[PDF] Towards Understanding Cryptocurrency Derivatives - CyLab

34602_2towards_understanding_cryptocurrency.pdf $BTF
4UVEZ
PG
#JU.&9 ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣ ␣␣␣␣ ␣ ␣␣␣␣␣ ␣ ␣ ␣␣␣␣␣␣␣ ␣␣␣ ␣ ␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣perpetual␣- ferings ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣- tably ␣␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ˆ ␣␣␣␣!␣ˆ␣␣- ing ␣ ! ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ - rency ␣␣␣␣␣␣␣␣Proceedings␣of␣the␣Web␣

Conference

␣2021␣(WWW␣'21),␣April␣1923,␣2021,␣Ljubljana,␣Slovenia.␣␣

␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ WWW ␣ '21, ␣ April ␣

1923,

␣ 2021,
␣

Ljubljana,

␣

Slovenia

␣ © ␣ ␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣ ␣␣␣␣␣␣␣␣␣ ␣␣␣ 1 ␣ ␣␣␣␣␣␣␣␣ 2 ␣ ␣ ␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣ ␣ ␣␣␣␣Arthur's␣Casino␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣8],␣␣␣- ribit ␣␣␣19],␣␣␣␣28],␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣ ␣ 3 ␣ ␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣ A

␣spot␣market␣␣␣␣␣␣␣␣␣␣␣␣␣␣

␣␣␣␣␣␣␣␣␣␣␣␣␣␣␣ ␣␣␣␣␣␣␣␣␣␣␣␣␣ ␣ ], ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ 2 In ␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ 3 A ␣ ␣␣␣␣␣␣␣␣␣␣␣ ␣ ␣ ␣ ␣ ␣ ␣

WWW"21,April19-23,2021,Ljubljana,SloveniaKyleSoska,Jin-DongDong,AlexKhodaverdian,ArielZetlin-Jones,BryanRoutledge,andNicolasChristin

- forgeable 4 5 chain 2

BACKGROUND

2.1

BitcoinandModernCryptocurrencies

- cies - gorithmically - spect. http://cryptotra de.cylab.cmu.edu on-chain o?-chain chain, 2.2

CustomerAccounts3BMEX

3BMEX

3BMEXip:205.193.117.159

Alice***@secmail.in

ip:65.153.158.203

Charlie***@protonmail.com

ip:166.123.218.220

Real-time price data

3BMEX

Daily Withdrawals3BMEX

Risk Management: Liquidation

Engine + Insurance Fund

BTC & ALT DerivativesSocial: Trollboxand PnL/ ROE

LeaderboardsIDPnL

3BMEXƋƉƋ͙+6.23 BTC

3BMEXdžϮ͙-1.44 BTC

ڭڭ

PnL/ Position / Order

MacrosX

ض

Bob2*@gmail.com

Figure

1:

SystemOverviewofBitmex

- location - sions 6 A 3BMEX TowardsUnderstandingCryptocurrencyDerivativesWWW"21,April19-23,2021,Ljubljana,Slovenia 2.3

FuturesandDerivatives

- mance - ties, locksproduct- tracts, options- chase

PerpetualContract

PerpetualBitcoinContract

indexprice C- - tracts, C% C ! " C   " C = -1"!% C - -/% C !" C " C ≥

0"01(-/%

C ) !≤100 C -/% C !=100" C fundingminimum maintenance

C•C+1•"""

A C +B

B≥0(C+B)

A C +B > 0 (C+B)   " C+B = " C +B-1 - A C +B - 1"% C+B B=1A C +1 =

0"001%

C +1 = % C = 10000
" C+1 =

0"01-0"001=0"099

equityvalue+ C +B - ized   + C +B = " C +B + -1-1"% C % C +B + C+1 " C +1 + C+B ≥ "-% C+B "=0"0035 A C +B = 0 (4) " +!% liquidation = % C " 1+! % liquidation > % C !/(1+!)

Perpetual

BitcoinSettledBitcoinPerpetualEthereum

Settled

EthereumPerpetualAltcoinsSettledAltcoins

3 DATA Price Data:

WWW"21,April19-23,2021,Ljubljana,SloveniaKyleSoska,Jin-DongDong,AlexKhodaverdian,ArielZetlin-Jones,BryanRoutledge,andNicolasChristin

7 base 28]-
dation

Trollbox

andLiquidations: REKT 4

LEDGERMETHODOLOGY

4.1

DetectionandFiltering

3BMEX - dition In maker taker (a) (b)

Figure

2:AhypotheticalBitcointransaction(a)withthreein-

puts fromtwoaddresseswhichgeneratestwooutputs(fees ignored) and the corresponding ?ow decomposition (b). 3BMEX 3BMEX

3BMEX-

dresses 3BMEX 3BMEX 3BMEX - dresses 3BMEX - ple, - ity internal external 4.2 Flows # UUV# V 0 1

201�2

- stance, A TowardsUnderstandingCryptocurrencyDerivativesWWW"21,April19-23,2021,Ljubljana,Slovenia As discussedinSection2.2,absentKYCrestrictions,asingleentity may operatemanyBitMEXaccounts.Wethuswanttodetectand cluster instancesinwhichauserownsseveralaccountstoinfer accurate customer demographics. The structureofcustomerdepositaddressesonBitMEXallow us toimproveontraditionalblockchainclusteringheuristics[34]. The keyinsightisthat,withafewexceptions,theentitysending funds toadepositaddressonBitMEXisalsotheownerofthe corresponding account.Anexceptiontothisruleariseswhena third partydepositsfundsintoaBitMEXaccountonbehalfofthe user.

