def get_event_center_dates(events):
return [Timing(evt.timing_descriptor).center_date for evt in events]
def get_event_timeline(events: 'list of events' = None,
symbol: 'str' = '',
expiries=None):
"""Get the Event Timeline in the form of a graph for a list of events.
Symbol is an optional parameters to display in the Graph Title"""
# Establish Event Information: events, center_dates, mean_moves
events = filter_discrete_events(events)
dates = get_event_center_dates(events)
event_mean_moves = get_event_mean_moves(events)
# Establish Color Scheme for Scatter Plot. Each unique event type has a different color (e.g. all Earnings events are the same color)
unique_event_types = set([type(evt) for evt in events])
color_scheme = OrderedDict([
(t[1], t[0]) for t in enumerate(unique_event_types, start=1)
])
# Assign color and size for each scatter point
event_types = [type(evt) for evt in events]
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]
# Instantiate the subplot
fig, ax1 = plt.subplots(1)
# Instantiate the Scatter Plot
# The height of each scatter point is proportional to the event magnitude.
bar_heights = event_mean_moves
ax1.scatter(dates,
bar_heights,
c=scatter_colors,
marker='s',
s=scatter_sizes)
# Set Error Bars for the scatter plot; for now no y_error_bars, although depending on how it looks, I would like to display the magnitude of events scenarios.
x_error_bars = [
Timing(evt.timing_descriptor).timing_duration * .475 for evt in events
]
y_error_bars = [0 for i in range(len(dates))]
#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')
# Create a thin bar graph extending the x-axis to each scatter point to enhance visualization.
bar_width = .5
ax1.bar(dates,
bar_heights,
width=bar_width,
label='Event Mean Move',
color='black'
#marker='s',
#s = 250
)
# Annotate the Scatter Points with the Event Names
for i in range(len(events)):
ax1.annotate(text=repr(events[i]),
xy=(dates[i], event_mean_moves[i]),
xytext=(dates[i], event_mean_moves[i] + .025075 +
scatter_sizes[i] * .01 * .01 * .025),
ha='center',
fontsize=10.0)
"""
texts = []
for i in range(len(events)):
txt = ax1.text(dates[i],
event_mean_moves[i],
repr(events[i]),
ha='center',
fontsize=10.0)
texts.append(txt)
adjust_text(texts)
"""
#fig.autofmt_xdate()
# Set x and y Tick Marks, Tick Labels, and Axis Labels.
# Set Tick Marks
# X Ticks
xticks = [
dt.date(2018,
dt.date.today().month, 1) + relativedelta(months=i)
for i in range(13)
]
ax1.set_xticks(xticks)
# Y Ticks
yticks = np.arange(0, max(event_mean_moves) + .05, .05)
ax1.set_yticks(yticks)
# Set Tick Labels
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()])
# Set Axis Labels
ax1.xaxis.label.set_color('darkblue')
ax1.yaxis.label.set_color('darkblue')
ax1.title.set_color('saddlebrown')
# Set Ticks Font Size, tick Rotations, and y-axis Location
ticks_fontsize = 10.0
plt.xticks(rotation=45, fontsize=ticks_fontsize)
plt.yticks(fontsize=ticks_fontsize)
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.tick_right()
# Set X-Axis Min and Max Dates.
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)
# Set Font and Size for x, y Axis Labels and Title
axis_label_fontsize = 12
plt.xlabel('Date', fontsize=axis_label_fontsize, fontweight='bold')
plt.ylabel('Event Magnitude',
fontsize=axis_label_fontsize,
fontweight='bold')
plt.title('{} Event Calendar'.format(symbol),
fontsize=axis_label_fontsize * 1.5,
fontweight='bold')
# Potential options I do not want to use for now. These commands turn off default features.
"""
ax1.yaxis.set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.spines['top'].set_visible(False)
"""
# For now I don't want to see term structure in the graph, but keeping the code here as an option for later.
"""
# Show Term Structure as a Line Graph
if expiries is not None:
expiries = [date for date in xticks if date > dt.date.today()]
term_struc = term_structure(events, expiries, metric = 'IV', mc_iterations = 10**5)
vols = term_struc.iloc[[term_struc.index.get_loc(1.00, method='nearest')], :].values.tolist()[0]
print(zip(expiries, vols))
ax1.plot(expiries,
vols,
#label = 'Term_Structure',
#color = 'black'
#marker='s',
#s = 250
)
ax1.set_yticks(np.arange(0, max(vols)+.05, .05))
"""
# Set High-Level Graph Parameters; Show Graph
#ax1.grid(True)
ax1.title.set_position([.525, 1.025])
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(loc='best')
plt.show()
return fig, ax1
from All_Events import sorted_events
from option_model.Event_Module import IdiosyncraticVol, TakeoutEvent, Earnings
from option_model.Timing_Module import Timing
import matplotlib.pyplot as plt
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
fig, ax1 = plt.subplots(1)
def sorted_events(self):
return sorted(
self.events,
key=lambda evt: Timing(evt.timing_descriptor).center_date)
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)
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()
from All_Events import sorted_events
from option_model.Event_Module import IdiosyncraticVol, TakeoutEvent
from option_model.Timing_Module import Timing
import matplotlib.pyplot as plt
#plt.style.use('bmh')
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]
scatter_sizes = [(mean_move*100)*50 for mean_move in event_mean_moves]
print(events, dates, event_mean_moves, event_types, sep='\n')