Informatica Economică vol.22, no. 2/2018 35
   DOI: 10.12948/issn14531305/22.2.2018.04
   Arbitrage Trading Systems for Cryptocurrencies.
   Design Principles and Server Architecture
   Cristian PĂUNA
   The Bucharest University of Economic Studies, Romania
   cristian.pauna@ie.ase.ro
   When dot.com has become a quaint idea, when electronic shops have lost
   the mass attention,
   while classical and margin trading has become obsolete, something new
   is coming: cryptocur-
   rencies. Hundreds of virtual coins have been invented for a single
   reason: the profit. The high
   price volatility of these new markets and the fact that the virtual
   coins price is not regulated by
   a central bank or a single exchange, gives us opportunities for
   arbitrage trading. The existence
   of important price differences makes possible the profit when an
   automated system buy cheaper
   and sell more expensive in the same time. This paper will present the
   general principles under-
   pinning the implementation of arbitrage trading software for virtual
   coins market. The very
   large number of cryptocurrencies and exchanges fundamentally change the
   server architecture
   of the trading software. The distributed price data in hundreds of
   sources and the technical
   differences of each of these data providers make all the things
   difficult to be implemented in a
   single application. The low-latency order calculation needed for the
   fast delivery before a sig-
   nificant price change, in the presence of thousands of price quotes
   coming from hundreds of
   distributed servers makes everything special.
   Keywords: Cryptocurrency (CRYC), Arbitrage trading (ART), Algorithmic
   trading (AT), High
   Frequency Trading (HFT), Automated trading software (ATS)
   Introduction
   Bitcoin (BTC), Litecoin (LTH), Ethereum
   (ETH), Ripple (RIP), thousands of virtual
   coins already exists. The Bitcoin “introduced
   in 2009”, “caught the interest of the main-
   stream media in 2012” [1]. After that, an un-
   defined number of virtual coins were in-
   vented. All these coins are named virtual just
   because they exist only in the electronic rep-
   resentation. They are also called cryptocurren-
   cies because they are designed to work as a
   currency like a medium of value exchange and
   the “cryptography is used to secure financial
   transactions” [2], to control the creation of
   new units, to verify the transfer of funds and
   to set the owner for each unit.
   The reason involving real money to buy this
   kind of virtual assets is of course the profit.
   The virtual coins value is fluctuating on the
   free markets as any asset, powered by specu-
   lative and marketing actions and sustained at
   least hypothetical by some economical pro-
   cesses involved in the background of each
   crypto coin. However, even it is a free market,
   the virtual coin market has its own character-
   istics, especially when it is about the volatility
   and “the flexibility in the supply schedule”
   [3].
   We are not so far away the days when Bitcoin
   almost touched 20.000 USD in a relative short
   period of time starting from couple of bucks
   and after that plunged back to 7.000 - 8.000
   USD in an even shorter interval. This kind of
   movements makes the investment in crypto-
   currencies an attractive one today. However,
   the high price volatility makes this investment
   to be a very risky one, drawdowns of more
   than 61.8% being very easily assimilated with
   a gambling game. For all that, keeping the
   money into a virtual currency hoping the
   value will increase after a while can be a prof-
   itable idea, but it is not the only one. All these
   assets are operating independently of a central
   bank or a central exchange.
   Without a central price regulator the quotes
   can be different from an exchange to another.
   That means we can find cases when and where
   we can buy cheap and sell more expensive the
   same thing in the same moment of time. This
   will be called an arbitrage trade. The unbal-
   anced price of the same asset in two different
   places will give us an instant profit.
   1
   36 Informatica Economică vol.22, no. 2/2018
   DOI: 10.12948/issn14531305/22.2.2018.04
   The question is how to find these opportuni-
   ties and how can we profit from all of these in
   the real time. The theory is easy, just find two
   exchanges with different quotes for the same
   equity, buy where the equity is cheaper and
   sell where it is more expensive.
   The problems start when we will consider the
   facts that there are hundreds of crypto pairs,
   hundreds of thousands of combination of
   these coins and hundreds of exchanges where
   you can buy and sell all of these assets. That
   means a human being cannot compare all
   these prices in order to find the best trade op-
   portunities, to build the orders and to send
   them to those exchanges before the price is
   changed. Only a computer can to manage all
   of these actions.
   This paper will present how an arbitrage trad-
   ing system for cryptocurrencies can be built,
   will reveal the main principles and practices
   and will also present some results in order to
   open or to grow the interest for this kind of
   software systems.
