Algorithmic trading

Algorithmic trading, also called automated trading,black-box trading, or algo trading, is the use ofelectronic platforms for entering trading orders with analgorithm which executes pre-programmed trading in-structions whose variables may include timing, price, orquantity of the order, or in many cases initiating the orderby automated computer programs. Algorithmic trading iswidely used by investment banks, pension funds, mutualfunds, and other buy-side (investor-driven) institutionaltraders, to divide large trades into several smaller tradesto manage market impact and risk.[1][2] Sell side traders,such as market makers and some hedge funds, provideliquidity to the market, generating and executing ordersautomatically.Many types of algorithmic or automated trading activitiescan be described as HFT. As a result, in February 2012,the Commodity Futures Trading Commission (CFTC)formed a special working group that included academicsand industry experts to advise the CFTC on how best todene HFT.[3][4] HFT strategies utilize computers thatmake elaborate decisions to initiate orders based on in-formation that is received electronically, before humantraders are capable of processing the information they ob-serve. Algorithmic trading and HFT have resulted in adramatic change of the market microstructure, particu-larly in the way liquidity is provided.[5]

Algorithmic trading may be used in any investment strat-egy, including market making, inter-market spreading,arbitrage, or pure speculation (including trend following).The investment decision and implementation may be aug-mented at any stage with algorithmic support or may op-erate completely automatically. One of the main issuesregarding HFT is the diculty in determining how prof-itable it is. A report released in August 2009 by theTABB Group, a nancial services industry research rm,estimated that the 300 securities rms and hedge fundsthat specialize in this type of trading took in a maximumof US$21 billion in prots in 2008,[6] which the authorscalled relatively small and surprisingly modest whencompared to the markets overall trading volume.

A third of all European Union and United States stocktrades in 2006 were driven by automatic programs, oralgorithms, according to Boston-based nancial servicesindustry research and consulting rm Aite Group.[7] Asof 2009, studies suggested HFT rms accounted for 60-73% of all US equity trading volume, with that numberfalling to approximately 50% in 2012.[8][9] In 2006, atthe London Stock Exchange, over 40% of all orders wereentered by algorithmic traders, with 60% predicted for2007. American markets and European markets gener-ally have a higher proportion of algorithmic trades thanother markets, and estimates for 2008 range as high asan 80% proportion in some markets. Foreign exchangemarkets also have active algorithmic trading (about 25%of orders in 2006).[10] Futures markets are consideredfairly easy to integrate into algorithmic trading,[11] withabout 20% of options volume expected to be computer-generated by 2010.[12] Bond markets are moving towardmore access to algorithmic traders.[13]

Algorithmic andHFT have been the subject of much pub-lic debate since the U.S. Securities and Exchange Com-mission and the Commodity Futures Trading Commis-sion said in reports that an algorithmic trade entered bya mutual fund company triggered a wave of selling thatled to the 2010 FlashCrash.[14][15][16][17][18][19][20][21] Thesame reports found HFT strategies may have contributedto subsequent volatility. As a result of these events, theDow Jones Industrial Average suered its second largestintraday point swing ever to that date, though pricesquickly recovered. (See List of largest daily changes inthe Dow Jones Industrial Average.) A July, 2011 reportby the International Organization of Securities Commis-sions (IOSCO), an international body of securities regu-lators, concluded that while algorithms and HFT tech-nology have been used by market participants to man-age their trading and risk, their usage was also clearlya contributing factor in the ash crash event of May 6,2010.[22][23] However, other researchers have reached adierent conclusion. One 2010 study found that HFT didnot signicantly alter trading inventory during the FlashCrash.[24] Some algorithmic trading ahead of index fund

1

2 1 HISTORY

rebalancing transfers prots from investors.[25][26][27]

1 HistoryComputerization of the order ow in nancial marketsbegan in the early 1970s, with some landmarks be-ing the introduction of the New York Stock Exchange'sdesignated order turnaround system (DOT, and laterSuperDOT), which routed orders electronically to theproper trading post, which executed them manually. Theopening automated reporting system (OARS) aided thespecialist in determining the market clearing openingprice (SOR; Smart Order Routing).Program trading is dened by the New York Stock Ex-change as an order to buy or sell 15 or more stocks valuedat over US$1 million total. In practice this means that allprogram trades are entered with the aid of a computer. Inthe 1980s, program trading became widely used in trad-ing between the S&P 500 equity and futures markets.In stock index arbitrage a trader buys (or sells) a stockindex futures contract such as the S&P 500 futures andsells (or buys) a portfolio of up to 500 stocks (canbe a much smaller representative subset) at the NYSEmatched against the futures trade. The program trade atthe NYSE would be pre-programmed into a computer toenter the order automatically into the NYSEs electronicorder routing system at a time when the futures price andthe stock index were far enough apart to make a prot.At about the same time portfolio insurance was designedto create a synthetic put option on a stock portfolio bydynamically trading stock index futures according to acomputer model based on the BlackScholes option pric-ing model.Both strategies, often simply lumped together as pro-gram trading, were blamed by many people (for exam-ple by the Brady report) for exacerbating or even startingthe 1987 stock market crash. Yet the impact of com-puter driven trading on stock market crashes is unclearand widely discussed in the academic community.[28]

Financial markets with fully electronic execution andsimilar electronic communication networks developed inthe late 1980s and 1990s. In the U.S., decimalization,which changed the minimum tick size from 1/16 of a dol-lar (US$0.0625) to US$0.01 per share, may have encour-aged algorithmic trading as it changed the market mi-crostructure by permitting smaller dierences between

the bid and oer prices, decreasing the market-makerstrading advantage, thus increasing market liquidity.This increased market liquidity led to institutional traderssplitting up orders according to computer algorithms sothey could execute orders at a better average price. Theseaverage price benchmarks aremeasured and calculated bycomputers by applying the time-weighted average priceor more usually by the volume-weighted average price.It is over. The trading that existed down the centurieshas died. We have an electronic market today. It is thepresent. It is the future.Robert Greifeld, NASDAQ CEO, April 2011[29]

