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Diffstat (limited to 'python/notebooks/Reto Report.ipynb')
| -rw-r--r-- | python/notebooks/Reto Report.ipynb | 183 |
1 files changed, 27 insertions, 156 deletions
diff --git a/python/notebooks/Reto Report.ipynb b/python/notebooks/Reto Report.ipynb index d0c16dea..5a95d1e0 100644 --- a/python/notebooks/Reto Report.ipynb +++ b/python/notebooks/Reto Report.ipynb @@ -169,20 +169,6 @@ "metadata": {}, "outputs": [], "source": [ - "from serenitas.utils.db2 import dbconn\n", - "import datetime\n", - "conn = dbconn('dawndb')\n", - "from risk.tranches import get_tranche_portfolio\n", - "tranches = get_tranche_portfolio(datetime.date(2023,2,17), conn, False)\n", - "jtd = tranches.jtd_single_names()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ "################################### Find the strategies that are not defined: undefined needs to be mapped in strat_map\n", "strats = pd.read_csv('/home/serenitas/edwin/Python/strat_map.csv')\n", "nav = go.get_net_navs()\n", @@ -207,15 +193,6 @@ "metadata": {}, "outputs": [], "source": [ - "undefined" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ "################################### Average Portfolio Sales Turnover\n", "#Rolling min(month from inception, 12 months) sum of (total bond sales proceeds + paydown)/monthly NAV\n", "warnings.filterwarnings('ignore')\n", @@ -256,54 +233,19 @@ " fund=fund,\n", " conn=dbconn(\"dawndb\"),\n", " asset_class =ac)\n", - "df_inst = pd.concat(df_inst)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "################################### Number of position (min/max/average) /position size (min/max/average) /Top 10 position size\n", - "portfolio = go.get_portfolio()\n", + "df_inst = pd.concat(df_inst)\n", "nav = go.get_net_navs()\n", - "exc_port_list = [None, 'SERCGLLC__SERCGLLC', 'CASH', 'SERCGLTD__SERCGLTD', 'GFS_HELPER_BUSINESS_UNIT', 'SER_TEST__SER_TEST']\n", - "exc_inst_list = ['CAD', 'CADF', 'SEREONUS', 'USD', 'USDF', 'USDLOAN', 'EUR', 'EURLOAN', 'USDCASHINT',\n", - " 'USDLOANOLD', 'USDSWAPFEE', 'EURF','CADCASHINT','COMMISSIONFEES', 'EURCASHINT', 'COMMUNICATIONFEES']\n", - "exc_inst_list2 = ['86359DUR6OLD2','004375DV0OLD4','32027GAD8OLD7','75406DAC7OLD7','86359DMN4OLD7','45661EAW4OLD7']\n", - "\n", - "portfolio = portfolio[~portfolio.port.isin(exc_port_list) &\n", - " ~portfolio.identifier.isin(exc_inst_list) &\n", - " ~portfolio.identifier.isin(exc_inst_list2)]\n", - "\n", - "all_positions = portfolio.groupby(['periodenddate', 'identifier'])['endbooknav'].sum() \n", - "num_pos = all_positions.groupby('periodenddate').count()\n", - "#min/max/mean number of positions\n", - "num_pos.min(), num_pos.max(), num_pos.mean()\n", - "\n", - "bonds = portfolio[(portfolio.custacctname == 'V0NSCLMAMB') &\n", - " (portfolio.identifier != 'USD') &\n", - " (portfolio.endqty != 0) &\n", - " (portfolio.port.isin(['MORTGAGES', 'STRUCTURED', 'CLO'])) &\n", - " (~portfolio.strat.isin(['MBSCDS']))]\n", - "\n", - "monthend_bonds = bonds.groupby(pd.Grouper(freq=\"M\"), group_keys=False).apply(\n", - " lambda df: df.loc[df.index[-1]]\n", - " )\n", - "monthend_bonds = monthend_bonds.groupby(['periodenddate', 