Option Pricing Engines

Vanilla Options

AnalyticEuropeanEngine

ql.AnalyticEuropeanEngine(GeneralizedBlackScholesProcess)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

engine = ql.AnalyticEuropeanEngine(process)

MCEuropeanEngine

ql.MCEuropeanEngine(GeneralizedBlackScholesProcess, traits, timeSteps=None, timeStepsPerYear=None, brownianBridge=False, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

steps = 2
rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCEuropeanEngine(process, rng, steps, requiredSamples=numPaths)

FdBlackScholesVanillaEngine

Note that this engine is capable of pricing both European and American payoffs!

ql.FdBlackScholesVanillaEngine(GeneralizedBlackScholesProcess, tGrid, xGrid, dampingSteps=0, schemeDesc=ql.FdmSchemeDesc.Douglas(), localVol=False, illegalLocalVolOverwrite=None)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

tGrid, xGrid = 2000, 200
engine = ql.FdBlackScholesVanillaEngine(process, tGrid, xGrid)

MCAmericanEngine

ql.MCAmericanEngine(GeneralizedBlackScholesProcess, traits, timeSteps=None, timeStepsPerYear=None, antitheticVariate=False, controlVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0, polynomOrder=2, polynomType=0, nCalibrationSamples=2048, antitheticVariateCalibration=None, seedCalibration=None)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

steps = 200
rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCAmericanEngine(process, rng, steps, requiredSamples=numPaths)

MCDigitalEngine

This engine prices american (ie. knock-in) cash-or-nothing payoffs only

ql.MCDigitalEngine(GeneralizedBlackScholesProcess, traits, timeSteps=None, timeStepsPerYear=None, brownianBridge=False, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

steps = 200
rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCDigitalEngine(process, rng, steps, requiredSamples=numPaths)

AnalyticHestonEngine

ql.AnalyticHestonEngine(HestonModel)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))

initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
v0 = 0.005
kappa = 0.8
theta = 0.008
rho = 0.2
sigma = 0.1

hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)
hestonModel = ql.HestonModel(hestonProcess)

engine = ql.AnalyticHestonEngine(hestonModel)

MCEuropeanHestonEngine

ql.MCEuropeanHestonEngine(HestonProcess, traits, timeSteps=None, timeStepsPerYear=None, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))

initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
v0 = 0.005
kappa = 0.8
theta = 0.008
rho = 0.2
sigma = 0.1

hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)

steps = 2
rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCEuropeanHestonEngine(hestonProcess, rng, steps, requiredSamples=numPaths)

FdHestonVanillaEngine

If a leverage function (and optional mixing factor) is passed in to this function, it prices using the Heston Stochastic Local Vol model

ql.FdHestonVanillaEngine(HestonModel, tGrid=100, xGrid=100, vGrid=50, dampingSteps=0, FdmSchemeDesc=ql.FdmSchemeDesc.Hundsdorfer(), leverageFct=LocalVolTermStructure(), mixingFactor=1.0)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))

initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
v0 = 0.005
kappa = 0.8
theta = 0.008
rho = 0.2
sigma = 0.1

hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)
hestonModel = ql.HestonModel(hestonProcess)

tGrid, xGrid, vGrid = 100, 100, 50
dampingSteps = 0
fdScheme = ql.FdmSchemeDesc.ModifiedCraigSneyd()

engine = ql.FdHestonVanillaEngine(hestonModel, tGrid, xGrid, vGrid, dampingSteps, fdScheme)

AnalyticPTDHestonEngine

ql.AnalyticPTDHestonEngine(PiecewiseTimeDependentHestonModel)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))

initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

times = [1.0, 2.0, 3.0]
grid = ql.TimeGrid(times)

v0 = 0.005
theta = [0.010, 0.015, 0.02]
kappa = [0.600, 0.500, 0.400]
sigma = [0.400, 0.350, 0.300]
rho = [-0.15, -0.10, -0.00]

kappaTS = ql.PiecewiseConstantParameter(times[:-1], ql.PositiveConstraint())
thetaTS = ql.PiecewiseConstantParameter(times[:-1], ql.PositiveConstraint())
rhoTS = ql.PiecewiseConstantParameter(times[:-1], ql.BoundaryConstraint(-1.0, 1.0))
sigmaTS = ql.PiecewiseConstantParameter(times[:-1], ql.PositiveConstraint())

for i, time in enumerate(times):
    kappaTS.setParam(i, kappa[i])
    thetaTS.setParam(i, theta[i])
    rhoTS.setParam(i, rho[i])
    sigmaTS.setParam(i, sigma[i])

hestonModelPTD = ql.PiecewiseTimeDependentHestonModel(riskFreeTS, dividendTS, initialValue, v0, thetaTS, kappaTS, sigmaTS, rhoTS, grid)
engine = ql.AnalyticPTDHestonEngine(hestonModelPTD)

