def send_wh_report(**kwargs):
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
ta_output_dir = dn.get_dated_directory_extension(folder_date=report_date, ext='ta')
strategy_frame = tpm.get_daily_pnl_snapshot(as_of_date=report_date, name='final')
total_pnl_row = strategy_frame[strategy_frame.alias == 'TOTAL']
report_date_str = cu.convert_datestring_format({'date_string': str(report_date), 'format_from': 'yyyymmdd', 'format_to': 'dd/mm/yyyy'})
config_output = su.read_config_file(file_name=dna.get_directory_name(ext='daily') + '/riskAndMargin.txt')
email_text = "Expected Maximum Drawdown: " + config_output['emd'] + "K" + \
"\nMargin: " + str(int(config_output['iceMargin']) + int(config_output['cmeMargin'])) + "K" + \
"\nNet Liquidating Value: " + config_output['pnl'] + "K" + \
"\n \nSee attached for individual strategy pnls."
se.send_email_with_attachment(send_from='*****@*****.**',
send_to=['*****@*****.**','*****@*****.**'],
sender_account_alias='wh_trading',
subject='Daily PnL for ' + report_date_str + ' is: ' + '${:,}'.format(total_pnl_row['daily_pnl'].iloc[0]),
email_text=email_text,
attachment_list = [ta_output_dir + '/' + 'pnl_final.xlsx'],
attachment_name_list=['PnLs.xlsx'])
def generate_portfolio_pnl_report(**kwargs):
if 'as_of_date' in kwargs.keys():
as_of_date = kwargs['as_of_date']
else:
as_of_date = exp.doubledate_shift_bus_days()
kwargs['as_of_date'] = as_of_date
ta_output_dir = dn.get_dated_directory_extension(folder_date=as_of_date, ext='ta')
daily_pnl_frame = tpm.get_daily_pnl_snapshot(**kwargs)
writer = pd.ExcelWriter(ta_output_dir + '/pnl.xlsx', engine='xlsxwriter')
daily_pnl_frame.to_excel(writer, sheet_name='strategies')
worksheet_strategies = writer.sheets['strategies']
worksheet_strategies.set_column('B:B', 30)
worksheet_strategies.freeze_panes(1, 0)
worksheet_strategies.autofilter(0, 0, len(daily_pnl_frame.index),
len(daily_pnl_frame.columns))
def generate_futures_butterfly_followup_report(**kwargs):
con = msu.get_my_sql_connection(**kwargs)
if 'as_of_date' in kwargs.keys():
as_of_date = kwargs['as_of_date']
else:
as_of_date = exp.doubledate_shift_bus_days()
kwargs['as_of_date'] = as_of_date
if 'writer' in kwargs.keys():
writer = kwargs['writer']
else:
ta_output_dir = dn.get_dated_directory_extension(folder_date=as_of_date, ext='ta')
writer = pd.ExcelWriter(ta_output_dir + '/followup.xlsx', engine='xlsxwriter')
strategy_frame = ts.get_open_strategies(**kwargs)
strategy_class_list = [sc.convert_from_string_to_dictionary(string_input=strategy_frame['description_string'][x])['strategy_class']
for x in range(len(strategy_frame.index))]
futures_butterfly_indx = [x == 'futures_butterfly' for x in strategy_class_list]
futures_butterfly_frame = strategy_frame[futures_butterfly_indx]
results = [sf.get_results_4strategy(alias=futures_butterfly_frame['alias'].iloc[x],
strategy_info_output=futures_butterfly_frame.iloc[x])
for x in range(len(futures_butterfly_frame.index))]
butterfly_followup_frame = pd.DataFrame(results)
butterfly_followup_frame['alias'] = futures_butterfly_frame['alias'].values
pnl_frame = pm.get_daily_pnl_snapshot(as_of_date=as_of_date, con=con)
risk_output = hr.get_historical_risk_4open_strategies(as_of_date=as_of_date, con=con)
