Six Examples of Quant Trading Strategies (and how to create them with no coding required)

Selecting the right trading strategy is one of the most critical components of investing. After all, investors have different objectives, time horizons and risk appetites, all of which lead to differences in strategies.

Trading strategies can be simple or complex, and which you decide to use will depend on the particulars of your analysis. If you want numbers and logic to be your guide, you may be interested in using quantitative trading to identify new market opportunities. However, quant trading isn’t one all-encompassing strategy. There are a variety of strategies that can be used within quant trading to analyze the market. 

This article will explore some of the most common quant trading strategies you can use for sound decision-making. At the bottom of the article, I outline the ways you can create these strategies without code.

What is Quant Trading?

Before looking at strategies, it’s important to understand quant trading as a whole. 

Quantitative trading, or quant trading, is a method of analyzing changes in securities’ prices by using advanced mathematical models and programming. It uses data, such as price and volume, in its analysis. Historically used only by professional investors at a firm, it is becoming increasingly popular with retail investors. Quant trading is desirable because of its logical approach. It does not run the risk of subjective factors, such as emotions, getting in the way. 

Human traders can easily be distracted by spur-of-the-moment feelings and may not always make rational decisions. However, quant trading is not fully automated like in algorithmic trading. Human traders can still play a crucial role in executing trades. 

There are four main steps in quant trading: Strategy identification, backtesting, execution, and risk management. 

• Strategy identification, which we will explore in greater detail, is the selection of a technique to be used in your mathematical model. 

• Once you decide upon a strategy, the model must be extensively backtested. Backtesting is when the model is trained on historical market data to optimize it before using it in the real-world. Backtesting is critical because it provides proof that the strategy works.

• Once backtesting is complete, the model is used in the real-time market to execute trades. 

• As always, with the trading process there are risks involved, so risk management techniques should be employed. These could include scenario analyses and stop-loss orders. 

For a more detailed overview, check out our complete guide to quant trading. 

Is Quant Trading Right for You?

While it may sound straightforward, quant trading is far from it. Advanced mathematics, programming skills, and knowledge of markets are required. Most professional quant traders have advanced degrees in quantitative areas, such as mathematics, computer science, or finance. 

However, there is not an educational requirement as a retail investor. If you do not already have advanced quantitative skills, there will be a steep learning curve. Despite its complexity, quant trading is often of interest due to its ability to digest large amounts of information and efficiently execute trades far beyond the capabilities of human traders. You will have to weigh the pros and cons to decide whether acquiring this advanced knowledge is worth it to you as a retail investor. 

Quantitative Trading Strategies

Here are some common strategies that are used to build mathematical models for quant trading. 

Momentum Investing

Momentum investing looks at trends in a security’s price movements. The idea is to buy stocks that are rising in price and to sell them just as they approach their peak. This is a short-term strategy, and the idea is fairly straightforward. These characteristics make it ideal to use in quant trading. A mathematical model can be created that detects stocks that are trending up or down, making it easier to execute trades. When executed correctly, momentum investing strategies take advantage of visible market trends and high momentum indicators to try and beat classic long-term investments such as index ETFs.

There are various indicators, which are technical tools, used to measure a stock’s momentum. The most common indicator is the relative strength index (RSI), which measures whether a security is overbought or oversold. Momentum investing can be an effective strategy due to its relative simplicity and ability to generate potential desired outcomes. You can find more details on momentum investing here. 

Trend Following

Trend following is sometimes seen as being interchangeable with momentum investing. While there are similarities between the two strategies, there are significant differences that should be highlighted. Both strategies involve looking at price movements and detecting patterns within them. However, trend following is a more long-term strategy to look at the bigger picture. Momentum investing is best used with equities, while trend following can work with a variety of assets, such as equities, bonds, and commodities. 

As with momentum investing, there are many indicators that can be used to measure trends, such as using a simple moving average. This looks at the average price of securities over a period of time. Because of its technical nature, trend following is well suited for quant trading. Mathematical models can be built to analyze these indicators and detect emerging patterns in market data. 

Mean Reversion

The theory behind mean reversion is that over the long term, security prices will gravitate toward an average. By this logic, any large fluctuation is unsustainable and will eventually return to the average. This is a great strategy to use in quant trading. You can program a model to detect markets with a long-term average, and the model can detect when prices are above or below that average. When prices are above the average, it could be a good time to sell at a profit. Although this may seem like a simple strategy, its beauty lies in its simplicity - often, stock price movements are driven by irrational behavior, and a mean reversion strategy can take advantage of that.

Statistical Arbitrage

Statistical arbitrage follows the same logic as mean reversion, but it is on a macro level. You may only be looking at a specific security with mean reversion. With statistical arbitrage, you are analyzing a large group of similar assets and determining an average price. For instance, you could look at a group of securities that are in the same industry, such as fast food. With quant trading, you could create a program that determines the average price for the fast food stocks and detects when prices diverge from this average. As with mean reversion, you can sell for a profit when prices are high. 

Algorithmic Pattern Recognition

Large institutional firms tend to make large trades by using algorithms. Because the firms do not want to affect the market price through their trade, they try to disguise it by spreading it out over multiple exchanges or brokers. Algorithmic pattern recognition attempts to uncover these large trades. A model can be programmed to recognize the patterns of firms’ large trades. While this strategy can be useful to identify stocks to purchase before the large trade drives the price up, it does require powerful high-frequency trading (HFT) systems. 

Sentiment Analysis

This strategy is unique as compared to the others we have discussed. Sentiment analysis is not concerned with market data, which may seem odd at first. However, in the Internet age there is a vast amount of non-financial data that can affect the market. Sentiment analysis usually analyzes text using natural language processing. This could be text from social media posts, news articles, or research reports.  

With quant trading, words can be categorized as positive or negative, with numerical values, or weights, assigned to them. A computer program can quickly read through many articles or social media posts, far more than a human can. The model can then determine the overall sentiment for the securities. Sentiment analysis can sometimes be tricky due to the subjectivity in classifying a sentiment as positive or negative. One should keep in mind that sentiment analysis is usually only one particular input or signal into a larger strategy. Sometimes, the stock price has already reacted before the sentiment has been spread, so one should exercise caution while deploying this strategy.

Quantitative trading is an excellent method for logically analyzing market data to make trades. It cuts out the flaws of manual trading, such as emotional decision making. It requires programming a model to identify potential trades. Before creating a model, a strategy must be identified to build the model on. Six of the most common strategies used in quant trading include momentum investing, trend following, mean reversion, statistical arbitrage, algorithmic pattern recognition, and sentiment analysis. While it can be difficult to achieve the high-level mathematical and computational knowledge required in quant trading, it has become increasingly popular among retail investors due to its efficiency and logic. 

What platforms can you use to create your own quant trading systems?

On the one hand, if you have a strong grasp of programming (Python is a common language to create algorithmic systems), you can build a quant 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

If you prefer to create a quant trading strategy with no-coding involved, check out Composer.

Composer is a no-code algorithmic trading platform. 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. Composer also integrates powerful AI capabilities including chatGPT to make your trading strategies even more robust and intelligent. Note: Composer does not currently support crypto trading or crypto exchanges (no current integrations with Kraken, Coinbase etc.).

If you want to get started, you can use the AI trading assistant to describe the strategy you want to create and it will build it for you e.g. "Create me a momentum trading strategy taking the top 2 of a basket of big tech stocks". Then either tweak the strategy in the no-code editor or using the AI assistant.

Sign up for free to test out the platform here.