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)
print(crbp.earnings_events)
print(TakeoutParams)
print(crbp.takeout_event)
print(crbp.events)
spread_price = spread_pricing(options, quantities, event_groupings[i], event_grouping_names[i], mc_iterations)
spread_prices.append(spread_price)
info = {'Level': event_grouping_names,
'Spread': spread_prices}
info = pd.DataFrame(info).set_index('Level')
return info
option_type = 'Put'
expiry = dt.date(2018, 8, 1)
option1 = Option(option_type, .975, expiry)
option2 = Option(option_type, .925, expiry)
option3 = Option(option_type, .95, expiry)
options = [option1, option2, option3]
quantities = [1, -1, 0]
spread_prices = spread_pricing_bid_ask(options, quantities, event_groupings, event_grouping_names, mc_iterations = 10**6)
print(spread_prices.round(3))
print(events_bid, events, events_ask, end = "\n")
sorted_events = sorted(events, key=lambda evt: Timing(evt.timing_descriptor).center_date)
pprint(sorted_events)
timing_descriptors = [evt.timing_descriptor for evt in events]
pprint(timing_descriptors)
center_dates = [Timing(timing_descriptor).center_date for timing_descriptor in timing_descriptors]
pprint(center_dates)
from All_Events import sorted_events
from Event_Module import IdiosyncraticVol, TakeoutEvent, Earnings
from Timing_Module import Timing
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import datetime as dt
from datetime import timedelta
events = [
evt for evt in sorted_events
if not isinstance(evt, (IdiosyncraticVol, TakeoutEvent))
]
dates = [Timing(evt.timing_descriptor).center_date for evt in events]
event_mean_moves = [evt.get_distribution().mean_move for evt in events]
color_scheme = {'Earnings': 1, 'Other': 2}
event_types = []
for evt in events:
if isinstance(evt, Earnings):
event_types.append('Earnings')
else:
event_types.append('Other')
scatter_colors = [color_scheme[event_type] for event_type in event_types]
scatter_sizes = [(mean_move * 100) * 50 for mean_move in event_mean_moves]
print(events, dates, event_mean_moves, event_types, sep='\n')
#X = pd.to_datetime(dates)
bar_heights = event_mean_moves
def get_event_timeline(events: 'list of events', symbol: 'str' = ''):
events = [evt for evt in events if not isinstance(evt, (IdiosyncraticVol, TakeoutEvent))]
dates = [Timing(evt.timing_descriptor).center_date for evt in events]
event_mean_moves = [evt.get_distribution().mean_move for evt in events]
color_scheme = {'Earnings': 1, 'Other': 2}
event_classes = set([type(evt) for evt in events])
color_scheme = {}
i = 1
for cls in event_classes:
color_scheme[cls] = i
i += 1
event_types = []
for evt in events:
event_types.append(type(evt))
scatter_colors = [color_scheme[event_type] for event_type in event_types]
scatter_sizes = [(mean_move*100)*50 for mean_move in event_mean_moves]
print(events, dates, event_mean_moves, event_types, sep='\n')
bar_heights = event_mean_moves
fig, ax1 = plt.subplots(1)
ax1.scatter(dates,
bar_heights,
c = scatter_colors,
marker = 's',
s = scatter_sizes)
# Set Error Bars
x_error_bars = [timedelta(5) for i in range(len(dates))]
y_error_bars = [0 for i in range(len(dates))]
x_error_bars = [Timing(evt.timing_descriptor).timing_duration*.475 for evt in events]
#y_error_bars = [evt.event_width*.475 for evt in events]
ax1.errorbar(dates,
bar_heights,
xerr = x_error_bars,
yerr = y_error_bars,
ls='none')
bar_width = .5
ax1.bar(dates,
bar_heights,
width = bar_width,
label = 'Event Mean Move',
color = 'black'
#marker='s',
#s = 250
)
for i in range(len(events)):
ax1.annotate(s = repr(events[i]),
xy = (dates[i], event_mean_moves[i]),
xytext = (dates[i], event_mean_moves[i]+.00875 + scatter_sizes[i]*.01*.01*.025),
ha='center',
fontsize=10.0)
#fig.autofmt_xdate()
# everything after this is turning off stuff that's plotted by default
"""
ax1.yaxis.set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.spines['top'].set_visible(False)
"""
#xticks = [dt.date.today(), dt.date(2018, 7, 1), dt.date(2018, 10, 1), dt.date(2019, 1, 1)]
#xticks = dates
xticks = [dt.date(2018, m, 1) for m in [4, 5, 6, 6, 7, 8, 9, 10, 11, 12]]
ax1.set_xticks(xticks)
ax1.set_yticks(np.arange(0, .20, .05))
ax1.set_xticklabels([t.strftime('%-m/%-d/%y') for t in xticks])
ax1.set_yticklabels(["{:.1f}%".format(y*100) for y in ax1.get_yticks()])
ax1.xaxis.label.set_color('darkblue')
ax1.yaxis.label.set_color('darkblue')
ax1.title.set_color('saddlebrown')
axes_fontsize = 10.0
plt.xticks(rotation=45, fontsize = axes_fontsize)
plt.yticks(fontsize = axes_fontsize)
ax1.xaxis.set_ticks_position('bottom')
#ax1.tick_params(axis ='y', direction = 'in', pad = -35)
ax1.yaxis.tick_right()
#ax1.get_yaxis().set_ticklabels([])
min_date = min([Timing(evt.timing_descriptor).event_start_date for evt in events])
max_date = max([Timing(evt.timing_descriptor).event_end_date for evt in events])
timeD = timedelta(20)
plt.xlim(min_date - timeD, max_date + timeD)
label_fontsize = 12
plt.xlabel('Date', fontsize = label_fontsize, fontweight = 'bold')
plt.ylabel('Event Magnitude', fontsize = label_fontsize, fontweight = 'bold')
#plt.yticks(np.arange(0, .3, .025))
plt.title('{} Event Calendar'.format(symbol), fontsize = label_fontsize*1.5, fontweight = 'bold' )
ax1.title.set_position([.525, 1.025])
#ax1.grid(True)
fig.patch.set_facecolor('xkcd:off white')
ax1.patch.set_facecolor('xkcd:pale grey')
fig.tight_layout()
fig.set_size_inches(8, 5)
plt.legend()
plt.show()
def sorted_events(self):
return sorted(
self.events,
key=lambda evt: Timing(evt.timing_descriptor).center_date)
from All_Events import sorted_events
from Event_Module import IdiosyncraticVol, TakeoutEvent, Earnings
from Timing_Module import Timing
import matplotlib.pyplot as plt
#plt.style.use('bmh')
import pandas as pd
import numpy as np
import datetime as dt
from datetime import timedelta
events = [
evt for evt in sorted_events
if not isinstance(evt, (IdiosyncraticVol, TakeoutEvent))
]
dates = [Timing(evt.timing_descriptor).center_date for evt in events]
event_mean_moves = [evt.get_distribution().mean_move for evt in events]
color_scheme = {'Earnings': 1, 'Other': 2}
event_classes = set([type(evt) for evt in events])
color_scheme = {}
i = 1
for cls in event_classes:
color_scheme[cls] = i
i += 1
event_types = []
for evt in events:
event_types.append(type(evt))
scatter_colors = [color_scheme[event_type] for event_type in event_types]