Thisoccurswhenausermakesadepositthroughamixing

service, orwhenanexchangeusestheirhot-wallettosendfunds on thecustomer'sbehalf.Anotherproblemcomesfromdusting attacks , inwhichanexternalentitysendsaverysmallamountof bitcoins toanaddress,hopingtherecipientwillspenditinaway that degrades their anonymity. We mitigatetheseexceptionsasfollows.Ser- vice addresses, such asexchangehot-walletsanddusters,aretypi- cally presentinalargenumberof?ows,toadiversegroupofdesti- nations.

WethusconsiderthenumberofuniqueBitMEXaccounts

accessed, thenumberofbitcoinstransactedandthedistributionof transaction sizes to infer whether an address belongs to a service. We thenclusterBitMEXaccountstogether, by iteratingoverall?owsfromexternaladdressesintoBitMEX accounts andbuildingupaconstraintsetasfollows.We?rstapply the rulethattwonon-serviceexternaladdresseswith?owsintothe same depositaddressareownedbythesameentity.Thiscaptures the notionthatonlytheBitMEXaccountownerwouldeverdeposit money intotheiraccount,sothatdepositsfromtwodistinctnon- service addressesmustactuallybelongtothesameowner.Wethen apply asecondconstraintthattwoBitMEXaccountsthatreceive deposits fromthesamenon-servicesexternaladdressareownedby the sameentity.Thesecondconstraintsimplyextendstheideaof ownership fromexternalBitcoinaddressestotheBitMEXaccounts that arebeingfunded.TheresultisasetofconstraintsonBitMEX accounts that induce a clustering. A fewservicesremainundetectedbyour service detectionheuristic,whichcausestheformationofafew very largeandlooselyconnectedclusters.Tobreakdownthese clusters, weusecommunitydetectiontechniques,speci?cally,La- bel Propagation[48].Thealgorithmworksby?rstassigningevery node inthegraphauniquelabelbeforerepeatedlyupdatingeach node's labeltobethelabelthatappearsthemostinitsneighbors. The algorithmterminateswheneachnodehasthelabelthatap- pears mostfrequentlyamongtheneighbors.Nodeswithidentical labels formasinglecommunityandareour?nalclusters.Thereare numerous communitydetectionalgorithms(see,e.g.,[52]).How- ever, mostofthemarecomputationallytooexpensiveforclustering

Bitcoin

addresses.Labelpropagationissuitable,evenwithourlarge dataset (¡4Mnodes),duetoitslinear-timecomputationandour expectation of very dense connections within communities. We onlyusedeposittransactionsfromexternaladdressestoin- ternal addressesforclustering.Indeed,othertransactions(internal- internal, orinternal-external)cannothelp,ingeneral,withoutad- ditional knowledge of how

BitMEX

internally moves funds. The Bitcoinledgerprovidesuswithaviewoftheaddressesthat are ownedbyBitMEXincludingover610,000addresses 9 that are used toreceivecustomerdepositswhichweutilizeinthissection to study the behavior of traders. As discussedabove,wecannotgenerallyinferhowmanybitcoins are creditedtoeachcustomeraccountatanypointintimesince that informationismaintainedusinganinternaldatabaseandis not synchronizedwiththeBitcoinledger.Theon-chain?owsinto customer depositaddressesdohoweverprovideuswithground- truth information regarding the funding of these accounts. We spotcheckedthedepositandwithdrawalhistoryofafew

BitMEX

accountswhichwereprovidedtousbyanonymouscontrib- utors, andfoundthatBitMEXappearstoprioritizeusingfundsfrom the customer'sdepositaddresstoful?lltheirwithdrawals.Accounts that madeunpro?tabletradesandwhosebalancefellbelowitson- chain valuesawtheirdepositaddressusedasasourceoffundsfor ful?lling thewithdrawalsofothercustomers.Thiscollectionofob- servations suggeststhatwithdrawalprocessingcausestheon-chain representation ofaccountstoconvergetotheinternaldatabase.The velocity withwhichaccountbalancesmaychangeandtherelative infrequency ofwithdrawalsmeansthattheon-chainbalanceofan individual accountisnotparticularlymeaningful,butthecollective distribution ofon-chainaccountbalancesmaystillyieldimportant insights about the distribution of wealth on the platform. 1HZ$FFRXQWV%LWFRLQ3ULFH - imately We usuallycannottellwhenacustomerofBitMEXisactively trading, butwecanstillapproximateactivitybyobservingthe on-chain depositsmadetocustomeraccounts.Figure3showsthe number ofcustomerdepositaddressesthatreceivedfundson-chain in a rolling

432-block

(approximately

3-day)

window. When a cus - tomer's addressreceivesfundsforthevery?rsttimeitisrecorded as anewaccountotherwiseitisrecordedasanexistingaccount.

