There are two parts:
1 Compute stock return using Capital Asset Pricing Model (CAPM)
2 Projects:
a) Market simulator: Read in an orders file that contains trade orders (buy and sell), compute a portfolio value for all the trades and other statistics and compare the portfolio's performance with that of $SPX
b) Technical analysis: Implement a basic technical indicator and an advanced one (Bollinger value) to detect events of interest, plot them and output events as trades to be fed into a market simulator
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 2 of Machine Learning for Trading by Georgia Tech