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RE SE A RCH F E AT U REAlgorithmicTradingGiuseppe Nuti, Mahnoosh Mirghaemi, Philip Treleaven, and Chaiyakorn YingsaereeUK Centre in Financial Computing, LondonTraders increasingly use automated systems for one or more stages ofthe trading process, yet the secrecy and complexity of the algorithmsprompt providing an overview of how these systems work.Advances in telecommunications and computertechnologies during the past decade have created increasingly global, dynamic, and complexfinancial markets, which in turn have stimulatedtrading by computer programs and the rise of systems foralgorithmic trading—also known as AT, algo, or black-box—to automate one or more stages of the trading process.These systems seek to capture fleeting anomalies inmarket prices, profit from statistical patterns within oracross financial markets, optimally execute orders, disguise a trader’s intentions, or detect and exploit rivals’strategies.1 Ultimately, profits drive any algorithmic trading system—whether in the form of cost savings, clientcommissions, or proprietary trading.As the “Electronic Trading” sidebar describes, institutional traders and managers of pension funds, mutualfunds, and hedge funds increasingly deploy algorithmictrading systems. These systems currently handle approximately 50 to 60 percent of all stocks traded in the US andEU.2 High-frequency algorithmic trading accounted for 60percent of US equity volumes in 2009, and it is a majordriver for computing and analytics innovation,3 especiallymachine learning and grid/GPU computing.0018-9162/11/ 26.00 2011 IEEEHowever, algorithmic trading is also of major concernto regulators, as the 6 May 2010 Flash Crash clearly illustrated.3 In this instance, the Dow Jones Industrial Averageplunged about 600 points in 5 minutes, causing a loss of 600 billion in the market value of US corporate stocks.This event revealed the lack of knowledge about highfrequency algorithmic trading and exposed its potentialvulnerability. Protecting against such events requires anin-depth understanding of the trading process.MARKET MICROSTRUCTURETo understand algorithmic trading, it is useful to consider the different types of trading, explore how a trade isexecuted in an exchange, and review the objectives andchallenges.Trade executionDealers generally execute their orders through a sharedcentralized order book that lists the buy and sell ordersfor a specific security ranked by price and order arrivaltime (generally on a first-in, first-out basis). This centralizedorder-driven trading system continuously tries to matchbuy and sell orders.Published by the IEEE Computer SocietyNOVEMBER 201161

RE SE A RCH F E AT U REELECTRONIC TRADINGWithin algorithmic trading, several closely related termsare used that are often confused. These include electronic trading, order-management systems, automatedtrading, systematic trading, and algorithmic trading.Broadly, electronic trading is any method of exchanging securities, stocks, bonds, foreign exchange (currency, Forex— or FX) andderivatives (options, futures, and so on). Within electronic trading,specialized programs bring together buyers and sellers throughelectronic media to create an exchange (such as Nasdaq). Ordermanagement systems facilitate and manage order execution,generally connecting to one or more electronic exchanges. Automated trading systems usually refer to trade execution programsthat automatically submit trades to an exchange.The distinguishing feature of algorithmic (referred to by somepeople as systematic) trading systems is the sophistication of theiranalysis and decision making. Broadly, these systems are deployedfor highly liquid markets and high-frequency trading, such as equities, futures, derivatives, bonds (US Treasuries), and foreign exchange(currencies). The essential characteristic of a highly liquid market isthat there are ready and willing buyers and sellers at all times.Central to these systems’ operation are financial protocols, suchas the Financial Information Exchange (FIX) protocol, a series ofmessaging specifications for the electronic communication oftrade-related messages. FIX messages are formed from severalfields, and each field is a tag-value pairing, separated from the nextfield by a delimiter (similar to XML). The TAG is a string representation of an integer that indicates the field’s meaning. The value is anarray of bytes holding a specific value for that TAG. The FIX protocolalso defines sets of fields comprising a particular message; withinthe sets of fields; some sets will be mandatory and others optional.There are various extensions to FIX, including FIXatdl, the FIX algorithmic trading definition language.As Figure 1 shows, the order book is divided into twoparts: buy orders on the left ranked with the highest priceat the top, and sell orders on the right ranked with thelowest price at the top. Orders are generally listed by priceand time priority, which means most exchanges prioritizeorders