9 Examples of Established Algorithmic Trading Strategies (And how to implement them without coding)

Algorithmic trading is one of the most effective intraday trading approaches in existence. As computer programs improve the ability to program increasingly complex and advanced algorithms, algorithmic trading continues to become more refined and also generate healthy returns. But algorithmic trading has a relatively high barrier to entry. Because it's dependent on the instructions programmed into the algorithm a trader uses, this trading method needs careful strategizing.

Thankfully, several robust algorithmic trading strategies have emerged. These strategies tend to deliver consistent results. Investors interested in learning more about the possibilities of algorithmic trading can use these well-established strategies. Here, we've gathered some of the most common strategies along with concrete examples of their performance.

If you're looking for the best algorithmic trading bot platforms, check out a comprehensive list here.

There's also a list of the best algorithmic trading platforms (with no coding involved) to execute the strategies at the bottom of this article.

What Is Algorithmic Trading?

Algorithmic trading is an automated trading process. Split-second buy and sell orders are made according to algorithms, sets of computer-programmed directions used to solve problems. Algorithms can be built with high levels of complexity, which is why computer programming is required to write these algorithms and execute them. These automated systems process order executions according to their preprogrammed trading instructions. Algorithms make their decisions by monitoring market variables like timing, price and volume. 

Algorithmic trading uses complex formulas, mathematical modeling and human oversight to automate the buy and sell decisions they make. Many algorithms have built-in, high-frequency trading technology to make hundreds or even thousands of executions in just a few seconds. This makes algorithmic trading incredibly efficient for intraday trading, as this speed would be almost impossible to achieve without it.

Top Algorithmic Trading Strategies 

As mentioned above, algorithmic trading requires careful programming of each algorithm used to make these crucial buy and sell decisions. Traders can create multiple trading strategies by simply switching out one algorithm for another depending on their needs or desires. Here are some of the most often-used algorithmic trading strategies and examples.

Momentum

Momentum trading is a classic day-trading strategy that has been delivering results for more than 80 years. It was only a matter of time before traders decided to leverage this investing method by combining it with algorithmic trading. Though future performance is never guaranteed, the fundamental idea behind momentum trading is to make predictions on future values based on values that have been previously observed. 

Examples of momentum trading in action are straightforward. Investing activity literally follows the momentum of a specific stock. If the price is rising, momentum trading strategy calls for purchasing that stock to drive the price higher until it reaches a certain threshold. Then, the strategy calls for a sale. Momentum trading is most useful in highly controlled situations with very short holds, making it ideal for algorithmic trading. You can read more about momentum trading here.

Trend Following

Trend following is also known as time-series momentum. It's related to momentum trading in that it seeks to generate profit through expectations that future asset price returns will be in the same direction of that asset's historical returns. 

Strategies for trend-following use closely defined market situations like range breakouts, volume profile skews or volatility jumps. The "simple moving average crossover" is one of the most well-known strategies. It works by identifying stocks that have short-period moving average values that surpass their long-period moving-average value. This triggers a buy order. If the inverse happens, this triggers a sell order.

Risk-On/ Risk-Off

Risk-on/ risk-off is a strategy where the changes in investor risk tolerance are monitored closely in response to global economic patterns. Under a risk-on/ risk-off strategy, periods when risk is perceived as low dictate that investors make higher-risk investments, with the reverse also being true.

Applying this strategy in practical terms is complex, as it involves monitoring several factors, including actions and statements made by global central banks, macroeconomic data, corporate earnings and others. Algorithms can be used to analyze these data points and help make determinations on whether the risk in a certain market is trending high or low.

Inverse Volatility

Inverse volatility strategy is often used in conjunction with markets for exchange-traded funds (ETFs). This strategy involves buying inverse volatility ETFs to hedge against portfolio risk by gaining exposure to volatility. Doing so makes it no longer necessary to buy options. Investors can see substantial returns if volatility remains low. This is because an inverse volatility ETF bets on market stability being the prevailing condition.

Practical use of this strategy includes using a specific metric: the Cboe Volatility Index (VIX). When an ETF's benchmark volatility rises, it loses value. Using algorithmic trading to monitor an ETF's volatility on the VIX can help automate buy and sell orders to limit losses and maximize gains.

Black Swan Catchers

The black swan event is a financial term that is used to describe an unpredictable event that lies beyond normal expectations but has potentially disastrous outcomes. Nicholas Taleb describes the Global Financial Crisis as a black swan event in his famous writings. Another more recent example would be the COVID-19 pandemic.