merged_frame1 = pd.merge(butterfly_followup_frame,pnl_frame, how='left', on='alias')
merged_frame2 = pd.merge(merged_frame1, risk_output['strategy_risk_frame'], how='left', on='alias')
butterfly_followup_frame = merged_frame2[['alias', 'ticker_head', 'holding_tr_dte', 'short_tr_dte',
'z1_initial', 'z1', 'QF_initial', 'QF',
'total_pnl', 'downside','recommendation']]
butterfly_followup_frame.rename(columns={'alias': 'Alias', 'ticker_head': 'TickerHead',
'holding_tr_dte': 'HoldingTrDte', 'short_tr_dte': 'ShortTrDte',
'z1_initial': 'Z1Initial', 'z1': 'Z1',
'QF_initial': 'QFInitial','total_pnl': 'TotalPnl',
'downside': 'Downside','recommendation':'Recommendation'}, inplace=True)
butterfly_followup_frame.sort('QF', ascending=False,inplace=True)
butterfly_followup_frame['Z1'] = butterfly_followup_frame['Z1'].round(2)
butterfly_followup_frame.to_excel(writer, sheet_name='butterflies')
worksheet_butterflies = writer.sheets['butterflies']
worksheet_butterflies.set_column('B:B', 26)
worksheet_butterflies.freeze_panes(1, 0)
worksheet_butterflies.autofilter(0, 0, len(butterfly_followup_frame.index),
len(butterfly_followup_frame.columns))
if 'con' not in kwargs.keys():
con.close()
return writer
def generate_vcs_followup_report(**kwargs):
if 'as_of_date' in kwargs.keys():
as_of_date = kwargs['as_of_date']
else:
as_of_date = exp.doubledate_shift_bus_days()
kwargs['as_of_date'] = as_of_date
ta_output_dir = dn.get_dated_directory_extension(folder_date=as_of_date, ext='ta')
con = msu.get_my_sql_connection(**kwargs)
if 'writer' in kwargs.keys():
writer = kwargs['writer']
else:
writer = pd.ExcelWriter(ta_output_dir + '/followup.xlsx', engine='xlsxwriter')
strategy_frame = ts.get_open_strategies(**kwargs)
strategy_class_list = [sc.convert_from_string_to_dictionary(string_input=strategy_frame['description_string'][x])['strategy_class']
for x in range(len(strategy_frame.index))]
vcs_indx = [x == 'vcs' for x in strategy_class_list]
vcs_frame = strategy_frame[vcs_indx]
results = [sf.get_results_4strategy(alias=vcs_frame['alias'].iloc[x],
strategy_info_output=vcs_frame.iloc[x])
for x in range(len(vcs_frame.index))]
vcs_followup_frame = pd.DataFrame(results)
vcs_followup_frame['alias'] = vcs_frame['alias'].values
pnl_frame = pm.get_daily_pnl_snapshot(**kwargs)
merged_frame1 = pd.merge(vcs_followup_frame,pnl_frame, how='left', on='alias')
vcs_followup_frame = merged_frame1[['alias', 'last_adjustment_days_ago','min_tr_dte', 'long_short_ratio',
'net_oev', 'net_theta', 'long_oev', 'short_oev', 'favQMove', 'total_pnl','recommendation']]
vcs_followup_frame['long_short_ratio'] = vcs_followup_frame['long_short_ratio'].round()
vcs_followup_frame['net_oev'] = vcs_followup_frame['net_oev'].round(1)
vcs_followup_frame['long_oev'] = vcs_followup_frame['long_oev'].round(1)
vcs_followup_frame['short_oev'] = vcs_followup_frame['short_oev'].round(1)
vcs_followup_frame['net_theta'] = vcs_followup_frame['net_theta'].round(1)
vcs_followup_frame.sort('total_pnl', ascending=False, inplace=True)
vcs_followup_frame.reset_index(drop=True,inplace=True)
vcs_followup_frame.loc[len(vcs_followup_frame.index)] = ['TOTAL', None, None, None, None, vcs_followup_frame['net_theta'].sum(),
None, None, None, vcs_followup_frame['total_pnl'].sum(), None]
