Algorithmic Trading: What It Is and How to Learn It

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Most traders or investors in the financial market dream of having a system that automatically trades for them without their needing to do anything else related to trading. Although no such system really exists, algorithmic trading is very close.

Based on a recent market report, the global algorithm market valued at USD 10.3 thousand in 2018 is expected to grow at a CAGR of 10% over a forecast period (2022-2027). The demand for a fast, reliable and profitable system is spearheading the growth of algorithmic trading.

However, despite the availability of various materials, a beginner with a non-technical background may find it very difficult to follow a systematic approach to learning algorithmic trading. There are a few better ways to learn algorithmic trading, but before that, take a look at some basic things you need to know:

What is Algorithmic Trading?

Algorithmic trading, also known as automatic trading or Algo, involves using a computer to automatically execute trades based on set parameters. Everything in Algo’s trading is data-driven, and you’re free to do other things while your computer takes care of your orders.

Algo traders use high frequency technology to enable trading companies to execute a large number of trades per second. However, despite being automated, auto trading still involves some form of human intervention from time to time to ensure that it is still operating efficiently.

The Difference Between Algorithmic Trading, Automated Trading and Quantitative Trading

Most traders think that Algo trading, automated trading and quantitative trading mean the same thing, but that’s not true.

Automated trading, as the name suggests, means that everything is done automatically. It involves using a computer to create and execute commands automatically. In practice, it is simply to automate the human manual trading process.

Algorithmic trading follows an algorithm to execute trades in accordance with an underlying trading strategy that can still operate manually with the human hand. Automated trading takes a step forward in automating the entire trading process.

On the other hand, quantitative trading, also known as quant or quant trading, involves the use of mathematical and statistical models and programming to analyze and execute orders.

One of the main differences between algo trading and quant trading is that quantitative trading uses a lot of data assets and mathematical models. In contrast, trading relies more on technical analysis.

How profitable is algorithmic trading?

Unlike the traditional trading method, algorithmic trading reduces the time spent observing and analyzing the market, allowing traders to focus on other aspects of their lives.

Trading algorithms take into account 92% of Forex tradesand they can be very profitable if implemented with proper risk management and a good trading plan.

Getting Started with Algorithmic Trading

Many resources on learning algorithmic trading online can be hard to digest. Yet, if you take your learning process systematically, no one can stop you from succeeding at Algo trading.

Here are three steps that any aspiring Algo-trader should focus on when learning algorithmic trading.

#1. Understand the key areas of algorithmic trading

Algorithmic trading has many facets. Therefore, a good understanding of the key areas is one of the first steps in creating a winning algorithm.

The main areas of algorithmic trading include:

Quantitative analysis

Quantitative analysis (quants) involves identifying patterns and creating patterns to access those patterns. The generated patterns are then used to predict the price movement of the securities.
If you are a technical or fundamental trader, you may need to start thinking about quantitative analysis.

Knowledge of financial markets

Naturally, the human mind is designed to learn by observation, and research has shown that spending time observing the chart will increase one’s knowledge of the financial market. This knowledge is crucial if you intend to build an algorithm based on the technique.

Even if you are a pro at technical analysis, be sure to brush up on your technical analysis skills from time to time. A thorough understanding of the following aspects will reduce your algorithmic trading learning curve.

  • Types of trading instruments
  • Business strategies
  • Good risk management
  • Pricing models.
Programming skills

After learning the basics, the next step is to move on to the more advanced aspect of algorithmic trading. If you’ve never compiled code before, it’s time to pick up some programming skills.

Most people consider this aspect the hardest part of learning algorithmic trading, but it’s not as complicated as you might think. Whatever strategy you intend to automate, you will need a programmer to implement your trading strategy.

A good knowledge of C++/Java/Python is necessary for a quant developer, and the best way to learn programming is to practice.

Among several programming languages, most traders prefer to use Python, and there are various algorithmic trading tools and platforms that allow you to create your own trading algorithm.

#2. Become an Algo trading professional

After understanding the fundamentals of Algo trading, the next step is to consider different ways to deepen your professional knowledge in this field.

Interestingly, you can also pursue a career in Algo trading.

First steps with books

Certain aspects of algo trading require a certain degree of mathematical and statistical knowledge, which can be difficult for beginners to understand. Learning through books may not be for everyone, but algorithmic trading books present a simple approach to learning the concepts of automated trading.

Although there are many good books on different algo trading strategies that you can consult, it is necessary to avoid complex mathematical concepts until the basics are understood.

For example, the book “Options, Futures, and Derivatives” by John C. Hull is considered a very good book for beginners.

Once you understand the basics, you can start developing your trading strategy, known as the alpha trading model. Ernest Chan’s Algorithmic Trading and Larry Harris’ Trading and Exchanges discuss trading systems and how to implement them.

Free Resources

In addition to best books on algo tradingyou can also take advantage of various free resources available such as:

  • Online webinars
  • youtube videos
  • Follow Algo trading blogs, such as Experfy Insights blog, where you can find out all about
  • Algo negotiation.
    You can also take free courses on online learning platforms like Udemy, Coursera, Udacity, and edX.

While beginners can get started with these free resources, it’s worth noting that some of these resources have their shortcomings.

For example, algorithmic books may not give you first-hand trading experience, and free courses may offer limited knowledge on the subject.

Learn from the professionals

Learning from a professional or an expert practitioner is one of the fastest ways to acquire a skill. Algo trading involves the use of programming languages ​​like Python. Therefore, it becomes necessary to learn from an expert so that you can interact with the expert while practicing their strategies alongside them.

Opt for MFE programs

Most students who wish to pursue research in the field of algo trading opt for MFE programs. However, if the objective is to make money, it becomes necessary to take a more professional route by placing oneself in the field of algorithmic trading.

Most MFE programs provide insight into mathematical concepts, and it pays to learn from the experience of others when you decide to apply for their courses.

Moreover, MFE programs also give you various opportunities to apply for jobs even when you are still learning.

#3. Learn more and practice on the job

After being placed in the field of algo trading, you can begin to implement your knowledge of algo trading in live markets for your business. You can also learn other processes to add to your workflow chain, as most companies use different approaches to automated trading.

For example, a low latency strategy trading company may be built on C++, while another company may only use Python. It becomes necessary to understand how both work.

In subtle terms, keep learning and practicing on the job. The learning never stops.

The scope of Algo trading is unlimited.

However, learning algorithmic trading requires knowledge of the main areas of trading and some degree of programming skills. It doesn’t have to be technical.

You can master this so-called “difficult” area by following the law learning process. Constant practice of what you have learned is necessary to master the trading industry.

Featured image: Gorodenkoff, iStock.

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