context peer-to-peer transactions on theBlockchain The most famous cryptocurrency OTCmarket is ldquoLocalBitcoin [176]rdquo

Programmable ldquosmartrdquo capabilities Some cryptocurren-cies can bring other benefits to holders including lim-ited ownership and voting rights Cryptocurrenciesmay also include a partial ownership interest in phys-ical 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 yearsa third kind of trading strategy which we call quantitativehas received increasing attention Such a trading strategy issimilar to a technical trading strategy because it uses trad-ing activity information on the exchange to make buying orselling decisions Quantitative traders build trading strate-gies with quantitative data which is mainly derived fromprice volume technical indicators or ratios to take advan-tage of inefficiencies in the market and are executed auto-matically by trading software Cryptocurrency market is dif-ferent from traditional markets as there are more arbitrageopportunities higher fluctuation and transparency Due tothese characteristics most traders and analysts prefer usingquantitative trading strategies in cryptocurrency markets

31 Cryptocurrency Trading Software SystemSoftware trading systems allow international transactions

process customer accounts and information and accept andexecute transaction orders [50] 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 [21]When developing a cryptocurrency trading system we mustconsider the capital market base asset investment plan andstrategies [190] Strategies are the most important part of aneffective 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 the professional third-party consulting companies andfast customer services

32 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 analysispairs 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 [116] Pairs trading is a systematic trading strat-egy that considers 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 [94] Papers shownin this section involve the analysis and comparison of tech-nical indicators pairs and informed trading amongst otherstrategies

33 Emergent Trading TechnologiesEmergent trading strategies for cryptocurrency include

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

331 Econometrics on CryptocurrencyEconometric methods apply a combination of statistical

and economic theories to estimate economic variables andpredict their values [244] Statistical models use mathe-matical equations to encode information extracted from thedata [152] In some cases statistical modeling techniquescan quickly provide sufficiently accurate models [24] Othermethods might be used such as sentiment-based predictionand long-and-short-term volatility classification based pre-diction [64] The prediction of volatility can be used tojudge the price fluctuation of cryptocurrencies which is alsovaluable for the pricing of cryptocurrency-related deriva-tives [147]

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 [96]) models to evaluate the fluctuationof cryptocurrencies [55] A linear statistical model is amethod to evaluate the linear relationship between pricesand an explanatory variable [196] When there exists 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 the time-series analysisis the autoregressive moving average (ARMA) model [69]

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

coin and other cryptocurrency trading strategies [185] 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 indi-cators (Google Trends Twitter etc) Technical indicators(price volume etc) and other Seasonal indicators (time ofday day of the 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 (eg price) or categorical(eg 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 appli-cation of Machine Learning to real applications and of ut-most importance in Financial applications We need to usestatistical approaches such as cross validation to validatethe model before we actually use it to make predictionsIn machine learning this is typically called ldquovalidationrdquoThe process of using machine learning technology to pre-dict cryptocurrency is shown in Figure 6

Depending on the formulation of the main learning loopwe can classify Machine Learning approaches into threecategories Supervised learning Unsupervised learning andReinforcement learning Supervised learning is used to de-rive a predictive function from labeled training data La-beled training data means that each training instance in-cludes inputs and expected outputs Usually these expectedoutputs 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 assetsUnsupervised learning tries to infer structure from unla-beled 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 utilises software agents trained to maximisea utility function which defines their objective this is flex-ible enough to allow agents to exchange short term returnsfor future ones In the financial sector some trading chal-lenges can be expressed as a game in which an agent aimsat maximising the return at the end of the period

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

34 Portfolio ResearchPortfolio theory advocates diversification of investments

to maximize returns for a given level of risk by allocatingassets strategically The celebrated mean-variance optimisa-tion is a prominent example of this approach [182] Gener-

ally crypto asset denotes a digital asset (ie cryptocurren-cies and derivatives) There are some common ways to builda diversified portfolio in crypto assets The first method isto diversify across markets which is to mix a wide vari-ety of investments within a portfolio of the cryptocurrencymarket The second method is to consider the industry sec-tor which is to avoid investing too much money in any onecategory Diversified investment of portfolio in the cryp-tocurrency market includes portfolio across cryptocurren-cies [175] and portfolio across the global market includingstocks and futures [140]

35 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 [48] Many experts pinpoint a cryptocurrency bubblein 2017 when the prices of cryptocurrencies grew by 900In 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

41 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 on 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 analysisregulatory 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 latterto the former When conducting this research we only con-sider those papers whose research focuses on cryptocurrencymarkets or a comparison of trading in those and other finan-cial markets

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



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

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 ldquocryptocurrency tradingrdquo here we mean one of the termslisted in Table 2 and discussed above

Some researchers gave a brief survey of cryptocurrency [4221] cryptocurrency systems [191] and cryptocurrency trad-ing opportunities [166] 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

42 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 We also chooseother public repositories like SSRN but we find that almostall academic papers in these platforms can also be retrievedvia Google Scholar consequently in our statistical analysiswe count those as Google Scholar hits We choose arXiv asanother source since it allows this survey to be contempo-rary with all the most recent findings in the area The in-terested reader is warned that these papers have not under-gone formal peer review The keywords used for searchingand collecting are listed below [Crypto] means the cryp-tocurrency market which is our research interest becausemethods might be different among different markets Weconducted 6 searches across the two repositories just beforeOctober 15 2019

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

To ensure high coverage we adopted the so-called snow-balling [250] method on each paper found through thesekeywords We checked papers added from snowballing meth-ods that satisfy the criteria introduced above until we reachedclosure

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 - - 60Overall - - 126

43 Collection ResultsTable 3 shows the details of the results from our paper

collection Keyword searches and snowballing resulted in126 papers across the six research areas of interest in Sec-tion 41

Figure 7 shows the distribution of papers published atdifferent research sites Among all the papers 4524 pa-pers are published in Finance and Economics venues suchas Journal of Financial Economics (JFE) Cambridge Centrefor Alternative Finance (CCAF) Finance Research LettersCentre for Economic Policy Research (CEPR) and Journalof Risk and Financial Management (JRFM) 476 papersare published in Science venues such as Public Library OfScience one (PLOS one) Royal Society open science andSAGE 1587 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) 476 papers are pub-lished in Physics Physicians venues (mostly in Physicsvenue) such as Physica A 1032 papers are published inAI and complex system venues such as Complexity and In-ternational Federation for Information Processing (IFIP) 1746papers are published in Others venues which contains inde-pendently published papers and dissertations 159 papersare published on arXiv The distribution of different venuesshows that cryptocurrency trading is mostly published in Fi-nance and Economics venues but with a wide diversity oth-erwise

44 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-



Table 4Review Schema

Classification Sec Topic

Cryptocurrency Trading Software System

51 Trading Infrastructure System52 Real-time Cryptocurrency Trading System53 Turtle trading system in Cryptocurrency market54 Arbitrage Trading Systems for Cryptocurrencies55 Comparison of three cryptocurrency trading systems

Systematic Trading61 Technical Analysis62 Pairs Trading63 Others

Emergent Trading Technologies71 Econometrics on cryptocurrency72 Machine learning technology73 Others

Portfolio and Cryptocurrency Assets 81 Research among cryptocurrency pairs and related factors82 Crypto-asset portfolio research

Market condition research 91 Bubbles and crash analysis92 Extreme condition

Others 10 Others related to Cryptocurrency Trading

Summary Analysis of Literature Review

111 Timeline112 Research distribution among properties113 Research distribution among categories and technologies114 Datasets used in cryptocurrency trading

Figure 7 Publication Venue Distribution

gent trading technologies in the cryptocurrency market Sec-tion 8 introduces research on cryptocurrency pairs and re-lated factors and crypto-asset portfolios research In Sec-tion 9 we discuss cryptocurrency market condition researchincluding bubbles crash analysis and extreme conditionsSection 10 introduces other research included in cryptocur-rency trading not 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 Systems51 Trading Infrastructure Systems

Following the development of computer science and cryp-tocurrency trading many cryptocurrency trading systemsbotshave 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 anadvanced backtesting engine [51] It supports five differentcryptocurrency exchanges

3 Commas is a proprietary payable cryptocurrency trad-ing system platform that can take profit and stop-loss ordersat the same time [1] Twelve different cryptocurrency ex-changes 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 [61] 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 exchangesII 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-neutral strate-gies that do not transfer funds between exchanges [36] 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 agnosticfluctuations (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 exchangesBuy 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 [227] 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 SDesigner - Free universal algorithm strategy appeasy to create strategies

II SData - free software that can automatically load andstore market data

III STerminal - free trading chart application (tradingterminal)

IV SShell - ready-made graphics framework that can bechanged according to needs and has a fully open sourcein C

V SAPI - a free C library for programmers using Vi-sual Studio Any trading strategies can be created inSAPI

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 toolsand strategy optimization through machine learning [108]Freqtrade has the following features

I Persistence Persistence is achieved through SQLitetechnology

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 sizeand 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 [86] Investors can track over 500coins of Bittrex Bitfinex GDAX Gemini and more Auto-mated technical analysis includes momentum RSI IchimokuCloud MACD etc The system gives alerts including EmailSlack Telegram etc CryptoSignal has two primary fea-tures First of all it offers modular code for easy implemen-tation of trading strategies Secondly it is easy to install withDocker

Ctubio is a C++ based low latency (high frequency)cryptocurrency trading system [87] 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 an analysis and visualization of the cryp-tocurrency trading system [57] It makes trading strategieseasy to express and backtest them on historical data (dailyand minute resolution) providing analysis and insights intothe performance of specific strategies Catalyst allows usersto share and organise data and build profitable data-driveninvestment strategies Catalyst not only supports the tradingexecution but also offers historical price data of all cryptoassets (from minute to daily resolution) Catalyst also hasbacktesting and real-time trading capabilities which enablesusers to seamlessly transit between the two different trad-ing modes Lastly Catalyst integrates statistics and machinelearning libraries (such as matplotlib scipy statsmodels andsklearn) to support the development analysis and visualiza-tion of the latest trading systems

Golang Crypto Trading Bot is a Go based cryptocur-rency trading system [117] 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

52 Real-time Cryptocurrency Trading SystemsAmit et al [21] developed a real-time Cryptocurrency

Trading System A real-time cryptocurrency trading systemis composed of clients servers and databases Traders usea web-application to login to the server to buysell cryptoassets The server collects cryptocurrency market data bycreating a script that uses the Coinmarket API Finally thedatabase collects balances trades and order book informa-tion from the server The authors tested the system with anexperiment that demonstrates user-friendly and secure expe-riences for traders in the cryptocurrency exchange platform

53 Turtle trading system in Cryptocurrencymarket

The original Turtle Trading system is a trend followingtrading system developed in the 1970s The idea is to gen-erate buy and sell signals on stock for short-term and long-term breakouts and its cut-loss condition which is measuredby Average true range (ATR) [144] The trading system willadjust the size of assets based on their volatility Essen-tially if a turtle accumulates a position in a highly volatile



