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Python for financial research (2018 workshop)

Vincent Grégoire, University of Melbourne

This repository contains material for the 2018 Python for financial research workshop for honours and Ph.D. students at the University of Melbourne.


The workshop is divided into four blocks of three hours each:

1. Introduction to Python programming

We will discuss what Python is and you will learn the basic structure of the language. You will also learn your way around the programming environment, including the two main editors for scientific Python, Spyder, and Jupyter. You will learn how to import and explore data using pandas by generating summary statistics and plotting graphs using matplotlib.

2. Introduction to data analysis using pandas and matplotlib

You will learn how to import, export and transform data using pandas, the panel data package for Python. You will also see how to do basic portfolio analysis while replicating a classic paper.

Recommended reading: Bondt, W.F. and Thaler, R., 1985. Does the stock market overreact?. The Journal of finance, 40(3), pp.793-805.

3. More data analysis using pandas and statsmodels

You will learn more advanced features of Python and pandas, including dealing with timestamps and estimating measures from daily and intraday data. You will also learn how to estimate OLS and panel regressions using statsmodels.

Recommended reading: Petersen, Mitchell A., 2009. Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches. Review of Financial Studies. 22, pp.435-480.

4. Other topics

In this block, you will be introduced briefly to other Python packages that can be helpful for research. We will look at an example of web scraping with textual analysis.


I recommend the Anaconda distribution, which is available for Windows, Mac OS and Linux. We are using the Python 3.6 version for the workshop.




Note: this code is for illustrative purpose, and does not necessarily show the correct or best way to do something, the main goal is to illustrate the Python language, its libraries, and some common use cases in research.

Block 1:

Block 2:

Block 3:

Block 4:


Material for a Python for Finance workshop at the University of Melbourne in 2018




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