Asian Options

AnalyticDiscreteGeometricAveragePriceAsianEngine

ql.AnalyticDiscreteGeometricAveragePriceAsianEngine(GeneralizedBlackScholesProcess)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

engine = ql.AnalyticDiscreteGeometricAveragePriceAsianEngine(process)

AnalyticContinuousGeometricAveragePriceAsianEngine

ql.AnalyticContinuousGeometricAveragePriceAsianEngine(GeneralizedBlackScholesProcess)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

engine = ql.AnalyticContinuousGeometricAveragePriceAsianEngine(process)

MCDiscreteGeometricAPEngine

ql.MCDiscreteGeometricAPEngine(GeneralizedBlackScholesProcess, traits, brownianBridge=False, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCDiscreteGeometricAPEngine(process, rng, requiredSamples=numPaths)

MCDiscreteArithmeticAPEngine

ql.MCDiscreteArithmeticAPEngine(GeneralizedBlackScholesProcess, traits, brownianBridge=False, antitheticVariate=False, controlVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCDiscreteArithmeticAPEngine(process, rng, requiredSamples=numPaths)

FdBlackScholesAsianEngine

Note that this engine will throw an error if asked to price Geometric averaging options. It only prices Discrete Arithmetic Asians.

ql.FdBlackScholesAsianEngine(GeneralizedBlackScholesProcess, tGrid=100, xGrid=100, aGrid=50)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

tGrid, xGrid, aGrid = 100, 100, 50
engine = ql.FdBlackScholesAsianEngine(process, tGrid=tGrid, xGrid=xGrid, aGrid=aGrid)

AnalyticDiscreteGeometricAveragePriceAsianHestonEngine

ql.AnalyticDiscreteGeometricAveragePriceAsianHestonEngine(HestonProcess)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

v0, kappa, theta, rho, sigma = 0.005, 0.8, 0.008, 0.2, 0.1
hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)

engine = ql.AnalyticDiscreteGeometricAveragePriceAsianHestonEngine(hestonProcess)

AnalyticContinuousGeometricAveragePriceAsianHestonEngine

ql.AnalyticContinuousGeometricAveragePriceAsianHestonEngine(HestonProcess)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

v0, kappa, theta, rho, sigma = 0.005, 0.8, 0.008, 0.2, 0.1
hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)

engine = ql.AnalyticContinuousGeometricAveragePriceAsianHestonEngine(hestonProcess)

MCDiscreteGeometricAPHestonEngine

ql.MCDiscreteGeometricAPHestonEngine(HestonProcess, traits, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0, timeSteps=None, timeStepsPerYear=None)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

v0, kappa, theta, rho, sigma = 0.005, 0.8, 0.008, 0.2, 0.1
hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)

rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCDiscreteGeometricAPHestonEngine(hestonProcess, rng, requiredSamples=numPaths)

MCDiscreteArithmeticAPHestonEngine

ql.MCDiscreteArithmeticAPHestonEngine(HestonProcess, traits, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0, timeSteps=None, timeStepsPerYear=None, controlVariate=False)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

v0, kappa, theta, rho, sigma = 0.005, 0.8, 0.008, 0.2, 0.1
hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)

rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCDiscreteArithmeticAPHestonEngine(hestonProcess, rng, requiredSamples=numPaths)

TurnbullWakemanAsianEngine

ql.TurnbullWakemanAsianEngine(GeneralizedBlackScholesProcess)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

engine = ql.TurnbullWakemanAsianEngine(process)

Barrier Options

BinomialBarrierEngine

ql.BinomialBarrierEngine(process, type, steps)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatVolTs = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.UnitedStates(), 0.2, ql.Actual365Fixed()))
bsm = ql.BlackScholesProcess(spotHandle, flatRateTs, flatVolTs)

binomialBarrierEngine = ql.BinomialBarrierEngine(bsm, 'crr', 200)

AnalyticBarrierEngine

ql.AnalyticBarrierEngine(process)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatVolTs = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.UnitedStates(), 0.2, ql.Actual365Fixed()))
bsm = ql.BlackScholesProcess(spotHandle, flatRateTs, flatVolTs)

analyticBarrierEngine = ql.AnalyticBarrierEngine(bsm)

