Artificial Intelligence for Trading Udacity Review [In-Depth Summary]
Are you looking for an Artificial Intelligence for Trading Udacity Review? If yes, first read my Artificial Intelligence for Trading Udacity Review and then decide whether to enroll in this Nanodegree program.
Artificial Intelligence for Trading Udacity Review/ AI for Trading Review
When I was redirected to the course material, I was overwhelmed to see the content. There was a lot of content available. First, I thought about how I complete this Nanodegree Program. But this content and project are designed and planned for 6 months.
So, I would suggest, after watching the content and projects, don’t panic and rush. Take your time and learn according to your pace.
Udacity Artificial Intelligence for Trading Nanodegree was divided into two sections-
- Quantitative Trading
- AI Algorithms in Trading
Quantitative Trading is known as Term 1, where there were 4 projects and 4 modules. Each module had some set of lessons. AI Algorithms in Trading is known as Term 2, and Term 2 had 4 projects and 4 modules. Again each module had some set of lessons. So, that’s how this “AI for Trading Nanodegree” was designed.
Term 1- Quantitative Trading
The first lesson was Introduction to the Nanodegree Programs. In this first lesson, I got to know about this Nanodegree Program such as the structure of the Nanodegree Program, who are the instructors of Nanodegree Program, what kind of support Udacity provides, etc.
Along with that, I learned about Quants from the lead Instructor, Jonathan Larkin, and got insights from a Quant. He explained that a “Quant is someone good at using Technology, math, and statistics to solve business problems.”
The first lesson was very informative and my half doubts were cleared regarding the Nanodegree structure and study plan.
The next lesson was common in every Nanodegree Program, where they explained Udacity Support features and what kind of help I received during the Nanodegree Program.
Project 1- Trading with Momentum
In this first project, I had to implement a momentum trading strategy and perform a statistical test to check whether there was alpha in the signal or not.
This was not a tough project. Udacity provided a textual description of how to generate a trading signal based on a momentum indicator.
Udacity also provided a Trading with Momentum Workspace and I had to submit the project from the workspace.
The best thing I found in Udacity was its Technical Mentor Support. Throughout the project, I asked them about my doubts to mentor and he helped me with this project.
Check Current Discount at-> Udacity Artificial Intelligence for Trading Nanodegree
Project 2- Breakout Strategy
In this project, I had to implement what I learned in the previous lessons such as I had to implement the breakout strategy, finding and removing any outliers, and testing to see if it has the potential to be profitable using a Histogram and P-Value.
Udacity provided a Breakout Strategy Workspace. Where I had to complete and submit the project.
To complete this project, previous knowledge of Pandas and Numpy is required.
Check Current Discount at-> Udacity Artificial Intelligence for Trading Nanodegree
Project 3- Smart Beta and Portfolio Optimization
This project had two parts that mean in the first part, I had to build a smart beta portfolio. After that, I had to calculate the tracking error.
In the next part, I had to rebalance the portfolio using quadratic programming and calculate the turnover to check the performance.
This project helped me to practice the math of portfolio optimization learned in previous lessons.
Udacity provided a Smart Beta and Portfolio Optimization Workspace where Pandas and Numpy packages were already imported.
This was not an easy project for me. Because there was math used in this project.
But the mentor helped me by telling me my mistakes and suggesting where to improve by showing the improved code.
Project 4- Alpha Research and Factor Modeling
In this project, I had to use the concepts learned in the previous lesson and build a statistical risk model using PCA.
I had to build a portfolio along with 5 alpha factors. And then I had to check the performance of these 5 alpha factors concerning factor-weighted returns, quantile analysis, Sharpe ratio, and turnover analysis.
At the end of this project, I had to optimize the portfolio by using the risk model and factors using multiple optimization formulations.
And here, Term 1 or Section 1 “Quantitative Trading” end.
Summary of Term 1
->If you ask me how was the content of term 1, I would say that term 1 was loaded with advanced and basic concepts of Quantitative Trading.
