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 aspects of the order such as the timing, price, or quantity of the order, or in many cases initiating the order without human intervention. Algorithmic Trading is widely used by pension funds, mutual funds, and other buy side (investor driven) institutional traders, to divide large trades into several smaller trades in order to manage market impact, and risk.[1][2] Sell side traders, such as market makers and some hedge funds, provide liquidity to the market, generating and executing orders automatically. A special class of algorithmic trading is “high-frequency trading” (HFT), in which computers make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe, in addition to which they can reduce costs of trading and improve shareholder returns.[citation needed]<\/p>\n
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 (“on auto-pilot”).<\/p>\n
A third of all EU and US stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based financial services industry research and consulting firm Aite Group.[3] As of 2009, high frequency trading firms account for 73% of all US equity trading volume.[4]<\/p>\n
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).[5] Futures and options markets are considered to be fairly easily integrated into algorithmic trading,[6] with about 20% of options volume expected to be computer generated by 2010.[7] Bond markets are moving toward more access to algorithmic traders.[8]<\/p>\n
One of the main issues regarding high frequency trading is the difficulty in determining just how profitable it is. A report released in August 2009 by the TABB Group, a financial services industry research firm, estimated that the 300 securities firms and hedge funds that specialize in rapid fire algorithmic trading took in roughly $21 billion in profits in 2008[9].<\/p>\n
Main Wikipedia article: High-frequency trading<\/strong><\/p>\n High-frequency trading uses computers that execute trades within milliseconds, or “with extremely low latency” in the jargon of the trade. In the U.S., high-frequency trading firms represent 2.0% of the approximately 20,000 firms operating today, but account for 73.0% of all equity trading volume.[21] As of the first quarter in 2009, total assets under management for hedge funds with high frequency trading strategies were $141 billion, down about 21% from their high.[22] The high frequency strategy was first made successful by Renaissance Technologies.[23] High frequency funds started to become especially popular in 2007 and 2008.[22] Many high frequency firms say they are market makers and that the liquidity they add to the market has lowered volatility and helped narrow spreads, but unlike traditional market makers, such as specialists on the New York Stock Exchange, they have few or no regulatory requirements.<\/p>\n These funds are highly dependent on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors.[4] The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform in order to benefit from implementing high frequency strategies.[4] Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by\u00a0competitors. There is also a very strong pressure to continuously add features or improvements to a particular algorithm, such as client specific modifications and various performance enhancing changes (regarding benchmark trading performance, cost reduction for the trading firm or a range of other implementations). This is due to the evolutionary nature of algorithmic trading strategies – they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. As a result, a significant proportion of net revenue from firms is spent on the R&D of these autonomous trading systems.[4]<\/p>\n Really want to get annoyed about HFT? <\/strong>The HFT houses are being fed confidential data which allows them to front run. They are being fed confidential information because there are many competing exchange. They are all competing for the trading dollars. To win the business, the exchanges have to provide information to their customers. In the old days, it was computer research. These days it’s research on order flow. <\/span><\/strong><\/span><\/p>\n Read<\/span> <\/strong>THEMIS_TRADING_White_Paper_–_Data_Theft_On_Wall_Street_–_05-11-10<\/strong><\/a><\/span><\/span><\/p>\n Here’s the paper’s conclusion (sorry the type is so small):<\/span><\/span><\/p>\n God bless English humor.<\/span><\/strong> Arriving for work at the U.K. Treasury last week, the incoming chief secretary, David Laws, found a note from his predecessor, Liam Byrne, summarizing England’s finances.<\/p>\n The note read; \u201cDear Chief Secretary, I\u2019m afraid to tell you there\u2019s no money left.\u201d<\/p>\n What a wonderful weekend. This was the view from my country house on Saturday evening. That’s the Catskills in the distance.<\/span><\/span><\/span><\/strong><\/p>\n![\"\"](\"https:\/\/www.technologyinvestor.com\/wp-content\/uploads\/2010\/05\/Conclusion.jpg\" "\\"Conclusion\\"")
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