A further encouragement for the adoption of algorith-mic trading in the nancial markets came in 2001 whena team of IBM researchers published a paper[30] at theInternational Joint Conference on Articial Intelligencewhere they showed that in experimental laboratory ver-sions of the electronic auctions used in the nancial mar-kets, two algorithmic strategies (IBMs own MGD, andHewlett-Packard's ZIP) could consistently out-performhuman traders. MGDwas a modied version of the GDalgorithm invented by Steven Gjerstad & John Dickhautin 1996/7;[31] the ZIP algorithm had been invented at HPby Dave Cli (professor) in 1996.[32] In their paper, theIBM team wrote that the nancial impact of their resultsshowingMGD and ZIP outperforming human traders ex-tquotedbl...might be measured in billions of dollars annu-ally extquotedbl; the IBM paper generated internationalmedia coverage.As more electronic markets opened, other algorithmictrading strategies were introduced. These strategies aremore easily implemented by computers, because ma-chines can react more rapidly to temporary mispric-ing and examine prices from several markets simulta-neously. For example Chameleon (developed by BNPParibas), Stealth (developed by the Deutsche Bank),Sniper and Guerilla (developed by Credit Suisse[33]),arbitrage, statistical arbitrage, trend following, and meanreversion.This type of trading is what is driving the new demandfor LowLatency Proximity Hosting andGlobal ExchangeConnectivity. It is imperative to understand what latencyis when putting together a strategy for electronic trad-ing. Latency refers to the delay between the transmis-sion of information from a source and the reception ofthe information at a destination. Latency has as a lower

2.3 Pairs trading 3

bound determined by the speed of light; this correspondsto about 3.3 milliseconds per 1,000 kilometers of opticalbre. Any signal regenerating or routing equipment in-troduces greater latency than this lightspeed baseline.

2 Strategies

2.1 Trading ahead of index fund rebalanc-ing

Most retirement savings, such as private pension funds or401(k) and individual retirement accounts in the US, areinvested in mutual funds, the most popular of which areindex funds which must periodically rebalance or ad-just their portfolio to match the new prices and marketcapitalization of the underlying securities in the stock orother index that they track.[34][35] Prots are transferredfrom investors to algorithmic traders, estimated to be atleast 21 to 28 basis points annually for S&P 500 indexfunds, and at least 38 to 77 basis points per year forRussell 2000 funds.[26] John Montgomery of BridgewayCapital Management says that the resulting poor in-vestor returns from trading ahead of mutual funds is theelephant in the room that shockingly, people are nottalking about.[27]

2.2 Trend following

Trend following is an investment strategy that tries to takeadvantage of long-term, medium-term, and short-termmovements that sometimes occur in various markets.The strategy aims to take advantage of a market trendon both sides, going long (buying) or short (selling) in amarket in an attempt to prot from the ups and downs ofthe stock or futures markets. Traders who use this ap-proach can use current market price calculation, movingaverages and channel breakouts to determine the gen-eral direction of the market and to generate trade signals.Traders who subscribe to a trend following strategy donot aim to forecast or predict specic price levels; theyinitiate a trade when a trend appears to have started, andexit the trade once the trend appears to have ended.[36]

2.3 Pairs trading

Pairs trading or pair trading is a long-short, ideallymarket-neutral strategy enabling traders to prot fromtransient discrepancies in relative value of close substi-tutes. Unlike in the case of classic arbitrage, in caseof pairs trading, the law of one price cannot guaranteeconvergence of prices. This is especially true when thestrategy is applied to individual stocks - these imperfectsubstitutes can in fact diverge indenitely. In theory thelong-short nature of the strategy should make it work re-gardless of the stock market direction. In practice, ex-ecution risk, persistent and large divergences, as well asa decline in volatility can make this strategy unprotablefor long periods of time (e.g. 2004-7). It belongs to widercategories of statistical arbitrage, convergence trading,and relative value strategies.[37]

2.4 Delta-neutral strategies

In nance, delta-neutral describes a portfolio of relatednancial securities, in which the portfolio value remainsunchanged due to small changes in the value of the un-derlying security. Such a portfolio typically contains op-tions and their corresponding underlying securities suchthat positive and negative delta components oset, result-ing in the portfolios value being relatively insensitive tochanges in the value of the underlying security.

2.5 Arbitrage

In economics and nance, arbitrage /rbtr/ is thepractice of taking advantage of a price dierence be-tween two or more markets: striking a combination ofmatching deals that capitalize upon the imbalance, theprot being the dierence between the market prices.When used by academics, an arbitrage is a transactionthat involves no negative cash ow at any probabilistic ortemporal state and a positive cash ow in at least one state;in simple terms, it is the possibility of a risk-free prot atzero cost. Example: One of the most popular Arbitragetrading opportunities is played with the S&P futures andthe S&P 500 stocks. During most trading days these twowill develop disparity in the pricing between the two ofthem. This happens when the price of the stocks whichare mostly traded on the NYSE and NASDAQ marketseither get ahead or behind the S&P Futures which aretraded in the CME market.

4 2 STRATEGIES

2.5.1 Conditions for arbitrage

Arbitrage is possible when one of three conditions is met:

1. The same asset does not trade at the same priceon all markets (the extquotedbllaw of one price ex-tquotedbl is temporarily violated).

2. Two assets with identical cash ows do not trade atthe same price.

3. An asset with a known price in the future does nottoday trade at its future price discounted at the risk-free interest rate (or, the asset does not have neg-ligible costs of storage; as such, for example, thiscondition holds for grain but not for securities).

Arbitrage is not simply the act of buying a product in onemarket and selling it in another for a higher price at somelater time. The long and short transactions should ideallyoccur simultaneously to minimize the exposure to mar-ket risk, or the risk that prices may change on one mar-ket before both transactions are complete. In practicalterms, this is generally only possible with securities andnancial products which can be traded electronically, andeven then, when rst leg(s) of the trade is executed, theprices in the other legs may have worsened, locking in aguaranteed loss. Missing one of the legs of the trade (andsubsequently having to open it at a worse price) is called'execution risk' or more specically 'leg-in and leg-outrisk'.[note 1]

In the simplest example, any good sold in one marketshould sell for the same price in another. Tradersmay, forexample, nd that the price of wheat is lower in agricul-tural regions than in cities, purchase the good, and trans-port it to another region to sell at a higher price. Thistype of price arbitrage is the most common, but this sim-ple example ignores the cost of transport, storage, risk,and other factors. True arbitrage requires that there beno market risk involved. Where securities are traded onmore than one exchange, arbitrage occurs by simultane-ously buying in one and selling on the other. Such si-multaneous execution, if perfect substitutes are involved,minimizes capital requirements, but in practice never cre-ates a self-nancing (free) position, as many sources in-correctly assume following the theory. As long as there issome dierence in the market value and riskiness of thetwo legs, capital would have to be put up in order to carrythe long-short arbitrage position.

See rational pricing, particularly arbitrage mechanics, forfurther discussion.