vcs_followup_frame.to_excel(writer, sheet_name='vcs')
worksheet_vcs = writer.sheets['vcs']
worksheet_vcs.set_column('B:B', 18)
worksheet_vcs.freeze_panes(1, 0)
worksheet_vcs.autofilter(0, 0, len(vcs_followup_frame.index),
len(vcs_followup_frame.columns))
if 'con' not in kwargs.keys():
con.close()
writer.save()
def main():
app = algo.Algo()
admin_dir = dn.get_directory_name(ext='admin')
risk_file_out = su.read_text_file(file_name=admin_dir +
'/RiskParameter.txt')
app.bet_size = float(risk_file_out[0])
con = msu.get_my_sql_connection()
date_now = cu.get_doubledate()
report_date = exp.doubledate_shift_bus_days()
report_date_list = [
exp.doubledate_shift_bus_days(shift_in_days=x) for x in range(1, 10)
]
overnight_calendars_list = []
for i in range(len(report_date_list)):
ocs_output = ocs.generate_overnight_spreads_sheet_4date(
date_to=report_date_list[i])
overnight_calendars = ocs_output['overnight_calendars']
overnight_calendars = \
overnight_calendars[overnight_calendars['tickerHead'].isin(['CL', 'HO', 'NG', 'C', 'W', 'KW', 'S', 'SM', 'BO', 'LC', 'LN', 'FC'])]
#isin(['CL', 'HO','NG', 'C', 'W', 'KW', 'S', 'SM', 'BO', 'LC', 'LN', 'FC'])]
overnight_calendars = overnight_calendars[
(overnight_calendars['ticker1L'] != '')
& (overnight_calendars['ticker2L'] != '')]
overnight_calendars['back_spread_price'] = np.nan
overnight_calendars['front_spread_price'] = np.nan
overnight_calendars['mid_ticker_price'] = np.nan
overnight_calendars['back_spread_ticker'] = [
overnight_calendars['ticker1'].iloc[x] + '-' +
overnight_calendars['ticker2'].iloc[x]
for x in range(len(overnight_calendars.index))
]
overnight_calendars['front_spread_ticker'] = [
overnight_calendars['ticker1L'].iloc[x] + '-' +
overnight_calendars['ticker2L'].iloc[x]
for x in range(len(overnight_calendars.index))
]
overnight_calendars['target_quantity'] = [
min(mth.ceil(app.bet_size / x),
app.total_traded_volume_max_before_user_confirmation)
for x in overnight_calendars['dollarNoise100']
]
overnight_calendars['alias'] = [
overnight_calendars['ticker1'].iloc[x] + '_' +
overnight_calendars['ticker2'].iloc[x] + '_ocs'
for x in range(len(overnight_calendars.index))
]
overnight_calendars['total_quantity'] = 0
overnight_calendars['total_risk'] = 0
overnight_calendars['holding_period'] = 0
#overnight_calendars['expiring_position_q'] = 0
overnight_calendars.reset_index(drop=True, inplace=True)
overnight_calendars_list.append(overnight_calendars)
overnight_calendars = overnight_calendars_list.pop(0)
open_strategy_frame = ts.get_filtered_open_strategies(
strategy_class_list=['ocs'], as_of_date=date_now)
for i in range(len(open_strategy_frame.index)):
position_manager_output = pm.get_ocs_position(
alias=open_strategy_frame['alias'].iloc[i],
as_of_date=date_now,
con=con)
trades_frame = ts.get_trades_4strategy_alias(
alias=open_strategy_frame['alias'].iloc[i], con=con)
datetime_now = cu.convert_doubledate_2datetime(date_now)
holding_period = (datetime_now - trades_frame['trade_date'].min()).days
if (not position_manager_output['empty_position_q']) & (
not position_manager_output['correct_position_q']):
print('Check ' + open_strategy_frame['alias'].iloc[i] +
' ! Position may be incorrect')
