Machine Learning Strategies In Trading

 

Both the tech and financial worlds are constantly on the move and this means that innovation runs non-stop. To gain an advantage, especially in the financial space, you have to find tools that allow you to have the best chance at an optimal result.

 

Machine learning (ML) is a prime example and if you aren’t familiar with it, you have come to the right place as below is a look into what it is and how it fits with trade. So let’s check out the background and development of machine learning in trading, common branches and obstacles that you may face when using it.

 

Contents:

Introduction to ML

- In algorithmic trade

- In predictive analysis

- In portfolio optimization

Common branches

- The supervised variety

- The unsupervised variety

- The reinforced variety

Obstacles that are faced when using it

- Quality information & accessibility

- Overstuffing of models

- Lack of training

- Using the tech properly

- Final thoughts

 

Introduction to ML

Machine learning is defined as a computer’s use of algorithms to learn and adapt to perform functions without any outright command. It is a subfield of AI that enables computers to learn patterns and make predictions based on data.

 

Using the human brain as a model, it learns for itself, making it more self-reliant than standard algorithms. This type of technology is incredibly versatile and has already found a place in the following fields:

- self-driving cars

- healthcare, for example, medical image analysis

- mobile applications

- search engines

- video recognition

- Natural Language Processing (NLP) like text analysis, voice recognition or speech recognition

- recommender systems

- predictive analytics and predictive modeling

- robotics and control systems

- finance: risk management and  algorithmic trading, and more

- cybersecurity

 

The above title has shown that it has found its way into the realm of trade. It has become an element of trade that has people rather excited because of just how useful it is.

 

In the financial market, vast amounts of data are generated on a daily basis, making it challenging for humans to analyze and make decisions based on this information. ML provides a solution by using algorithms to analyze and identify patterns in financial data, which can be used to make better investment decisions. Some of the common uses include:

 

A. In algorithmic trade

This is a strategy in which an algorithm recognizes patterns based on past information and traders make decisions based on that. It still requires input on the end from humans, who may not be as accurate. The only difference is that the AI element takes human emotion and error out of the equation, making the decisions a lot more accurate because the patterns learned are easier to process.

 

B. In predictive analysis

In the same breath as the previous use, one may point to the ability to accurately predict outcomes as a strength of straying from mathematical models. This is only possible if the past results provided are accurate. If this is the case, the speedy way in which this piece of tech operates will churn out predictions faster than any human could.

 

C. In portfolio optimization

Portfolio optimization is simply the process in which the best stock available is selected for trades or investment. This is something that requires the analysis of tons of information, something that no human does as quickly and accurately.

 

These are just a few cardinal areas where a tool like this would make partaking in these activities easier. As a result, its importance in the field will carry on as it further develops.

 

Common branches

This branch of AI is applicable to trade in many different ways. As such, the term itself doubles as an umbrella that has many different categories that best suit certain aspects of the trade. Three of those include:

 

A. The supervised variety

This category focuses on labeled datasets as guides that train mainly regression and decision tree algorithms to better classify information and accurately predict outcomes. This makes it suitable for use in prediction-based activities that require accuracy.

 

B. The unsupervised variety

This is the opposite of its supervised counterpart and relies on clustering algorithms and neural networks to find patterns in large groupings. As such, they can't exactly predict outcomes, but lead traders down some proven paths.

 

C. The reinforced variety

This version follows models as they perform tasks based on the likely positive or negative reward. In the sense of trade, an agent would look at as much information as possible and gauge through historical results in particular, whether a strategy is effective or not. It may also be an indicator of whether or not strategies should be upgraded.

 

Obstacles that are faced when using it

As useful as such tech is, it is not exactly perfect. There are some obstacles you have to expect if you want to use it in the stock or crypto market and they are:

 

A. Quality information & accessibility

The data provided may not always be the best. If this is the case, the results will be misleading. So, all information has to be screened and verified.

 

B. Overstuffing of models

This occurs when a model can't handle large amounts of information at a time. The result of this is slow performance.

 

C. Lack of training

An adequate amount of data is needed for proper training. Without said abundance of information, models will find situations they can't handle.

 

Using the tech properly

With all the pitfalls that you may encounter while using the technology of this kind in trade, it's important to see just how to best apply it. To do so, a team would need to properly implement the AI and this requires the following:

- adequate skills in feature selection and data engineering

- similar aptitude when it comes to the area of domains

- a keen eye to better choose and access models

 

Final thoughts

The fact that machine learning further automates the process is why great focus is put on it in this and many other fields. One thing of note is that how you use the solution will often depend on the type of branch you want to use.

 

That said, the tech is relatively new and based on what experts say, the amount of improvement it can undergo at this rate is quite small. This doesn't completely shut the door on it, which is already incredibly useful. But it does put a question mark on the future, which is exciting.

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