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My solutions to the Machine Learning for Trading course exercises.
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Felix Martin e577ce34d2 Add support for trading view data 2021-06-20 15:04:30 -04:00
assess_learners Finish report for project 3. 2020-09-26 10:52:05 -04:00
crypto_eval Add support for trading view data 2021-06-20 15:04:30 -04:00
defeat_learners Finish project 4 2020-10-05 20:01:29 -04:00
manual_strategy Fix picture link in project 6 report 2020-10-15 13:11:40 -04:00
marketsim Make marketsim support buying and selling via sign of shares 2020-10-27 19:53:55 -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 report for project 3. 2020-09-26 10:52:05 -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
qlearning_robot Implement dyna-q to finish project 7 2020-10-19 08:56:24 -04:00
strategy_evaluation Finish project 8 and course! 2020-11-10 12:33:42 -05:00
zips Add template for project 8 2020-10-15 16:46:50 -04:00
.gitignore Add util because I cannot remember why I would ignore it 2021-06-19 09:47:35 -04:00
LICENSE Add readme and license. 2020-08-05 17:05:32 -04:00
README.md Add project 8 report to readme 2020-11-10 12:41:50 -05:00
util.py Add support for trading view data 2021-06-20 15:04:30 -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. The summer 2020 page is here.

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 mplfinance to plot candlestick-charts. You can install mplfinance via pip and find the tutorial here.

pip install mplfinance --user

I have included the archived version of the exercise. To extract them run the following command. The -n flag makes unzip never overwrite existing files.

unzip -n zips/*.zip -d ./

Reports