Algorithmic trading involves executing trade orders using algorithms based on predefined instructions. But a common question that aspirants often find themselves asking is:
What academic background is needed for algorithmic trading?
The answer is quite straightforward!
Certain undergraduate and postgraduate degrees cover subjects that provide the essential skills for getting started with algorithmic trading. Having one of these degrees, which we have mentioned in this blog, can aid in thoroughly learning and understanding the necessary concepts.
Additionally, this blog addresses frequently asked questions from professionals and students looking to start algorithmic trading from scratch.
This blog covers:
- Overview of algorithmic trading
- Undergraduate and postgraduate degrees for algorithmic trading
- Resources for learning algorithmic trading
- Case studies of success despite unrelated backgrounds
- Frequently asked questions about education required for algorithmic trading
Overview of algorithmic trading
Algorithmic trading automates trade execution using computer algorithms based on predefined criteria. This method enhances efficiency and precision, allowing trades to be executed at speeds and frequencies beyond human capability.
Algorithms are sets of instructions based on market conditions that dictate when to buy or sell. The speed at which these algorithms operate allows trades to be executed in milliseconds, and their precision reduces human error, ensuring accurate timing and volume of trades. Enhance your trades with an advanced Algorithmic Trading Platform.
The benefits of algorithmic trading include the automation of repetitive tasks, the ability to backtest strategies on historical data, and the elimination of emotional decision-making.
However, there are risks involved, such as system failures that can lead to significant losses and the potential for over-optimisation, where strategies that perform well in backtesting may not do as well in live markets. However, despite these risks being there, measures can be taken to avoid the same with certain trading related risk management techniques such as putting stop loss, position sizing etc.
Going forward, here is a video which you can watch to get an overview of learning algorithmic trading.
Let us now look at some undergraduate and postgraduate degrees that can hel you pursue algorithmic trading.
Undergraduate and postgraduate degrees for algorithmic trading
In this section, I have listed degrees that are beneficial for aspiring algorithmic traders. Algorithmic trading encompasses various job roles, such as quantitative analyst, quantitative developer, and risk analyst. Based on your skill set, you can choose to specialise in a particular role.
But, you need to have a basic know-how of all the other roles simultaneously for better coordination while working with employees having the above-mentioned job roles. For example, if you specialise as a quantitative analyst, you must understand the basic coding skills of a quantitative developer to communicate the data models you create effectively. Similarly, if you are a quantitative developer, a basic understanding of risk analysis is crucial to ensure that the algorithms you develop adhere to the firm’s risk management strategies.
For instance, while showing the maximum drawdown for a stock, the meaning of maximum drawdown needs to be well understood so that you can code the right conditional statements.
To get started with algorithmic trading, certain undergraduate and postgraduate degrees are especially beneficial. The degrees mentioned below typically cover subjects that provide the essential skills and knowledge required for this field of algorithmic trading.
Undergraduate & Postgraduate degrees:
|
Degrees |
Skills that will be gained to make a base for learning algorithmic trading |
|
Computer Science |
Programming, Hardware & Architecture |
|
Mathematics/Statistics |
Statistics & Probability, Linear Algebra and Calculus |
|
Finance & Economics |
Fundamental analysis, Trading/Finance (Basics of markets), Risk management, Econometrics and Portfolio management |
|
Financial engineering |
Machine learning, Statistics and Probability Theory, Stochastic calculus, Risk management, Programming, Quantitative analysis, Econometrics, Derivative pricing and Portfolio management |
And, if you already possess any of the above-mentioned degrees, then you can focus on the skills which you haven’t acquired by learning from the resources we will discuss next.
Resources for learning algorithmic trading
Whether you are looking to learn missed-out skills or to gain in-depth know-how on existing skills, the resources below will serve the purpose:
Learning tracks
In the learning tracks, each learning track consists of a bundle of courses and an easy transition from beginner-level courses to advanced-level courses.
Here are a couple of learning tracks specifically for algorithmic trading beginners.
Learning Track: Algorithmic Trading for Beginners for:
- Python Programming
- Trading with machine learning and mathematical concepts
- Trading strategies: quantitative trading strategies, day trading strategies, options trading (Basics of markets)
This learning track is tailored to provide a comprehensive introduction to algorithmic trading for beginners, equipping learners with essential tools and knowledge to navigate the world of automated trading. From programming basics to applying machine learning and crafting trading strategies, this track builds a solid foundation for your trading journey.
Learning Track: Machine Learning and Deep Learning in Financial Markets for:
- Introduction to machine learning
- Basic concepts of machine learning such as decision trees and neural network in trading
Courses
As far as the individual courses are concerned, there is a particular course, that is, Executive Programme in Algorithmic Trading (EPAT) which can be taken up. A 6-month long comprehensive algo trading course builds the knowledge and expertise in:
- Quantitative analysis
- Statistics
- Trading
Blogs
- Python for trading section includes a lot of blogs to get started with Python for trading. You can learn about important libraries and their installation, how to debug your code and write simple to advanced algorithms for trading. Moreover, the blogs are also there to help you learn backtesting with Python.
- Automated trading section consists of all the blogs to learn how to automate your trades using different tools and platforms: Python, R, Interactive Brokers, Alpaca, Zerodha, Blueshift and many others.
- Machine learning section includes blogs to help learn basics to advanced concepts in machine learning and its implementation in financial markets.
- Portfolio and risk management section will help you learn everything from portfolio construction to analysis, optimisation and risk management. Moreover, you will learn from market practitioners who share their knowledge and downloadable files for free.
Having said that, numerous case studies show how algorithmic trading can even be learned from scratch in case you have already graduated or post-graduated from an unrelated field.
