Common Strategies of Algorithmic Trading

Common Strategies of Algorithmic Trading

Capital Markets CIO Outlook | Monday, April 19, 2021

Algorithmic trading has emerged as a new way for financial firms to gain an advantage over other market participants in the last decade, as long as they use this powerful method correctly.

Fremont, CA: Algorithmic trading is a type of automation in which a computer is programmed to perform a series of acts, such as buying or selling an asset in response to market data changes. This form of trading can passively enter and exit positions at a pace and frequency that is difficult for a normal trader to achieve on their own.

The pace at which algo-trading executes trades allows traders to get the best possible rates by preventing significant market fluctuations, lowering transaction costs, and reducing the risk of human error. Over the last decade, high-frequency trading (HFT) has become the most common application of the technology, especially among large financial institutions. Its popularity among major banks, insurers, and hedge funds stem from its ability to quickly position large amounts of orders across several markets using various algorithmic trading strategies.

Here are two popular trading techniques:

Trend following

This is the most commonly used algorithmic trading strategy for traders looking to benefit from various stock market scenarios. Traders can monitor anything from moving averages and channel breakouts to price fluctuations and other technical analysis metrics, including on-balance volume using algorithmic trading (OBV).

Since they are one of the easiest to incorporate, many traders program trend tracking strategies into algorithmic trading platforms.


Arbitrage, which is the practice of purchasing dual-listed security from one exchange and simultaneously selling it at a higher price point on another, is another common tactic that can be boosted by the power of algorithmic trading platforms. While experienced traders can identify these arbitrage opportunities, by developing an algorithm that can recognize price differentials across several markets, it would be possible to execute trades and produce profits on a much larger scale.

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