Unlike

manyservicesincryptocurrency,BitMEX'spopularityin- creased dramaticallywiththedeclineinBitcoin'spricein2018, reaching acrescendoinNovember2018whenthepricetumbled 9 As of

February

8th, 2020.

WWW"21,April19-23,2021,Ljubljana,SloveniaKyleSoska,Jin-DongDong,AlexKhodaverdian,ArielZetlin-Jones,BryanRoutledge,andNicolasChristin

Figure

4:Heatmapofthein?owsofBitcointoBitMEXby

time, brokendownbythewealthtieroftherecipientac- count batchedby3-dayblocks.Brightyellowindicatesa ?ow of 400
bitcoins or more. 1 C84A =blog 10

¹1º 10c

5.2

SizeandWealthDistribution

This oins Also Value

Entity

TypeCoins%Supply

BitMEX

Exchange215,4761.17%2.370

Table

1:Thenumberofbitcoinsheldbyseveralsigni?cant

entities as of July 31,
2020.
6HWWOHG%LWFRLQ)XWXUHV3HUSHWXDO(WKHUHXP)XWXUHV

6HWWOHG(WKHUHXP)XWXUHV3HUSHWXDO$OWFRLQ)XWXUHV6HWWOHG$OWFRLQ)XWXUHV

9RODWLOLW\

Figure

5:Theabsolutevolumetradedinvariousinstrument

categories onBitMEXovertimesmoothedusinga3-daysim- ple moving average. %7&%7&!%7&%LWFRLQ3ULFH

Figure

6:ThetotalnumberofbitcoinsheldbyBitMEXover

time brokendownbythetiersofaddressesholdingthose coins. TowardsUnderstandingCryptocurrencyDerivativesWWW"21,April19-23,2021,Ljubljana,Slovenia addresses thatareholdingthem.BitMEXthrivedfollowingthecol- lapse inthepriceofBitcoinin2018,growingitsassetsuntilthesum- mer of2019whereitbrie?ydippedbeforepeakingaround310,000 bitcoins onMarch13,2020.InSeptember2020,theUnitedStates

Department

ofJusticeindictmentofBitMEX[2]leadtoamaterial decline inbitcoinsheldbytheexchange.Thesetrendsmirrorwhat we observewithrespecttotradingvolumesasshowninFigure5 where thetradedvolumeofproductsonBitMEXreallyexplodedin popularity through2018andinto2019.Anumberofe?ortstoiden- tify washtradingofpopularcryptocurrencyexchanges[29,40,49] have failedto?ndanyonBitMEXandconsistentlyrankitamong the most transparent exchanges.

Figure

7isaheatmapofhowwealthisdistributedonBitMEX

accounts over time.In2017attheheightoftheretailmania,most of thewealthonBitMEXwasconcentratedintoaccountsthatheld 10 bitcoinsorless.Aswediscussinsection6.2,November2018 culminated inamassiveliquidationeventoflongcontractsthat simultaneously shiftedthewealthdemographicstowardshigher- tier accountsholdingthemajorityofcoinswhilemanylowertier accounts werewipedout.Thispatternappearstohaveoccured again inSeptember2019;however,furtherinspectionindicates that inthisevent,BitMEXseeminglycon?scatedtensofthousands of bitcoinsandplacedthemintospecialvanityaccountsthathad never receivedanyexternaldepositsbefore.Onepossibilityisthat these accountsconstitutetheinsurancefundthattheexchange maintains, andthemovementsimplyconsolidatedcoinsthathad been earmarkedfortheinsurancefund.Curiouslythisshiftoffunds occurred withinmomentsofasharpdeclineinthepriceofBitcoin of over20%,andfurtherresearchisneededtodetermineifthis played acausalroleinthepricemovementorifitwasmerelya coincidence. The on-chainactivityofaccountssuggeststhatsomeactorsare engaging withBitMEXinsophisticatedways.We?rstderived clusters ofaccountsusingthemethodologydescribedinSection4.3.

Someone

maychoosetointeractwithBitMEXthroughmultiple accounts forafewreasons.First,BitMEX'sriskmanagementre- stricts theleverageoflargepositions( ¡

200bitcoins)whichcan

be

circumventedbysplittingapositionacrossmultiplesmalleraccounts.Second,sophisticatedtradersmayusetherate-limited

API serviceprovidedbyBitMEXtoperformautomatedtrading.A trader canmultiplextheircommandsthroughmultipleaccountsto increase theire?ectiveratelimitsorrunseparatealgorithmsand strategies ondi?erentaccountsaltogether.Third,tradingbitcoin is uniquebecausethe?owsthattraderscreatebetweenexchanges are public,andsosophisticatedtradersmaywishtoobfuscatethese movements to mitigate the impact of being front-run. After applyingbothourrule-basedandcommunitydetection algorithms forclustering,weidenti?edthatabout90%ofaccounts are notpartofaclusterwhilelessthan1%belongtoclustersof5 or moreaccounts.Wedidhoweverdiscoverhundredsofproli?c clusters, the largest of which include 50
accounts or more. DGGUVDGGUVDGGUV DGGUV DGGUVDGGUVDGGUV DGGUV

Figure

8characterizestheclustersbyaccountbalance,andav-

erage numberofdeposits.InFigure8(a),weseethatclustersizes of

6to10accountsonaverageappeartohaveahigheramountof

wealth peraccountthanclustersofothersizes,albeitnotbyalarge margin. Thesingletonclusters,ontheotherhand,aresigni?cantly lower thanalltheothersizesofclusters.Thisplateausuggeststhat the accountwealthmaybeintentionallylimitedasdiscussedbefore and largedepositsarescaledhorizontallyforminglargerclusters.

Figure

8(b)suggeststhatlargerclustersalsoengageinahigher

number ofdepositsthanthesingletonclusters.Largenumbers of on-chaintransactionsmaybeasignthattheaccountisbeing used aspartofanarbitragestrategywherethetradermanagesac- counts on multipleexchangesthatarefrequentlyreconciledusing on-chain transactions.Theseobservationssuggestthat,inaddition to retailspeculators,BitMEXisutilizedbyhighlysophisticated traders whichechoestheclaimsmadebyaprofessionalmarket marker [18]abouttheusefulnessofderivativesincryptocurrency. An exampleofalargeclusterwouldbetheonerootedfrom

Bitcoin

address

1KiJkugknjgW6AHXNgVQgNuo3b5DqsVFmk,which

owns

86BitMEXaccounts.Thisaddresshassentapproximately

13,900

bitcoinstoBitMEXbuthasextractedover72,100bitcoins from it. We complementouron-chainevaluationofBitMEXwithananal- ysis ofitsusers.We?rstlookatthesite-wideIRC-likechatroom known as the trollbox , before analyzing leveraged positions.

WWW"21,April19-23,2021,Ljubljana,SloveniaKyleSoska,Jin-DongDong,AlexKhodaverdian,ArielZetlin-Jones,BryanRoutledge,andNicolasChristin

Trollbox

usersconsistofamixoftraders,administratorsandau- tomated botsthatpostinformationsuchasalivefeedofposition liquidations ontheplatform.Thetrollboxalsosupportsmacrossuch as /position,/orders,/pnl,/rpnl,whichdisplayunforgeable facts about the account of the user issuing them. (QJOLVK &KLQHVH 5XVVLDQ -DSDQHVH )UHQFK 6SDQLVK 6.1.1 Generalstatistics.TheBitMEXtrollboxisahighlyproli?c messaging system,with57.8millionmessagesfromNovember 14th,

2014toFebruary10th,2021.Figure9showsa7-daymov-

ing averageofthetrollboxmessagevolume,brokendownbylan- guage.

Since2018,thetrollboxhassustainedanaverageofover

2,000 messagesperhourwithfrequentspikesabove3,000messages per hour.Thepopularityofthetrollboxcloselymirrorsthetotal trading volumeonBitMEXshowninFigure5.Thisfarsurpasses other mediumsofcryptocurrencydiscussionsuchasthepopu- lar cryptocurrencysubreddits /r/cryptocurrency,/r/bitcoin, /r/bitcoinmarkets,/r/ethfinanceand/r/ethereumwhichav- erage just above 200
comments per hour.

Likely

owingtoSouthKorea'scryptocurrencyfrenzy[38],Ko- rean becameinmid-2018themostpopularlanguage,followedby

English;

Chinese

is a distant third. )UHQFK -DSDQHVH 5XVVLDQ &KLQHVH (QJOLVK .RUHDQ In

Figure10,weorganizethemessagesbytimeofdayintoone-

hour bucketsandnormalizeeachbucketbyvolume.Asurprisingly small amountoftemporalcorrelationoccursamonglanguages.RussianandChineseexhibitpatternswherethemostcommon hour ofthedayismorethantwiceasproli?castheleastcom- mon houroftheday.Thisisunderstandablesinceasigni?cant concentration ofpeoplewhospeaktheselanguagesliveinafew consecutive timezones.Englishmessagesontheotherhandare relatively timeinvariantandlikelyre?ectstheglobaldistribution of

English-speaking

traders. The relativeinvarianceoftheKoreanmessagevolumetothe time ofdayisfarmoresurprising.While94%ofKoreanspeakers live intheGMT+9timezone[1],themostproli?choursoftheday for the

Koreanlanguageonlycontainaround50%moremessages

than theleastpopularhour.Thisisindramaticcontrasttothe trends observedinhobbiessuchasvideogames[51]wheretheratio between peakandtroughsisregularly3orhigher.Unliketraditional ?nancial markets,cryptocurrencymarketsareactive24/7.Korean traders seemtobeactiveatallhoursoftheday,indicatingthat trading may be an all-consuming activity for many of them. 6.1.2 Sentiment.TofurtherourunderstandingofBitMEXtraders, we nextdescribeasentimentanalysisofthetrollboxmessages.The in?uence ofBitcoinprice?uctuationsonusermoodshouldindeed reveal the timeframes on which traders operate.

