Python for Financial Analysis and Algorithmic Trading (

Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!

Created by: Jose Portilla

Produced in 2020

What you will learn

  • Use NumPy to quickly work with Numerical Data
  • Use Pandas for Analyze and Visualize Data
  • Use Matplotlib to create custom plots
  • Learn how to use statsmodels for Time Series Analysis
  • Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
  • Use Exponentially Weighted Moving Averages
  • Use ARIMA models on Time Series Data
  • Calculate the Sharpe Ratio
  • Optimize Portfolio Allocations
  • Understand the Capital Asset Pricing Model
  • Learn about the Efficient Market Hypothesis
  • Conduct algorithmic Trading on Quantopian

Quality Score

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Video Quality
Qualified Instructor
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Course Depth & Coverage

Overall Score : 88 / 100

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Course Description

Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you!
This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We'll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!
We'll cover the following topics used by financial professionals:
  • Python Fundamentals
  • NumPy for High Speed Numerical Processing
  • Pandas for Efficient Data Analysis
  • Matplotlib for Data Visualization
  • Using pandas-datareader and Quandl for data ingestion
  • Pandas Time Series Analysis Techniques
  • Stock Returns Analysis
  • Cumulative Daily Returns
  • Volatility and Securities Risk
  • EWMA (Exponentially Weighted Moving Average)
  • Statsmodels
  • ETS (Error-Trend-Seasonality)
  • ARIMA (Auto-regressive Integrated Moving Averages)
  • Auto Correlation Plots and Partial Auto Correlation Plots
  • Sharpe Ratio
  • Portfolio Allocation Optimization
  • Efficient Frontier and Markowitz Optimization
  • Types of Funds
  • Order Books
  • Short Selling
  • Capital Asset Pricing Model
  • Stock Splits and Dividends
  • Efficient Market Hypothesis
  • Algorithmic Trading with Quantopian
  • Futures Trading
Who this course is for:
  • Someone familiar with Python who wants to learn about Financial Analysis!

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Instructor Details

Jose Portilla

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

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77 total reviews

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The content covered in this course is so vast and i doubt anything else on Udemy on the subject of "python in finance" comes close to it. Lots of supplementary topics to make the course more understandable.highly recommended.

It was a little advanced for me. Certain issues with codes since course was developed a few years ago. Preferably update them with what works now instead of us having to ask in Q&A. Thank you for teaching.

Even if you're not really into trading securities, this is an excellent course in time series analysis... I only wish I had something like this in my University Econometric Courses. This course would serve well any student pushing through a more mathematical based curriculum. Jose provides a practical hands on approach to analyzing (and understanding) time series data sets. If you're only interested in trading, I highly recommend starting at the beginning and going through each lecture. There is something to learn at each step for even the most experienced analyst. Well done Jose... Thanks!

Amazing course and its structure, required practice to get familiar with various functions, modules available and lot to explore new stuff. Excellent resources provided with detailed explanation for each topic. Great!Thank you!

Parts 82-85 need to be explained in a bit more detail and reasoning, it felt rushed especially that this is an important part of the course with a lot of detail.

The crash course in Python basics was very good, however the Finance/algorythmic Trading part of the course appeared to be a bit outdated at times and needed some updating. Plus, most of the course was too much focused on US equities; which is good for us investors but not so much for people living outside of the US.

The Quantopian part of the course is not as well explained as the rest. And I would prefer a more extensive section from the financial analysis part. Still a good course though.

So far the course has been an excellent match for me! Though I had some experience coding in Python, it has taught me a lot, i.e. about Matplotlib visualization capabilities. I love it that it's right to the point and that Jose is pretty clear and uncomplicated when it comes to explaining the different topics. Would love to see a second episode of this course to go deeper into algotrading.Updated on 09/12/2019 --> Maintain my previous review and just would like to strongly underscore that the community needs a second part of this course.

Very useful and well taught course. Good overview of financial concepts and python. I just wish it covered APIs outside of Quantopian, market making, and more technical indicators.

Professor is very organized and the material is very helping to understand the content. Just need to update some topics that is not working anymore.

I spent about an hour trying to get the folder to come up in Jupyter. Eventually I found that I had to create it, using the command line with - jupyter notebook 'C:Deep LearningU-PyForTrading' That worked.

The course is good. If you have some Python knowledge before start it, you will enjoy the course from the beginning, however, if you do not have familiarity with Python, you will take a while to catch up.