[Quantlib-users] Integrating Open Source Risk Engine (ORE) with QuantLib-Python for CVA/DVA Calculations

[Quant <qua...@gm...>]

Dear QuantLib User,

I would appreciate some guidance on integrating the Open Source Risk Engine
(ORE) with QuantLib-Python for calculating CVA and DVA on OTC derivatives,
like Interest Rate Swaps (IRS) and Cross-Currency Basis Swaps (CCBS).

I currently use QuantLib-Python for market data extraction, yield curve
construction and instrument valuation. I have also installed ORE and
successfully run its exposure and XVA examples.

My objective is to leverage QuantLib-Python for market data and trade
preparation, while using ORE's simulation and valuation framework to
generate future exposure profiles and calculate CVA/DVA.

I would appreciate guidance on the following:

   1.

   Is there a recommended way to integrate ORE directly with
   QuantLib-Python?
   2.

   Are there Python bindings or APIs that allow QuantLib-Python
   instruments, curves and market data to be passed directly to ORE?
   3.

   If direct integration is not supported, what is the recommended approach
   for transferring trade details and market data between the two systems?
   4.

   Can ORE simulate future market scenarios and perform pathwise
   revaluation of IRS and CCBS trades to generate exposure profiles for
   CVA/DVA calculations?
   5.

   Would you recommend running ORE as a standalone engine, with
   QuantLib-Python handling market data and trade preparation?

Any examples, documentation or recommendations on the most appropriate
architecture would be greatly appreciated.

Thanks,

Nk