def get_scv_signals(**kwargs):
ticker = kwargs['ticker']
date_to = kwargs['date_to']
con = msu.get_my_sql_connection(**kwargs)
if 'futures_data_dictionary' in kwargs.keys():
futures_data_dictionary = kwargs['futures_data_dictionary']
else:
futures_data_dictionary = {
x: gfp.get_futures_price_preloaded(ticker_head=x)
for x in [cmi.get_contract_specs(ticker)['ticker_head']]
}
aligned_indicators_output = ops.get_aligned_option_indicators(
ticker_list=[ticker], settle_date=date_to, con=con)
if not aligned_indicators_output['success']:
return {
'downside': np.NaN,
'upside': np.NaN,
'theta': np.NaN,
'realized_vol_forecast': np.NaN,
'real_vol20_current': np.NaN,
'imp_vol': np.NaN,
'imp_vol_premium': np.NaN,
'q': np.NaN
}
hist = aligned_indicators_output['hist']
current = aligned_indicators_output['current']
vcs_output = vcs.generate_vcs_sheet_4date(date_to=date_to, con=con)
if 'con' not in kwargs.keys():
con.close()
clean_indx = hist['c1']['profit5'].notnull()
clean_data = hist[clean_indx]
if clean_data.empty:
downside = np.NaN
upside = np.NaN
else:
last_available_align_date = clean_data.index[-1]
clean_data = clean_data[clean_data.index >= last_available_align_date -
dt.timedelta(5 * 365)]
profit5 = clean_data['c1']['profit5']
percentile_vector = stats.get_number_from_quantile(
y=profit5.values,
quantile_list=[1, 15, 85, 99],
clean_num_obs=max(100, round(3 * len(profit5.values) / 4)))
downside = (percentile_vector[0] + percentile_vector[1]) / 2
upside = (percentile_vector[2] + percentile_vector[3]) / 2
realized_vol_output = rvue.forecast_realized_vol_until_expiration(
ticker=ticker,
futures_data_dictionary=futures_data_dictionary,
date_to=date_to)
realized_vol_forecast = realized_vol_output['realized_vol_forecast']
real_vol20_current = realized_vol_output['real_vol20_current']
imp_vol = current['imp_vol'][0]
imp_vol_premium = 100 * (imp_vol - realized_vol_forecast) / imp_vol
q = np.NaN
if vcs_output['success']:
vcs_pairs = vcs_output['vcs_pairs']
selected_pairs = vcs_pairs[vcs_pairs['ticker2'] == ticker]
if not selected_pairs.empty:
q = 100 - selected_pairs['Q'].mean()
return {
'downside': downside,
'upside': upside,
'theta': current['theta'][0],
'realized_vol_forecast': realized_vol_forecast,
'real_vol20_current': real_vol20_current,
'imp_vol': imp_vol,
'imp_vol_premium': imp_vol_premium,
'q': q
}
def get_vcs_panel_plot(**kwargs):
ticker_list = kwargs['ticker_list']
if 'diagnostics_q' in kwargs.keys():
diagnostics_q = kwargs['diagnostics_q']
else:
diagnostics_q = False
#con = msu.get_my_sql_connection(**kwargs)
option_indicator_output = ops.get_aligned_option_indicators(
ticker_list=ticker_list, settle_date=kwargs['report_date'])
hist = option_indicator_output['hist']
new_index = list(range(len(hist.index)))
contract_change_indx = (hist['c1']['ticker_year'] -
hist['c1']['ticker_year'].shift(1) != 0).values
front_contract_year = hist['c1']['ticker_year'].astype('int') % 10
contract_change_indx[0] = False
x_tick_locations = [x for x in new_index if contract_change_indx[x]]
x_tick_values = [
cmi.letter_month_string[int(hist['c1']['ticker_month'].values[x]) - 1]
+ str(front_contract_year.values[x]) for x in new_index
if contract_change_indx[x]
]
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)),
hist['c1']['imp_vol'] / hist['c2']['imp_vol'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('atmVolRatio')
plt.grid()
plt.show()
fwd_var = hist['c2']['cal_dte'] * (hist['c2']['imp_vol']**
2) - hist['c1']['cal_dte'] * (
hist['c1']['imp_vol']**2)
fwd_vol_sq = fwd_var / (hist['c2']['cal_dte'] - hist['c1']['cal_dte'])
fwd_vol_adj = np.sign(fwd_vol_sq) * ((abs(fwd_vol_sq)).apply(np.sqrt))
hist['fwd_vol_adj'] = fwd_vol_adj
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)), hist['fwd_vol_adj'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('Fwd Vol')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)),
hist['c1']['close2close_vol20'] / hist['c2']['close2close_vol20'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('realVolRatio')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)),
hist['c1']['imp_vol'] / hist['c1']['close2close_vol20'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('atmRealVolRatio')
plt.grid()
plt.show()
if diagnostics_q:
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)), hist['c1']['ticker_year'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('ticker_year')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)),
hist['c1']['tr_dte'] - hist['c2']['tr_dte'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('tr dte diff')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)), hist['c1']['tr_dte'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('tr dte')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(range(len(hist.index)), hist['c1']['ticker_month'])
