Learn Algorithmic Trading: Steps to Become an Algo Trading Professional

Imagine having a strategy that looks at market data, processes it, and then tells you when to place trades—without any manual intervention. That’s the power of algorithmic trading: it transforms your trading ideas into automated systems that handle trades for you.

The process of algo trading involves creating a trading strategy, coding it into a program, and then testing it using historical data through backtesting. Once refined, the algorithm is deployed in live markets to automatically execute trades, allowing you to capitalise on opportunities without constant manual input. If you are someone who wants to learn all about algo-trading, you’re in the right place! This blog covers the steps to become an algo trading professional. And one of the most crucial steps is “learning”.

EPAT (Executive Programme in Algorithmic Trading), is an algorithmic trading course that offers a comprehensive 6-month curriculum for learning algorithmic trading. EPAT is designed for both beginners and even seasoned traders who are now aspiring to establish their own trading desk. The programme includes over 120 hours of live online lectures and 150+ hours of recorded material.

EPAT covers a broad range of topics, including Python, market microstructure, data analysis, and machine learning, all taught by world-class faculty. Not only this, EPAT also provides ongoing support after completion and access to an extensive network of placement partners. This blog will cover the essential steps for learning algorithmic trading to become an algo trading professional.

Here’s what we have covered in this blog:

Key things to know about algorithmic trading

In algorithmic trading, the trading signals (buy/sell decisions), in the financial market, are generated based on a set of instructions. The trades can be placed either manually or by semi/fully automating the order placement and execution process. The key feature of algorithmic trading is reducing human intervention in the process of trading. ⁽¹⁾

On that note, algorithmic trading does not mean it is free from human intervention. Algorithmic trading has caused the focus of human intervention to shift from trading to a more behind-the-scenes role, which involves devising newer alpha-seeking strategies regularly. The video below explains the role of human intervention clearly.

But you cannot intervene or rather even build a system without learning about algo trading. So do you need to have a PhD in Physics, Mathematics, or Engineering Sciences to build sophisticated quant strategies and models for trading? Maybe in the earlier days, but not anymore.

Recently, there has been explosive growth in the online education industry, offering comprehensive algorithmic trading programs to aspiring algorithmic traders. This has made it possible to enter this domain without going through the long (8-10 years) academic route.

Consequently, there is a growing demand to learn algorithmic trading.

Trading strategies can be categorised as per the holding time of the trades.

• Low-frequency - Involves fewer trades with a latency (time taken between order generation and exchange receiving the order) of more than 20 milliseconds.
• Medium-frequency - More frequent trades in which the latency is between 1 to 20 milliseconds.
• High-frequency - This trading type gets executed in an automated way in less than 1 millisecond of latency. HFTs hold their trade positions for a very short time and execute millions of trades every day.

Moving forward, we will now discuss the question that comes to the mind of both beginners in the trading domain and seasoned traders alike. And that question is, “Why should I learn algorithmic trading?”

Why should you learn algorithmic trading?

As trading and financial markets embrace technological advancements, algorithmic trading, and high-frequency trading are gaining widespread acceptance across global exchanges. Within a decade, they have become the predominant trading methods in developed markets and are swiftly gaining traction in emerging economies. For a trader, it is imperative to know that learning algorithmic trading gives you an advantage over manual trading amidst this technological shift.

There is a growing demand to “learn algorithmic trading”. The reason is the growing “demand for algorithmic trading” in the financial sector.

Reputed global banks and investment giants are investing in Quants for the future of trading. Back in 2019, Bloomberg reported, "JPMorgan Arms Coders With Trading Licences as Quants Advance". ⁽²⁾

In early 2020, Forbes reported:

• Citigroup started planning to hire 2,500 programmers for its trading and investment banking units. Citigroup’s 75% of trades were electronic by 2019.
• Goldman Sachs started hiring Coders, Data Scientists, and Engineers for their Trading floor. ⁽³⁾

According to research, “Approximately 80% of investments are either quant-based or fully passive, with only one-fifth of trades actively plotted out by sentient lifeforms.” ⁽⁴⁾

Moreover, as per the numbers in August 2024, the sub-reddit Algo Trading on Reddit has 1.8 Million active users, with thousands joining in daily. The growth indicates the interest of people towards this growing domain.

Hence, banks, investors, and financial institutions are drawn towards this field which is rising quickly and has been adopted globally.

Whether you are a complete beginner or a seasoned trader, algorithmic trading can be learnt from scratch and even if you are already in an unrelated professional background.

Recommended reads:

• 10 Doubts About Algorithmic Trading You Should Clarify
• Blindness couldn’t stop Pranav’s pursuit of Algo Trading
• Akshat's story: Guitarist, Entrepreneur, and now an Algo Trader
• 17 years of Engineering in Japan to Algorithmic Trading | Praveen Singh from India
• An Algorithmic Trading Guide For Retail Traders
• How Can Algorithmic Trading Add Value To Finance & Tech Grads?
• How Can An MBA In Finance Become A Quant?

