Issue in Pricing Barrier Options using MCBarrierEngine in QuantLib Python

Question

Extremely sorry for bugging the community again, but I am struggling with finding proper documentation of QuantLib Python. I am trying to price Barrier Option using MC Simulation. Here is the code:

from QuantLib import *

import matplotlib.pyplot as plt

barrier, barrierType, optionType, rebate = (80.0, Barrier.DownIn, Option.Call, 0.0)
underlying, strike, rf, sigma, maturity, divYield = (100, 105, .05, 0.2, 12, 0.0)

Grids = (5, 10, 25, 50, 100, 1000, 50000)

maxG = Grids[-1]

today = Settings.instance().evaluationDate

maturity_date = today + int(maturity)

process = BlackScholesMertonProcess(QuoteHandle(SimpleQuote(underlying)),
                                    YieldTermStructureHandle(FlatForward(today, divYield, Thirty360())),
                                    YieldTermStructureHandle(FlatForward(today, rf, Thirty360())),
                                    BlackVolTermStructureHandle(BlackConstantVol(
                                        today, NullCalendar(), sigma, Thirty360())))

option = BarrierOption(barrierType, barrier, rebate,PlainVanillaPayoff(optionType, strike),
                       EuropeanExercise(maturity_date))


steps = 2

rng = "lowdiscrepancy"
 
numPaths = 500000

traits=50000

engine = MCBarrierEngine(process, traits)

option.setPricingEngine(engine)

trueValue = option.NPV()

print(trueValue)

Here is the output:

runfile('C:/Users/nitin.kapai/Documents/Exam_v1/QuantLib Code/Barrier_DownIn_MC_Simulation_QuantLib.py', wdir='C:/Users/nitin.kapai/Documents/Exam_v1/QuantLib Code')
Traceback (most recent call last):

  File "C:\Users\nitin.kapai\Documents\Exam_v1\QuantLib Code\Barrier_DownIn_MC_Simulation_QuantLib.py", line 35, in <module>
    engine = MCBarrierEngine(process, traits)

  File "C:\Users\nitin.kapai\Anaconda3\lib\site-packages\QuantLib\QuantLib.py", line 12067, in MCBarrierEngine
    traits = traits.lower()

AttributeError: 'int' object has no attribute 'lower'

Any suggestion/feedback would be greatly appreciated

Answer 1

A few things... firstly, I've attached a correction to your code at the bottom. It runs, but gives a solution of 0.0. Not sure why that is, but the code runs at least, can you work out the pricing problem yourself?

You've used the import * pattern here. It seems easy now, but it will cause you trouble in future, because when you come to work out where each import has come from, you won't know where... this is really best avoided! (trust me, I've done it and regretted).

You also had some trouble with ql arguments. We're working on documenting them all over at ReadTheDocs but are a bit slow due to day jobs, in the meantime I recommend the following commands (one of the two should help): help(ql.MCBarrierEngine) or help(ql.MCBarrierEngine()), should make sense of the arguments that Python QuantLib is expecting.

Here is the solution to your problem above:

from QuantLib import *

import matplotlib.pyplot as plt

barrier, barrierType, optionType, rebate = (80.0, Barrier.DownIn, Option.Call, 0.0)
underlying, strike, rf, sigma, maturity, divYield = (100, 105, .05, 0.2, 12, 0.0)

Grids = (5, 10, 25, 50, 100, 1000, 50000)

maxG = Grids[-1]

today = Settings.instance().evaluationDate

maturity_date = today + int(maturity)

process = BlackScholesMertonProcess(QuoteHandle(SimpleQuote(underlying)),
                                    YieldTermStructureHandle(FlatForward(today, divYield, Thirty360())),
                                    YieldTermStructureHandle(FlatForward(today, rf, Thirty360())),
                                    BlackVolTermStructureHandle(BlackConstantVol(
                                        today, NullCalendar(), sigma, Thirty360())))

option = BarrierOption(barrierType, barrier, rebate,PlainVanillaPayoff(optionType, strike),
                       EuropeanExercise(maturity_date))


steps = 2
rng = "lowdiscrepancy"
numPaths = 500000

engine = MCBarrierEngine(process, traits=rng, timeSteps=steps, requiredSamples=numPaths)

option.setPricingEngine(engine)

trueValue = option.NPV()

print(trueValue)