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Learn Algorithmic Trading: A Step By Step Guide

For beginners who want to venture into algorithmic trading, this article will serve as a guide to all the things that are essential to get you trading the algorithmic way.

Acquire knowledge in quantitative analysis, trading, programming and learn from the experience of market practitioners in this step-by-step guide as it guides you through the basics and covers all the questions that you would need to know to learn algorithmic trading.

Quick look:

  • Here is What You Should Know
  • Difference Between Algorithmic Trading, Quantitative Trading, Automated Trading, And High-Frequency Trading
  • Steps To Becoming An Algorithmic Trading Professional
  • Frequently Asked Questions about how to learn Algorithmic Trading

With the boom in technological advancements in trading and financial market applications, algorithmic trading and high-frequency trading is being welcomed and accepted by exchanges all over the world.

Within a decade, it is the most common way of trading in the developed markets and is rapidly spreading in developing economies. It is essential to learn algorithmic trading to trade the markets profitably.

Here is What You Should Know

An important point to note here is that automated trading does not mean it is free from human intervention. Automated trading has caused the focus of human intervention to shift from the process of trading to a more behind-the-scenes role, which involves devising newer alpha-seeking strategies regularly.

In the past, entry into algorithmic trading firms used to be restricted to PhDs in Physics, Mathematics, or Engineering Sciences, who could build sophisticated for Algorithmic trading. However, in recent years there has been an explosive growth of the online education industry, offering comprehensive algorithmic trading programs to wannabe algorithmic traders. This has made it possible to get into this domain without having to go through the long (8-10 years) academic route.

Difference Between Algorithmic Trading, Quantitative Trading, and Automated Trading

There is often a lot of confusion between algorithmic trading, automated trading, and HFT (high-frequency) trading. Let us start by defining algorithmic trading first.

Difference In

Algorithmic Trading – Algorithmic trading means turning a trading idea into an algorithmic trading strategy via an algorithm. The algorithmic trading strategy thus created can be backtested with historical data to check whether it will give good returns in real markets. The algorithmic trading strategy can be executed either manually or in an automated way.

Quantitative Trading – Quantitative trading involves using advanced mathematical and statistical models for creating and executing an algorithmic trading strategy.

Automated Trading – Automated trading means completely automating the order generation, submission, and order execution process.

Trading strategies can be categorized as low-frequency, medium-frequency, and high-frequency strategies as per the holding time of the trades.

High-Frequency Trading (HFT) – High-frequency trading strategies are algorithmic strategies that get executed in an automated way in quick time, usually on a sub-second time scale. Such strategies hold their trade positions for a very short time and try to make wafer-thin profits per trade, executing millions of trades every day.

Steps To Becoming An Algo Trading Professional

In the sections below, we outline the core areas that any aspiring algorithmic trader ought to focus on to learn algorithmic trading. We also present our readers with a comprehensive picture of the different ways and means through which these essential skill sets can be acquired.

Step 1: Core Areas Of Algorithmic Trading

Algorithmic trading is a multi-disciplinary field that requires knowledge in three domains, namely,

  • Quantitative Analysis/Modeling
  • Programming Skills
  • Trading/Financial Markets Knowledge

Quantitative Analysis

If you are a trader who is used to trade using fundamental and technical analysis, you would need to shift gears to start thinking quantitatively. Problem-solving skills are highly valued by recruiters across trading firms.

  • Working on statistics, time-series analysis, statistical packages such as Excel, Matlab, R should be your favorite activities.
  • Exploring historical data from exchanges and designing new algorithmic trading strategies should excite you.

Trading Knowledge

This knowledge will be crucial when you interact with the quants and will help in creating robust programs. A professional Coder/Developer in a trading firm is expected to have a good fundamental knowledge of financial markets such as:

  • types of trading instruments (stocks, options, currencies, etc.),
  • types of strategies (Trend Following, Mean Reversal, etc.),
  • arbitrage opportunities,
  • options pricing models, and
  • risk management

View some popular algo strategies here -> Algorithmic Trading Strategies, Paradigms, and Modelling Ideas

Programming Skills

The strategies created by the Technical Analyst are implemented in the live markets by the Programmers.

If you want to excel in the technology-driven domain of automated trading, you should be willing to learn new skills and you shouldn’t be disinclined to any field. So if you have never printed “hello world” by compiling your own coding program, it’s time to download the compiler of your interest – C++/Java/Python/Ruby and start doing it!

The best way is that you should have an expert programmer who can do the coding for you, instead of learning programming u should focus on technical analysis and strategy building.

Step 2: How To Become An Algo Trading Professional?

Getting started with books

Algorithmic trading books are a great resource to learn algo trading. You will find many good books written on different algorithmic trading topics by some well-known authors. As an example,

  • To hone your knowledge of derivatives, the “Options, Futures, and Derivatives” book authored by John C. Hull is considered a very good read for beginners.
  • For algorithmic trading, one can read the “Algorithmic Trading: Winning Strategies and Their Rationale” book by Dr. Ernest Chan.