2.6 Mean reversion

Mean reversion is a mathematical methodology some-times used for stock investing, but it can be applied toother processes. In general terms the idea is that botha stocks high and low prices are temporary, and that astocks price tends to have an average price over time.An example of a mean-reverting process is the Ornstein-Uhlenbeck stochastic equation.Mean reversion involves rst identifying the trading rangefor a stock, and then computing the average price usinganalytical techniques as it relates to assets, earnings, etc.When the current market price is less than the averageprice, the stock is considered attractive for purchase, withthe expectation that the price will rise. When the currentmarket price is above the average price, the market priceis expected to fall. In other words, deviations from theaverage price are expected to revert to the average.The Standard deviation of the most recent prices (e.g.,the last 20) is often used as a buy or sell indicator.Stock reporting services (such as Yahoo! Finance, MSInvestor, Morningstar, etc.), commonly oer moving av-erages for periods such as 50 and 100 days. While report-ing services provide the averages, identifying the high andlow prices for the study period is still necessary.

2.7 Scalping

Scalping (trading) is a method of arbitrage of small pricegaps created by the bid-ask spread. Scalpers attempt toact like traditional market makers or specialists. Tomakethe spreadmeans to buy at the bid price and sell at the askprice, to gain the bid/ask dierence. This procedure al-lows for prot even when the bid and ask do not moveat all, as long as there are traders who are willing to takemarket prices. It normally involves establishing and liqui-dating a position quickly, usually within minutes or evenseconds.The role of a scalper is actually the role of market mak-ers or specialists who are to maintain the liquidity andorder ow of a product of a market. A market makeris basically a specialized scalper. The volume a marketmaker trades are many times more than the average in-

5dividual scalpers. A market maker has a sophisticatedtrading system to monitor trading activity. However, amarket maker is bound by strict exchange rules while theindividual trader is not. For instance, NASDAQ requireseach market maker to post at least one bid and one askat some price level, so as to maintain a two-sided marketfor each stock represented.

2.8 Transaction cost reductionMost strategies referred to as algorithmic trading (aswell as algorithmic liquidity-seeking) fall into the cost-reduction category. The basic idea is to break down alarge order into small orders and place them in the mar-ket over time. The choice of algorithm depends on vari-ous factors, with the most important being volatility andliquidity of the stock. For example, for a highly liquidstock, matching a certain percentage of the overall or-ders of stock (called volume inline algorithms) is usuallya good strategy, but for a highly illiquid stock, algorithmstry to match every order that has a favorable price (calledliquidity-seeking algorithms).The success of these strategies is usually measured bycomparing the average price at which the entire orderwas executed with the average price achieved through abenchmark execution for the same duration. Usually, thevolume-weighted average price is used as the benchmark.At times, the execution price is also compared with theprice of the instrument at the time of placing the order.A special class of these algorithms attempts to detect al-gorithmic or iceberg orders on the other side (i.e. if youare trying to buy, the algorithm will try to detect ordersfor the sell side). These algorithms are called sning al-gorithms. A typical example is Stealth.Some examples of algorithms are TWAP, VWAP, Imple-mentation shortfall, POV, Display size, Liquidity seeker,and Stealth. Modern algorithms are often optimally con-structed via either static or dynamic programming .[38]

2.9 Strategies that only pertain to darkpools

Recently, HFT, which comprises a broad set of buy-side as well as market making sell side traders, hasbecome more prominent.[39] These algorithms or tech-niques are commonly given names such as Stealth (de-veloped by the Deutsche Bank), Iceberg, Dagger,

Guerrilla, Sniper, BASOR (developed by QuodFinancial) and Snier.[40] Dark pools are alternativeelectronic stock exchanges where trading takes placeanonymously, with most orders hidden or iceberged.[41]Gamers or sharks sni out large orders by pingingsmall market orders to buy and sell. When several smallorders are lled the sharks may have discovered the pres-ence of a large iceberged order.Now its an arms race, said Andrew Lo, director of theMassachusetts Institute of Technologys Laboratory forFinancial Engineering. Everyone is building more so-phisticated algorithms, and the more competition exists,the smaller the prots.[42]

3 High-frequency trading

Main article: High-frequency trading

In 2009, high frequency trading strategies total assetsunder management were $141 billion. By comparison,the overall hedge fund industry peaked at $1.93 tril-lion in 2007.[43] The HFT strategy was rst made suc-cessful by Renaissance Technologies.[44] High-frequencyfunds started to become especially popular in 2007 and2008.[43] Many HFT rms are market makers and pro-vide liquidity to the market, which has lowered volatilityand helped narrow Bid-oer spreads making trading andinvesting cheaper for other market participants.[43][45][46]HFT has been a subject of intense public focus sincethe U.S. Securities and Exchange Commission and theCommodity Futures Trading Commission stated thatboth algorithmic and HFT contributed to volatility in the2010 Flash Crash. Among the major U.S. high frequencytrading rms are Chicago Trading, Virtu Financial, Tim-ber Hill, ATD, GETCO, and Citadel LLC. [47]

There are four key categories of HFT strategies: market-making based on order ow, market-making based ontick data information, event arbitrage and statistical ar-bitrage. All portfolio-allocation decisions are made bycomputerized quantitative models. The success of HFTstrategies is largely driven by their ability to simultane-ously process volumes of information, something ordi-nary human traders cannot do.

6 5 STRATEGY IMPLEMENTATION

3.1 Market making

Market making is a set of HFTs strategies that involvesplacing a limit order to sell (or oer) above the currentmarket price or a buy limit order (or bid) below the cur-rent price to benet from the bid-ask spread. AutomatedTrading Desk, which was bought by Citigroup in July2007, has been an active market maker, accounting forabout 6% of total volume on both NASDAQ and the NewYork Stock Exchange.[48]

3.2 Statistical arbitrage

Another set of HFT strategies is classical arbitrage strat-egy might involve several securities such as coveredinterest rate parity in the foreign exchange market whichgives a relation between the prices of a domestic bond, abond denominated in a foreign currency, the spot price ofthe currency, and the price of a forward contract on thecurrency. If the market prices are suciently dierentfrom those implied in the model to cover transaction costthen four transactions can be made to guarantee a risk-free prot. HFT allows similar arbitrages using modelsof greater complexity involving many more than 4 secu-rities. The TABB Group estimates that annual aggregateprots of low latency arbitrage strategies currently ex-ceed US$21 billion.[8]

A wide range of statistical arbitrage strategies have beendeveloped whereby trading decisions are made on thebasis of deviations from statistically signicant relation-ships. Like market-making strategies, statistical arbi-trage can be applied in all asset classes.

