Financial Engineering and Artificial Intelligence in Python

Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!

Register for this Course

$54.99 $219.99 USD 75% OFF!

Login or signup to register for this course

Have a coupon? Click here.

Course Data

Lectures: 157
Length: 23h 10m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course Description

Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?

Today, you can stop imagining, and start doing.

This course will teach you the core fundamentals of financial engineering, with a machine learning twist.

We will cover must-know topics in financial engineering, such as:

  • Exploratory data analysis, significance testing, correlations, alpha and beta
  • Time series analysis, simple moving average, exponentially-weighted moving average
  • Holt-Winters exponential smoothing model
  • Efficient Market Hypothesis
  • Random Walk Hypothesis
  • Time series forecasting ("stock price prediction")
  • Modern portfolio theory
  • Efficient frontier / Markowitz bullet
  • Mean-variance optimization
  • Maximizing the Sharpe ratio
  • Convex optimization with Linear Programming and Quadratic Programming
  • Capital Asset Pricing Model (CAPM)
  • Algorithmic trading (VIP only)
  • Statistical Factor Models (VIP only)
  • Regime Detection with Hidden Markov Models (VIP only)

In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:

  • Regression models
  • Classification models
  • Unsupervised learning
  • Reinforcement learning and Q-learning

We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance.

As the author of ~30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn't help but wander into the vast and complex world of financial engineering.

This course is for anyone who loves finance or artificial intelligence, and especially if you love both!

Whether you are a student, a professional, or someone who wants to advance their career - this course is for you.

Thanks for reading, I will see you in class!

Suggested Prerequisites:

  • Matrix arithmetic
  • Probability
  • Decent Python coding skills
  • Numpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses!)

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.



4 Lectures · 20min
  1. Introduction and Outline (06:39) (FREE preview available)
  2. Scope of the course (03:48)
  3. How to Practice (05:39)
  4. Warmup (Optional) (04:46)

Getting Set Up

2 Lectures · 05min
  1. Where to get the code (02:06)
  2. Temporary 403 Errors (02:58)

Financial Basics

31 Lectures · 03hr 25min
  1. Financial Basics Section Introduction (05:32)
  2. Getting Financial Data (07:20)
  3. Getting Financial Data (Code) (07:16)
  4. Understanding Financial Data (05:05)
  5. Understanding Financial Data (Code) (12:08)
  6. Dealing with Missing Data (05:58)
  7. Dealing with Missing Data (Code) (07:00)
  8. Returns (09:15)
  9. Adjusted Close, Stock Splits, and Dividends (11:30)
  10. Adjusted Close (Code) (03:49)
  11. Back to Returns (Code) (07:21)
  12. QQ-Plots (05:29)
  13. QQ-Plots (Code) (07:19)
  14. The t-Distribution (03:55)
  15. The t-Distribution (Code) (08:07)
  16. Skewness and Kurtosis (07:34)
  17. Confidence Intervals (10:28)
  18. Confidence Intervals (Code) (02:16)
  19. Statistical Testing (14:18)
  20. Statistical Testing (Code) (07:07)
  21. Covariance and Correlation (08:16)
  22. Covariance and Correlation (Code) (05:56)
  23. Alpha and Beta (06:55)
  24. Alpha and Beta (Code) (08:09)
  25. Mixture of Gaussians (06:41)
  26. Mixture of Gaussians (Code) (06:13)
  27. Volatility Clustering (03:03)
  28. Price Simulation (03:04)
  29. Price Simulation (Code) (02:34)
  30. Financial Basics Section Summary (02:20)
  31. Suggestion Box (03:10)

Time Series Analysis

31 Lectures · 04hr 59min
  1. Time Series Analysis Section Introduction (06:52)
  2. Efficient Market Hypothesis (11:17)
  3. Random Walk Hypothesis (14:25)
  4. The Naive Forecast (06:45)
  5. Simple Moving Average (Theory) (04:17)
  6. Simple Moving Average (Code) (08:41)
  7. Exponentially-Weighted Moving Average (Theory) (11:07)
  8. Exponentially-Weighted Moving Average (Code) (11:05)
  9. Simple Exponential Smoothing for Forecasting (Theory) (10:13)
  10. Simple Exponential Smoothing for Forecasting (Code) (10:24)
  11. Holt's Linear Trend Model (Theory) (07:55)
  12. Holt's Linear Trend Model (Code) (03:11)
  13. Holt-Winters (Theory) (11:20)
  14. Holt-Winters (Code) (08:00)
  15. Autoregressive Models - AR(p) (12:51)
  16. Moving Average Models - MA(q) (03:31)
  17. ARIMA (10:45)
  18. ARIMA in Code (pt 1) (20:25)
  19. Stationarity (12:20)
  20. Stationarity Code (09:50)
  21. ACF (Autocorrelation Function) (10:10)
  22. PACF (Partial Autocorrelation Funtion) (06:55)
  23. ACF and PACF in Code (pt 1) (08:26)
  24. ACF and PACF in Code (pt 2) (07:03)
  25. Auto ARIMA and SARIMAX (09:41)
  26. Model Selection, AIC and BIC (09:52)
  27. ARIMA in Code (pt 2) (14:39)
  28. ARIMA in Code (pt 3) (16:21)
  29. ACF and PACF for Stock Returns (07:35)
  30. Forecasting (09:14)
  31. Time Series Analysis Section Conclusion (04:12)

