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import pandas as pd
from functools import reduce
from position import get_list
from db import dbengine
from dates import bus_day, imm_dates
def pnl_explain(identifier, start_date = None, end_date = None,
engine = dbengine("dawndb")):
""" if start_date is None, pnl since inception"""
trades = pd.read_sql_query("SELECT * FROM bonds where identifier=%s", engine,
params=(identifier,), parse_dates=['trade_date', 'settle_date'],
index_col=['settle_date'])
marks = pd.read_sql_query("SELECT * FROM marks where identifier=%s", engine,
params=(identifier,), parse_dates = ['date'], index_col='date')
factors = pd.read_sql_query("SELECT * FROM factors_history where identifier=%s", engine,
params=(identifier,), parse_dates = ['last_pay_date', 'prev_cpn_date'],
index_col=['last_pay_date'])
for key in ['faceamount', 'principal_payment', 'accrued_payment']:
trades.loc[~trades.buysell, key] = -trades[key][~trades.buysell]
df = (marks[['price']].join(factors, how='outer').
join(trades[['principal_payment', 'accrued_payment', 'faceamount']], how='outer'))
df.sort_index(inplace=True)
if start_date is None:
start_date = trades.index.min()
if end_date is None:
end_date = pd.datetime.today()
dates = pd.date_range(start_date, end_date, freq = bus_day)
keys1 = ['price','factor', 'coupon', 'prev_cpn_date']
df[keys1] = df[keys1].fillna(method='ffill')
keys2 = ['losses', 'principal','interest', 'faceamount','accrued_payment', 'principal_payment']
df[keys2] = df[keys2].fillna(value=0)
df.faceamount = df.faceamount.cumsum()
keys = keys1 + ['faceamount']
df1 = df.reindex(dates, keys, method='ffill')
keys = ['losses', 'principal','interest', 'accrued_payment', 'principal_payment']
df2 = df.reindex(dates, keys, fill_value=0)
daily = pd.concat([df1, df2], axis = 1)
daily['unrealized_pnl'] = daily.price.diff() * daily.factor.shift()/100 * daily.faceamount
daily['realized_pnl'] = (daily.price/100*daily.factor.diff()+daily.principal/100) * daily.faceamount
daily['clean_nav'] = daily.price/100 * daily.factor * daily.faceamount
daily['realized_accrued'] = daily.interest/100 * daily.faceamount
days_accrued = daily.index - daily.prev_cpn_date
daily['accrued'] = days_accrued.dt.days/360*daily.coupon/100*daily.factor*daily.faceamount
extra_pnl = daily.clean_nav.diff() - daily.principal_payment
daily.loc[daily.principal_payment>0 , 'unrealized_pnl'] += extra_pnl[daily.principal_payment>0]
daily.loc[daily.principal_payment<0, 'realized_pnl'] += extra_pnl[daily.principal_payment<0]
daily['realized_accrued'] -= daily.accrued_payment
daily['unrealized_accrued'] = daily.accrued.diff() + daily.realized_accrued
return daily[['clean_nav', 'accrued', 'unrealized_pnl', 'realized_pnl', 'unrealized_accrued',
'realized_accrued']].iloc[1:,]
def pnl_explain_list(id_list, start_date = None, end_date = None, engine = dbengine("dawndb")):
return {identifier: pnl_explain(identifier, start_date, end_date, engine)
for identifier in id_list}
def cds_explain(index, series, tenor, attach = None, detach = None,
start_date = None, end_date = None, engine = dbengine('serenitasdb')):
if attach is None:
quotes = pd.read_sql_query("SELECT date, (100-closeprice) AS upfront FROM index_quotes " \
"WHERE index=%s AND series=%s AND tenor=%s ORDER BY date",
