def get_distribution(self, expiry):
time_to_expiry = get_time_to_expiry(expiry)
distribution_df = copy.deepcopy(self.event_input_distribution_df)
distribution_df.loc[:,
'Pct_Move'] *= self.mult * self.idio_mult * math.sqrt(
time_to_expiry)
distribution_df.loc[:,
'Relative_Price'] = distribution_df.loc[:,
'Pct_Move'] + 1
return Distribution(distribution_df)
def get_distribution(self, expiry=None, *args, **kwargs):
event_by_expiry = event_prob_by_expiry(self.timing_descriptor, expiry)
event_not_by_expiry = 1 - event_by_expiry
if event_by_expiry == 0:
return get_no_event_distribution()
elif event_by_expiry == 1:
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)
else:
no_event_scenario_info = {
'State': ['No_Event'],
'Prob': [event_not_by_expiry],
'Pct_Move': [0],
'Relative_Price': [1.0]
}
no_event_scenario_df = pd.DataFrame(
no_event_scenario_info).set_index(
'State').loc[:, ['Prob', 'Pct_Move', 'Relative_Price']]
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
distribution_df.loc[:, 'Prob'] *= event_by_expiry
distribution_df = distribution_df.append(no_event_scenario_df)
return Distribution(distribution_df)
def get_distribution(self, expiry: 'dt.date', *args, **kwargs):
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_info = {
'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 = pd.DataFrame(distribution_info).set_index(
'States').loc[:, ['Prob', 'Pct_Move', 'Relative_Price']]
return Distribution(distribution_df)
from option_model.Option_Module import Option, OptionPriceMC
# Define Events
event8_info = pd.read_excel('CLVS_RiskScenarios.xlsx',
header=[0],
index_col=[0, 1],
sheet_name='Sub_States')
event0 = IdiosyncraticVol('CLVS', .05)
event1 = SysEvt_PresElection('CLVS', .02, 'Q2_2018')
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, 'Q2_2018', 'FDA_Approval')
event6 = TakeoutEvent('CLVS', 1)
event7 = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Q2_2018',
'Ph3_Data')
event8 = Event('CLVS', Distribution_MultiIndex(event8_info), 'Q3_2018',
'Elagolix_Approval')
events = [
event0, event1, event2, event3, event4, event5, event6, event7, event8
]
#events = [event0, event8]
events = [
event0, event1, event2, event3, event4, event5, event6, event7, event8
]
events = [event0, event6]
events = [event0, event2, event3, event7]
# Define Expiries
expiry1 = dt.date(2018, 5, 21)
def mc_simulation():
expiry = dt.date(2018, 12, 1)
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_distribution(events):
mc_simulation = np.zeros(mc_iterations)
for event in events:
distribution = event.get_distribution(expiry)
mc_simulation += distribution.mc_simulation(mc_iterations)
return mc_simulation
@my_time_decorator
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
@my_time_decorator
def event_groupings_df(event_groupings):
i = 0
for grouping in event_groupings:
mc_distribution = get_total_distribution(grouping)
prices = get_option_prices(mc_distribution).loc[:, ['IVs']]
if event_groupings.index(grouping) == 0:
prices_df = prices
else:
prices_df = pd.merge(prices_df,
prices,
left_index=True,
right_index=True)
graph_MC_distribution(mc_distribution)
i += 1
return prices_df
event_df = event_groupings_df(event_groupings)
print(event_df)
expiries = [expiry3]
# Define Events
event8_info = pd.read_excel('CLVS_RiskScenarios.xlsx',
header = [0],
index_col = [0,1],
sheet_name = 'Sub_States')
idio = IdiosyncraticVol('CLVS', .05)
takeout = TakeoutEvent('CLVS', 2)
pres_elec = SysEvt_PresElection('CLVS', .02)
earns_q2 = Earnings('CLVS', .05, 'Q2_2018', 'Q2_Earnings')
earns_q3 = Earnings('CLVS', .05, 'Q3_2018', 'Q3_Earnings')
earns_q4 = Earnings('CLVS', .05, 'Q4_2018', 'Q4_Earnings')
event5 = Event('CLVS', .1, 'Q2_2018', 'FDA_Approval')
data = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Q2_2018', 'Ph3_Data')
elagolix = ComplexEvent('CLVS', Distribution_MultiIndex(event8_info), 'Q2_2018', 'Elagolix_Approval')
events = [idio, takeout, earns_q2, earns_q3, earns_q4, elagolix]
#events = [takeout]
#events = [idio, elagolix]
events_bid = [event.event_bid for event in events]
events_ask = [event.event_ask for event in events]
events_high_POS = [idio, elagolix.event_high_prob_success]
