Hands-On Python for Finance

Video description

The practical guide to using data-driven algorithms in Finance

About This Video

  • Use libraries like Numpy, Pandas, Scipy and Matplotlib for data analysis, manipulation and visualization
  • Implement common Time Series evaluation techniques, including development of forecasting models and linear models for forecasting
  • Make use of Monte Carlo method to simulate portfolio ending values, value options and calculate Value at Risk

In Detail

Did you know Python is the one of the best solution to quantitatively analyse your finances by taking an overview of your timeline? This hands-on course helps both developers and quantitative analysts to get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

You will begin with a primer to Python and its various data structures.Then you will dive into third party libraries. You will work with Python libraries and tools designed specifically for analytical and visualization purposes. Then you will get an overview of cash flow across the timeline. You will also learn concepts like Time Series Evaluation, Forecasting, Linear Regression and also look at crucial aspects like Linear Models, Correlation and portfolio construction. Finally, you will compute Value at Risk (VaR) and simulate portfolio values using Monte Carlo Simulation which is a broader class of computational algorithms.

With numerous practical examples through the course, you will develop a full-fledged framework for Monte Carlo, which is a class of computational algorithms and simulation-based derivatives and risk analytics.


This course is for developers and analysts with some background in programming language and are interested in a concrete framework for using Python to augment or replace spreadsheet applications for financial tasks.

Product information

  • Title: Hands-On Python for Finance
  • Author(s): Matthew Macarty
  • Release date: February 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781789800975