If you are pricing and risk-managing complex derivatives books then Pandas is not the right tool for the job. Pandas does not include derivatives pricing models

If you are pricing and risk-managing complex derivatives books then Pandas is not the right tool for the job. Pandas does not include derivatives pricing models, daycount conventions, schedule generation and algorithmic differentiation which are all basic ingredients in a modern derivatives pricing framework. In general what you are concerned with here is computing the fair value of each instrument usually as an expectation in a simulation or as a solution to a partial differential equation, and the first derivatives and sometimes second-order derivatives of the fair value with respect to the model parameters. Pandas makes a lot of work in empirical finance nicer, data cleaning, time series work, etc. But when pricing and risk-managing a derivatives book as a market maker you are less concerned with inferring the future from the past, and more with interpolating current data in such a way that you do not open yourself to arbitrage. I worked as a model quant for a year a couple of years ago in a bank in the team responsible for developing and implementing the derivatives pricing models. Everything was written in (a lot of) C++, but I suspected and still suspect that you could get the same performance by leveraging one of the off-the-shelf deep learning libraries with a lot less code. Maybe you would have to write a few central components in C++ and assembly to get the exact same performance. I am glad to see someone actually following through on that idea.