Random stuff to do later:

• get stock data and insert it into the database (https://www.dataquest.io/blog/sql-insert-tutorial/)

• setup database server with ansible

• create ansible playbook for basic server setup, including swap setup (Ubuntu 18.04 LTS and CentOs 8)

• plot moving average turbulent waves for stocks and crypto

• make double bottom or double top detection equivalents for crypto

• write simple moving average trading advisor (almost trading bot without calling any APIs)

• write stock screener that would send emails with alerts (weekly RSI or something like that)

• compute various indicators in python (dont forget about McGinley dynamic indicator)

• aggregate daily data into weekly candles and then compute weekly RSI

• plot stock candles in python

• checkout DataQuest courses (https://www.dataquest.io/directory/)

• check DataCamp courses (https://www.datacamp.com/tracks/career)

• write LSTM neural network for time series

• learn Chef or Salt Stack (well, maybe just Chef)

• check Datastructures and Algorithms course on Udemy

• rewrite top or bottom screeners with moving average or exp. moving average that is that is moved backwards by its window size (or maybe half a window size, needs some testing)

• mount some directories to docker container (https://stackoverflow.com/questions/23439126/how-to-mount-a-host-directory-in-a-docker-container)

• check regression in python

  • https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/
  • https://www.analyticsvidhya.com/blog/2018/03/introduction-regression-splines-python-codes/

• check smoothing in python, mainly Savitzky-Golay filter

  • https://plot.ly/python/#peak-analysis
  • https://stats.stackexchange.com/questions/36309/how-do-i-find-peaks-in-a-dataset
  • https://plot.ly/python/smoothing/
  • https://stackoverflow.com/questions/28855928/python-smoothing-data
  • https://scipy-cookbook.readthedocs.io/items/SavitzkyGolay.html
  • https://www.swharden.com/wp/2008-11-17-linear-data-smoothing-in-python/
  • https://stackoverflow.com/questions/20618804/how-to-smooth-a-curve-in-the-right-way
  • https://www.mikulskibartosz.name/smoothing-time-series-in-python-using-savitzky-golay-filter/

• check kubernetes and docker on for machine learning (https://www.mikulskibartosz.name/a-comprehensive-guide-to-putting-a-machine-learning-model-in-production/)

• read machine learning stuff on https://www.mikulskibartosz.name/

• read everything at https://blog.quantinsti.com/

• some algorithms https://www.youtube.com/watch?v=p65AHm9MX80

• create Sobel filter to detect edges with Open CV library

• summary of lsof command

• make notes about GNU screen command

• details about rpm packages

• maybe data-ops.info domain

• how to bootstrap new server with ansible https://www.rubytreesoftware.com/resources/ansible-bootstrap-playbook/ https://blog.scottlowe.org/2015/05/26/bootstrap-servers-ansible/ https://github.com/mrmierzejewski/ansible-bootstrap-ubuntu https://github.com/robertdebock/ansible-role-bootstrap

• check logrotate

machine learning for finance Packt book:
https://subscription.packtpub.com/book/data/9781789346411/10/ch10lvl1sec42/decision-trees

decision tree for stock market
https://blog.quantinsti.com/decision-tree/

nice backtests:
https://www.pythonforfinance.net/2017/07/31/bollinger-band-trading-strategy-backtest-in-python/

Interesting stuff from quantisti and kaggle (opportunities for expansion):

• https://blog.quantinsti.com/predicting-stock-trends-technical-analysis-random-forests/
• https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs
• https://www.kaggle.com/mtszkw/using-xgboost-for-stock-trend-prices-prediction
• https://www.kaggle.com/mtszkw/visualizing-technical-indicators-for-trading-etfs
• https://www.kaggle.com/taronzakaryan/stock-prediction-lstm-using-pytorch

simple pytorch algorithm to make basic neural net:

• https://www.reddit.com/r/learnmachinelearning/comments/9z3elx/pytorch_neural_net_training_with_iris_dataset/

Iris flower dataset with pytorch

• https://medium.com/@ozgur.ersoz3/iris-flowers-classification-with-pytorch-cd80c8aeeb2c