"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?
well, actually...quantpylib is the just the part of the quant research that I have automated or integrated into a quant `ecosystem` of libraries. It doesn't have some of the statistical tools that I use, but those are very script-based and situation dependent, and hence iffy to put as a quant package.
of course, the library is also not a strategy. It is used to research, analyse and ideate strategies but in itself is not one, which of course is separate
I was just curious about what other tools might be needed when researching strategies outside of what the quantpylib already had built in. I am currently in a "statistical soundness" phase and have been writing some scripts for statistical testing. The process has me wondering if most researchers tend to just write their own code for what they need or if the use outside libraries. Or both.
Anyway, I guess I am just trying to nail down the "process" of researching strategies a little more. Having no formal education on statistics or quantitative analysis, it is difficult to find information on the research process itself and the basic measures that researchers look for and how they look for them.
"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?
well, actually...quantpylib is the just the part of the quant research that I have automated or integrated into a quant `ecosystem` of libraries. It doesn't have some of the statistical tools that I use, but those are very script-based and situation dependent, and hence iffy to put as a quant package.
of course, the library is also not a strategy. It is used to research, analyse and ideate strategies but in itself is not one, which of course is separate
Thanks for the reply.
I was just curious about what other tools might be needed when researching strategies outside of what the quantpylib already had built in. I am currently in a "statistical soundness" phase and have been writing some scripts for statistical testing. The process has me wondering if most researchers tend to just write their own code for what they need or if the use outside libraries. Or both.
Anyway, I guess I am just trying to nail down the "process" of researching strategies a little more. Having no formal education on statistics or quantitative analysis, it is difficult to find information on the research process itself and the basic measures that researchers look for and how they look for them.