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 optimally_get_mc_distribution_for_IdiosyncraticVol(event, expiry):
default_vol = .10
magnitude_mult = event.at_the_money_vol / default_vol
time_mult = math.sqrt(get_time_to_expiry(expiry))
#print('Time Mult:', time_mult)
mc_distribution = (IdiosyncraticVolDist -
1) * magnitude_mult * time_mult + 1
#print('Mean MC Dist:', np.mean(mc_distribution))
logger.info('IdioVol: {}'.format(np.average(mc_distribution)))
print('MC Dist HERE', mc_distribution)
return mc_distribution
def get_distribution(self, expiry: 'dt.date', *args, **kwargs):
ref_date = dt.date.today()
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_df = pd.DataFrame({'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.set_index('States', inplace=True)
distribution_df = distribution_df.loc[:, ['Prob', 'Pct_Move', 'Relative_Price']]
distribution_df = Distribution(distribution_df)
return 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)
def new_methodology(events, expiry, mc_iterations=10**4):
events = establish_events(events, expiry)
if not events:
return np.ones(mc_iterations)
mc_distributions = []
for evt in events:
if isinstance(evt, IdiosyncraticVol):
mc_distribution = (IdiosyncraticVolDist - 1) * (
evt.at_the_money_vol / .10) * math.sqrt(
get_time_to_expiry(expiry)) + 1
#print('IdioVol: {}'.format(np.average(mc_distribution)))
elif isinstance(evt, Earnings):
mc_distribution = (EarningsDist - 1) * (evt.mean_move /
1.0504) + 1
#print('Earnings: {}'.format(np.average(mc_distribution)))
else:
mc_distribution = evt.get_distribution(expiry).mc_simulation(
mc_iterations)
#print('Other: {}'.format(np.average(mc_distribution)))
mc_distributions.append(mc_distribution)
return get_tot_mc_distribution(mc_distributions)
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-------------------------------#
capital_allocation = 100 * 10**6
portfolio_max_drawdown_percent = .05
portfolio_max_drawdown = portfolio_max_drawdown_percent * capital_allocation
time_to_expiry = get_time_to_expiry(expiry)
Xguess = min(max(.1, 4 - 4 * 5 * time_to_expiry), .9)
Xguess = .5
increment = .05
print('Xguess: {:.2f}'.format(Xguess))
optimal_size = find_maximum(f=kelly_criterion,
Xguess=Xguess,
increment=increment,
print_guesses=True,
probs=probs,
pct_moves=pct_moves)
#capital_deployed = maximum*(1/-max_loss)
capital_deployed = optimal_size
max_drawdown = max_loss * capital_deployed
max_drawdown_event_only = max_loss_event_only * capital_deployed