elif position_manager_output['correct_position_q']:
ticker_head = cmi.get_contract_specs(
position_manager_output['sorted_position']
['ticker'].iloc[0])['ticker_head']
position_name = ''
if position_manager_output['scale'] > 0:
position_name = ticker_head + '_long'
else:
position_name = ticker_head + '_short'
app.ocs_portfolio.order_send(ticker=position_name,
qty=abs(
position_manager_output['scale']))
app.ocs_portfolio.order_fill(ticker=position_name,
qty=abs(
position_manager_output['scale']))
ticker1 = position_manager_output['sorted_position'][
'ticker'].iloc[0]
ticker2 = position_manager_output['sorted_position'][
'ticker'].iloc[1]
selection_indx = overnight_calendars[
'back_spread_ticker'] == ticker1 + '-' + ticker2
if sum(selection_indx) == 1:
overnight_calendars.loc[
selection_indx,
'total_quantity'] = position_manager_output['scale']
overnight_calendars.loc[
selection_indx, 'total_risk'] = position_manager_output[
'scale'] * overnight_calendars.loc[selection_indx,
'dollarNoise100']
overnight_calendars.loc[
selection_indx,
'alias'] = open_strategy_frame['alias'].iloc[i]
overnight_calendars.loc[selection_indx,
'holding_period'] = holding_period
app.ocs_risk_portfolio.order_send(
ticker=position_name,
qty=abs(position_manager_output['scale'] *
overnight_calendars.loc[selection_indx,
'dollarNoise100']))
app.ocs_risk_portfolio.order_fill(
ticker=position_name,
qty=abs(position_manager_output['scale'] *
overnight_calendars.loc[selection_indx,
'dollarNoise100']))
else:
for j in range(len(overnight_calendars_list)):
overnight_calendars_past = overnight_calendars_list[j]
selection_indx = overnight_calendars_past[
'back_spread_ticker'] == ticker1 + '-' + ticker2
if sum(selection_indx) == 1:
overnight_calendars_past.loc[
selection_indx,
'total_quantity'] = position_manager_output[
'scale']
overnight_calendars_past.loc[
selection_indx,
'total_risk'] = position_manager_output[
'scale'] * overnight_calendars_past.loc[
selection_indx, 'dollarNoise100']
overnight_calendars_past.loc[
selection_indx,
'alias'] = open_strategy_frame['alias'].iloc[i]
overnight_calendars_past.loc[
selection_indx, 'holding_period'] = holding_period
app.ocs_risk_portfolio.order_send(
ticker=position_name,
qty=abs(position_manager_output['scale'] *
overnight_calendars_past.loc[
selection_indx, 'dollarNoise100']))
app.ocs_risk_portfolio.order_fill(
ticker=position_name,
qty=abs(position_manager_output['scale'] *
overnight_calendars_past.loc[
selection_indx, 'dollarNoise100']))
if j > 1:
overnight_calendars_past.loc[
selection_indx, 'butterflyMean'] = np.nan
overnight_calendars_past.loc[
selection_indx, 'butterflyNoise'] = np.nan
overnight_calendars = overnight_calendars.append(
overnight_calendars_past[selection_indx])
break
overnight_calendars.reset_index(drop=True, inplace=True)
overnight_calendars['working_order_id'] = np.nan
spread_ticker_list = list(
set(overnight_calendars['back_spread_ticker']).union(
overnight_calendars['front_spread_ticker']))
back_spread_ticker_list = list(overnight_calendars['back_spread_ticker'])
theme_name_list = set([
x + '_long' for x in back_spread_ticker_list
]).union(set([x + '_short' for x in back_spread_ticker_list]))
ocs_alias_portfolio = aup.portfolio(ticker_list=theme_name_list)
