def get_intraday_vcs(**kwargs):
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
atm_vol_ratio = kwargs['atm_vol_ratio']
vcs_output = vcs.generate_vcs_sheet_4date(date_to=report_date)
vcs_pairs = vcs_output['vcs_pairs']
if 'id' in kwargs.keys():
id = kwargs['id']
ticker1 = vcs_pairs['ticker1'].iloc[id]
ticker2 = vcs_pairs['ticker2'].iloc[id]
else:
ticker1 = kwargs['ticker1']
ticker2 = kwargs['ticker2']
ticker_head = cmi.get_contract_specs(ticker1)['ticker_head']
ticker_class = cmi.ticker_class[ticker_head]
vcs_output = ops.get_vcs_signals(ticker_list=[ticker1, ticker2],
settle_date=report_date,
atm_vol_ratio=atm_vol_ratio)
q = vcs_output['q']
q1 = vcs_output['q1']
filter_out = of.get_vcs_filters(data_frame_input=pd.DataFrame.from_dict({
'tickerHead': [ticker_head],
'tickerClass': [ticker_class],
'Q': [q],
'Q1': [q1]
}),
filter_list=['long2', 'short2'])
if filter_out['selected_frame'].empty:
validQ = False
else:
validQ = True
return {
'ticker1': ticker1,
'ticker2': ticker2,
'Q': q,
'Q1': q1,
'validQ': validQ
}
def generate_vcs_formatted_output(**kwargs):
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
output_dir = ts.create_strategy_output_dir(strategy_class='vcs',
report_date=report_date)
vcs_output = vcs.generate_vcs_sheet_4date(date_to=report_date)
vcs_pairs = vcs_output['vcs_pairs']
filter_out = of.get_vcs_filters(data_frame_input=vcs_pairs,
filter_list=['long2', 'short2'])
good_vcs_pairs = filter_out['selected_frame']
vcs_pairs_w_selected_columns = vcs_pairs[[
'ticker1', 'ticker2', 'tickerHead', 'tickerClass', 'trDte1', 'trDte2',
'Q', 'Q1', 'fwdVolQ', 'downside', 'upside', 'atmVolRatio', 'fwdVol',
'realVolRatio', 'atmRealVolRatio', 'theta'
]]
good_vcs_pairs_w_selected_columns = good_vcs_pairs[[
'ticker1', 'ticker2', 'tickerHead', 'tickerClass', 'trDte1', 'trDte2',
'Q', 'Q1', 'fwdVolQ', 'downside', 'upside', 'atmVolRatio', 'fwdVol',
'realVolRatio', 'atmRealVolRatio', 'theta'
]]
writer = pd.ExcelWriter(output_dir + '/' + futil.xls_file_names['vcs'] +
'.xlsx',
engine='xlsxwriter')
vcs_pairs_w_selected_columns.to_excel(writer, sheet_name='all')
good_vcs_pairs_w_selected_columns.to_excel(writer, sheet_name='good')
worksheet_good = writer.sheets['good']
worksheet_all = writer.sheets['all']
worksheet_good.freeze_panes(1, 0)
worksheet_all.freeze_panes(1, 0)
worksheet_good.autofilter(0, 0,
len(good_vcs_pairs_w_selected_columns.index),
len(good_vcs_pairs_w_selected_columns.columns))
worksheet_all.autofilter(0, 0, len(vcs_pairs_w_selected_columns.index),
len(vcs_pairs_w_selected_columns.columns))
writer.save()
def generate_vcs_formatted_output(**kwargs):
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
output_dir = ts.create_strategy_output_dir(strategy_class='vcs', report_date=report_date)
vcs_output = vcs.generate_vcs_sheet_4date(date_to=report_date)
vcs_pairs = vcs_output['vcs_pairs']
filter_out = of.get_vcs_filters(data_frame_input=vcs_pairs, filter_list=['long2', 'short2'])
good_vcs_pairs = filter_out['selected_frame']
vcs_pairs_w_selected_columns = vcs_pairs[['ticker1', 'ticker2', 'tickerHead', 'tickerClass', 'trDte1', 'trDte2', 'Q',
'Q1', 'fwdVolQ', 'downside', 'upside', 'atmVolRatio', 'fwdVol','realVolRatio',
'atmRealVolRatio','theta']]
good_vcs_pairs_w_selected_columns = good_vcs_pairs[['ticker1', 'ticker2', 'tickerHead', 'tickerClass', 'trDte1', 'trDte2', 'Q',
'Q1', 'fwdVolQ', 'downside', 'upside', 'atmVolRatio', 'fwdVol','realVolRatio',
'atmRealVolRatio','theta']]
writer = pd.ExcelWriter(output_dir + '/' + futil.xls_file_names['vcs'] + '.xlsx', engine='xlsxwriter')
vcs_pairs_w_selected_columns.to_excel(writer, sheet_name='all')
good_vcs_pairs_w_selected_columns.to_excel(writer, sheet_name='good')
worksheet_good = writer.sheets['good']
worksheet_all = writer.sheets['all']
worksheet_good.freeze_panes(1, 0)
worksheet_all.freeze_panes(1, 0)
worksheet_good.autofilter(0, 0, len(good_vcs_pairs_w_selected_columns.index),
len(good_vcs_pairs_w_selected_columns.columns))
worksheet_all.autofilter(0, 0, len(vcs_pairs_w_selected_columns.index),
len(vcs_pairs_w_selected_columns.columns))
def get_intraday_vcs(**kwargs):
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
id = kwargs['id']
atm_vol_ratio = kwargs['atm_vol_ratio']
vcs_output = vcs.generate_vcs_sheet_4date(date_to=report_date)
vcs_pairs = vcs_output['vcs_pairs']
ticker1 = vcs_pairs['ticker1'].iloc[id]
ticker2 = vcs_pairs['ticker2'].iloc[id]