Table 5Comparison of existing cryptocurrency trading systems Exchange Language andPopularity denote the number of the exchanges that are supported by this softwareprogramming 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 [51]

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 [61] 13k

optional normalized dataBlackBird Strategy is market-neutral 8 C++ Yes GitHub [36] 47k

strategy not transfer funds between exchangesStockSharp Free C library 48 C Yes GitHub [227] 26k

free trading charting applicationFreqtrade Strategy Optimization by machine learning 2 Python Yes GitHub [108] 24k

Calculate edge position sizingCryptoSignal Technical analysis trading system 4 Python Yes GitHub [86] 19kCtubio Low latency 1 C++ Yes GitHub [87] 17kCatalyst Analysis and visualization of system 4 Python Yes GitHub [57] 17kseamless transition between live

and back-testingGoLang Sandbox environment simulation 7 Go Yes GitHub [117] 277

market it will be offset by a low volatility position Ex-tended Turtle Trading system is improved with smaller timeinterval spans and introduces a new rule by using exponen-tial moving average (EMA) Three EMA values are used totrigger the ldquobuyrdquo signal 30EMA (Fast) 60EMA (Slow)100EMA (Long) The author of [144] performed backtest-ing and comparing both trading systems (Original Turtle andExtended Turtle) on 8 prominent cryptocurrencies Throughthe experiment Original Turtle Trading System achieved an1859 average net profit margin (percentage of net profitover total revenue) and 3594 average profitability (per-centage of winning trades over total numbers of trades) in87 trades through nearly one year Extended Turtle Trad-ing System achieved 11441 average net profit margin and5275 average profitability in 41 trades through the sametime interval This research showed how Extended TurtleTrading System compared can improve over Original TurtleTrading System in trading cryptocurrencies

54 Arbitrage Trading Systems forCryptocurrencies

Christian [205] 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 exchangesAs 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 SQLare reliable solution due to the large amounts of relationaldata The author used the system to catch arbitrage oppor-tunities on 25 May 2018 among 787 cryptocurrencies on 7different exchanges The research paper [205] listed the bestten trading signals made by this system from 186 availablefound signals The results showed that the system caughtthe trading signal of ldquoBTG-BTCrdquo to get a profit of up to

49544 when arbitraging to buy in Cryptopia exchangeand sell in Binance exchange Another three well-traded ar-bitrage signals (profit expectation around 20 mentioned bythe author) were found on 25 May 2018 Arbitrage TradingSoftware System introduced in that paper presented generalprinciples and implementation of arbitrage trading systemin the cryptocurrency market

55 Comparison of three cryptocurrency tradingsystems

Real-time trading systems use real-time functions to col-lect data and generate trading algorithms Turtle trading sys-tem and arbitrage trading system have shown a sharp con-trast in their profit and risk behaviour Using Turtle tradingsystem in cryptocurrency markets got high returns with highrisk Arbitrage trading system is inferior in terms of revenuebut also has a lower risk One feature that turtle trading sys-tem and arbitrage trading system have in common is theyperformed well in capturing alpha

6 Systematic Trading61 Technical Analysis

Many researchers have focused on technical indicators(patterns) analysis for trading on cryptocurrency marketsExamples of studies with this approach include ldquoTurtle Souppattern strategyrdquo [233] ldquoNem (XEM) strategyrdquo [236] ldquoAmaz-ing Gann Box strategyrdquo [234] ldquoBusted Double Top Pat-tern strategyrdquo [235] and ldquoBottom Rotation Trading strat-egyrdquo [237] Table 6 shows the comparison among thesefive classical technical trading strategies using technical in-dicators ldquoTurtle soup pattern strategyrdquo [233] used a 2-daybreakout of price in predicting price trends of cryptocurren-cies This strategy is a kind of chart trading pattern ldquoNem(XEM) strategyrdquo combined Rate of Change (ROC) indica-tor and Relative Strength Index (RSI) in predicting pricetrends [236] ldquoAmazing Gann Boxrdquo predicted exact pointsof increase and decrease in Gann Box which are used to



Table 6Comparison among five classical technical trading strategies

Technical trading strategy Core Methods Tecchnical toolspatternsTurtle Soup pattern [233] 2-daybreakout of price Chart trading patterns

Nem (XEM) [236] Price trends combined ROC amp RSI Rate of Change indictor (ROC)Relative strength index (RSI)

Amazing Gann Box [234] Predict exact points of rises and fallsin Gann Box (catch explosive trends)

Candlestick boxcharts withFibonacci Retracement

Busted Double Top Pattern [235] Bearish reversal trading pattern thatgenerates a sell signal Price chart pattern

Bottom Rotation Trading [237] Pick the bottom before the reversalhappens Price chart pattern box chart

catch explosive trends of cryptocurrency price [234] Tech-nical analysis tools such as candlestick and box charts withFibonacci Retracement based on golden ratio are used in thistechnical analysis Fibonacci Retracement uses horizontallines to indicate where possible support and resistance lev-els are in the market ldquoBusted Double Top Patternrdquo used aBearish reversal trading pattern which generates a sell sig-nal to predict price trends [235] ldquoBottom Rotation Tradingrdquois a technical analysis method that picks the bottom beforethe reversal happens This strategy used a price chart patternand box chart as technical analysis tools

Sungjoo et al [122] 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 mar-ket pressure and diversity measures have been used to quan-tify the attractiveness of technical patterns With extendedexperiments the GP system is shown to find successfullyattractive technical patterns which are useful for portfoliooptimization Hudson et al [130] applied almost 15 000to technical trading rules (classified into MA rules filterrules support resistance rules oscillator rules and channelbreakout rules) This comprehensive study found that tech-nical trading rules provide investors with significant pre-dictive power and profitability Corbet et al [82] analysedvarious technical trading rules in the form of the movingaverage-oscillator and trading range break-out strategies togenerate higher returns in cryptocurrency markets By usingone-minute dollar-denominated Bitcoin close-price data thebacktest showed variable-length moving average (VMA) ruleperforms best considering it generates the most useful sig-nals in high frequency trading

62 Pairs TradingPairs trading is a trading strategy that attempts to ex-

ploit the mean-reversion between the prices of certain secu-rities Miroslav [105] investigated the applicability of stan-dard pairs trading approaches on cryptocurrency data withthe benchmarks of Gatev et al [115] The pairs trading strat-egy is constructed in two steps Firstly suitable pairs witha stable long-run relationship are identified Secondly thelong-run equilibrium is calculated and pairs trading strategyis defined by the spread based on the values The researchalso extended intra-day pairs trading using high frequency

data Overall the model was able to achieve a 3 monthlyprofit in Miroslavrsquos experiments [105] Broek [47] ap-plied pairs trading based on cointegration in cryptocurrencytrading and 31 pairs were found to be significantly cointe-grated (within sector and cross-sector) By selecting fourpairs and testing over a 60-day trading period the pairs trad-ing strategy got its profitability from arbitrage opportunitieswhich rejected the Efficient-market hypothesis (EMH) forthe cryptocurrency market Lintihac et al [174] proposed anoptimal dynamic pair trading strategy model for a portfolioof assets The experiment used stochastic control techniquesto calculate optimal portfolio weights and correlated the re-sults with several other strategies commonly used by practi-tioners including static dual-threshold strategies Thomas etal [171] proposed a pairwise trading model incorporatingtime-varying volatility with constant elasticity of variancetype The experiment calculated the best pair strategy byusing a finite difference method and estimated parametersby generalised moment method

63 OthersOther systematic trading methods in cryptocurrency trad-

ing mainly include informed trading Using USD BTC ex-change rate trading data Feng et al [104] found evidenceof informed trading in the Bitcoin market in those quantilesof the order sizes of buyer-initiated (seller-initiated) ordersare abnormally high before large positive (negative) eventscompared to the quantiles of seller-initiated (buyer-initiated)orders this study adopts a new indicator inspired by the vol-ume imbalance indicator [93] The evidence of informedtrading in the Bitcoin market suggests that investors profiton their private information when they get information be-fore it is widely available

7 Emergent Trading Technologies71 Econometrics on cryptocurrency

Copula-quantile causality analysis and Granger-causalityanalysis are methods to investigate causality in cryptocur-rency trading analysis Bouri et al [41] applied a copula-quantile causality approach on volatility in the cryptocur-rency market The approach of the experiment extended theCopula-Granger-causality in distribution (CGCD) methodof Lee and Yang [170] 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 (9095 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 Elie et al [42] examined the causal linkages amongthe volatility of leading cryptocurrencies via the frequency-domain test of Bodart and Candelon [38] and distinguishedbetween temporary and permanent causation The resultsshowed that permanent shocks are more important in ex-plaining Granger causality whereas transient shocks dom-inate the causality of smaller cryptocurrencies in the longterm Badenhorst [13] attempted to reveal whether spotand derivative market volumes affect Bitcoin price volatilitywith the Granger-causality method and ARCH (11) Theresult shows spot trading volumes have a significant posi-tive effect on price volatility while the relationship betweencryptocurrency volatility and the derivative market is uncer-tain Elie et al [45] used a dynamic equicorrelation (DECO)model and reported evidence that the average earnings equi-librium correlation changes over time between the 12 lead-ing cryptocurrencies The results showed increased cryp-tocurrency market consolidation despite significant price de-clined in 2018 Furthermore measurement of trading vol-ume and uncertainty are key determinants of integration

Several econometrics methods in time-series researchsuch as GARCH and BEKK have been used in the liter-ature on cryptocurrency trading Conrad et al [81] usedthe GARCH-MIDAS model to extract long and short-termvolatility components of the Bitcoin market The technicaldetails of this model decomposed the conditional varianceinto the low-frequency and high-frequency components Theresults identified that SampP 500 realized volatility has a nega-tive and highly significant effect on long-term Bitcoin volatil-ity and SampP 500 volatility risk premium has a significantlypositive effect on long-term Bitcoin volatility Ardia et al [8]used the Markov Switching GARCH (MSGARCH) modelto test the existence of institutional changes in the GARCHvolatility dynamics of Bitcoinrsquos logarithmic returns More-over a Bayesian method was used for estimating model pa-rameters and calculating VaR prediction The results showedthat MSGARCH models clearly outperform single-regimeGARCH for Value-at-Risk forecasting Troster et al [239]performed general GARCH and GAS (Generalized Auto-regressive Score) analysis to model and predict Bitcoinrsquos re-turns and risks The experiment found that the GAS modelwith heavy-tailed distribution can provide the best out-of-sample prediction and goodness-of-fit attributes for Bitcoinrsquosreturn and risk modeling The results also illustrated theimportance of modeling excess kurtosis for Bitcoin returnsCharles et al [65] studied four cryptocurrency markets in-cluding Bitcoin Dash Litecoin and Ripple Results showedcryptocurrency returns are strongly characterised by the pres-

ence of jumps as well as structural breaks except the Dashmarket Four GARCH-type models (ie GARCH APARCHIGARCH and FIGARCH) and three return types with struc-tural breaks (original returns jump-filtered returns and jump-filtered returns) are considered The research indicated theimportance of jumps in cryptocurrency volatility and struc-tural 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 [63] 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 levelsCaporale et al [52] examined persistence in the cryptocur-rency market by Rescaled range (RS) analysis and frac-tional integration The results of the study indicated thatthe market is persistent (there is a positive correlation be-tween its past and future values) and that its level changesover time Khuntin et al [154] applied the adaptive markethypothesis (AMH) in the predictability of Bitcoin evolvingreturns The consistent test of Dominguez and Lobato [89]generalized spectral (GS) of Escanciano and Velasco [98]are applied in capturing time-varying linear and nonlineardependence in bitcoin returns The results verified EvolvingEfficiency in Bitcoin price changes and evidence of dynamicefficiency in line with AMHrsquos claims

