def run_takeout_by_expiry():
event = TakeoutEvent('NBIX', 1)
option_type = 'Call'
option_type_2 = 'Put'
strike = 1.0
expiries = [
dt.date(2018, 4, 20),
dt.date(2018, 7, 20),
dt.date(2018, 10, 20),
dt.date(2019, 1, 20),
dt.date(2019, 4, 20)
]
# Preliminary Print Statements
print("Ann. Takeout Prob: {:.1f}%, Premium: {:.1f}%".format(
event.takeout_prob * 100, event.takeout_premium * 100))
for expiry in expiries:
option = Option(option_type, strike, expiry)
option2 = Option(option_type_2, strike, expiry)
distribution = event.get_distribution(expiry)
price = OptionPrice(distribution, option)
price2 = OptionPrice(distribution, option2)
straddle = price + price2
print(
"T.O. by {:%m/%d/%Y}: {:.1f}%".format(
expiry,
distribution.distribution_df.loc["Takeout", "Prob"] * 100),
"\n" * 0)
print("Mean Move: {:.1f}%".format(distribution.mean_move * 100))
print("Straddle: {:.1f}%".format(straddle * 100), "\n" * 1)
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 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 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(name, mc_distribution):
strikes = np.arange(.5, 1.55, .05)
prices = []
for strike in strikes:
if strike >= 1.0:
option_type = 'Call'
else:
option_type = 'Put'
option = Option(option_type, strike, expiry)
prices.append(OptionPriceMC(option, mc_distribution))
prices_info = {'Strikes': strikes, 'Prices': prices}
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('Prices', mc_distribution)
if event_groupings.index(grouping) == 0:
prices_df = prices
else:
#prices_df.join(prices.loc[:, 'Prices'])
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)