diff options
Diffstat (limited to 'python/notebooks')
| -rw-r--r-- | python/notebooks/Allocation Reports.ipynb | 71 | ||||
| -rw-r--r-- | python/notebooks/Curve Trades.ipynb | 42 | ||||
| -rw-r--r-- | python/notebooks/Curve cap.ipynb | 24 | ||||
| -rw-r--r-- | python/notebooks/Option Trades.ipynb | 55 | ||||
| -rw-r--r-- | python/notebooks/Reto Report.ipynb | 216 | ||||
| -rw-r--r-- | python/notebooks/Single Names Monitoring.ipynb | 32 | ||||
| -rw-r--r-- | python/notebooks/VaR.ipynb | 71 | ||||
| -rw-r--r-- | python/notebooks/tranche and swaption portfolio strategy.ipynb | 287 | ||||
| -rw-r--r-- | python/notebooks/tranches numbers.ipynb | 72 |
9 files changed, 723 insertions, 147 deletions
diff --git a/python/notebooks/Allocation Reports.ipynb b/python/notebooks/Allocation Reports.ipynb index 1a30f348..b20f77d0 100644 --- a/python/notebooks/Allocation Reports.ipynb +++ b/python/notebooks/Allocation Reports.ipynb @@ -14,6 +14,9 @@ "import numpy as np\n", "\n", "from db import dbengine\n", + "from yieldcurve import YC\n", + "from quantlib.termstructures.yield_term_structure import YieldTermStructure\n", + "\n", "engine = dbengine('dawndb')\n", "Sengine = dbengine('serenitasdb')" ] @@ -53,8 +56,19 @@ "#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", - "pnl_alloc_last_month = pnl_alloc.xs(report_date)" + "pnl_alloc['strat_return'] = pnl_alloc.mtdtotalbookpl / pnl_alloc.begbooknav" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#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()" ] }, { @@ -64,6 +78,7 @@ "outputs": [], "source": [ "#Plot this month's PNL\n", + "pnl_alloc_last_month = pnl_alloc.xs(report_date)\n", "ax = pnl_alloc_last_month['strat_return'].plot(kind='bar', figsize = (6,6), width = .35)\n", "ax.set_xlabel('Strategy')\n", "ax.set_ylabel('Return (%)')\n", @@ -154,6 +169,15 @@ "metadata": {}, "outputs": [], "source": [ + "turnover.to_clipboard(sep='\\t')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "#Number of bond positions by strategy by month\n", "df = go.get_portfolio()\n", "df = df[(df.custacctname == 'V0NSCLMAMB') &\n", @@ -265,6 +289,16 @@ "metadata": {}, "outputs": [], "source": [ + "a.to_clipboard()\n", + "#b.to_clipboard()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "#RMBS Risk - need RMBS Positions and Risks\n", "sql_string = \"select date, duration, series from on_the_run where index = 'HY'\"\n", "duration = pd.read_sql_query(sql_string, Sengine, parse_dates=['date'], index_col=['date'])\n", @@ -288,10 +322,11 @@ " right_index=True,\n", " by='cusip')\n", "df_with_trades['hold'] = (df_with_trades.index - df_with_trades.trade_date2).dt.days/365\n", - "holding_period = df_with_trades.groupby('timestamp').apply(lambda df: sum(df.endbooknav * df.hold)/sum(df.endbooknav))\n", + "holding_period = df_with_trades.groupby('timestamp').apply(lambda df: sum(df.endbookmv * df.hold)/sum(df.endbookmv))\n", "ax = holding_period.plot(legend=False, title='Average Holding Period')\n", "ax.set_xlabel('date')\n", - "ax.set_ylabel('Years')" + "ax.set_ylabel('Years')\n", + "holding_period[-1]" ] }, { @@ -300,7 +335,31 @@ "metadata": {}, "outputs": [], "source": [ - "engine.dispose()" + "#Calculate Interests allocation \n", + "sql_string = \"select periodenddate, strat, sum(endqty) as bal, counterparty \" \\\n", + " \"from valuation_reports where invid = 'USDLOAN' and \" \\\n", + " \"extract(month from periodenddate) = %s and \" \\\n", + " \"extract(year from periodenddate) = %s \" \\\n", + " \"group by periodenddate, strat, counterparty \" \\\n", + " \"order by periodenddate desc\"\n", + "df = pd.read_sql_query(sql_string, dbengine('dawndb'), \n", + " parse_dates=['periodenddate'],\n", + " index_col=['strat', 'counterparty'],\n", + " params=[report_date.month, report_date.year])\n", + "df['day_frac'] = -(df.groupby(level=['strat','counterparty'])['periodenddate'].transform(lambda s:\n", + " s.diff().astype('timedelta64[D]') / 360)).astype(float)\n", + "df = df.fillna(0)\n", + "r = {}\n", + "yc = YieldTermStructure()\n", + "for t in df['periodenddate'].unique():\n", + " yc.link_to(YC(evaluation_date=pd.Timestamp(t)))\n", + " r[pd.Timestamp(t)] = (float(yc.zero_rate(.083333)))\n", + "rates = pd.DataFrame.from_dict(r, orient='index')\n", + "df = df.reset_index().set_index('periodenddate', drop=False).join(rates)\n", + "df = df.rename(columns={0: 'rate'})\n", + "df = df.set_index(['strat','counterparty'], append=True)\n", + "df['interest'] = df['rate'] * df['day_frac'] * df['bal']\n", + "interests = df['interest'].groupby(level=['counterparty','strat']).sum()" ] }, { @@ -327,7 +386,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/python/notebooks/Curve Trades.ipynb b/python/notebooks/Curve Trades.ipynb index b2cde72e..05b05f89 100644 --- a/python/notebooks/Curve Trades.ipynb +++ b/python/notebooks/Curve Trades.ipynb @@ -9,7 +9,6 @@ "import analytics.curve_trades as ct\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import pandas as pd\n", "import numpy as np\n", "import graphics as g\n", "import globeop_reports as go\n", @@ -42,7 +41,7 @@ "outputs": [], "source": [ "index = w.value\n", - "report_date = (pd.datetime.today() - pd.offsets.BDay(2)).date()" + "report_date = (pd.datetime.today() - pd.offsets.BDay(5)).date()" ] }, { @@ -92,7 +91,7 @@ "metadata": {}, "outputs": [], "source": [ - "rolling = 10\n", + "rolling = 20\n", "years = 5\n", "ret = ct.curve_returns(index, rolling, years)\n", "if index == 'IG':\n", @@ -187,9 +186,9 @@ "source": [ "#Theta with 3-5-10 Strategy\n", "df = ct.ratio_within_series(param='duration')\n", - "s = - df.theta2['3yr'] / df.duration_ratio_to_5yr['3yr'] \\\n", - " + 2 * df.theta2['5yr'] \\\n", - " - df.theta2['10yr'] / df.duration_ratio_to_5yr['10yr']\n", + "s = - df.theta['3yr'] / df.duration_ratio_to_5yr['3yr'] \\\n", + " + 2 * df.theta['5yr'] \\\n", + " - df.theta['10yr'] / df.duration_ratio_to_5yr['10yr']\n", "s.dropna().unstack(-1).plot()" ] }, @@ -200,7 +199,7 @@ "outputs": [], "source": [ "#Theta with 5-10 Strategy: buy sell 5y, buy 10y\n", - "s = df.theta2['5yr'] - df.theta2['10yr'] / df.duration_ratio_to_5yr['10yr']\n", + "s = df.theta['5yr'] - df.theta['10yr'] / df.duration_ratio_to_5yr['10yr']\n", "s.dropna().unstack(-1).plot()" ] }, @@ -211,8 +210,8 @@ "outputs": [], "source": [ "#Relative Spread Difference\n", - "spread_ratio = ct.ratio_within_series(param = 'closespread')\n", - "spread_ratio.groupby(level = ['date']).last()['closespread_ratio_to_5yr'].plot()" + "spread_ratio = ct.ratio_within_series(param = 'close_spread')\n", + "spread_ratio.groupby(level = ['date']).last()['close_spread_ratio_to_5yr'].plot()" ] }, { @@ -263,9 +262,9 @@ "sql_string = \"SELECT closespread FROM index_quotes where index = %s and series = %s and tenor = %s and date = %s\"\n", "spread_df = pd.read_sql_query(sql_string, dbengine('serenitasdb'),\n", " params=[index, ct.on_the_run(index), '5yr', report_date])\n", - "spread_range = (1+ spread_shock) * spread_df.iloc[0][0]\n", - "#need to max it at the closest maturity date\n", - "date_range = pd.bdate_range(report_date, report_date + 180* pd.offsets.DateOffset(), freq='5B')\n", + "spread_range = np.round((1+ spread_shock) * spread_df.iloc[0][0], 2)\n", + "closest_mat = min([t.end_date for t in portf.trades])\n", + "date_range = pd.bdate_range(report_date, min(closest_mat, (report_date + 180* pd.offsets.DateOffset()).date()), freq='5B')\n", "curve_per = np.arange(.01, .99, .1)\n", "\n", "df = run_curve_scenarios(portf, spread_range, date_range, curve_per)" @@ -277,8 +276,21 @@ "metadata": {}, "outputs": [], "source": [ - "df_plot = df[df.curve_per == curve_per[5]]\n", - "g.plot_time_color_map(df_plot, spread_range, attr='pnl')" + "#plot steepness scenario at current spread\n", + "df_plot = df.set_index(['spread', 'curve_per'], append=True)\n", + "df_plot = df_plot.xs(round(spread_df.iloc[0][0], 2), level = 'spread')\n", + "df_plot.name = 'pnl'\n", + "g.plot_color_map(df_plot, spread_range)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Plot the shape of the scenario that was run above\n", + "ct.plot_curve_shape(report_date)" ] }, { @@ -371,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/python/notebooks/Curve cap.ipynb b/python/notebooks/Curve cap.ipynb index 4ce1f2f6..1a42524e 100644 --- a/python/notebooks/Curve cap.ipynb +++ b/python/notebooks/Curve cap.ipynb @@ -58,6 +58,28 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "with init_bbg_session(BBG_IP) as session:\n", + " hist_data_210 = retrieve_data(session, [\"USSW2 Curncy\", \"USSW10 Curncy\"], [\"PX_LAST\"], start_date=pd.datetime(1994, 1, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.concat(hist_data).unstack(level=0)\n", + "df.columns = ['2y_swap', '10y_swap']\n", + "df['diff'] = df['10y_swap'] - df['2y_swap']\n", + "df.plot(title='Historical swap rates')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] } ], @@ -77,7 +99,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/python/notebooks/Option Trades.ipynb b/python/notebooks/Option Trades.ipynb index d43d86ee..6d60669c 100644 --- a/python/notebooks/Option Trades.ipynb +++ b/python/notebooks/Option Trades.ipynb @@ -57,7 +57,7 @@ " df = df.set_index(['date', 'price', 'vol_shock'])\n", " sort_order = [True, False]\n", " else:\n", - " ss_df['spread'] = portf.indices[0].spread * (1 + df.spread_shock)\n", + " df['spread'] = portf.indices[0].spread * (1 + df.spread_shock)\n", " df = df.set_index(['date', 'spread', 'vol_shock'])\n", " sort_order = [True, True]\n", " \n", @@ -83,15 +83,56 @@ "outputs": [], "source": [ "#Ad hoc\n", + "index = 'IG'\n", + "series = 31\n", + "value_date = datetime.date(2018, 11, 18)\n", + "option_delta = CreditIndex(index, series, '5yr', value_date)\n", + "option_delta.spread = 76.5\n", + "option1 = BlackSwaption(option_delta, datetime.date(2019, 2, 20), 65, option_type=\"payer\")\n", + "option2 = BlackSwaption(option_delta, datetime.date(2019, 2, 20), 90, option_type=\"payer\")\n", + "option3 = BlackSwaption(option_delta, datetime.date(2019, 2, 20), 60, option_type=\"payer\")\n", + "option1.sigma = .425\n", + "option2.sigma = .59\n", + "option3.sigma = .0\n", + "option1.notional = 300_000_000\n", + "option2.notional = 300_000_000\n", + "option3.notional = 1\n", + "option1.direction = 'Long'\n", + "option2.direction = 'Short'\n", + "option3.direction = 'Long'\n", + "option_delta.notional = 1\n", + "option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta + option3.notional * option3.delta\n", + "option_delta.direction = 'Seller' if option_delta.notional > 0 else 'Buyer'\n", + "option_delta.notional = abs(option_delta.notional)\n", + "portf = Portfolio([option1, option2, option3, option_delta], trade_ids=['opt1', 'opt2', 'opt3', 'delta'])\n", + "#Plot Scenarios Inputs: Portfolio, spread shock tightening%, spread shock widening%, snapshot period)\n", + "portf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_trade_scenarios(portf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Ad hoc\n", "index = 'HY'\n", "series = 30\n", "value_date = datetime.date(2018, 6, 7)\n", "option_delta = CreditIndex(index, series, '5yr', value_date)\n", - "#option_delta.spread = 66\n", - "option_delta.price = 106.75\n", - "option1 = BlackSwaption(option_delta, datetime.date(2018, 8, 15), 103, option_type=\"payer\")\n", - "option2 = BlackSwaption(option_delta, datetime.date(2018, 8, 15), 101.5, option_type=\"payer\")\n", - "option3 = BlackSwaption(option_delta, datetime.date(2018, 8, 15), 100, option_type=\"payer\")\n", + "option_delta.spread = 66\n", + "option1 = BlackSwaption(option_delta, datetime.date(2018, 10, 17), 55, option_type=\"receiver\")\n", + "option2 = BlackSwaption(option_delta, datetime.date(2018, 10, 17), 57.5, option_type=\"receiver\")\n", + "option3 = BlackSwaption(option_delta, datetime.date(2018, 10, 17), 60, option_type=\"receiver\")\n", "option1.sigma = .47\n", "option2.sigma = .53\n", "option3.sigma = .69\n", @@ -255,7 +296,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/python/notebooks/Reto Report.ipynb b/python/notebooks/Reto Report.ipynb new file mode 100644 index 00000000..c91cd918 --- /dev/null +++ b/python/notebooks/Reto Report.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import pandas.tseries.offsets as off\n", + "import globeop_reports as go\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import exploration.VaR as var\n", + "\n", + "from analytics.curve_trades import curve_pos, on_the_run\n", + "from analytics.index_data import get_index_quotes\n", + "from analytics.scenarios import run_portfolio_scenarios\n", + "from analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface, DualCorrTranche\n", + "from db import dbconn, dbengine\n", + "\n", + "conn = dbconn('dawndb')\n", + "dawndb = dbengine('dawndb')\n", + "serenitasdb = dbengine('serenitasdb')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#PNL Allocation\n", + "date = datetime.date.today() - off.BDay(1)\n", + "report_date = date - off.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 = 'False'\"\n", + "df = pd.read_sql_query(sql_string, dbengine('dawndb'),\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", + "sql_string = \"SELECT * from cashflow_history\"\n", + "cf = pd.read_sql_query(sql_string, dbengine('dawndb'), 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": [ + "#Calculate amount of stress for reports\n", + "df = get_index_quotes('HY', list(range(on_the_run('HY') - 10, on_the_run('HY') + 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": [ + "#Current tranche and swaptions positions\n", + "t_sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n", + " \"OVER (partition by security_id, attach) AS ntl_agg \"\n", + " \"FROM cds WHERE swap_type='CD_INDEX_TRANCHE' AND termination_cp IS NULL \"\n", + " \"AND trade_date <= %s\")\n", + "swaption_sql_string = (\"select id, security_desc from swaptions where date(expiration_date) \"\n", + " \"> %s and swap_type = 'CD_INDEX_OPTION' \"\n", + " \"AND trade_date <= %s AND termination_date iS NULL\")\n", + "index_sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n", + " \"OVER (partition by security_id, attach) AS ntl_agg \"\n", + " \"FROM cds WHERE swap_type='CD_INDEX' AND termination_cp IS null \"\n", + " \"AND folder = 'IGOPTDEL' OR folder = 'HYOPTDEL' \"\n", + " \"AND trade_date <= %s\")\n", + "with conn.cursor() as c:\n", + " #Get Tranche Trade Ids\n", + " c.execute(t_sql_string, (date,))\n", + " t_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n", + " #Get Swaption Trade Ids\n", + " c.execute(swaption_sql_string, (date, date))\n", + " swaption_trades = c.fetchall()\n", + " #Get Index/deltas Trade Ids\n", + " c.execute(index_sql_string, (date,))\n", + " index_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n", + " \n", + "portf = Portfolio([DualCorrTranche.from_tradeid(dealid) for dealid in t_trade_ids],\n", + " t_trade_ids)\n", + "for row in swaption_trades:\n", + " option_delta = CreditIndex(row[1].split()[1], row[1].split()[3][1:], '5yr', date)\n", + " option_delta.mark()\n", + " portf.add_trade(BlackSwaption.from_tradeid(row[0], option_delta), 'opt_' + str(row[0]))\n", + "for index_id in index_trade_ids:\n", + " portf.add_trade(CreditIndex.from_tradeid(index_id), 'index_' + str(index_id))\n", + " \n", + "#Update manually - positive notional = long risk\n", + "non_trancheSwap_risk_notional = 49119912 \n", + "\n", + "portf.add_trade(CreditIndex('HY', on_the_run('HY'), '5yr', value_date = date, notional = -non_trancheSwap_risk_notional), 'port')\n", + " \n", + "portf.value_date = date\n", + "portf.mark(interp_method=\"bivariate_linear\")\n", + "portf.reset_pv()\n", + " \n", + "vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, \n", + " portf.swaptions[0].index.series, \n", + " value_date=date, \n", + " interp_method = \"bivariate_linear\")\n", + "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", + "vol_shock = [0]\n", + "corr_shock = [0]\n", + "spread_shock = widen + tighten\n", + "date_range = [pd.Timestamp(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", + "scens.sum(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/python/notebooks/Single Names Monitoring.ipynb b/python/notebooks/Single Names Monitoring.ipynb index 0c32e617..62a1383a 100644 --- a/python/notebooks/Single Names Monitoring.ipynb +++ b/python/notebooks/Single Names Monitoring.ipynb @@ -23,7 +23,7 @@ "outputs": [], "source": [ "w = widgets.Dropdown(\n", - " options=['IG', 'HY', 'EU'],\n", + " options=['IG', 'HY', 'EU', 'XO'],\n", " value='IG',\n", " description='Index:',\n", " disabled=False,\n", @@ -37,7 +37,7 @@ "metadata": {}, "outputs": [], "source": [ - "w_1 = widgets.IntSlider(value=30, min=22, max=30, description = 'Series')\n", + "w_1 = widgets.IntSlider(value=31, min=22, max=31, description = 'Series')\n", "w_1" ] }, @@ -47,7 +47,7 @@ "metadata": {}, "outputs": [], "source": [ - "trade_date = (pd.datetime.today() - pd.offsets.BDay(1)).date()\n", + "value_date = (pd.datetime.today() - pd.offsets.BDay(2)).date()\n", "index_type = w.value\n", "series = w_1.value" ] @@ -59,8 +59,8 @@ "outputs": [], "source": [ "sql_string = \"select * from index_members(%s, %s)\"\n", - "df = pd.read_sql_query(sql_string, engine, params=(index_type + str(series), trade_date), index_col=['markit_ticker'])\n", - "df1 = pd.read_sql_query(sql_string, engine, params=(index_type + str(series-1), trade_date), index_col=['markit_ticker'])" + "df = pd.read_sql_query(sql_string, engine, params=(index_type + str(series), value_date), index_col=['markit_ticker'])\n", + "df1 = pd.read_sql_query(sql_string, engine, params=(index_type + str(series-2), value_date), index_col=['markit_ticker'])" ] }, { @@ -89,7 +89,7 @@ "metadata": {}, "outputs": [], "source": [ - "date_range = pd.bdate_range(trade_date - 52 * pd.offsets.Week(), trade_date, freq='5B')\n", + "date_range = pd.bdate_range(value_date - 52 * pd.offsets.Week(), value_date, freq='5B')\n", "index = MarkitBasketIndex(index_type, series, ['5yr'])\n", "default_prob = {}\n", "maturity = np.array([np.datetime64(index.maturities[0]).view('int') + 134774])\n", @@ -107,10 +107,13 @@ "outputs": [], "source": [ "#Top 20 highest cumulative\n", - "top20 = default_prob.unstack(-1)[default_prob[trade_date].nlargest(20).index]\n", + "top20 = default_prob.unstack(-1)[default_prob[value_date].nlargest(20).index]\n", "top20.index.name='date'\n", "top20.columns.name='tickers'\n", - "top20.plot(title=f'market implied default probabilities to {index.maturities[0]}')" + "ax = top20.plot(title=f'market implied default probabilities to {index.maturities[0]}', figsize=(10,6))\n", + "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=1)\n", + "ax.set(xlabel='date', ylabel='probability')\n", + "plt.tight_layout()" ] }, { @@ -118,16 +121,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "title = 'market implied default probabilities to ' + str(temp.maturities[0])\n", - "fig, ax = plt.subplots()\n", - "ax.plot(dp_top.T)\n", - "ax.set(xlabel='date', ylabel='probability',\n", - " title=title)\n", - "ax.legend(dp_top.index, loc='upper center', bbox_to_anchor=(1.3, 1), ncol=1)\n", - "fig.set_size_inches(10, 6)\n", - "fig.tight_layout()" - ] + "source": [] } ], "metadata": { @@ -146,7 +140,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/python/notebooks/VaR.ipynb b/python/notebooks/VaR.ipynb index 3bb3e83d..178d72b3 100644 --- a/python/notebooks/VaR.ipynb +++ b/python/notebooks/VaR.ipynb @@ -10,13 +10,16 @@ "from analytics.index_data import get_index_quotes\n", "from analytics.scenarios import run_portfolio_scenarios\n", "from analytics import Swaption, BlackSwaption, CreditIndex, BlackSwaptionVolSurface, Portfolio, ProbSurface, DualCorrTranche\n", - "from db import dbconn\n", + "from db import dbconn, dbengine\n", "\n", "import datetime\n", "import exploration.VaR as var\n", "import pandas as pd\n", + "import numpy as np\n", "\n", - "conn = dbconn('dawndb')" + "conn = dbconn('dawndb')\n", + "dawndb = dbengine('dawndb')\n", + "serenitasdb = dbengine('serenitasdb')" ] }, { @@ -25,7 +28,7 @@ "metadata": {}, "outputs": [], "source": [ - "date = (datetime.date.today() - pd.tseries.offsets.BDay(1)).date()\n", + "date = (datetime.date.today() - pd.tseries.offsets.BDay(3)).date()\n", "report_date = (date + pd.tseries.offsets.BMonthEnd(-1)).date()\n", "index_type = \"IG\"\n", "quantile = .025" @@ -63,7 +66,8 @@ "outputs": [], "source": [ "#Mortgage Hedge VaR - use IG spread relative move for VaR\n", - "df = var.get_pos(date, 'HEDGE_MBS')\n", + "df = pd.read_sql_query(\"SELECT * from list_cds_marks_by_strat(%s) where strategy ='HEDGE_MBS'\",\n", + " dawndb, params=(date,))\n", "portf = Portfolio([CreditIndex(row.p_index, row.p_series, row.tenor,\n", " report_date, -row.notional)\n", " for row in df[['p_index', 'tenor', 'p_series', 'notional']].