Katsiampa et al [150] 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 effectsThe 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 [149]further researched an asymmetric diagonal BEKK modelto examine conditional variances of five cryptocurrenciesthat are significantly affected by both previous squared er-rors and past conditional volatility The experiment testedthe null hypothesis of the unit root against the stationar-ity hypothesis Once stationarity is ensured ARCH LMis tested for ARCH effects to examine the requirement ofvolatility modeling in return series Moreover volatilityco-movements among cryptocurrency pairs are also testedby the multivariate GARCH model The results confirmedthe non-normality and heteroskedasticity of price returns incryptocurrency markets The finding also identified the ef-fects of cryptocurrenciesrsquo volatility dynamics due to majornews Hultman [131] set out to examine GARCH (11)bivariate-BEKK (11) and a standard stochastic model toforecast the volatility of Bitcoin A rolling window approach



is used in these experiments Mean absolute error (MAE)Mean squared error (MSE) and Root-mean-square deviation(RMSE) are three loss criteria adopted to evaluate the de-gree of error between predicted and true values The re-sult shows the following rank of loss functions GARCH(11) gt bivariate-BEKK (11) gt Standard stochastic for allthe three different loss criteria in other words GARCH(11)appeared best in predicting the volatility of Bitcoin Wavelettime-scale persistence analysis is also applied in the predic-tion and research of volatility in cryptocurrency markets [202]The results showed that information efficiency (efficiency)and volatility persistence in the cryptocurrency market arehighly sensitive to time scales measures of returns and volatil-ity and institutional changes Adjepong et al [202] con-nected with similar research by Corbet et al [85] and showedthat GARCH is quicker than BEKK to absorb new informa-tion regarding the data

72 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 [128] The rapid development ofmachine learning in recent years has promoted its applica-tion to cryptocurrency trading especially in the predictionof cryptocurrency returns

721 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 intrinsic variation of techniquesand wide adoption

Classification Algorithms Classification in machinelearning has the objective of categorising incoming objectsinto different categories as needed where we can assignlabels to each category (eg up and down) Naive Bayes(NB) [216] Support Vector Machine (SVM) [247] K-NearestNeighbours (KNN) [247] Decision Tree (DT) [109] Ran-dom Forest (RF) [173] and Gradient Boosting (GB) [111]algorithms habe been used in cryptocurrency trading basedon papers we collected NB is a probabilistic classifier basedon Bayesrsquo theorem with strong (naive) conditional indepen-dence assumptions between features [216] SVM is a su-pervised learning model that aims at achieving high mar-gin classifiers connecting to learning bounds theory [256]SVMs assign new examples to one category or another mak-ing it a non-probabilistic binary linear classifier [247] al-though some corrections can make a probabilistic interpre-tation of their output [153] KNN is a memory-based orlazy learning algorithm where the function is only approx-imated locally and all calculations are being postponed toinference time [247] DT is a decision support tool algo-rithm that uses a tree-like decision graph or model to seg-ment input patterns into regions to then assign an associ-ated label to each region [109] RF is an ensemble learn-

ing method The algorithm operates by constructing a largenumber of decision trees during training and outputting theaverage consensus as predicted class in the case of classi-fication or mean prediction value in the case of regression[173] GB produces a prediction model in the form of anensemble of weak prediction models [111]

Clustering Algorithms Clustering is a machine learn-ing technique that involves grouping data points in a waythat each group shows some regularity [137] 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 clusterrsquos centroid than any other centroid [245]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 [164] 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) [164] ScatterplotSmoothing is a technology to fit functions through scatterplots to best represent relationships between variables [110]

Deep Learning Algorithms Deep learning is a moderntake on artificial neural networks (ANNs) [257] 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 animalrsquos brain The system ldquolearnsrdquoto perform tasks including the prediction by considering ex-amples Deep learningrsquos superior accuracy comes from highcomputational complexity cost Deep learning algorithmsare currently the basis for many modern artificial intelli-gence applications [231] Convolutional neural networks(CNNs) [168] Recurrent neural networks (RNNs) [188]Gated recurrent units (GRUs) [70] Multilayer perceptron(MLP) and Long short-term memory (LSTM) [67] networksare the most common deep learning technologies used incryptocurrency trading A CNN is a specific type of neu-ral network layer commonly used for supervised learningCNNs have found their best success in image processingand natural language processing problems An attempt touse CNNs in cryptocurrency can be shown in [143] AnRNN is a type of artificial neural network in which con-nections between nodes form a directed graph with possi-ble loops This structure of RNNs makes them suitable forprocessing time-series data [188] due to the introduction ofmemory in the recurrent connections They face neverthe-less for the vanishing gradients problem [203] and so dif-ferent variations have been recently proposed LSTM [67]is a particular RNN architecture widely used LSTMs haveshown to be superior to nongated RNNs on financial time-series problems because they have the ability to selectivelyremember patterns for a long time A GRU [70] is anothergated version of the standard RNN which has been used in



crypto trading [91] Another deep learning technology usedin cryptocurrency trading is Seq2seq which is a specificimplementation of the EncoderndashDecoder architecture [251]Seq2seq was first aimed at solving natural language process-ing problems but has been also applied it in cryptocurrencytrend predictions in [226]

Reinforcement Learning Algorithms Reinforcementlearning (RL) is an area of machine learning leveraging theidea that software agents act in the environment to maximizea cumulative reward [230] Deep Q-Learning (DQN) [120]and Deep Boltzmann Machine (DBM) [219] are commontechnologies used in cryptocurrency 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 [120] DBM is a typeof binary paired Markov random field (undirected probabil-ity graphical model) with multiple layers of hidden randomvariables [219] It is a network of randomly coupled randombinary units

722 Research on Machine Learning ModelsIn the development of machine learning trading signals

technical indicators have usually been used as input fea-tures Nakano et al [193] 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 the next period from the input data Data is pre-processed to construct a training dataset that 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 indicatorsThe 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 the 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 pricetrends Most researchers have focused on the comparisonof different classification and regression machine learningmethods Sun et al [229] used random forests (RFs) withfactors in Alpha01 [141] (capturing features from the his-tory of the cryptocurrency market) to build a prediction modelThe experiment collected data from API in cryptocurrencyexchanges and selected 5-minute frequency data for back-testing The results showed that the performances are pro-portional to the amount of data (more data more accurate)and the factors used in the RF model appear to have differentimportance For example ldquoAlpha024rdquo and ldquoAlpha032rdquo fea-tures appeared as the most important in the model adopted(The alpha features come from paper ldquo101 Formulaic Al-

phas [141]) Vo et al [243] applied RFs in High-Frequencycryptocurrency Trading (HFT) and compared it with deeplearning models Minute-level data is collected when util-ising a forward fill imputation method to replace the NULLvalue (ie 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 [260] inves-tigated the profitability of an algorithmic trading strategybased on training an SVM model to identify cryptocurren-cies with high or low predicted returns The results showedthat the performance of the SVM strategy was the fourth be-ing better only than SampP BampH strategy which simply buys-and-hold the SampP index (There are other 4 benchmarkstrategies in this research)The authors observed that SVMneeds a large number of parameters and so is very proneto overfitting which caused its bad performance Barnwalet al [18] 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 models the relationship be-tween unknown and known data while generative classifiersmodel the prediction indirectly through the data generationdistribution [198] 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 [10] com-pared a variety of classification algorithms including SVMNB and RF in predicting next-day price trends of a givencryptocurrency The results showed that due to the hetero-geneity and volatility of cryptocurrenciesrsquo financial instru-ments forecasting models based on a series of forecasts ap-peared better than a single classification technology in trad-ing cryptocurrencies Madan et al [179] modeled the 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 [242] compared RF SVM GB andLR to predict the price of Bitcoin The results showed thatSVM achieved the highest accuracy of 6231 and preci-sion value 077 among binomial classification machine learn-ing algorithms

Different deep learning models have been used in find-ing patterns of price movements in cryptocurrency marketsZhengy et al [258] 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 LSTMis more 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 the future state ofa time series for cryptocurrencies is highly dependent on itshistoric evolution Kwon et al [165] used an LSTM modelwith 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 Specificallyit has a performance improvement of about 7 over the GBmodel in 10-minute price 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 (includ-ing single regression and XGBoost-augmented regression)and the LSTM model on forecasting daily cryptocurrencyprices They found methods based on gradient boosting de-cision trees worked best when predictions were based onshort-term windows of 510 days while LSTM worked bestwhen predictions were based on 50 days of data The rel-ative importance of the features in both models are com-pared and an optimised portfolio composition (based on ge-ometric mean return and Sharpe ratio) is discussed in thispaper Phaladisailoed et al [207] chose regression mod-els (Theil-Sen Regression and Huber Regression) and deeplearning-based models (LSTM and GRU) to compare theperformance of predicting the rise and fall of Bitcoin priceIn terms of two common measure metrics MSE and R-Square (R2) GRU shows the best accuracy Fan et al [100]applied an autoencoder-augmented LSTM structure in pre-dicting the mid-price of 8 cryptocurrency pairs Level-2limit order book live data is collected and the experimentachieved 78 accuracy of price movements prediction inhigh frequency trading (tick level) This research improvedand verified the view of Sirignano et al [224] that univer-sal models have better performance than currency-pair spe-cific models for cryptocurrency markets Moreover ldquoWalk-throughrdquo (ie retrain the original deep learning model it-self when it appears to no longer be valid) is proposed asa method to optimise the training of a deep learning modeland shown to significantly improve the prediction accuracy

Researchers have also focused on comparing classicalstatistical models and machinedeep learning models Raneet al [214] described classical time series prediction meth-ods and machine learning algorithms used for predictingBitcoin price Statistical models such as Autoregressive In-tegrated Moving Average models (ARIMA) Binomial Gen-eralized Linear Model and GARCH are compared with ma-chine learning models such as SVM LSTM and Non-linearAuto-Regressive with Exogenous Input Model (NARX) Theobservation and results showed that the NARX model is thebest model with nearly 52 predicting accuracy based on10 seconds interval Rebane et al [215] compared tradi-tional models like ARIMA with a modern popular modellike seq2seq in predicting cryptocurrency returns The re-

sult showed that the seq2seq model exhibited demonstra-ble improvement over the ARIMA model for Bitcoin-USDprediction but the seq2seq model showed very poor perfor-mance in extreme cases The authors proposed performingadditional investigations such as the use of LSTM insteadof GRU units to improve the performance Similar modelswere also compared by Stuerner et al [228] who exploredthe superiority of automated investment approach in trendfollowing and technical analysis in cryptocurrency tradingSamuel et al [206] explored the vector autoregressive model(VAR model) a more complex RNN and a hybrid of the twoin residual recurrent neural networks (R2N2) in predictingcryptocurrency returns The RNN with ten hidden layers isoptimised for the setting and the neural network augmentedby VAR allows the network to be shallower quicker andto have a better prediction than an RNN RNN VAR andR2N2 models are compared The results showed that theVAR model has phenomenal test period performance andthus props up the R2N2 model while the RNN performspoorly This research is an attempt at optimisation of modeldesign and applying to the prediction on cryptocurrency re-turns