for i in range(len(overnight_calendars.index)):
if overnight_calendars.loc[i, 'total_quantity'] > 0:
position_name = overnight_calendars.loc[
i, 'back_spread_ticker'] + '_long'
ocs_alias_portfolio.order_send(
ticker=position_name,
qty=overnight_calendars.loc[i, 'total_quantity'])
ocs_alias_portfolio.order_fill(
ticker=position_name,
qty=overnight_calendars.loc[i, 'total_quantity'])
elif overnight_calendars.loc[i, 'total_quantity'] < 0:
position_name = overnight_calendars.loc[
i, 'back_spread_ticker'] + '_short'
ocs_alias_portfolio.order_send(
ticker=position_name,
qty=-overnight_calendars.loc[i, 'total_quantity'])
ocs_alias_portfolio.order_fill(
ticker=position_name,
qty=-overnight_calendars.loc[i, 'total_quantity'])
app.price_request_dictionary['spread'] = spread_ticker_list
app.price_request_dictionary['outright'] = overnight_calendars[
'ticker1'].values
app.overnight_calendars = overnight_calendars
app.open_strategy_list = list(open_strategy_frame['alias'])
app.ocs_alias_portfolio = ocs_alias_portfolio
app.ticker_list = list(
set(overnight_calendars['ticker1']).union(
overnight_calendars['ticker2']).union(
set(overnight_calendars['ticker1L'])).union(
set(overnight_calendars['ticker2L'])))
app.output_dir = ts.create_strategy_output_dir(strategy_class='ocs',
report_date=report_date)
app.log = lg.get_logger(file_identifier='ib_ocs', log_level='INFO')
app.con = con
app.pnl_frame = tpm.get_daily_pnl_snapshot(as_of_date=report_date)
print('Emre')
app.connect(client_id=2)
app.run()
def generate_ocs_followup_report(**kwargs):
if 'as_of_date' in kwargs.keys():
as_of_date = kwargs['as_of_date']
else:
as_of_date = exp.doubledate_shift_bus_days()
kwargs['as_of_date'] = as_of_date
broker = kwargs['broker']
ta_output_dir = dn.get_dated_directory_extension(folder_date=as_of_date,
ext='ta')
con = msu.get_my_sql_connection(**kwargs)
if 'writer' in kwargs.keys():
writer = kwargs['writer']
else:
writer = pd.ExcelWriter(ta_output_dir + '/followup.xlsx',
engine='xlsxwriter')
strategy_frame = ts.get_open_strategies(**kwargs)
strategy_class_list = [
sc.convert_from_string_to_dictionary(
string_input=strategy_frame['description_string'][x])
['strategy_class'] for x in range(len(strategy_frame.index))
]
ocs_indx = [x == 'ocs' for x in strategy_class_list]
ocs_frame = strategy_frame[ocs_indx]
if ocs_frame.empty:
writer.save()
return
results = [
sf.get_results_4strategy(alias=ocs_frame['alias'].iloc[x],
strategy_info_output=ocs_frame.iloc[x],
con=con,
broker=broker,
date_to=as_of_date)
for x in range(len(ocs_frame.index))
]
ocs_followup_frame = pd.DataFrame(results)
ocs_followup_frame['alias'] = ocs_frame['alias'].values
kwargs['name'] = 'final'
pnl_frame = pm.get_daily_pnl_snapshot(**kwargs)
merged_frame1 = pd.merge(ocs_followup_frame,
pnl_frame,
how='left',
on='alias')
ocs_followup_frame = merged_frame1[[
'alias', 'dollar_noise', 'time_held', 'daily_pnl', 'total_pnl', 'notes'
]]
ocs_followup_frame.reset_index(drop=True, inplace=True)
ocs_followup_frame.loc[max(ocs_followup_frame.index) + 1] = [
'TOTAL', np.nan, np.nan, ocs_followup_frame['daily_pnl'].sum(),
ocs_followup_frame['total_pnl'].sum(), ''
]
date_from30 = cu.doubledate_shift(as_of_date, 30)