\n", @@ -136,7 +140,6 @@ " \"FROM cds WHERE swap_type='CD_INDEX' AND termination_cp IS null \"\n", " \"AND folder = 'IGOPTDEL' OR folder = 'HYOPTDEL' \"\n", " \"AND trade_date <= %s\")\n", - "conn = dbconn('dawndb')\n", "with conn.cursor() as c:\n", " #Get Tranche Trade Ids\n", " c.execute(t_sql_string, (date,))\n", @@ -158,14 +161,18 @@ " portf.add_trade(CreditIndex.from_tradeid(index_id), 'index_' + str(index_id))\n", " \n", "#Update manually - positive notional = long risk\n", - "non_trancheSwap_risk_notional = 33763230\n", - "portf.add_trade(CreditIndex('HY', on_the_run('HY'), '5yr', value_date = date, notional = -non_trancheSwap_risk_notional), 'port')\n", + "non_trancheSwap_risk_notional = 49119912 \n", + "\n", + "portf.add_trade(CreditIndex('HY', on_the_run('HY'), '5yr', value_date = date, notional = -non_trancheSwap_risk_notional), 'bond')\n", " \n", "portf.value_date = date\n", - "portf.mark()\n", + "portf.mark(interp_method=\"bivariate_spline\")\n", "portf.reset_pv()\n", " \n", - "vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, portf.swaptions[0].index.series, value_date=date)\n", + "vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, \n", + " portf.swaptions[0].index.series, \n", + " value_date=date, \n", + " interp_method = \"bivariate_spline\")\n", "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", "vol_shock = [0]\n", "corr_shock = [0]\n", @@ -187,8 +194,50 @@ "metadata": {}, "outputs": [], "source": [ - "var.cleared_cds_margins(report_date)" + "spread_shock = np.arange(-.4, 2.2, .2)\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", + "scens.sum(axis=1)\n", + "\n", + "risk_notional = [t.notional * t._index.duration for t in portf.indices]\n", + "portf.trades[0]._index.duration()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Calculate the margins for cleared CDS required for each strategy\n", + "df = pd.read_sql_query(\"SELECT * from list_cds_marks_by_strat(%s)\",\n", + " dawndb, params=(date,))\n", + "percentile = .95 #monthly 90%tile case.\n", + "shocks, widen, tighten, onTR_dur, onTR_spread = {}, {}, {}, {}, {}\n", + "for ind in ['IG', 'HY', 'EU']:\n", + " shocks[ind], onTR_spread[ind], onTR_dur[ind] = rel_spread_diff(date, index=ind)\n", + " widen[ind] = shocks[ind].quantile(percentile)\n", + " tighten[ind] = shocks[ind].quantile(1-percentile)\n", + "\n", + "df['onTR_notional'] = df.apply(lambda df:\n", + " df.notional * df.factor * df.duration / onTR_dur[df.p_index], axis=1)\n", + "df['widen'] = df.apply(lambda df:\n", + " df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * widen[df.p_index]/10000, axis=1)\n", + "df['tighten'] = df.apply(lambda df:\n", + " df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * tighten[df.p_index]/10000, axis=1)\n", + "delta_alloc = df.groupby('strategy').sum()\n", + "delta_alloc['total'] = delta_alloc.apply(lambda df: max(abs(df.widen), abs(df.tighten)), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -207,7 +256,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/python/notebooks/tranche and swaption portfolio strategy.ipynb b/python/notebooks/tranche and swaption portfolio strategy.ipynb index 1b0e0a08..c0355fb0 100644 --- a/python/notebooks/tranche and swaption portfolio strategy.ipynb +++ b/python/notebooks/tranche and swaption portfolio strategy.ipynb @@ -18,7 +18,7 @@ "from datetime import date\n", "from graphics import plot_color_map\n", "\n", - "value_date = (pd.datetime.today() - pd.offsets.BDay(2)).date()" + "value_date = (pd.datetime.today() - pd.offsets.BDay(1)).date()" ] }, { @@ -27,7 +27,50 @@ "metadata": {}, "outputs": [], "source": [ - "#Construct IG Swaption Portfolio\n", + "def color_plots(portf, scens, options_names):\n", + " sort_order = [True, False]\n", + " scens_pnl = scens.xs('pnl', axis=1, level=1)\n", + " just_spread = scens_pnl.xs((0,0), level=['corr_shock', 'vol_shock'])\n", + " combined = just_spread.sum(axis=1)\n", + " combined.name = 'combined_pnl'\n", + " plot_color_map(combined, sort_order)\n", + "\n", + " swaptions_only = just_spread[options_names].sum(axis=1)\n", + " swaptions_only.name = 'swaptions_pnl'\n", + " plot_color_map(swaptions_only, sort_order)\n", + "\n", + " tranches_only = just_spread[[x for x in portf.trade_ids if x not in options_names]].sum(axis=1)\n", + " tranches_only.name = 'tranches_pnl'\n", + " plot_color_map(tranches_only, sort_order)\n", + "\n", + " #Plot delta, swaption delta is in protection terms: switch to risk terms\n", + " sort_order = [True, False]\n", + " scens_delta = scens.xs('delta', axis=1, level=1)\n", + " scens_delta = scens_delta.mul(pd.Series(portf.notionals))\n", + " if 'delta' in portf.trade_ids:\n", + " scens_delta['delta'] = portf.notionals['delta']\n", + " scens_delta = scens_delta.xs((0,0), level=['corr_shock', 'vol_shock'])\n", + "\n", + " combined = scens_delta.sum(axis=1)\n", + " combined.name = 'Combined Delta'\n", + " plot_color_map(combined, sort_order)\n", + "\n", + " swaptions_only = scens_delta[options_names].sum(axis=1)\n", + " swaptions_only.name = 'Swaptions Only Delta'\n", + " plot_color_map(swaptions_only, sort_order)\n", + "\n", + " tranches_only = scens_delta[[x for x in portf.trade_ids if x not in options_names]].sum(axis=1)\n", + " tranches_only.name = 'Tranches Only Delta'\n", + " plot_color_map(tranches_only, sort_order)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Package 1\n", "index = 'IG'\n", "series = 30\n", "option_delta = CreditIndex(index, series, '5yr', value_date=value_date)\n", @@ -42,22 +85,17 @@ "option2.notional = 300_000_000\n", "option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta\n", "\n", - "#Get current Tranche positions\n", - "sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n", - " \"OVER (partition by