3.3 Event arbitrage

A subset of risk, merger, convertible, or distressed se-curities arbitrage that counts on a specic event, such asa contract signing, regulatory approval, judicial decision,etc., to change the price or rate relationship of two ormore nancial instruments and permit the arbitrageur toearn a prot.[49]

Merger arbitrage also called risk arbitrage would be anexample of this. Merger arbitrage generally consists ofbuying the stock of a company that is the target of atakeover while shorting the stock of the acquiring com-pany. Usually the market price of the target companyis less than the price oered by the acquiring company.

The spread between these two prices depends mainly onthe probability and the timing of the takeover being com-pleted as well as the prevailing level of interest rates. Thebet in a merger arbitrage is that such a spread will even-tually be zero, if and when the takeover is completed.The risk is that the deal breaks and the spread massivelywidens.

4 Low-latency tradingHFT is often confused with low-latency trading that usescomputers that execute trades within microseconds, orwith extremely low latency in the jargon of the trade.Low-latency traders depend on ultra-low latency net-works. They prot by providing information, such ascompeting bids and oers, to their algorithms microsec-onds faster than their competitors.[8] The revolutionaryadvance in speed has led to the need for rms to havea real-time, colocated trading platform to benet fromimplementing high-frequency strategies.[8] Strategies areconstantly altered to reect the subtle changes in the mar-ket as well as to combat the threat of the strategy be-ing reverse engineered by competitors. There is also avery strong pressure to continuously add features or im-provements to a particular algorithm, such as client spe-cic modications and various performance enhancingchanges (regarding benchmark trading performance, costreduction for the trading rm or a range of other imple-mentations). This is due to the evolutionary nature of al-gorithmic trading strategies they must be able to adaptand trade intelligently, regardless of market conditions,which involves being exible enough to withstand a vastarray of market scenarios. As a result, a signicant pro-portion of net revenue from rms is spent on the R&D ofthese autonomous trading systems.[8]

5 Strategy implementationMost of the algorithmic strategies are implemented us-ing modern programming languages, although some stillimplement strategies designed in spreadsheets. Increas-ingly, the algorithms used by large brokerages and as-set managers are written to the FIX Protocols Algorith-mic Trading Denition Language (FIXatdl), which al-lows rms receiving orders to specify exactly how theirelectronic orders should be expressed. Orders built usingFIXatdl can then be transmitted from traders systems via

6.2 Concerns 7

the FIX Protocol.[50] Basic models can rely on as little asa linear regression, while more complex game-theoreticand pattern recognition[51] or predictive models can alsobe used to initiate trading. Neural networks and geneticprogramming have been used to create these models.

6 Issues and developmentsAlgorithmic trading has been shown to substantially im-prove market liquidity[52] among other benets. How-ever, improvements in productivity brought by algorith-mic trading have been opposed by human brokers andtraders facing sti competition from computers.

6.1 Cyborg FinanceTechnological advances in nance, particularly thoserelating to algorithmic trading, has increased nancialspeed, connectivity, reach, and complexity while simulta-neously reducing its humanity. Computers running soft-ware based on complex algorithms have replaced humansin many functions in the nancial industry. Finance isessentially becoming an industry where machines andhumans share the dominant roles transforming mod-ern nance into what one scholar has called, cyborgnance.[53]

6.2 ConcernsWhile many experts laud the benets of innovation incomputerized algorithmic trading, other analysts have ex-pressed concern with specic aspects of computerizedtrading.

The downside with these systems is theirblack box-ness, Mr. Williams said. Tradershave intuitive senses of how the world works.But with these systems you pour in a bunch ofnumbers, and something comes out the otherend, and its not always intuitive or clear whythe black box latched onto certain data or rela-tionships. [42]

The Financial Services Authority hasbeen keeping a watchful eye on the develop-ment of black box trading. In its annual report

the regulator remarked on the great benets ofeciency that new technology is bringing tothe market. But it also pointed out that greaterreliance on sophisticated technology and mod-elling brings with it a greater risk that systemsfailure can result in business interruption. [54]

UK Treasury minister Lord Myners haswarned that companies could become theplaythings of speculators because of auto-matic high-frequency trading. Lord Mynerssaid the process risked destroying the relation-ship between an investor and a company.[55]

Other issues include the technical problem of latency orthe delay in getting quotes to traders,[56] security and thepossibility of a complete system breakdown leading to amarket crash.[57]

Goldman spends tens of millions of dol-lars on this stu. They havemore people work-ing in their technology area than people on thetrading desk...The nature of the markets haschanged dramatically. [58]

On August 1, 2012 Knight Capital Group experienceda technology issue in their automated trading system,[59]causing a loss of $440 million.

This issue was related to Knights installa-tion of trading software and resulted in Knightsending numerous erroneous orders in NYSE-listed securities into the market. This softwarehas been removed from the companys systems.[..] Clients were not negatively aected by theerroneous orders, and the software issue waslimited to the routing of certain listed stocksto NYSE. Knight has traded out of its entireerroneous trade position, which has resulted ina realized pre-tax loss of approximately $440million.

Some have claimed that algorithmic trading and HFTcontributed to volatility during the May 6, 2010 FlashCrash,[14][16] when the Dow Jones Industrial Averageplunged about 600 points only to recover those losseswithin minutes. At the time, it was the second largestpoint swing, 1,010.14 points, and the biggest one-daypoint decline, 998.5 points, on an intraday basis in DowJones Industrial Average history.[60]

8 7 TECHNICAL DESIGN

6.3 Recent developmentsFinancial market news is now being formatted by rmssuch as Need To Know News, Thomson Reuters, DowJones, and Bloomberg, to be read and traded on via algo-rithms.

