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My solutions to the Machine Learning for Trading course exercises.
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Felix Martin 927c5eb9de Finish project 2. 2020-09-07 21:24:45 -04:00
martingale Fix mistake in previous solution and finish report for project 1. 2020-08-07 15:55:12 -04:00
optimize_something Finish project 2. 2020-09-07 21:24:45 -04:00
playground Start with optimize something exercise. Also add a playground for testing candlestick plotting via mplfinance. 2020-08-28 22:36:43 -04:00
zips Start with optimize something exercise. Also add a playground for testing candlestick plotting via mplfinance. 2020-08-28 22:36:43 -04:00
.gitignore Start with optimize something exercise. Also add a playground for testing candlestick plotting via mplfinance. 2020-08-28 22:36:43 -04:00
LICENSE Add readme and license. 2020-08-05 17:05:32 -04:00
README.md Finish project 2. 2020-09-07 21:24:45 -04:00

README.md

ML4T

This is my solution to the ML4T course exercises. The main page for the course is here. The page contains a link to the assignments. There are eight projects in total.

To set up the environment I have installed the following packages on my Linux Manjaro based system.

sudo pacman -S python-pandas --asdeps python-pandas-datareader python-numexpr \
               python-bottleneck python-jinja python-scipy python-matplotlib  \
               python-numpy

I am also using the wonderful mplfinance. You can install mplfinance via pip and find the tutorial here.

pip install mplfinance --user

Use unzip with the -n flag to extract the archives for the different exercises. This makes sure that you do not override any of the existing files. I might add a makefile to automize this later.

unzip -n zips/20Spring_martingale.zip -d ./
unzip -n zips/19fall_optimize_something.zip -d ./

Reports