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-rw-r--r--python/notebooks/swaption_risk.ipynb58
-rw-r--r--python/notebooks/swaption_scenarios.ipynb (renamed from python/notebooks/Option Trades.ipynb)210
2 files changed, 165 insertions, 103 deletions
diff --git a/python/notebooks/swaption_risk.ipynb b/python/notebooks/swaption_risk.ipynb
index 8c38ecf8..00b5db55 100644
--- a/python/notebooks/swaption_risk.ipynb
+++ b/python/notebooks/swaption_risk.ipynb
@@ -84,64 +84,6 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": [
- "from analytics.scenarios import run_portfolio_scenarios\n",
- "from analytics import BlackSwaptionVolSurface, CreditIndex\n",
- "import analytics\n",
- "import datetime\n",
- "import numpy as np\n",
- "\n",
- "today = datetime.datetime.now()\n",
- "yesterday = datetime.date.today() - pd.offsets.BDay()\n",
- "\n",
- "portf = get_swaption_portfolio(yesterday, conn, source_list=['GS'])\n",
- "for i, amt in hedges.iteritems():\n",
- " portf.add_trade(CreditIndex(i[:2], i[2:4], '5yr', value_date=yesterday, notional=amt), ('delta', i))\n",
- "\n",
- "vol_surface = {}\n",
- "for trade in portf.swaptions:\n",
- " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
- " value_date=today.date(), interp_method = \"bivariate_linear\")\n",
- " vol_surface[(trade.index.index_type, trade.index.series, trade.option_type)] = vs[vs.list(source='GS', option_type=trade.option_type)[-1]]\n",
- "\n",
- "#Set original_pv as of yesterday's EOD levels, don't reset PV after this time\n",
- "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
- "portf.reset_pv()\n",
- "\n",
- "#set ref to today's levels\n",
- "portf.value_date = today\n",
- "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
- "\n",
- "spread_shock = np.round(np.arange(-.1, .1, .01), 4)\n",
- "scens = run_portfolio_scenarios(portf, [today], params=['pnl', 'hy_equiv', 'sigma'],\n",
- " spread_shock=spread_shock,\n",
- " vol_shock=[0],\n",
- " corr_shock=[0],\n",
- " vol_surface=vol_surface)\n",
- "pnl = scens.xs('pnl', level = 2, axis=1).sum(axis=1)\n",
- "hy_equiv = scens.xs('hy_equiv', level = 2, axis=1).sum(axis=1)\n",
- "\n",
- "ig = CreditIndex('IG', 32, '5yr', value_date = today)\n",
- "ig.mark()\n",
- "\n",
- "pnl.index = pnl.index.set_levels((1+pnl.index.get_level_values('spread_shock')) * ig.spread, level = 'spread_shock')\n",
- "hy_equiv.index = pnl.index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pnl, hy_equiv"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
"source": []
}
],
diff --git a/python/notebooks/Option Trades.ipynb b/python/notebooks/swaption_scenarios.ipynb
index 32cd5b04..13639918 100644
--- a/python/notebooks/Option Trades.ipynb
+++ b/python/notebooks/swaption_scenarios.ipynb
@@ -9,17 +9,20 @@
"import datetime\n",
"import pandas as pd\n",
"import numpy as np\n",
+ "import analytics\n",
+ "import math\n",
"\n",
"from graphics import plot_color_map\n",
"from analytics import Swaption, BlackSwaption, BlackSwaptionVolSurface, CreditIndex, Portfolio\n",
"from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios, run_portfolio_scenarios_module\n",
"from scipy.interpolate import SmoothBivariateSpline\n",
- "from db import dbengine\n",
+ "from utils.db import dbconn, dbengine\n",
+ "from risk.swaptions import get_swaption_portfolio\n",
"\n",
- "from db import dbconn\n",
- "from analytics import init_ontr\n",
"conn = dbconn('dawndb')\n",
- "init_ontr()\n",
+ "dawn_engine = dbengine('dawndb')\n",
+ "conn.autocommit=True\n",
+ "analytics.init_ontr()\n",
"pd.options.display.float_format = \"{:,.2f}\".format"
]
},
@@ -29,7 +32,46 @@
"metadata": {},
"outputs": [],
"source": [
- "#Delta Chart: Red = Long Risk, Blue = Short Risk"
+ "portf = get_swaption_portfolio(datetime.date.today() - pd.offsets.BDay(), conn, source_list=['GS'])\n",
+ "\n",
+ "hedges = pd.read_sql_query(\"SELECT security_desc, notional FROM list_cds_positions_by_strat(%s) \"\n",
+ " \"WHERE folder in ('IGOPTDEL', 'HYOPTDEL')\",\n",
+ " conn, params=(datetime.date.today(),))\n",
+ "\n",
+ "for i, r in hedges.iterrows():\n",
+ " portf.add_trade(CreditIndex(r['security_desc'].split(\" \")[1],\n",
+ " r['security_desc'].split(\" \")[3][1:],\n",
+ " '5yr', value_date=datetime.date.today() - pd.offsets.BDay(),\n",
+ " notional = r['notional']), ('delta', i))\n",
+ "\n",
+ "vol_surface = {}\n",
+ "for trade in portf.swaptions:\n",
+ " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
+ " value_date=datetime.date.today(), interp_method = \"bivariate_linear\")\n",
+ " vol_surface[(trade.index.index_type, trade.index.series, trade.option_type)] = vs[vs.list(source='GS', option_type=trade.option_type)[-1]]\n",
+ "\n",
+ "#Set original_pv as of yesterday's EOD levels, don't reset PV after this time\n",
+ "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
+ "portf.reset_pv()\n",
+ "\n",
+ "#set ref to today's levels\n",
+ "portf.value_date = datetime.date.today()\n",
+ "portf.mark(interp_method=\"bivariate_linear\", source_list=['GS'])\n",
+ "\n",
+ "spread_shock = np.round(np.arange(-.1, .1, .01), 4)\n",
+ "scens = run_portfolio_scenarios(portf, [datetime.datetime.now()], params=['pnl', 'hy_equiv', 'sigma'],\n",
+ " spread_shock=spread_shock,\n",
+ " vol_shock=[0],\n",
+ " corr_shock=[0],\n",
+ " vol_surface=vol_surface)\n",
+ "pnl = scens.xs('pnl', level = 2, axis=1).sum(axis=1)\n",
+ "hy_equiv = scens.xs('hy_equiv', level = 2, axis=1).sum(axis=1)\n",
+ "\n",
+ "ig = CreditIndex('IG', 32, '5yr')\n",
+ "ig.mark()\n",
+ "\n",
+ "pnl.index = pnl.index.set_levels((1+pnl.index.get_level_values('spread_shock')) * ig.spread, level = 'spread_shock')\n",
+ "hy_equiv.index = pnl.index"
]
},
{
@@ -38,17 +80,26 @@
"metadata": {},
"outputs": [],
"source": [
- "#Trade Analysis - see if the trade is net positive gamma\n",
+ "pnl, hy_equiv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Trade Analysis - pre-trade analytics\n",
"index = 'IG'\n",
"series = 32\n",
"option_delta = CreditIndex(index, series, '5yr') \n",
- "option_delta.spread = 64\n",
- "option1 = BlackSwaption(option_delta, datetime.date(2019, 7, 17), 60, option_type=\"payer\") \n",
- "option2 = BlackSwaption(option_delta, datetime.date(2019, 7, 17), 75, option_type=\"payer\") \n",
- "option1.sigma = .394 \n",
- "option2.sigma = .52\n",
- "option1.notional = 200_000_000 \n",
- "option2.notional = 200_000_000 \n",
+ "option_delta.spread = 60\n",
+ "option1 = BlackSwaption(option_delta, datetime.date(2019, 9, 17), 90, option_type=\"payer\") \n",
+ "option2 = BlackSwaption(option_delta, datetime.date(2019, 11, 19), 90, option_type=\"payer\") \n",
+ "option1.sigma = .6\n",
+ "option2.sigma = .58\n",
+ "option1.notional = 100_000_000 \n",
+ "option2.notional = 100_000_000 \n",