   2 Arbitrage trading
   As we already presented, the arbitrage trading
   means to buy cheap and to sell more expen-
   sive the same equity in the same moment of
   time in two places where the quote prices are
   different. The arbitrage trading is not a new
   idea. It was used since years to trade shares.
   “Stocks are matched into pairs with minimum
   distance between normalized historical
   prices” [4] and arbitrage trading strategies can
   be used with any stock exchange in order to
   make profit.
   Usual, the classical arbitrage trade is involv-
   ing the same volume of equity in the both
   trades. For each trade (i), the profit (P
   i
   ) is
   equal with the difference between the sell
   price (sell
   i
   ) and the buy price (buy
   i
   ) multiplied
   by the volume traded (V
   i
   ).
   For this simple trade idea, the trading software
   must compare continuously the quotes from
   different exchanges and find the highest price
   differences. But the classical arbitrage trading
   is not the only one trading opportunity which
   can be found in the cryptocurrency markets.
   The virtual coins are traded in pairs. The price
   of a pair, for example BTCETH if we will
   consider Bitcoin and Ethereum, means how
   much ETH we need to sell in order to buy a
   specified unit of BTC. When an exchange ask
   you a specified price to buy BTCETH and an-
   other exchange offers you a higher price for
   the same pair, a classical arbitrage trade can
   be executed and the profit will result from the
   price difference. But sometimes the depend-
   ency between two coins is not expressed in the
   same way in all exchanges. There are cases
   when an exchange will quote the price for
   BTCETH and another exchange will quote the
   price for ETHBTC. The virtual coins involved
   are the same but the price in built in the re-
   versed way. For this case, to find an arbitrage
   trade the software must compare the price
   from the first exchange with the reversed price
   into the second. Now we are talking about a
   reversed arbitrage trade. In this case the profit
   will be:
   The execution of the trades is usual simple. A
   trading order means to buy or sell the speci-
   fied pair at a specified price with an estab-
   lished volume. Of course an order can be exe-
   cuted by the exchange only if there are enough
   coin units to sell in that exchange in order to
   buy the ordered volume from the first coin.
   This validation must to be done by the trading
   software before to send the orders to the ex-
   changes, based on the real-time capital data
   provided by each exchange. An arbitrage
   trade means at least two orders in two differ-
   ent exchanges.
   Another type of arbitrage trades is involving
   more pairs. Sometimes the product of two,
   three or more pair quotes is imbalanced be-
   tween two exchanges or even in the same ex-
   change. For understanding, letʼs consider the
   next inequalities:
   BTCETH*ETHLTC<BTCLTC (3)
   and
   BTCETH*ETHLTC*LTCRIP<BTCRIP (4)
   Informatica Economică vol.22, no. 2/2018 37
   DOI: 10.12948/issn14531305/22.2.2018.04
   In cases like these, multiple arbitrage trades
   can be possible in order to make profit. In the
   first exchange will be executed more trades
   and in the second only one. The software will
   buy the lowest price for multiple pairs and sell
   the last one on a higher price. The profit for
   multiple arbitrage trades will be expressed in
   this case by the formula:
   For the next level of multi arbitrage trades,
   when we buy more pairs and sell more pairs
   in order to earn the difference between those
   operations, the profit will be expressed by the
   next equation:
   where (nBUY) is the number of buy trades
   and (nSELL) is the number of sell trades made
   simultaneous. This kind of trades can be found
   using two different exchanges but sometimes
   the difference can be found even in the same
   exchange. The multiple arbitrage trades are
   more complex to be validated and executed
   and a serious computational power is needed
   in order to deliver all the trading signals and
   orders in real time. Only low-latency trading
   software can manage this process in order to
   deliver the orders in that short time needed to
   execute them.
   3. Cryptocurrency dictionaries
   One of the most important parts of an arbi-
   trage trading system for cryptocurrencies is
   the dictionary. Due to a very high number of
   cryptocurrencies quotes involved and because
   there is not a standard for naming the coins
   and pairs, a lot of false trading signals can be
   built without a strict codification methodol-
   ogy. Each exchange has its own coin and pair
   codes. We can find cases when the same coin
   can be named or coded differently.
   For example BTC can be the symbol for
   Bitcoin in the most exchanges but some of
   them will code the Bitcoin with XBC or BTX.
   In order to compare the price of the same cur-
   rencies, a codification dictionary must to be
   implemented. The trading software must have
   its own codification rules and all codes from
   all exchanges must to be included into the
   trading software database.
