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.
In the past several years algo trading has been gaining traction with both retails and institutional traders. Popular platforms for algorithmic
trading include Horizon, MetaTrader, NinjaTrader, IQBroker, and Quantopian.
Algorithmic trading is not an attempt to make a trading profit. It is simply a way to minimize 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.
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
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 passive index investors to active investors, some of whom are algorithmic traders specifically exploiting the index rebalance effect. The magnitude of these losses incurred by passive investors has been estimated at 21-28bp per year for the S&P 500 and 38-77bp per year for the Russell 2000. 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. It belongs to wider categories of statistical arbitrage, convergence trading, and relative value strategies.
In finance, delta-neutral describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to small changes in the value of the underlying security. Such a portfolio typically contains options and their corresponding underlying securities such that positive and negative delta components offset, resulting in the portfolio's value being relatively insensitive to changes in the value of the underlying security.
In economics and finance, arbitrage is the practice of taking advantage of a price difference between two or more markets: striking
a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices. When used by academics,
an arbitrage is a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state;
in simple terms, it is the possibility of a risk-free profit at zero cost.
Example: One of the most popular Arbitrage trading opportunities is played with the S&P futures and the S&P 500 stocks. During most trading days these two will develop disparity in the pricing between the two of them. This happens when the price of the stocks which are mostly traded on the NYSE and NASDAQ markets either get ahead or behind the S&P Futures which are traded in the CME market.