Trollbox

messagesaresimilartosentences,andaveragearound eight wordspermessage.Messageshowevercontainalotofslang, profanity, emojis,ASCII-artandcommunityspeci?ctermssuchas asset tickers, 12 which makespre-trainedsentimentmodelspoorly suited; likewise,theabsenceofanyground-truthlabelmakestrain- ing anewmodeldifcult.However,akeyinsightisthattheaverage mood of thetrollboxisstilllikelycorrelatedwiththepriceaction of

Bitcoin,

and that correlation allows us to extract some signal. Thus, we?rstautomaticallyassignlabelstotrollboxmessages based ?uctuationsinthepriceofBitcoin.Parameterizingthelabel assignment algorithmallowsustoadjustthetime-frameconsid- ered forthelabel.Wethentakethislabeleddataanduseitto train aconvolutionalneuralnetworkfollowingtheapproachof Kim [

26]usingtheCoreNLP[32]opensourcednaturallanguage

processing package and its

Python

variant

Stanza

[47].Alabeling of messagesdrawnfromtime-framessynchronizedwiththemood of

BitMEXtradersshouldproduceahigherperformancemodel

than one produced by labels drawn from orthogonal time-frames.

Intuitively,

wewanttoassignlabelstomessagestocapture trader excitementwhenthepriceisgoinguprapidly,anddespair or capitulationwhenitisgoingdown.Technicalindicators 13 allow us to mathematically describe price ?uctuations. In particular,theRelativeStrengthIndex(RSI)[50]takesasinput the pricehistory ? ofanassetandatimeparameterfandoutputsa value intherange »0•100¼todescribethemomentumofthatasset's price attimeC,basedon?uctuationsover(roughly)theprevious

15f.(WereferthereadertoWilder[50]foraformalde?nition.)

We scorethesentimentofeachmessageby?rstcomputing the RSIvalueatthetimethemessageappearsinthetrollbox.We partition thespaceofRSIvaluesinto?veranges,»0•30¼,¹30•43¼, ¹43•57¼,¹57•70¼,¹70•100¼,whichwemaptothesentimentlabels 12 A tickersymbolisanarrangementofcharacters,typicallyletters,whichrepresents a particular asset or market which is traded publicly. 13 A technicalindicatorisaheuristicorpattern-basedsignalthatisproducedbythe price, volume,and/oropeninterestofasecurityorcontractandusedbytraderswho follow technical analysis. TowardsUnderstandingCryptocurrencyDerivativesWWW"21,April19-23,2021,Ljubljana,Slovenia

0•4.Wechosethisspecicpartitioningasitnearlybalancedthe

number ofmessagesassignedeachlabelwhenusingf=1hour. The higherthescore,themorepositivethesentiment.Wethentag the messagewiththecorrespondingsentimentlabel.Forinstance, a message issuedwhen"(f=37istaggedwithsentimentvalue1. Ζ ȴ

Figure

11:CNNsaccuracyforsentimentpredictionwhen

trained usingtrollboxdatawithlabelsderivedfromRSI,us- ing di?erent time parameters.

Besides

labeling,weremovedallautomatedmessages,macros and knownbots.Wesanitizedtheremainingmessagesbyremoving special characters,URLs,andusernames.Wealsosanitizednumbers to avoidsituationswherethepriceofBitcoinmightbeusedto inuence themodel;howeverwedidleaveinpunctuationasthat may beinuentialinthesentimentofmessages.Wetraineda separate modelforeachlanguageandusedlanguage-specicpre- computed wordvectormappings[35] 14 for each.Foreachmodel,we balanced thetrainingandtestingdataacrossclassesbyre-sampling the minority classes to match the majority class. Figure11showstheperformanceoftheclassierswhentrained with labelsderivedfromtheRSIusingatime-spanparameterrang- ing fromoneminutetooneweek(10,080minutes).Arandomly labeled datasetexpectedlyproducesaclassierwithjust20%accu- racy inthe5-classpredictiontask,soallRSIlabellingsproduceda signal that encodes some information about sentiment. The localmaximaatf=5minutesimpliesthatconversationin the trollboxislargelyfocusedonpriceactionfromthelasthouror so (15f=75minutes).Manualinspectionconrmsthatuserswho have recentlymadeprotabletradesaredisproportionatelyprolic in thetrollboxrelativetothosewhohavenot.Thequalityofthe trained modelsfallsountilf=1•440minutesor1dayandreally takes oatf=10•080minutesor1week.Thissuggeststhatthe sentiment ofthetrollboxisalsolargelyimpactedbythetrendof the marketovertheprevious15weeks.Wesuspectthatthisisdue to survivorbiaswheretraderswhose(bearishorbullish)outlook on themarkethasbeensupportedbythepricetrendareprolic, while manytraderswhoseoutlookhasbeencontradictedbythe market trend have dropped out of the platform. 6.2

