diff options
Diffstat (limited to 'python/globeop_reports.py')
| -rw-r--r-- | python/globeop_reports.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/python/globeop_reports.py b/python/globeop_reports.py index e44641b9..bb2c01db 100644 --- a/python/globeop_reports.py +++ b/python/globeop_reports.py @@ -16,7 +16,7 @@ def get_monthly_pnl(group_by = ['identifier']): index_col=['date']) df_pnl['identifier'] = df_pnl.invid.str.replace("_A$", "") pnl_cols = ['bookunrealmtm', 'bookrealmtm', 'bookrealincome', 'bookunrealincome', 'totalbookpl'] - monthend_pnl = df_pnl.groupby(pd.TimeGrouper('M')).apply(lambda df: df.loc[df.index[-1]]) + monthend_pnl = df_pnl.groupby(pd.Grouper(freq='M')).apply(lambda df: df.loc[df.index[-1]]) return monthend_pnl.groupby(['date'] + group_by)[['mtd' + col for col in pnl_cols]].sum() def get_portfolio(report_date = False): @@ -41,8 +41,8 @@ def curr_port_PNL(date = datetime.date.today(), asset_class='Subprime'): def trade_performance(): sql_string = "SELECT * FROM bonds" - df_trades = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['lastupdate', 'trade_date', 'settle_date']) - df_trades = df_trades[df_trades.asset_class == 'Subprime'] + df_trades = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates={'lastupdate': {'utc': True}, 'trade_date': {}, 'settle_date': {}}) + df_trades = df_trades[df_trades['asset_class'] == 'Subprime'] df_pnl = get_monthly_pnl() df_sell = df_trades[df_trades.buysell == False].groupby('identifier').last().reset_index() @@ -167,14 +167,14 @@ def num_bond_by_strat(): df = get_portfolio() df = df[(df.custacctname == 'V0NSCLMAMB') & ~(df.invid.isin(['USD', 'CAD', 'EUR'])) & (df.endqty > 0)] - df = df.groupby(pd.TimeGrouper('M')).apply(lambda df: df.loc[df.index[-1]]) + df = df.groupby(pd.Grouper(freq='M')).apply(lambda df: df.loc[df.index[-1]]) return df.groupby(['periodenddate', 'port']).identifier.nunique().unstack() def num_bond_trades(): sql_string = "SELECT * FROM bonds" df = pd.read_sql_query(sql_string, dbengine('dawndb'), parse_dates=['trade_date'], index_col=['trade_date']) - df = df.groupby([pd.TimeGrouper('M'), 'buysell']).identifier.count().unstack() + df = df.groupby([pd.Grouper(freq='M'), 'buysell']).identifier.count().unstack() idx = pd.date_range(df.index[0], df.index[-1], freq = 'M') return df.reindex(idx, fill_value = 0) @@ -217,12 +217,12 @@ def calc_trade_performance_stats(): temp = {} temp1 = {} for x, df1 in df.groupby('winners'): - for y, df2 in df1.groupby(pd.TimeGrouper(freq='A')): + for y, df2 in df1.groupby(pd.Grouper(freq='A')): import pdb; pdb.set_trace() y = y.date().year results.loc[y] = df2[df2.days_held.notnull()].mean()[['curr_face','initialinvestment', 'days_held']] #results.loc[] = len(df2[df2.winners == x].index)/len(df) - df[df.days_held.notnull()]['days_held'].groupby(pd.TimeGrouper(freq='A')).mean() + df[df.days_held.notnull()]['days_held'].groupby(pd.Grouper(freq='A')).mean() |