based on the best price, and, if two or more ordersare inserted at the same price, priority is given to the firstorder to be inserted. Notable exceptions include the UKthree-month interest rates contracts (known as Short Sterling) on the London International Financial Futures and(a)Quantity5,0008,00010,00015,000BuyOrder Book – ABC Inc.Price99989795Price99100101103SellOptions Exchange (LIFFE). These contracts prioritize boththe arrival time and order size, such that a large order canhave priority over a smaller trade, even if the larger orderwas inserted after the smaller one.Buy orders that a sell order can fully or partially matchare automatically traded. Many variants to the order bookmodel exist. Different exchanges will accept differentorder types, including limit orders, as per our example,market orders, stop-loss orders, and so on. In developingan algorithmic trading system, knowledge of the marketmicrostructure—the detailed process governing how tradesoccur and orders interact in a specific market—is of paramount importance.Recently, both media and financial regulators havefocused their attention on algorithmic trading in a bid toexplain some unusual phenomena in the market. The abnormal drop in US equity markets in the Flash Crash, andsubsequent reversal of the move, is a good example of howmarket microstructure can have a drastic effect—and possibly an undesirable one from a regulator’s viewpoint—onprice dynamics.4 In this case, interconnected exchanges haddifferent market microstructures for the securities in question, and the main exchange (by volume) had specific rulesdesigned to interrupt trading during periods of high volatilityand volume. Smaller satellite exchanges did not have thesame safety features. Automated order-routing programsdirected trades to the smaller exchanges which, because ofa lack of liquidity, caused several share prices to plummet.However, there is still uncertainty about the underlyingcauses of the Flash Crash, especially as one of the theoriesexplored by the US Securities and Exchange Commission’sinvestigation attributed the cause of the crash to an abnormally large order erroneously inserted by a human trader.Although the jury is still out on the crash’s ultimate cause,algorithmic trading systems had an amplifying effect onthe severity and breadth of that day’s equity markets’decline.Trading objectivesBanks are usually thought of as intermediaries, actingas brokers or market makers or servicing clients by 0,0001,00015,000BuyOrder Book – ABC 001,00015,0003,000(b)Figure 1. An example trade order book (a) before matching a trade and (b) after matching a trade. Buy orders, which are generallylisted by price and time priority, are ranked with the highest price at the top, while sell orders are ranked with the lowest price at thetop.62COMPUTER

or selling stocks or bonds. However, most banks and fundsalso engage in proprietary trading on their own accounts.Proprietary trading occurs when an institution activelytrades stocks, bonds, options, commodities, derivatives, orother financial instruments with its own money as opposedto its customers’ money.The type of trading—broker or proprietary—will shapethe algorithmic trading strategy’s design; typically, brokeralgorithmic trading systems seek to minimize the costof trading by optimizing the execution strategy—thatis, minimize market impact cost or time to execution,optimize the price, and so on—whereas proprietary algorithmic trading systems seek to maximize profits againstsome measure of financial risk. In practice, all algorithmstarget profits, either in the form of cost savings or tradingprofit and loss; the difference is in who participates inthe profits—the clients versus the trading firm—and whotakes on the trading risk. Generally, the institution thatis taking the risk also takes the lion’s share of the profits,whereas an institution acting only as intermediary collects a brokerage fee.Current proprietary trading strategies include index arbitrage, statistical arbitrage, merger arbitrage, fundamentalanalysis, volatility arbitrage, and macrotrading.Trading processAn intuitive way to classify algorithmic trading isthrough the separate processes being automated within atrade’s life cycle. As Figure 2 illustrates, algorithmic trading can be used at any stage of the trading process and forFinancial informationNewsFinancial marketsHistorical dataPretrade analysisExchangesTrading signalMarket-makingProprietary tradingECNsTrade executionAgency executionPrincipal tradingFigure 2. Algorithmic trading systems. The figure shows thethree stages of algorithmic trading—pretrade analysis, tradingsignal, and trade execution—and the two major firm types:agency execution and principal trading.various purposes, including market making, spread trading(also known as relative value or basis trading), arbitrage,and macrotrading. Algorithmic trading therefore coversa wide variety of systems. In trade-execution programs,for example, the algorithm might decide aspects