723 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 [167] used daily news and social mediadata labeled on actual price changes rather than on positiveand negative sentiment By this approach the predictionon price is replaced with positive and negative sentimentThe experiment acquired cryptocurrency-related news arti-cle headlines from the website like ldquocryptocoinsnewsrdquo andtwitter API Weights are taken in positive and negative wordsin the cryptocurrency market Authors compared LogisticRegression (LR) Linear Support Vector Machine (LSVM)and NB as classifiers and concluded that LR is the bestclassifier in daily price prediction with 439 of price in-creases correctly predicted and 619 of price decreasescorrectly forecasted Smuts [225] conducted a similar bi-nary sentiment-based price prediction method with an LSTMmodel using Google Trends and Telegram sentiment Indetail the sentiment was extracted from Telegram by us-ing a novel measure called VADER [132] The backtest-ing reached 76 accuracy on the test set during the firsthalf of 2018 in predicting hourly prices Nasir et al [195]researched the relationship between cryptocurrency returnsand search engines The experiment employed a rich setof established empirical approaches including VAR frame-work copulas approach and non-parametric drawings of timeseries The results found that Google searches exert signif-icant influence on Bitcoin returns especially in the short-term intervals Kristoufek [162] discussed positive and neg-ative feedback on Google trends or daily views on WikipediaThe author mentioned different methods including Cointe-



gration Vector autoregression and Vector error-correctionmodel to find causal relationships between prices and searchedterms in the cryptocurrency market The results indicatedthat search trends and cryptocurrency prices are connectedThere is also a clear asymmetry between the effects of in-creased interest in currencies above or below their trend val-ues from the experiment Young et al [156] analysed usercomments and replies in online communities and their con-nection with cryptocurrency volatility After crawling com-ments and replies in online communities authors tagged theextent of positive and negative topics Then the relation-ship between price and the number of transactions of cryp-tocurrency is tested according to comments and replies toselected data At last a prediction model using machinelearning based on selected data is created to predict fluctu-ations in the cryptocurrency market The results show theamount of accumulated data and animated community ac-tivities exerted a direct effect on fluctuation in the price andvolume of a cryptocurrency

Phillips et al [212] applied dynamic topic modeling andHawkes model to decipher relationships between topics andcryptocurrency price movements The authors used LatentDirichlet allocation (LDA) model for topic modeling whichassumes each document contains multiple topics to differentextents The experiment showed that particular topics tendto precede certain types of price movements in the cryp-tocurrency market and the authors proposed the relation-ships could be built into real-time cryptocurrency tradingLi et al [172] analysed Twitter sentiment and trading vol-ume and an Extreme Gradient Boosting Regression TreeModel in the prediction of ZClassic (ZCL) cryptocurrencymarket Sentiment analysis using natural language process-ing from the Python package ldquoTextblobrdquo assigns impactfulwords a polarity value Values of weighted and unweightedsentiment indices are calculated on an hourly basis by sum-ming weights of coinciding tweets which makes us com-pare this index to ZCL price data The model achieveda Pearson correlation of 0806 when applied to test datayielding a statistical significance at the p lt 00001 levelFlori [107] relied on a Bayesian framework that combinesmarket-neutral information with subjective beliefs to con-struct diversified investment strategies in the Bitcoin mar-ket The result shows that news and media attention seem tocontribute to influence the demand for Bitcoin and enlargethe perimeter of the potential investors probably stimulatingprice euphoria and upwards-downwards market dynamicsThe authorsrsquo research highlighted the importance of newsin guiding portfolio re-balancing Elie et al [39] comparedthe ability of newspaper-based metrics and internet search-based uncertainty metrics in predicting bitcoin returns Thepredictive power of Internet-based economic uncertainty-related query indices is statistically stronger than that ofnewspapers in predicting bitcoin returns

Similarly Colianni et al [80] Garcia et al [113] Za-muda et al [254] et al used sentiment analysis technol-ogy applying it in the cryptocurrency trading area and hadsimilar results Colianni et al [80] cleaned data and ap-

plied supervised machine learning algorithms such as logis-tic regression Naive Bayes and support vector machinesetc on Twitter Sentiment Analysis for cryptocurrency trad-ing Garcia et al [113] applied multidimensional analy-sis and impulse analysis in social signals of sentiment ef-fects and algorithmic trading of Bitcoin The results veri-fied the long-standing assumption that transaction-based so-cial media sentiment has the potential to generate a posi-tive return on investment Zamuda et al [254] adopted newsentiment analysis indicators and used multi-target portfo-lio selection to avoid risks in cryptocurrency trading Theperspective is rationalized based on the elastic demand forcomputing resources of the cloud infrastructure A gen-eral model evaluating the influence between userrsquos networkAction-Reaction-Influence-Model (ARIM) is mentioned inthis research Bartolucci et al [19] researched cryptocur-rency prices with the ldquoButterfly effectrdquo which means ldquois-suesrdquo of the open-source project provides insights to im-prove prediction of cryptocurrency prices Sentiment po-liteness emotions analysis of GitHub comments are appliedin Ethereum and Bitcoin markets The results showed thatthese metrics have predictive power on cryptocurrency prices

724 Reinforcement LearningDeep reinforcement algorithms bypass prediction and

go straight to market management actions to achieve highcumulated profit [126] Bu et al [49] 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 an 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 models(LSTM CNN MLP etc) this model achieved the high-est profit even facing an extreme market situation (recorded24 of the profit while cryptocurrency market price dropsby -64) Juchli [138] 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 [177] 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



73 OthersAtsalakis et al [9] 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 investmentreturn obtained through trading simulation is 7121 higherthan the investment return obtained through a simple buyand hold strategy This application is proposed for the firsttime in the forecasting of Bitcoin price movements Topo-logical data analysis is applied to forecasting price trends ofcryptocurrency markets in [155] The approach is to har-ness topological features of attractors of dynamical systemsfor arbitrary temporal data The results showed that themethod can effectively separate important topological pat-terns and sampling noise (like bidndashask bounces discrete-ness of price changes differences in trade sizes or infor-mational content of price changes etc) by providing theo-retical results Kurbucz [163] designed a complex methodconsisting of single-hidden layer feedforward neural net-works (SLFNs) to (i) determine the predictive power of themost frequent edges of the transaction network (a publicledger that records all Bitcoin transactions) on the futureprice of Bitcoin and (ii) to provide an efficient techniquefor applying this untapped dataset in day trading The re-search found a significantly high accuracy (6005) for theprice movement classifications base on information that canbe obtained using a small subset of edges (approximately045 of all unique edges) It is worth noting that Kondoret al [157 159] firstly published some papers giving analy-sis on transaction networks on cryptocurrency markets andapplied related research in identifying Bitcoin users [139]Abay et al [2] attempted to understand the network dynam-ics behind the Blockchain graphs using topological featuresThe results showed that standard graph features such as thedegree distribution of transaction graphs may not be suffi-cient to capture network dynamics and their potential im-pact on Bitcoin price fluctuations Maurice et al [202] ap-plied wavelet time-scale persistence in analysing returns andvolatility in cryptocurrency markets The experiment exam-ined the long-memory and market efficiency characteristicsin cryptocurrency markets using daily data for more thantwo years The authors employed a log-periodogram re-gression method in researching stationarity in the cryptocur-rency market and used ARFIMA-FIGARCH class of mod-els in examining long-memory behaviour of cryptocurren-cies across time and scale In general experiments indicatedthat heterogeneous memory behaviour existed in eight cryp-tocurrency markets using daily data over the full-time periodand across scales (August 25 2015 to March 13 2018)

8 Portfolio and Cryptocurrency Assets81 Research among cryptocurrency pairs and

related factorsJi et al [135] 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 [88] and built positivenegative re-turns and volatility connectedness networks Furthermorethe 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 [201] 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 Elie et al [43] found the presence of jumps wasdetected in a series of 12 cryptocurrency returns and signif-icant jumping activity was found in all cases More resultsunderscore the importance of the jump in trading volume forthe formation of cryptocurrency leapfrogging

Some researchers explored the relationship between cryp-tocurrency and different factors including futures gold etcHale et al [123] 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 the cryp-tocurrency market Werner et al [161] focused on the asym-metric interrelationships between major currencies and cryp-tocurrencies The results of multiple fractal asymmetric de-trending cross-correlation analysis show evidence of signif-icant persistence and asymmetric multiplicity in the cross-correlation between most cryptocurrency pairs and ETF pairsBai et al [14] studied a trading algorithm for foreign ex-change on a cryptocurrency Market using the AutomatedTriangular Arbitrage method Implementing a pricing strat-egy implementing trading algorithms and developing a giventrading simulation are three problems solved by this researchKang et al [146] examined the hedging and diversificationproperties of gold futures versus Bitcoin prices by usingdynamic conditional correlations (DCCs) and wavelet co-herence DCC-GARCH model [95] is used to estimate thetime-varying correlation between Bitcoin and gold futuresby modeling 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 [92] applied the GARCH modeland the exponential GARCH model in analysing similarities



between Bitcoin gold and the US dollar The experimentsshowed that Bitcoin gold and the US dollar have similari-ties 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 Baur etal [20] extended the research of Dyhrberg et al the samedata and sample periods are tested [92] with GARCH andEGARCH-(11) 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 theUS dollar Bouri et al [40] studied the relationship be-tween Bitcoin and energy commodities by applying DCCsand GARCH (11) models In particular the results showedthat Bitcoin is a strong hedge and safe haven for energycommodities Kakushadze [142] proposed factor models forthe cross-section of daily cryptoasset returns and providedsource code for data downloads computing risk factors andbacktesting for all cryptocurrencies and a host of variousother digital assets The results showed that cross-sectionalstatistical arbitrage trading may be possible for cryptoas-sets subject to efficient executions and shorting Beneki etal [25] tested hedging abilities between Bitcoin and Ethereumby a multivariate BEKK-GARCH methodology and impulseresponse analysis within VAR model The results indicateda volatility transaction from Ethereum to Bitcoin which im-plied possible profitable trading strategies on the cryptocur-rency derivatives market Guglielmo et al [54] examinedthe week effect in cryptocurrency markets and explored thefeasibility of this indicator in trading practice Student t-testANOVA KruskalndashWallis and MannndashWhitney tests were car-ried out for cryptocurrency data in order to compare timeperiods that may be characterised by anomalies with othertime periods When an anomaly is detected an algorithmwas established to exploit profit opportunities (MetaTraderterminal in MQL4 is mentioned in this research) The re-sults showed evidence of anomaly (abnormal positive re-turns on Mondays) in the Bitcoin market by backtesting in2013-2016

