{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import datetime\n", "from pandas.tseries.offsets import BDay, MonthEnd\n", "import globeop_reports as go\n", "import pandas as pd\n", "import analytics\n", "import numpy as np\n", "\n", "from analytics.index_data import get_index_quotes\n", "from analytics.scenarios import run_portfolio_scenarios\n", "from analytics import BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio,DualCorrTranche\n", "\n", "from utils.db import dbconn, dbengine\n", "\n", "from risk.tranches import get_tranche_portfolio\n", "from risk.bonds import subprime_risk, clo_risk, crt_risk\n", "\n", "dawn_engine = dbengine('dawndb')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#PNL Allocation\n", "date = datetime.date.today() - BDay(1)\n", "report_date = date - MonthEnd(1)\n", "report_date" ] }, { "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", "m_pnl = go.get_monthly_pnl(['strat', 'custacctname'])\n", "m_pnl = m_pnl.reset_index().merge(strats, on=['strat', 'custacctname'], how='left')\n", "undefined = m_pnl[m_pnl.pnl.isna()].groupby(['strat', 'custacctname']).last()\n", "#Get PNL Allocation\n", "#Input latest NAVS to: '/home/serenitas/edwin/Python/subscription_fee_data.csv'\n", "pnl_alloc = m_pnl.groupby(['date', 'pnl']).sum()\n", "pnl_alloc = pnl_alloc.join(nav.begbooknav)\n", "pnl_alloc['strat_return'] = pnl_alloc.mtdtotalbookpl / pnl_alloc.begbooknav\n", "#rolling 12 months PNL per strategy - copy to RiskMonitor\n", "start_date = report_date - pd.tseries.offsets.MonthEnd(11)\n", "rolling_return = pnl_alloc[start_date:report_date].groupby('pnl').sum()['strat_return']\n", "rolling_return.to_clipboard()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Average Portfolio Sales Turnover - as of last monthend from today\n", "#(total Bond Sales Proceeds + paydown)/average starting 12 months NAV\n", "nav = go.get_net_navs()\n", "sql_string = \"SELECT * FROM bonds WHERE buysell IS False\"\n", "df = pd.read_sql_query(sql_string, dawn_engine,\n", " parse_dates={'lastupdate':{'utc':True}, 'trade_date': {}, 'settle_date':{}},\n", " index_col = 'trade_date')\n", "df = df.groupby(pd.Grouper(freq='M')).sum()\n", "#Now get portfolio paydown per month\n", "portfolio = go.get_portfolio()\n", "portfolio = portfolio[(portfolio.custacctname == 'V0NSCLMAMB') &\n", " (portfolio.port == 'MORTGAGES') &\n", " (portfolio.identifier != 'USD') &\n", " (portfolio.endqty != 0)]\n", "portfolio = portfolio.set_index('identifier', append=True)\n", "portfolio = portfolio['endqty'].groupby(['identifier', 'periodenddate']).sum()\n", "portfolio = portfolio.reset_index('identifier')\n", "cf = pd.read_sql_query(\"SELECT * FROM cashflow_history\", dawn_engine,\n", " parse_dates=['date'],\n", " index_col=['date']).sort_index()\n", "df_1 = pd.merge_asof(cf, portfolio.sort_index(), left_index=True, right_index=True, by='identifier')\n", "df_1 = df_1.dropna(subset=['endqty'])\n", "df_1 = df_1[(df_1.principal_bal != 0) & (df_1.principal != 0)]\n", "df_1['paydown'] = df_1.apply(lambda df: df.endqty/df.principal_bal * df.principal, axis=1)\n", "paydowns = df_1.paydown.groupby(pd.Grouper(freq='M')).sum()\n", "temp = pd.concat([paydowns, df.principal_payment, df.accrued_payment], axis=1).fillna(0)\n", "turnover = temp.rolling(12).sum().sum(axis=1)/ nav.begbooknav.rolling(12).mean()\n", "turnover[12:].plot()\n", "turnover[-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "position_date = (datetime.date.today() - BDay(1)).date()\n", "shock_date = (datetime.date.today() - BDay(1)).date()\n", "spread_date = shock_date\n", "(position_date, spread_date, shock_date)\n", "analytics.init_ontr(spread_date)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Calculate amount of stress for reports\n", "from analytics.curve_trades import on_the_run\n", "df = get_index_quotes('HY', list(range(on_the_run('HY', spread_date) - 10, on_the_run('HY', spread_date) + 1)),\n", " tenor=['5yr'], years=5)\n", "df = df.xs('5yr', level='tenor')['close_spread'].groupby(['date', 'series']).last()\n", "\n", "widen, tighten = [], []\n", "#approximately 1,3,6 months move (22 each months)\n", "for days in [22, 66, 132]: \n", " calc = df.unstack().pct_change(freq= str(days)+'B').stack().groupby('date').last()\n", " widen.append(calc.max())\n", " tighten.append(calc.min())\n", "pd.DataFrame([widen, tighten], columns=['1M', '3M', '6M'], index=['widen', 'tighten'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#tranche