Options Pricing in Excel with QuantLib
One tool for working with finance in Python is QuantLib. If you're building a basic model, there's a good chance it's already implemented in QuantLib; if you're working with something more advanced, it provides a good starting point.
The script reads in this data and returns a vanilla option price. This is using the Black-Scholes-Merton process for pricing, but many other options models are built in - the Heston/Hull White models and numerical models, for example. Here's where the computations are done:
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# basic option payoff = PlainVanillaPayoff(opt_type, strike) exercise = EuropeanExercise(maturity) europeanOption = VanillaOption(payoff, exercise) # handle setups underlyingH = QuoteHandle(SimpleQuote(underlying)) flatTermStructure = YieldTermStructureHandle(FlatForward(settlementDate, riskFreeRate, dayCounter)) dividendYield = YieldTermStructureHandle(FlatForward(settlementDate, dividendYield, Actual365Fixed())) flatVolTS = BlackVolTermStructureHandle(BlackConstantVol(settlementDate, calendar, volatility, dayCounter)) # done bsmProcess = BlackScholesMertonProcess(underlyingH, dividendYield, flatTermStructure, flatVolTS) # method: analytic europeanOption.setPricingEngine(AnalyticEuropeanEngine(bsmProcess)) value = europeanOption.NPV() return value
The main part of this code is selecting what kind of model to use - computing the value of a vanilla option just scratches the surface of what QuantLib can do.
The output, with real time stock prices from Yahoo finance:
To use QuantLib with DataNitro, you need to compile Python bindings for it. Instructions for this are available on the QuantLib site. A compiled version of QuantLib for Python is also available with the enterprise version of DataNitro.