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Two years ago, I participated in the Kaggle competition Using News to Predict Stock Movements hosted by Two Sigma which is a hedge fund that uses AI, machine learning, and distributed computing, for its trading strategies.

I got a gold medal, 221st of 2927 competitors, in this competition. This is my first exposure to quantitative finance(quants), which is the use of mathematical and statistical methods in finance and investment management. This competition let me experience the charm of the combination of finance and AI. So I decided to learn the knowledge of quantitative finance or have the opportunity to start my professional career in quantitative finance in the future.

Refer to a video about how to learn quantitative finance on YouTube and answers of it on Quora, the normal way to quants a career is having an MSc or Ph.D. in quantitative finance or financial engineering. This way is more suitable for undergraduates who have just left campus. Another way is to obtain relevant certifications, which are suitable for those who have several years of work experience. One of the most valuable certifications is the Certificate in Quantitative Finance (CQF). It is founded by Dr. Paul Wilmott who is best known as a leading expert on quantitative finance and author of the best-selling book Paul Wilmott On Quantitative Finance.

For me, CQF is a good option. It can study part-time, its faculty is packed with leading practitioners from around the world, all courses are online, you can access their Lifelong Learning Library.

However, I have not made up my mind to apply for CQF, because it’s not a cheap course — coming in at close to $20K, and the other reason it’s not widely known outside the quants community compared to CFA(Chartered Financial Analyst), FRM(Financial Risk Manager).

Finally, I decided to learn QF through MOOC and online resources at this stage.

Photo by Visual Stories || Micheile on Unsplash

There exist two separate branches of finance that require advanced quantitative techniques: the Q quant of derivative pricing, whose task is to “extrapolate the present” and as the sell-side in the market; and P quant of quantitative risk and portfolio management, whose task is to “model the future” and as the buy-side in the market.

I think no matter whether my future job is P quant or Q quant, the following courses are for me to learn. I don’t want to limit my vision in some domain too early. What’s more, most works in the financial industry are between them now.

There are three core skills in quantitative finance:

Mathematics and Statistics
Computer Science, Data Science, and Machine Learning
Finance

Based on these core skills, I selected the following online courses to start learning.

Core Courses

1. Financial Markets

Instructor: Professor Robert Shiller (Nobel Laureate in 2013)

Offered by: Yale University

MOOC platform: Coursera

Fee: Free

Hours to complete: Approx. 33 hours

I took Professor Shiller’s Financial Markets as the first course of my quantitative finance learning. I chose this course not only because it gives us a comprehensive understanding of financial markets, but also Professor Shiller is a specialist in behavioral finance. Behavioral finance is to help understand why people make certain financial choices and how those choices can affect markets. In particular, it can help us explain the various phenomena of the stock market under COVID-19, such as WallStreetBets(WSB) event, the GameStop short squeeze that caused losses for some U.S. firms, and short-sellers in a few days in early 2021.

2. Statistical Inference

Instructor: Brian Caffo, Ph.D.

Offered by: Johns Hopkins University

MOOC platform: Coursera

Fee: 7-day free trial

Hours to complete: Approx. 54 hours

This course will make you understand the broad directions of statistical inference and use this information for making informed choices in analyzing data. If you did not study statistics courses in college, you should take this course before starting the following study.

3. Mathematical Methods for Quantitative Finance

Instructor: Paul F. Mende, Egor Matveyev

Offered by: Massachusetts Institute of Technology (MIT)

MOOC platform: edX

Fee: Free (Optional upgrade available)

Hours to complete: Approx. 12 weeks (10–14 hours per week)

You will learn the mathematical foundations essential for financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and applied computational techniques in R.

4. Financial Engineering and Risk Management Specialization

Offered by: Columbia University

MOOC platform: Coursera

Fee: 7-day free trial

Hours to complete: Approx. 7 months

There are 5 Courses in this Specialization:

Introduction to Financial Engineering and Risk Management
Term-Structure and Credit Derivatives
Optimization Methods in Asset Management
Advanced Topic in Derivative Pricing
Computational Methods in Pricing and Model Calibration

These courses cover derivative pricing, asset allocation, portfolio optimization, real options, commodity, energy derivatives, and algorithmic trading in the quantitative finance area.

