These are the course materials for the CFA of Minnesota Python training on November 7, 2019.
This course will cover the fundamentals of performing Pythonic data analysis in a finance context.
Location:
121 South 8th Street
Suite 200 – Marquette Room (Skyway Level)
Minneapolis, MN 55402
Time:
8:00a-5:00p
Please feel free to e-mail me with any question: pritam@umn.edu.
Instructor: Pritam Dalal (pritam@umn.edu)
TA: Charles Fossey (fosse067@umn.edu)
TA: Yifei Luo (luo00115@umn.edu)
TA: Juan Malaver Alvarado (malav015@umn.edu)
All of our analysis will occur in Jupyter Notebooks. I recommend that you use the Anaconda Distribution for this course, and by doing so you will have all the software and packages needed to follow along.
Instructions for installing Anaconda can be found in slides/01_download_installation.html.
There are a series of Jupyter Notebooks in the tutorials/ folder. Prior to class, feel free to launch those in Jupyter or JupyterLab. These are the tutorials we will work through during the training.
01 - introduction to Jupyter
02 - basics of python syntax and data structures
03 - numpy and pandas introduction
04 - DataFrame indexing and slicing
05 - DataFrame masking
06 - calculating returns (comparison, if-else, iteration)
07 - writing functions
08 - .apply() to calculate option payoffs
09 - aggregation and grouping (part 1)
10 - joining DataFrames (inner joins and left-joins)
11 - aggregation and grouping (part 2)
12 - line graphs with pandas
13 - bar charts with pandas
14 - scatter plots with pandas
15 - visualization with seaborn
16 - pandas_datareader
I have provided several exercises, along with solutions, for you to work through after the class.
01 - computing volume weighted average price
02 - an introduction to options data
03 - calculating pnl on a stock investment
04 - occ volume analysis
05 - TNA vs IWM - a volatility analysis
06 - calculating pnl on an option
07 - analysis on SPY calls