ticker_head = cmi.get_contract_specs(ticker1)['ticker_head']
ticker_class = cmi.ticker_class[ticker_head]
vcs_output = ops.get_vcs_signals(ticker_list=[ticker1, ticker2],settle_date=report_date,atm_vol_ratio=atm_vol_ratio)
q = vcs_output['q']
q1 = vcs_output['q1']
filter_out = of.get_vcs_filters(data_frame_input=pd.DataFrame.from_items([('tickerHead', [ticker_head]),
('tickerClass', [ticker_class]),
('Q', [q]), ('Q1', [q1])]), filter_list=['long2', 'short2'])
if filter_out['selected_frame'].empty:
validQ = False
else:
validQ = True
print(ticker1)
print(ticker2)
print('Q: ' + str(q))
print('Q1: ' + str(q1))
print('Valid?: ' + str(validQ))
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
}
try:
log.info('update_options_greeks')
ogl.update_options_greeks_4date(con=con, settle_date=folder_date)
except Exception:
log.error('update_options_greeks failed', exc_info=True)
try:
log.info('load_ticker_signals')
osl.load_ticker_signals_4settle_date(con=con, settle_date=folder_date)
except Exception:
log.error('load_ticker_signals failed', exc_info=True)
try:
log.info('generate_vcs_sheet')
vcs.generate_vcs_sheet_4date(con=con, date_to=folder_date)
except Exception:
log.error('generate_vcs_sheet failed', exc_info=True)
try:
osf.generate_vcs_formatted_output(report_date=folder_date)
prep.prepare_strategy_daily(strategy_class='vcs', report_date=folder_date)
except Exception:
pass
try:
log.info('generate_scv_sheet')
osf.generate_scv_formatted_output(report_date=folder_date)
prep.prepare_strategy_daily(strategy_class='scv', report_date=folder_date)
except Exception:
log.error('generate_scv_sheet failed', exc_info=True)
def main():
app = algo.Algo()
report_date = exp.doubledate_shift_bus_days()
todays_date = cu.get_doubledate()
con = msu.get_my_sql_connection()
vcs_output = vcs.generate_vcs_sheet_4date(date_to=report_date)
vcs_pairs = vcs_output['vcs_pairs']
filter_out = of.get_vcs_filters(data_frame_input=vcs_pairs,
filter_list=['long2', 'short2'])
vcs_pairs = filter_out['selected_frame']
vcs_pairs = vcs_pairs[vcs_pairs['downside'].notnull()
& vcs_pairs['upside'].notnull()]
# &(vcs_pairs.tickerClass!='Energy')
vcs_pairs = vcs_pairs[(vcs_pairs['trDte1'] >= 50)
& (vcs_pairs.tickerClass != 'Metal') &
(vcs_pairs.tickerClass != 'FX') &
(vcs_pairs.tickerClass != 'Energy')]
vcs_pairs = vcs_pairs[((vcs_pairs['Q'] <= 30) &
(vcs_pairs['fwdVolQ'] >= 30)) |
((vcs_pairs['Q'] >= 70) &
(vcs_pairs['fwdVolQ'] <= 70))]
vcs_pairs.reset_index(drop=True, inplace=True)
vcs_pairs['underlying_ticker1'] = [
omu.get_option_underlying(ticker=x) for x in vcs_pairs['ticker1']
]
vcs_pairs['underlying_ticker2'] = [
omu.get_option_underlying(ticker=x) for x in vcs_pairs['ticker2']
]
vcs_pairs['underlying_tickerhead'] = [
cmi.get_contract_specs(x)['ticker_head']
for x in vcs_pairs['underlying_ticker1']
]
futures_data_dictionary = {
x: gfp.get_futures_price_preloaded(ticker_head=x)
for x in vcs_pairs['underlying_tickerhead'].unique()
}
proxy_output_list1 = [
up.get_underlying_proxy_ticker(
ticker=x,
settle_date=report_date,
futures_data_dictionary=futures_data_dictionary)
for x in vcs_pairs['underlying_ticker1']
]
vcs_pairs['proxy_ticker1'] = [x['ticker'] for x in proxy_output_list1]
vcs_pairs['add_2_proxy1'] = [x['add_2_proxy'] for x in proxy_output_list1]
proxy_output_list2 = [
up.get_underlying_proxy_ticker(
ticker=x,
settle_date=report_date,
futures_data_dictionary=futures_data_dictionary)
for x in vcs_pairs['underlying_ticker2']
]
vcs_pairs['proxy_ticker2'] = [x['ticker'] for x in proxy_output_list2]
vcs_pairs['add_2_proxy2'] = [x['add_2_proxy'] for x in proxy_output_list2]
vcs_pairs['expiration_date1'] = [
int(
exp.get_expiration_from_db(instrument='options', ticker=x,
con=con).strftime('%Y%m%d'))
for x in vcs_pairs['ticker1']
]
vcs_pairs['expiration_date2'] = [
int(
exp.get_expiration_from_db(instrument='options', ticker=x,
con=con).strftime('%Y%m%d'))
for x in vcs_pairs['ticker2']
]
vcs_pairs['interest_date1'] = [
grfs.get_simple_rate(as_of_date=report_date, date_to=x)['rate_output']
for x in vcs_pairs['expiration_date1']
]
vcs_pairs['interest_date2'] = [
grfs.get_simple_rate(as_of_date=report_date, date_to=x)['rate_output']
for x in vcs_pairs['expiration_date2']
]
vcs_pairs['exercise_type'] = [
cmi.get_option_exercise_type(ticker_head=x)
for x in vcs_pairs['tickerHead']
]
admin_dir = dna.get_directory_name(ext='admin')
