def get_distribution(self, *args, **kwargs):
distribution_df = copy.deepcopy(self.event_input_distribution_df)
distribution_df.loc[:, 'Pct_Move'] *= self.mult * self.idio_mult
distribution_df.loc[:,
'Relative_Price'] = distribution_df.loc[:,
'Pct_Move'] + 1
return Distribution(distribution_df)
def get_distribution(self, expiry: 'dt.date', *args, **kwargs):
ref_date = dt.date.today()
time_to_expiry = get_time_to_expiry(expiry)
prob_takeout_by_expiry = time_to_expiry * self.takeout_prob
prob_no_takeout_by_expiry = 1 - prob_takeout_by_expiry
relative_price_takeout = (1 + self.takeout_premium)
relative_price_no_takeout = 1-(prob_takeout_by_expiry*self.takeout_premium) / (prob_no_takeout_by_expiry)
distribution_df = pd.DataFrame({'States': ['Takeout', 'No Takeout'],
'Prob': [prob_takeout_by_expiry, prob_no_takeout_by_expiry],
'Relative_Price': [relative_price_takeout, relative_price_no_takeout],
'Pct_Move': [self.takeout_premium, relative_price_no_takeout-1]
})
distribution_df.set_index('States', inplace=True)
distribution_df = distribution_df.loc[:, ['Prob', 'Pct_Move', 'Relative_Price']]
distribution_df = Distribution(distribution_df)
return distribution_df
def mc_simulation(expiry=None, mc_iterations=10**5):
# Define Events
event1 = TakeoutEvent('CLVS', 1)
event2 = SysEvt_PresElection('CLVS', .015)
event3 = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Ph3_Data')
event4 = Event('CLVS', .025, 'Investor_Day')
event5 = Event('CLVS', .15, 'FDA_Approval')
event6 = Event('CLVS', .15, 'Q1_Earnings')
event7 = Event('CLVS', .05, 'Q2_Earnings')
events1 = [event6]
events2 = [event6, event7]
events3 = [event5, event6, event7]
events4 = [event1, event5, event6, event7]
events5 = [event1, event3, event5, event6, event7]
event_groupings = [events1, events2, events3, events4, events5]
@my_time_decorator
def get_total_mc_distribution(events,
expiry=None,
symbol=None,
mc_iterations=10**4):
"""Add together the MC Distributions of individual events. Return the Total MC Distribution."""
total_mc_distribution = np.zeros(mc_iterations)
for event in events:
distribution = event.get_distribution(expiry)
total_mc_distribution += distribution.mc_simulation(mc_iterations)
return total_mc_distribution
@my_time_decorator
def get_option_prices_from_mc_distribution(mc_distribution, strikes=None):
if strikes is None:
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)
option_prices.append(option_price)
implied_vol = get_implied_volatility(option, 1.0, 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)
return prices_df
@my_time_decorator
def event_groupings_df(event_groupings):
i = 0
for grouping in event_groupings:
mc_distribution = get_total_mc_distribution(grouping,
expiry=expiry)
prices = get_option_prices_from_mc_distribution(
mc_distribution,
strikes=np.arange(.5, 1.55, .05)).loc[:, ['Prices', 'IVs']]
if event_groupings.index(grouping) == 0:
prices_df = prices
else:
prices_df = pd.merge(prices_df,
prices,
left_index=True,
right_index=True)
get_mc_histogram(mc_distribution)
i += 1
return prices_df
event_df = event_groupings_df(event_groupings)
print(event_df)
def mc_simulation(expiry=None, timing_descriptor=None, mc_iterations=10**5):
timing_descriptor = 'Q2_2018'
# Define Events
event1 = SysEvt_PresElection('CLVS', .02, timing_descriptor)
event2 = Event('CLVS', .05, 'Q2_2018', 'Q2_Earnings')
event3 = Event('CLVS', .05, 'Q3_2018', 'Q3_Earnings')
event4 = Event('CLVS', .075, 'Q3_2018', 'Investor_Day')
event5 = Event('CLVS', .1, '2H_2018', 'FDA_Approval')
event6 = TakeoutEvent('CLVS', 1)
event7 = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Q2_2018',
'Ph3_Data')
events1 = [event1]
events2 = [event1, event2]
events3 = [event1, event2, event3]
events4 = [event1, event2, event3, event4]
events5 = [event1, event2, event3, event4, event5]
events6 = [event1, event2, event3, event4, event5, event6]
events7 = [event1, event2, event3, event4, event5, event6, event7]
event_groupings = [
events1, events2, events3, events4, events5, events6, events7
]
@my_time_decorator
def get_total_mc_distribution(events,
expiry=None,
symbol=None,
mc_iterations=10**4):
"""Add the simulation results of individual events to return the total simulated distribution."""
distributions = map(lambda evt: evt.get_distribution(expiry), events)
mc_distributions = map(lambda dist: dist.mc_simulation(mc_iterations),
distributions)
return sum(mc_distributions)
@my_time_decorator
def get_option_sheet_from_mc_distribution(mc_distribution,
expiry=None,
strikes=None):
if strikes is None:
strikes = np.arange(.5, 1.55, .05)
call_options = [Option('Call', strike, expiry) for strike in strikes]
call_prices = list(
map(lambda option: OptionPriceMC(option, mc_distribution),
call_options))
call_IVs = list(
map(
lambda option, option_price: get_implied_volatility(
option, option_price), call_options, call_prices))
put_options = [Option('Put', strike, expiry) for strike in strikes]
put_prices = list(
map(lambda option: OptionPriceMC(option, mc_distribution),
put_options))
put_IVs = list(
map(
lambda option, option_price: get_implied_volatility(
option, option_price), put_options, put_prices))
option_sheet_info = {
'Strike': strikes,
'Price': call_prices,
'IV': call_IVs
}
option_sheet = pd.DataFrame(option_sheet_info).round(2)
option_sheet.set_index('Strike', inplace=True)
option_sheet = option_sheet.loc[:, ['Price', 'IV']]
print(option_sheet)
#iterables = [['Price', 'IV'], ['Group 1']]
#index = pd.MultiIndex.from_product(iterables, names=['Option Info', 'Event Grouping'])
#option_sheet.rename(columns=index)
#print(index)
return option_sheet
@my_time_decorator
def get_option_sheet_by_event_groupings(event_groupings, expiry):
# i = 0
# for grouping in event_groupings:
# mc_distribution = get_total_mc_distribution(grouping, expiry = expiry)
# prices = get_option_sheet_from_mc_distribution(mc_distribution, strikes = np.arange(.5, 1.55, .05)).loc[:, ['Price','IV']]
#
# if event_groupings.index(grouping) == 0:
# prices_df = prices
# else:
# prices_df = pd.merge(prices_df, prices, left_index=True, right_index=True)
#
# get_mc_histogram(mc_distribution)
#
# i += 1
# return prices_df
mc_distributions = list(
map(
lambda event_grouping: get_total_mc_distribution(
event_grouping, expiry), event_groupings))
[
get_mc_histogram(mc_distribution)
for mc_distribution in mc_distributions
]
option_sheets = list(
map(
lambda dist: get_option_sheet_from_mc_distribution(
dist, expiry).loc[:, ['IV']], mc_distributions))
return reduce(
lambda x, y: pd.merge(x, y, left_index=True, right_index=True),
option_sheets)
event_df = get_option_sheet_by_event_groupings(event_groupings, expiry)
print(event_df)