def run2():
expiry = dt.date(2018, 5, 1)
event1 = TakeoutEvent('CLVS', 1)
event2 = SysEvt_PresElection('CLVS', .02)
event3 = SystematicEvent('CLVS', .1, 'Ph3_Data')
event4 = SystematicEvent('CLVS', .05, 'Investor_Day')
event5 = SystematicEvent('CLVS', .3, 'FDA Approval')
distribution1 = event1.get_distribution(expiry)
distribution2 = event2.get_distribution()
distribution3 = event3.get_distribution()
distribution4 = event4.get_distribution()
distribution5 = event5.get_distribution()
added_distribution = distribution1 + distribution2 + distribution3 + distribution4 + distribution5
print(added_distribution)
print(added_distribution.distribution_df)
rprint(distribution1.mean_move, distribution2.mean_move,
distribution3.mean_move, distribution4.mean_move,
distribution5.mean_move, added_distribution.mean_move)
def run():
if __name__ == "__main__":
#-------------------PresElection Setup-----------------#
PresElectionParams = pd.read_csv(
"/Users/paulwainer/Environments/finance_env/PresElectionParams.csv"
)
PresElectionParams.set_index('Stock', inplace=True)
# Create PresElection Events Dict
PresElection_Evts = {}
for stock, move_input in PresElectionParams.itertuples():
PresElection_Evts[stock] = SysEvt_PresElection(stock, move_input)
#-------------------Takeout Setup-----------------#
TakeoutParams = pd.read_csv("TakeoutParams.csv")
TakeoutParams.set_index('Stock', inplace=True)
# Create Takeout Events Dict
Takeout_Evts = {}
for stock, bucket in TakeoutParams.itertuples():
Takeout_Evts[stock] = TakeoutEvent(stock, bucket)
takeout_dict = {}
for stock, event in Takeout_Evts.items():
takeout_dict[stock] = (event.takeout_prob, event.takeout_premium)
takeout_df = pd.DataFrame(takeout_dict).T.round(3)
takeout_df.rename(columns={0: 'Prob', 1: 'Premium'}, inplace=True)
takeout_df.rename_axis('Stock', inplace=True)
evt = SystematicEvent('ZFGN', .20)
evt2 = Event()
evt3 = SysEvt_PresElection('GM', .05)
evt4 = TakeoutEvent('NBIX', 1)
print("\n\n\nAll Events---\n", Event.instances, "\n")
print("Systematic Event---\n", SystematicEvent.instances, "\n")
print("Presidential Election---\n", SysEvt_PresElection.instances,
"\n")
print("Takeout Event---\n", TakeoutEvent.instances, "\n")
print(takeout_df.sort_values('Premium', ascending=False))
def run3():
# Define Events
event1 = TakeoutEvent('CLVS', 1)
event2 = SysEvt_PresElection('CLVS', .02)
event3 = SystematicEvent('CLVS', .1, 'Ph3_Data')
event4 = SystematicEvent('CLVS', .05, 'Investor_Day')
event5 = SystematicEvent('CLVS', .3, 'FDA_Approval')
event6 = SystematicEvent('CLVS', .05, 'Q1_Earnings')
event7 = SystematicEvent('CLVS', .05, 'Q2_Earnings')
expiry = dt.date(2018, 5, 1)
events = [event2, event3, event4]
added_distribution = event1.get_distribution(expiry)
for event in events:
added_distribution += event.get_distribution()
rprint(added_distribution.mean_move)
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)