risk_file_out = su.read_text_file(file_name=admin_dir +
'/RiskParameter.txt')
vcs_risk_parameter = 5 * 2 * float(risk_file_out[0])
vcs_pairs['long_quantity'] = vcs_risk_parameter / abs(
vcs_pairs['downside'])
vcs_pairs['short_quantity'] = vcs_risk_parameter / vcs_pairs['upside']
vcs_pairs['long_quantity'] = vcs_pairs['long_quantity'].round()
vcs_pairs['short_quantity'] = vcs_pairs['short_quantity'].round()
vcs_pairs['alias'] = [
generate_vcs_alias(vcs_row=vcs_pairs.iloc[x])
for x in range(len(vcs_pairs.index))
]
vcs_pairs['call_mid_price1'] = np.nan
vcs_pairs['put_mid_price1'] = np.nan
vcs_pairs['call_mid_price2'] = np.nan
vcs_pairs['put_mid_price2'] = np.nan
vcs_pairs['call_iv1'] = np.nan
vcs_pairs['put_iv1'] = np.nan
vcs_pairs['call_iv2'] = np.nan
vcs_pairs['put_iv2'] = np.nan
vcs_pairs['underlying_mid_price1'] = np.nan
vcs_pairs['underlying_mid_price2'] = np.nan
vcs_pairs['proxy_mid_price1'] = np.nan
vcs_pairs['proxy_mid_price2'] = np.nan
vcs_pairs['current_strike1'] = np.nan
vcs_pairs['current_strike2'] = np.nan
ta_folder = dn.get_dated_directory_extension(folder_date=todays_date,
ext='ta')
app.vcs_pairs = vcs_pairs
app.con = con
app.futures_data_dictionary = futures_data_dictionary
app.report_date = report_date
app.todays_date = todays_date
app.log = lg.get_logger(file_identifier='vcs', log_level='INFO')
app.trade_file = ta_folder + '/trade_dir.csv'
app.vcs_risk_parameter = vcs_risk_parameter
app.connect(client_id=3)
app.run()
def get_results_4strategy(**kwargs):
signal_input = dict()
if 'futures_data_dictionary' in kwargs.keys():
signal_input['futures_data_dictionary'] = kwargs[
'futures_data_dictionary']
if 'date_to' in kwargs.keys():
date_to = kwargs['date_to']
else:
date_to = exp.doubledate_shift_bus_days()
if 'datetime5_years_ago' in kwargs.keys():
signal_input['datetime5_years_ago'] = kwargs['datetime5_years_ago']
if 'strategy_info_output' in kwargs.keys():
strategy_info_output = kwargs['strategy_info_output']
else:
strategy_info_output = ts.get_strategy_info_from_alias(**kwargs)
if 'broker' in kwargs.keys():
broker = kwargs['broker']
else:
broker = 'abn'
con = msu.get_my_sql_connection(**kwargs)
strategy_info_dict = sc.convert_from_string_to_dictionary(
string_input=strategy_info_output['description_string'])
strategy_class = strategy_info_dict['strategy_class']
pnl_frame = tpm.get_daily_pnl_snapshot(as_of_date=date_to, broker=broker)
pnl_frame = pnl_frame[pnl_frame['alias'] == kwargs['alias']]
strategy_position = ts.get_net_position_4strategy_alias(
alias=kwargs['alias'], as_of_date=date_to)
if strategy_class == 'futures_butterfly':
ticker_head = cmi.get_contract_specs(
strategy_info_dict['ticker1'])['ticker_head']
if not strategy_position.empty:
total_contracts2trade = strategy_position['qty'].abs().sum()
t_cost = cmi.t_cost[ticker_head]
QF_initial = float(strategy_info_dict['QF'])
z1_initial = float(strategy_info_dict['z1'])
bf_signals_output = fs.get_futures_butterfly_signals(
ticker_list=[
strategy_info_dict['ticker1'], strategy_info_dict['ticker2'],
strategy_info_dict['ticker3']
],
aggregation_method=int(strategy_info_dict['agg']),
contracts_back=int(strategy_info_dict['cBack']),
date_to=date_to,
**signal_input)
if bf_signals_output['success']:
aligned_output = bf_signals_output['aligned_output']
current_data = aligned_output['current_data']
holding_tr_dte = int(
strategy_info_dict['trDte1']) - current_data['c1']['tr_dte']
success_status = True
QF = bf_signals_output['qf']
z1 = bf_signals_output['zscore1']
short_tr_dte = current_data['c1']['tr_dte']
second_spread_weight = bf_signals_output['second_spread_weight_1']
if strategy_position.empty:
recommendation = 'CLOSE'
elif (z1_initial>0)&(holding_tr_dte > 5) &\
(bf_signals_output['qf']<QF_initial-20)&\
(pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade):
recommendation = 'STOP'
elif (z1_initial<0)&(holding_tr_dte > 5) &\
(bf_signals_output['qf']>QF_initial+20)&\
(pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade):
recommendation = 'STOP'
elif (current_data['c1']['tr_dte'] < 35)&\
(pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade):
recommendation = 'STOP'
elif (current_data['c1']['tr_dte'] < 35)&\
(pnl_frame['total_pnl'].iloc[0] < 3*t_cost*total_contracts2trade):
recommendation = 'WINDDOWN'
else:
recommendation = 'HOLD'
else:
success_status = False
QF = np.nan
z1 = np.nan
short_tr_dte = np.nan
