diff options
Diffstat (limited to 'python/pnl_explain_old.py')
| -rw-r--r-- | python/pnl_explain_old.py | 243 |
1 files changed, 243 insertions, 0 deletions
diff --git a/python/pnl_explain_old.py b/python/pnl_explain_old.py new file mode 100644 index 00000000..25af238e --- /dev/null +++ b/python/pnl_explain_old.py @@ -0,0 +1,243 @@ +import numpy as np +import pandas as pd + +from db import dbengine +from dates import bus_day, imm_dates, yearfrac + +def get_daycount(identifier, engine=dbengine("dawndb")): + """ retrieve daycount and paydelay for a given identifier""" + conn = engine.raw_connection() + with conn.cursor() as c: + c.execute("SELECT day_count, pay_delay FROM securities WHERE identifier=%s", + (identifier,)) + try: + a, b = c.fetchone() + except TypeError: + conn.commit() + return None, None + conn.commit() + return a, b + +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']) + for key in ['faceamount', 'principal_payment', 'accrued_payment']: + trades.loc[~trades.buysell, key] = -trades[key][~trades.buysell] + if start_date is None: + start_date = trades.trade_date.min() + + ## take care of multiple trades settling on the same date + trades = (trades. + groupby('settle_date')[['faceamount', 'principal_payment', 'accrued_payment']]. + sum()) + 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']) + factors = factors.set_index('prev_cpn_date', drop=False) + daycount, delay = get_daycount(identifier, engine) + + df = (marks[['price']]. + join([factors[['prev_cpn_date', 'coupon', 'factor']], + trades[['principal_payment', 'accrued_payment', 'faceamount']]], + how='outer')) + factors = factors.set_index('last_pay_date') + df = df.join(factors[['principal', 'losses', 'interest']], how='outer') + df.sort_index(inplace=True) + + 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') + + ## overwrite the factor to 1 in case of zero factor bond + df['orig_factor'] = df['factor'] + if identifier.endswith('_A'): + df.loc[df.price.notnull() & (df.factor==0), 'factor'] = 1 + + keys2 = ['losses', 'principal','interest', 'faceamount','accrued_payment', 'principal_payment'] + df[keys2] = df[keys2].fillna(value=0) + df.faceamount = df.faceamount.cumsum() + keys = keys1 + ['faceamount', 'orig_factor'] + df1 = df.reindex(df.index.union(dates), keys, method='ffill') + keys = ['losses', 'principal','interest', 'accrued_payment', 'principal_payment'] + df2 = df.reindex(df.index.union(dates), keys, fill_value=0) + daily = pd.concat([df1, df2], axis = 1) + daily = daily[(start_date-1):end_date] + daily['unrealized_pnl'] = daily.price.diff() * daily.factor.shift()/100 * daily.faceamount + daily['clean_nav'] = daily.price/100 * daily.factor * daily.faceamount + ## realized pnl due to factor change + daily['realized_pnl'] = daily.price/100 * daily.factor.diff() * daily.faceamount.shift() + ## realized pnl due to principal payment + if delay: + daily['realized_pnl'] = (daily['realized_pnl']. + add(daily.principal/100 * daily.faceamount.shift(delay, 'D'), + fill_value=0)) + else: + daily['realized_pnl'] = (daily['realized_pnl']. + add(daily.principal/100 * daily.faceamount.shift(), + fill_value=0)) + if delay: + daily['realized_accrued'] = daily.interest/100 * daily.faceamount.shift(delay, 'D') + else: + daily['realized_accrued'] = daily.interest/100 * daily.faceamount.shift() + daily['realized_accrued'] = daily['realized_accrued'].fillna(value=0) + daily['accrued'] = yearfrac(daily.prev_cpn_date, daily.index.to_series(), daycount) * \ + daily.coupon/100 * daily.orig_factor * daily.faceamount + daily['unrealized_accrued'] = daily.accrued.diff() + daily.realized_accrued + cols = ['unrealized_pnl', 'realized_pnl', 'realized_accrued', 'clean_nav'] + daily[cols] = daily[cols].fillna(value=0) + 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 + + return daily[['clean_nav', 'accrued', 'unrealized_pnl', 'realized_pnl', 'unrealized_accrued', + 'realized_accrued']] + +def pnl_explain_list(id_list, start_date = None, end_date = None, engine = dbengine("dawndb")): + return {(identifier, strategy): pnl_explain(identifier, start_date, end_date, engine) + for identifier, strategy 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__": + engine = dbengine("dawndb") + from position import get_list_range + ## CLO + # clo_list = get_list_range(engine, '2015-01-01', '2015-12-31', 'CLO') + # df = pnl_explain_list(clo_list.identifier.tolist(), '2015-01-01', '2015-12-31', engine) + # df = pd.concat(df) + # df_agg = df.groupby(level=1).sum() + ## subprime + subprime_list = get_list_range(engine, '2015-09-30', '2015-10-31', 'Subprime') + df_subprime = pnl_explain_list(subprime_list[['identifier', 'strategy']].to_records(index=False), + '2015-09-30', '2015-10-31', engine) + df_subprime = pd.concat(df_subprime, names=['identifier', 'strategy', 'date']) + # monthly_pnl = (df_subprime.reset_index('strategy', drop=True). + # reset_index('identifier'). + # groupby('identifier'). + # resample('M', how='sum')) + # ## daily pnl by strategy + #df_agg = df_subprime.groupby(level=['date', 'strategy']).sum() + # ## monthly pnl by strategy + # df_monthly = df_agg.reset_index('strategy').groupby('strategy').resample('M', 'sum') + # df_monthly = df_monthly.swaplevel('strategy', 'date').sort_index() + # monthly_pnl = df_monthly.groupby(level='date')[['unrealized_accrued', 'unrealized_pnl', 'realized_pnl']].sum().sum(axis=1) + # # df_agg[['realized_accrued','unrealized_accrued', + # # 'realized_pnl', 'unrealized_pnl']].sum(axis=1).cumsum().plot(x_compat=True) + # # cds_df = cds_explain_strat(['SER_IGINX', 'SER_IGMEZ'], None, None, 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') |