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text Courtesy Of Wikipedia
An automated trading system (ATS) is a computer trading program that automatically submits trades to an exchange. As of the year 2010 more than 70% of the stock shares traded on the NYSE and NASDAQ are generated from automated trading systems.[citation needed] They are designed to trade stocks, futures and forex based on a predefined set of rules which determine when to enter a trade, when to exit it and how much to invest in it.
An example of an early ATS is Instinet. This allows traders to input trades invisibly to the market, with a crossing price determined by a VWAP measure. Instinet also enables anonymous conversations and negotiations to take place between bidders, and so reduces informational costs to the participants.
Trading system designers / programmers often test their automated trading systems on historical or current market data in order to determine whether the underlying algorithm guiding the system is profitable or not. Backtesting software are special trading platforms which enable trading system designer to develop and test their trading systems on historical market data while aiming to produce optimal historical results.High-frequency trading (HFT) is the use of sophisticated technological tools to trade securities like stocks or options, and is typically characterized by several distinguishing features:[1][2][3]
It is highly quantitative, employing computerized algorithms to analyze incoming market data and implement proprietary trading strategies;
An investment position is held only for very brief periods of time - from seconds to hours - and rapidly trades into and out of those positions, sometimes thousands or tens of thousands of times a day;[4]
At the end of a trading day there is no net investment position;
It is mostly employed by proprietary firms or on proprietary trading desks in larger, diversified firms;
It is very sensitive to the processing speed of markets and of their own access to the market;
Many high-frequency traders provide liquidity and price discovery to the markets through market-making and arbitrage trading; high-frequency traders also take liquidity to manage risk or lock in profits.
Positions are taken in equities, options, futures, ETFs, currencies, and other financial instruments that can be traded electronically.[5]
High-frequency traders compete on a basis of speed with other high-frequency traders, not long-term investors (who typically look for opportunities over a period of weeks, months, or years), and compete for very small, consistent profits.[6][7] As a result, high-frequency trading has been shown to have a potential Sharpe ratio (measure of reward per unit of risk) thousands of times higher than the traditional buy-and-hold strategies.[8]
Aiming to capture just a fraction of a penny per share or currency unit on every trade, high-frequency traders move in and out of such short-term positions several times each day. Fractions of a penny accumulate fast to produce significantly positive results at the end of every day.[2] High-frequency trading firms do not employ significant leverage, do not accumulate positions, and typically liquidate their entire portfolios on a daily basis.[7]
By 2010 high-frequency trading accounted for over 70% of equity trades in the US and was rapidly growing in popularity in Europe and Asia.
Algorithmic and high-frequency trading were both found to have contributed to volatility in the May 6, 2010 Flash Crash, when high-frequency liquidity providers were in fact found to have withdrawn from the market.[9][10][11][12][13][14][15][16] A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010."[1][17]