holding_tr_dte = np.nan
second_spread_weight = np.nan
recommendation = 'MISSING DATA'
result_output = {
'success': success_status,
'ticker_head': ticker_head,
'QF_initial': QF_initial,
'z1_initial': z1_initial,
'QF': QF,
'z1': z1,
'short_tr_dte': short_tr_dte,
'holding_tr_dte': holding_tr_dte,
'second_spread_weight': second_spread_weight,
'recommendation': recommendation
}
elif strategy_class == 'spread_carry':
trades4_strategy = ts.get_trades_4strategy_alias(**kwargs)
grouped = trades4_strategy.groupby('ticker')
net_position = pd.DataFrame()
net_position['ticker'] = (grouped['ticker'].first()).values
net_position['qty'] = (grouped['trade_quantity'].sum()).values
net_position = net_position[net_position['qty'] != 0]
net_position['ticker_head'] = [
cmi.get_contract_specs(x)['ticker_head']
for x in net_position['ticker']
]
price_output = [
gfp.get_futures_price_preloaded(ticker=x, settle_date=date_to)
for x in net_position['ticker']
]
net_position['tr_dte'] = [
np.nan if x.empty else x['tr_dte'].values[0] for x in price_output
]
results_frame = pd.DataFrame()
unique_tickerhead_list = net_position['ticker_head'].unique()
results_frame['tickerHead'] = unique_tickerhead_list
results_frame['ticker1'] = [None] * len(unique_tickerhead_list)
results_frame['ticker2'] = [None] * len(unique_tickerhead_list)
results_frame['qty'] = [None] * len(unique_tickerhead_list)
results_frame['pnl'] = [None] * len(unique_tickerhead_list)
results_frame['downside'] = [None] * len(unique_tickerhead_list)
results_frame['indicator'] = [None] * len(unique_tickerhead_list)
results_frame['timeHeld'] = [None] * len(unique_tickerhead_list)
results_frame['recommendation'] = [None] * len(unique_tickerhead_list)
spread_carry_output = osc.generate_spread_carry_sheet_4date(
report_date=date_to)
spread_report = spread_carry_output['spread_report']
pnl_output = tpnl.get_strategy_pnl(**kwargs)
pnl_per_tickerhead = pnl_output['pnl_per_tickerhead']
for i in range(len(unique_tickerhead_list)):
net_position_per_tickerhead = net_position[
net_position['ticker_head'] == unique_tickerhead_list[i]]
net_position_per_tickerhead.sort_values('tr_dte',
ascending=True,
inplace=True)
selected_spread = spread_report[
(spread_report['ticker1'] ==
net_position_per_tickerhead['ticker'].values[0])
& (spread_report['ticker2'] ==
net_position_per_tickerhead['ticker'].values[1])]
results_frame['qty'][i] = net_position_per_tickerhead[
'qty'].values[0]
if selected_spread.empty:
results_frame['ticker1'][i] = net_position_per_tickerhead[
'ticker'].values[0]
results_frame['ticker2'][i] = net_position_per_tickerhead[
'ticker'].values[1]
else:
results_frame['ticker1'][i] = selected_spread[
'ticker1'].values[0]
results_frame['ticker2'][i] = selected_spread[
'ticker2'].values[0]
selected_trades = trades4_strategy[
trades4_strategy['ticker'] ==
results_frame['ticker1'].values[i]]
price_output = gfp.get_futures_price_preloaded(
ticker=results_frame['ticker1'].values[i],
settle_date=pd.to_datetime(
selected_trades['trade_date'].values[0]))
results_frame['timeHeld'][i] = price_output['tr_dte'].values[
0] - net_position_per_tickerhead['tr_dte'].values[0]
results_frame['pnl'][i] = pnl_per_tickerhead[
unique_tickerhead_list[i]].sum()
if unique_tickerhead_list[i] in ['CL', 'B', 'ED']:
results_frame['indicator'][i] = selected_spread[
'reward_risk'].values[0]
if results_frame['qty'][i] > 0:
results_frame['recommendation'][i] = 'STOP'
elif results_frame['qty'][i] < 0:
if results_frame['indicator'][i] > -0.06:
results_frame['recommendation'][i] = 'STOP'
else:
results_frame['recommendation'][i] = 'HOLD'
else:
results_frame['indicator'][i] = selected_spread[
'q_carry'].values[0]
if results_frame['qty'][i] > 0:
if results_frame['indicator'][i] < 19:
results_frame['recommendation'][i] = 'STOP'
else:
results_frame['recommendation'][i] = 'HOLD'
elif results_frame['qty'][i] < 0:
if results_frame['indicator'][i] > -9:
results_frame['recommendation'][i] = 'STOP'
else:
results_frame['recommendation'][i] = 'HOLD'
if results_frame['qty'][i] > 0:
results_frame['downside'][i] = selected_spread[
'downside'].values[0] * results_frame['qty'][i]
else:
results_frame['downside'][i] = selected_spread[
'upside'].values[0] * results_frame['qty'][i]
return {'success': True, 'results_frame': results_frame}
elif strategy_class == 'vcs':
greeks_out = sg.get_greeks_4strategy_4date(alias=kwargs['alias'],
as_of_date=date_to)