'identifier']).sum()\n", - "nav.index.rename('periodenddate', inplace=True)\n", - "monthend_bonds = monthend_bonds.merge(nav, left_index=True, right_index=True, suffixes=('_bond', '_fund'))\n", - "monthend_bonds['percentage'] = monthend_bonds.endbooknav_bond/monthend_bonds.endbooknav_fund\n", + "monthend_bonds = df_inst.merge(nav['endbooknav'], left_on = 'date', right_on= 'date')\n", + "monthend_bonds['percentage'] = monthend_bonds.usd_market_value/monthend_bonds.endbooknav\n", "last_date = monthend_bonds.index.get_level_values(0).max() \n", "latest = monthend_bonds.loc[last_date]\n", "#min/max/mean position size\n", "latest['percentage'][latest['percentage']>0.0000001].min(), latest['percentage'].max(), latest['percentage'].mean()\n", "#10 largest positions\n", - "ten_largest = monthend_bonds.groupby('periodenddate').apply(lambda df: df['percentage'].nlargest(10).sum())\n", + "ten_largest = monthend_bonds.groupby('date').apply(lambda df: df['percentage'].nlargest(10).sum())\n", "print(\"ten largest position - min/max/mean\", ten_largest.min(), ten_largest.max(), ten_largest.mean())\n", "#5 largest positions in the last 5 years\n", - "five_largest = monthend_bonds.groupby('periodenddate').apply(lambda df: df['percentage'].nlargest(5).sum())\n", + "five_largest = monthend_bonds.groupby('date').apply(lambda df: df['percentage'].nlargest(5).sum())\n", "print(\"five largest position - min/max/mean\",\n", " five_largest[datetime.date.today() - pd.DateOffset(years=5):].min(),\n", " five_largest[datetime.date.today() - pd.DateOffset(years=5):].max(),\n", @@ -316,23 +258,6 @@ "metadata": {}, "outputs": [], "source": [ - "rmbs_pos = subprime_risk(position_date, conn, dbengine(\"rmbs_model\"), fund=fund)\n", - "crt_pos = crt_risk(position_date, conn, fund=fund)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ "################################### Average Traded Volume\n", "nav = go.get_net_navs()\n", "sql_string = \"SELECT * FROM bond_trades where fund='SERCGMAST'\"\n", @@ -400,23 +325,32 @@ "outputs": [], "source": [ "################################## Calculate Historical Bond Duration/Yield\n", + "from serenitas.utils.db import dbengine\n", "fund = 'SERCGMAST'\n", - "CBM = CustomBusinessMonthEnd(calendar=np.busdaycalendar())\n", + "bdd = np.busdaycalendar(holidays=['2021-05-31', \n", + " '2021-05-28',\n", + " '2021-05-27',\n", + " '2021-05-26',\n", + " '2020-12-31',\n", + " '2020-12-30'])\n", + "CBM = CustomBusinessMonthEnd(calendar=bdd)\n", "dates = pd.bdate_range(\"2015-1-31\", datetime.date.today() - MonthEnd(1), \n", " freq=CBM)\n", "bond_stats=pd.DataFrame()\n", - "with dawn_pool.connection() as conn, dbconn(\"etdb\") as et_conn:\n", - " for d in dates:\n", - " sub = subprime_risk(d.date(), dawnconn , dbengine(\"rmbs_model\"), fund=fund)\n", - " sub=sub[sub.pv1>0]\n", - " crt = crt_risk(d.date(), dawnconn , dbengine(\"crt\"), fund=fund)\n", - " clo = clo_risk(d.date(), dawnconn , dbconn(\"etdb\"), fund=fund)\n", - " bonds = pd.concat([sub,crt,clo]).dropna(subset=['modDur', 'usd_market_value'])\n", - " bond_stats.at[d, 'dur']= sum(bonds.notional * bonds.factor * bonds.modDur)/sum(bonds.notional * bonds.factor)\n", - " bond_stats.at[d, 'yield'] = (sum(bonds.usd_market_value * bonds.modDur * bonds.bond_yield) /\n", - " sum(bonds.usd_market_value * bonds.modDur))\n", - " bond_stats.at[d, 'swap_rate'] = (sum(bonds.usd_market_value * bonds.modDur * bonds.swap_rate) /\n", - " sum(bonds.usd_market_value * bonds.modDur))\n", + "for d in dates:\n", + " sub = subprime_risk(d.date(), dbconn('dawndb') , dbengine(\"rmbs_model\"), fund=fund)\n", + " sub=sub[sub.pv1>0]\n", + " crt = crt_risk(d.date(), dbconn('dawndb') , fund=fund)\n", + " clo = clo_risk(d.date(), dbconn('dawndb') , dbconn(\"etdb\"), fund=fund)\n", + " crt.rename(columns={'duration': 'modDur'}, inplace=True)\n", + " bonds = pd.concat([sub,crt,clo]).dropna(subset=['modDur', 'usd_market_value'])\n", + " bonds['gross_carry'] = bonds.bond_yield * bonds.usd_market_value\n", + " bond_stats.at[d, 'gross_yield'] = sum(bonds.gross_carry)/sum(bonds.usd_market_value)\n", + " bond_stats.at[d, 'dur'] = sum(bonds.notional * bonds.factor * bonds.modDur)/sum(bonds.notional * bonds.factor)\n", + " bond_stats.at[d, 'yield'] = (sum(bonds.usd_market_value * bonds.modDur * bonds.bond_yield) /\n", + " sum(bonds.usd_market_value * bonds.modDur))\n", + " bond_stats.at[d, 'swap_rate'] = (sum(bonds.usd_market_value * bonds.modDur * bonds.swap_rate) /\n", + " sum(bonds.usd_market_value * bonds.modDur))\n", "bond_stats['dm'] = bond_stats['yield'] - bond_stats['swap_rate']" ] }, @@ -467,15 +401,6 @@ "metadata": {}, "outputs": [], "source": [ - "bond_stats.to_clipboard()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ "################################## FX Exposure, any net CAD/EUR exposures are FX exposure. \n", "################### doesn't add up to 1 including the USD as we now sum up all the NAVs after adjusting the Futures\n", "nav = go.get_net_navs()\n", @@ -616,60 +541,6 @@ "income.T.groupby(pd.Grouper(freq='A')).sum().T.sort_index().to_clipboard()\n", "appreciation.T.groupby(pd.Grouper(freq='A')).sum().T.sort_index().to_clipboard()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#####our jump test\n", - "from serenitas.analytics import DualCorrTranche\n", - "from serenitas.analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface\n", - "trade = DualCorrTranche('HY', 29, '5yr', attach=0, detach=15, corr_attach=np.nan, \n", - " corr_detach=.35, tranche_running=100, notional=-10000000, use_trunc=True)\n", - "portf = Portfolio([trade, ], ['trade', ])\n", - "portf.mark()\n", - "jtd = portf.jtd_single_names()\n", - "conn = serenitas_pool.getconn()\n", - "surv_curves = load_all_curves(conn, spread_date)\n", - "serenitas_pool.putconn(conn)\n", - "surv_curves['spread'] = surv_curves['curve'].apply(lambda sc: sc.to_series(forward=False)[5] * (1-sc.recovery_rates[5]))\n", - "jtd_sabo = jtd[[jtd.columns[0]]].join(surv_curves.groupby(level=0).first()[['name', 'company_id', 'spread']])\n", - "jtd_sabo.columns = ['jtd', 'name', 'company_id', 'spread']\n", - "jtd_sabo = jtd_sabo.groupby(['company_id', 'name']).sum()\n", - "jtd_sabo = jtd_sabo.sort_values('spread', ascending=False)\n", - "top_5_avg_loss = jtd_sabo.nlargest(5, columns='jtd')['jtd'].mean()\n", - "widest_5_total_loss = jtd_sabo.nlargest(5, columns='spread')['jtd'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { |