Welcome to RiskFlow

RiskFlow is a python framework for performing derivatives pricing and related quantitative finance by utilizing google's tensorflow library. Riskflow is designed to work either on CPU's or nvidia GPU's via CUDA.


  • Fast prototyping and interactive scripting of new instruments in Python
  • Theoretical documentation for the pricing and simulation of financial derivatives
  • Monte Carlo simulation of a portfolio of trades through time allowing fast calculations
  • Automatic Derivatives for sensitivities calculation via tensorflow


Similar to other open source quantitative finance libraries (like quantlib), the motivations for RiskFlow are:

  • Stop re-inventing the wheel. Robust implementations of standard pricing functions (like Black Scholes) have been written multiple times and as a result of regulation, have had to be independently validated as many times.
  • Encouraging open collaboration via the philosophy of open source software.

Libraries like quantlib already do an excellent job of the above. RiskFlow attempts to also:

  • Make use of modern GPU's to perform full portfolio monte carlo simulation.
  • Provide theoretical documentation as part of the library thereby encouraging model validation (which can then be added to the library).
  • Standardize the way in which market and trade data is loaded and stored in the form of JSON files.
  • Offer a simpler alternative to quantlib by utilizing python as its main development language.


Although most major asset classes have been implemented, there is still considerable room for refinement. In addition to adding more assets/pricing functions, there is also the following:

  • Bootstrapping yield curves from benchmark FRA's and swaps
  • Bootstrapping volatility surfaces for FX and interest rates
  • Incorporating Wrong way risk during the Monte Carlo simulation
  • Calibration of risk neutral price models from market data
  • Non linear interpolation of yield curves/vol surfaces.

Non linear interpolation of yield curves would allow efficient memory storage for GPU's and more precise sensitivities to market benchmarks.