Computers are now being used to gener-ate news stories about company earnings re-sults or economic statistics as they are re-leased. And this almost instantaneous infor-mation forms a direct feed into other comput-ers which trade on the news.[61]

The algorithms do not simply trade on simple news sto-ries but also interpret more dicult to understand news.Some rms are also attempting to automatically assignsentiment (deciding if the news is good or bad) to newsstories so that automated trading can work directly on thenews story.[62]

Increasingly, people are looking at allforms of news and building their own indica-tors around it in a semi-structured way, as theyconstantly seek out new trading advantagessaid Rob Passarella, global director of strat-egy at Dow Jones Enterprise Media Group.His rm provides both a low latency news feedand news analytics for traders. Passarella alsopointed to new academic research being con-ducted on the degree to which frequent Googlesearches on various stocks can serve as trad-ing indicators, the potential impact of variousphrases and words that may appear in Securi-ties and Exchange Commission statements andthe latest wave of online communities devotedto stock trading topics.[62]

Markets are by their very nature conver-sations, having grown out of coee houses andtaverns, he said. So the way conversationsget created in a digital society will be used toconvert news into trades, as well, Passarellasaid.[62]

There is a real interest in moving the pro-cess of interpreting news from the humansto the machines says Kirsti Suutari, globalbusiness manager of algorithmic trading at

Reuters. More of our customers are ndingways to use news content to make money.[61]

An example of the importance of news reporting speed toalgorithmic traders was an advertising campaign by DowJones (appearances included pageW15 of theWall StreetJournal, on March 1, 2008) claiming that their servicehad beaten other news services by 2 seconds in reportingan interest rate cut by the Bank of England.In July 2007, Citigroup, which had already developed itsown trading algorithms, paid $680million for AutomatedTrading Desk, a 19-year-old rm that trades about 200million shares a day.[63] Citigroup had previously boughtLava Trading and OnTrade Inc.In late 2010, The UK Government Oce for Science ini-tiated a Foresight project investigating the future of com-puter trading in the nancial markets,[64] led by DameClara Furse, ex-CEO of the London Stock Exchange andin September 2011 the project published its initial nd-ings in the form of a three-chapter working paper avail-able in three languages, along with 16 additional papersthat provide supporting evidence.[65] All of these nd-ings are authored or co-authored by leading academicsand practitioners, and were subjected to anonymous peer-review. The Foresight projects nal report noted thatthough HFT created the potential for periodic liquidity,it found that HFTwas benecial to liquidity and price for-mation and helped to lower transaction costs. [66][67]

In September 2011, RYBN has launched ADM8,[68] anopen source Trading Bot prototype, already active on thenancial markets.

7 Technical designThe technical designs of such systems are not standard-ized. Conceptually, the design can be divided into logicalunits:[69]

1. The data stream unit (the part of the systems thatreceives data (e.g. quotes, news) from externalsources)

2. The decision or strategy unit3. The execution unit

With the wide use of social networks, some systems im-plement scanning or screening technologies to read posts

9of users extracting human sentiment and inuence thetrading strategies. [70]

8 Eects

Though its development may have been prompted by de-creasing trade sizes caused by decimalization, algorith-mic trading has reduced trade sizes further. Jobs oncedone by human traders are being switched to comput-ers. The speeds of computer connections, measured inmilliseconds and even microseconds, have become veryimportant.[71][72]

More fully automated markets such as NASDAQ, Di-rect Edge and BATS, in the US, have gained marketshare from less automated markets such as the NYSE.Economies of scale in electronic trading have contributedto lowering commissions and trade processing fees, andcontributed to international mergers and consolidation ofnancial exchanges.Competition is developing among exchanges for thefastest processing times for completing trades. For exam-ple, in June 2007, the London Stock Exchange launcheda new system called TradElect that promises an aver-age 10 millisecond turnaround time from placing an or-der to nal conrmation and can process 3,000 ordersper second.[73] Since then, competitive exchanges havecontinued to reduce latency with turnaround times of 3milliseconds available. This is of great importance tohigh-frequency traders, because they have to attempt topinpoint the consistent and probable performance rangesof given nancial instruments. These professionals areoften dealing in versions of stock index funds like theE-mini S&Ps, because they seek consistency and risk-mitigation along with top performance. They must l-ter market data to work into their software programmingso that there is the lowest latency and highest liquidityat the time for placing stop-losses and/or taking prof-its. With high volatility in these markets, this becomes acomplex and potentially nerve-wracking endeavor, wherea small mistake can lead to a large loss. Absolute fre-quency data play into the development of the traders pre-programmed instructions.[74]

Spending on computers and software in the nancial in-dustry increased to $26.4 billion in 2005.[1]

9 Communication standardsAlgorithmic trades require communicating considerablymore parameters than traditional market and limit or-ders. A trader on one end (the extquotedblbuy side ex-tquotedbl) must enable their trading system (often calledan extquotedblorder management system extquotedbl orextquotedblexecution management system extquotedbl)to understand a constantly proliferating ow of new al-gorithmic order types. The R&D and other costs to con-struct complex new algorithmic orders types, along withthe execution infrastructure, and marketing costs to dis-tribute them, are fairly substantial. What was needed wasa way that marketers (the extquotedblsell side extquot-edbl) could express algo orders electronically such thatbuy-side traders could just drop the new order types intotheir system and be ready to trade them without constantcoding custom new order entry screens each time.FIX Protocol LTD http://www.fixprotocol.org is a tradeassociation that publishes free, open standards in thesecurities trading area. The FIX language was origi-nally created by Fidelity Investments, and the associa-tion Members include virtually all large and many mid-sized and smaller broker dealers, money center banks, in-stitutional investors, mutual funds, etc. This institutiondominates standard setting in the pretrade and trade ar-eas of security transactions. In 2006-2007 several mem-bers got together and published a draft XML standardfor expressing algorithmic order types. The standardis called FIX Algorithmic Trading Denition Language(FIXatdl).[75] The rst version of this standard, 1.0 wasnot widely adopted due to limitations in the specication,but the second version, 1.1 (released in March 2010) isexpected to achieve broad adoption and in the processdramatically reduce time-to-market and costs associatedwith distributing new algorithms.

10 AlgorithmsSome common trading algorithms include:[76][77]

List of algorithms

-TILT- 2-step 2200 BTUs

10 10 ALGORITHMS

4-Wheel Drive 60-Step The Abyss Algo Mountains Almost Human Apollo Asimovs Nightmare The Awakening Back to School The Bagman Bankers Ball Bankers Blitz BAT Cave BAT Code BAT Discovery BAT Dribble BAT Fence BAT Hats BAT Horizon BAT Lego Bat Pig Batastic Batsicles BBOBomber The Beach Beyond the Blue Wall Bid Stuer The Bird Blast This Blockhead

Blotter Blue Bandsaw The Blue Bidder Blue Blaster Blue Blind Blue Blocker Blue Flicker Blue Ice The Blue Pig Blue Stubble Blue Thicket Blue Wave Blue Zinger Bluegrass Boston Buck'r Boston Shue Boston Zapper Bot Town Bot Wars Botastic BOTvsBOT The Bridge Bristles Broken BAT Broken Highway Broken SKY Broken Zanti Buckaroo Banzai The Bug The Bunker