FdBlackScholesBarrierEngine

ql.FdBlackScholesBarrierEngine(process, tGrid=100, xGrid=100, dampingSteps=0, FdmSchemeDesc=ql.FdmSchemeDesc.Douglas(), localVol=False, illegalLocalVolOverwrite=None)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatVolTs = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.UnitedStates(), 0.2, ql.Actual365Fixed()))
bsm = ql.BlackScholesProcess(spotHandle, flatRateTs, flatVolTs)

fdBarrierEngine = ql.FdBlackScholesBarrierEngine(bsm)

FdBlackScholesRebateEngine

ql.FdBlackScholesRebateEngine(process, tGrid=100, xGrid=100, dampingSteps=0, FdmSchemeDesc=ql.FdmSchemeDesc.Douglas(), localVol=False, illegalLocalVolOverwrite=None)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatVolTs = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.UnitedStates(), 0.2, ql.Actual365Fixed()))
bsm = ql.BlackScholesProcess(spotHandle, flatRateTs, flatVolTs)

fdRebateEngine = ql.FdBlackScholesRebateEngine(bsm)

AnalyticBinaryBarrierEngine

ql.AnalyticBinaryBarrierEngine(process)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatVolTs = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.UnitedStates(), 0.2, ql.Actual365Fixed()))
bsm = ql.BlackScholesProcess(spotHandle, flatRateTs, flatVolTs)

analyticBinaryBarrierEngine = ql.AnalyticBinaryBarrierEngine(bsm)

FdHestonBarrierEngine

If a leverage function (and optional mixing factor) is passed in to this function, it prices using the Heston Stochastic Local Vol model

ql.FdHestonBarrierEngine(HestonModel, tGrid=100, xGrid=100, vGrid=50, dampingSteps=0, FdmSchemeDesc=ql.FdmSchemeDesc.Hundsdorfer(), leverageFct=LocalVolTermStructure(), mixingFactor=1.0)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatDividendTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))

v0, kappa, theta, sigma, rho = 0.01, 2.0, 0.01, 0.01, 0.0
hestonProcess = ql.HestonProcess(flatRateTs, flatDividendTs, spotHandle, v0, kappa, theta, sigma, rho)
hestonModel = ql.HestonModel(hestonProcess)

hestonBarrierEngine = ql.FdHestonBarrierEngine(hestonModel)

AnalyticDoubleBarrierEngine

ql.AnalyticDoubleBarrierEngine(process)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatVolTs = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.UnitedStates(), 0.2, ql.Actual365Fixed()))
bsm = ql.BlackScholesProcess(spotHandle, flatRateTs, flatVolTs)

analyticDoubleBarrierEngine = ql.AnalyticDoubleBarrierEngine(bsm)

AnalyticDoubleBarrierBinaryEngine

ql.AnalyticDoubleBarrierBinaryEngine(process)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatVolTs = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.UnitedStates(), 0.2, ql.Actual365Fixed()))
bsm = ql.BlackScholesProcess(spotHandle, flatRateTs, flatVolTs)

analyticDoubleBinaryBarrierEngine = ql.AnalyticDoubleBarrierBinaryEngine(bsm)

FdHestonDoubleBarrierEngine

If a leverage function (and optional mixing factor) is passed in to this function, it prices using the Heston Stochastic Local Vol model

ql.FdHestonDoubleBarrierEngine(HestonModel, tGrid=100, xGrid=100, vGrid=50, dampingSteps=0, FdmSchemeDesc=ql.FdmSchemeDesc.Hundsdorfer(), leverageFct=LocalVolTermStructure(), mixingFactor=1.0)

today = ql.Date().todaysDate()

spotHandle = ql.QuoteHandle(ql.SimpleQuote(100))
flatRateTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
flatDividendTs = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))

v0, kappa, theta, sigma, rho = 0.01, 2.0, 0.01, 0.01, 0.0
hestonProcess = ql.HestonProcess(flatRateTs, flatDividendTs, spotHandle, v0, kappa, theta, sigma, rho)
hestonModel = ql.HestonModel(hestonProcess)

hestonDoubleBarrierEngine = ql.FdHestonDoubleBarrierEngine(hestonModel)

Basket Options

MCEuropeanBasketEngine

ql.MCEuropeanBasketEngine(GeneralizedBlackScholesProcess, traits, timeSteps=None, timeStepsPerYear=None, brownianBridge=False, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0)