ticker_portfolio = greeks_out['ticker_portfolio']
options_position = greeks_out['options_position']
if ticker_portfolio.empty and not options_position.empty:
result_output = {
'success': False,
'net_oev': np.NaN,
'net_theta': np.NaN,
'long_short_ratio': np.NaN,
'recommendation': 'MISSING DATA',
'last_adjustment_days_ago': np.NaN,
'min_tr_dte': np.NaN,
'long_oev': np.NaN,
'short_oev': np.NaN,
'favQMove': np.NaN
}
elif ticker_portfolio.empty and options_position.empty:
result_output = {
'success': False,
'net_oev': np.NaN,
'net_theta': np.NaN,
'long_short_ratio': np.NaN,
'recommendation': 'EMPTY',
'last_adjustment_days_ago': np.NaN,
'min_tr_dte': np.NaN,
'long_oev': np.NaN,
'short_oev': np.NaN,
'favQMove': np.NaN
}
else:
min_tr_dte = min([
exp.get_days2_expiration(ticker=x,
date_to=date_to,
instrument='options',
con=con)['tr_dte']
for x in ticker_portfolio['ticker']
])
net_oev = ticker_portfolio['total_oev'].sum()
net_theta = ticker_portfolio['theta'].sum()
long_portfolio = ticker_portfolio[
ticker_portfolio['total_oev'] > 0]
short_portfolio = ticker_portfolio[
ticker_portfolio['total_oev'] < 0]
short_portfolio['total_oev'] = abs(short_portfolio['total_oev'])
long_oev = long_portfolio['total_oev'].sum()
short_oev = short_portfolio['total_oev'].sum()
if (not short_portfolio.empty) & (not long_portfolio.empty):
long_short_ratio = 100 * long_oev / short_oev
long_portfolio.sort_values('total_oev',
ascending=False,
inplace=True)
short_portfolio.sort_values('total_oev',
ascending=False,
inplace=True)
long_ticker = long_portfolio['ticker'].iloc[0]
short_ticker = short_portfolio['ticker'].iloc[0]
long_contract_specs = cmi.get_contract_specs(long_ticker)
short_contract_specs = cmi.get_contract_specs(short_ticker)
if 12*long_contract_specs['ticker_year']+long_contract_specs['ticker_month_num'] < \
12*short_contract_specs['ticker_year']+short_contract_specs['ticker_month_num']:
front_ticker = long_ticker
back_ticker = short_ticker
direction = 'long'
else:
front_ticker = short_ticker
back_ticker = long_ticker
direction = 'short'
if 'vcs_output' in kwargs.keys():
vcs_output = kwargs['vcs_output']
else:
vcs_output = ovcs.generate_vcs_sheet_4date(date_to=date_to)
vcs_pairs = vcs_output['vcs_pairs']
selected_result = vcs_pairs[
(vcs_pairs['ticker1'] == front_ticker)
& (vcs_pairs['ticker2'] == back_ticker)]
if selected_result.empty:
favQMove = np.NaN
else:
current_Q = selected_result['Q'].iloc[0]
q_limit = of.get_vcs_filter_values(
product_group=long_contract_specs['ticker_head'],
filter_type='tickerHead',
direction=direction,
indicator='Q')
if direction == 'long':
favQMove = current_Q - q_limit
elif direction == 'short':
favQMove = q_limit - current_Q
else:
long_short_ratio = np.NaN
favQMove = np.NaN
trades_frame = ts.get_trades_4strategy_alias(**kwargs)
trades_frame_options = trades_frame[trades_frame['instrument'] ==
'O']
last_adjustment_days_ago = len(
exp.get_bus_day_list(
date_to=date_to,
datetime_from=max(
trades_frame_options['trade_date']).to_pydatetime()))
if favQMove >= 10 and last_adjustment_days_ago > 10:
recommendation = 'STOP-ratio normalized'
elif min_tr_dte < 25:
recommendation = 'STOP-close to expiration'
elif np.isnan(long_short_ratio):
recommendation = 'STOP-not a proper calendar'
else:
if long_short_ratio < 80:
if favQMove < 0:
recommendation = 'buy_options_to_grow'
else:
recommendation = 'buy_options_to_shrink'
elif long_short_ratio > 120:
if favQMove < 0:
recommendation = 'sell_options_to_grow'
else:
recommendation = 'sell_options_to_shrink'
else:
recommendation = 'HOLD'
result_output = {
'success': True,
'net_oev': net_oev,
'net_theta': net_theta,
'long_short_ratio': long_short_ratio,
'recommendation': recommendation,
'last_adjustment_days_ago': last_adjustment_days_ago,
'min_tr_dte': min_tr_dte,
'long_oev': long_oev,
'short_oev': short_oev,
'favQMove': favQMove
}
elif strategy_class == 'ocs':
datetime_to = cu.convert_doubledate_2datetime(date_to)
time_held = (datetime_to.date() -
strategy_info_output['created_date'].date()).days
notes = ''
strategy_position = ts.get_net_position_4strategy_alias(
alias=kwargs['alias'], as_of_date=date_to, con=con)
if len(strategy_position.index) == 0:
tpnl.close_strategy(alias=kwargs['alias'],
close_date=date_to,
broker=broker,
con=con)
result_output = {
'success': True,
'time_held': time_held,
'dollar_noise': np.nan,
'notes': 'closed'
}