such astiming, price, and the order’s quantity splits. Other systemsmight automate the complete trading process.As the “Algorithmic Trading System Components” sidebar describes, the trading process can be split into fourstylized steps: pretrade analysis, trading signal generation,trade execution, and post-trade analysis.ALGORITHMIC TRADING SYSTEM COMPONENTSFigure A shows the major components of an algorithmictrading system and the steps at which they occur.Pretrade analysis includes three mathematical models: The alpha model predicts the future behavior of the financial instruments to trade. The risk model evaluates the levels of exposure/risk associated with the financial instruments. The transaction cost model calculates the (potential) costsassociated with trading the financial instruments.Trading signal generation consists of the portfolio construction model. This model takes as its inputs the results of the alpha,risk, and transaction cost models and decides what portfolio offinancial instruments should be owned going forward and in whatquantities.At trade execution, the execution model executes the trades,making several decisions with constraints on (actual) transactioncosts and trading duration. The most general decision is the trading strategy followed by the venue and order onTradeexecutionData(Real-time/historical; t modelPortfolio igure A. Steps at which the algorithmic trading systemcomponents occur.NOVEMBER 201163

RE SE A RCH F E AT U RETrading signalTrade executionSelected stockPretrade analysisStock PositionBARC.L BUYIAPR.L BUYVOD.L BUY Trading decisionStockBARC.LIAPR.LVOD.L SideBUYBUYBUY Financial informationfrom Reuters and BloombergAmount40,000,00025,000,00010,000,000 Trading planDAY 1BUY BARC.LBUY IAPR.LBUY VOD.L DAY 2 5,000,0003,000,0001,000,000 London StockExchangeFigure 3. Example algorithmic trading system. The pretrade analysis selects a group of stocks to buy and passes this information tothe trading signal component, which determines the quantity of each stock to buy. Finally, the trade execution determines the trading plan by selecting the exchanges and associated quantities.Pretrade analysis is the most common use of algorithmswithin a trading environment. It encompasses any systemthat uses financial data or news to analyze certain properties of an asset. It can be as simple as a method to value acompany, or it can involve state-of-the-art algorithms thatuse artificial intelligence techniques to scan news or Twitter feeds to forecast asset price volatility. Pretrade analysis,as a stand-alone algorithmic trading system, stops short ofgenerating a trade signal. Human traders use the outputto make trading decisions that are most likely based on aselection of trading signals and some discretionary input.The next step in automating the trading process istrading signal generation. Systematic asset managers andtrading institutions often use this level of automation.Human traders can execute the generated signal if theyrequire further discretionary input or—more often thannot—the trade is not executable electronically because ofthe order size versus the market liquidity. This level of automation is generally applicable to all but high-frequencytrading, where complete automation is a prerequisite.The third step is trade order execution. Algorithmic trading can execute trades and place orders in one or moreexchanges. A human trader can make the actual tradingdecision, in which case the algorithm only optimizes theexecution (this is often associated with agency trading).If the trading decision is generated algorithmically, moreoften than not the trade is proprietary.ALGORITHMIC TRADING SYSTEM EXAMPLEThe structure and operation of an actual algorithmictrading system depends on which stages of the tradingprocess are being automated, whether the system is supporting broker or proprietary trading, and what type ofsecurities are being traded—for example, equities, bonds,or currencies.To illustrate an algorithmic trading system’s operation,we use a simple example of a fully automated system for64COMPUTERthe equity market—specifically, from the FTSE (Financial Times and London Stock Exchange) 100 index. In thisexample, the objective is to replicate the FTSE 100 indexby trading a subset of stocks (that is, not the entire index)that provides a similar performance to the index whileminimizing the transaction costs naturally incurred whenrebalancing any replicating portfolio.In pretrade analysis, the system compares and contrasts the historical performances of several index-trackingstrategies to help the user select the strategy that best suitscurrent market conditions. The other components worktoward implementing the chosen replicating strategy. Specifically, the trading signal component selects the stocks forconstructing the benchmark portfolio and determines theweight of each stock in the portfolio.After all of the portfolio composition decisions havebeen made, the trade-execution component determineshow best to execute the trade