Free resources

In addition to the algorithmic trading books, beginners can,

  • follow various blogs on algorithmic trading;
  • watch YouTube videos,
  • catch trading podcasts,
  • attend online webinars, or
  • one can also register for the free courses that are available on various online learning portals like CourseraUdemyUdacityedX, & Open Intro.

Although these free resources are a good starting point, one should note that some of these have their own shortcomings.

  • For example, algorithmic trading books do not give you hands-on experience in trading.
  • Free courses on online portals can be subject-specific and may offer very limited knowledge to serious learners.
  • Another important point to note is the lack of interaction with experienced market practitioners when you opt for some of these free courses.

Learn from Professionals/Experts/Market Practitioners

The building blocks in learning Algorithmic trading are Statistics, Derivatives, Matlab/R, and Programming languages. It becomes necessary to learn from the experiences of market practitioners, which you can do only by implementing strategies practically alongside them.


You can join any organization as a trainee or intern to get familiarized with their work ethics and market best practices. If it’s not possible for you to join any such organization then you can opt for classroom courses/workshops or paid online courses. Most of the classroom courses/workshops are delivered in the form of 2 days to 2 weeks long workshops or as a part of Financial Engineering degree programs.

Self-learning Online

On the online front, there are online learning portals such as Coursera, Udemy, Udacity, edX, & Open Intro, that have expert faculty from mathematics and computer science backgrounds who share their experiences and strategy ideas/tactics with you during the course.

Step 3: Get Placed, Learn More, And Implement On The Job

Once you get placed in an algorithmic trading firm, you are expected to apply and implement your algorithmic trading knowledge in real markets for your firm. As a recruit, you are also expected to know other processes as well, which are part of your workflow chain.

As an example, firms that trade low latency strategies will usually have their own platform, whereas, in trading firms where latency is not a critical parameter, trading platforms can be based on a programming language. Thus, it becomes essential for wannabe and new Algo Developers to have an understanding of both worlds.

Recruits working on specific projects may be given brief training to get a good grasp of the subject. Trading firms usually make their recruits spend time on different desks (e.g. Algo Trading Desk, Programming, Risk Management Desk) which gives them a fair understanding of the work process followed in the organization.

To put it in subtle words,

Learning in the algorithmic world never stops!!

Frequently Asked Questions about Algorithmic Trading

Here are some of the most commonly asked questions on Algorithmic Trading.

Question: How to go step-by-step to algorithmic trading from 0 to 90?
Reply: So if you are starting from 0 the key thing to note here is that algorithmic trading typically would have 3 major pillars on which the whole algo trading stands.

  • Statistics & Econometrics
  • Financial Computing
  • Quantitative Trading Strategies

If your knowledge in all these three domains is 0 then the first thing will be to learn about it. There are a lot of resources available out there. There are a lot of resources that are freely available to start with and then progress towards automating.

  • In case you are new to trading strategies then learn about them.
  • If you are already a trader but are looking at automation then you can use some broker API and start automating your strategy.
  • But if you are already doing that, in that case, you can move ahead and get a medium-frequency trading strategy and code it on a vendor platform.
  • If you are an expert programmer yourself or you have a team of expert programmers then you can build your own API as well and build your own trading platform as well.
  • You can code your strategy on that platform and if everything is well set then as an institution or a prop house you can venture out into the high-frequency domain.

That’s typically 0 to 90.

Question: I’m a trader but I don’t know how to program. How should I get started with Algorithmic Trading?
Reply: The good part is for most of the tasks that you would need to do in algorithmic trading, you don’t need hardcore programming expertise in the programming languages, but if you have that, that’s great but even if you don’t have that or have a decent understanding of programming languages, that also works.

Although if you wanted to focus only on the Trading, Technical Analysis, and Strategy building then you should hire Algo Trading Developer like Myalgomate™

Another good part is we have seen so many people who do not have a programming background but have been able to pick up programming languages like Python with much more ease in comparison to the difficulty they use to face with C++ or Java. Though, it will need a lot of effort, time, and commitment on your side if you have never done programming in your life before.



This article gives an overview of algorithmic trading, the core areas to focus on, and the resources that serious aspiring traders can explore to learn algorithmic trading.

Disclaimer: All data and information provided in this article are for informational purposes only. Myalgomate™ makes no representations as to accuracy, completeness, correctness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.

Rishi Ajmera

Started my trading career at the age of 17 and still learning! Developing fully automated trading systems for equities, derivatives, commodities, and cryptocurrencies since 2016. ‘Learn, Unlearn and Relearn!’ – That’s my motto. Hobby – Reading. So many books, so little time! Listen to me on algo trading: Write us at Twitter: @rishi_ajmera


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