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I'm brand new to QuantLib and have the following class that I'm using to price European options:

import QuantLib as ql

class European_Option:
    def __init__(self, underlying_price, strike_price, maturity_date, risk_free_rate, implied_volatility, calculation_date):
        self.underlying_price = underlying_price
        self.strike_price = strike_price
        self.maturity_date = ql.Date(maturity_date, '%d-%m-%Y')
        self.risk_free_rate = risk_free_rate
        self.implied_volatility = implied_volatility
        self.calculation_date = ql.Date(calculation_date, '%d-%m-%Y')
        
        day_count = ql.Actual365Fixed()
        calendar = ql.UnitedStates()
        
        dividend_rate = 0.01

        option_type = ql.Option.Call
    
        payoff = ql.PlainVanillaPayoff(option_type, self.strike_price)
        exercise = ql.EuropeanExercise(self.maturity_date)
        european_option = ql.VanillaOption(payoff, exercise)
    
        spot_handle = ql.QuoteHandle(
            ql.SimpleQuote(self.underlying_price))

        flat_ts = ql.YieldTermStructureHandle(
            ql.FlatForward(self.calculation_date, 
                           self.risk_free_rate, 
                           day_count))
        
        dividend_yield = ql.YieldTermStructureHandle(
            ql.FlatForward(self.calculation_date, 
                           dividend_rate, 
                           day_count))
        
        flat_vol_ts = ql.BlackVolTermStructureHandle(
            ql.BlackConstantVol(self.calculation_date, 
                                calendar, 
                                self.implied_volatility, 
                                day_count))
        
        bsm_process = ql.BlackScholesMertonProcess(spot_handle, 
                                                   dividend_yield, 
                                                   flat_ts, 
                                                   flat_vol_ts)
        
    def get_price(self):
        european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
        bs_price = european_option.NPV() / spot_price
        return bs_price
    
    def get_delta(self):
        delta = european_option.delta()
        return delta
    
    def get_gamma(self):
        gamma = european_option.gamma()
        return gamma
    
    def get_vega(self):
        vega = european_option.vega()
        return gamma
    
    def get_theta(self):
        theta = european_option.theta()

Next, I instantiate an object named foo as follows:

foo = European_Option(400,
                      500,
                      '15-06-2022',
                      0.01,
                      0.375,
                      '20-01-2022'
                        )

Then, I call the get_delta() method:

foo.get_delta()

I'm seeing the following error:

NameError: name 'european_option' is not defined

I have european_option defined as:

european_option = ql.VanillaOption(payoff, exercise)

So, I'm confused as to what's happening here.

My goal is to be able to use this class to be able to get the price and Greeks of thousands of options in a csv file (by creating a Pandas dataframe and then adding columns to the dataframe with the price and Greeks).

Can anyone help me with this?

Thanks!

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1 Answer 1

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This is not really a QuantLib related error.

You defined european_option locally in your __init__ function and then you are trying to access european_option locally in your get_delta() function, so they are not the same variable.

Use self.european_option = ql.VanillaOption(payoff, exercise) to define this variable in the scope of the class instance and then refer to it as self.european_option in your functions.

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  • $\begingroup$ Are you suggesting that self.european_option = ql.VanillaOption(payoff, exercise) should be indented 4 spaces (i.e. not part of the __init__ function? If this is the case, then do all of the other variables that are currently under the self.x variables have to be moved out of the __init__ function, too? (sorry, I'm new to OOO paradigm) $\endgroup$
    – equanimity
    Jan 26, 2022 at 15:34

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