DF_SGP_tot['Value_'] = DF_SGP_tot['SustG'] / DF_SGP_tot['PE_f12m']
DF_SGP_tot['freq'], DF_SGP_tot['StyleName'] = 'M', 'SGP_f2yr'
# Append All Factors
#*------------
firstCheck_duplicates(DF_PEf12m,
DF_DYf12m,
DF_EPS_fwd,
DF_ROE_fwd,
DF_EGP_tot,
DF_SGP_tot,
add_cols=['RGN_TP_CD'])
#*------------
cols = [
'BASE_DT', 'TMSRS_CD', 'Code', 'RGN_TP_CD', 'Value_', 'freq', 'StyleName'
]
#*------------
secondCheck_columns(DF_PEf12m[cols], DF_DYf12m[cols], DF_EPS_fwd[cols],
DF_ROE_fwd[cols], DF_EGP_tot[cols], DF_SGP_tot[cols])
#*------------
DF_GARP = reduce(lambda x, y: pd.DataFrame.append(x, y, sort=False), [
DF_PEf12m[cols], DF_DYf12m[cols], DF_EPS_fwd[cols], DF_ROE_fwd[cols],
DF_EGP_tot[cols], DF_SGP_tot[cols]
])
DF_GARP.reset_index(drop=True, inplace=True)
# Save!
save_batch(bkfil, DF_GARP, mapping, fileName)
from batch_utils.fill_monitor import print_fillReport
out = print_fillReport(bkfil, freq, DF_GARP)
batch_monitor_msg(i, tot_n, st_time)
batch_finish_msg(tot_n, st_time)
DF_accruals_tot = substitute_Value(DF_accruals_yr, DF_accruals_qtr)
DF_accrualsS_tot = substitute_Value(DF_accrualsS_yr, DF_accrualsS_qtr)
#*------------
firstCheck_duplicates(DF_accruals_tot, DF_accrualsS_tot)
#*------------
# Map Code -> TMSRS_CD
# Should customize columns by needed ones.
# MUST HAVE 'BASE_DT', 'TMSRS_CD', 'Code', 'Value_', 'RGN_TP_CD', 'freq'
cols = [
'BASE_DT', 'TMSRS_CD', 'Code', 'Value_yr', 'Value_qtr', 'Value_',
'RGN_TP_CD', 'freq', 'ref'
]
DF_accruals_tot = add_mapped_tick(DF_accruals_tot, trim_codeMap)[cols]
DF_accrualsS_tot = add_mapped_tick(DF_accrualsS_tot, trim_codeMap)[cols]
DF_accruals_tot['StyleName'] = 'Accruals'
DF_accrualsS_tot['StyleName'] = 'Accruals.S'
#*------------
secondCheck_columns(DF_accruals_tot, DF_accrualsS_tot)
#*------------
DF = DF_accruals_tot.append(DF_accrualsS_tot)
# Save!
save_batch(bkfil, DF, mapping, fileName)
from batch_utils.fill_monitor import print_fillReport
out = print_fillReport(bkfil, freq, DF)
DF_epsg_trail = add_mapped_tick(DF_epsg_trail, trim_codeMap)[cols]
DF_ROE_trail['StyleName'] = 'ROE_l2yr'
DF_Payout_trail['StyleName'] = 'Payout_l2yr'
DF_epsg_trail['StyleName'] = 'EPSg_l2yr'
#*------------
secondCheck_columns(DF_ROE_trail, DF_Payout_trail, DF_epsg_trail)
#*------------
DF = reduce(lambda x, y: pd.DataFrame.append(x, y, sort=False),
[DF_ROE_trail, DF_Payout_trail])
DF.reset_index(drop=True, inplace=True)
# Save!
save_batch(bkfil, DF, mapping, fileName + '_ROEnPayout')
# Save!
save_batch(bkfil, DF_epsg_trail, mapping, fileName + '_EPSg')
from batch_utils.fill_monitor import print_fillReport
out = print_fillReport(bkfil, freq, DF)
out = print_fillReport(bkfil, freq, DF_epsg_trail)
# <<Sustainable Growth Script>>
dir_ = 'save_total' if bkfil else 'save_batch'
DF_IBES_Payout = pd.read_pickle('{}/IBES_refIBES_Payout.p'.format(dir_))
DF_IBES_ROE = pd.read_pickle('{}/IBES_refIBES_ROE.p'.format(dir_))
# Merge IBES with Worldscope for (normalized) Payout & ROE (comb. average)
TMP = DF_IBES_Payout[['BASE_DT', 'TMSRS_CD',
TMP['freq'] = 'curr'
DF_Value = reduce(lambda x, y: pd.DataFrame.append(x, y, sort=True),
[DF_Value, TMP, ps_DF, pfcf_DF])
batch_monitor_msg(i, tot_n, st_time)
batch_finish_msg(tot_n, st_time)
#*------------
firstCheck_duplicates(DF_Value, add_cols=['Name'])
