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:
(as usual, this coupon lasts only 30 days, so don’t wait!)
This is a MASSIVE (18 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
- 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.
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. These include:
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.
Please note: The VIP coupon will work only for the next month (ending Oct 8, 2020). 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.
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?