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

sets Corbet et al [83] gave a systematic analysis of cryp-tocurrencies as financial assets Brauneis et al [46] ap-plied the Markowitz mean-variance framework in order toassess the risk-return benefits of cryptocurrency portfoliosIn an out-of-sample analysis accounting for transaction costthey found that combining cryptocurrencies enriches the setof lsquolowrsquo-risk cryptocurrency investment opportunities Interms of the Sharpe ratio and certainty equivalent returnsthe 1∕N-portfolio (ie ldquonaiverdquo 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 optimal

portfolios Castro et al [56] 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 handalso increase the risk exposure

Bedi et al [22] 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 EuroJapanese 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 the sharpdisparity in Bitcoin trading volumes across national curren-cies from a portfolio theory perspective Similar researchhas been done by Antipova et al [7] 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 [102] proposed a set of modelsthat 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) modelThe results revealed the superiority of the t-copulaskewed-tGARCH model for market risk and the ZPP-based modelsfor credit risk Qiang et al [134] examined the commondynamics of bitcoin exchanges Using a connectivity met-ric based on the actual daily volatility of the bitcoin pricethey found that Coinbase is undoubtedly the market leaderwhile Binance performance is surprisingly weak The re-sults also suggested that safer asset extraction is more im-portant for volatility linkages between Bitcoin exchangesrelative to trading volumes

Trucios et al [240] 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 [129] 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 optimisationthe sets of optimal cryptocurrency portfolios were built intheir experiments

Jiang et al [136] proposed a two-hidden-layer CNN thattakes the historical price of a group of cryptocurrency assetsas 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 Portfolio



and 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 [99] 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 Research91 Bubbles and Crash Analysis

Phillips and Yu proposed a methodology to test for thepresence of cryptocurrency bubble [68] which is extendedby Shaen et al [84] The method is based on supremumAugmented DickeyndashFuller (SADF) to test for the bubblethrough the inclusion of a sequence of forwarding recur-sive right-tailed ADF unit root tests An extended method-ology generalised SADF (GSAFD) is also tested for bub-bles within cryptocurrency data The research concludedthat there is no clear evidence of a persistent bubble in cryp-tocurrency markets including Bitcoin or Ethereum Bouriet al [44] 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 [208 209] (who ap-plied a recursive augmented Dickey-Fuller algorithm whichis called PSY test) and Landsnes et al [97] 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 the authors found the probit model to perform betterthan the linear models

Phillips et al [210] 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 an epidemic de-tection mechanism is used in social media to predict cryp-tocurrency price bubbles which classify bubbles throughepidemic and non-epidemic labels Experiments have demon-strated a strong relationship between Reddit usage and cryp-tocurrency prices This work also provides some empirical

evidence that bubbles mirror the social epidemic-like spreadof an investment idea Guglielmo et al [53] examined theprice overreactions in the case of cryptocurrency tradingSome parametric and non-parametric tests confirmed the pres-ence of price patterns after overreactions which identifiedthat the next-day price changes in both directions are biggerthan after ldquonormalrdquo 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 [62] analysed the high unconditionalvolatility of cryptocurrency from a standard log-normal stochas-tic volatility model to discontinuous jumps of volatility andreturns The experiment indicated the importance of in-corporating permanent jumps to volatility in cryptocurrencymarkets

92 Extreme conditionDifferently from traditional fiat currencies cryptocur-

rencies are risky and exhibit heavier tail behaviour Paraskeviet al [151] found extreme dependence between returns andtrading volumes Evidence of asymmetric return-volume re-lationship in the cryptocurrency market was also found bythe experiment 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 2018 incryptocurrency [253] Yaya et al [253] researched the per-sistence and dependence of Bitcoin on other popular alter-native coins before and after the 201718 crash in cryptocur-rency markets The result showed that higher persistence ofshocks is expected after the crash due to speculations in themind of cryptocurrency traders and more evidence of non-mean reversions implying chances of further price fall incryptocurrencies

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 [160] and Yang [252] analysed market dy-namics and behavioural anomalies respectively to under-stand effects of market behaviour in the cryptocurrency mar-ket Krafft et al discussed potential ultimate causes poten-tial behavioural mechanisms and potential moderating con-textual factors to enumerate possible influence of GUI andAPI on cryptocurrency markets Then they highlighted thepotential social and economic impact of human-computerinteraction in digital agency design Yang on the otherhand applied behavioural theories of asset pricing anoma-lies in testing 20 market anomalies using cryptocurrencytrading data The results showed that anomaly research fo-cused more on the role of speculators which gave a newidea to research the momentum and reversal in the cryp-tocurrency market Cocco et al [75] implemented a mech-anism to form a Bitcoin price and specific behaviour foreach type of trader including the initial wealth distribution



following Paretorsquos law order-based transaction and pricesettlement mechanism Specifically the model reproducedthe unit root attributes of the price series the fat tail phe-nomenon the volatility clustering of price returns the gen-eration of Bitcoins hashing power and power consumption

Leclair [169] and Vidal-Thomaacutes et al [241] analysed theexistence of herding in the cryptocurrency market Leclairapplied herding methods of Huang and Salmon [133] in esti-mating the market herd dynamics in the CAPM frameworkVidal-Thomaacutes et al analyse the existence of herds in thecryptocurrency market by returning the cross-sectional stan-dard (absolute) deviations Both their findings showed sig-nificant evidence of market herding in the cryptocurrencymarket Makarov et al [180] studied price impact and arbi-trage dynamics in the cryptocurrency market and found that85 of the variations in bitcoin returns and the idiosyncraticcomponents of order flow play an important role in explain-ing the 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 [119] 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 [127 259] regulatory frameworkanalysis [220] data mining technology in cryptocurrencytrading [204] application of efficient market hypothesis inthe cryptocurrency market [223] and artificial financial mar-kets for studying a cryptocurrency market [74] Hilemanet al [127] segmented the cryptocurrency industry into fourkey sectors exchanges wallets payments and mining Theygave a benchmarking study of individuals data regulationcompliance practices costs of firms and a global map ofmining in the cryptocurrency market in 2017 Zhou et al [259]discussed the status and future of computer trading in thelargest group of Asia-Pacific economies and then consid-ered algorithmic and high frequency trading in cryptocur-rency markets as well Shanaev et al [220] used data on120 regulatory events to study the implications of cryptocur-rency regulation and the results showed that stricter regula-tion of cryptocurrency is not desirable Akhilesh et al [204]used the average absolute error calculated between the ac-tual and predicted values of the market sentiment of differ-ent cryptocurrencies on that day as a method for quantifyingthe uncertainty They used the comparison of uncertaintyquantification methods and opinion mining to analyse cur-rent market conditions Sigaki et al [223] used permutationentropy and statistical complexity on the sliding time win-dow returned by the price log to quantify the dynamic ef-ficiency of more than four hundred cryptocurrencies As aresult the cryptocurrency market showed significant com-

pliance with efficient market assumptions Cocco et al [74]described an agent-based artificial cryptocurrency market inwhich heterogeneous agents buy or sell cryptocurrenciesThe proposed simulator is able to reproduce some real sta-tistical properties of price returns observed in the Bitcoinreal market Marko [200] considered the future use of cryp-tocurrencies as money based on the long-term value of cryp-tocurrencies Neil et al [112] analysed the influence of net-work effect on the competition of new cryptocurrency mar-kets Bariviera and Merediz-Sola [17] gave a survey basedon hybrid analysis which proposed a methodological hybridmethod for a comprehensive literature review and providedthe latest technology in the cryptocurrency economics liter-ature

There also exists some research and papers introducingthe basic process and rules of cryptocurrency trading in-cluding findings of Hansel et al [124] Kate [148] Garzaet al [114] Ward et al [248] and Fantazzini et al [101]Hansel et al [124] introduced the basics of cryptocurrencyBitcoin and Blockchain ways to identify the profitable trendsin the market ways to use Altcoin trading platforms suchas GDAX and Coinbase methods of using a crypto wal-let to store and protect the coins in their book Kate etal [148] set six steps to show how to start an investmentwithout any technical skills in the cryptocurrency marketThis book is an entry-level trading manual for starters learn-ing cryptocurrency trading Garza et al [114] simulatedan automatic cryptocurrency trading system which helpsinvestors limit systemic risks and improve market returnsThis paper is an example to start designing an automaticcryptocurrency trading system Ward et al [248] discussedalgorithmic cryptocurrency trading using several general al-gorithms and modifications thereof including adjusting theparameters used in each strategy as well as mixing mul-tiple strategies or dynamically changing between strategiesThis paper is an example to start algorithmic trading in cryp-tocurrency market Fantazzini et al [101] introduced theR packages Bitcoin-Finance and bubble including financialanalysis of cryptocurrency markets including Bitcoin

A community resource that is a platform for scholarlycommunication about cryptocurrencies and Blockchains isldquoBlockchain research network see [197]

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

111 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 the first decentralised cryptocurrency Bitcoin [192]It is considered to be the start of cryptocurrency In 2010



Figure 8 Timeline of cryptocurrency trading research

the first cryptocurrency exchange was founded which meanscryptocurrency would not be an OTC market but traded onexchanges based on an auction market system

In 2013 Kristoufek [162] concluded that there is a strongcorrelation between Bitcoin price and the frequency of ldquoBit-coinrdquo search queries in Google Trends and Wikipedia In2014 Lee and Yang [170] 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 [66] discussed the bubble and spec-ulation of Bitcoin and cryptocurrencies In 2016 Dyhrbergexplored Bitcoin volatility using GARCH models combinedwith gold and US dollars [92]

From late 2016 to 2017 machine learning and deep learn-ing technology were applied in the prediction of cryptocur-rency return In 2016 McNally et al [184] predicted Bit-coin price using the LSTM algorithm Bell and Zbikowskiet al [23 255] applied SVM algorithm to predict trends ofcryptocurrency price In 2017 Jiang et al [136] used doubleQ-network and pre-trained it using DBM for the predictionof cryptocurrencies portfolio weights

In recent years several research directions including crossasset portfolios [22 56 46] transaction network applica-tions [163 44] machine learning optimisation [214 9 258]have been considered in the cryptocurrency trading area

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

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

Over one-third (3810) of the papers research predic-tion of returns Another one-third of papers focus on re-searching bubbles and extreme conditions and the relation-ship between pairs and portfolios in cryptocurrency tradingThe remaining researching topics (prediction of volatilitytrading system technical trading and others) have roughlyone-third share

113 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 different technical perspectives

Among all the 126 papers 87 papers (6905) cover sta-tistical methods and machine learning categories These pa-