security_id, attach) AS ntl_agg \"\n", - " \"FROM cds WHERE swap_type='CD_INDEX_TRANCHE' AND termination_cp IS NULL\")\n", - "conn = dbconn('dawndb')\n", - "with conn.cursor() as c:\n", - " c.execute(sql_string)\n", - " trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n", - "portf = Portfolio([DualCorrTranche.from_tradeid(dealid) for dealid in trade_ids],\n", - " trade_ids)\n", + "equity = DualCorrTranche('IG', 29, '5yr', attach=0, detach=3, corr_attach=np.nan, \n", + " corr_detach=.35, tranche_running=100, notional=-40000000, use_trunc=True)\n", + "mezz = DualCorrTranche('IG', 29, '5yr', attach=7, detach=15, corr_attach=.45, \n", + " corr_detach=.55, tranche_running=100, notional=240000000, use_trunc=True)\n", + "portf = Portfolio([equity, mezz], ['equity', 'mezz'])\n", "portf.trades.extend([option1, option2, option_delta])\n", "portf.trade_ids.extend(['opt1', 'opt2', 'delta'])\n", "\n", - "spread_shock = np.arange(-.3, 1.1, .1)\n", + "spread_shock = np.round(np.arange(-.3, 1, .1),2)\n", "corr_shock = np.arange(0, .1, 0.1)\n", - "vol_shock = np.arange(-.1, .3, 0.1)\n", + "vol_shock = np.arange(0, 0.1, 0.1)\n", "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", "date_range = pd.date_range(value_date, earliest_expiry, periods=5)\n", "vs = BlackSwaptionVolSurface(index, series, value_date=value_date)\n", @@ -67,12 +105,14 @@ "portf.mark()\n", "portf.reset_pv()\n", "\n", - "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n", + "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\", 'delta'],\n", " spread_shock=spread_shock,\n", " corr_shock=corr_shock,\n", " vol_shock=vol_shock,\n", " vol_surface=vol_surface)\n", - "scens = round(scens,2)" + "scens = round(scens,2)\n", + "\n", + "color_plots(portf, scens, ['opt1', 'opt2', 'delta'])" ] }, { @@ -81,11 +121,41 @@ "metadata": {}, "outputs": [], "source": [ - "sort_order = [True, False]\n", - "output = scens.xs((0,0), level=['corr_shock', 'vol_shock']).sum(axis=1)\n", - "(1+output.index.get_level_values(1)) * portf.swaptions[0].ref\n", - "output.name = 'pnl'\n", - "plot_color_map(output, sort_order)" + "#simple IG package: sell OTM swaption vs. short 3-7 delta neutral at start\n", + "index = 'IG'\n", + "series = 30\n", + "option_delta = CreditIndex(index, series, '5yr', value_date=value_date)\n", + "option_delta.spread = 60\n", + "option2 = BlackSwaption(option_delta, date(2018, 11, 21), 85, option_type=\"payer\")\n", + "option2.sigma = .588\n", + "option2.direction = 'Short'\n", + "option2.notional = 500_000_000\n", + "option_delta.notional = 1\n", + "\n", + "mezz = DualCorrTranche('IG', 29, '5yr', attach=7, detach=15, corr_attach=.45, \n", + " corr_detach=.55, tranche_running=100, notional=50000000, use_trunc=True)\n", + "portf = Portfolio([option2, mezz], ['opt2', 'mezz'])\n", + "\n", + "spread_shock = np.round(np.arange(-.3, 1, .1),2)\n", + "corr_shock = np.arange(0, .1, 0.1)\n", + "vol_shock = np.arange(0, 0.1, 0.1)\n", + "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", + "date_range = pd.date_range(value_date, earliest_expiry, periods=5)\n", + "vs = BlackSwaptionVolSurface(index, series, value_date=value_date)\n", + "ps = ProbSurface(index, series, value_date=value_date)\n", + "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", + "portf.value_date = value_date\n", + "portf.mark()\n", + "portf.reset_pv()\n", + "\n", + "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\", 'delta'],\n", + " spread_shock=spread_shock,\n", + " corr_shock=corr_shock,\n", + " vol_shock=vol_shock,\n", + " vol_surface=vol_surface)\n", + "scens = round(scens,2)\n", + "\n", + "color_plots(portf, scens, ['opt2'])" ] }, { @@ -94,6 +164,48 @@ "metadata": {}, "outputs": [], "source": [ + "#simple HY package: sell OTM swaption vs. short 3-7 delta neutral at start\n", + "index = 'HY'\n", + "series = 30\n", + "option_delta = CreditIndex(index, series, '5yr', value_date=value_date)\n", + "option_delta.price = 106.75\n", + "option2 = BlackSwaption(option_delta, date(2018, 11, 21), 102, option_type=\"payer\")\n", + "option2.sigma = .469\n", + "option2.direction = 'Short'\n", + "option2.notional = 200_000_000\n", + "\n", + "mezz = DualCorrTranche('HY', 29, '5yr', attach=15, detach=25, corr_attach=.35, \n", + " corr_detach=.45, tranche_running=100, notional=16000000, use_trunc=True)\n", + "portf = Portfolio([option2, mezz], ['opt2', 'mezz'])\n", + "\n", + "spread_shock = np.round(np.arange(-.3, 1, .1),2)\n", + "corr_shock = np.arange(0, .1, 0.1)\n", + "vol_shock = np.arange(0, 0.1, 0.1)\n", + "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", + "date_range = pd.date_range(value_date, earliest_expiry, periods=5)\n", + "vs = BlackSwaptionVolSurface(index, series, value_date=value_date)\n", + "ps = ProbSurface(index, series, value_date=value_date)\n", + "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", + "portf.value_date = value_date\n", + "portf.mark()\n", + "portf.reset_pv()\n", + "\n", + "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\", 'delta'],\n", + " spread_shock=spread_shock,\n", + " corr_shock=corr_shock,\n", + " vol_shock=vol_shock,\n", + " vol_surface=vol_surface)\n", + "scens = round(scens,2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(1+output.index.get_level_values(1)) * portf.swaptions[0].ref\n", + "\n", "#negative notional == sell protection\n", "hy_tranche = DualCorrTranche('HY', 29, '5yr', attach=0, detach=15, corr_attach=np.nan, \n", " corr_detach=.35, tranche_running=500, notional=-10000000)\n", @@ -112,15 +224,60 @@ "metadata": {}, "outputs": [], "source": [ + "#Current tranche and swaptions positions\n", + "t_sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n", + " \"OVER (partition by security_id, attach) AS ntl_agg \"\n", + " \"FROM cds WHERE swap_type='CD_INDEX_TRANCHE' AND termination_cp IS NULL\")\n", + "swaption_sql_string = (\"select id, security_desc from swaptions where date(expiration_date) \"\n", + " \"> %s and swap_type = 'CD_INDEX_OPTION'\")\n", + "index_sql_string = (\"SELECT id, sum(notional * case when protection='Buyer' then -1 else 1 end) \"\n", + " \"OVER (partition by security_id, attach) AS ntl_agg \"\n", + " \"FROM cds WHERE swap_type='CD_INDEX' AND termination_cp IS null and folder = 'IGOPTDEL'\")\n", + "conn = dbconn('dawndb')\n", + "with conn.cursor() as c:\n", + " c.execute(t_sql_string)\n", + " t_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n", + " c.execute(swaption_sql_string, (value_date,))\n", + " swaption_trades = c.fetchall()\n", + " c.execute(index_sql_string)\n", + " index_trade_ids = [dealid for dealid, ntl in c if ntl != 0]\n", + " \n", + "portf = Portfolio([DualCorrTranche.from_tradeid(dealid) for dealid in t_trade_ids],\n", + " t_trade_ids)\n", + "for row in swaption_trades:\n", + " option_delta = CreditIndex(row[1].split()[1], row[1].split()[3][1:], '5yr', value_date)\n", + " option_delta.mark()\n", + " portf.add_trade(BlackSwaption.from_tradeid(row[0], option_delta), 'opt_' + str(row[0]))\n", + "for index_id in index_trade_ids:\n", + " portf.add_trade(CreditIndex.from_tradeid(index_id), 'index_' + str(index_id))\n", + " \n", + "spread_shock = np.round(np.arange(-.3, 1, .1),2)\n", + "corr_shock = np.arange(0, .1, 0.1)\n", + "vol_shock = np.arange(0, 0.1, 0.1)\n", + "#date_range = pd.date_range(value_date, date(2018,12,31), periods=5)\n", + "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", + "date_range = pd.date_range(value_date, earliest_expiry, periods=5)\n", + "portf.swaptions[0]\n", + "vs = BlackSwaptionVolSurface(portf.swaptions[0].index.index_type, portf.swaptions[0].index.series, value_date=value_date)\n", + "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", + "portf.value_date = value_date\n", + "portf.mark()\n", + "portf.reset_pv()\n", "\n", - "##\n", - "scens_more = run_portfolio_scenarios(portf, date_range, params=['pnl', 'delta'],\n", + "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\", 'delta'],\n", " spread_shock=spread_shock,\n", " corr_shock=corr_shock,\n", " vol_shock=vol_shock,\n", " vol_surface=vol_surface)\n", - "#swaption delta is in protection terms: switch to risk terms\n", - "swaption_scens.delta = -swaption_scens.delta" + "\n", + "color_plots(portf, scens, ['opt_20', 'opt_21', 'index_954'])\n", + "\n", + "#sort_order = [True, False]\n", + "#scens_pnl = scens.xs('pnl', axis=1, level=1)\n", + "#just_spread = scens_pnl.xs(0, level=1)\n", + "#combined = just_spread.sum(axis=1)\n", + "#combined.name = 'tranche_pnl'\n", + "#plot_color_map(combined, sort_order)" ] }, { @@ -263,7 +420,7 @@ "swaption_scens.delta = -swaption_scens.delta\n", "\n", "notional = 30_000_000_000\n", - "t = bkt.TrancheBasket('IG', '29', '3yr')\n", + "t = bkt.TrancheBasket('IG', '29', '5yr')\n", "t.build_skew()\n", "#get back to 17bps, .36 delta\n", "port_spread = 67\n", @@ -280,70 +437,28 @@ "metadata": {}, "outputs": [], "source": [ - "#IG Bullish Risk Reversal vs. shorting IG 7-15 risk\n", "index = 'IG'\n", - "series = 30\n", - "option_delta = Index.from_name(index, series, '5yr', value_date)\n", - "option_delta.spread = 62\n", - "option1 = BlackSwaption(option_delta, date(2018, 9, 19), 60, option_type=\"receiver\")\n", - "option2 = BlackSwaption(option_delta, date(2018, 9, 19), 90, option_type=\"payer\")\n", - "option1.sigma = .344\n", - "option2.sigma = .585\n", - "option1.notional = 200_000_000\n", - "option2.notional = 400_000_000\n", - "option1.direction = 'Long'\n", - "option2.direction = 'Short'\n", - "option_delta.notional = 1\n", - "option_delta.direction = 'Seller' if option_delta.notional > 0 else 'Buyer'\n", - "option_delta.notional = abs(option_delta.notional)\n", - "portf = Portfolio([option1, option2, option_delta])\n", - "#Plot Scenarios Inputs: Portfolio, spread shock tightening%, spread shock widening%, snapshot period)\n", - "portf\n", + "series = 29\n", + "ss = DualCorrTranche('IG', 29, '5yr', attach=15, detach=100, corr_attach=.59685, \n", + " corr_detach=.7, tranche_running=100, notional=-230000000)\n", + "mezz = DualCorrTranche('IG', 29, '5yr', attach=7, detach=15, corr_attach=.46984, \n", + " corr_detach=.59685, tranche_running=100, notional=50000000)\n", + "portf = Portfolio([ss, mezz], ['ss', 'mezz'])\n", "\n", + "spread_shock = np.round(np.arange(-.3, 1, .1), 2)\n", + "corr_shock = np.arange(0, .1, 0.1)\n", + "vol_shock = np.arange(0, 0.1, 0.1)\n", + "date_range = pd.date_range(value_date, date(2018,12,31), periods=5)\n", + "#portf.value_date = value_date\n", + "#portf.mark()\n", "portf.reset_pv()\n", - "#Run Swaption sensitivities\n", - "#Set Shock range\n", - "shock_min = -.5\n", - "shock_max = 1\n", - "spread_shock = np.arange(shock_min, shock_max, 0.1)\n", - "#Set Date range\n", - "earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n", - "date_range = pd.bdate_range(value_date, earliest_expiry - pd.offsets.BDay(), freq='10B')\n", - "#Setup Vol Surface\n", - "vs = BlackSwaptionVolSurface(index,series, value_date=value_date)\n", - "ps = ProbSurface(index,series, value_date=value_date)\n", - "vol_surface = vs[vs.list(option_type='payer')[-1]]\n", - "swaption_scens = run_portfolio_scenarios(portf, date_range, spread_shock, np.array([0]),\n", - " vol_surface, params=[\"pnl\", \"delta\"])\n", - "#swaption delta is in protection terms: switch to risk terms\n", - "swaption_scens.delta = -swaption_scens.delta\n", - "\n", - "notional = -100_000_000\n", - "t = bkt.TrancheBasket('IG', '29', '5yr')\n", - "t.build_skew()\n", - "spread_range = (1+ spread_shock) * option_delta.spread\n", - "tranches_scens = run_tranche_scenarios_rolldown(t, spread_range, date_range, corr_map=False)\n", - "tranches_scens = notional*tranches_scens.xs('7-15', axis=1, level=1)\n", "\n", - "#Create snapshot of the the first scenario date\n", - "total_scens = swaption_scens.reset_index().merge(tranches_scens.reset_index(), \n", - " left_on=['date', 'spread'], \n", - " right_on=['date', 'spread_range'], \n", - " suffixes=['_s', '_t'])\n", - "total_scens['pnl'] = total_scens['pnl_s'] + total_scens['pnl_t']\n", - "total_scens['delta'] = total_scens['delta_s'] + total_scens['delta_t']\n", - "total_scens_single_date = total_scens.set_index('date').xs(date_range[0])\n", - "total_scens_single_date = total_scens_single_date.set_index('spread', drop=True)\n", - "\n", - "#tranche positions delta at different spreads\n", - "ax = total_scens_single_date.delta_t.plot(title = 'delta vs. spread levels')\n", - "ax.ticklabel_format(style='plain')\n", - "plt.tight_layout()\n", + "scens = run_portfolio_scenarios(portf, date_range, params=[\"pnl\", 'delta'],\n", + " spread_shock=spread_shock,\n", + " corr_shock=corr_shock)\n", + "scens = round(scens,2)\n", "\n", - "#Tranche + Swaptions positions delta at different spreads\n", - "ax1 = total_scens_single_date.delta.plot()\n", - "ax1.ticklabel_format(style='plain')\n", - "plt.tight_layout()" + "color_plots(portf, scens, ['opt1', 'opt2', 'delta'])" ] }, { @@ -379,7 +494,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/python/notebooks/tranches numbers.ipynb b/python/notebooks/tranches numbers.ipynb index fc936b7a..d655f8ba 100644 --- a/python/notebooks/tranches numbers.ipynb +++ b/python/notebooks/tranches numbers.ipynb @@ -54,7 +54,7 @@ "outputs": [], "source": [ "sql_string = '''SELECT a.*, b.trancheupfrontmid, b.indexrefspread, b.tranchedelta, b.trancherunningmid\n", - " FROM risk_numbers_new a \n", + " FROM risk_numbers a \n", " join tranche_quotes b on a.tranche_id = b.id\n", " where a.index <> 'EU'\n", " '''\n", @@ -435,6 +435,74 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "#Back test tranche pair trades \n", + "sql_str = \"select * from markit_tranche_quotes a left join \" \\\n", + " \"(select index, series, version, basketid from index_version) b \" \\\n", + " \"using (basketid) order by quotedate asc\"\n", + "index_columns=['index', 'series', 'version', 'tenor', 'attach', 'detach']\n", + "df = pd.read_sql_query(sql_str, engine, parse_dates=['quotedate'], index_col=index_columns)\n", + "df['day_frac'] = (df.groupby(index_columns)['quotedate'].\n", + " transform(lambda s: s.\n", + " diff().astype('timedelta64[D]') / 360))\n", + "df['close_price'] = 1-df['upfront_mid']\n", + "df = df.set_index('quotedate', append=True)\n", + "df['price_return'] = df['close_price'].groupby(level=index_columns).diff()\n", + "df['price_return'] += df.day_frac * df.tranche_spread/10000\n", + "df = df.drop(['basketid', 'upfront_bid', 'upfront_ask', 'upfront_mid', 'index_price', 'day_frac', 'tranche_spread', 'close_price'], axis=1)\n", + "df = df.dropna()\n", + "#Focus on IG\n", + "ig_tranches = df.xs(('IG', '5yr', 23), level = ['index', 'tenor', 'series'])\n", + "ig_tranches = ig_tranches.reset_index(['version', 'detach'], drop=True)\n", + "ig_tranches = ig_tranches.unstack(level='attach')\n", + "ig_tranches.columns = ig_tranches.columns.droplevel()\n", + "\n", + "#carry strat = long 15-100 and short 7-15: 4.6 by 1, 50bps IA\n", + "carrystrat = (4.6 * ig_tranches[15] - ig_tranches[7])/.05\n", + "cum_return = (carrystrat+1).cumprod()\n", + "cum_return.plot()\n", + "#equity gamma strat = long 0-3 and short 7-15: 1 by 6, 12% IA\n", + "equitygammastrat = (1 * ig_tranches[0] - 6 * ig_tranches[7])/.12\n", + "cum_return = (equitygammastrat+1).cumprod()\n", + "cum_return.plot()\n", + "#mezz gamma strat = long 3-7 and short 7-15: 1 by 2.75, 2.5% IA\n", + "mezzgammastrat =(1 * ig_tranches[3] - 2.75 * ig_tranches[7])/.025\n", + "cum_return = (mezzgammastrat+1).cumprod()\n", + "cum_return.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r=[]\n", + "r.append(carrystrat.mean()/carrystrat.std() * math.sqrt(252))\n", + "r.append(equitygammastrat.mean()/equitygammastrat.std() * math.sqrt(252))\n", + "r.append(mezzgammastrat.mean()/mezzgammastrat.std() * math.sqrt(252))\n", + "r" + ] + }, + { + "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": [] } ], @@ -473,7 +541,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.1" } }, "nbformat": 4, |