Let us now check out the essential steps for becoming a professional in the algorithmic trading domain.

Steps for becoming a professional

Step 1: Learn the necessary skills for algorithmic trading from the various resources

Below are the necessary skills for learning algorithmic trading.

• Financial markets - This knowledge will be crucial when you interact with the quants and will help in creating robust programs. 

This knowledge should be about:

• • Types of trading instruments (stocks, options, currencies etc.),
  • Types of strategies (Trend Following, Mean Reversal etc.),
  • Arbitrage opportunities,
  • Options pricing models, and
  • Risk management.

• Programming - If you want to excel in the technology-driven domain of algorithmic trading, you should be willing to learn new skills. A very important step in learning algorithmic trading is knowing how to program.

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 and start doing it! The best way to learn programming is to PRACTISE!

Sound knowledge of programming languages like Python is a prerequisite for a Quant Developer job (quant jobs) in trading firms. It is also interesting to learn that Python is the most preferred choice among traders.

Recommended read:

Python trading libraries

• Quantitative analysis for trading strategies - If you are a trader who is used to trading with fundamental analysis only, you would need to shift gears to start thinking quantitatively as well. Problem-solving skills are highly valued by recruiters across trading firms. Working on statistics,  time-series analysis should be your preferred activity. Exploring historical data from exchanges and designing new algorithmic trading strategies should excite you.
• Data science and machine learning - Data science and machine learning are another important part of learning algorithmic trading. Rather, it is better to say they empower algorithmic trading by enabling the analysis of vast datasets, identifying patterns, and optimising trading strategies.

Resources to learn the skills mentioned above

Books to learn Algorithmic Trading

You will find many good books written on different algorithmic trading topics by some well-known authors that can help you learn algorithmic trading. Here are some useful books that can help:

• Find a list of good reads here → Essential Books on Algorithmic Trading.
• FREE Algorithmic Trading Book - A Rough and Ready Guide  and Python Basics Handbook.
• To hone your knowledge of derivatives, the “Options, Futures, and Derivatives” book authored by John C. Hull is considered a very good read.
• For algorithmic trading, one can read the “Algorithmic Trading: Winning Strategies and Their Rationale” book by Dr. Ernest Chan.

Free resources to learn Algorithmic Trading

In addition to the books mentioned above, you can refer to various free resources to learn algorithmic trading:

• Blogs - Follow and read various blogs on algorithmic trading
• Videos - Watch YouTube videos to learn algorithmic trading
• Podcasts - Catch trading podcasts
• Webinars - Attend online webinars about algorithmic trading
• Free courses - One can also register for the free courses such as the learning track of 8 free-courses meant for beginners in quantitative trading.
• Workshops - Attend workshops like this one - Algorithmic Trading 3-day Workshop - Complete Recording and Slides
• Websites - Learn algorithmic trading from some of the best websites for Quants

For further understanding, consider diving into an algo trading book to strengthen your foundation alongside these free resources.

Recommended read:

Top 10 Blogs on Algorithmic Trading | 2023

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

• Algorithmic trading books do not give you hands-on experience in trading.
• Free courses to learn algorithmic trading on online portals can be subject-specific and may offer very limited knowledge to serious learners.
• Lack of interaction with experienced market practitioners when you opt for some of these free courses.

Paid resource for learning algorithmic trading

A 6 month comprehensive course on Algorithmic Trading with certification

Keeping in mind the need for an online certification programme for working professionals in the domain of Algorithmic and Quantitative Trading, QuantInsti offers a comprehensive hands-on course called the Executive Programme in Algorithmic Trading (EPAT).

The objective of EPAT is to make you market-ready for the world of algorithmic trading upon successful completion of the coursework. Hundreds of course participants from over 70+ countries working across different sectors such as financial markets, technology, and quantitative finance have benefited from the programme in various ways.

Modules of EPAT:

EPAT is an online part-time course in which you get access to the following modules:

Features of EPAT:

The salient features of the EPAT algorithmic trading course are in the image below.

Moreover, the webinar mentioned below will help you with the information regarding how the algorithmic trading course (EPAT) is of help while learning the integral skills needed for algorithmic trading.

How EPAT Can Help You! | WEBINAR

Recommended read:

Success stories

Step 2: Learn about algorithmic trading strategies

Developing trading strategies is the core of algorithmic trading since the execution of algorithm-based trades depends on these strategies. The strategies are the rules or the defined set of instructions in the form of algorithms to carry out trades.

Learn how to build and improve trading strategies with in-depth algorithmic trading courses that cover both beginner and advanced concepts.

This video below mentions the top 15 algorithmic trading strategies in terms of popularity.

Step 3: Learn backtesting as it is crucial for algorithmic trading

Backtesting allows you to test your trading strategies using historical data to see how they would have performed in the past. This step is crucial because it helps you understand the potential risks and rewards before applying the strategies in real markets.

