I understand that QR is designed to be piloted from JupyterLab and/or the command line. However, for obvious reasons I’d like to debug in something like PyCharm.
With previous projects I’ve used PyCharm to iron-out problems in JupyterLab notebooks and their dependencies. Upon getting the code right in the IDE, I would then restart the JupyterLab kernal and re-run the notebook, which isn’t ideal but I find to be an acceptable trouble-shooting workflow.
With docker I don’t know how to implement a similar approach, since the python files kinda don’t exist outside of JupyterLab (or the container). As a case in point, when a moonshot backtest is run from PyCharm, at a minimum the strategy file must reside over in codeload where it’s difficult to debug.
Passing code around through a code repository does not strike me as an appropriate solution, and I’m not sure whether Docker shared-volumes is suitable either.
Just wondering what workflow you use to develop and debug the strategies presented in your blog.