Portfolio Optimization and CAPM

24 Lectures · 03hr 05min
  1. Portfolio Optimization Section Introduction (03:35)
  2. The S&P500 (02:46)
  3. What is Risk? (07:03)
  4. Why Diversify? (08:28)
  5. Describing a Portfolio (pt 1) (09:51)
  6. Describing a Portfolio (pt 2) (06:30)
  7. Visualizing Random Portfolios and Monte Carlo Simulation (pt 1) (13:07)
  8. Visualizing Random Portfolios and Monte Carlo Simulation (pt 2) (15:07)
  9. Maximum and Minimum Portfolio Return (09:35)
  10. Maximum and Minimum Portfolio Return in Code (04:59)
  11. Mean-Variance Optimization (07:26)
  12. The Efficient Frontier (07:23)
  13. Mean-Variance Optimization And The Efficient Frontier in Code (09:13)
  14. Global Minimum Variance (GMV) Portfolio (01:56)
  15. Global Minimum Variance (GMV) Portfolio in Code (02:14)
  16. Optimal Portfolio Closed-Form Solution (20:11)
  17. Optimal Portfolio Closed-Form Solution in Code (05:38)
  18. Sharpe Ratio (08:01)
  19. Maximum Sharpe Ratio in Code (06:35)
  20. Portfolio with a Risk-Free Asset and Tangency Portfolio (09:52)
  21. Risk-Free Asset and Tangency Portfolio in Code (02:16)
  22. Capital Asset Pricing Model (CAPM) (12:26)
  23. Problems with Markowitz Portfolio Theory and Robust Estimation (09:13)
  24. Portfolio Optimization Section Conclusion (02:25)

VIP: Algorithmic Trading

9 Lectures · 01hr 06min
  1. Algorithmic Trading Section Introduction (02:55)
  2. Trend-Following Strategy (13:14)
  3. Trend-Following Strategy in Code (pt 1) (08:27)
  4. Trend-Following Strategy in Code (pt 2) (09:38)
  5. Machine Learning-Based Trading Strategy (07:53)
  6. Machine Learning-Based Trading Strategy in Code (09:25)
  7. Classification-Based Trading Strategy in Code (03:40)
  8. Using a Random Forest Classifier for Machine Learning-Based Trading (05:00)
  9. Algorithmic Trading Section Summary (05:56)

VIP: The Basics of Reinforcement Learning

12 Lectures · 01hr 56min
  1. Reinforcement Learning Section Introduction (06:34)
  2. Elements of a Reinforcement Learning Problem (20:18)
  3. States, Actions, Rewards, Policies (09:24)
  4. Markov Decision Processes (MDPs) (10:07)
  5. The Return (04:56)
  6. Value Functions and the Bellman Equation (09:53)
  7. What does it mean to “learn”? (07:18)
  8. Solving the Bellman Equation with Reinforcement Learning (pt 1) (09:49)
  9. Solving the Bellman Equation with Reinforcement Learning (pt 2) (12:04)
  10. Epsilon-Greedy (06:09)
  11. Q-Learning (14:15)
  12. How to Learn Reinforcement Learning (05:56)

VIP: Reinforcement Learning for Algorithmic Trading

5 Lectures · 52min
  1. Trend-Following Strategy with Reinforcement Learning API (12:33)
  2. Trend-Following Strategy Revisited (Code) (09:14)
  3. Q-Learning in an Algorithmic Trading Context (07:39)
  4. Representing States (07:27)
  5. Q-Learning for Algorithmic Trading in Code (15:33)

VIP: Statistical Factor Models and Unsupervised Machine Learning

4 Lectures · 01hr 01min
  1. Statistical Factor Models (Beginner) (15:41)
  2. Statistical Factor Models (Intermediate) (10:09)
  3. Statistical Factor Models (Advanced) (19:50)
  4. Statistical Factor Models (Code) (16:13)

VIP: Regime Detection and Sequence Modeling with Hidden Markov Models

5 Lectures · 01hr 09min
  1. Why Sequence Models? (pt 1) (14:06)
  2. Why Sequence Models? (pt 2) (12:14)
  3. HMM Parameters (09:26)
  4. HMM Tasks and the Viterbi Algorithm (15:15)
  5. HMM for Modeling Volatility Clustering in Code (18:38)

VIP: Predicting Recessions

2 Lectures · 15min
  1. Predicting A Recession (Demo Walkthrough) (13:05)
  2. Predicting A Recession Discussion (02:40)

VIP: Experiments

1 Lectures · 13min
  1. Let's Beat the Naive Forecast (13:25)

Course Summary and Common Questions

12 Lectures · 01hr 40min
  1. Final Thoughts and Course Summary (06:10)
  2. Creating Your Personalized Trading Strategy (14:01)
  3. Applying This Course (08:30)
  4. Trading APIs and Deploying Your Strategy in the Real World (05:53)
  5. High Frequency Trading (HFT) (03:54)
  6. The Importance of Data (09:14)
  7. Why do I have to learn statistics to learn finance? (10:37)
  8. Get a Plug-and-Play Trading Bot Without Math (05:11)
  9. Slippage and Bid-Ask Spread (03:16)
  10. Eliminating the Naive Mindset (pt 1) (16:01)
  11. Eliminating the Naive Mindset (pt 2) (15:13)
  12. VIP: Finance Enthusiasts, Beware of Marketers! (02:03)

Setting Up Your Environment (Appendix/FAQ by Student Request)

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

6 Lectures · 01hr 07min
  1. Beginner's Coding Tips (13:22)
  2. How to Code Yourself (part 1) (15:55)
  3. How to Code Yourself (part 2) (09:24)
  4. Proof that using Jupyter Notebook is the same as not using it (12:29)
  5. Python 2 vs Python 3 (04:38)
  6. How to use Github & Extra Coding Tips (Optional) (11:12)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:49)
This website is using cookies. That's Fine