# Create a StochasticProcessArray for the various underlyings
underlying_spots = [100., 100., 100., 100., 100.]
underlying_vols = [0.1, 0.12, 0.13, 0.09, 0.11]
underlying_corr_mat = [[1, 0.1, -0.1, 0, 0], [0.1, 1, 0, 0, 0.2], [-0.1, 0, 1, 0, 0], [0, 0, 0, 1, 0.15], [0, 0.2, 0, 0.15, 1]]

today = ql.Date().todaysDate()
day_count = ql.Actual365Fixed()
calendar = ql.NullCalendar()

riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.0, day_count))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.0, day_count))

processes = [ql.BlackScholesMertonProcess(ql.QuoteHandle(ql.SimpleQuote(x)),
                                          dividendTS,
                                          riskFreeTS,
                                          ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, calendar, y, day_count)))
             for x, y in zip(underlying_spots, underlying_vols)]

multiProcess = ql.StochasticProcessArray(processes, underlying_corr_mat)

# Create the pricing engine
rng = "pseudorandom"
numSteps = 500000
stepsPerYear = 1
seed = 43

engine = ql.MCEuropeanBasketEngine(multiProcess, rng, timeStepsPerYear=stepsPerYear, requiredSamples=numSteps, seed=seed)

Cliquet Options

Forward Options

ForwardEuropeanEngine

This engine in python implements the C++ engine QuantLib::ForwardVanillaEngine (notice the subtle name change)

ql.ForwardEuropeanEngine(process)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))
process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

engine = ql.ForwardEuropeanEngine(process)

MCForwardEuropeanBSEngine

ql.MCForwardEuropeanBSEngine(process, traits, timeSteps=None, timeStepsPerYear=None, brownianBridge=False, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
volatility = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.1, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

process = ql.BlackScholesMertonProcess(initialValue, dividendTS, riskFreeTS, volatility)

rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCForwardEuropeanBSEngine(process, rng, timeStepsPerYear=12, requiredSamples=numPaths)

AnalyticHestonForwardEuropeanEngine

ql.AnalyticHestonForwardEuropeanEngine(process, integrationOrder=144)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

v0, kappa, theta, rho, sigma = 0.005, 0.8, 0.008, 0.2, 0.2
hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)

engine = ql.AnalyticHestonForwardEuropeanEngine(hestonProcess)

MCForwardEuropeanHestonEngine

ql.MCForwardEuropeanHestonEngine(hestonProcess, traits, timeSteps=None, timeStepsPerYear=None, antitheticVariate=False, requiredSamples=None, requiredTolerance=None, maxSamples=None, seed=0, controlVariate=False)

today = ql.Date().todaysDate()
riskFreeTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.05, ql.Actual365Fixed()))
dividendTS = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, ql.Actual365Fixed()))
initialValue = ql.QuoteHandle(ql.SimpleQuote(100))

v0, kappa, theta, rho, sigma = 0.005, 0.8, 0.008, 0.2, 0.2
hestonProcess = ql.HestonProcess(riskFreeTS, dividendTS, initialValue, v0, kappa, theta, sigma, rho)

rng = "pseudorandom" # could use "lowdiscrepancy"
numPaths = 100000

engine = ql.MCForwardEuropeanHestonEngine(hestonProcess, rng, timeStepsPerYear=12, requiredSamples=numPaths)

Option Pricing Engines

Vanilla Options

AnalyticEuropeanEngine

MCEuropeanEngine

FdBlackScholesVanillaEngine

MCAmericanEngine

MCDigitalEngine

AnalyticHestonEngine

MCEuropeanHestonEngine

FdHestonVanillaEngine

AnalyticPTDHestonEngine

Asian Options

AnalyticDiscreteGeometricAveragePriceAsianEngine

AnalyticContinuousGeometricAveragePriceAsianEngine

MCDiscreteGeometricAPEngine

MCDiscreteArithmeticAPEngine

FdBlackScholesAsianEngine

AnalyticDiscreteGeometricAveragePriceAsianHestonEngine

AnalyticContinuousGeometricAveragePriceAsianHestonEngine

MCDiscreteGeometricAPHestonEngine

MCDiscreteArithmeticAPHestonEngine

TurnbullWakemanAsianEngine

Barrier Options

BinomialBarrierEngine

AnalyticBarrierEngine

FdBlackScholesBarrierEngine

FdBlackScholesRebateEngine

AnalyticBinaryBarrierEngine

FdHestonBarrierEngine

AnalyticDoubleBarrierEngine

AnalyticDoubleBarrierBinaryEngine

FdHestonDoubleBarrierEngine

Basket Options

MCEuropeanBasketEngine

Cliquet Options

Forward Options

ForwardEuropeanEngine

MCForwardEuropeanBSEngine

AnalyticHestonForwardEuropeanEngine

MCForwardEuropeanHestonEngine

Quanto Options