elif strategy_position['qty'].sum() != 0:
result_output = {
'success': True,
'time_held': time_held,
'dollar_noise': np.nan,
'notes': 'check position'
}
else:
strategy_position['cont_indx'] = [
cmi.get_contract_specs(x)['cont_indx']
for x in strategy_position['ticker']
]
strategy_position.sort_values('cont_indx',
ascending=True,
inplace=True)
ocs_output = ocs.generate_overnight_spreads_sheet_4date(
date_to=date_to)
overnight_calendars = ocs_output['overnight_calendars']
selection_indx = (overnight_calendars['ticker1'] == strategy_position['ticker'].iloc[0])&\
(overnight_calendars['ticker2'] == strategy_position['ticker'].iloc[1])
if sum(selection_indx) > 0:
dollar_noise = (overnight_calendars.loc[
selection_indx, 'dollarNoise100'].values[0]) * abs(
strategy_position['qty'].iloc[0])
else:
dollar_noise = np.nan
result_output = {
'success': True,
'time_held': time_held,
'dollar_noise': dollar_noise,
'notes': 'hold'
}
elif strategy_class == 'skpt':
long_ticker = strategy_position.loc[strategy_position['qty'] > 0,
'ticker'].iloc[0]
short_ticker = strategy_position.loc[strategy_position['qty'] < 0,
'ticker'].iloc[0]
long_data = gsp.get_stock_price_preloaded(ticker=long_ticker,
data_source='iex',
settle_date_to=date_to)
short_data = gsp.get_stock_price_preloaded(ticker=short_ticker,
data_source='iex',
settle_date_to=date_to)
merged_data = pd.merge(long_data[['close', 'settle_datetime']],
short_data[['close', 'settle_datetime']],
how='inner',
on='settle_datetime')
merged_data.set_index('settle_datetime', drop=True, inplace=True)
intaday_output_long = pweb.DataReader(long_ticker, 'iex-tops')
intaday_output_short = pweb.DataReader(short_ticker, 'iex-tops')
merged_data = merged_data.append(
pd.DataFrame(
{
'close_x': intaday_output_long.iloc[4].values[0],
'close_y': intaday_output_short.iloc[4].values[0]
},
index=[dt.datetime.now()]))
signal_output = spt.backtest(merged_data, 'close_x', 'close_y')
return {
'long_ticker': long_ticker,
'short_ticker': short_ticker,
'zScoreC': signal_output['data_frame']['zScore'].iloc[-1],
'zScore': signal_output['data_frame']['zScore'].iloc[-2]
}
else:
result_output = {'success': False}
if 'con' not in kwargs.keys():
con.close()
return result_output
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}
opl.update_options_price_database_from_cme_files(con=con, settle_date=folder_date)
except Exception:
pass
try:
ogl.update_options_greeks_4date(con=con, settle_date=folder_date)
except Exception:
pass
try:
osl.load_ticker_signals_4settle_date(con=con, settle_date=folder_date)
except Exception:
pass
try:
vcs.generate_vcs_sheet_4date(con=con,date_to=folder_date)
except Exception:
pass
try:
osf.generate_vcs_formatted_output(report_date=folder_date)
prep.prepare_strategy_daily(strategy_class='vcs', report_date=folder_date)
except Exception:
pass
try:
osf.generate_scv_formatted_output(report_date=folder_date)
prep.prepare_strategy_daily(strategy_class='scv', report_date=folder_date)
except Exception:
pass
def get_results_4strategy(**kwargs):
signal_input = dict()
if 'futures_data_dictionary' in kwargs.keys():
signal_input['futures_data_dictionary'] = kwargs['futures_data_dictionary']
if 'date_to' in kwargs.keys():
date_to = kwargs['date_to']
else:
date_to = exp.doubledate_shift_bus_days()
if 'datetime5_years_ago' in kwargs.keys():
signal_input['datetime5_years_ago'] = kwargs['datetime5_years_ago']
if 'strategy_info_output' in kwargs.keys():
strategy_info_output = kwargs['strategy_info_output']
else:
strategy_info_output = ts.get_strategy_info_from_alias(**kwargs)
con = msu.get_my_sql_connection(**kwargs)
strategy_info_dict = sc.convert_from_string_to_dictionary(string_input=strategy_info_output['description_string'])
#print(kwargs['alias'])
strategy_class = strategy_info_dict['strategy_class']
pnl_frame = tpm.get_daily_pnl_snapshot(as_of_date=date_to)
pnl_frame = pnl_frame[pnl_frame['alias']==kwargs['alias']]
strategy_position = ts.get_net_position_4strategy_alias(alias=kwargs['alias'],as_of_date=date_to)
if strategy_class == 'futures_butterfly':
ticker_head = cmi.get_contract_specs(strategy_info_dict['ticker1'])['ticker_head']
if not strategy_position.empty:
total_contracts2trade = strategy_position['qty'].abs().sum()
t_cost = cmi.t_cost[ticker_head]
QF_initial = float(strategy_info_dict['QF'])
z1_initial = float(strategy_info_dict['z1'])
bf_signals_output = fs.get_futures_butterfly_signals(ticker_list=[strategy_info_dict['ticker1'],
