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)
class Stock(object):
event8_info = pd.read_excel('CLVS_RiskScenarios.xlsx',
header=[0],
index_col=[0, 1],
sheet_name='Sub_States')
fda_meeting = Event('CLVS', .1, 'Q2_2018', 'FDA Meeting')
data = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Q2_2018',
'Ph3_Data')
elagolix = ComplexEvent('CLVS', Distribution_MultiIndex(event8_info),
dt.date(2018, 6, 1), 'Elagolix Approval')
all_other_events = [data, fda_meeting, elagolix]
def __init__(self, stock):
self.stock = stock
@property
def idio_vol(self):
return IdiosyncraticVol(self.stock, .05)
@property
def earnings_events(self):
return get_earnings_events(self.stock)
@property
def takeout_event(self):
return TakeoutEvent(self.stock, TakeoutParams.loc[self.stock,
'Bucket'])
@property
def other_events(self):
if self.stock == 'CLVS':
return self.all_other_events
else:
return []
@property
def events(self):
return self.earnings_events + [self.takeout_event
] + [self.idio_vol] + self.other_events
@property
def sorted_events(self):
return sorted(
self.events,
key=lambda evt: Timing(evt.timing_descriptor).center_date)
def get_event_timeline(self):
get_event_timeline(self.events, self.stock)
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)
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
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
no_event_scenario = {
'State': ['No_Event'],
'Prob': [event_not_by_expiry],
'Pct_Move': [0],
'Relative_Price': [1.0]
}
no_event_scenario = pd.DataFrame(no_event_scenario).set_index(
'State').loc[:, ['Prob', 'Pct_Move', 'Relative_Price']]
distribution_df = distribution_df.append(no_event_scenario)
return Distribution(distribution_df)
return self.earnings_events + [self.takeout_event]
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, dt.date(2018, 5, 15), 'Q2_2018')
earns_q3 = Earnings('CLVS', .05, dt.date(2018, 8, 15), 'Q3_2018')
earns_q4 = Earnings('CLVS', .05, dt.date(2018, 11, 15), 'Q4_2018')
fda_meeting = Event('CLVS', .1, 'Q2_2018', 'FDA Meeting')
data = Event('CLVS', Distribution(pd.read_csv('CLVS.csv')), 'Q2_2018',
'Ph3_Data')
elagolix = ComplexEvent('CLVS', Distribution_MultiIndex(event8_info),
dt.date(2018, 6, 1), 'Elagolix Approval')
events = [
idio, takeout, pres_elec, earns_q2, earns_q3, earns_q4, fda_meeting,
elagolix
]
earnings = get_earnings_events('CLVS')
sorted_events = sorted(
events, key=lambda evt: Timing(evt.timing_descriptor).center_date)
print(earnings)
crbp = Stock('CRBP')
print(crbp)
print(crbp.stock)
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), 'Q3_2018',
'Elagolix_Approval')
events = [event0, event1, event2, event3, event4, event5, event6]
#events = [event0, event6]
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')
from Event_Module import IdiosyncraticVol
import datetime as dt
from Distribution_Module import Distribution, float_to_bs_distribution
import numpy as np
import pandas as pd
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)
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)