diff options
| -rw-r--r-- | python/exploration/curve_trades.py | 24 |
1 files changed, 10 insertions, 14 deletions
diff --git a/python/exploration/curve_trades.py b/python/exploration/curve_trades.py index 3c0a25ad..d17b35ed 100644 --- a/python/exploration/curve_trades.py +++ b/python/exploration/curve_trades.py @@ -245,20 +245,18 @@ def spot_forward(index='IG', series=None, tenors=['3yr', '5yr', '7yr', '10yr']): def curve_pos(trade_date, index='IG'): ''' - Input trade_date and index + trade_date : :class:`datetime.date` + index : string + one of 'IG', 'HY' or 'ITRX' + Returns a Portfolio of curve trades ''' sql_string = "SELECT * FROM cds where trade_date < %s" df = pd.read_sql_query(sql_string, dawndb, parse_dates=['trade_date', 'maturity'], params=[trade_date]) - if index is 'IG': - df = df[df['folder'] == 'SER_IGCURVE'] - elif index is 'HY': - df = df[df['folder'] == 'SER_HYCURVE'] - else: - df = df[df['folder'] == 'SER_ITRXCURVE'] - df.notional = df.apply(lambda x: x.notional * -1 if x.protection == 'Buyer' else x.notional, axis = 1) - df = df.groupby(['security_id', 'maturity']).sum()['notional'] + df = df[df['folder'] == f'SER_{index}CURVE'] + df.notional = df.notional.where(df.protection == 'Seller', -df.notional) + df = df.groupby(['security_id', 'maturity'])['notional'].sum() df = df.iloc[df.nonzero()[0]].reset_index() sql_string = "SELECT * FROM index_maturity LEFT JOIN index_version USING (index, series)" @@ -270,18 +268,18 @@ def curve_pos(trade_date, index='IG'): sql_string = "SELECT closespread FROM index_quotes where index = %s and series = %s and tenor = %s and date = %s" for i, row in df[['index', 'tenor', 'series', 'notional']].iterrows(): temp = Index.from_name(row['index'], row.series, row.tenor) - temp.value_date = trade_date.date() + temp.value_date = trade_date if row.notional > 0: temp.direction = 'Seller' temp.notional = abs(row.notional) spread_df = pd.read_sql_query(sql_string, serenitasdb, - params=[row['index'], row.series, row.tenor, trade_date.date()]) + params=[row['index'], row.series, row.tenor, trade_date]) temp.spread = spread_df.iloc[0][0] indices.append(temp) return Portfolio(indices) -def curve_shape(trade_date, index = 'IG', percentile=.95): +def curve_shape(trade_date, index='IG', percentile=.95): ''' Returns a function to linearly interpolate between the curve based on maturity (in years)''' @@ -302,5 +300,3 @@ def curve_shape(trade_date, index = 'IG', percentile=.95): df = df.reset_index().merge(lookup_table, on=['tenor']) df['year_frac'] = (df.maturity - pd.to_datetime(trade_date)).dt.days/365 return interp1d(np.hstack([0, df.year_frac]), np.hstack([0, df.spread])) - - |