strategy_info_dict['ticker2'],
strategy_info_dict['ticker3']],
aggregation_method=int(strategy_info_dict['agg']),
contracts_back=int(strategy_info_dict['cBack']),
date_to=date_to,**signal_input)
aligned_output = bf_signals_output['aligned_output']
current_data = aligned_output['current_data']
holding_tr_dte = int(strategy_info_dict['trDte1'])-current_data['c1']['tr_dte']
if strategy_position.empty:
recommendation = 'CLOSE'
elif (z1_initial>0)&(holding_tr_dte > 5) &\
(bf_signals_output['qf']<QF_initial-20)&\
(pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade):
recommendation = 'STOP'
elif (z1_initial<0)&(holding_tr_dte > 5) &\
(bf_signals_output['qf']>QF_initial+20)&\
(pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade):
recommendation = 'STOP'
elif (current_data['c1']['tr_dte'] < 35)&\
(pnl_frame['total_pnl'].iloc[0] > 3*t_cost*total_contracts2trade):
recommendation = 'STOP'
elif (current_data['c1']['tr_dte'] < 35)&\
(pnl_frame['total_pnl'].iloc[0] < 3*t_cost*total_contracts2trade):
recommendation = 'WINDDOWN'
else:
recommendation = 'HOLD'
result_output = {'success': True,'ticker_head': ticker_head,
'QF_initial':QF_initial,'z1_initial': z1_initial,
'QF': bf_signals_output['qf'],'z1': bf_signals_output['zscore1'],
'short_tr_dte': current_data['c1']['tr_dte'],
'holding_tr_dte': holding_tr_dte,
'second_spread_weight': bf_signals_output['second_spread_weight_1'],'recommendation': recommendation}
elif strategy_class == 'spread_carry':
trades4_strategy = ts.get_trades_4strategy_alias(**kwargs)
grouped = trades4_strategy.groupby('ticker')
net_position = pd.DataFrame()
net_position['ticker'] = (grouped['ticker'].first()).values
net_position['qty'] = (grouped['trade_quantity'].sum()).values
net_position = net_position[net_position['qty'] != 0]
net_position['ticker_head'] = [cmi.get_contract_specs(x)['ticker_head'] for x in net_position['ticker']]
price_output = [gfp.get_futures_price_preloaded(ticker=x, settle_date=date_to) for x in net_position['ticker']]
net_position['tr_dte'] = [x['tr_dte'].values[0] for x in price_output]
results_frame = pd.DataFrame()
unique_tickerhead_list = net_position['ticker_head'].unique()
results_frame['tickerHead'] = unique_tickerhead_list
results_frame['ticker1'] = [None]*len(unique_tickerhead_list)
results_frame['ticker2'] = [None]*len(unique_tickerhead_list)
results_frame['qty'] = [None]*len(unique_tickerhead_list)
results_frame['pnl'] = [None]*len(unique_tickerhead_list)
results_frame['downside'] = [None]*len(unique_tickerhead_list)
results_frame['indicator'] = [None]*len(unique_tickerhead_list)
results_frame['timeHeld'] = [None]*len(unique_tickerhead_list)
results_frame['recommendation'] = [None]*len(unique_tickerhead_list)
spread_carry_output = osc.generate_spread_carry_sheet_4date(report_date=date_to)
spread_report = spread_carry_output['spread_report']
pnl_output = tpnl.get_strategy_pnl(**kwargs)
pnl_per_tickerhead = pnl_output['pnl_per_tickerhead']
for i in range(len(unique_tickerhead_list)):
net_position_per_tickerhead = net_position[net_position['ticker_head'] == unique_tickerhead_list[i]]
net_position_per_tickerhead.sort('tr_dte',ascending=True,inplace=True)
selected_spread = spread_report[(spread_report['ticker1'] == net_position_per_tickerhead['ticker'].values[0]) &
(spread_report['ticker2'] == net_position_per_tickerhead['ticker'].values[1])]
results_frame['ticker1'][i] = selected_spread['ticker1'].values[0]
results_frame['ticker2'][i] = selected_spread['ticker2'].values[0]
results_frame['qty'][i] = net_position_per_tickerhead['qty'].values[0]
selected_trades = trades4_strategy[trades4_strategy['ticker'] == results_frame['ticker1'].values[i]]
price_output = gfp.get_futures_price_preloaded(ticker=results_frame['ticker1'].values[i],
settle_date=pd.to_datetime(selected_trades['trade_date'].values[0]))
results_frame['timeHeld'][i] = price_output['tr_dte'].values[0]-net_position_per_tickerhead['tr_dte'].values[0]
results_frame['pnl'][i] = pnl_per_tickerhead[unique_tickerhead_list[i]].sum()
if unique_tickerhead_list[i] in ['CL', 'B', 'ED']:
results_frame['indicator'][i] = selected_spread['reward_risk'].values[0]
if results_frame['qty'][i] > 0:
results_frame['recommendation'][i] = 'STOP'
elif results_frame['qty'][i] < 0:
if results_frame['indicator'][i] > -0.06:
results_frame['recommendation'][i] = 'STOP'
