Hi!
I just replied on another thread - I’ve started working on a vectorized (both single asset and portfolio) backtesting framework. I think it could be a low hanging fruit (it’s really not rocket science), high leverage project, which can establish some of the primites that other package could be using as well.
I’m planning to open my code up as soon as I get a bit more familiar with Julia package creation - hopefully this week.
The goal would be to create a simple-to-use portfolio backtester (and also an alpha research tool similar to alphalens) that can be used to easily layer quantiative portfolio management strategies on top of each other, like volatility targeting, trend-following, etc.
Let me know if anyone is interested in helping out, on any level!
It’d be great to do this under the JuliaQuant organization, potentially?
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I’m interested. My specialty is in stochastic processes/filtering and I have already gained some experience developing Julia packages, e.g. FeedbackParticleFilters.jl.
How far evolved is the JuliaQuant ecosystem in your view? Would the backtester just provide functions for the backtesting itself, importing financial time series etc. or would it also need to provide functionality to describe portfolios and strategies themselves?
What do you mean by vectorized? Is that something regarding the data structures you have in mind, or a finance concept?
I would be interested to participate in this project. Can you contact me on Zulip (Andrey Oskin)? We can organize channel there where everyone can join and it wouldn’t be lost.
I have done the smallest amount of initial work that one could possibly do on the topic! Maybe this could help generate some ideas, but the stuff here is pretty trivial.
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It’d be great to do this under the JuliaQuant organization, potentially?
Ah, great. If you’r ready to transfer the repo to JuliaQuant, I can help for that. The only thing I can do is giving you the committer bit. 
Hi, I would also be interested in this projects
We organized stream in Zulip: Zulip Everyone is welcome 
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