diff options
Diffstat (limited to 'python/pnl_explain.py')
| -rw-r--r-- | python/pnl_explain.py | 107 |
1 files changed, 66 insertions, 41 deletions
diff --git a/python/pnl_explain.py b/python/pnl_explain.py index 360dc45f..44325893 100644 --- a/python/pnl_explain.py +++ b/python/pnl_explain.py @@ -3,6 +3,10 @@ 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")): @@ -39,11 +43,13 @@ def pnl_explain(identifier, start_date = None, end_date = None, 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['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 + 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] @@ -56,36 +62,37 @@ def pnl_explain_list(id_list, start_date = None, end_date = None, engine = dbeng 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, +def cds_explain(index, series, tenor, attach = np.nan, detach = np.nan, 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", + if np.isnan(attach) or np.isnan(detach): + 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)) + 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'], + index_col='lastdate', + params = (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', + 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 " \ + "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" + factors = pd.read_sql_query(sqlstring, engine, parse_dates=['lastdate'], + index_col='lastdate', params = (attach, detach, index, series, tenor)) if start_date is None: start_date = quotes.index.min() @@ -93,11 +100,10 @@ FROM index_desc WHERE index=%s AND series=%s AND tenor=%s ORDER BY lastdate end_date = pd.datetime.today() #we use tranche_factor - if attach: + if not np.isnan(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') @@ -112,27 +118,45 @@ FROM index_desc WHERE index=%s AND series=%s AND tenor=%s ORDER BY lastdate df = (quotes. join(factors[['factor']], how='left'). join(recovery).join(yearfrac)) - if attach: + if not np.isnan(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.factor = df.factor.bfill() + df.loc[df.factor.isnull(), 'factor'] = factors.factor.iat[-1] + df['clean_nav'] = df.upfront * df.factor/100 + 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>0), 0) df['unrealized_accrued'] = df.unrealized_accrued.where(df.unrealized_accrued.isnull()| - (df.unrealized_accrued>0), df.accrued) + (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 + 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 convert_to_none(x): + return None if np.isnan(x) else x -def cds_explain2(dealid): - pass +def cds_explain_strat(strat, start_date, end_date, engine = dbengine("dawndb")): + 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 == strat) & + (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) + trade_df = cds_explain(r.index, r.series, r.tenor, r.attach, r.detach, + max(r.trade_date, pd.Timestamp(start_date)), end_date, + engine) + trade_df.loc[r.upfront_settle_date, 'clean_nav'] + + trade_df.loc[r.upfront_settle_date, 'realized_pnl'] trade_df.loc[r.upfront_settle_date, 'realized_pnl'] + df[key] = df.get(key, 0) + r.notional * trade_df + return pd.concat(df) if __name__=="__main__": workdate = pd.datetime.today() @@ -141,4 +165,5 @@ if __name__=="__main__": 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') + cds_df = cds_explain_strat('SER_IGCURVE', '2015-09-01', '2015-12-08', engine) + #cds_df = cds_explain('HY', 21, '5yr', 25, 35, '2014-07-18') |