Algorithmic trading http://en.wikipedia.org/wiki/Algorithmic_trading In electronic financial markets, algorithmic trading or automated trading, also known as algo trading, black-box trading, or robo trading, is the use of computer programs for entering trading orders with the computer algorithm deciding on certain aspects of the order such as the timing, price, or even the final quantity of the order. It is widely used by hedge funds, pension funds, mutual funds, and other institutional traders to divide up a large trade into several smaller trades in order to manage market impact, opportunity cost, and risk.[1] It is also used by hedge funds and similar traders to make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information. Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically. A third of all EU and US stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based consulting firm Aite Group LLC. By 2010, that figure will reach 50 percent, according to Aite.[2] In 2006 at the London Stock Exchange, over 40% of all orders were entered by algo traders, with 60% predicted for 2007. American markets and equity markets generally have a higher proportion of algo trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algo trading (about 25% of orders in 2006). [3] Futures and options markets are considered to be fairly easily integrated into algorithmic trading[4], with about 20% of options volume expected to be computer generated by 2010.[5] Bond markets are moving toward more access to algorithmic traders.[6] History Computerization of the order flow in financial markets began in the early 1970s with some landmarks being the introduction of the New York Stock Exchange’s “designated order turnaround” system (DOT, and later SuperDOT) which routed orders electronically to the proper trading post to be executed manually, and the "opening automated reporting system" (OARS) which aided the specialist in determining the market clearing opening price. Communication Standards Algorithmic trades require communicating considerably more parameters than traditional market and limit orders. decimalization. The program trade at the NYSE would be pre-programmed into a computer to enter the order automatically into the NYSE’s electronic order routing system at a time when the futures price and the stock index were far enough apart to make a profit. mean reversion. In stock index arbitrage a trader would buy (sell) a stock index futures contract such as the S&P 500 futures and sell (buy) a portfolio of up to 500 stocks at the NYSE matched against the futures trade. statistical arbitrage.S. In the U. This decreased market liquidity led to institutional traders splitting up orders according to computer algorithms in order to execute their orders at a better average price. trend following..e unweighted) average price TWAP or more usually by the volume weighted average price VWAP. decreasing the market-makers' trading advantage. In practice this means that all program trades are entered with the aid of a computer. These strategies are more easily implemented by computers because machines can react more rapidly to temporary mispricing and examine prices from several markets simultaneously. Financial markets with fully electronic execution and similar electronic communication networks developed in the late 1980s and 1990s.Program trading is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks valued at over $1 million total. which changed the minimum tick size from 1/16th of a dollar ($0.” were blamed by many people (for example by the Brady report) for exacerbating or even starting the 1987 stock market crash. may have encouraged algorithmic trading as it changed the market microstructure by permitting smaller differences between the bid and offer prices. thus decreasing market liquidity. In the 1980s program trading became widely used in trading between equity and futures markets. A trader on one end (the “buy side“) must . other algorithmic trading strategies became possible including arbitrage. These average price benchmarks are measured and calculated by computers by applying the time weighted (i. often simply lumped together as “program trading. As more electronic markets opened.0625) to $0.01 per share. Both strategies. At about the same time portfolio insurance was designed to create a synthetic put option on a stock portfolio by dynamically trading stock index futures according to a computer model based on the Black-Scholes option pricing model. including example XML files and sample code may be found at: http://www.enable their trading system (often called an “Order Management System” or “Execution Management System”) to understand a constantly proliferating flow of new algorithmic order types. Arbitrage A classical arbitrage strategy might involve three or four securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond.[7] Transaction cost reduction Large orders are broken down into several smaller orders and entered into the market over time. Credit Suisse. Citigroup. mutual funds. FIXatdl is now in broad beta testing with the following firms participating: Barclays. Fidelity Investments. along with the execution infrastructure. More information on FIXatdl. These algorithms or techniques are commonly given names such as "iceberging".fixprotocol. "Guerrilla". The success of this strategy may be measured by the average purchase price against the VWAP for the market over that time period. If the market prices are sufficiently different from those implied in . This institution dominates standard setting in the pretrade and trade areas of security transactions. "benchmarking". are fairly substantial. etc. This basic strategy is called "iceberging". Members include virtually all large and many midsize and smaller broker dealers. Goldman Sachs. The R&D and other costs to construct complex new algorithmic orders types. and UBS AG. a bond denominated in a foreign currency. Cheuvreux. Currently targeting 2009 for final release. The standard is called FIX Algorithmic Trading Definition Language (FIXatdl). One algorithm designed to find hidden orders or icebergs is called "Guerrilla". institutional investors. and the price of a forward contract on the currency. open standards in the securities trading area.fixprotocol. Pragma@Weeden. ITG. JPMorgan Chase. What was needed was a way that marketers (the “sell side”) could express algo orders electronically such that buyside traders could just drop the new order types into their system and be ready to trade them without constant coding custom new order entry screens each time. In 2006-2007 several members got together and published a draft XML standard for expressing algorithmic order types. FIX Protocol LTD http://www. Merrill Lynch. the spot price of the currency. money center banks. Morgan Stanley.org is a trade association that publishes free.org/working_groups/algowg/documents Strategies Many different algorithms have been developed to implement different trading strategies. "Sniper" and "Snif-fer". and marketing costs to distribute them. "Dagger". Bloomberg Tradebook. NeoNet. with most orders hidden or "iceburged.. Any type of algo trading which depends on the programming skills of other algo traders is called gaming."[9] Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell." said Joe Saluzzi. An algorithm designed to discover which markets are most volatile or unstable is called "Snif-fer". dumb elephants leaving big footprints.the model to cover transactions cost then four transactions can be made to guarantee a risk-free profit. Automated Trading Desk. .. Market Making Market making involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price in order to benefit from the bid-ask spread. They then front run the order. accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange. “Everyone is building more sophisticated algorithms. "If you're getting tapped by odd lots. head of equity trading at Themis Trading... Algorithmic trading allows similar arbitrages using models of greater complexity involving many more than 4 securities. and the more competition exists. When several small orders are filled the sharks may have discovered the presence of a large iceburged order. director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering..[8] More Complicated Strategies A "benchmarking" algorithm is used by traders attempting to mimic an index's return.”[10] Issues and Developments More sophisticated models and intelligent programs have created the question of whether the models will break down. "They look for big. which was bought by Citigroup in July 2007. the smaller the profits. has been an active market maker. if it happens 40 times. Neural networks and genetic programming have been used to create these models. Dark pools are alternative electronic stock exchanges where trading takes place anonymously. “Now it’s an arms race.you're being gamed." [9] Any sort of pattern recognition or predictive model can be used to initiate algo trading.” said Andrew Lo. as the need for stability. to be read and traded on via algorithms. And this almost instantaneous information forms a direct feed into other computers which trade on the news. But with these systems you pour in a bunch of numbers. “Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. and it’s not always intuitive or clear why the black box latched onto certain data or relationships..The nature of the markets has changed dramatically. Some firms are also attempting to automatically assign sentiment (deciding if the news is good or bad) to news stories so that automated trading can work directly on the news story. They have more people working in their technology area than people on the trading desk.”[10] Regulators in Great Britain are watching the development of algo trading. especially for new entrants. Dow Jones. bandwidth and speed is even higher than for regular order execution. But it also pointed out that ‘greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption’. and Thomson Financial.” Mr. and something comes out the other end.[12] security and front running..”[15] The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news. . In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. Williams said."[14] Financial market news is now being formatted by firms such as Reuters.”