Academic Backgrounds That Are Fit For Algorithmic Trading – Part I

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.

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.

Visit QuantInsti website to watch the video on learning algorithmic trading.

Let us now look at some undergraduate and postgraduate degrees that can help 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)

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

1. 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.
2. 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.
3. Machine learning section includes blogs to help learn basics to advanced concepts in machine learning and its implementation in financial markets.
4. 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.

Stay tuned for Part II for case studies insights and FAQs

Originally posted on QuantInsti blog.