September 8, 2020
Financial Engineering and Artificial Intelligence in Python
The complete Financial Engineering course has arrived
Hello once again friends!
Today, I am announcing the VIP version of my latest course: Financial Engineering and Artificial Intelligence in Python.
If you don’t want to read my little spiel just click here to get your VIP coupon:
https://www.udemy.com/course/ai-finance/?couponCode=FINANCEVIP (expires Oct 9, 2020)
https://www.udemy.com/course/ai-finance/?couponCode=FINANCEVIP17 (expires Feb 21, 2022)
(as usual, this coupon lasts only 30 days, so don’t wait!)
This is a MASSIVE (20 hours) Financial Engineering course covering the core fundamentals of financial engineering and financial analysis from scratch. We will go in-depth into all the classic topics, such as:
- Exploratory data analysis, significance testing, correlations
- Alpha and beta
- Advanced Pandas Data Frame manipulation for time series and finance
- Time series analysis, simple moving average, exponentially-weighted moving average
- Holt-Winters exponential smoothing model
- ARIMA and SARIMA
- 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
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.
List of VIP-only Contents
As with my Tensorflow 2 release, some of the VIP content will be a surprise and will be released in stages. Currently, the entirety of the Algorithmic Trading sections are VIP sections. Newly added VIP sections include Statistical Factor Models and “The Lazy Programmer Bonus Offer”. Here’s a full list:
Classic Algorithmic Trading – Trend Following Strategy
You will learn how moving averages can be applied to do algorithmic trading.
Machine Learning-Based Trading Strategy
Forecast returns in order to determine when to buy and sell.
Reinforcement Learning-Based (Q-Learning) Trading Strategy
I give you a full introduction to Reinforcement Learning from scratch, and then we apply it to build a Q-Learning trader. Note that this is *not* the same as the example I used in my Tensorflow 2, PyTorch, and Reinforcement Learning courses. I think the example included in this course is much more principled and robust.
Statistical Factor Models
The CAPM is one of the most renowned financial models in history, but did you know it’s only the simplest factor model, with just a single factor? To go beyond just this single factor model, we will learn about statistical factor models, where the multiple “factors” are found automatically using only the data.
Regime Detection with Hidden Markov Models (HMMs)
In the first section on financial basics, we learn how to model the distribution of returns. But can we really say “the” distribution, as if there is only one?
One important “stylized fact” about returns is that volatility “clusters” or “persists”. That is, large returns tend to be surrounded by more large returns, and small returns by more small returns.
In other words, returns are actually nonstationary and to build a more accurate model we should not assume that they all come from the same distribution at all times.
Using HMMs, we can model this behavior. HMMs allow you to model hidden state sequences (high volatility and low volatility regimes), from which observations (the actual returns) are generated.
The Lazy Programmer Bonus Offer
There are marketers out there who want to capitalize on your enthusiastic interest in finance, and unfortunately what they are teaching you is utter and complete garbage.
They will claim that they can “predict stock prices with LSTMs” and show you charts like this with nearly perfect stock price predictions.
Hint: if they can do this, why do they bother putting effort into making courses? Wouldn’t they already be billionaires?
Have you ever wondered if you are taking such a course from a fake data scientist / marketer? If so, just send me a message, and I will tell you whether or not you are taking such a course. (Hint: many of you are) I will give you a list of mistakes they made so you can look out for them yourself, and avoid “learning” things which will ultimately make YOU look very bad in front of potential future employers.
Believe me, if you ever try to get a job in machine learning or data science and you talk about a project where you “predicted stock prices with LSTMs”, all you will be demonstrating is how incompetent you are. I don’t want to see any of my students falling for this! Save yourself from this embarrassing scenario by taking the “Lazy Programmer Offer”!
Please note: The VIP coupon will work only for the next month (starting from the coupon creation time). It’s unknown whether the VIP period will renew after that time.
After that, although the VIP content will be removed from Udemy, all who purchased the VIP course will get permanent free access to these VIP contents on deeplearningcourses.com.
In case it’s not clear, the process is very easy. For those folks who want the “step-by-step” instructions:
STEP 1) I announce the VIP content will be removed.
STEP 2) You email me with proof that you purchased the course during the VIP period. Do NOT email me earlier as it will just get buried.
STEP 3) I will give you free access to the VIP materials for this course on deeplearningcourses.com.
Benefits of taking this course
- Learn the knowledge you need to work at top tier investment firms
- Gain practical, real-world quantitative skills that can be applied within and outside of finance
- Make better decisions regarding your own finances
Personally, I think this is the most interesting and action-packed course I have created yet. My last few courses were cool, but they were all about topics which I had already covered in the past! GANs, NLP, Transfer Learning, Recommender Systems, etc etc. all just machine learning topics I have covered several times in different libraries. This course contains new, fresh content and concepts I have never covered in any of my courses, ever.
This is the first course I’ve created that extends into a niche area of AI application. It goes outside of AI and into domain expertise. An in-depth topic such as finance deserves its own course. This is that course. These are topics you will never learn in a generic data science or machine learning course. However, as a student of AI, you will recognize many of our tools and methods being applied, such as statistical inference, supervised and unsupervised learning, convex optimization, and optimal control. This allows us to go deeper than your run of the mill financial engineering course, and it becomes more than just the sum of its parts.
So what are you waiting for?