One free software meant for backtesting is Blueshift which can help you get an idea about your strategies’ performance.

Also, this video tutorial will help you gain clarity about how backtest and live trade using Blueshift:

Recommended course:

Backtesting Trading Strategies

Step 4: Know all about paper trading for a smooth trading experience

Paper trading is the practice of simulating trades without using real money. It helps you gain experience and confidence by allowing you to test your strategies in a risk-free environment. This practice is important for refining your approach before you start trading with actual funds.

Step 5: Explore different roles to find the best fit for you in the algo trading domain

Algorithmic trading offers various roles, from quantitative analyst to software developer. Each role requires different skills and focuses on different aspects of trading. Understanding these roles will help you identify which one aligns best with your strengths and career goals, ensuring a more successful and satisfying career in the field.

Step 6: Network to find out more about becoming an algo trading professional

It becomes necessary to learn algorithmic trading from the experiences of market practitioners since they have experience of the mistakes they made which can contribute greatly to one’s learning journey.

Step 7: Get placed while focusing on all the skills

To kick off your career, you can join any organisation as a trainee or an intern and get familiarised with the basic work ethics and best practices.

It is often seen that students who would like to get placed in high-frequency trading firms or in quantitative roles, go for MFE programs.

• Most of the MFE programs give a very good overview of mathematical concepts including Calculus, PDE and Pricing Models.
• For learning algorithmic trading and quantitative trading strategies, what is also required is the implementation of these skills/theories on actual market data under a simulated environment.
• It is always better to get trained by practitioners and traders themselves if the aim is to experience a smooth learning journey leading to practical achievements.
• If you would like to pursue research in these fields, then taking an academic route is recommended.

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 new recruit, you are also expected to have knowledge of other processes as well, which are part of your workflow chain.

As an example, firms which trade low latency strategies will usually have their platform built on C++, whereas in trading firms where latency is not a critical parameter, trading platforms can be based on a programming language like Python.

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

Recommended reads:

• Algorithmic Trading Strategies, Paradigms And Modelling Ideas
• Paper Trading: Trading using virtual money!
• How to Backtest a Trading Strategy
• Algorithmic Trading in Commodity Markets

The workshop video below is a comprehensive video, covering almost everything you need to know about learning algorithmic trading.

Moreover, if you want to learn to build trading algorithms from the ground up, you will find the below session to be useful. It provides you with a solid foundation in algorithmic trading and equips you with the necessary skills to create and test your own trading strategies using real market data.

Build Your Trading Algorithm from Scratch | Algo Trading Tutorial

Step 8: Business aspirants can set up own algorithmic trading desk

In case you are one of the traders who wishes to set up their own algorithmic trading desk, you must learn about the same. This includes knowledge of trading infrastructure, strategy development, risk management, compliance, and the technology needed to execute trades efficiently. Mastering these aspects will enable you to create a robust and successful trading operation tailored to your goals.

Recommended read:

Setting-Up An Algo Trading Desk

The video below mentions how learning from EPAT can help set up your own algorithmic trading desk.

Also, here is another video which discusses how to set up your algo trading desk.

As we have reached the end of this blog, the most important take away that we would like to leave you with is the fact that“Learning in the algorithmic world never stops!!”

Frequently asked questions about how to learn algorithmic trading and career opportunities

Q: How can I stay updated in the field of algorithmic trading?

A: Continuous learning through online courses, industry conferences, networking with professionals, and staying informed about market trends and technological advancements is essential.

Q: What are some common algorithmic trading strategies?

A: Common strategies include trend-following, mean-reversion, arbitrage, and market-making. Each has its own set of rules and risk factors.

Q: How important is backtesting in algorithmic trading?

A: Backtesting is crucial as it allows you to test your strategies against historical data to evaluate their effectiveness before applying them in live markets.

Q: What career opportunities are available in algorithmic trading?

A: Career options include quantitative analyst, quantitative developer, algorithmic trader, risk analyst etc. Each role requires specific skills and expertise in trading and finance.

Q: Do I need a financial background to work in algorithmic trading?

A: While a financial background is helpful, strong analytical, mathematical, and programming skills are often quite critical in this field.

Conclusion

This blog gives an overview of algorithmic trading, the core areas to focus on, and the resources that serious aspiring algorithmic traders can explore to learn algorithmic trading. Do let us know your thoughts on it and feel free to share any suggestions in the comments below.

In case you are also interested in developing lifelong skills that will always assist you in improving your trading strategies, you can explore our course EPAT. In this algo trading course, you will be trained in statistics & econometrics, programming, machine learning and quantitative trading methods, so you are proficient in every skill necessary to excel in quantitative & algorithmic trading. Know more about EPAT now!

Author: Chainika Thakar (Originally compiled by Viraj Bhagat)

Note: The original post has been revamped on 16^th September 2024 for recentness, and accuracy.

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