5. Fundamentals of Quantitative Modeling

Instructor: Richard Waterman

Offered by: University of Pennsylvania

MOOC platform: Coursera

Fee: 7-day free trial

Hours to complete: Approx. 8 hours

This course cover key ideas and process of quantitative modeling, including linear models and optimization, probabilistic models(tree-based models, Monte Carlo simulations, and Markov chains), and regression models.

6. Deep Learning Specialization

Instructor: Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh

Offered by: DeepLearning.AI

MOOC platform: Coursera

Fee: 7-day free trial

Hours to complete: Approx. 5 months

There are 5 Courses in this Specialization:

Neural Networks and Deep Learning
Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
Structuring Machine Learning Projects
Convolutional Neural Networks
Sequence Models

I choose this specialization project as an AI entry course. These courses have almost all the knowledge and skills of deep learning.

I took these courses 3 years ago and started my deep learning career. I highly recommend this course. Dr. Ng is chairman and Co-Founder of Coursera, as a pioneer both in machine learning and online education. Dr. Ng can help you easily understand the complex concepts and formulas of deep learning, so you only need some basic mathematics and computer knowledge to complete this course.

7. Machine Learning for Trading Specialization

Instructor: Jack Farmer, Ram Seshadri

Offered by: Google Cloud, New York Institute of Finance

MOOC platform: Coursera

Fee: 7-day free trial

Hours to complete: Approx. 3 months

There are 3 Courses in this Specialization:

Introduction to Trading, Machine Learning & GCP
Using Machine Learning in Trading and Finance
Reinforcement Learning for Trading Strategies

After finishing Deep Learning Specialization courses, this 3-course Specialization focused on how to construct effective trading strategies using Machine Learning and Python.

8. Blockchain and Money

Instructor: Prof. Gary Gensler

Offered by: MITOPENCOURSEWARE

MOOC platform: YouTube

Fee: Free

Hours to complete: Approx. 24 months

Finally, no matter what your attitude towards Bitcoin, you should listen to this course offered by Professor Gensler, the current chair of the U.S. Securities and Exchange Commission, at MIT.

These are core courses. If you find that you lack some knowledge during the above learning process, you can search for related courses or videos through MOOC platforms such as edX, Coursera, and YouTube.

Practices

After completing the above courses, you should already have basic quantitative finance skills. Next, you should practice.
There are two ways:

Finance Competitions

Kaggle
Kaggle is an online community of data scientists and machine learning practitioners. Some well-known hedge fund companies often hold competitions in Kaggle. You can participate in these competitions and solve real-world problems with quantitative finance and machine learning enthusiasts all over the world. There are many masters hidden in the Kaggle community. They will often share their model design concepts in the discussions, and you will gain a lot here.
Kaggle also offers a web-based machine learning programming environment and some GPU hours for free.
NumerAI
Numerai is an AI-run, crowd-sourced hedge fund. Numerai hosts a weekly tournament, in which data scientists submit their predictions in exchange for the potential to earn rewards paid in a cryptocurrency called Numeraire.

Backtesting and trading

backtrader — A feature-rich Python framework for backtesting and trading
Quantconnect — the World’s leading algorithmic trading platform

You can use these platforms to design your own quantitative financial strategies and back-testing these platforms. Not only do they provide a complete backtesting framework, but their community also has many resources for us to use and learn.

Conclusion

The above is my quantitative finance learning plan. I have already started the learning journey from Financial Market, and I hope you will join my team. I will continue to update this plan in the future.

Resource:

Some quantitative finance companies:

Useful Videos:

https://youtu.be/6M9YsJKtxyQ

James Simons is an American mathematician, the founder of Renaissance Technologies, a quantitative hedge fund.

If you like to study mathematics, please watch the video below.

Stochastic Partial Differential Equations by Martin Hairer, who is a mathematician working in the field of stochastic analysis. In 2014 he was awarded the Fields Medal, one of the highest honors a mathematician can achieve.