expiry4 = dt.date(2018, 8, 21)
expiry5 = dt.date(2018, 9, 21)
expiry6 = dt.date(2018, 10, 21)
expiry6 = dt.date(2018, 11, 21)
expiry6 = dt.date(2018, 12, 21)
expiries = [expiry1, expiry2, expiry3, expiry4, expiry5, expiry6]
expiries = [expiry1, expiry3, expiry5]
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', 4)
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, event6]
import pandas as pd
import datetime as dt
from functools import reduce
from biotech_class_run_9 import get_total_mc_distribution, get_option_sheet_from_mc_distribution
from option_model.Event_Module import IdiosyncraticVol, SysEvt_PresElection, Event, TakeoutEvent
from option_model.Distribution_Module import Distribution, Distribution_MultiIndex
from paul_resources import show_mc_distributions_as_line_chart
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 = [
def get_vol_surface(events, expiry):
return get_vol_surface_from_event_grouping(events, expiry)
def get_vol_surface_spline(vol_surface):
strikes = vol_surface.index.values.tolist()
vols = vol_surface.iloc[:, 0].values.tolist()
return interp1d(strikes, vols, kind='cubic')
#---------------------------------I optimize for speed again below here---------------------------------------------#
EarningsDist = Earnings('CRBP', .05, 'Q2_2018').get_distribution(
dt.date(2018, 7, 1)).mc_simulation(10**4)
IdiosyncraticVolDist = IdiosyncraticVol(
'CRBP', .10).get_distribution(dt.date.today() +
timedelta(365)).mc_simulation(10**4)
#@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."""
"""
events = [evt for evt in events if event_prob_by_expiry(evt.timing_descriptor, expiry) > 0]
distributions = map(lambda evt: evt.get_distribution(expiry), events)
mc_distributions = map(lambda dist: dist.mc_simulation(mc_iterations), distributions)
"""
def idio_vol(self):
return IdiosyncraticVol(self.stock, .10)
expiry4 = dt.date(2018, 8, 21)
expiry5 = dt.date(2018, 9, 21)
expiry6 = dt.date(2018, 10, 21)
expiry7 = dt.date(2018, 11, 21)
expiry8 = dt.date(2018, 12, 21)
expiries = [expiry1, expiry2, expiry3, expiry4, expiry5, expiry6]
expiries = [expiry1, expiry3, expiry5]
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]
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)
from option_model.Stock_Module import Stock
from option_model.Option_Module import get_time_to_expiry
from option_model.Event_Module import IdiosyncraticVol
from option_model.Distribution_Module import Distribution, mc_distribution_to_distribution
from CreateMC import get_total_mc_distribution_from_events_vanilla
from Optimization_Formulas import find_maximum, kelly_criterion
# Paul General Formulas
from utility.graphing import get_histogram_from_array
# Declare Expiry
expiry = dt.date(2019, 5, 15)
# Declare Events
event = Stock.elagolix
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'))