This post reviews NumPy main components and functionality, with attention to the needs of Data Science and Machine Learning practitioners, and people who aspire to become a data professional.
Archives for July 2020
Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.
The plotting functionality in the popular Python data analysis library Pandas has always been one of my go-to methods for super quick charts. However, the available visualisations have always been fairly basic and not particularly pretty.
It’s easy to get carried away with the wealth of data and free open-source tools available for data science. After spending a little bit of time with the quandl financial library and the prophet modeling library, I decided to try some simple stock data exploration. Several days and 1000 lines of Python later, I ended up with a complete stock analysis and prediction tool. Although I am not confident (or foolish) enough to use it to invest in individual stocks, I learned a ton of Python in the process and in the spirit of open-source, want to share my results and code so others can benefit.