history_frame = ts.select_strategies(close_date_from=date_from30,
close_date_to=as_of_date,
con=con)
strategy_class_list = [
sc.convert_from_string_to_dictionary(
string_input=history_frame['description_string'][x])
['strategy_class'] for x in range(len(history_frame.index))
]
ocs_indx = [x == 'ocs' for x in strategy_class_list]
ocs_history_frame = history_frame[ocs_indx]
pnl_past_month = ocs_history_frame['pnl'].sum()
as_of_datetime = cu.convert_doubledate_2datetime(as_of_date)
date_from7 = as_of_datetime + dt.timedelta(days=-7)
ocs_short_history_frame = ocs_history_frame[
ocs_history_frame['close_date'] >= date_from7]
pnl_past_week = ocs_short_history_frame['pnl'].sum()
ocs_followup_frame.loc[max(ocs_followup_frame.index) + 1] = [
'WEEKLY PERFORMANCE', np.nan, np.nan, np.nan, pnl_past_week, ''
]
ocs_followup_frame.loc[max(ocs_followup_frame.index) + 1] = [
'MONTHLY PERFORMANCE', np.nan, np.nan, np.nan, pnl_past_month, ''
]
ocs_followup_frame['total_pnl'] = ocs_followup_frame['total_pnl'].astype(
int)
ocs_followup_frame.to_excel(writer, sheet_name='ocs')
worksheet_ocs = writer.sheets['ocs']
worksheet_ocs.freeze_panes(1, 0)
worksheet_ocs.set_column('B:B', 26)
worksheet_ocs.autofilter(0, 0, len(ocs_followup_frame.index),
len(ocs_followup_frame.columns))
if 'con' not in kwargs.keys():
con.close()
writer.save()
def generate_vcs_followup_report(**kwargs):
if 'as_of_date' in kwargs.keys():
as_of_date = kwargs['as_of_date']
else:
as_of_date = exp.doubledate_shift_bus_days()
kwargs['as_of_date'] = as_of_date
ta_output_dir = dn.get_dated_directory_extension(folder_date=as_of_date,
ext='ta')
con = msu.get_my_sql_connection(**kwargs)
if 'writer' in kwargs.keys():
writer = kwargs['writer']
else:
writer = pd.ExcelWriter(ta_output_dir + '/followup.xlsx',
engine='xlsxwriter')
strategy_frame = ts.get_open_strategies(**kwargs)
strategy_class_list = [
sc.convert_from_string_to_dictionary(
string_input=strategy_frame['description_string'][x])
['strategy_class'] for x in range(len(strategy_frame.index))
]
vcs_indx = [x == 'vcs' for x in strategy_class_list]
vcs_frame = strategy_frame[vcs_indx]
if len(vcs_frame.index) == 0:
return writer
results = [
sf.get_results_4strategy(alias=vcs_frame['alias'].iloc[x],
strategy_info_output=vcs_frame.iloc[x])
for x in range(len(vcs_frame.index))
]
vcs_followup_frame = pd.DataFrame(results)
vcs_followup_frame['alias'] = vcs_frame['alias'].values
kwargs['name'] = 'final'
pnl_frame = pm.get_daily_pnl_snapshot(**kwargs)
merged_frame1 = pd.merge(vcs_followup_frame,
pnl_frame,
how='left',
on='alias')
vcs_followup_frame = merged_frame1[[
'alias', 'last_adjustment_days_ago', 'min_tr_dte', 'long_short_ratio',
'net_oev', 'net_theta', 'long_oev', 'short_oev', 'favQMove',
'total_pnl', 'recommendation'
]]
vcs_followup_frame['long_short_ratio'] = vcs_followup_frame[
'long_short_ratio'].round()
vcs_followup_frame['net_oev'] = vcs_followup_frame['net_oev'].round(1)
vcs_followup_frame['long_oev'] = vcs_followup_frame['long_oev'].round(1)
vcs_followup_frame['short_oev'] = vcs_followup_frame['short_oev'].round(1)
vcs_followup_frame['net_theta'] = vcs_followup_frame['net_theta'].round(1)
vcs_followup_frame.sort_values('total_pnl', ascending=False, inplace=True)
vcs_followup_frame.reset_index(drop=True, inplace=True)
vcs_followup_frame.loc[len(vcs_followup_frame.index)] = [
'TOTAL', None, None, None, None, vcs_followup_frame['net_theta'].sum(),
None, None, None, vcs_followup_frame['total_pnl'].sum(), None
]
vcs_followup_frame.to_excel(writer, sheet_name='vcs')
worksheet_vcs = writer.sheets['vcs']
worksheet_vcs.set_column('B:B', 18)
worksheet_vcs.freeze_panes(1, 0)
worksheet_vcs.autofilter(0, 0, len(vcs_followup_frame.index),
len(vcs_followup_frame.columns))
if 'con' not in kwargs.keys():
con.close()
return writer
def generate_futures_butterfly_followup_report(**kwargs):
con = msu.get_my_sql_connection(**kwargs)
if 'as_of_date' in kwargs.keys():
as_of_date = kwargs['as_of_date']
else:
as_of_date = exp.doubledate_shift_bus_days()
kwargs['as_of_date'] = as_of_date
if 'writer' in kwargs.keys():
writer = kwargs['writer']
else:
ta_output_dir = dn.get_dated_directory_extension(
folder_date=as_of_date, ext='ta')
writer = pd.ExcelWriter(ta_output_dir + '/followup.xlsx',
engine='xlsxwriter')
strategy_frame = ts.get_open_strategies(**kwargs)
strategy_class_list = [
sc.convert_from_string_to_dictionary(
string_input=strategy_frame['description_string'][x])
['strategy_class'] for x in range(len(strategy_frame.index))
]
futures_butterfly_indx = [
x == 'futures_butterfly' for x in strategy_class_list
]
futures_butterfly_frame = strategy_frame[futures_butterfly_indx]
results = [
sf.get_results_4strategy(
alias=futures_butterfly_frame['alias'].iloc[x],
strategy_info_output=futures_butterfly_frame.iloc[x])
for x in range(len(futures_butterfly_frame.index))
]
butterfly_followup_frame = pd.DataFrame(results)
butterfly_followup_frame['alias'] = futures_butterfly_frame['alias'].values
pnl_frame = pm.get_daily_pnl_snapshot(as_of_date=as_of_date,
con=con,
name='final')
risk_output = hr.get_historical_risk_4open_strategies(
as_of_date=as_of_date, con=con)
merged_frame1 = pd.merge(butterfly_followup_frame,
pnl_frame,
how='left',
on='alias')
merged_frame2 = pd.merge(merged_frame1,
risk_output['strategy_risk_frame'],
how='left',
on='alias')
butterfly_followup_frame = merged_frame2[[
'alias', 'ticker_head', 'holding_tr_dte', 'short_tr_dte', 'z1_initial',
'z1', 'QF_initial', 'QF', 'total_pnl', 'downside', 'recommendation'
]]
butterfly_followup_frame.rename(columns={
'alias': 'Alias',
'ticker_head': 'TickerHead',
'holding_tr_dte': 'HoldingTrDte',
'short_tr_dte': 'ShortTrDte',
'z1_initial': 'Z1Initial',
'z1': 'Z1',
'QF_initial': 'QFInitial',
'total_pnl': 'TotalPnl',
'downside': 'Downside',
'recommendation': 'Recommendation'
},
inplace=True)
butterfly_followup_frame.sort_values('QF', ascending=False, inplace=True)
butterfly_followup_frame['Z1'] = butterfly_followup_frame['Z1'].round(2)
butterfly_followup_frame.to_excel(writer, sheet_name='butterflies')
worksheet_butterflies = writer.sheets['butterflies']
worksheet_butterflies.set_column('B:B', 26)
worksheet_butterflies.freeze_panes(1, 0)
worksheet_butterflies.autofilter(0, 0, len(butterfly_followup_frame.index),
len(butterfly_followup_frame.columns))
if 'con' not in kwargs.keys():
con.close()
return writer
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_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