11

CancelBot CancelBot Jr. Cancelled Check Cannons Cannons 2 The Carnival Castle Wall Changing Tide Cherokee Nation The Circus Comes to Town City Of BATS City Under Siege The Click Clockwork Orange Clogged Artery Continental Crust Control Tower Crazy Eyes The Crown Danger Will Robinson Day Trippin The Dead Pool The Deep The Deer Hunter Deer vs. Bat Depth Ping Detox Dinosaur Hunt Dirty Glaciers Don't Tread On Me

Double Dip Double Pole, Double Throw The Drowning Early Discovery Early Riser Enchanted Forest EPIC Zapper Eraser Head Faster Zapper Flag Repeater The Flood Flutter Focus The Follower Fred Frog Pond From Above From Below Full Moon Rising Fuzzy Orange Gold Finger Gone Fishing Good Luck Human The Green Flash The Green Hornet Ground Strike Hairline Heart Attack High EQ High Tide

12 10 ALGORITHMS

Hummingbird I'm A PC Inner Chart Jump Shot Junior Just Ask The Knife Landmine Life and Death Lightning Strike Living On The Edge Local Dump Low Tide Made in America Mainframe Mannie, Moe and Jack Marco Polo Market Share Master Blaster Maxy-Zapper Meteors The Monster Monster Mash Morning Zanti The Morphing NARA Zapper No Joy No Reason Obstructus Maximus One Ping Only

Orange Crush Orange Marmalade The Outer Limits Pacic Rim The Palace Penny Pincher The Pepsi Challenge Periscopes Petting Zoo Pinger Plate Shift Platform Drilling The Port Power Line Power Tower Puzzle Pieces The Quota Quota Catcher Quota Machine The Raceway Racing Stripe Railway The Ramp Red Sky at Night Red Tide Redline Repeater Wars Robot Fight Robot Hunting Rock Star

13

Rollerball The Ron Rougue Wave The Rover Runawa S.O.S. Scissors Scoaw Sea Level Sea of BATS Sea of BATS Star The Search Search Bots The Seekers Seen Too Much Seizure Shades of Blue The Shredder Simple BAT Single Track Social Buttery Solar Flare Soylent Blue The Spartan Spastic BAT Street Lamps Stubby Triangles Sunshowers T1 Killer Take Two

Tank Tracks

Teslas Cathedral

Test Pattern

Them

tHigh EQ

The Thin Blue Line

Thin Blue Line

Things that make you go 'hmmmm'

The Tickler

To The Moon, Alice!

Trade Dominator

Twilight

Wading Pool

Wake Up Call

Warp 15

Waste Pool

When the Levee Breaks

Wild Thing

Wild Thing Edge

Yellow Picket Fence

Yellow Snow

You Don't Know Jack

Zanti Mahem

The Zanti Mist

Zapata

Zappa Street

Zapper Clone

Zero to Sixty

14 13 REFERENCES

11 See also FIXatdl Alternative trading system Articial intelligence Complex event processing Electronic trading platform 2010 Flash Crash High-frequency trading Mirror trading Technical analysis

12 Notes[1] As an arbitrage consists of at least two trades, the

metaphor is of putting on a pair of pants, one leg (trade)at a time. The risk that one trade (leg) fails to execute isthus 'leg risk'.

13 References[1] Moving markets Shifts in trading patterns are making

technology ever more important, The Economist, Feb 2,2006

[2] Algorithmic Trading: Hype or Reality?

[3] CFTC Panel Urges Broad Denition of High-FrequencyTrading, Bloomberg News, June 20, 2012

[4] Futures Trading Commission Votes to Establish a NewSubcommittee of the Technology Advisory Committee(TAC) to focus on High Frequency Trading, February 9,2012, Commodity Futures Trading Commission

[5] Easley, D., M. Lpez de Prado, M. O'Hara: The Mi-crostructure of the 'Flash Crash': Flow Toxicity, Liquid-ity Crashes and the Probability of Informed Trading, TheJournal of Portfolio Management, Vol. 37, No. 2, pp.118128, Winter, 2011, SSRN 1695041

[6] Opalesque (4 August 2009). Opalesque Exclusive: High-frequency trading under the microscope.

[7]

[8] Rob Iati, The Real Story of Trading Software Espionage,AdvancedTrading.com, July 10, 2009

[9] Times Topics: High-Frequency Trading, The New YorkTimes, December 20, 2012

[10] A London Hedge Fund That Opts for Engineers, NotM.B.A.s by Heather Timmons, August 18, 2006

[11] Looking for options Derivatives drive the battle of the ex-changes, April 15, 2007, Economist.com

[12] Algorithmic trading, Ahead of the tape, The Economist383 (June 23, 2007), June 21, 2007: 85

[13] MTS to mull bond access, The Wall Street Journal Eu-rope, April 18, 2007: 21

[14] Lauricella, Tom (2 Oct 2010). How a Trading AlgorithmWent Awry. The Wall Street Journal.

[15] Mehta, Nina (1 Oct 2010). Automatic Futures TradeDrove May Stock Crash, Report Says. Bloomberg.

[16] Bowley, Graham (1 Oct 2010). Lone $4.1 Billion SaleLed to 'Flash Crash' in May. The New York Times.

[17] Spicer, Jonathan (1 Oct 2010). Single U.S. trade helpedspark Mays ash crash. Reuters.

[18] Goldfarb, Zachary (1 Oct 2010). Report examinesMays'ash crash,' expresses concern over high-speed trading.Washington Post.

[19] Popper, Nathaniel (1 Oct 2010). extquotedbl$4.1-billiontrade set o Wall Street 'ash crash,' report nds. LosAngeles Times.

[20] Younglai, Rachelle (5 Oct 2010). U.S. probes com-puter algorithms after ash crash extquotedbl extquot-edbl. Reuters.

[21] Spicer, Jonathan (15 Oct 2010). Special report: Glob-ally, the ash crash is no ash in the pan. Reuters.

[22] TECHNICAL COMMITTEE OF THE INTERNA-TIONAL ORGANIZATION OF SECURITIES COM-MISSIONS (July 2011), Regulatory Issues Raised by theImpact of Technological Changes onMarket Integrity andEciency, IOSCO Technical Committee, retrieved July12, 2011

[23] Huw Jones (July 7, 2011). Ultra fast trading needs curbs-global regulators. Reuters. Retrieved July 12, 2011.

[24] Tuzun, Tugkan (May 5, 2014), The Flash Crash: The Im-pact of High Frequency Trading on an Electronic Market(Social Science Research Network)

15

[25] Amery, Paul (November 11, 2010). Know Your En-emy. IndexUniverse.eu. Retrieved 26 March 2013.