plt.xticks(x_tick_locations, x_tick_values)
plt.ylabel('ticker_month')
plt.grid()
plt.show()
def get_scv_signals(**kwargs):
ticker = kwargs['ticker']
date_to = kwargs['date_to']
con = msu.get_my_sql_connection(**kwargs)
if 'futures_data_dictionary' in kwargs.keys():
futures_data_dictionary = kwargs['futures_data_dictionary']
else:
futures_data_dictionary = {x: gfp.get_futures_price_preloaded(ticker_head=x) for x in [cmi.get_contract_specs(ticker)['ticker_head']]}
aligned_indicators_output = ops.get_aligned_option_indicators(ticker_list=[ticker],
settle_date=date_to, con=con)
if not aligned_indicators_output['success']:
return {'downside': np.NaN, 'upside': np.NaN, 'theta': np.NaN,
'realized_vol_forecast': np.NaN,
'real_vol20_current': np.NaN,
'imp_vol': np.NaN,
'imp_vol_premium': np.NaN,
'q': np.NaN}
hist = aligned_indicators_output['hist']
current = aligned_indicators_output['current']
vcs_output = vcs.generate_vcs_sheet_4date(date_to=date_to,con=con)
if 'con' not in kwargs.keys():
con.close()
clean_indx = hist['c1']['profit5'].notnull()
clean_data = hist[clean_indx]
if clean_data.empty:
downside = np.NaN
upside = np.NaN
else:
last_available_align_date = clean_data.index[-1]
clean_data = clean_data[clean_data.index >= last_available_align_date-dt.timedelta(5*365)]
profit5 = clean_data['c1']['profit5']
percentile_vector = stats.get_number_from_quantile(y=profit5.values,
quantile_list=[1, 15, 85, 99],
clean_num_obs=max(100, round(3*len(profit5.values)/4)))
downside = (percentile_vector[0]+percentile_vector[1])/2
upside = (percentile_vector[2]+percentile_vector[3])/2
realized_vol_output = rvue.forecast_realized_vol_until_expiration(ticker=ticker,
futures_data_dictionary=futures_data_dictionary,
date_to=date_to)
realized_vol_forecast = realized_vol_output['realized_vol_forecast']
real_vol20_current = realized_vol_output['real_vol20_current']
imp_vol = current['imp_vol'][0]
imp_vol_premium = 100*(imp_vol-realized_vol_forecast)/imp_vol
q = np.NaN
if vcs_output['success']:
vcs_pairs = vcs_output['vcs_pairs']
selected_pairs = vcs_pairs[vcs_pairs['ticker2'] == ticker]
if not selected_pairs.empty:
q = 100-selected_pairs['Q'].mean()
return {'downside': downside, 'upside': upside, 'theta': current['theta'][0],
'realized_vol_forecast': realized_vol_forecast,
'real_vol20_current': real_vol20_current,
'imp_vol': imp_vol,
'imp_vol_premium': imp_vol_premium,
'q': q}
def get_vcs_panel_plot(**kwargs):
ticker_list = kwargs['ticker_list']
if 'diagnostics_q' in kwargs.keys():
diagnostics_q = kwargs['diagnostics_q']
else:
diagnostics_q = False
#con = msu.get_my_sql_connection(**kwargs)
option_indicator_output = ops.get_aligned_option_indicators(ticker_list=ticker_list,settle_date=kwargs['report_date'])
hist = option_indicator_output['hist']
new_index = list(range(len(hist.index)))
contract_change_indx = (hist['c1']['ticker_year']-hist['c1']['ticker_year'].shift(1) != 0).values
front_contract_year = hist['c1']['ticker_year'].astype('int') % 10
contract_change_indx[0] = False
x_tick_locations = [x for x in new_index if contract_change_indx[x]]
x_tick_values = [cmi.letter_month_string[int(hist['c1']['ticker_month'].values[x])-1]+
str(front_contract_year.values[x]) for x in new_index if contract_change_indx[x]]
plt.figure(figsize=(16, 7))
plt.plot(hist['c1']['imp_vol']/hist['c2']['imp_vol'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('atmVolRatio')
plt.grid()
plt.show()
fwd_var = hist['c2']['cal_dte']*(hist['c2']['imp_vol']**2)-hist['c1']['cal_dte']*(hist['c1']['imp_vol']**2)
fwd_vol_sq = fwd_var/(hist['c2']['cal_dte']-hist['c1']['cal_dte'])
fwd_vol_adj = np.sign(fwd_vol_sq)*((abs(fwd_vol_sq)).apply(np.sqrt))
hist['fwd_vol_adj'] = fwd_vol_adj
plt.figure(figsize=(16, 7))
plt.plot(hist['fwd_vol_adj'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('Fwd Vol')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(hist['c1']['close2close_vol20']/hist['c2']['close2close_vol20'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('realVolRatio')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(hist['c1']['imp_vol']/hist['c1']['close2close_vol20'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('atmRealVolRatio')
plt.grid()
plt.show()
if diagnostics_q:
plt.figure(figsize=(16, 7))
plt.plot(hist['c1']['ticker_year'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('ticker_year')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(hist['c1']['tr_dte']-hist['c2']['tr_dte'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('tr dte diff')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(hist['c1']['tr_dte'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('tr dte')
plt.grid()
plt.show()
plt.figure(figsize=(16, 7))
plt.plot(hist['c1']['ticker_month'])
plt.xticks(x_tick_locations,x_tick_values)
plt.ylabel('ticker_month')
plt.grid()
plt.show()
