What is Algorithmic Trading?
Algorithmic Trading (or simply Algo-trading) refers to automating buy and sell orders by utilizing programs developed to make trades depending on a specific market condition.
Algorithms are computer programs with a set of instructions configured to be carried out in a specific sequence and they are used across various industries. Institutional traders such as mutual funds, hedge funds and investment banks develop algorithmic trading strategies to execute large orders at a frequency too high for human traders.
Many algorithm trading strategies are developed for high-frequency trading (HFT), which involves leveraging fast-paced financial market data, high-end computerized trading, and analysis tools to execute large numbers of orders within a specified period of time across a range of assets under management. Popular trading strategies include arbitrage, pure speculations, trend following, and index fund rebalancing.
Algo-trading allows traders to make trading decisions in response to predicted market actions or price movements. Algorithmic traders develop the algo-softwares which execute these trades without human intervention.
Benefits of Algo-trading
Besides a faster execution time which reduces the risk of slippage and losing out on favourable positions, algorithmic trading systems helps reduce the risk of making emotional decisions associated with human traders.
There is always the risk of human errors due to stress, fatigue, and many other factors with manual trading. Algo-trading helps scalpers (i.e. short-term traders) to minimize risk by automating trading processes. This way, traders are hedge their positions against drastic price movements when they are asleep or away from their trading stations.
In addition, a good algo-software can help new retail traders earn some profit with little or no knowledge of the financial markets. The challenge here may be to identify a good algo-software as the field is rife with scams.
Algo-trading also helps market makers create sufficient market liquidity given the high frequency of executable trades in very short time intervals, and arbitrageurs can also identify arbitrage opportunities at faster speeds.
Finally, many traders use algo-trading to test out complex algorithms and new strategies, considering historical price data before using them in real-world market scenarios.