Figure 9 Research distribution among cryptocurrency trad-ing properties

Table 7Search hits of research distribution in all trading areas

Technology Category Google Scholar hits Google hits Arxiv hitsStatistical methods 122M 62M 1240Machine learning methods 483K 150M 520

pers basically research technical-level cryptocurrency trad-ing including mathematical modeling 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 87 papers 75 papers(8621) present statistical methods and technologies in cryp-tocurrency trading research and 138 papers research ma-chine learning applied to cryptocurrency trading (cf Fig-ure 10) It is interesting to mention that there are 16 pa-pers (184) applying and comparing more than one tech-nique in cryptocurrency trading More specifically Bach etal [12] Alessandretti et al [5] Vo et al [243] Phaladis-ailoed et al [207] Siaminos [222] Rane et al [214] 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 we cansee that most research findings focused on statistical meth-ods in trading which means most of the research on tradi-tional markets still focused on using statistical methods fortrading But we observed that machine learning in tradinghad a higher degree of attention It might because the tra-ditional technical and fundamental have been arbitraged sothe market has moved in recent years to find new anomaliesto exploit Meanwhile the results also showed there existmany opportunities for research in the widely studied areasof machine learning applied to trade in cryptocurrency mar-kets (cf Section 12)

1131 Research Distribution among Statisticalmethods

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



Figure 10 Research distribution among cryptocurrencytrading technologies and methods

Basic regression methods include regression methods(Linear Regression) function estimation and CGCD methodLinear Classifiers and Clustering include SVM and KNNalgorithm Time-series analysis include GARCH modelBEKK model ARIMA model Wavelet time-scale methodDecision 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

1132 Research Distribution among MachineLearning Categories

Papers using machine learning account for 2278 (cfFigure 10) of the total We further classified these papersinto three categories (vii) ANNs (viii) LSTMRNNGRUsand (ix) DLRL

The figure also shows that methods based on LSTMRNN and GRU are the most popular in this subfield

ANNs contains papers researching ANN applications incryptocurrency trading such as back propagation (BP) NNLSTMRNNGRUs include papers using neural networksthat exploit the temporal structure of the data a technologyespecially suitable for time series prediction and financialtrading DLRL includes 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

114 Datasets used in Cryptocurrency TradingTables 8ndash10 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 the datasets in this table contain market dataand mediaInternet data with emotional or statistical labelsTable 10 gives two examples of datasets used in the col-lected papers that are not covered in the first two tables

The column ldquoCurrencyrdquo shows the types of cryptocur-rencies included this shows that Bitcoin is the most com-monly used currency for cryptocurrency researches Thecolumn ldquoDescriptionrdquo shows a general description and typesof datasets The column ldquoData Resolutionrdquo means latencyof the data (eg used in the backtest) ndash this is useful to dis-tinguish between high-frequency trading and low-frequencytrading The column ldquoTime rangerdquo 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 (ie the task) cf column ldquoUsagerdquoldquoData Sourcesrdquo gives details on where the data is retrievedfrom including cryptocurrency exchanges aggregated cryp-tocurrency index and user forums (for sentiment analysis)

Alexander et al [6] made an investigation of cryptocur-rency data as well They summarised data collected from152 published and SSRN discussion papers about cryptocur-rencies and analysed their data quality They found that lessthan half the cryptocurrency papers published since January2017 employ correct data

12 Opportunities in Cryptocurrency TradingThis section discusses potential opportunities for future

research in cryptocurrency tradingSentiment-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 a larger volumeof media input (eg adding video sources) in sentimentanalysis 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 the holding period directly proportional tothe risk 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



Table 8Datasets (13)Market Data

Research Source Description Currency Data Resolution Time Range Usage Data Sources

Bouri et al [41] pricevolatilitydetrended volume data

BitcoinEthereum5 other cryptocurrencies

daily From 20130101To 20171231

Prediction of volatilityreturn CoinMarketCap

Nakano et al [193] high frequency pricevolume data

Bitcoin 15min From 20160731To 20180124

Prediction of return Poloniex

Bu et al [49] three pieces time slice fordifferent research objectives

Bitcoin and seven altcoins Not mentioned From 20160514To 20160703From 20180101To 20180131From 20170701To 20170731

Maximum profit with DRL Not mentioned

Sun et al [229] price volatility ETC-USDTother 12 cryptocurrencies

1 minute5 minutes30 minutesone hourone day

From August 2017To December 2018

Prediction of return Binance Bitfinex

Guo et al [121] volatilityorder book data

Bitcoin hourly volatility observationsorder book snapshots

From September 2015To April 2017

Prediction of volatility Not mentioned

Vo et al [243] timestampsthe OHLC prices etc

Bitcoin 1minute From Starting 2015To End 2016

Prediction of return Bitstamp Btce BtcnCoinbase Coincheck and Kraken

Ross et al [210] price Bitcoinother 3 cryptocurrencies

daily From April 2015To September 2016

Predicting bubbles CryptoCompare

Yaya et al [253] price Bitcoinother 12 cryptocurrencies

daily From 20150807To 20181128

Bubbles and crashes Coin Metrics

Brauneis et al [46] individual pricetrading volume

500 most capitalizedCryptocurrencies

daily From 20150101To 20171231

Portfolios management CoinMarketCap

Feng et al [104] order-level USDBTCtrading data

Bitcoin order-level From 20110913To 20170717

Trading strategy Bitstamp

Table 9Datasets (23)Sentiment-based data

Research Source Description Currency Time range Usage Data Sources

Kim et al [156] Online cryptocurrency communities dataand market data

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

Prediction of fluctuation Each communityrsquos HTML page

Phillips et al [212] Social media data and orice data Bitcoin and Ethereum From 20160830To 20170830

Predict Mutual-Excitation ofCryptocurrency Market Returns

Reddit

Smtus [225] Hourly data on price and trading volumeand search terms from Google Trends

Bitcoin Ethereumand their respective pricedrivers

From 20171201To 20180630

Prediction of price Google Trends Telegram chat groups

Lamon et al [167] Daily price data and cryptocurrencyrelated news article headlines

Bitcoin Ethereum Litecoin From 20170101To 20171130

Prediction of price Kaggle news headline

Phillips et al [211] Price and social media factors from Reddit Bitcoin Ethereum Monero From 20100910To 20170531 (Bitcoin)Others can reference the paper

Waveletcoherence analysis of price BraveNewCoin

Kang et al [145] Market data and posts and commentsincluding metadata

Bitcoin From 20091122To 20180202

Relationships Between BitcoinPrices and User Groups inOnline Community

Bitcoin forum

Researchers can differentiate between long-term and short-term trading in cryptocurrency trading by applying wavelettechnology analysing bubble regimes [211] and consider-ing price explosiveness [44] hypotheses for short-term and

long-term researchThe existing work is mainly about showing the differ-

ences between long and short-term cryptocurrency tradingLong-term trading means less time would cost in trend trac-

Table 10Datasets (33)Others

Research Source Description Time range Usage Data Sources

Kurbucz [163 158] Raw and preprocessed data of allBitcoin transactions and daily returns

From 20161109To 20180205

Predicting the price of Bitcoinwith transaction network

Bitcoin network dataset [189]

Bedi et al [22] A diversified portfolio including equityfixedincome real estate alternativeinvestments commodities and money market

From July 2010To December 2018

Cross-currency including cryptocurrencyresearching portfolios

Portfolio sources [22]



ing and simple technical indicators in market analysis Short-term trading can limit overall risk because small positionsare used in every transaction But market noise (interfer-ence) and short transaction time might cause some stress inshort term trading It might also be interesting to explorethe extraction of trading signals time series research appli-cation to portfolio management the relationship between ahuge 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 trendsThere have been some studies discussing correlations be-tween cryptocurrencies and other financial markets [14656] which can be used to predict the direction of the cryp-tocurrency market

Considering the characteristics of cryptocurrency thecorrelation between cryptocurrency and other assets still re-quires further research Possible breakthroughs might beachieved with principal component analysis the relation-ship between cryptocurrency and other currencies in extremeconditions (ie 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 [68] corre-lation between cryptocurrency bubbles and indicators likevolatility index (VIX) [97] (which is a ldquopanic indexrdquo to mea-sure the implied volatility of SampP500 Index Options) spillovereffects in cryptocurrency market [178]

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 (ie Omori law [249]) 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 the traditional financial market It might also beinteresting to apply this method to trading in cryptocurrencymarkets

Public nature of Blockchain technology Investiga-tions on the connections between the formation of a givencurrencyrsquos transaction network and its price has increasedrapidly in recent years the growing attention on user iden-tification [139] 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 [183]pointed out that investors learn about mispricing in stock

markets from academic publications Similarly cryptocur-rency market predictability could also be affected by re-search papers in the area A possible attempt is to try newpricing methods applying real-time market changes Con-sidering the proportion of informed traders increasing inthe cryptocurrency market in the pricing process is anotherbreaking point (looking for a balance between alpha tradingand trading research literature)

13 ConclusionsWe provided a comprehensive overview and analysis of

the research work on cryptocurrency trading This surveypresented a nomenclature of the definitions and current stateof the art The paper provides a comprehensive survey of126 cryptocurrency trading papers and analyses the researchdistribution that characterise the cryptocurrency trading lit-erature We further summarised the datasets used for exper-iments and analysed the research trends and opportunities incryptocurrency trading

We expect this survey to be beneficial to academics (egfinance researchers) and quantitative traders alike The sur-vey represents a quick way to get familiar with the litera-ture on cryptocurrency trading and can motivate more re-searchers to contribute to the pressing problems in the areafor example along the lines we have identified

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

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[3] Adeyanju C 2019 What crypto exchanges do to comply withkyc aml and cft regulations httpscointelegraphcomnewswhat-crypto-exchanges-do-to-comply-with-kyc-aml-and-cft-regulations [Online Accessed January 11 2020]

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[6] Alexander C Dakos M 2020 A critical investigation of cryp-tocurrency data and analysis Quantitative Finance 20 173ndash188

[7] Antipova V 2019 Building and testing global investment portfo-lios using alternative asset classes Masterrsquos thesis Vytautas Mag-nus University Lithuania

[8] Ardia D Bluteau K Ruumlede M 2019 Regime changes in bitcoingarch volatility dynamics Finance Research Letters 29 266ndash271

[9] Atsalakis GS Atsalaki IG Pasiouras F Zopounidis C 2019Bitcoin price forecasting with neuro-fuzzy techniques EuropeanJournal of Operational Research 276 770ndash780

[10] 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 ACMp 1

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[12] Bach WG Kasper L 2018 On Machine Learning Based Cryp-tocurrency Trading Masterrsquos thesis Aalborg University Denmark

[13] Badenhorst JJ et al 2019 Effect of Bitcoin spot and derivativetrading volumes on price volatility PhD thesis University of Pre-toria

[14] Bai S Robinson F 2019 Automated Triangular Arbitrage ATrading Algorithm for Foreign Exchange on a Cryptocurrency Mar-ket Bachelorrsquos thesis KTH Royal Institute of Technology Sweden

