Skills you'll gain: Machine Learning, Finance, Leadership and Management, Cloud Computing, Cloud Platforms, Risk Management, Strategy, Applied Machine Learning, Artificial Neural Networks, Entrepreneurship, Investment Management, Marketing, Probability & Statistics, Sales, Securities Trading, Strategy and Operations, Business Psychology, Computer Programming, General Statistics, Mathematics, Python Programming, Reinforcement Learning, Statistical Programming
Intermediate · Specialization · 1-3 Months
Skills you'll gain: Finance, Investment Management, Risk Management, Accounting, Business Analysis, Data Analysis, Entrepreneurship, Financial Analysis, Leadership and Management, Market Research, Research and Design
Beginner · Course · 1-4 Weeks
Skills you'll gain: Machine Learning, Artificial Neural Networks, Business Psychology, Cloud Computing, Computer Programming, Entrepreneurship, Finance, General Statistics, Investment Management, Leadership and Management, Marketing, Mathematics, Probability & Statistics, Python Programming, Reinforcement Learning, Sales, Statistical Programming, Strategy, Strategy and Operations
Intermediate · Course · 1-4 Weeks
Skills you'll gain: Finance, Accounting, Securities Trading, Banking, Cash Management, Financial Analysis, Financial Management, General Accounting, Generally Accepted Accounting Principles (GAAP), Investment Management, Accounts Payable and Receivable, Audit, Business Analysis, Data Analysis, Design and Product, Financial Accounting, Leadership and Management, Risk Management
Beginner · Course · 1-4 Weeks
Skills you'll gain: Algorithms, Computer Science, Theoretical Computer Science, Computer Programming, Data Structures, Python Programming, Computational Thinking, Graph Theory, Mathematics, Probability & Statistics
Intermediate · Course · 1-4 Weeks
Skills you'll gain: Accounting, Algorithms, Brand Management, Business Psychology, Change Management, Communication, Corporate Accouting, Entrepreneurship, Finance, General Accounting, General Statistics, Generally Accepted Accounting Principles (GAAP), Human Resources, Investment Management, Journalism, Leadership and Management, Marketing, Operations Research, Planning, Probability & Statistics, Project Management, Research and Design, Sales, Strategy, Strategy and Operations, Supply Chain and Logistics, Theoretical Computer Science, Training, Visual Design, Writing
Intermediate · Course · 1-3 Months
Algorithmic trading, also known as automated trading or “algo trading,” is the use of computers and high-speed internet connections to execute large volumes of trading in financial markets much faster than would be possible for human traders. “Algos” leverage machine learning algorithms, typically created using reinforcement learning techniques in Python, to build high-frequency trading strategies that can make orders based on electronically-received information on variables like time, share price, and volume.
Understanding algorithmic trading is critically important to understanding financial markets today. It is estimated that algorithms are responsible for 80% of trading on U.S. stock markets, and it is widely used by investment banks, hedge funds, and other institutional investors. There are debates over the impacts of this rapid change in the market; some argue that it has benefitted traders by increasing liquidity, while others fear the speed of trading has created more volatility.
However, there is no question that algo trading is here to stay, and day traders as well as finance professionals need to understand how they work at a minimum - and, ideally, be able to make use of these powerful tools themselves.
Because of their ubiquity in today’s financial markets, a baseline familiarity with algorithmic trading is increasingly essential for careers as a trader, analyst, portfolio manager, or other finance jobs. These highly-paid professionals may work at institutions such as banks, asset management firms, and hedge funds, and they are increasingly adding courses in algorithms, machine learning, and other related areas to their education in order to understand this critical topic.
Career opportunities in this field are also attracting professionals with high-level computer science skills, who have gained nearly as high of a profile in the finance industry as algorithmic trading itself. Quantitative analysts, or “quants,” are highly prized for their ability to apply their programming skills to massive datasets, statistics, and other high-velocity market inputs to create the mathematical models required for algorithmic trading and other financial engineering techniques.
In a sense, then, algorithmic trading is where finance and programming meet, giving professionals with the ability to span these worlds the opportunity to create enormous value for their firms.
Absolutely. Coursera offers a wealth of courses and Specializations about relevant topics in both finance and computer science, including opportunities to learn specifically about algorithmic trading. These courses are offered by top-ranked schools from around the world such as New York University and the Indian School of Business, as well as leading companies like Google Cloud.
In addition to being able to access a high-quality education remotely from anywhere in the world, learning online through Coursera offers other advantages. The ability to virtually attend lectures and complete coursework on a flexible schedule makes online courses ideal for working professionals in finance or computer programming that want to add algorithmic trading to their skillset. And the lower cost of online courses compared to on-campus alternatives means that this high-value education can be surprisingly affordable.
The skills and experience that you might need to already have before starting to learn algorithmic trading are generally financial in nature, covering areas like programming skills, knowledge of trading and financial markets, and a solid understanding of financial modeling and quantitative analysis. These are deep subjects that would involve having a fundamental basis of mathematics concepts, data science, and programming capabilities. You might also learn more about algorithmic trading in other ways, from studying online webinars, taking online courses, reading informative blogs, or watching video content. Conversely, you might also attend college to gain a degree in mathematics, computer science, or statistical analysis. Having a good education would be a good benefit before starting to learn algorithmic trading.
The kind of people that are best suited for work that involves algorithmic trading are people who are comfortable working with numbers, data, computer algorithms, and financial concepts. People working in algorithmic trading are known as ‘quants’, short for quantitative analysts, or financial quantitative analysts. A person who works as a quant uses knowledge, skills, and experience to help financial organizations generate profits while reducing risk. These quants must be able to analyze data, develop statistical scenarios, and implement complex mathematical models for banks, hedge funds, and investment firms to make smart decisions about pricing structures, investments, and risk management opportunities.
You might know if learning algorithmic trading is right for you if you have a sharp mind that can scan and analyze numbers in math, data, and financial areas quickly and decisively. You would most likely love aspects of technology and finance, working with programming languages, scrutinizing data, crunching numbers, and having a good grasp of principles with ratios and percentages. If you want to learn if algorithmic trading is right for you, then you might want to take online courses in statistical modeling, quantitative analyses, financial trading, computer programming and related areas to gauge your interest and capability for this subject.