else:
results_frame['recommendation'][i] = 'HOLD'
else:
results_frame['indicator'][i] = selected_spread['q_carry'].values[0]
if results_frame['qty'][i] > 0:
if results_frame['indicator'][i] < 19:
results_frame['recommendation'][i] = 'STOP'
else:
results_frame['recommendation'][i] = 'HOLD'
elif results_frame['qty'][i] < 0:
if results_frame['indicator'][i] > -9:
results_frame['recommendation'][i] = 'STOP'
else:
results_frame['recommendation'][i] = 'HOLD'
if results_frame['qty'][i] > 0:
results_frame['downside'][i] = selected_spread['downside'].values[0]*results_frame['qty'][i]
else:
results_frame['downside'][i] = selected_spread['upside'].values[0]*results_frame['qty'][i]
return {'success': True, 'results_frame': results_frame}
elif strategy_class == 'vcs':
greeks_out = sg.get_greeks_4strategy_4date(alias=kwargs['alias'], as_of_date=date_to)
ticker_portfolio = greeks_out['ticker_portfolio']
if ticker_portfolio.empty:
min_tr_dte = np.NaN
result_output = {'success': False, 'net_oev': np.NaN, 'net_theta': np.NaN, 'long_short_ratio': np.NaN,
'recommendation': 'EMPTY', 'last_adjustment_days_ago': np.NaN,
'min_tr_dte': np.NaN, 'long_oev': np.NaN, 'short_oev': np.NaN, 'favQMove': np.NaN}
else:
min_tr_dte = min([exp.get_days2_expiration(ticker=x,date_to=date_to,instrument='options',con=con)['tr_dte'] for x in ticker_portfolio['ticker']])
net_oev = ticker_portfolio['total_oev'].sum()
net_theta = ticker_portfolio['theta'].sum()
long_portfolio = ticker_portfolio[ticker_portfolio['total_oev'] > 0]
short_portfolio = ticker_portfolio[ticker_portfolio['total_oev'] < 0]
short_portfolio['total_oev']=abs(short_portfolio['total_oev'])
long_oev = long_portfolio['total_oev'].sum()
short_oev = short_portfolio['total_oev'].sum()
if (not short_portfolio.empty) & (not long_portfolio.empty):
long_short_ratio = 100*long_oev/short_oev
long_portfolio.sort('total_oev', ascending=False, inplace=True)
short_portfolio.sort('total_oev', ascending=False, inplace=True)
long_ticker = long_portfolio['ticker'].iloc[0]
short_ticker = short_portfolio['ticker'].iloc[0]
long_contract_specs = cmi.get_contract_specs(long_ticker)
short_contract_specs = cmi.get_contract_specs(short_ticker)
if 12*long_contract_specs['ticker_year']+long_contract_specs['ticker_month_num'] < \
12*short_contract_specs['ticker_year']+short_contract_specs['ticker_month_num']:
front_ticker = long_ticker
back_ticker = short_ticker
direction = 'long'
else:
front_ticker = short_ticker
back_ticker = long_ticker
direction = 'short'
if 'vcs_output' in kwargs.keys():
vcs_output = kwargs['vcs_output']
else:
vcs_output = ovcs.generate_vcs_sheet_4date(date_to=date_to)
vcs_pairs = vcs_output['vcs_pairs']
selected_result = vcs_pairs[(vcs_pairs['ticker1'] == front_ticker) & (vcs_pairs['ticker2'] == back_ticker)]
if selected_result.empty:
favQMove = np.NaN
else:
current_Q = selected_result['Q'].iloc[0]
q_limit = of.get_vcs_filter_values(product_group=long_contract_specs['ticker_head'],
filter_type='tickerHead',direction=direction,indicator='Q')
if direction == 'long':
favQMove = current_Q-q_limit
elif direction == 'short':
favQMove = q_limit-current_Q
else:
long_short_ratio = np.NaN
favQMove = np.NaN
trades_frame = ts.get_trades_4strategy_alias(**kwargs)
trades_frame_options = trades_frame[trades_frame['instrument'] == 'O']
last_adjustment_days_ago = len(exp.get_bus_day_list(date_to=date_to,datetime_from=max(trades_frame_options['trade_date']).to_datetime()))
if favQMove >= 10 and last_adjustment_days_ago > 10:
recommendation = 'STOP-ratio normalized'
elif min_tr_dte<25:
recommendation = 'STOP-close to expiration'
elif np.isnan(long_short_ratio):
recommendation = 'STOP-not a proper calendar'
else:
if long_short_ratio < 80:
if favQMove < 0:
recommendation = 'buy_options_to_grow'
else:
recommendation = 'buy_options_to_shrink'
elif long_short_ratio > 120:
if favQMove < 0:
recommendation = 'sell_options_to_grow'
else:
recommendation = 'sell_options_to_shrink'
else:
recommendation = 'HOLD'
result_output = {'success': True, 'net_oev': net_oev, 'net_theta': net_theta, 'long_short_ratio': long_short_ratio,
'recommendation': recommendation, 'last_adjustment_days_ago': last_adjustment_days_ago,
'min_tr_dte': min_tr_dte, 'long_oev': long_oev, 'short_oev': short_oev, 'favQMove': favQMove}
else:
result_output = {'success': False}
if 'con' not in kwargs.keys():
con.close()
return result_output