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[17] Bariviera AF Merediz-Sola I 2020 Where do we stand in cryp-tocurrencies economic research a survey based on hybrid analysisarXiv preprint arXiv200309723

[18] Barnwal A Bharti H Ali A Singh V 2019 Stackingwith neural network for cryptocurrency investment arXiv preprintarXiv190207855

[19] Bartolucci S Destefanis G Ortu M Uras N Marchesi MTonelli R 2019 The butterfly affect Impact of development prac-tices on cryptocurrency prices

[20] Baur DG Dimpfl T Kuck K 2018 Bitcoin gold and the usdollarndasha replication and extension Finance Research Letters 25103ndash110

[21] Bauriya A Tikone A Nandgaonkar P Sakure KS 2019 Real-time cryptocurrency trading system International Research Journalof Engineering and Technology (IRJET) 06

[22] Bedi P Nashier T 2020 On the investment credentials of bitcoinA cross-currency perspective Research in International Businessand Finance 51 101087

[23] Bell T 2016 Bitcoin trading agents University of Southampton [24] Ben-Akiva M McFadden D Train K Walker J Bhat C Bier-

laire M Bolduc D Boersch-Supan A Brownstone D BunchDS et al 2002 Hybrid choice models progress and challengesMarketing Letters 13 163ndash175

[25] Beneki C Koulis A Kyriazis NA Papadamou S 2019 In-vestigating volatility transmission and hedging properties betweenbitcoin and ethereum Research in International Business and Fi-nance 48 219ndash227

[26] Binance 2020a Binance partners with coinfirm to protect the globalcryptocurrency economy and ensure compliance with fatf aml ruleshttpswwwbinancecomenblog386484403820867584Binance-Partners-with-Coinfirm-to-Protect-the-Global-Cryptocurrency-Economy-and-Ensure-Compliance-with-FATF-AML-Rules[Online Accessed February 11 2020]

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[35] Bitstamp 2020c Terms of use httpswwwbitstampnetterms-of-

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[37] Bloomberg 2020 Coinbase inc httpswwwbloombergcomprofilecompany0776164DUS [Online Accessed February 112020]

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[39] Bouri E Gupta R 2019 Predicting bitcoin returns Comparingthe roles of newspaper-and internet search-based measures of uncer-tainty Finance Research Letters 101398

[40] Bouri E Jalkh N Molnaacuter P Roubaud D 2017 Bitcoin forenergy commodities before and after the december 2013 crash di-versifier hedge or safe haven Applied Economics 49 5063ndash5073

[41] Bouri E Lau CKM Lucey B Roubaud D 2019a Tradingvolume and the predictability of return and volatility in the cryp-tocurrency market Finance Research Letters 29 340ndash346

[42] Bouri E Lucey B Roubaud D 2020a The volatility surpriseof leading cryptocurrencies Transitory and permanent linkages Fi-nance Research Letters 33 101188

[43] Bouri E Roubaud D Shahzad SJH 2020b Do bitcoin andother cryptocurrencies jump together The Quarterly Review ofEconomics and Finance 76 396ndash409

[44] Bouri E Shahzad SJH Roubaud D 2019b Co-explosivity inthe cryptocurrency market Finance Research Letters 29 178ndash183

[45] Bouri E Vo XV Saeed T 2020c Return equicorrelation inthe cryptocurrency market Analysis and determinants Finance Re-search Letters 101497

[46] Brauneis A Mestel R 2019 Cryptocurrency-portfolios in amean-variance framework Finance Research Letters 28 259ndash264

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

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[49] Bu SJ Cho SB 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 468ndash480

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[54] Caporale GM Plastun A 2019 The day of the week effect inthe cryptocurrency market Finance Research Letters 31

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[56] Castro JG Tito EAH Brandatildeo LET Gomes LL 2019Crypto-assets portfolio optimization under the omega measure TheEngineering Economist 1ndash21

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[64] Chang CC Hsieh PF Wang YH 2015 Sophistication senti-ment and misreaction Journal of Financial and Quantitative Anal-ysis 50 903ndash928

[65] Charles A Darneacute O et al 2019 Volatility estimation for cryp-tocurrencies Further evidence with jumps and structural breaksEconomics Bulletin 39 954ndash968

[66] Cheah ET Fry J 2015 Speculative bubbles in bitcoin marketsan empirical investigation into the fundamental value of bitcoinEconomics Letters 130 32ndash36

[67] Cheng J Dong L Lapata M 2016 Long short-term memory-networks for machine reading arXiv preprint arXiv160106733

[68] Cheung A Roca E Su JJ 2015 Crypto-currency bubbles anapplication of the phillipsndashshindashyu (2013) methodology on mt goxbitcoin prices Applied Economics 47 2348ndash2358

[69] Choi B 2012 ARMA model identification Springer Science ampBusiness Media

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

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[81] Conrad C Custovic A Ghysels E 2018 Long-and short-term cryptocurrency volatility components A garch-midas analysisJournal of Risk and Financial Management 11 23

[82] Corbet S Eraslan V Lucey B Sensoy A 2019a The effective-ness of technical trading rules in cryptocurrency markets FinanceResearch Letters 31 32ndash37

[83] 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 182ndash199

[84] Corbet S Lucey B Yarovaya L 2018a Datestamping the bit-coin and ethereum bubbles Finance Research Letters 26 81ndash88

[85] Corbet S Meegan A Larkin C Lucey B Yarovaya L 2018bExploring the dynamic relationships between cryptocurrencies andother financial assets Economics Letters 165 28ndash34

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[90] Doran MD 2014 A forensic look at bitcoin cryptocurrency PhDthesis Utica College

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

[92] Dyhrberg AH 2016 Bitcoin gold and the dollarndasha garch volatil-ity analysis Finance Research Letters 16 85ndash92

[93] Easley D Engle RF OrsquoHara M Wu L 2008 Time-varyingarrival rates of informed and uninformed trades Journal of FinancialEconometrics 6 171ndash207

[94] Elliott RJ Van Der Hoek J Malcolm WP 2005 Pairs tradingQuantitative Finance 5 271ndash276

[95] Engle R 2002 Dynamic conditional correlation A simple class ofmultivariate generalized autoregressive conditional heteroskedastic-ity models Journal of Business amp Economic Statistics 20 339ndash350

[96] Engle RF Kroner KF 1995 Multivariate simultaneous general-ized arch Econometric theory 11 122ndash150

[97] Enoksen FA Landsnes CJ 2019 What Can Predict Bubbles inCryptocurrency Prices Masterrsquos thesis University of StavangerNorway

[98] Escanciano JC Velasco C 2006 Generalized spectral tests forthe martingale difference hypothesis Journal of Econometrics 134151ndash185

[99] Estalayo I Del Ser J Osaba E Bilbao MN Muhammad KGaacutelvez 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 755ndash761

[100] Fang F Chung W Ventre C Basios M Kanthan L Li L WuF 2019 Ascertaining price formation in cryptocurrency marketswith deeplearning arXiv preprint arXiv200300803

[101] Fantazzini D 2019 Quantitative finance with r and cryptocurren-cies Amazon KDP ISBN-13 978ndash1090685315

[102] 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 19ndash69

[103] Farell R 2015 An analysis of the cryptocurrency industry [104] Feng W Wang Y Zhang Z 2018 Informed trading in the bitcoin

market Finance Research Letters 26 63ndash70[105] Fil M 2019 Pairs trading in cryptocurrency markets Univerzita

Karlova Fakulta sociaacutelniacutech ved [106] Flood R Hodrick RJ Kaplan P 1986 An evaluation of recent

evidence on stock market bubbles National Bureau of EconomicResearch Cambridge Mass USA

[107] Flori A 2019 News and subjective beliefs A bayesian approachto bitcoin investments Research in International Business and Fi-nance 50 336ndash356

[108] Fretrade 2020 Freqtrade httpsgithubcomfreqtradefreqtrade[Online Accessed January 26 2020]

[109] Friedl MA Brodley CE 1997 Decision tree classification ofland cover from remotely sensed data Remote sensing of environ-ment 61 399ndash409

[110] Friedman J Tibshirani R 1984 The monotone smoothing ofscatterplots Technometrics 26 243ndash250



[111] Friedman JH 2001 Greedy function approximation a gradientboosting machine Annals of statistics 1189ndash1232

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

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

[114] Garza P 2019 Formal automatic trading in the cryptocurrency eraPhD thesis Politecnico di Torino

[115] Gatev E Goetzmann WN Rouwenhorst KG 2006 Pairs trad-ing Performance of a relative-value arbitrage rule The Review ofFinancial Studies 19 797ndash827

[116] Gerritsen DF Bouri E Ramezanifar E Roubaud D 2019 Theprofitability of technical trading rules in the bitcoin market FinanceResearch Letters

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[119] Griffin JM Shams A 2019 Is bitcoin really un-tethered Avail-able at SSRN 3195066

[120] Gu S Lillicrap T Sutskever I Levine S 2016 Continuousdeep q-learning with model-based acceleration in InternationalConference on Machine Learning pp 2829ndash2838

[121] 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 989ndash994

[122] Ha S Moon BR 2018 Finding attractive technical patterns incryptocurrency markets Memetic Computing 10 301ndash306

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

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

[125] Harwick C 2016 Cryptocurrency and the problem of intermedia-tion The Independent Review 20 569ndash588

[126] Henderson P Islam R Bachman P Pineau J Precup DMeger D 2018 Deep reinforcement learning that matters inThirty-Second AAAI Conference on Artificial Intelligence

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

[128] Holmes G Donkin A Witten IH 1994 Weka A machinelearning workbench in Proceedings of ANZIISrsquo94-Australian NewZealnd Intelligent Information Systems Conference IEEE pp 357ndash361

[129] 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

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

[131] Hultman H 2018 Volatility Forecasting An Empirical Study onBitcoin Using Garch and Stochastic Volatility models Masterrsquos the-sis Lund University Sweden

[132] Hutto CJ 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

[133] Hwang S Salmon M 2004 Market stress and herding Journalof Empirical Finance 11 585ndash616

[134] Ji Q Bouri E Kristoufek L Lucey B 2019a Realised volatil-ity connectedness among bitcoin exchange markets Finance Re-search Letters 101391

[135] Ji Q Bouri E Lau CKM Roubaud D 2019b Dynamic con-nectedness and integration in cryptocurrency markets InternationalReview of Financial Analysis 63 257ndash272

[136] Jiang Z Liang J 2017 Cryptocurrency portfolio management

with deep reinforcement learning in 2017 Intelligent Systems Con-ference (IntelliSys) IEEE pp 905ndash913

[137] 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 IEEEpp 150ndash152

[138] Juchli M 2018 Limit order placement optimization with DeepReinforcement Learning Learning from patterns in cryptocurrencymarket data Masterrsquos thesis TU Delft Electrical EngineeringNetherlands

[139] Juhaacutesz PL Steacuteger J Kondor D Vattay G 2018 A bayesianapproach to identify bitcoin users PloS one 13

[140] Kajtazi A Moro A 2019 The role of bitcoin in well diversifiedportfolios A comparative global study International Review ofFinancial Analysis 61 143ndash157

