# Quantlib - model changes in option value on day of expiry

I'm trying to model option value changes during the progression of the last trading day before expiry. All option pricing Quantlib examples that I've seen work with day-level granularity. I'm wondering if Quantlib can support a finer granularity in its models, and if so, how this might be set up? Thanks in advance.

N.B. I'm using the Python wrapper for Quantlib at this time.

It's possible, but you'll need to recompile both the C++ library and the Python wrappers. On Windows, you'll have to edit ql/userconfig.hpp and uncomment the line
//#    define QL_HIGH_RESOLUTION_DATE

On other systems, you'll have to pass the flag --enable-intraday when you call ./configure. This will cause the library to use the Boost.Date library internally, giving you (in principle) microsecond granularity.