[11] Other issues include the technical problem of latency or the delay in getting quotes to traders. and the possibility of a complete system breakdown leading to a market crash. Firms which have not developed their own algorithmic trading have had to buy competing firms. “The Financial Services Authority has been keeping a watchful eye on the development of black box trading. “Traders have intuitive senses of how the world works.“The downside with these systems is their black box-ness.[13] The cost of developing and maintaining algorithms is still relatively high. "Goldman spends tens of millions of dollars on this stuff. Bloomberg. 2008) claiming that their service had beaten other news services by 2 seconds in reporting an interest rate cut by the Bank of England. and can process 3. algorithmic trading has reduced trade sizes further.“There is a real interest in moving the process of interpreting news from the humans to the machines” says Kiristi Suutani. Effects Though its development may have been prompted by decreasing trade sizes caused by decimalization. The speeds of computer connections.[1] Brokers have found it more difficult to monitor the risk of their clients' positions. Citigroup. [17] [18] More fully automated markets such as NASDAQ have gained market share from less automated markets such as the NYSE. especially for clients such as hedge funds See also • • Artificial Intelligence Complex Event Processing . Competition is developing among exchanges for the fastest processing times for completing trades. measured in milliseconds. markets.S. and contributed to international mergers and consolidation of financial exchanges.[16] Citigroup had previously bought Lava Trading and OnTrade Inc. on March 1. have become very important. paid $680 million for Automated Trading Desk. “More of our customers are finding ways to use news content to make money. For example the London Stock Exchange. Jobs once done by human traders are being switched to computers. In July 2007. started a new system called TradElect.000 orders per second.4 billion in 2005.[19] Spending on computers and software in the financial industry increased to $26. Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees. which promises an average 10 millisecond turnaround time from placing an order to final confirmation.”[15] An example of the importance of reporting speed to algorithmic traders was an advertising campaign by Dow Jones (appearances included page W15 of the Wall Street Journal. in June 2007. which had already developed its own trading algorithms. global business manager of algorithmic trading at Reuters. accounting for about 6 percent of trading volume in U. a 19 year old firm that trades about 200 million shares a day. The Wall Street Journal Europe. 2007 18. report warns. ^ Looking for options Derivatives drive the battle of the exchanges. June 19.• • • • • • Dark pools of liquidity Electronic Communication Network Electronic trading Implementation shortfall Investment strategy Quantitative trading References 1. The Wall Street Journal. 2007.3 . 2007 8. 14. 2007 Citigroup to expand electronic trading capabilities by buying Automated Trading Desk. 2007 19. p. accessed July 4. p. ^ Cracking The Street's New Math. April 15. Iran Daily May 7. ^ Pleasures and Pains of Cutting-Edge Technology Mar 19. Financial Times. ^ Enter algorithmic trading systems race or lose returns. 2007): p. ^ Trading with the help of 'guerrillas' and 'snipers. ^ a b "If you're reading this. Citigroup to expand electronic trading capabilities by buying Automated Trading Desk. ^ a b Rob Curren. c5. 2006 12. July 2.A. accessed July 4. ^ "LSE leads race for quicker trades" by Alistair MacDonald The Wall Street Journal Europe. ^ MTS to mull bond access. 2006 11. 2007. October 2. Feb 2. November 23. 6. The Economist. ^ The Ultimate Money Machine.' Financial Times. 2008. 21 7.com/finance/displaystory. ^ Siemon's Case Study Automated Trading Desk. August 18. 2007 15. ^ The Associated Press. 2007 3. ^ The Associated Press. April 16. 2007.1 16. Not M. 2007. ^ "Algorithmic trading. p. 2007. April 18. Economist. Ahead of the tape". 2006 2. 2008 10. 2007 17. 2006 4. accessed July 4. it's too late: a machine got here first. August 19.cfm? story_id=9370718. The Economist.economist.’s by Heather Timmons. http://www. 2007 9. ^ a b Moving markets Shifts in trading patterns are making technology ever more important." The Financial Times. August 26. ^ Black box traders are on the march The Telegraph. March 8. p.com 5. Watch Out for Sharks in Dark Pools. July 2. ^ a b Artificial intelligence applied heavily to picking stocks by Charles Duhigg. 2006 13. Available at WSJ Blogs retrieved August 19.B. The Economist 383 (June 23. March 19. Algorithmic trades are sweeping the stock market. ^ A London Hedge Fund That Opts for Engineers. 85. ^ Dodgy tickers. 2005 FT Mandate Special Report on Algorithmic Trading Advanced Trading Magazine: Algorithmic Trading Resource Center Advanced Trading Magazine Paper on the empirical efficiency of combining algorithmic trading strategies Retrieved from "http://en. April 18.wikipedia. Cracking the Street's New Math.External links • • • • Business Week.org/wiki/Algorithmic_trading" .
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