[141] Kakushadze Z 2016 101 formulaic alphas Wilmott 2016 72ndash81[142] Kakushadze Z 2018 Cryptoasset factor models Algorithmic Fi-

nance 1ndash18[143] Kalchbrenner N Grefenstette E Blunsom P 2014 A convo-

lutional neural network for modelling sentences arXiv preprintarXiv14042188

[144] 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 1ndash4

[145] 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

[146] Kang SH McIver RP Hernandez JA 2019b Co-movementsbetween bitcoin and gold A wavelet coherence analysis PhysicaA Statistical Mechanics and its Applications 120888

[147] Kat HM Heynen RC 1994 Volatility prediction A comparisonof the stochastic volatility garch (1 1) and egarch (1 1) modelsJournal of Derivatives 2

[148] Kate C 2018 Cryptocurrency trading for beginners 6-steps actionplan to your first investment

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

[150] Katsiampa P Corbet S Lucey BM 2018a Volatility spillovereffects in leading cryptocurrencies A bekk-mgarch analysis Avail-able at SSRN 3232912

[151] Katsiampa P Gkillas K Longin F 2018b Cryptocur-rency market activity during extremely volatile periods URLhttpsssrncomabstract=3220781 doihttpsdxdoiorg102139ssrn3220781

[152] Kaufman PJ 2013 Trading Systems and Methods+ Website vol-ume 591 John Wiley amp Sons

[153] Keerthi SS Shevade SK Bhattacharyya C Murthy KRK2001 Improvements to plattrsquos smo algorithm for svm classifier de-sign Neural computation 13 637ndash649

[154] Khuntia S Pattanayak J 2018 Adaptive market hypothesis andevolving predictability of bitcoin Economics Letters 167 26ndash28

[155] Kim K Kim J Rinaldo A 2018 Time series featurization viatopological data analysis an application to cryptocurrency trendforecasting arXiv preprint arXiv181202987

[156] Kim YB Kim JG Kim W Im JH Kim TH Kang SJKim CH 2016 Predicting fluctuations in cryptocurrency transac-tions based on user comments and replies PloS one 11 e0161197

[157] Kondor D Csabai I Szuumlle J Poacutesfai M Vattay G 2014a In-ferring the interplay between network structure and market effectsin bitcoin New Journal of Physics 16 125003

[158] 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 httpsdoiorg1010882F1367-26302F162F122F125003 doi1010881367-26301612125003



[159] Kondor D Poacutesfai M Csabai I Vattay G 2014c Do the richget richer an empirical analysis of the bitcoin transaction networkPloS one 9

[160] Krafft PM Della Penna N Pentland AS 2018 An experimen-tal study of cryptocurrency market dynamics in Proceedings of the2018 CHI Conference on Human Factors in Computing SystemsACM p 605

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[162] Kristoufek L 2013 Bitcoin meets google trends and wikipediaQuantifying the relationship between phenomena of the internet eraScientific reports 3 3415

[163] Kurbucz MT 2019 Predicting the price of bitcoin by the mostfrequent edges of its transaction network Economics Letters 184108655

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[165] Kwon DH Kim JB Heo JS Kim CM Han YH 2019Time series classification of cryptocurrency price trend based on arecurrent lstm neural network Journal of Information ProcessingSystems 15

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

[167] Lamon C Nielsen E Redondo E 2017 Cryptocurrency priceprediction using news and social media sentiment SMU Data SciRev 1 1ndash22

[168] Lawrence S Giles CL Tsoi AC Back AD 1997 Facerecognition A convolutional neural-network approach IEEE trans-actions on neural networks 8 98ndash113

[169] Leclair EM 2018 Herding in the cryptocurrency market Econ5029 final research Carleton University Canada

[170] Lee TH Yang W 2014 Granger-causality in quantiles betweenfinancial markets Using copula approach International Review ofFinancial Analysis 33 70ndash78

[171] Li TN Tourin A 2016 Optimal pairs trading with time-varying volatility International Journal of Financial Engineering3 1650023

[172] Li TR Chamrajnagar A Fong X Rizik N Fu F 2019Sentiment-based prediction of alternative cryptocurrency price fluc-tuations using gradient boosting tree model Frontiers in Physics 798

[173] Liaw A Wiener M et al 2002 Classification and regression byrandomforest R news 2 18ndash22

[174] Lintilhac PS Tourin A 2017 Model-based pairs trading in thebitcoin markets Quantitative Finance 17 703ndash716

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[179] Madan I Saluja S Zhao A 2015 Automated bitcoin trad-ing via machine learning algorithms URL httpcs229 stanfordeduproj2014Isaac 20Madan 20

[180] Makarov I Schoar A 2020 Trading and arbitrage in cryptocur-rency markets Journal of Financial Economics 135 293ndash319

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[182] Markowitz H 1952 Portfolio selection The Journal of Finance 777ndash91

[183] McLean RD Pontiff J 2016 Does academic research destroystock return predictability The Journal of Finance 71 5ndash32

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[185] 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 339ndash343

[186] Merediz-Solagrave I Bariviera AF 2019 A bibliometric analysis ofbitcoin scientific production Research in International Business andFinance 50 294ndash305

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[190] Molina J 2019 Develop your crypto-trading system using plainlogic part 1 URL httpsmediumcomswlhdevelop-your-crypto-trading-system-using-plain-logic-part-1-caac02f0a37d

[191] Mukhopadhyay U Skjellum A Hambolu O Oakley J Yu LBrooks R 2016 A brief survey of cryptocurrency systems in2016 14th annual conference on privacy security and trust (PST)IEEE pp 745ndash752

[192] Nakamoto S 2009 Bitcoin open source implementation of p2pcurrency P2P foundation 18

[193] Nakano M Takahashi A Takahashi S 2018 Bitcoin technicaltrading with artificial neural network Physica A Statistical Me-chanics and its Applications 510 587ndash609

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[204] Patil AP Akarsh T Parkavi A 2018 A study of opinion miningand data mining techniques to analyse the cryptocurrency market



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[205] Pauna C 2018 Arbitrage trading systems for cryptocurrenciesdesign principles and server architecture Informatica Economica22 35ndash42

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

[207] 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 506ndash511

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[211] Phillips RC Gorse D 2018a Cryptocurrency price driversWavelet coherence analysis revisited PloS one 13 e0195200

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[216] Rish I et al 2001 An empirical study of the naive bayes clas-sifier in IJCAI 2001 workshop on empirical methods in artificialintelligence pp 41ndash46

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[224] Sirignano J Cont R 2019 Universal features of price formationin financial markets perspectives from deep learning QuantitativeFinance 19 1449ndash1459

[225] Smuts N 2019 What drives cryptocurrency prices An investiga-tion of google trends and telegram sentiment ACM SIGMETRICS

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[229] 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 647ndash652

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[231] Sze V Chen YH Yang TJ Emer JS 2017 Efficient process-ing of deep neural networks A tutorial and survey Proceedings ofthe IEEE 105 2295ndash2329

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[241] Vidal-Tomaacutes D Ibaacutentildeez AM Farinoacutes JE 2019 Herding in thecryptocurrency market Cssd and csad approaches Finance Re-search Letters 30 181ndash186

[242] Virk DS 2017 Prediction of Bitcoin Price using Data MiningMasterrsquos thesis National College of Ireland

[243] Vo A Yost-Bremm C 2018 A high-frequency algorithmic trad-ing strategy for cryptocurrency Journal of Computer InformationSystems 1ndash14

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

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

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markets and omori processes occurring on all scales Physical Re-view E 76 016109

[250] 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 1ndash10

[251] Xu Z Wang S Zhu F Huang J 2017 Seq2seq fingerprintAn unsupervised deep molecular embedding for drug discovery inProceedings of the 8th ACM International Conference on Bioinfor-matics Computational Biology and Health Informatics pp 285ndash294

[252] Yang H 2018 Behavioral anomalies in cryptocur-rency markets Available at SSRN 3174421 URLhttpsssrncomabstract=3174421 doihttpsdxdoiorg102139ssrn3174421

[253] Yaya OS Ogbonna EA Olubusoye OE 2018 How persistentand dependent are pricing of bitcoin to other cryptocurrencies beforeand after 201718 crash

[254] Zamuda A Crescimanna V Burguillo JC Dias JM Wegrzyn-Wolska K Rached I Gonzlez H Senkerik R Pop C CioaraT et al 2019 Forecasting cryptocurrency value by sentiment anal-ysis An hpc-oriented survey of the state-of-the-art in the cloud erain High-Performance Modelling and Simulation for Big Data Ap-plications Springer pp 325ndash349

[255] Zbikowski K 2016 Application of machine learning algorithmsfor bitcoin automated trading in Machine Intelligence and BigData in Industry Springer pp 161ndash168

[256] 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 53ndash62

[257] Zhang S Yao L Sun A Tay Y 2019 Deep learning based rec-ommender system A survey and new perspectives ACM ComputSurv 52 URL httpsdoiorg1011453285029 doi1011453285029

[258] 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 15ndash19

[259] Zhou H Kalev PS 2019 Algorithmic and high frequency tradingin asia-pacific now and the future Pacific-Basin Finance Journal53 186ndash207

[260] Slepaczuk R Zenkova M 2018 Robustness of support vectormachines in algorithmic trading on cryptocurrency market CentralEuropean Economic Journal 5 186 ndash 205



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 [166] surveyed efficiency and profitable tradingopportunities in cryptocurrency markets Ahamad et al [4]and Sharma et al [221] gave a brief survey on cryptocur-rencies Ujan et al [191] gave a brief survey of cryptocur-rency systems Ignasi et al [186] performed a bibliometricanalysis of bitcoin literature These relate work outcomesfocused on specific area in cryptocurrency including cryp-tocurrencies and cryptocurrency market introduction cryp-tocurrency systems platforms bitcoin literature review etcTo the best of our knowledge no previous work has pro-vided a comprehensive survey particularly focused on cryp-tocurrency 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 126 cryptocurrency trading papersacross 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

Figure 2 Tree structure of the contents in this paper

Figure 3 Workflow of Blockchain transaction

21 Blockchain211 Blockchain Technology Introduction

Blockchain is a digital ledger of economic transactionsthat can be used to record not just financial transactions butany object with an intrinsic value [232] In its simplestform a Blockchain is a series of immutable data recordswith timestampswhich 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 ldquoA paysX Bitcoins to Brdquo that is signed by A using its private keyThis 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 signerrsquos publickey denoted as the algorithm in Figure 3) If the verificationis valid then the block is added to the chain all other blocksadded after it will ldquoconfirmrdquo that transaction For exampleif a transaction is contained in block 502 and the length ofthe blockchain is 507 blocks it means that the transactionhas 5 confirmations (507-502) [218]

212 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 into







































































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daily From 20130101To 20171231























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daily From 20130101To 20171231























daily From 20150807To 20181128




daily From 20150101To 20171231












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From 20171201To 20180630










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cryptocurrency trading: a comprehensive survey

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