diff options
Diffstat (limited to 'python/pnl_explain.py')
| -rw-r--r-- | python/pnl_explain.py | 17 |
1 files changed, 10 insertions, 7 deletions
diff --git a/python/pnl_explain.py b/python/pnl_explain.py index 44325893..642fb175 100644 --- a/python/pnl_explain.py +++ b/python/pnl_explain.py @@ -13,7 +13,7 @@ def pnl_explain(identifier, start_date = None, end_date = None, """ 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']) + index_col=['trade_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, @@ -65,7 +65,7 @@ def pnl_explain_list(id_list, start_date = None, end_date = None, engine = dbeng def cds_explain(index, series, tenor, attach = np.nan, detach = np.nan, start_date = None, end_date = None, engine = dbengine('serenitasdb')): if np.isnan(attach) or np.isnan(detach): - quotes = pd.read_sql_query("SELECT date, (100-closeprice) AS upfront " \ + 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'], @@ -124,7 +124,7 @@ def cds_explain(index, series, tenor, attach = np.nan, detach = np.nan, coupon = factors.coupon.iat[0]/10000 df.factor = df.factor.bfill() df.loc[df.factor.isnull(), 'factor'] = factors.factor.iat[-1] - df['clean_nav'] = df.upfront * df.factor/100 + 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() | @@ -153,9 +153,12 @@ def cds_explain_strat(strat, start_date, end_date, engine = dbengine("dawndb")): 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 + trade_df = r.notional*trade_df + if pd.Timestamp(start_date) <= r.trade_date: + extra_pnl = trade_df.clean_nav.iat[0]+trade_df.accrued.iat[2] + r.upfront + trade_df.unrealized_pnl.iat[2] = extra_pnl + trade_df = trade_df.iloc[2:] + df[key] = df.get(key, 0) + trade_df return pd.concat(df) if __name__=="__main__": @@ -165,5 +168,5 @@ 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_strat('SER_IGCURVE', '2015-09-01', '2015-12-08', engine) + cds_df = cds_explain_strat('SER_IGMEZ', '2014-09-18', '2015-12-08', engine) #cds_df = cds_explain('HY', 21, '5yr', 25, 35, '2014-07-18') |