This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.
Why Take This Course? #### By the end of this course, you should be able to:
- Understand data structures used for algorithmic trading.
- Know how to construct software to access live equity data, assess it, and make trading decisions.
- Understand 3 popular machine learning algorithms and how to apply them to trading problems.
- Understand how to assess a machine learning algorithm's performance for time series data (stock price data).
- Know how and why data mining (machine learning) techniques fail.
- Construct a stock trading software system that uses current daily data.
#### Some limitations/constraints:
- We use daily data. This is not an HFT course, but many of the concepts here are relevant.
- We don't interact (trade) directly with the market, but we will generate equity allocations that you could trade if you wanted to.
This course is composed of three _mini-courses_:
- Mini-course 1: Manipulating Financial Data in Python
- Mini-course 2: Computational Investing
- Mini-course 3: Machine Learning Algorithms for Trading
Each mini-course consists of about 7-10 short lessons. Assignments and projects are interleaved.
**Fall 2015 OMS students**: There will be two tests - one midterm after mini-course 2, and one final exam.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
I enjoyed learning about how machine learning applies to trading strategy, but was very disappointed that mini-courses 2 and 3 included no coding assignments! This, despite the instructors repeated assurances that we'd be building cool things later in the course. I'm feeling a bit swindled, particularly given the pleasant experience I've had with the two previous courses taken at Udacity, which both had lots of coding challenges sprinkled throughout the course. :(
All in all I'm extremely disappointed. The combination of theory and practical coding was great in the first third of the course, and I really learned a lot! But when things were starting to get really interesting, they gave up! No more Python and no more practical examples. All that was left was just tedious examples of extremely basic financial theory. Honestly, I don't undeerstand what they were aiming for here.
Wichaiditsornpon@gmail.comcompleted this course, spending 1 hours a week on it and found the course difficulty to be very easy.
I don't know why people so complain about no code in session 2 and 3. All I can say about this course is "I want more" I want more knowledge about how machine learning can apply to trading, I want more about Financial more about ML not Python this course is intro us to trading not how to create it's no python code? it would be great if instructor add more code in session 2 and 3 but this is not programming course it's ML for Trading
I started with Python and Pandas in the course , which was very helpful as that had many programming stuff.Then I jumped on to Machine Learning part as I was very interested in that part of the course.But was disappointed after that to see that the ML part includes only Theory with no practicals and coding in python
Implementation of theory using Python is missing in 2nd and 3rd part of the course. It is indeed very frustrating and it doesn't provide a glimpse into how to implement those ideas or even a starting point to develop them further. Students are just left with theoretical concepts and no intuition to implement them. I have to say it was very disappointing from 2nd part onwards.
If you are expecting practical lessons on how to actually use machine learning for trading don't waste your time with this course. Everything is basic or just briefly described. It does not show you how to take the models described and actually put them to use with data to create an algortihm. Extremely disappointing course.
this course was great in the first and second but in the third it's should be intro to machine learning. I just want more detail about ML for Trading. But this course was great overall no coding in part 2 3 no problem they teach in first part you all should be applied it's your selft