Types of Algo Trading Strategies

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Algorithmic trading (algo trading, automated trading, or black-box trading) is basically a process where computers are programmed to make trading decisions using defined sets of rules in hopes of enhancing profits.

These defined sets of rules are primarily based on timing, price, quantity and mathematical models. The core idea behind algo trading is to generate profits at maximum speed and frequency in a manner impossible for typical human traders. 

Aside from creating profit opportunities for retail traders, algo trading makes markets more liquid and trading more systematic by eliminating the impact of human emotions on trading activities.

Despite all these favourable characteristics, algorithmic trading requires specific trading strategies to be effective, for example, following trends in moving averages, channel breakouts, or price level movements. These strategies need to have a set of rules that a computer programme can follow, and needs to be extensively tested before being deployed.

In this article, we explore some of the main algo trading strategies used by many retail traders to maximise profit.

algo trading
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Key algorithmic trading strategies in FX

Algo trading strategies can be based on either technical or fundamental analysis. However, a notable key denominator is that they all rely on quantifiable information that can be extensively backtested for accuracy. 

1. Mean Reversion

The first strategy on our list is mean reversion, a strategy that seeks to exploit the tendency of multiple asset prices to revert to their mean, after specific points in time where they become either oversold or overbought. 

Retail traders following the mean reversion framework usually assume that the price of the stock will ultimately revert to its long-term ‘average price’. 

As a result, they hope to be able to purchase assets when they are trading at the lower end of a price range. When these assets approach the centre of the trading range or a moving average, the traders can choose to sell them and thus, make profits. 

Conversely, when prices are trading at the higher end of a trading range, the strategy would look to sell the asset, in order to buy it back later at a cheaper price after the price has reverted to its mean.

Overall, the mean reversion algorithmic system operates under the fundamental premise that the marketplace is ranging 80% of the time, and prices usually gravitate towards their mean price. As such, they exploit historical price data to determine the average price of assets, and open purchase or sell orders in anticipation of their current prices coming back to their average price.

2. High-Frequency Trading Strategies

Also known as scalping, high-frequency trading is a strategy that employs automated systems to execute hundreds of FX trades in a fraction of a second. High-frequency trading allows traders to trade substantial blocks within a very short time utilising algorithms.  

Such trading adds significant liquidity to the market; however, the liquidity produced only lasts for seconds – which is a limited time for non-HFT traders to benefit from it.

3. Trend Following

Generally speaking, trend following is one of the oldest strategies utilised by traders. This strategy entails leveraging algorithms to continuously monitor the market for reliable indicators of a trending market.

In practice, such traders utilise technical analysis, market patterns and indicators to make decisions, with the goal of buying assets when prices break determined resistance levels and selling assets that fall below support levels. 

For the most part, this algorithmic strategy is popular due to its functionality and ease of use in contrast to other algorithmic trading strategies. 

In summary, the premise of trend following is to follow the trends of prices while assuming that momentum will continue indefinitely until there are particular signals or indicators otherwise. 

4. Sentiment Analysis

This algo strategy is determined by crowd reactions following recent and relevant news and data releases, and either buy or sell assets according to their predictions of crowd reactions.

Overall, the central goal of this strategy is to take large quantities of unstructured data, such as newspaper articles, industry reports, social media posts, videos, blog posts, and analyse it to capture short-term price changes and obtain quick benefits.

5. Iceberging Trading

The iceberg trading system is usually adopted by large financial institutions that prefer to maintain secrecy about their positions on the forex market. As such, they tend to divide their large orders into smaller positions and execute them under different brokers instead of with only one broker, or at disparate times instead of all at once..

6. Statistical Arbitrage 

This algorithmic trading strategy comprises a set of quantitatively driven trading strategies that look to exploit relative price movements across hundreds of financial instruments by analysing price differences and patterns. 

Typically, traders utilise statistical arbitrage to generate higher-than-usual profits by exploiting statistical mispricing or price inefficiencies of assets. Overall, statistical arbitrage strategies are a subset of mean reversion strategies, involving complex quantitative models and requiring substantial computational power.

The most popular statistical arbitrage algo strategy is the pairs trading strategy which is used to trade the differentials between two assets or markets, for example, taking a long position in one asset while simultaneously taking an equal-sized short position in another asset. 

How to choose an algorithmic trading strategy?

Despite the several trading strategies shared above, finding the one that will work satisfactorily for you can be an overarching challenge, especially if you’re not a programmer. So, here are three core tips you can consider as you choose an algo strategy:

  • Clearly define the type of strategy: Though the list of aforementioned strategies isn’t fully exhaustive, it is still prudent to understand how each strategy works and its caveats. For example, trend following strategies exclusively works when markets are trending, while news-based strategies only workaround news events. Make sure to deploy a strategy tailored to your environment. The strategy you choose should match your risk profile, time commitment, and technical understanding.  
  • Carefully evaluate its sophistication: Overall, algo trading strategies are intricate as they are deployed with hundreds of lines of complex code and require quantitative trading skills to manage. Remember that the more sophisticated a strategy, the harder it will be to eventually fix or adjust if you do not truly understand how it is constructed. 
  • Extensively backtest for performance:  To comprehensively determine your risk parameters, you can first analyze your trading system based on historical statistics such as win rate, average wins and losses, and standard deviation etc. Extensive backtesting allows traders to see how the strategy operates in diverse market conditions whilst also visualising the typical results they might expect. This gives you a better idea of whether the strategy is well-suited to your goals.

You can use demo accounts to carefully evaluate your algorithm in real-time without risking any money. However, do note that demo accounts aren’t ideal for high-frequency trading strategy evaluation which would benefit from real-world trials to compensate for slippage and fills that happen in the live environment.

algo trading strategies
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All things considered, today’s modern investment analysts and retail traders require state-of-the-art tools to compete favourably in the financial market. 

With over 50% of all trades in the global equity markets being algorithmic, algo execution has established itself as a vital trading tool for systematic and disciplined execution of trades. Traders should select their algo trading strategies carefully, based on their risk profile and performance goals. 

The article is a part of our comprehensive guide on Algo trading

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