[26] Petajisto, Antti (2011). The index premium and its hid-den cost for index funds. Journal of Empirical Finance18: 271288. doi:10.1016/j.jempn.2010.10.002. Re-trieved 26 March 2013.

[27] Rekenthaler, John (FebruaryMarch 2011). TheWeighting Game, and Other Puzzles of Indexing. Morn-ingstar Advisor. pp. 5256 [56]. Retrieved 26 March2013.

[28] Sornette (2003), Critical Market Crashes

[29] BOWLEY, GRAHAM (April 25, 2011). Preserving aMarket Symbol. The New York Times. Retrieved 7 Au-gust 2014.

[30] Agent-Human Interactions in the Continuous DoubleAuction, IBM T.J.Watson Research Center, August 2001

[31] Price Formation in Double Auctions, Games and Eco-nomic Behavior, 22(1):1-29, S. Gjerstad and J. Dickhaut,January 1998

[32] Minimal Intelligence Agents for Bargaining Behavioursin Market-Based Environments, Hewlett-Packard Labo-ratories Technical Report 97-91 extquotedbl, D. Cli, Au-gust 1997

[33] Algo Arms Race Has a Leader For Now, NYU SternSchool of Business, December 18, 2006

[34] High-Frequency Firms Tripled Trades in Stock Rout,Wedbush Says. Bloomberg/Financial Advisor. August12, 2011. Retrieved 26 March 2013.

[35] Siedle, Ted (March 25, 2013). Americans Want MoreSocial Security, Not Less. Forbes. Retrieved 26 March2013.

[36] Anatomy of a Trend Following Trade - The Short Exit

[37] The Application of Pairs Trading to Energy Futures Mar-kets

[38] Jackie Shen (2013), A Pre-Trade Algorithmic TradingModel under Given Volume Measures and Generic PriceDynamics (GVM-GPD), available at SSRN or DOI.

[39] Wilmott, Paul (July 29, 2009). Hurrying into the NextPanic. New York Times. p. A19. Retrieved July 29,2009.

[40] Trading with the help of 'guerrillas and 'snipers extquot-edbl, Financial Times, March 19, 2007

[41] Rob Curren, Watch Out for Sharks in Dark Pools, TheWall Street Journal, August 19, 2008, p. c5. Available atWSJ Blogs retrieved August 19, 2008

[42] Articial intelligence applied heavily to picking stocks byCharles Duhigg, November 23, 2006

[43] Georey Rogow, Rise of the (Market) Machines, TheWall Street Journal, June 19, 2009

[44] High-frequency nance and the hedge fund category ofthe future

[45] Hendershott, Terrence, Charles M. Jones, and Albert J.Menkveld. Does Algorithmic Trading Improve Liquid-ity? extquotedbl, Journal of Finance (forthcoming), 2010

[46] Jovanovic, Boyan, and Albert J. Menkveld. Middlemenin Securities Markets, working paper, 2010

[47] James E. Hollis (Sep 2013). HFT: Boon? Or ImpendingDisaster? extquotedbl. Cutter Associates. Retrieved July1, 2014.

[48] Citigroup to expand electronic trading capabilities bybuying Automated Trading Desk, The Associated Press(International Herald Tribune), July 2, 2007, retrievedJuly 4, 2007

[49] Event Arb Denition Amex.com, September 4th 2010

[50] FIXatdl - An Emerging Standard, FIXGlobal, December2009

[51] Preis, T.; Paul, W.; Schneider, J. J. (2008), Fluctuationpatterns in high-frequency nancial asset returns, EPL(Europhysics Letters) 82 (6): 68005, doi:10.1209/0295-5075/82/68005

[52] HENDERSHOTT, TERRENCE, CHARLES M.JONES, AND ALBERT J. MENKVELD. Does Algo-rithmic Trading Improve Liquidity? extquotedbl, Journalof Finance, 2010

[53] Lin, TomC.W., TheNew Investor, 60 UCLA 678 (2013),available at: http://ssrn.com/abstract=2227498

[54] Black box traders are on the march The Telegraph, 27August 2006

[55] Myners super-fast shares warning BBC News, Tuesday 3November 2009.

[56] Enter algorithmic trading systems race or lose returns, re-port warns, Financial Times, October 2, 2006

[57] Cracking The Streets New Math, Algorithmic trades aresweeping the stock market.

16 14 EXTERNAL LINKS

[58] The Associated Press, July 2, 2007 Citigroup to expandelectronic trading capabilities by buying Automated Trad-ing Desk, accessed July 4, 2007

[59] Knight Capital Group Provides Update Regarding August1st Disruption To Routing In NYSE-listed Securities

[60] Lauricella, Tom, and McKay, Peter A. Dow Takes aHarrowing 1,010.14-Point Trip, OnlineWall Street Jour-nal, May 7, 2010. Retrieved May 9, 2010

[61] If you're reading this, its too late: a machine got hererst, The Financial Times, April 16, 2007, p.1

[62] Sentiment Algos

[63] Siemons Case Study Automated Trading Desk, accessedJuly 4, 2007

[64] http://www.bis.gov.uk/foresight/our-work/projects/current-projects/computer-trading

[65] http://www.bis.gov.uk/foresight/our-work/projects/current-projects/computer-trading/working-paper

[66] HFT study calls for Regulatory Refocus, The Trade News,October 23, 2012

[67]

[68] http://rybn.org/ANTI/ADM8/

[69] Brodin, basic trading system architecture, start-algotrading.com, July , 2014

[70] C Fiedler, design high-frequency system (algorithmictrading system), dbcoretech.com, June , 2010

[71] Dodgy tickers, The Economist, March 8, 2007

[72] Pleasures and Pains of Cutting-Edge Technology Mar 19,2007

[73] LSE leads race for quicker trades by Alistair MacDon-ald The Wall Street Journal Europe, June 19, 2007, p.3

[74] Milliseconds are focus in algorithmic trades. Reuters.May 11, 2007.

[75] FIXatdl version 1.1 released March, 2010

[76] Crop Circle of the Day Quote Stung and Strange Se-quences. Nanex. 29 September 2010. Archived fromthe original on 23 June 2011. Retrieved 2011-07-28.

[77] Kevin Slavin (12 July 2011). How algorithms shape ourworld. TEDGlobal 2011.

