Good-To-Know: 4 Effective Quantitative Trading Strategies

The trading market today is very different from what it was years ago. It has become more sophisticated and efficient. The strategies that worked ten years ago are no longer applicable today.

So, how are you going to become successful in the trading market nowadays? Well, here are four quantitative trading strategies you should know.

1. Alternative Data

Alternative data refers to non-traditional data that has predictive value in the financial market. Some popular examples of alternative data are location data, satellite imagery, weather data, consumer expenditure data, and web-scrapped data.

In addition to traditional trading data, traders need quality alternative data to stay ahead of the competition. That’s why traders and hedge funds collect alternative data from reliable sources. They either buy the information or collect the data themselves.

Some examples of effective alternative data strategies are the following:

• Walmart parking lot satellite images allow hedge funds to determine the number of people going to Walmart, which allows them to predict Walmart’s sales figures.
• Surveyors record the number of trucks leaving the factory to predict Company A’s revenue.
• Social medial foot traffic data helps traders to predict a restaurant’s sales figure.

2. Obscure and Small Markets

One of the effective trading strategies nowadays is performing trades in obscure and small markets. Obscure markets are those that are less popular and regulated. Small markets can only absorb a small amount of trading volume and do not involve large price movements.

So, why should you trade in these types of markets? Well, the answer is simple. Obscure and small markets are less efficient, and they offer more opportunities. Those opportunities provide consistent profits.

You have to take into account that once the market gets popular and attracts big players, the market behavior will change, and opportunities get eroded significantly.

3. High-Frequency Trading

High-frequency trading (HFT) defines trading that requires high computing and communication speeds. Traders use communication speed to profit and outwit other traders. HFT is characterized by expensive software infrastructure, low trading profit, and a large volume of trades.

There are several types of HTF, which include the following:

• Arbitrage trades – It is when an asset is priced differently on two exchanges, and the trader buys the cheaper one.
• Latency Arbitrage – This is a strategy where HFT traders profit to the detriment of slower trading investors. So, the HFT hedge fund will buy the stocks and then sell them back to the slower hedge fund for a small profit.
• Statistical Arbitrage – This is a strategy that employs large, diverse portfolios that are traded on a very short-term basis. When the stocks diverge, the HFT trader will buy the cheaper one.
• Index Arbitrage – This strategy is designed to track the returns of an index like the S&P500.
• Reaction to news – In this strategy, HFT trader needs to analyze the news and fire the trade. The trader who reacts the fastest wins.

Since HFT involves fast communication speed, HFT funds spend hundreds of millions on software and hardware infrastructure to reduce communication and computing speed. 

The most important thing when it comes to HFT is to react faster than your rivals.

4. Machine Learning

Machine learning (ML) is the ability of computers to learn by analyzing data or through their own experience. 

Here’s a simple explanation of how it works:

Traditional computing rules will be:

If an image has four legs, fur, pointy ears, and whiskers, label it a cat.

Machine learning rules will be:

Give the computers 1,000 pictures of cats and 1,000 pictures that are not cats. The computer analyzes the pictures, and then the computer will be able to tell if a picture contains a cat.

So, why machine learning is beneficial? Here are some of the advantages:

• It can analyze large volumes of data.
• It understands texts.
• It can interpret images.
• It can come up with creative solutions.
• It can analyze and make predictions fast.

So, what now?

These strategies work, but it takes a lot of hard work. Executing these strategies is not straightforward. You will experience failure, and you need to improve. You can always try, fail, and improve until you get successful results.

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