   In addition there are cases when the code of a
   pair into an exchange can be the code of an-
   other pair into another exchange. For example
   SCAPPC is representing the pair of SC re-
   ported to the APPC coin in the most ex-
   changes but it others it is the pair between
   SCA and PPC. All these codes SC, SCC, PPC
   and APPC are valid crypto coin codes. This is
   the case when a profitable arbitrage trade can
   be found if we will compare the price of the
   SCAPPC pair into two exchanges where that
   code represent different coin pairs. To avoid
   these type of false signals, in the trading soft-
   ware dictionary must to be inserted each pair
   code from each exchange related with the real
   pairs that are quoted. A codification using a
   separator is a very good implementation. With
   this additional rule, the code will looks like
   SC-APPC and SCA-PPC and the confusion is
   avoided.
   From this reason, the cryptocurrencies dic-
   tionary implementation and continuous opti-
   mization is a very significant part of the arbi-
   trage trading software for virtual coins. More,
   the current practice added to this reason an-
   other important one. In fact more exchanges
   are changing the codification of the crypto
   coins and pairs occasionally. This means reli-
   able trading software must have the possibility
   for an easy change of a coin code or pair code,
   in order to save time and to trade the most sig-
   nificant profit opportunities without a signifi-
   cant change in the code of that software.
   In this moment (2018 year) there are more
   than 3000 virtual coins on the free market
   listed in more than 300 exchanges and we are
   still counting. Not on the last place, the fact
   that new crypto coins are invented every day
   and they are added often into the exchanges
   quotes makes the dictionary to be a significant
   tool in order to keep the trading software up-
   dated with the reality of cryptocurrency mar-
   kets.
   38 Informatica Economică vol.22, no. 2/2018
   DOI: 10.12948/issn14531305/22.2.2018.04
   4 Data structure
   The price quotes are received from the ex-
   changes or from the brokerage companies in
   real-time in different data structures. Each ex-
   change has its own API application and the
   data structure of each exchange is different.
   Even all data sources are available by an API
   access, the differences between data structures
   delivered by each exchange make everything
   more complex. The arbitrage software must
   be adapted to import all the piece quotes from
   each exchange according each technical spec-
   ifications.
   Another difference is given by the fact that
   some exchanges will deliver all the price
   quotes on a single interrogation of their API,
   others exchanges will deliver the quotes in
   different interrogations, one request for each
   currency pair price. The third level of diffi-
   culty is occurred for those exchanges that ac-
   cept a limited number of requests in a speci-
   fied time interval. All of these facts make con-
   nection with each exchange to be a dedicated
   and laborious job.
   Whatever are the difficulties to implement the
   data connection with the exchanges, once the
   price quote is received in real-time, it must to
   be recorded into the application database. Be-
   cause the update interval is random, each
   quote will be inserted as a separate record re-
   lated with the exchange code, with the pair
   code and with the updated time. For those pro-
   cesses that need the historical price quotes, be-
   cause of the large number of the records, usual
   the history is simplified and represented sepa-
   rately as historical records which will include
   the next components, that become a standard
   for a historical price data series [5]: open price
   of a time interval (OPEN), maximal price of a
   time interval (MAX), minimal price of a time
   interval (MIN), close price of a time interval
   (CLOSE), where the time interval has usual
   one of the next values: one minute (1M), five
   minutes (5M), fifteen minutes (15M), thirty
   minutes (30M), one hour = 60 minutes (1H),
   four hour = 240 minutes (4H), one day = 1440
   minutes (1D), one week = 10080 minutes
   (1W), one calendar month (1M), one calendar
   year (1Y).
   Usual start time interval and data about the
   traded volumes are also available together
   with the price quotes. Advanced software will
   record also the current spread for the equities,
   which is the difference between the buy and
   the sell price at a moment of time.
   All price data quotes from hundreds of ex-
   changes and for hundred of thousand of cryp-
   tocurrency pairs must to be made in real time.
   In addition the data history must to be recorder
   for future operations. Using a relational data-
   base is a fact. In addition, the huge number of
   CRUD operations and the low-latency needed
   for the trading signals and orders push the ap-
   plication to use multi server architecture pre-
   sented in Figure 1. In the current practice,
   more servers will do the acquisition processes
   of the real time price data from the exchanges,
   a different server will makes all CRUD (cre-
   ate, record, update, delete) operations and a
   different one will compute the trading signals
   and will build and deliver the low-latency
   trading orders. All of these operations must to
   be completed in real-time, before the price is
   changing significantly the values in order to
   make possible the arbitrage trades.
