def hlf_amc(dic_data, input_dic, period='half_no'):
aua = combined.total_aua(dic_data, input_dic)
aua_margins = general.fillna_monthly(
input_dic['aua margin']).reindex(index=general.month_end_series)
return (aua['discretionary_aua'] * (aua_margins['hlf_amc'] / 12)).groupby(
['financial_year',
period]).sum(min_count=1) #.map(general.compound_growth_rate))
def summary_avg_aua_dist(data_dic, input_dic, period='financial_year'):
if (period == 'financial_year') or (period == 'calendar_year'):
df = combined.total_aua(data_dic, input_dic).groupby(period).mean()
elif period=='month_no':
df = combined.total_aua(data_dic, input_dic).groupby(['calendar_year',period]).mean()
else:
df = combined.total_aua(data_dic, input_dic).groupby(['financial_year',period]).mean()
avg_aua_funds = df['Funds']
avg_aua_shares = df['Shares']
avg_aua_hlf_amc = df['HLF']
avg_aua_cash = df['Cash']
avg_aua_cash_service = df['cash_service_aua']
avg_aua_cash_service.name = 'Cash Service'
if general.last_result_month == 6:
temp = general.recent_end_year + 1
else:
temp = general.recent_end_year
result = pandas.concat([avg_aua_funds,avg_aua_shares,avg_aua_hlf_amc,avg_aua_cash,avg_aua_cash_service],axis='columns')
return result.loc[temp:,:]
def pms_advice_fee(dic_data, input_dic, period='half_no'):
aua = combined.total_aua(dic_data, input_dic)
aua_margins = general.fillna_monthly(input_dic['aua margin']).reindex(index=general.month_end_series)
net_revenue = (aua['pms_aua'] * (aua_margins['pms_advice']/12))
if period=='month_no':
result = net_revenue
elif (period=='financial_year' or period=='calendar_year'):
result = net_revenue.groupby(period).sum()
else:
result = net_revenue.groupby(['financial_year',period]).sum()
return result#.map(general.compound_growth_rate))
def cash_interest(dic_data, input_dic, period='half_no'):
aua = combined.total_aua(dic_data, input_dic)
annual_libor_revenue = aua['vantage_cash_aua']*general.account_cash_dist['sipp']*0.8*(general.annual_libor_mean(dic_data)/12)#.map(general.compound_growth_rate)
overnight_libor = dic_data['Index price'].loc[:, 'Overnight LIBOR'].fillna(method='ffill').reindex(index=general.month_end_series).fillna(method='ffill')
overnight_libor_revenue = aua['vantage_cash_aua']*(general.account_cash_dist['sipp'] * 0.2 + (1 - general.account_cash_dist['sipp']))*(overnight_libor/12)#.map(general.compound_growth_rate)
gross_revenue = (annual_libor_revenue + overnight_libor_revenue)
net_revenue = gross_revenue - aua['vantage_cash_aua'] * general.account_cash_dist['sipp'] * (0.0005/12) # effective from Nov-2017
if period=='month_no':
result = net_revenue
elif (period=='financial_year' or period=='calendar_year'):
result = net_revenue.groupby(period).sum()
else:
result = net_revenue.groupby(['financial_year',period]).sum()
return result
def aua_analysis(dic_data, input_dic):
df = combined.total_aua(
dic_data, input_dic).reset_index().set_index('month_end').reindex(
index=general.semi_annual_series)
df.index.name = 'month_end'
df = df.reset_index().set_index(
['month_end', 'financial_year', 'quarter_no', 'half_no'])
result = pandas.DataFrame(index=df.index, columns=aua_cols)
result.loc[:, 'Cash Service AUA'] = df['cash_service_aua']
result.loc[:, 'Vantage AUA'] = df['vantage_aua']
result.loc[:, 'PMS AUA'] = df['pms_aua']
result.loc[:, 'HLMM Funds AUA'] = df['discretionary_aua']
result.loc[:,
'Total AUA'] = result.loc[:,
'Vantage AUA'] + result.loc[:,
'PMS AUA'] + result.loc[:,
'Cash Service AUA']
return result.groupby(['financial_year', 'quarter_no',
'half_no']).sum(min_count=1)
def cash_interest(dic_data, input_dic, period='half_no'):
aua = combined.total_aua(dic_data, input_dic)
sipp_rebate = (input_dic['cash interest rebate'].fillna(
method='ffill').reindex(
index=general.month_end_series))['sipp_cash_interest_rebt']
others_rebate = (input_dic['cash interest rebate'].fillna(
method='ffill').reindex(
index=general.month_end_series))['others_cash_interest_rebt']
one_month_libor = general.annual_libor_mean(dic_data, '1_month LIBOR')
three_month_libor = general.annual_libor_mean(dic_data, '3_month LIBOR')
annual_libor_revenue = aua['vantage_cash_aua'] * general.account_cash_dist[
'sipp'] * 0.8 * (general.annual_libor_mean(dic_data, 'Annual LIBOR') /
12) #.map(general.compound_growth_rate)
three_month_libor_revenue = aua['vantage_cash_aua'] * (
1 - general.account_cash_dist['sipp']) * 0.6 * (three_month_libor / 12)
one_month_libor_revenue = aua['vantage_cash_aua'] * (
1 - general.account_cash_dist['sipp']) * 0.2 * (one_month_libor / 12)
overnight_libor = dic_data['Index price'].loc[:, 'Overnight LIBOR'].fillna(
method='ffill').reindex(index=general.month_end_series).fillna(
method='ffill')
overnight_libor_revenue = aua['vantage_cash_aua'] * (
general.account_cash_dist['sipp'] * 0.2 +
((1 - general.account_cash_dist['sipp'])) * 0.2) * (
overnight_libor / 12) #.map(general.compound_growth_rate)
gross_revenue = (annual_libor_revenue + overnight_libor_revenue +
three_month_libor_revenue + one_month_libor_revenue)
net_revenue = gross_revenue - aua[
'vantage_cash_aua'] * general.account_cash_dist['sipp'] * (
sipp_rebate / 12) - aua['vantage_cash_aua'] * (
1 - general.account_cash_dist['sipp']) * (others_rebate / 12)
if period == 'month_no':
result = net_revenue
elif (period == 'financial_year' or period == 'calendar_year'):
result = net_revenue.groupby(period).sum(min_count=1)
else:
result = net_revenue.groupby(['financial_year',
period]).sum(min_count=1)
return result
def hlf_revenue_margin(data_dic, input_dic):
df1 = revenue.hlf_amc_daily(data_dic, input_dic, period='month_no')
df2 = combined.total_aua(data_dic, input_dic)['discretionary_aua'].groupby(
['calendar_year', 'month_no']).sum()
return df1 / df2.reindex(df1.index)