Catching a black swan, so to speak, is an investment strategy that leverages the intense market volatility following such an event. It revolves around finding speculative markets like options contracts and others that traditionally skyrocket whenever a black swan shows up. These so-called tail risk strategies can benefit strongly from using algorithmic trading to monitor market levels, identify black swan events and trigger investment in the opportunities these events leave behind.

Index Fund Rebalancing

Index funds are linked to benchmark indices. Each fund has a defined period where it goes through a rebalancing to bring its holdings in line with its index. When this occurs, algorithmic traders can capitalize on the event. The trades that this rebalancing brings can offer profits of anywhere between 20 to 80 basis points, depending on how many stocks are in the index fund prior to rebalancing.

Algorithmic trading systems excel in these environments as they can make buy and sell decisions much more quickly than human beings. An algorithm can initiate rebalancing trades in the timeliest manner. This provides untold opportunities for the best and most advantageous prices, maximizing profit opportunities.

Mean Reversion

Mean reversion strategies are based on the temporal nature of high and low asset prices. The concept is that assets will revert to their average (or mean) value periodically.  The challenge, therefore, is to identify when such a mean reversion is about to take place and act accordingly. If a reversion is poised to drive the price higher, it's time to buy; likewise, if reversions are going to drop the price, it's time to sell.

Algorithmic trading is the perfect tool to both identify and define a price range for an asset. Then, whenever the price of that asset breaks out of its defined range and indicates a mean reversion, the algorithm can be configured to automatically place the appropriate trades.

Market Timing

Market timing strategy is all about waiting until the perfect moment to buy or sell an asset. This strategy can be hit or miss, as an investor can wait for an asset to hit what they perceive to be an all-time low only to see the price drop even further after investing in it. Likewise, investors can miss out on profit if they sell an asset when a perceived high hits, only to watch its value climb higher after their sale.

Algorithmic trading can help smooth out these issues with market timing. By analyzing current market trends and comparing them against historical activity, algorithms can aid in determining whether an investor's timing choices are accurate. While still not perfect, using algorithmic trading in this way can reduce false starts by a significant margin.

Arbitrage

Arbitrage investment is when an investor simultaneously purchases and sells the same asset in different markets. The goal of arbitrage is to profit from small — often infinitesimal differences — in the listed price of the asset. Arbitrage is most effective when exploiting short-lived price variations in the same or nearly identical assets in different forms or different markets.

Arbitrage requires pinpoint accuracy in buy and sell orders to maximize profit. This is where algorithmic trading can be extremely helpful. Algorithms can monitor relevant markets for timing and help eliminate issues related to risk and transaction costs quicker than a human trader can. 

How can I implement algorithmic trading strategies without code?

In short, algorithmic trading is just sending a set of instructions to computers to automate the trades you want to make. In all the examples above, the structure is very similar to: when X happens, do Y. In the past you would need to code to create these algorithms (for computers to execute) but given the recent advancements in technology, you can also set up algorithmic trading strategies without any code.

If you have a strong grasp of programming (Python is a common language to create algorithmic systems), you can build an algorithmic trading system from scratch. Check out the following example code tutorials:

• Using Python and Alpaca to create a SMA algorithmic trading bot

• Using Python and Alpaca to create an RSI algorithmic trading bot

What no-code platforms can I use to create and implement algorithmic trading systems?

If you prefer to create an algorithmic trading strategy with no-coding involved, check out the following platforms:

• Composer: Composer is a no-code algorithmic trading platform (with an AI copilot powered by ChatGPT4). It offers an intuitive drag-and-drop interface that allows you to build, test, and deploy sophisticated trading strategies with ease. With a comprehensive library of pre-built indicators, professional grade data, and trading actions, Composer empowers you to create a wide range of strategies without writing a single line of code.

• EquBot AI Watson: EquBot uses IBM Watson to make trading decisions, including analysis of news articles and social media. If you're looking for an algorithmic trading strategy that uses alternative data this could be a platform to consider.

• Tickeron: Tickeron is a subsidiary of SAS Global Corp. with customizable AI bots, providing dynamic price alerts for trade timing for stock, exchange-traded fund (ETF), forex and crypto pattern recognition. Tickeron’s AI pattern recognition surfaces daily top-ranked stock price patterns and provides a confidence level for trading ideas. It also has robust trend forecasting tools that can help provide predictions for future price levels.

Check out this article for the full list.