engine, parse_dates=['date'],
index_col='date', params = (index, series, tenor))
factors = pd.read_sql_query("""
SELECT indexfactor/100 AS indexfactor, cumulativeloss/100 AS cumulativeloss
lastdate FROM index_desc WHERE index=%s AND series=%s AND tenor=%s ORDER BY lastdate
""",
engine, parse_dates=['lastdate'], index_col='lastdate',
params = (attach, detach, index, series, tenor))
else:
#we take the latest version available
quotes = pd.read_sql_query("SELECT DISTINCT ON (quotedate) quotedate, upfront_mid AS upfront, "\
"tranche_spread FROM markit_tranche_quotes " \
"JOIN index_version USING (basketid) " \
"WHERE index=%s AND series=%s " \
"AND tenor=%s AND attach=%s AND detach=%s " \
"ORDER by quotedate, version desc",
engine, parse_dates=['quotedate'], index_col='quotedate',
params = (index, series, tenor, attach, detach))
factors = pd.read_sql_query("""
SELECT tranche_factor(%s::smallint, %s::smallint, indexfactor, cumulativeloss/100),
indexfactor/100 AS indexfactor, cumulativeloss/100 AS cumulativeloss, lastdate
FROM index_desc WHERE index=%s AND series=%s AND tenor=%s ORDER BY lastdate
""",
"postgresql://serenitas_user@debian/serenitasdb",
parse_dates=['lastdate'], index_col='lastdate',
params = (attach, detach, index, series, tenor))
if start_date is None:
start_date = quotes.index.min()
if end_date is None:
end_date = pd.datetime.today()
#we use tranche_factor
if attach:
factors['factor'] = factors.tranche_factor
else:
factors['factor'] = factors.indexfactor
dates = pd.date_range(start_date, end_date, freq = bus_day)
yearfrac = imm_dates(start_date, end_date)
yearfrac = yearfrac.to_series().reindex(dates, method='ffill')
yearfrac = yearfrac.index-yearfrac
yearfrac = (yearfrac.dt.days+1)/360
yearfrac.name = 'yearfrac'
quotes = quotes.reindex(dates, method='ffill')
recovery = -factors.indexfactor.diff()-factors.cumulativeloss.diff()
recovery.name = 'recovery'
recovery = recovery.shift(-1)
recovery = recovery.reindex(dates, fill_value=0).shift()
df = (quotes.
join(factors[['factor']], how='left').
join(recovery).join(yearfrac))
if attach:
coupon = df.tranche_spread.iat[0]/10000
else:
coupon = factors.coupon.iat[0]/10000
df.indexfactor = df.indexfactor.bfill()
df.loc[df.indexfactor.isnull(), 'indexfactor'] = factors.factor.iat[-1]
df['clean_nav'] = df.upfront*df.factor
df['accrued'] = df.yearfrac*coupon*df.factor
df['unrealized_accrued'] = df.accrued.diff()
df['realized_accrued'] = -df.unrealized_accrued.where(df.unrealized_accrued.isnull() |
(df.unrealized_accrued<0), 0)
df['unrealized_accrued'] = df.unrealized_accrued.where(df.unrealized_accrued.isnull()|
(df.unrealized_accrued>0), df.accrued)
df.loc[df.realized_accrued>0, 'realized_accrued'] += df.loc[df.realized_accrued>0, 'unrealized_accrued']
df['unrealized_pnl'] = df.upfront.diff() * df.factor.shift()/100
df['realized_pnl'] = df.upfront/100*df.indexfactor.diff()+df.recovery
return df
def cds_explain2(dealid):
pass
if __name__=="__main__":
workdate = pd.datetime.today()
engine = dbengine("dawndb")
clo_list = get_list(engine, workdate, 'CLO')
df = pnl_explain_list(clo_list.identifier.tolist(), '2015-10-30', '2015-11-30', engine)
df = pd.concat(df)
df_agg = df.groupby(level=1).sum()
cds_df = cds_explain('HY', 21, '5yr', 25, 35, '2014-07-18')
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