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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
import numpy as np
from psycopg2.extensions import register_adapter, AsIs
register_adapter(np.int64, lambda x: AsIs(x))
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, trades[['principal_payment', 'accrued_payment', 'faceamount']]],
how='outer'))
df.sort_index(inplace=True)
if start_date is None:
start_date = trades.trade_date.ix[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 compute_tranche_factors(df, attach, detach):
attach, detach = attach/100, detach/100
df['indexrecovery'] = 1-df.indexfactor-df.cumulativeloss
df = df.assign(tranche_loss = lambda x: (x.cumulativeloss-attach)/(detach-attach),
tranche_recov = lambda x: (x.indexrecovery-(1-detach))/(detach-attach))
df[['tranche_loss', 'tranche_recov']] = df[['tranche_loss', 'tranche_recov']].clip(lower=0, upper=1)
df['tranche_factor'] = 1-df.tranche_loss - df.tranche_recov
return df
def cds_explain(index, series, tenor, attach = np.nan, detach = np.nan,
start_date = None, end_date = None, engine = dbengine('serenitasdb')):
cds_trade = np.isnan(attach) or np.isnan(detach)
if cds_trade:
quotes = pd.read_sql_query("SELECT date, (100-closeprice)/100 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))
else:
#we take the latest version available
sqlstring = "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"
quotes = pd.read_sql_query(sqlstring, engine, parse_dates=['quotedate'],
index_col='quotedate',
params = (index, series, tenor, int(attach), int(detach)))
sqlstring = "SELECT indexfactor/100 AS indexfactor, coupon, " \
"cumulativeloss/100 AS cumulativeloss, lastdate " \
"FROM index_desc WHERE index=%s AND series=%s AND tenor=%s " \
"ORDER BY lastdate"
factors = pd.read_sql_query(sqlstring, engine, parse_dates=['lastdate'],
params = (index, series, tenor))
if start_date is None:
start_date = quotes.index.min()
if end_date is None:
end_date = pd.datetime.today()
if not cds_trade:
coupon = quotes.tranche_spread.iat[0]/10000
factors = compute_tranche_factors(factors, attach, detach)
factors['factor'] = factors.tranche_factor
factors['recovery'] = factors.tranche_recov
else:
coupon = factors.coupon.iat[0]/10000
factors['factor'] = factors.indexfactor
factors['recovery'] = 1-factors.indexfactor-factors.cumulativeloss
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')
if factors.shape[0]==1 or dates[-1] > max(factors.lastdate):
factors.lastdate.iat[-1] = dates[-1]
else:
factors = factors.iloc[:-1]
try:
factors = (factors.set_index('lastdate', verify_integrity=True).
reindex(dates, ['factor', 'recovery'], method='bfill'))
except ValueError:
pdb.set_trace()
factors.recovery = factors.recovery.diff()
df = quotes.join([factors[['factor', 'recovery']], yearfrac])
#df.loc[df.factor.isnull(), 'factor'] = 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()
df['realized_pnl'] = df.upfront/100*df.factor.diff()+df.recovery
return df[['clean_nav', 'accrued', 'unrealized_accrued', 'realized_accrued',
'unrealized_pnl', 'realized_pnl']]
def cds_explain_strat(strat, start_date, end_date, engine = dbengine("dawndb")):
if not pd.core.common.is_list_like(strat):
strat = [strat]
cds_positions = pd.read_sql_table("orig_cds", engine,
parse_dates = ['trade_date', 'upfront_settle_date'],
index_col='dealid')
cds_positions = cds_positions.ix[cds_positions.folder.isin(strat) &
(end_date is None or \
cds_positions.upfront_settle_date<=pd.Timestamp(end_date))]
cds_positions.loc[cds_positions.protection=='Seller', "notional"] *= -1
df = {}
for r in cds_positions.itertuples():
key = (r.index, r.series, r.tenor)
if start_date is not None:
start_date = max(r.trade_date, pd.Timestamp(start_date))
else:
start_date = r.trade_date
trade_df = cds_explain(r.index, r.series, r.tenor, r.attach, r.detach,
start_date, end_date, engine)
trade_df = r.notional * trade_df
if start_date is None or (start_date <= r.trade_date):
trade_df.realized_accrued.iat[3] -= trade_df.accrued.iat[0]
extra_pnl = trade_df.clean_nav.iat[0] + trade_df.accrued.iat[0] + r.upfront
trade_df.unrealized_pnl.iat[0] = extra_pnl
trade_df.loc[:3, 'unrealized_accrued'] = 0
df[key] = trade_df.add(df.get(key, 0), fill_value=0)
return pd.concat(df)
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(), None, '2015-11-30', engine)
df = pd.concat(df)
df_agg = df.groupby(level=1).sum()
cds_df = cds_explain_strat(['SER_IGINX', 'SER_IGMEZ'], None, '2015-12-08', engine)
#cds_df = cds_explain_strat(['SER_HYMEZ'], None, '2015-03-10', engine)
#cds_df2 = cds_explain_strat('SER_IGCURVE', None, None, engine)
#cds_df = cds_explain('HY', 21, '5yr', 25, 35, '2014-07-18')
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