#*------------
# Map Code -> TMSRS_CD
# Should customize columns by needed ones.
# MUST HAVE 'BASE_DT', 'TMSRS_CD', 'Code', 'Value_', 'RGN_TP_CD', 'freq'
cols = ['BASE_DT', 'TMSRS_CD', 'Code', 'Value_', 'RGN_TP_CD', 'freq', 'Name']
DF_Value = DF_Value.groupby('Name').apply(
lambda x: add_mapped_tick(x, trim_codeMap))
DF_Value.index = DF_Value.index.droplevel(level=1)
DF_Value.reset_index(drop=True, inplace=True)
DF_Value = DF_Value[cols]
DF_Value.rename(columns={'Name': 'StyleName'}, inplace=True)
DF_Value['ref'] = None
#*------------
secondCheck_columns(DF_Value)
#*------------
# Save!
save_batch(bkfil, DF_Value, mapping, fileName)
from batch_utils.fill_monitor import print_fillReport
out = print_fillReport(bkfil, freq, DF_Value)
TMP = reduce(lambda x, y: pd.DataFrame.append(x, y, sort=False), [
get_value_col(TMP0, 'Payout_fy0'),
get_value_col(TMP1, 'Payout_fy1'),
get_value_col(TMP2, 'Payout_fy2')
])
tmp_ = TMP.groupby(['BASE_DT', 'TMSRS_CD'], as_index=False)['Value_'].count()
TMP = TMP.merge(tmp_.rename(columns={'Value_': 'CNT'}),
on=['BASE_DT', 'TMSRS_CD'])
TMP_ = TMP[TMP['CNT'] >= 2]
Payout_mean = TMP_.groupby(['BASE_DT', 'TMSRS_CD'],
as_index=False)['Value_'].mean()
Payout_mean.rename(columns={'Value_': 'Payout_avg'}, inplace=True)
# END AVERAGE calculation-------
DF_IBES_Payout = pd.merge(DF_IBES_Payout,
Payout_mean,
on=['BASE_DT', 'TMSRS_CD'],
how='left')
DF_IBES_Payout['StyleName'] = 'IBES_Payout'
save_batch(bkfil, DF_IBES_EPS, mapping, fileName[:-4] + '_EPS')
save_batch(bkfil, DF_IBES_DPS, mapping, fileName[:-4] + '_DPS')
save_batch(bkfil, DF_IBES_Payout, mapping, fileName[:-4] + '_Payout')
from batch_utils.fill_monitor import print_fillReport
out = print_fillReport(bkfil, freq, DF_IBES_EPS)
out = print_fillReport(bkfil, freq, DF_IBES_DPS)
out = print_fillReport(bkfil, freq, DF_IBES_Payout, chk_col='Payout_fy1')
['Code', 'RGN_TP_CD', 'BASE_DT']).reset_index(drop=True)
#*------------
firstCheck_duplicates(DF_IBES_EPS, add_cols=['RGN_TP_CD'])
#*------------
DF_IBES_EPS = chg_to_type(DF_IBES_EPS, chg_col=['Code', 'BASE_DT', 'RGN_TP_CD'])
DF_IBES_EPS = add_mapped_tick(
DF_IBES_EPS, trim_codeMap, on=['Code', 'RGN_TP_CD'])
cols = ['BASE_DT', 'TMSRS_CD', 'Code', 'RGN_TP_CD', 'FY0', 'Px0',
'ActValue0', 'EPS_fy1', 'EPS_fy1_prev', 'EPS_chg_pct']
DF_IBES_EPS.dropna(subset=['EPS_chg_pct'], axis=0, inplace=True)
DF_IBES_EPS = DF_IBES_EPS[cols]
cols = ['BASE_DT', 'TMSRS_CD', 'Code', 'RGN_TP_CD',
'EPS_fy1', 'EPS_fy1_prev', 'EPS_chg_pct']
DF_final = DF_IBES_EPS[cols].copy()
DF_final['freq'], DF_final['ref'] = 'M', None
DF_final.rename(columns={'EPS_chg_pct': 'Value_'}, inplace=True)
DF_final['StyleName'] = 'EPSRev3M'
#*------------
secondCheck_columns(DF_final)
#*------------
save_batch(bkfil, DF_final, mapping, fileName + '3M')
from batch_utils.fill_monitor import print_fillReport
out = print_fillReport(bkfil, freq, DF_final)
