Algorithmic Trading

Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes per-programmed trading instructions whose variables may include timing, price, or quantity of the order, or in many cases initiating the order by a "robot", without human intervention. Algorithmic trading is widely used by investment banks, pension funds, mutual funds, and other buy-side (investor-driven) institutional traders, to divide large trades into several smaller trades to manage market impact and risk. Sell side traders, such as market makers and some hedge funds, provide liquidity to the market, generating and executing orders automatically. Algorithmic Trading is helpful when quick decisions are imperative in market trading. In recent years, it has become very popular with investment banks, pension funds and mutual funds, as it has proven to be extremely profitable for some of Wall Street’s biggest firms. The main advantages of Algorithmic Trading includes, the speed of order entry in to the market, controlling or minimizing human emotions, enforcing discipline in your trading, the ability to back test, the ability to obtain consistency and diversify your trading. Selecting the platform for your own Algorithmic Trading System has become much easier now that Trader Design provide many options on which you can create your trading strategy. A special class of algorithmic trading is "high-frequency trading" (HFT), which is often most profitable during periods of high market volatility. During the past years, companies such as Algorates have employed HFT strategies, recording high profits even during periods in which the markets have seen steep declines. Financial market news is now being formatted by firms such as Need To Know News, Thomson Reuters, Dow Jones, and Bloomberg, to be read and traded on via algorithms."Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms a direct feed into other computers which trade on the news." The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news. 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. Rob Passarella, global director of strategy at Dow Jones Enterprise Media Group said "Increasingly, people are looking at all forms of news and building their own indicators around it in a semi-structured way," as they constantly seek out new trading advantages. His firm provides both a low latency news feed and news analytics for traders. Passarella also pointed to new academic research being conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators, the potential impact of various phrases and words that may appear in Securities and Exchange Commission statements and the latest wave of online communities devoted to stock trading topics. He told that the "Markets are by their very nature conversations, having grown out of coffee houses and taverns". So the way conversations get created in a digital society will be used to convert news into trades, as well, Passarella said. "There is a real interest in moving the process of interpreting news from the humans to the machines" says Kirsti Suutari, global business manager of algorithmic trading at Reuters. "More of our customers are finding ways to use news content to make money." An example of the importance of news reporting speed to algorithmic traders was an advertising campaign by Dow Jones(appearances included page W15 of the Wall Street Journal, on March 1, 2008) claiming that their service had beaten other news services by 2 seconds in reporting an interest rate cut by the Bank of England. In July 2007, Citigroup, which had already developed its own trading algorithms, paid $680 million for Automated Trading Desk, a 19-year-old firm that trades about 200 million shares a day. Citigroup had previously bought Lava Trading and On Trade Inc. In late 2010, The UK Government Office for Science initiated a Foresight project investigating the future of computer trading in the financial markets, led by Dame Clara Furse, ex-CEO of the London Stock Exchange and in September 2011 the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additional papers that provide supporting evidence. All of these findings are authored or co-authored by leading academics and practitioners, and were subjected to anonymous peer-review. The Foresight project is set to conclude in late 2012.In September 2011, RYBN has launched "ADM8", an open source Trading Bot prototype, already active on the financial markets.

All of these findings are authored or co-authored by leading academics and practitioners, and were subjected to anonymous peer-review. The Foresight project is set to conclude in late 2012.In September 2011, Rybn has launched "