There are nine parts:
1 Reading, slicing and plotting stock data
2 Working with many stocks at once
3 Using Numpy
4 Statistical analysis of time series
5 Incomplete data
6 Histograms and scatter plots
7 Sharpe ratio & other portfolio statistics
8 Optimizers: Building a parameterized model
9 Projects:
a) Portfolio analysis: Assess a portfolio by computing statistics such as cumulative return, average period return, standard deviation, Sharpe ratio and end value of portfolio
b) Optimizing a portfolio: Find an optimal allocation of stocks in a portfolio and compute its statistics
You need Python 2.7+, and the following packages: pandas, numpy, scipy and matplotlib.
Data files can be downloaded from this link or from Yahoo Finance
Place the data into a directory named 'data' and it should be one level above this repository.
To run any script file, use:
python <script.py>Source: Part 1 of Machine Learning for Trading by Georgia Tech