Python and Statistics for Financial Analysis

This is a compact course on statistical analysis using python on downloaded historical stock prices. You learn how to calculate moving averages (MA), buy signals based on MA, strategy profits, stock return frequency distributions, Value at Risk (VaR), confidence intervals on average returns, hypothesis tests, p-values, linear regression, diagnostics like Durbin Watson test, normality validation, residuals. You apply a multiple linear regression model to predict daily return of SPY based on other stock prices, and you evaluate the model using RMSE, R2, Sharpe Ratio and Maximum Drawdown.

Everything is illustrated with jupyter notebooks.

If you are not at all familiar with the terminology used above I recommend to take a statistics course first. Not all the concepts are very well explained, don't hesitate to look them up on the internet. The inststurtor's English pronontiation is not very good and sometimes difficult to understand.

Testing is mainly done through quizzes. The notebooks have some trivial ungraded fill-in exercises. You are not really encouraged to experiment in this course.

Executive summary: Recommend, but i personally did not like it and could spend my time better on harder and more useful courses.

Complete review:
The course is well arranged in terms of what contents they show in each module and it is quite practical, with some python codes to help us understand and play with the topics.
But there were some things that kept me wondering that I should be spending my time another course:
- The material (videos, transcripts and notebooks) have gramatical mistakes that compromised my and other people's understanding. I have checked some discussions that were all about mistake in singular and plural in the transcripts that made it hard for the students to actually understand the meaning of the sentences. No hard feelings to the Professor, but his accent is not easy to understand, as I usually watch other native courses in 1.5 - 2.0 speed. This is not something that I would expect: that the defficulty of the course was to understand the sentences and not content;
- The notebooks are quite good in the beginning, when they act as a practical example and also have a blank space with activities we can elaborate on. But by the end, these activities are no more and the notebooks are not but the code that was shown in the lessons. Thumbs down to the course;
- MANY concepts are shown but without being properly explained or referenced. Everything seems simplified and only taught for the sake of the course, like explaining all the hypothesis validation stuff and not using them in the regression model.

The course is well arranged in terms of what contents they show in each module and it is quite practical, with some python codes to help us understand and play with the topics.
But there were some things that kept me wondering that I should be spending my time another course:
- The material (videos, transcripts and notebooks) have gramatical mistakes that compromised my and other people's understanding. I have checked some discussions that were all about mistake in singular and plural in the transcripts that made it hard for the students to actually understand the meaning of the sentences. No hard feelings to the Professor, but his accent is not easy to understand, as I usually watch other native courses in 1.5 - 2.0 speed. This is not something that I would expect: that the defficulty of the course was to understand the sentences and not content;
- The notebooks are quite good in the beginning, when they act as a practical example and also have a blank space with activities we can elaborate on. But by the end, these activities are no more and the notebooks are not but the code that was shown in the lessons. Thumbs down to the course;
- MANY concepts are shown but without being properly explained or referenced. Everything seems simplified and only taught for the sake of the course, like explaining all the hypothesis validation stuff and not using them in the regression model.

This course is very much useful. I enjoyed the course and learned a lot from it. The content is well organised and focused on practical situations. The course is well arranged in terms of what contents they show in each module and it is quite practical, with some python codes to help us understand and play with the topics.
But there were some things that kept me wondering that I should be spending my time another course:
- The material (videos, transcripts and notebooks) have gramatical mistakes that compromised my and other people's understanding. I have checked some discussions that were all about mistake in singular and plural in the transcripts that made it hard for the students to actually understand the meaning of the sentences. No hard feelings to the Professor, but his accent is not easy to understand, as I usually watch other native courses in 1.5 - 2.0 speed. This is not something that I would expect: that the defficulty of the course was to understand the sentences and not content;
- The notebooks are quite good in the beginning, when they act as a practical example and also have a blank space with activities we can elaborate on. But by the end, these activities are no more and the notebooks are not but the code that was shown in the lessons. Thumbs down to the course;
- MANY concepts are shown but without being properly explained or referenced. Everything seems simplified and only taught for the sake of the course, like explaining all the hypothesis validation stuff and not using them in the regression model.

I have mixed feelings about the course. It shows very practical aspects of building trading stategy in Python, which is still quite unique topic here. It also offers a lot of practice and ready to use and modify solutions delivered as Jupyter notebooks. This course definitely expect you to know a bit about statistics and also to know Python programming, on basic level at least. On the other hand I think the course does not cover the topic deep enough, we've got only some simple linear regression model based on some not-so-creative feature engineering. It does not cover such aspects as HFT vs swing trading strategies, using slipage and transaction costs to evaluate strategy, managing invested capital and many more. I've expected a bit more, to be honest. The course is well done as ready-to-use implementation of very simple concept - but there's nothing more to expect here.

Overall, I would recommend this for a beginner of python. The classes, until the end in my opinion, were easy to follow. Some of the quizzes were a little strange and unorganized where a few of the answers honestly didnt quite make sense, but for the most part were good.

In regards to the actual content, I had learned econometrics, so this wasnt all too foreign, but for a new student i can understand this might be hard to grasp in just 5 minutes. It wasnt explained in depth efficiently, although you wouldnt necessarily need to memorize the formulas. I felt like the hypothesis testing couldve been explained better, specifically p values and confidence intervals. Additionally, maybe brushing upon the rest of the contents in the summary model such as kurtosis, Durbin Watson etc... because i felt those were not explained all too well.

I have to give you credit because its hard to make such a short video combining python and statistics in such a short time. I managed to finish this course in about a week after learning python in depth for about a month already. However, I think this would be amazing for beginners to just grasp at the straws of how statistical finance operates in portfolio strategies. I would be interested if there were other free courses but in more depth because i am technically a beginner, but not to this level of amateur. Its all really a relative description.

Nevertheless, I still had learned some things from this course, which is why i think it merits 3 stars. I had to use my brain for those quizzes but some of questions didnt necessarily reflect the material being taught in a clear manner. For a complete amateur who doesnt know anything about python, I would recommend this course, unless you dont have this amount of time to learn it. There are other courses who could probably teach the same material in about a week than a month. However, this was a great effort and I appreciate the work you had put into this. Thank you!

This course is very much useful. I enjoyed the course and learned a lot from it. The content is well organised and focused on practical situations. The course is well arranged in terms of what contents they show in each module and it is quite practical, with some python codes to help us understand and play with the topics.
But there were some things that kept me wondering that I should be spending my time another course:
- The material (videos, transcripts and notebooks) have gramatical mistakes that compromised my and other people's understanding. I have checked some discussions that were all about mistake in singular and plural in the transcripts that made it hard for the students to actually understand the meaning of the sentences. No hard feelings to the Professor, but his accent is not easy to understand, as I usually watch other native courses in 1.5 - 2.0 speed. This is not something that I would expect: that the defficulty of the course was to understand the sentences and not content;
- The notebooks are quite good in the beginning, when they act as a practical example and also have a blank space with activities we can elaborate on.