This shows that you need not worry if you are already a professional in some other field and now wish to switch to algorithmic trading completely or partly. Let us see how with this section on case studies next.
Case studies of success despite unrelated backgrounds
We will now discuss the case studies of individuals who began algorithmic trading from scratch for making a career in algorithmic trading and became successful due to their grit, determination, and willingness to learn all the necessary skills.
- Starting with no programming background, Melvin Soon is now an Algo Trader!
Melvin Soon, a Chemical Engineer at an Oil and Gas MNC, was always intrigued by algorithmic trading.
Despite not having any background in one of the most essential skills needed for trading such as Python knowledge, he decided to pursue his interest and enrolled in the EPAT course at QuantInsti in 2022. Starting with no coding knowledge, Melvin dedicated himself to mastering Python and algorithmic trading techniques. By actively rewriting codes, using Python notebooks, and applying real-life examples, he quickly became proficient. Melvin's commitment to learning transformed him into a confident algorithmic trader.
- Megan’s journey in achieving goals through learning Algo Trading!
Megan Lester, a Biochemistry student at the University of Bristol, transitioned into algorithmic trading through Quantra’s free quantitative trading courses. Initially possessing only basic Python skills, Megan enrolled in Quantra's eight-course track, which introduced her to Python applications in finance, including Numpy and Pandas. She found the Jupyter notebooks and coding exercises particularly helpful. The courses provided clear instructions and hints, enabling her to grasp complex concepts. Megan's initial goal to apply Python in finance was achieved, and she continues to expand her knowledge with advanced courses.
Now you know that you can also begin with algorithmic trading from scratch, even if you have not graduated in the relevant subjects.
Next, there is a set of interesting frequently asked questions revolving around the academic background for algorithmic trading.
Frequently asked questions about education required for algorithmic trading
Here is a list of questions and answers that are frequently asked relating to algorithmic trading.
Q: Do I need a degree to start algorithmic trading?
A: While a degree is not mandatory, having formal education in relevant subjects such as finance, economics, computer language etc. can provide a strong foundation and make it easier to understand complex concepts in algorithmic trading.
For those without a formal degree, enrolling in algorithmic trading courses can be an effective way to acquire the necessary skills and knowledge to succeed in this field.
Q: Can someone with no prior technical knowledge do algorithmic trading?
A: Yes, it is okay to not have existing technical knowledge initially. But for doing algorithmic trading you can learn the technicalities with ease. Such technicalities include programming, creating machine learning algorithms, and applying quantitative trading strategies. We discussed in the blog above the learning resources that can help you equip these relevant skills or technicalities.
See this inspiring story of Zahra, who began her trading journey at age 17. Later, she could manage the transition from a manual trader to a top algorithmic trader with the help of EPAT. If Zahra can do it, so can you!
Q: Would it make more sense to do an MFE or any quantitative analytics course before getting enrolled in an algorithmic trading course?
A: It is always good to be carrying some knowledge from MFE or other graduate courses. That is why we have mentioned the graduate and postgraduate degrees that help to gain the basic knowledge required for algorithmic trading. A quantitative analytics course can help with making it easier to grasp the fundamental concepts of algorithmic trading.
For instance, the knowledge of advanced mathematics (probability theory, stochastic calculus, partial differential equations, numerical analysis, statistics, econometrics) and/or the ability to programme using a programming language like Python.
On the other hand, you can opt for a course like EPAT that provides you with the entire know-how of algorithmic trading right from scratch. To enroll in such a course, you do not need a prior course. The stories of Narciso Perez (an entrepreneur’s journey into algo trading) and Shubhrabaran (dentist and a part time trader) provide a deeper insight into the helpfulness of such a programme.
Whether you choose to follow a structured course or explore independent resources, a Guide to learn algo trading can help you navigate your journey more effectively. It can offer a clear path to mastering programming, understanding market trends, and developing strategies that align with your trading goals.
Q: We only see PhDs, math scholars, hard-core programmers and IITians in the domain. Is that true for the majority?
A: While many think that only the PhD holder, a C++ programmer or an IITian get into the algorithmic trading domain, it is not completely true. The truth is that, for doing algorithmic trading, you need knowledge of fundamental concepts such as programming, machine learning, trading etc.
But, being from a different discipline is not an obstacle. If you remain dedicated towards the algorithmic trading domain, you can get enrolled in a course which will equip you with the required knowledge.
Q: How important are programming skills in algorithmic trading?
A: Programming skills are crucial as they allow you to develop, test, and implement trading algorithms. Python is the most commonly used language in this field.
Q: Are there online courses or certifications that can help?
A: QuantInsti is one of the helpful platforms for selecting online courses from and also to get certification in the algorithmic trading field. The courses offered can help with both foundational knowledge and practical skills.
Q: Do I need to know advanced mathematics for algorithmic trading?
A: A good understanding of advanced stock market mathematics, particularly in areas like statistics, probability, and calculus, is important for developing and analysing trading strategies.
Q: How can I gain practical experience in algorithmic trading?
A: Practical experience can be gained through internships, personal trading accounts, paper trading platforms, and participation in trading competitions.
Conclusion
As a student, a degree which helps you gain the required skills for algorithmic trading is surely a plus. The missed-out skills can be gained with a professional course and the knowledge you gained from your academic background will help you acquire the foundational base for learning algorithmic trading. Also, being in any other field for years can not be an obstacle if you have a passion for algorithmic trading. With the right guidance and training, you can gain the knowledge required for algorithmic trading and take the right steps further.
Enhance your algorithmic trading expertise with our all-encompassing algo trading course, covering topics from statistics and econometrics to financial computing, technology, machine learning, and beyond!
Author: Chainika Thakar
Note: The original post has been revamped on 13th August 2024 for recentness, and accuracy.
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