An algorithmic trading strategy is a high-frequency and high-volume algorithmic system that exploits the fluctuations in securities markets to generate profits. Such algorithmic systems get usually programmed with complex mathematical models, which help them decide when to trade based on incoming market data. This blog post will present before you some of the critical algorithmic trading strategies and get them automated for execution.
What are Algorithmic Trading Strategies?
Many algorithmic strategies have been developed over the years by both academics as well as practitioners. These algorithmic systems or algorithms work on mathematical models based on price data from securities markets. They use this data input and some rules defined by humans to buy/sell assets automatically, e.g. taking an equity position, i.e. long positions, short positions, or remaining flat (neutral) in the market.
Why use algorithmic trading strategies?
Algorithmic trading strategies can get used to make algorithmic or automated buy and sell decisions for a portfolio of securities. Some benefits include:
Reduced transaction costs: Algorithms use quantitative models that exploit market inefficiencies, thus leading to lower transaction costs than the manual way of doing things. It also results in lower slippage as algorithmic trades are usually made on large volumes due to their high-frequency nature.
Better Resource Utilization: An algorithmic system can process much more data than any human could manually, which means it takes better-informed decisions. It will have access to many different sources, including fundamental analysis, news feeds, social media sentiment analysis etc., all at once, whereas humans will have to do this sequentially.
Reduced Risk: Humans are often risk-averse, and algorithmic trading systems work in real-time; thus, they can make more knowledgeable decisions on when and how much of their portfolio should be sold for minimizing risks. Compared to humans, Algorithms need not suffer from mental errors that could lead to losses, e.g. emotions such as fear or greed affecting decision-making, etc.
Types of algorithmic trading strategies
There are broadly two types of algorithmic trading strategies:
Trend Following Strategies: These algorithmic systems try to identify and exploit trends in the market. They do so by looking at price data and then making buy/sell decisions accordingly. E.g. moving average crossovers, trend lines etc.
Arbitrage Strategies: These algorithmic systems look for pricing discrepancies between different markets or securities and exploit these opportunities for profits. It is done by buying an asset undervalued and selling it in another market where it is overvalued, e.g. pair trading, triangular arbitrage etc.
The future of algorithmic trading systems
Many algorithmic traders use algorithmic trading systems and hedge funds to execute their trades. Although algorithmic trading is still a nascent market, there has been significant growth in the industry over the years, with an increasing number of firms building algorithmic or quantitative strategies. Algorithms have also become more accessible than ever before, with platforms allowing algorithmic traders to create and run their strategies on big data. Many experts predict that algorithmic trading will continue to grow in the future; with some even saying that eventually, all trades across securities markets will get automated.
Conclusion
An algorithmic trading strategy is a set of rules or instructions used by an algorithmic system for buy/sell decisions, along with quantitative models built on price data from various sources. Some key examples are trend following, arbitrage etc. Algorithms have become very familiar over the years, especially among hedge funds that use them widely for executing their trades. As time goes on, more market participants might adopt algorithmic trading systems too! If you are looking for more information about algorithmic trading strategies or automated execution in general, visit algorithmictrading.net today!