positions\n", "conn = dawn_engine.raw_connection()\n", "portf = get_tranche_portfolio(position_date, conn, False, 'SERCGMAST')\n", "\n", "#swaption positions\n", "swaption_sql_string = (\"select id, folder, expiration_date from swaptions where expiration_date > %s \"\n", " \"AND swap_type = 'CD_INDEX_OPTION' \"\n", " \"AND trade_date <= %s AND termination_date IS NULL\")\n", "\n", "with conn.cursor() as c:\n", " c.execute(swaption_sql_string, (position_date, position_date))\n", " for trade_id, strat, expiration_date in c:\n", " if expiration_date > shock_date:\n", " portf.add_trade(BlackSwaption.from_tradeid(trade_id), (strat, trade_id))\n", "conn.close()\n", "\n", "#index positions\n", "df = pd.read_sql_query(\"SELECT * from list_cds_positions_by_strat(%s)\",\n", " dawn_engine, params=(position_date,))\n", "df_curve = df[df.folder.str.contains(\"CURVE\")]\n", "df_no_curve = df[~df.folder.str.contains(\"CURVE\")]\n", "for t in df_no_curve.itertuples(index=False):\n", " portf.add_trade(CreditIndex(redcode=t.security_id, maturity=t.maturity, notional=t.notional),\n", " (t.folder, t.security_desc))\n", " \n", "#separately add in curve delta\n", "curve_portf = Portfolio([CreditIndex(redcode=t.security_id, maturity=t.maturity, notional=t.notional)\n", " for t in df_curve.itertuples(index=False)])\n", "curve_portf.value_date = spread_date\n", "curve_portf.mark()\n", "portf.add_trade(CreditIndex('HY', on_the_run('HY', spread_date), '5yr', \n", " value_date=spread_date, \n", " notional=curve_portf.hy_equiv), ('curve_trades', ''))\n", "\n", "mysql_engine = dbengine('rmbs_model')\n", "mysqlcrt_engine = dbengine('crt')\n", "\n", "#get bond risks:\n", "with dbconn('etdb') as etconn, dbconn('dawndb') as dawnconn:\n", " rmbs_pos = subprime_risk(position_date, dawnconn, mysql_engine)\n", " clo_pos = clo_risk(position_date, dawnconn, etconn)\n", " crt_pos = crt_risk(position_date, dawnconn, mysqlcrt_engine)\n", "duration = analytics._ontr.risky_annuity\n", "rmbs_pos['hy_equiv'] = rmbs_pos['delta_yield']/duration * 100\n", "crt_pos['hy_equiv'] = crt_pos['delta_yield']/duration * 100\n", "notional = rmbs_pos['hy_equiv'].sum() + clo_pos['hy_equiv'].sum() + crt_pos['hy_equiv'].sum()\n", "portf.add_trade(CreditIndex('HY', on_the_run('HY', spread_date), '5yr', \n", " value_date = spread_date, \n", " notional = -notional), ('bonds', ''))\n", " \n", "portf.value_date = spread_date\n", "portf.mark(interp_method=\"bivariate_linear\")\n", "portf.reset_pv()\n", "\n", "vol_surface = {}\n", "for trade in portf.swaptions:\n", " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n", " value_date=spread_date, interp_method = \"bivariate_linear\")\n", " vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n", "vol_shock = [0]\n", "corr_shock = [0, -.1]\n", "spread_shock = tighten + [0] + widen\n", "date_range = [pd.Timestamp(shock_date)]\n", "\n", "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n", " spread_shock=spread_shock,\n", " vol_shock=vol_shock,\n", " corr_shock=corr_shock,\n", " vol_surface=vol_surface)\n", "\n", "attrib = (scens.\n", " reset_index(level=['date'], drop=True).\n", " groupby(level=0, axis=1).sum())\n", "attrib.columns.name = 'strategy'\n", "results = attrib.xs((widen[2], 0.), level=['spread_shock', 'corr_shock']).unstack('strategy')\n", "results.name = 'pnl'\n", "#results.to_clipboard(header=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Run set of scenario\n", "spread_shock = np.round(np.arange(-.2, 1, .025), 3)\n", "scens = run_portfolio_scenarios(portf, date_range, params=['pnl', 'delta'],\n", " spread_shock=spread_shock,\n", " vol_shock=vol_shock,\n", " corr_shock=[0],\n", " vol_surface=vol_surface)\n", "\n", "pnl = scens.xs('pnl', axis=1, level=2)\n", "pnl = pnl.xs((0,0), level=['vol_shock', 'corr_shock'])\n", "\n", "scenarios = (pnl.\n", " reset_index(level=['date'], drop=True).\n", " groupby(level=0, axis=1).sum())\n", "\n", "options = ['HYOPTDEL', 'HYPAYER', 'HYREC', 'IGOPTDEL', 'IGPAYER', 'IGREC']\n", "tranches = ['HYMEZ', 'HYINX', 'HYEQY', 'IGMEZ', 'IGINX', 'IGEQY', 'IGSNR', 'IGINX', 'BSPK']\n", "\n", "scenarios['options'] = scenarios[set(scenarios.columns).intersection(options)].sum(axis=1)\n", "scenarios['tranches'] = scenarios[set(scenarios.columns).intersection(tranches)].sum(axis=1)\n", "\n", "synthetic = scenarios[['options', 'tranches', 'curve_trades']]\n", "synthetic['total'] = synthetic.sum(axis = 1)\n", "synthetic.plot()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }