diff options
Diffstat (limited to 'python/analytics/curve_trades.py')
| -rw-r--r-- | python/analytics/curve_trades.py | 22 |
1 files changed, 6 insertions, 16 deletions
diff --git a/python/analytics/curve_trades.py b/python/analytics/curve_trades.py index b6c17436..98361cad 100644 --- a/python/analytics/curve_trades.py +++ b/python/analytics/curve_trades.py @@ -306,12 +306,7 @@ def pos_pnl_abs(portf, value_date, index='IG', rolling=6, years=3): lookup_table = pd.read_sql_query(sql_string, serenitasdb, parse_dates=['maturity'], params=[index, series]) lookup_table['year_frac'] = (lookup_table.maturity - pd.to_datetime(value_date)).dt.days/365 - indices = [] - for i, ind in enumerate(portf.indices): - indices.append(deepcopy(ind)) - portf_copy = Portfolio(indices) - portf_copy.reset_pv() - + portf_copy = deepcopy(portf) portf_copy.reset_pv() r = [] @@ -328,19 +323,14 @@ def curve_scen_table(portf): ''' Runs PNL scenario on portf by shocking different points on the curve - off-the-runs shocks are duration linearly interpolated''' - value_date = portf.value_date - - indices = [] - for i, ind in enumerate(portf.indices): - indices.append(deepcopy(ind)) - portf_copy = Portfolio(indices) + portf_copy = deepcopy(portf) portf_copy.reset_pv() index = portf_copy.indices[0].index_type series = on_the_run(index) sql_string = "SELECT tenor, maturity FROM index_maturity where index = %s and series = %s" lookup_table = pd.read_sql_query(sql_string, serenitasdb, parse_dates=['maturity'], params=[index, series]) - lookup_table['year_frac'] = (lookup_table.maturity - pd.to_datetime(value_date)).dt.days/365 + lookup_table['year_frac'] = (lookup_table.maturity - pd.to_datetime(portf_copy.value_date)).dt.days/365 lookup_table = lookup_table.iloc[[0,1,3]] year_frac = lookup_table.year_frac.tolist() @@ -348,17 +338,17 @@ def curve_scen_table(portf): shock = 10 #shocks in bps r = [] - tenor_shock = pd.DataFrame(0, index=lookup_table['year_frac'], columns=['shock']) for t_frac, t_ten in zip(year_frac, tenors): + tenor_shock = pd.DataFrame(0, index=lookup_table['year_frac'], columns=['shock']) tenor_shock.loc[t_frac] = -shock for w_frac, w_ten in zip(year_frac, tenors): tenor_shock.loc[w_frac] = 0 if t_ten == w_ten else shock #f is the shock amount interpolated based on tenor f = interp1d(np.hstack([0, year_frac]), np.hstack([tenor_shock.shock.iloc[0], tenor_shock.shock])) for i, ind in enumerate(portf_copy.indices): - ind.spread = max(0, portf.indices[i].spread + f((ind.end_date - value_date).days/365)) + ind.spread = max(0, portf.indices[i].spread + f((ind.end_date - portf_copy.value_date).days/365)) r.append([t_ten, w_ten] + [portf_copy.pv - portf.pv]) - tenor_shock.loc[w_frac] = 0 + tenor_shock.loc[w_frac] = -shock if t_ten == w_ten else 0 tenor_shock.loc[t_frac] = 0 return pd.DataFrame.from_records(r, columns=['tighter', 'wider', 'pnl']) |