I am creating a Python library to simulate stock strategies on historical data, specifically made for learners and hobbyists.
Used ChatGPT for debugging, used GitHub Copilot to create some parts of the documentation, and testing code. Also used ChatGPT for learning certain stock performance metrics such as calculating sharpe ratio
Hi all,
Side note: I am building an open-source Python library to simulate stocks to test user-created strategies. I am mainly focusing on making it as simple as possible while preserving usability. I did the initial release in December, but haven’t touched it since then, and I am slowly getting back to work.
I have been recently working on adding built-in indicators support for basic ones, such as SMA. And adding a new function to my StockData class that would take in these indicator functions and automatically add their result to itself.
I think this would make it easier to create more advanced strategies involving these indicators, also save people from having to create their own function or rely on external libraries, and manage them, which would make things unnecessarily complicated. Also, since my whole goal is to make this lightweight, relying on the outside doesn’t help with this goal.
Adding indicators were failrly simple as I already had them in the early development of the library; however, implementing the function to take in these indicators and add them to the data is a lot harder than I anticipated. For some reason, I keep getting errors, and the data formatting gets messed up.
Aside from this goal, I am also working on improving the overall architecture of the library, but I am planning to release it as v0.2.0, and release all these indicator improvements as part of v0.1.1
Log in to leave a comment