QuantLib on CoCalc

import QuantLib as ql
import matplotlib.pyplot as plt

ql.__version__

# option data
maturity_date = ql.Date(15, 1, 2016)
spot_price = 127.62
strike_price = 130
volatility = 0.20 # the historical vols for a year
dividend_rate =  0.0163
option_type = ql.Option.Call

risk_free_rate = 0.001
day_count = ql.Actual365Fixed()
calendar = ql.UnitedStates()

calculation_date = ql.Date(8, 5, 2015)
ql.Settings.instance().evaluationDate = calculation_date

# construct the European Option
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
exercise = ql.EuropeanExercise(maturity_date)
european_option = ql.VanillaOption(payoff, exercise)

spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
flat_ts = ql.YieldTermStructureHandle(
    ql.FlatForward(calculation_date, risk_free_rate, day_count))
dividend_yield = ql.YieldTermStructureHandle(
    ql.FlatForward(calculation_date, dividend_rate, day_count))
flat_vol_ts = ql.BlackVolTermStructureHandle(
    ql.BlackConstantVol(calculation_date, calendar, volatility, day_count))
bsm_process = ql.BlackScholesMertonProcess(spot_handle, dividend_yield,
                                           flat_ts, flat_vol_ts)

european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
bs_price = european_option.NPV()
print(f"The theoretical price is {bs_price}")

def binomial_price(bsm_process, steps):
    binomial_engine = ql.BinomialVanillaEngine(bsm_process, "crr", steps)
    european_option.setPricingEngine(binomial_engine)
    return european_option.NPV()


steps = range(2, 100, 1)
prices = [binomial_price(bsm_process, step) for step in steps]

plt.plot(steps, prices, label="Binomial Tree Price", lw=2, alpha=0.6)
plt.plot([0, 100], [bs_price, bs_price],
         "r--",
         label="BSM Price",
         lw=2,
         alpha=0.6)
plt.xlabel("Steps")
plt.ylabel("Price")
plt.title("Binomial Tree Price For Varying Steps")
plt.legend()