Announcing Crypto and Other Backtesting Logic ; Quantpylib
We are proud to bring enhancements to the backtesting logic of the quantpylib’s simulator library.
https://hangukquant.github.io/simulator/simulator/#crypto-currencies-fees-and-customization
The module is designed to work seamlessly with the quantpylib.datapoller library. All you have to do is construct your alpha string, define your trading intervals, and pass it into our no-code back-tester. You can also choose to add execution fees, funding rates, positional inertia, portfolio volatility - and we take care of all of the work, including instrument size, instrument volatility targeting, portfolio volatility targeting and so forth …
quantpylib is quickly becoming one of my core tools in my own quantitative research process. It is indeed becoming quite a powerful tool…a powerful one I say. If you a reader and do not have access yet…read this!
"quantpylib is quickly becoming one of my core tools in my own quantitative research process."
What would you say is one of the biggest differences between your researching process and the process presented in quantpylib? Rephrased, what capability/functionality doesn't quantpylib have that you use in your own research process?