events_low_POS = [idio, elagolix.event_low_prob_success]
events_max_optionality = [idio, elagolix.event_max_optionality]
# Define Event Groupings
event_groupings = {}
for i in range(len(events)):
event_groupings[i] = [events[i] for i in range(i+1)]
import pandas as pd
import datetime as dt
# Paul Modules
from option_model.Event_Module import Event, ComplexEvent
from option_model.Distribution_Module import Distribution, Distribution_MultiIndex
analyst_day = Event('NBIX', .075, dt.date(2018, 12, 1), 'Analyst Day')
ingrezza_info = pd.read_csv(
'/Users/paulwainer/Paulthon/Events/Parameters/CLVS.csv')
ingrezza_data = Event('NBIX', Distribution(ingrezza_info), 'Q4_2018',
'Ingrezza Data')
elagolix_info = pd.read_excel(
'/Users/paulwainer/Paulthon/Events/Parameters/CLVS_RiskScenarios2.xlsx',
header=[0],
index_col=[0, 1],
sheet_name='Sub_States')
elagolix_approval = ComplexEvent('NBIX',
Distribution_MultiIndex(elagolix_info),
dt.date(2018, 11, 15), 'Elagolix Approval')
all_other_events = [analyst_day, ingrezza_data, elagolix_approval]
from option_model.Event_Module import IdiosyncraticVol
import datetime as dt
from option_model.Distribution_Module import Distribution, float_to_bs_distribution
event = IdiosyncraticVol('CLVS', .1)
distribution = event.get_distribution(dt.date(2018, 5, 10))
print(distribution.mean_move, distribution.average_move, distribution.straddle)
distribution.get_histogram()
event_input_distribution = Distribution(event.event_input_distribution_df)
print(event_input_distribution.mean_move,
event_input_distribution.average_move, event_input_distribution.straddle)
event_input_dist = event.event_input
print(event_input_dist.mean_move, event_input_dist.average_move,
event_input_dist.straddle)
dist = float_to_bs_distribution(.5)
print("HERE", dist.mean_move, dist.average_move, dist.straddle)
expiries = [expiry3]
# Define Events
event8_info = pd.read_excel('CLVS_RiskScenarios.xlsx',
header = [0],
index_col = [0,1],
sheet_name = 'Sub_States')
event0 = IdiosyncraticVol('CLVS', .15)
event1 = SysEvt_PresElection('CLVS', .02, 'Q2_2018')
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, 'Q2_2018', 'FDA_Approval')
event6 = TakeoutEvent('CLVS', 2)
event7 = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Q2_2018', 'Ph3_Data')
event8 = Event('CLVS', Distribution_MultiIndex(event8_info), 'Q2_2018', 'Elagolix_Approval')
events = [event0, event1, event2, event3, event4, event5]
events = [event8]
event0_bid = IdiosyncraticVol('CLVS', .125)
event1_bid = SysEvt_PresElection('CLVS', .01, 'Q2_2018')
event2_bid = Event('CLVS', .03, 'Q2_2018', 'Q2_Earnings')
event3_bid = Event('CLVS', .03, 'Q3_2018', 'Q3_Earnings')
event4_bid = Event('CLVS', .05, 'Q3_2018', 'Investor_Day')
event5_bid = Event('CLVS', .075, 'Q2_2018', 'FDA_Approval')
event6_bid = TakeoutEvent('CLVS', 3)
event7_bid = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Q2_2018', 'Ph3_Data')
event8_bid = Event('CLVS', Distribution_MultiIndex(event8_info), 'Q3_2018', 'Elagolix_Approval')
#events_bid = [event0_bid, event1_bid, event2_bid, event3_bid, event4_bid, event5_bid, event6_bid]
#events_bid = [event0_bid]
vol = IdiosyncraticVol('NBIX', .20)
# Create MC_Distribution and distribution_df for the Combined Event
df = event.event_input_distribution_df
mc_dist = get_total_mc_distribution_from_events_vanilla([event, vol],
expiry=expiry,
mc_iterations=10**5)
get_histogram_from_array(mc_dist,
bins=2 * 10**1 + 1,
title='Expected GE Distribution')
mc_distribution_to_distribution(mc_dist,
bins=5 * 10**1 + 1,
to_file=True,
file_name='Elagolix_Dist')
combined_dist = Distribution(pd.read_csv('Elagolix_Dist.csv'))
df = combined_dist.distribution_df
#print(df.to_string())
# Source Probs, Pct_Moves, Max_Loss for the Combined Event
probs = df.loc[:, 'Prob'].tolist()
pct_moves = df.loc[:, 'Pct_Move'].tolist()
max_loss = min(pct_moves)
event_pct_moves = event.event_input_distribution_df.loc[:, 'Pct_Move'].tolist()
print(event_pct_moves)
max_loss_event_only = min(event_pct_moves)
#max_loss = -1
#pct_moves = [pct_move/-max_loss for pct_move in pct_moves]
#-----------------------------Portfolio Parameters-------------------------------#