LeverageAnalysis

One ofthebestrecordsontheleverageusedbytradersatBitMEX comes fromablogpost[25]byBitMEXCEOArthurHayeswherehe details ground-truthinformationabouttheleveragetradersapplied to theirpositionsbetweenMay2018andApril2019.Hisanalysis 14 These mappingsarepubliclyavailable:https://code.google.com/archive/p/word2vec/. took snapshotsonthelastdayofeachmonthandcalculatedthe eective leverageofeachpositionontheXBTUSDperpetualBitcoin futures instrument.Overthistimeperiod,Hayesshowsthatthe weighted averageleverageoflongandshortpositionswasaround

2035x

withshortpositionsbrieyaveragingunder20xleveragein

November

andDecember2018.Additionally,theaverageeective leverage oflongpositionsisonaveragehigherthanthatofshort positions, butthereissignicantvolatility,andshortpositionswere more leveraged during three of the twelve months analyzed. We supplementHayes'analysisbycoveringallactivityonBit- MEX incontinuoustimeuptoAugustof2020andextendingour investigation toalltradedinstruments.Unfortunately,wegener- ally cannotknowtheleverageofatrader'sposition,sowecannot directly replicateHayes'experiment.Insteadweexploretrader leverage and risk by looking at liquidation events.

Without

user verication,BitMEXwasunabletoknowtheiden- tities oftradersonitsplatform.Asaresult,whenatrader'saccount becomes overdrawn,BitMEXhadnorecoursetoseekadditional funds fromtheuserthoughatraditionalmargincallprocess.Instead,

BitMEX

tookovertheriskypositioninaprocesscalledliquidation.

Liquidation

eventsarebroadcastpubliclythroughbothanAPI feed andviaanautomatedREKTbotinthetrollbox.Thesepublic events includetheinstrumentthatthepositionwastakenon,the size of the position, and the liquidation timestamp.

Liquidations

overtime.

Figure

12showsa7-daymovingaverage

of thedailyvolumeofcontractsliquidatedonBitMEX,adjustedto US dollars,andcomparesittotheBitcoinprice.Asexpected,the amount ofdailyliquidatedcontractspickedupwiththetrading volume in2018followingthemarkettopandspikedwithincreases in pricevolatility,peakingashighas1billiondollarsinaggregate in asingleweekinNovember2018.Mostof2018wascharacterized by signicantliquidationevents(¿USD100M)everyfewweeks which coincideswiththepriceuctuatinginrapiddiscretejumps,a pattern referredtobythecommunityasbarts.Althoughbartsshare a strongcorrelationtotheseliquidationevents,furtherresearchis needed todetermineifleveragedBitcointradingplaysacausalrole in barts oriftheseliquidationsaremerelyasymptomoftheprice action.

AlsonotethataftercontactingBitMEXaboutourresearch

in

Novemberof2020,theREKTbotwasdisabledinthetrollbox

until eventual being re-enabled in

January

2021.
As Figure12shows,signicantliquidationstendtodispropor- tionately occurtothelongsideofcontractswithaggregatelong liquidations regularlyspikingaboveshortliquidations.Curiously, this observationholdsevenwhenthepriceofBitcoinistrendingup as seeninJuly2019.Twonotableexceptionsoccured.OnApril1, 2019
alargecoordinatedpurchaseofBitcointookplaceontheBT- CUSD spotmarketsatCoinbase,KrakenandBitstamp,andresulted in a25%increaseinthespot(andthereforeindex)priceofBitcoin, causing theliquidationofoverUSD400Mofshortcontracts.On

October

24252019,PRCpresidentXiJinpingdeclaredthatChina

aspires to become a world leaderinblockchaintechnology,which triggered a short-lived bull run.

Liquidations

overinstruments. Table

2aggregatesliquidations

by productcategorytoilluminateanytrendsspecictoaparticular instrument class.AsFigure12suggested,thereisanasymmetry among thevolumeinliquidatedlongandshortcontracts.Thistrend is presentregardlessoftheinstrumentandunderlyingassetthatis

WWW"21,April19-23,2021,Ljubljana,SloveniaKyleSoska,Jin-DongDong,AlexKhodaverdian,ArielZetlin-Jones,BryanRoutledge,andNicolasChristin

'DLO\/LTXLGDWHG6KRUWV'D\0RYLQJ$YHUDJH%LWFRLQ3ULFH

(USD,inbillions)(USD,inbillions)(USD,inbillions)

Perpetual

Bitcoin

30"4818"3048"781"671"26%0"76%

Settled

Bitcoin

2"521"043"562"422"06%0"85%

Perpetual

Ethereum

1"290"902"191"430"61%0"43%

Settled

Ethereum

0"290"100"392"862"64%0"92%

Perpetual

Altcoins

0"060"020"083"241"23%0"38%

Settled

Altcoins

1"310"551"872"382"13%0"98%

: being traded,howevertheratiobetweenlongandshortliquidation volume is somewhat unstable. The fractionofliquidationsovertotaltradingvolumeisaproxy for evaluatingtheriskofaninstrument:highervolume-normalized liquidation denoteinstrumentswithriskierpositions.Inallcases, settled futuresappearriskierthanperpetualswaps.Additionally,

Bitcoin

andaltcoinfutureshaveverysimilarvolume-normalized characteristics whileEthereumsettledfuturesappearriskierfor both longsandshorts,andEthereumperpetualfuturesappearto be safer.Theseresultsareinterestingsincetheinstrumentsin these categoriessupportdi?erentamountsofleverage,withBitcoin allowing upto100xleverage,Ethereumallowingupto50x,and altcoins being a mix that typically ranges from 20x to

33.33x.

