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from glob import iglob
import os
import pandas as pd
from itertools import chain
from dates import bus_day
from db import dbengine
import datetime
def get_globs(fname, years=['2013', '2014', '2015', '2016', '2017']):
basedir = '/home/serenitas/Daily'
globs = [iglob(os.path.join(basedir,
year,
"{0}_*/{0}*/Reports/{1}.csv".format(year, fname)))
for year in years]
for year in years[-2:]:
globs.append(iglob(os.path.join(basedir,
'{0}-*/Reports/{1}.csv'.format(year,
fname))))
return globs
def read_valuation_report(f):
date = pd.Timestamp(f.parts[4])
if date >= pd.Timestamp('2013-02-06'):
df = pd.read_csv(f, parse_dates=['KnowledgeDate', 'PeriodEndDate'])
else:
df = pd.read_csv(f)
df['KnowledgeDate'] = date
df['PeriodEndDate'] = date - bus_day
df['row'] = df.index
if 'AccountingPeriod' in df:
del df['AccountingPeriod']
if 'CounterParty' in df:
del df['CounterParty']
df = df.rename(columns={'CounterPartyCode': 'counterparty'})
if "Strat" in df:
df.Strat = df.Strat.str.replace("^(SERCGMAST__){1,2}(M_|SER_)?", "", 1)
if "Port" in df:
df.Port = df.Port.str.replace("^(SERCGMAST__){1,2}(SERG__|SERG_)?", "", 1)
df.columns = df.columns.str.lower()
return df
def valuation_reports():
df = pd.concat(read_valuation_report(f) for f in
chain.from_iterable(get_globs('Valuation_Report')))
# There can be duplicates in case of holidays
df = df.sort_values(['periodenddate', 'row', 'knowledgedate'])
df = df.drop_duplicates(['periodenddate', 'row'], 'last')
df.to_sql('valuation_reports', dbengine('dawndb'), if_exists='append', index=False)
def read_pnl_report(f):
df = pd.read_csv(f)
df.Strat = df.Strat.str.replace("^(SERCGMAST__){1,2}(M_|SER_)?", "", 1)
df.Port = df.Port.str.replace("^(SERCGMAST__){1,2}(SERG__|SERG_)?", "", 1)
df['LongShortIndicator'] = df['LongShortIndicator'].str.strip()
df.columns = df.columns.str.lower().str.replace(" ", "")
return df
def pnl_reports():
df = {}
for f in chain.from_iterable(get_globs('Pnl*')):
if not (f.endswith("Pnl.csv") and f.endswith("Pnl_Report.csv")):
continue
date = pd.Timestamp(f.rsplit('/', 3)[1])
date = date - bus_day
df[date] = read_pnl_report(f)
df = pd.concat(df, names=['date', 'row']).reset_index()
df.to_sql('pnl_reports', dbengine('dawndb'), if_exists='append', index=False)
def read_cds_report(f):
df = pd.read_csv(f)
df2 = pd.read_csv(f.parent / "All_Report.csv")
def drop_zero_count(df):
for col in df:
vc = len(df[col].value_counts())
if vc == 0:
del df[col]
continue
drop_zero_count(df)
drop_zero_count(df2)
contract = df['Contractual Definition']
contract = contract.where(contract.isin(['ISDA2014', 'ISDA2003Cred']), 'ISDA2014')
df['Contractual Definition'] = contract
to_drop = ['Bloomberg Yellow key', 'Created User', 'Last Modified User',
'Last Modified Date', 'Fund Long Name', 'Instrument Sub Type',
'Netting Id', 'Client', 'Trade Status', 'Position Status',
'Clearing Broker', 'Settle Mode', 'Off Price', 'On Price',
'Price Ccy', 'VAT', 'SEC Fee', 'Clearing Fee',
'Trading Notional', 'BBGID']
df = df.drop(to_drop,
axis=1, errors='ignore')
df2 = df2.drop(to_drop,
axis=1, errors='ignore')
df.columns = df.columns.str.lower().str.replace(" ", "_")
df2.columns = df2.columns.str.lower().str.replace(" ", "_")
df.calendar = df.calendar.str.replace(" ", "")
df = df.rename(columns={'direction': 'buy/sell'})
df.roll_convention = df.roll_convention.str.title()
df = df[df.strategy != 'SER_TEST']
df.loc[df.strategy == 'SERCGMAST__MBSCDS', 'strategy'] = 'MBSCDS'
df.strategy = df.strategy.str.replace("SER_", "")
df['buy/sell'] = df['buy/sell'].astype('category')
df['buy/sell'].cat.categories = ['Buyer', 'Seller']
del df['independent_%']
df2 = df2.rename(columns={'independent_%': 'independent_perc'})
df.prime_broker = df.prime_broker.where(df.prime_broker != 'NONE')
return df.set_index('gtid').join(df2.set_index('gtid')[
df2.columns.difference(df.columns)]).reset_index()
def cds_reports():
df = {}
for f in chain.from_iterable(get_globs('CDS_Report')):
date = pd.Timestamp(f.rsplit('/', 3)[1])
old_report = date <= pd.Timestamp('2017-02-28') or date == pd.Timestamp('2017-03-02')
date = date - bus_day
df[date] = read_cds_report(f, old_report)
df = pd.concat(df, names=['date', 'row']).reset_index()
return df
def monthly_pnl_bycusip(df, strats):
df = df[(df.strat.isin(strats)) & (df.custacctname=='V0NSCLMAMB')]
pnl_cols = ['bookunrealmtm', 'bookrealmtm', 'bookrealincome', 'bookunrealincome', 'totalbookpl']
return df.groupby('invid').resample('M').last()[['mtd'+col for col in pnl_cols]]
if __name__=='__main__':
valuation_reports()
pnl_reports()
df_val = pd.read_hdf('globeop.hdf', 'valuation_report')
df_pnl = pd.read_hdf('globeop.hdf', 'pnl')
nav = df_val[df_val.Fund == 'SERCGMAST'].groupby('PeriodEndDate')['EndBookNAV'].sum()
subprime_strats = ['MTG_GOOD', 'MTG_RW', 'MTG_IO','MTG_THRU', 'MTG_B4PR']
clo_strats = ['CLO_BBB', 'CLO_AAA', 'CLO_BB20']
## daily pnl by cusip
#subprime_daily_pnl = daily_pnl_bycusip(df_pnl, subprime_strats)
df_monthly = monthly_pnl_bycusip(df_pnl, subprime_strats)
#df_monthly.loc[idx[ts('2015-01-01'):ts('2015-01-31'),:],:]
# clo = df_pnl[df_pnl.Strat.isin(clo_strats)]
# clo_monthly_pnl = clo.groupby(level=0).sum()['MTD TotalBookPL'].resample('M').last()
# clo.groupby(level=0).sum()['2015-12-01':'2015-12-31']
df_val.set_index(['custacctname', 'periodenddate', 'invid', 'strat'])
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