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