This post explains valuing American Options using QuantLib and Python

In [1]:

```
import QuantLib as ql
import matplotlib.pyplot as plt
%matplotlib inline
ql.__version__
```

Out[1]:

'1.9.2'

In [2]:

```
# option data
maturity_date = ql.Date(15, 1, 2016)
spot_price = 127.62
strike_price = 130
volatility = 0.20 # the historical vols or implied vols
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
```

`AmericanExercise`

instead of `EuropeanExercise`

to pass into the `VanillaOption`

to construct an American option.

In [3]:

```
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
settlement = calculation_date
am_exercise = ql.AmericanExercise(settlement, maturity_date)
american_option = ql.VanillaOption(payoff, am_exercise)
eu_exercise = ql.EuropeanExercise(maturity_date)
european_option = ql.VanillaOption(payoff, eu_exercise)
```

The Black-Scholes-Merton process is constructed here.

In [4]:

```
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)
```

The value of the American option can be computed using a Binomial Engine using the CRR approach.

In [5]:

```
steps = 200
binomial_engine = ql.BinomialVanillaEngine(bsm_process, "crr", steps)
american_option.setPricingEngine(binomial_engine)
print (american_option.NPV())
```

In [6]:

```
def binomial_price(option, bsm_process, steps):
binomial_engine = ql.BinomialVanillaEngine(bsm_process, "crr", steps)
option.setPricingEngine(binomial_engine)
return option.NPV()
steps = range(5, 200, 1)
eu_prices = [binomial_price(european_option, bsm_process, step) for step in steps]
am_prices = [binomial_price(american_option, bsm_process, step) for step in steps]
# theoretican European option price
european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
bs_price = european_option.NPV()
```

In [7]:

```
plt.plot(steps, eu_prices, label="European Option", lw=2, alpha=0.6)
plt.plot(steps, am_prices, label="American Option", lw=2, alpha=0.6)
plt.plot([5,200],[bs_price, bs_price], "r--", label="BSM Price", lw=2, alpha=0.6)
plt.xlabel("Steps")
plt.ylabel("Price")
plt.ylim(6.7,7)
plt.title("Binomial Tree Price For Varying Steps")
plt.legend()
```

Out[7]:

<matplotlib.legend.Legend at 0x85b24e0>

quantlib python finance

- QuantLib Python Tutorials With Examples
- On the Convergence of Hull White Monte Carlo Simulations
- Valuing Options on Commodity Futures Using QuantLib Python
- Short Interest Rate Model Calibration in QuantLib Python
- QuantLib Python Cookbook Announcement

I am Goutham Balaraman, and I explore topics in quantitative finance, programming, and data science. You can follow me @gsbalaraman.

Updated posts from this blog and transcripts of Luigi's screencasts on YouTube is compiled into QuantLib Python Cookbook .