   Fig. 1. Server architecture for a cryptocur-
   rency arbitrage system
   Informatica Economică vol.22, no. 2/2018 39
   DOI: 10.12948/issn14531305/22.2.2018.04
   5 SQL needed
   Due to the large amount of data, first reasons
   to use a relational database are to control data
   redundancy, to provide the data integrity and
   to avoid data inconsistency. The concurrency
   control needed by using several servers to up-
   date the same data warehouse with the real-
   time price quotes will be also provided by a
   relational database. In addition, to restrict the
   data access to different levels of authorization
   and to provide reliable data backup and recov-
   ery, the usage of an SQL relational database
   will be the solution, even today when NoSQL
   databases are in trend.
   In the context of a large volume of CRUD op-
   erations and data requests, SQL will provide
   the low-latency data responses. The most sig-
   nificant example is the signal generator
   server. To see how much can be the time re-
   sponse difference we have compared a repeti-
   tive process to build the arbitrage trading sig-
   nals and an SQL procedure to extract the same
   signals from the database. In the first case, a
   classical procedure to build the trading signals
   was wrote to find better prices for each price
   quote in the database using only repetitive
   code statements.
   For a database including more than 1,000,000
   real-time quotes, the process to build the arbi-
   trage trading signals was longer than two
   minutes on a dedicated server with 2x3GHz
   processor core and 4 GB RAMM Burst. This
   time is too long for reliable trading software.
   In more than two minutes the quotes can be
   significantly changes, meaning those signals
   have a low chance to be executed after the de-
   livery on the brokerage account.
   In the second case it was implemented an SQL
   procedure to build the arbitrage trading sig-
   nals with the same amount of data. The results
   using SQL are conclusive; the time response
   was about 0.25 seconds which is a realistic
   time for the trading software. In Figure 2 is
   presented a sample code for node.js using
   callback functions and SQL to build the arbi-
   trage trading signals.
   Fig. 2. node.js callback functions for the classical arbitrage trading
   signals module.
   40 Informatica Economică vol.22, no. 2/2018
   DOI: 10.12948/issn14531305/22.2.2018.04
   6 API data server
   A special particularity of the arbitrage trading
   systems is the API server that collects real-
   time quotes from each data source. Each ex-
   change has organized an API application in
   order to permit restricted and real-time access
   to the data. To interrogate that API, to receive
   low-latency data and to insert it in the local
   database or warehouse, we need to organize a
   special server process for each exchange. In
   the Figure 3 it is presented the code for a data
   acquisition process using an API. The “Ex-
   change” object represents the Rest API proce-
   dure module distributed by each exchange.
   “bookTickers” is the function from this mod-
   ule that exports the price data. Each exchange
   has its own exporting functions, the name of
   this function and the “data” structure deliv-
   ered by this function are different, and conse-
   quently the code function must to be adapted
   for each particular case.
   The data delivered by each exchange is usual
   a JSON object [6] including different data re-
   lated with the current state of the market. Be-
   cause the structure of this JSON object is dif-
   ferent from an exchange to another, a general
   procedure cannot be built. In the example pre-
   sented, the “setInterval” function is a prede-
   fined node.js function to repeat the process. In
   our case we set to repeat the price quotes im-
   port each minute (60 sec * 1000 = 60000).
   Setting this time is also a procedure depending
   on each exchange. There are exchanges that
   allows one per minute requests, other ex-
   changes will allow interrogations one time at
   two or five minutes and others at ten or larger
   interval. Usually when the request interval is
   not respected, the API can cancel the connec-
   tion with the server and no data will be re-
   ceived from that source. In the example
   above, the “updateQuote” function is a com-
   mon function to update the records with the
   piece quotes, this function will be used for any
   exchange server, once the data is reduced to
   an established data structure.
   Fig. 3. node.js call back functions for the real-time price data
   acquisition server
   Important is the fact that the real-time repeti-
   tive price process is a server process, orga-
   nized for each exchange and depending on the
   particularities of each API data source. The
   update time is also depending on the data
   source. The usage of the node.js server “is
   quite lightweight and efficient” [7] and permit
   a fast and simple implementation of these en-
   tire server processes. When the number of
   these modules is a significant one, using
   node.js we can distribute the repetitive proce-
   dures on more distributed servers, as a meas-
   ure to keep the data delivery time under those
   limits to ensure the low-latency response.
   7 Arbitrage trading signals
   In order to consider why we have to do all
   these technical efforts, in this chapter it will
   be presented a real case of arbitrage trading
   signals with cryptocurrencies. Using theCryp-
   toTrader [8] the next signals were obtained at
   25 May 2018 using 787 cryptocurrencies
   listed on 7 different exchanges.
