def run_takeout_by_expiry():
event = TakeoutEvent('NBIX', 1)
option_type = 'Call'
option_type_2 = 'Put'
strike = 1.0
expiries = [
dt.date(2018, 4, 20),
dt.date(2018, 7, 20),
dt.date(2018, 10, 20),
dt.date(2019, 1, 20),
dt.date(2019, 4, 20)
]
# Preliminary Print Statements
print("Ann. Takeout Prob: {:.1f}%, Premium: {:.1f}%".format(
event.takeout_prob * 100, event.takeout_premium * 100))
for expiry in expiries:
option = Option(option_type, strike, expiry)
option2 = Option(option_type_2, strike, expiry)
distribution = event.get_distribution(expiry)
price = OptionPrice(distribution, option)
price2 = OptionPrice(distribution, option2)
straddle = price + price2
print(
"T.O. by {:%m/%d/%Y}: {:.1f}%".format(
expiry,
distribution.distribution_df.loc["Takeout", "Prob"] * 100),
"\n" * 0)
print("Mean Move: {:.1f}%".format(distribution.mean_move * 100))
print("Straddle: {:.1f}%".format(straddle * 100), "\n" * 1)
def get_option_prices(mc_distribution):
strikes = np.arange(.5, 1.55, .05)
option_prices = []
implied_vols = []
for strike in strikes:
if strike >= 1.0:
option_type = 'Call'
else:
option_type = 'Put'
option = Option(option_type, strike, expiry)
option_price = OptionPriceMC(option, mc_distribution)
implied_vol = get_implied_volatility(option, 1.0, option_price)
option_prices.append(option_price)
implied_vols.append(implied_vol)
prices_info = {
'Strikes': strikes,
'Prices': option_prices,
'IVs': implied_vols
}
prices_df = pd.DataFrame(prices_info).round(3)
prices_df.set_index('Strikes', inplace=True)
#prices_df.rename_axis(name, inplace=True)
return prices_df
def get_option_prices(name, mc_distribution):
strikes = np.arange(.5, 1.55, .05)
prices = []
for strike in strikes:
if strike >= 1.0:
option_type = 'Call'
else:
option_type = 'Put'
option = Option(option_type, strike, expiry)
prices.append(OptionPriceMC(option, mc_distribution))
prices_info = {'Strikes': strikes, 'Prices': prices}
prices_df = pd.DataFrame(prices_info).round(3)
prices_df.set_index('Strikes', inplace=True)
#prices_df.rename_axis(name, inplace=True)
return prices_df