Algorithmic trading

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Algorithmic trading is a method of executing a large order (too large to fill all at once) 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.
Algorithmic trading is not an attempt to make a trading profit. It is simply a way to minimise the cost, market impact and risk in execution of an order. 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, these encompass trading strategies that are heavily reliant on complex mathematical formulas and high-speed computer programs.
Much of the rest of this article should be moved to the page on automated trading systems. 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.

Trading ahead of index fund rebalancing
Pairs trading
Most retirement savings, such as private pension funds or 401(k) and individual retirement accounts in the US, are invested in mutual funds, the most popular of which are index funds which must periodically "rebalance" or adjust their portfolio to match the new prices and market capitalization of the underlying securities in the stock or other index that they track. Profits are transferred from investors to algorithmic traders, estimated to be at least 21 to 28 basis points annually for S&P 500 index funds, and at least 38 to 77 basis points per year for Russell 2000 funds. John Montgomery of Bridgeway Capital Management says that the resulting "poor investor returns" from trading ahead of mutual funds is "the elephant in the room" that "shockingly, people are not talking about."

Pairs trading or pair trading is a long-short, ideally market-neutral strategy enabling traders to profit from transient discrepancies in relative value of close substitutes. Unlike in the case of classic arbitrage, in case of pairs trading, the law of one price cannot guarantee convergence of prices. This is especially true when the strategy is applied to individual stocks – these imperfect substitutes can in fact diverge indefinitely. In theory the long-short nature of the strategy should make it work regardless of the stock market direction. In practice, execution risk, persistent and large divergences, as well as a decline in volatility can make this strategy unprofitable for long periods of time (e.g. 2004-7). It belongs to wider categories of statistical arbitrage, convergence trading, and relative value strategies.

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