6KRUW/LTXLGDWLRQV /RQJ6KRUW/LTXLGDWLRQV - tions

Figure

13hintsatthehow

much liquidationvolumeiscontributedbypositionsofdi?erent sizes. ThisplotwasformedbypartitioningpositionsizesintoUSD

50,000-buckets

and plotting thecumulativevalueofallliquidated positions uptoaparticularvalue.50%oflongliquidationscome from positionsofUSD1.6Mandunder,while50%ofshortliquida- tions comefrompositionsUSD950Kandunder.Themonotonely decreasing slopeofbothcurvesimpliesthatadisproportionatefrac- tion oftotalliquidationscomesfromsmallerpositionsizes.This could potentiallybeduetobetterriskmanagementandlowerper- sonal risktoleranceoftraderswhomanagelargerpositionsorasys- tematic fallacyoftraderswhoarereluctanttoselltheirlosers[41].

Additionally,

thedi?erencebetweenthecumulativelongandshort liquidations formsa(blackdashed)curvewithpositiveslopeatall points.

Thisshowsthereisalwaysagreaterliquidationvolumeof

long positions regardless of size. One keytounderstand- ing whyliquidationsoccuristostudythepriceactionofBitcoin leading uptoaliquidationevent.InFigure14wepartitionedthe history ofBitMEXinto1week(168hour)sectionsandcomputed the maximumandminimumpricethatwastradedontheXBTUSD perpetual

Bitcoininstrument

15 within eachsectionalongwith the totalUSDvalueofalllongliquidationsacrossallinstruments. If thepriceatthebeginningoftheintervalislowerthanatthe end, thedi?erenceisassignedapositivevalue,otherwisethedif- ference isde?nedtobenegative.InFigure14(a),whenwe?ta linear regressiontothedistribution,theslopeofliquidationsof 15 We usedtheBitcoinspotpriceonCoinbasetoanalyzedatabeforetheXBTUSD instrument existed. TowardsUnderstandingCryptocurrencyDerivativesWWW"21,April19-23,2021,Ljubljana,Slovenia

ȫȫȫ

0D['HYLDWLRQ/RQJ/LTXLGDWLRQV$ERYH5HJUHVVLRQ

0D['HYLDWLRQ/RQJ/LTXLGDWLRQV%HORZ5HJUHVVLRQ

΍

ȫȫȫ

0D['HYLDWLRQ6KRUW/LTXLGDWLRQV$ERYH5HJUHVVLRQ

0D['HYLDWLRQ6KRUW/LTXLGDWLRQV%HORZ5HJUHVVLRQ

΍ - mum long contractsisunsurprisinglysteeperwhenthepriceistrending down. Whatislessintuitiveisthatthevolumeoflongliquidations is positively correlated withincreasesinprice,thatis,astheprice of

Bitcoin

trends upandthegapbetweentheminimumandmaxi- mum pricetradedwithinaweekexpands,thevolumeofliquidated long contractsincreases.Thiscouldpotentiallybeexplainedbyan increase involatilityduringweekswithsigni?cantpriceexpansion. Late

Juneearly

July2019,asseeninFigure12,isagoodexample:

while thepricetrendedup,signi?cantvolumesoflongliquidations were observed.Althoughwerestricthereouranalysisofliquidated position sizestotheXBTUSDinstrument,wenotednearlyidentical trends on the

Ethereum

and altcoin instruments. By contrast,Figure14(b)displaysthesamelinearregression analysis forshortcontracts.Againasexpected,theslopeofthe regression islesssteepwhenthepriceisgoingdownandsteeper when thepriceisincreasingandgoingagainsttheposition.In- terestingly, theslopeoftheregressionsforshortcontractsare signi?cantly lesssteepthenthoseforlongliquidations,andasthe price isincreasing,weexpecttoobservehighervolumesoflong liquidations than short liquidations.

BitMEXand

forthatmatter,relatedcryptocurrencyderivative marketsraises anumberofimportantquestionsregardingwhether the serviceito?ersisasocietallydesirable,orevenanetpositive for cryptocurrency adoption. The demandforleveragedexposure to cryptocurrencyfromretailspeculatorsandprofessionaltraders alike isclearlypresent,basedonthelevelofactivityweobservedon the platform.However,communityanecdotes[42],coupledtoour own leverageandliquidationanalysissuggeststhatproductslike those tradedonBitMEXexacerbatelargemovesinunderlyingasset price.

Historyhastaughtusthatcommodityspeculation[

33]using

derivatives canhaveundesirableconsequences:cryptocurrencies are simply the newest manifestation of this issue. More speci?cally, the complexity of the derivative instruments o?ered, pairedwiththetremendousamountofliquidationswe observe, particularlyofmodestsize,suggeststhatnotallsmall, retail tradersfullyunderstandthehighrisksinvolved.Similar concerns inthepasthavemotivatedpoliciestorestrictcertain ?nancial o?erings to accredited investors. We didnotstudytheimpactof geo-fencing.

Thiscouldbedonebycheckingtheon-chain?owsfor

systematic di?erencesbeforeandafterBitMEXimplementedthis policy (roughlyinNov./Dec.-2018).Severalotherexchangessuch as Bybit[12]currentlyrelyongeo-fencing,sounderstandingits efcacy could have profound consequences. There mayalsobesigni?cantstructureintheon-chaintransac- tions thatBitMEXgeneratesforful?llingwithdrawalsthatcould further enhanceourunderstandingoftradingbehavior.Another potentially valuablesignalwedidnotuseliesinthemillionsof ground-truth position,order,andpro?ts-and-lossesdatapointsthat traders andbotspostedinthetrollboxalongwiththepublicleader- board of the most pro?table accounts. We reachedouttoBitMEXinNovember 2020
withadraftofthepaperandtheanalysiswebsite.BitMEX representatives respondedwiththefollowingstatement,without elaborating any further: We willnotprovidespeci?ccommentsonyourpaper as itcontainsvariousinaccurateand/ormisleading statements thatdonotproperlyre?ecttheplatform's structure andoperationsandalsodonotre?ectthe platform's userveri?cationrequirementsthatarein place for all customers. While thereisample?nancialliteratureonthestudyofderivatives trading, cryptocurrencyderivativestradingisnovelenoughto have remainedmostlyunexploredsaveforHayes'aforementioned analysis [25].Onthe?nanceside,theworkofBhardwajetal.[6] studies ahistoryofcommodityfutureswhichmirrorsourown e?orts tostudycryptocurrencyfutures.Incryptocurrencies,the closest relatedworkcomesfromGandaletal.[20]whoperformed a postmortemanalysisoftheMt.Goxbitcoinexchange.Theyhad the bene?toftheexchange'sback-enddatabase,whilewesourced various publicsignalstoreconstructBitMEX'shistory.Mooreand

Christin

[36]lookedatearlycryptocurrencyexchanges(20082013), and observedthatanti-moneylaunderingprecautionswererare, and exchangeswerefrequentlycompromised.Ourwork,almost a decadelater,showsthat,whilethe?nancialinstrumentshave become farmorecomplex,cryptocurrencytraders'riskappetite remains high.Decentralizedexchangeshaverecentlybeenthefocus of anumberofresearchpapers,inparticular,onhowtoattackthem. For instance,Daianetal.[

16]examinedvariousattemptsatgaming

decentralized platformsforpro?t;whileimportant,suchattacksare less relevantinthecontextofcentralizedplatformssuchasBitMEX. Last, fromamethodsstandpoint,webuilduponthemethodologyof

Meiklejohn

etal.[

34]andMöseretal.[37]forclusteringaddresses

and tainting?ows.WewerealsoinspiredbyKoganetal.[27]and by LoughranandMcDonald[30]forrelatingthepricesignalsofa market to sentiment within textual data.

Through

theinnovationofitscomplexyetintuitiveperpetualfu- tures instrument,BitMEXbecameamulti-billiondollarexchange that transformedthelandscapeforcryptocurrencyderivatives. While wecannotafrmthatderivativesproductsliketheones

WWW"21,April19-23,2021,Ljubljana,SloveniaKyleSoska,Jin-DongDong,AlexKhodaverdian,ArielZetlin-Jones,BryanRoutledge,andNicolasChristin

- tions - ulators. - count 10

ACKNOWLEDGMENTS

REFERENCES

Ethnologue(17thed.)

AvailableatSSRN3452255

- ing - custody-acquires-xapos-institutional-business-becoming-the-world-s-largest - crypto-2c1b46fc94c4.

Proc.2020IEEESymp.Sec.Priv.

JournalofMonetaryEconomics

arXiv preprint arXiv:1408.5882

Proc.2009AnnualConf.NorthAm.Chap.Assoc.

Comp.

Linguistics

- modity. -

5574233-185408.pdf.

TheJournalofFinance

Proc.52ndAnn.Meet-

ing

Assoc.

Comp.

Ling.:

System

Demonstrations

USSenate,Washington,May

Proc. 2013

Internet

Measurement

Conf. arXivpreprintarXiv:1309.4168 Proc. 2013
IFCA

Financial

Crypto.

Proc. 2013
APWG eCrime - ero - monero-and-eos-try-the-kimchi-premium/.

TheJournalof

Finance

Proc.58th

Ann.

Meeting

Assoc.

Comp.

Ling.:

System

Demonstrations

Phys.Rev.E

- tocurrency

Newconceptsintechnicaltradingsystems

SteamDB.

- munity

Sci.Reports