   Informatica Economică vol.22, no. 2/2018 41
   DOI: 10.12948/issn14531305/22.2.2018.04
   Table 1. Arbitrage trading signals made by theCryptoTrader on
   25.05.2018
   Arbitrage
   trading sig-
   nal
   Buy
   exchange
   Buy price
   Sell
   exchange
   Sell price
   Profit [%]
   BTG-BTC
   Cryptopia
   0.00101454
   Binance
   0.00604100
   495.44
   CTM-BTC
   Cryptopia
   0.00000929
   Binance
   0.00004469
   381.05
   BTC-STEEM
   Poloniex
   0.00029422
   Bittrex
   0.00036467
   23.94
   BTC-SBD
   Bittrex
   0.00023894
   Poloniex
   0.00026653
   11.55
   BTC-OMNI
   Bittrex
   0.00338255
   Poloniex
   0.00367822
   8.74
   BTC-SYS
   Poloniex
   0.00004503
   Bittrex
   0.00004895
   8.71
   BTC-GBP
   Kraken
   5398.10
   Gdax
   5653.55
   4.73
   BTC-NAV
   Bittrex
   0.00012728
   Poloniex
   0.00013294
   4.45
   BTC-DOGE
   Poloniex
   0.00000046
   Bittrex
   0.00000048
   4.35
   BTC-PINK
   Bittrex
   0.00000250
   Poloniex
   0.00000258
   3.20
   As we can see in the Table 1, the profit expec-
   tation is a considerable one. The signals pre-
   sented are the best ten arbitrage trading sig-
   nals from a list of about 186 available signals
   found at that moment by the trading software.
   Any investor who has available money into
   those exchanges can trade that signal in order
   to make profit. Usual the signal is not availa-
   ble for a very long period of time. This is the
   reason to use trading software that has to find
   the trading opportunities and to trade them
   with low-latency orders sent direct to the ex-
   changes.
   The arbitrage trading software permits a very
   fast order delivery for each signal direct to the
   exchanges into the investorʼs accounts. If the
   orders arrived before the significant change of
   the price quotes, they will be executed and the
   profit will be immediately received into the
   investorʼs account. However, time to time,
   some signals are false. Some exchanges de-
   liver price quotes for some cryptocurrency
   pairs but they refuse to execute orders at those
   prices. This is usual the fact when the profit
   expectation of a signal is too high. Even so,
   signals with profit expectation under 999%
   can be well executed in the real practice. The
   limit is imposed by the exchange by the traded
   volume which cannot exceed a specified
   value. Some exchanges limit the trading vol-
   ume depending on the investorʼs profile. How-
   ever, with all that limits, scheduling the exe-
   cution of a signal to be repeated time to time
   as a function of the trading software will per-
   mit the profit for capitals higher than the trad-
   ing limits imposed by each exchange. Usual
   signals with the profit expectation around
   20% are well traded.
   8 Conclusions
   Cryptocurrency market price is not regulated
   by a central bank. Arbitrage trading opportu-
   nities can be found on the free markets in or-
   der to make profit. Because of the large num-
   ber of virtual coins and exchanges, finding
   and computing the arbitrage trading signals
   must to be done by the automated trading soft-
   ware. A crypto dictionary is a must to have in
   the trading software with cryptocurrencies be-
   cause of the important codes differences be-
   tween exchanges. In addition, this module will
   permit all frequent changes in the codification
   and new virtual coins to be managed.
   The technical differences between data
   sources impose an individual server process to
   be organized for each data source. A high
   number of data sources mean a high number
   of server processes. This implies implementa-
   tion of more data acquisition process servers
   distributed in different technical resources.
   Due to the large amount of relational data, re-
   lational database and SQL is a reliable solu-
   tion in order to ensure the low-latency re-
   sponse of the trading software. Arbitrage trad-
   ing software with cryptocurrencies delivers a
   significant profit.
   42 Informatica Economică vol.22, no. 2/2018
   DOI: 10.12948/issn14531305/22.2.2018.04
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   Cristian PĂUNA graduated the Faculty of Cybernetics, Statistics and
   Eco-
   nomic Informatics of the Academy of Economic Studies in 1999 and also
   he
   is a graduate of the Faculty of Aircrafts of the Polytechnic University
   of Bu-
   charest in 1995. He got the title of Master of Science in Special
   Aerospace
   Engineering in 1996. In the last decades he had a sustained activity in
   software
   development industry, especially applied in the financial trading
   domain.
   Based on several original mathematical algorithms he is the author of
   more
   automated trading software. At present he is the Principal Software
   Developer of Algo Trading
   Service Ltd. and he is involved as PhD student in Economic Informatics
   Doctoral School of the
   Academy of Economic Studies.