->It was a fun and challenging journey with term 1. My domain is computer science and I didn’t learn any such concepts in the past, that’s why understanding these concepts was a little bit tough for me. But thanks to the Nanodegree Instructors who explained the concepts visually that method helped me to catch the concept.
->Term 1 required previous math knowledge, especially in statistics, linear algebra, and calculus. Without having math knowledge, I would not suggest starting this Nanodegree.
->Overall, I learned various new and interesting concepts in Term 1 related to finance and trading. These concepts will help me in the future.
Check Current Discount at-> Udacity Artificial Intelligence for Trading Nanodegree
After Term 1, the exciting part began for me which was Term 2-> AI Algorithms in Trading. Let’s see what I learned in Term 2 and how the content was.
Term 2- AI Algorithms in Trading
This was the first lesson of term 2, where the instructors explained what they will teach in this term. Along with that, there were two interviews added. One is an Interview with Gordon Ritter. Gordon Ritter is the Professor and portfolio manager. Another Interview was with Justin Sheetz. Justin Sheetz is an investment strategist and quant research analyst. They explained their experience in the field.
Project 5- NLP on Financial Statements
This was the first project of Term 2. In this project, I had to perform NLP Analysis on 10-k financial statements and generate an alpha factor.
This project required two corpora to run this project. The first one is the stopwords corpus for removing stopwords and the second one is the wordnet for lemmatizing.
Quotemedia and Loughran-McDonald sentiment word lists are used as a dataset. This was a challenging as well as a fun project.
Project 6- Sentiment Analysis with Neural Networks
In this project, I had to use the concepts learned in the previous lessons. For this project, I had to build my deep learning model that can predict if any particular message is positive or negative.
The messages I had to use were from StockTwits, a social network for investors and traders.
I had to use a five-point scale for capturing a sentiment: very negative, negative, neutral, positive, and very positive.
Project 7- Combining Signals for Enhanced Alpha
In this project, I had to use the following alpha factors:
- Momentum 1 Year Factor
- Mean Reversion 5 Day Sector Neutral Smoothed Factor
- Overnight Sentiment Smoothed Factor
The objective of this project was to combine signals on a random forest for enhanced alpha.
Project 8- Backtesting
This was the final project of this Nanodegree Program. In this project, I had to build a fairly realistic backtester that uses the Barra data. Gordon designed this project.
This backtester performed portfolio optimization that includes transaction costs. I also used
The technical mentor support helped me too much in this project.
Summary of Term 2
->If you asked me how was the content and projects of term 2, I would say it was worth the money.
-> Term 2 was a perfect balance between theory and hands-on.
->The projects covered in Term 2 were challenging and not easy. But these projects help my Resume.
->The content was updated and advanced.
->I liked the method of explanation of each instructor. They tried to make the complex concepts easier. And they did it so well.
->The quizzes and exercises were well-designed and helpful to revise the learned concepts.
Final Thought
I would recommend this Udacity Artificial Intelligence for Trading Nanodegree to those who are not beginners and have previous knowledge in Python Programming, statistics, linear algebra, and calculus.
Udacity will also provide some Free additional courses in the Naodegree program. These courses will cover the following topics- python basics, linear algebra, Jupyter Notebook basics, and statistics. You can learn the prerequisites from these elective courses.
Enroll-> Udacity Artificial Intelligence for Trading Nanodegree
Now it’s time to wrap up this Artificial Intelligence for Trading Udacity Review.
Conclusion
I hope this Artificial Intelligence for Trading Udacity Review helped you to decide whether to enroll in this program or not.
If you found this Artificial Intelligence for Trading Udacity Review/AI for Trading Review helpful, you can share it with others. And if you have any doubts or questions, feel free to ask me in the comment section.
All the Best!
NOTE- Some of the links in the post are Affiliate Links. This means if you click on the link and purchase the course, I will receive an affiliate commission at no extra cost to you😊.
