Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume to send small slices of the order (child orders) out to the market over time. They were developed so that traders do not need to constantly watch a stock and repeatedly send those slices out manually. Popular “algos” include Percentage of Volume, Pegged, VWAP, TWAP, Implementation shortfall, Target close. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders.
It is widely used by investment banks, pension funds, mutual funds, and hedge funds because these institutional traders need to execute large orders in markets that cannot support all of the size at once. The term is also used to mean automated trading system. These do indeed have the goal of making a profit. Also known as black box trading, Quant or Quantitative trading, these encompass trading strategies that are heavily reliant on complex mathematical formulas and high-speed computer programs.
Such systems run strategies including market making, inter-market spreading, arbitrage, or pure speculation such as trend following. Many fall into the category of high-frequency trading (HFT), which are characterized by high turnover and high order-to-trade ratios. As a result, in February 2012, the Commodity Futures Trading Commission CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided