Algorithmic Trading
Overview
Algorithmic trading, also known as algo-trading or black-box trading, is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading was developed so traders can respond to market changes at speeds and frequencies that are beyond human capability. It uses complex formulas, combined with mathematical models and human oversight, to make decisions about trading stocks, options, futures and other financial instruments.
History
The concept of algorithmic trading first came into existence in the 1970s, when the New York Stock Exchange introduced the "designated order turnaround" system (DOT). The DOT system was capable of routing orders to the correct trading post automatically. The term 'algorithmic trading' was coined in the early 1980s when stock market firms started using sets of rules for executing trades automatically.
In the mid-1980s, program trading became widely used in trading between the S&P 500 equity and futures markets. In the 1990s, with the advent of the internet, algorithmic trading evolved into high-frequency trading (HFT) and started to influence all financial markets.
Types of Algorithmic Trading
There are various types of algorithmic trading. Each type has its unique set of algorithms and methodologies.
Trend Following Strategies
Trend following strategies are algorithms that monitor trends in the stock market to forecast future trends. These strategies do not aim to outperform the market, but simply ride the wave of the market.
Mean Reversion
Mean reversion strategy assumes that prices, returns, or various economic indicators tend to move to the historical average or mean over time. This strategy is based on the concept that the high and low prices of an asset are a temporary phenomenon that revert to their mean value periodically.
Scalping
Scalping is a strategy that involves quickly entering and exiting the market with the aim of capturing small profits on short-term market movements.
Momentum Trading
Momentum trading involves buying securities that are trending up and selling securities that are trending down.
Advantages and Disadvantages
Like any trading method, algorithmic trading has its advantages and disadvantages.
Advantages
Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. It can also be less expensive, as lower transaction costs can help to reduce the costs of trading. Furthermore, it allows for backtesting, which means applying trading rules to historical market data to determine the viability of the idea.
Disadvantages
Despite the advantages, algorithmic trading is not without risks. These include mechanical failures, system crashes, and anomalies that could result in errant orders or missing orders. There is also the risk of over-optimization, which refers to excessive curve-fitting that produces a trading plan unreliable in live trading.
Regulatory and Ethical Considerations
Algorithmic trading has been the focus of much attention and controversy. The FCA and other regulatory bodies around the world have introduced stricter rules on algorithmic trading in light of these concerns.
Future of Algorithmic Trading
The future of algorithmic trading is likely to be influenced by technology, regulation, and market changes. Developments in technology such as artificial intelligence and machine learning could lead to the development of new algorithms and strategies.