to minimize market impactand timing risk. In the example in Figure 3, strategies intrading signal generation and trade execution are formulated as an optimization problem with multiobjectiveconstraints. Algorithms for solving these problems mightuse quadratic programming, genetic programming, or particle swarm optimization.PRETRADE ANALYSISPretrade analysis in an algorithmic trading system generally involves both analyzing financial data or news withthe aim of forecasting future price movement or volatilityand generating trading signals when a trading opportunityoccurs. Broadly, the three categories of techniques usedto perform this include fundamental analysis, technicalanalysis, and quantitative analysis.Fundamental analysisFundamental analysis involves a detailed study of related information that might affect asset prices with the aim

of determining the asset’s fair value (or its potential futureprice movements). Relevant information might include theoverall state of two countries’ economies (such as unemployment figures), interest rates, gross domestic product,or national policies. The idea that the current market pricefor an asset is not equivalent to its fair value contradictsthe somewhat controversial efficient-market hypothesis,which suggests that the current price is a reflection of allthe available information.5Fundamental analysis typically generates trading signals when the current asset price differs from the fair valueobtained using discount models for the analysis; ratios,such as the price-to-earnings ratio; or certain fundamentalproperties, such as earnings yielding more than twice theAAA-rated bond yield. In the past few decades, analystsand traders have used advanced mathematical and statistical models from machine learning and computationalstatistics to determine the relationship between the stockprice’s future value and its fundamental quantities with theaim of identifying stocks with a potential to appreciate (ordepreciate) significantly.Technical analysisTechnical analysis aims to predict future price movements based on asset price history and, sometimes, relatedtrading information such as trade volume. By assumingthat the market’s price reflects all relevant information,technical analysis seeks to identify and exploit price movement patterns rather than examine the underlying factorsaffecting asset prices.Many popular technical analysis techniques are basedon the premise that asset prices move in trends, hencetechnical trading systems typically generate entry signals when a new trend is identified and generate exitsignals when a trend ends. Traditionally, they determinetrends by analyzing the continuation patterns (such asthe ascending symmetrical triangle pattern) and reversalpatterns in a chart, as well as trend lines, support, and resistance areas. Technical traders also use indicators suchas moving averages, advance-decline lines and ratios, therelative strength index, and the stochastic oscillator, whichindicates market momentum. Recently, more complexmodeling techniques have included trading rules formulated using genetic programming, or statistical time seriesforecasting methods such as autoregressive fractionallyintegrated moving-average models and neural networks.Quantitative analysisQuantitative analysis treats asset prices as random anduses mathematical and statistical analysis to find a suitablemodel for describing this randomness. This type of analysishas dominated the financial industry in recent decades,forming a solid foundation for portfolio theory, derivativespricing, and risk management.Although fundamental analysis and technical analysisalso use mathematical and statistical methods, they areprimarily concerned with the deterministic relationship between the asset price and related information. In contrast,quantitative analysis focuses on an asset price’s stochasticbehavior. Consequently, quantitative analysis is generallyrelated to the pricing of derivative products, such as optionsand swaps, whose fair value relies on the underlying asset’sstochastic property as well as the analysis of the temporalconvergence and divergence of price movements of pairsand baskets of assets.When used within an algorithmic trading system, quantitative analysis typically generates trading signals whenthe current asset price differs from the asset’s fair value,such as in statistical arbitrage, which attempts to profitfrom pricing inefficiencies. The most commonly used andsimplest case of statistical arbitrage is pairs trading, whichtries to identify divergence of the correlated prices of twostocks.Assessing the complexity of the tradingrules is useful because it highlights t

the algorithmic trading strategy’s design; typically, broker algorithmic trading systems seek to minimize the cost of trading by optimizing the execution strategy—that is, minimize market impact cost or time to execution, optimize the price, and so on—whereas proprietary algo - rithmic

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