14 External links Automated Trader Magazine: Algorithmic Tradingdenition and reference

What is a Trading System? Advanced Trading Magazine: Algorithmic TradingResource Center Advanced Trading Magazine

Motley Fool denition and references for Algorith-mic Trading

BusinessWeek.com SEC Risks Harm With High-Frequency Trading Curbs, CME CEO Says

Findings Regarding the Market Events of May 6,2010, Report of the stas of the CFTC and SEC tothe Joint Advisory Committee on Emerging Regu-latory Issues, September 30, 2010

The Flash Crash: The Impact of High FrequencyTrading on an Electronic Market, Andrei A. Kir-ilenko (Commodity Futures Trading Commission)Albert S. Kyle (University of Maryland; NationalBureau of Economic Research (NBER)) MehrdadSamadi (Commodity Futures Trading Commission)Tugkan Tuzun (University of Maryland Robert H.Smith School of Business), October 1, 2010

Regulatory Issues Raised by the Impact of Techno-logical Changes on Market Integrity and Eciency,Technical Committee of the International Organi-zation of Securities Commissions, July, 2011

17

15 Text and image sources, contributors, and licenses15.1 Text

Algorithmic trading Source: http://en.wikipedia.org/wiki/Algorithmic_trading?oldid=626378821 Contributors: Edward, Jdoherty,JASpencer, Sunray, Superm401, Bnn, Khalid hassani, Beland, Quarl, Karol Langner, Elroch, Now3d, Seer, Leibniz, Bender235, Flash-green, Vipul, Cretog8, Jimg, Alex.g, Woohookitty, Mrkwpalmer, GregorB, Waldir, Ronnotel, Rjwilmsi, X1011, Lmatt, Digitalme, Gw-ernol, Dili, Rsrikanth05, Yahya Abdal-Aziz, Rjlabs, LisaCarrol, Glich, Sameer81, Fram, JLaTondre, Killerandy, Groyolo, Capitalist,DocendoDiscimus, Veinor, SmackBot, DomQ, Ohnoitsjamie, Anwar saadat, Oli Filth, Pdek, Mitsuhirato, Smallbones, DMacks, Lam-biam, Polihale, Kuru, Giric, Evenios, Ckatz, Hu12, Papertiger, Iridescent, Chrismartins, Trade2tradewell, Eastlaw, Mellery, CmdrObot,The ed17, Justin.short, Jesse Viviano, Joelholdsworth, John M Baker, DumbBOT, Faust.ua, Diabloblue, Headbomb, Michael A. White,Wai Wai, Pi314159, Adam Chlipala, Deective, Davewho2, Barek, MER-C, TAnthony, Magioladitis, Eganesan, VoABot II, Nakkisormi,SSZ, JohnLai, Strateg68, DGG, Kgeis, Jim.henderson, EdBever, Foober, NerdyNSK, LiKaShing, Tsachin, DMCer, Bonadea, HighK-ing, Squids and Chips, Scls19fr, JLBernstein, Fxcxt, Lars67, Lamro, Chikichiki, Willisja, Gocmanac 88, Flyer22, Jcl365, Vanished userojwejuerijaksk344d, Jojalozzo, Cheesefondue, HotBridge, Bbosh, PixelBot, Pladook, Iohannes Animosus, ChrisHodgesUK, Vagieray,Erwinduke, XLinkBot, Alexius08, Hrufat, Addbot, Kmassaro, Lihaas, Bonewith, MMoranTraders, , Zorrobot, Zentrader, Yobot,Legobot II, AnomieBOT, Materialscientist, LilHelpa, NOrbeck, N419BH, Smallman12q, Hersfold tool account, Judymmay, FrescoBot,SUPER-QUANT-HERO, Wikiphile1603, Citation bot 1, Pulkinya, John abraham 516, Mastishka, Beancrush, TobeBot, Trappist themonk, Krassotkin, Oliver H, JV Smithy, Skakkle, Tbhotch, Mean as custard, Saurael, RjwilmsiBot, Sargdub, DASHBot, Keithsan, John ofReading, WikitanvirBot, Dewritech, DominicConnor, Solarra, Pkilhaney, SCWilkinson, Borealis112, Lippia, Akerans, MedoJoe, Anto-nio1998, Dbcoretech, Zuijiadeai, Be gottlieb, PaulTheOctopus, Zfeinst, Jeeries2010, Tradinginfo, Aberdeen01, Financestudent, Sbandit,AndyTheGrump, Albertjmenkveld, Sunmylondon10, ClueBot NG, MarketsGuy, Dutyaxistubenovel, Frietjes, Fearquit87, Statoman71, Al-pha7248, Rossmix, Scorpy55, Loopylip, Astellix, BG19bot, Sledge 1981, Furkhaocean, Patrick.carter, Aisteco, Asa5520, BattyBot, Chris-Gualtieri, Whileworth, Fritzbunwalla, Norefer42, Seaman4516, Krishan.nagpal, Igitus, Bananasoldier, EllenCT, Comp.arch, Tonytonov,Ari.benesh, WallStreet7177, WPGA2345, FinancialResearchGroup, ICP Group Finance, Mickeyhsue, Wallstreet1234, PranavKSangha-dia, TDKSociety, Brodin.sat, Sunvshade, Gragre123, Fundron1, Rmh789, MpowerTrading, Potemkin70 and Anonymous: 246

15.2 Images File:Assorted_United_States_coins.jpg Source: http://upload.wikimedia.org/wikipedia/commons/5/5e/Assorted_United_States_

coins.jpg License: Public domain Contributors: Own work Original artist: Elembis File:Chicklet-currency.jpg Source: http://upload.wikimedia.org/wikipedia/commons/9/9f/Chicklet-currency.jpg License: Public do-

main Contributors: ? Original artist: ? File:Nuvola_apps_kaboodle.svg Source: http://upload.wikimedia.org/wikipedia/commons/1/1b/Nuvola_apps_kaboodle.svg License:

LGPL Contributors: http://ftp.gnome.org/pub/GNOME/sources/gnome-themes-extras/0.9/gnome-themes-extras-0.9.0.tar.gz Originalartist: David Vignoni / ICON KING

15.3 Content license Creative Commons Attribution-Share Alike 3.0

HistoryStrategiesTrading ahead of index fund rebalancingTrend followingPairs tradingDelta-neutral strategiesArbitrageConditions for arbitrage

Mean reversionScalpingTransaction cost reductionStrategies that only pertain to dark pools

High-frequency tradingMarket makingStatistical arbitrageEvent arbitrage

Low-latency tradingStrategy implementationIssues and developmentsCyborg FinanceConcernsRecent developments

Technical designEffectsCommunication standardsAlgorithmsSee alsoNotesReferencesExternal linksText and image sources, contributors, and licensesTextImagesContent license