"option1.direction = 'Long' \n",
"option2.direction = 'Short' \n",
"option_delta.notional = option1.delta * option1.notional + option2.delta * option2.notional\n",
@@ -61,7 +112,21 @@
"metadata": {},
"outputs": [],
"source": [
- "portf"
+ "date_range = pd.bdate_range(portf.value_date, portf.value_date + pd.tseries.offsets.BDay(30), freq='3B')\n",
+ "vol_shock = np.arange(-.15, .31, 0.01)\n",
+ "spread_shock = np.arange(-.2, 2, 0.01)\n",
+ "vol_surface = {}\n",
+ "for trade in portf.swaptions:\n",
+ " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
+ " value_date=portf.value_date, interp_method = \"bivariate_linear\")\n",
+ " vol_surface[(trade.index.index_type, trade.index.series)] = vs[vs.list(option_type='payer')[-1]]\n",
+ "\n",
+ "df = run_portfolio_scenarios(portf, date_range, params=[\"pnl\"],\n",
+ " spread_shock = spread_shock,\n",
+ " vol_shock = vol_shock,\n",
+ " vol_surface = vol_surface)\n",
+ "df = df.reset_index()\n",
+ "df.vol_shock = df.vol_shock.round(2)"
]
},
{
@@ -151,7 +216,7 @@
"outputs": [],
"source": [
"plot_trade_scenarios(portf)\n",
- "plot_trade_scenarios(portf, -.15, .8, vol_time_roll=False)"
+ "plot_trade_scenarios(portf, -.15, .5, vol_time_roll=False)"
]
},
{
@@ -248,36 +313,49 @@
"metadata": {},
"outputs": [],
"source": [
- "#Current Positions\n",
- "today = datetime.date.today()\n",
- "swaption_sql_string = (\"select id, folder, expiration_date 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, folder, 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 trade_date <= %s AND maturity > %s\")\n",
- "conn = dawn_engine.raw_connection()\n",
- "with conn.cursor() as c:\n",
- " c.execute(swaption_sql_string, (today, today))\n",
- " swaption_trades = [[dealid, f\"{folder}_{dealid}\", expiration_date] for dealid, folder, expiration_date in c]\n",
- " c.execute(index_sql_string, (today, today))\n",
- " index_trades = [[dealid, f\"{folder}_{dealid}\"] for dealid, folder, ntl in c if ntl != 0]\n",
- "conn.close()\n",
+ "#Set up Portfolio\n",
+ "from risk.swaptions import get_swaption_portfolio\n",
+ "from risk.tranches import get_tranche_portfolio\n",
+ "rundate = datetime.date(2019,6,21)\n",
"\n",
- "portf = Portfolio([BlackSwaption.from_tradeid(dealid) for dealid, _, _ in swaption_trades],\n",
- " [trade_id for _, trade_id, _ in swaption_trades])\n",
- "index_trades = list(filter(lambda x: \"CURVE\" not in x[1], index_trades))\n",
- "index_trades = list(filter(lambda x: \"SER_IGINDX\" not in x[1], index_trades))\n",
- "index_trades = list(filter(lambda x: \"HEDGE_MBS\" not in x[1], index_trades))\n",
- "index_trades = list(filter(lambda x: \"IGINX\" not in x[1], index_trades))\n",
+ "portf = get_swaption_portfolio(rundate, conn)\n",
"\n",
- "for trade_id, name in index_trades:\n",
- " portf.add_trade(CreditIndex.from_tradeid(trade_id), name)\n",
- " \n",
- "portf.value_date = today\n",
- " \n",
- "results = calc_simple_scenario(portf, shock_min=-.3, shock_max=.3)"
+ "#index positions\n",
+ "df = pd.read_sql_query(\"SELECT * from list_cds_positions_by_strat(%s)\",\n",
+ " dawn_engine, params=(rundate,))\n",
+ "df = df[df.folder.str.contains(\"OPT\")]\n",
+ "for t in df.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",
+ "portf.value_date = rundate\n",
+ "portf.mark(interp_method=\"bivariate_linear\")\n",
+ "portf.reset_pv()\n",
+ "\n",
+ "#------------------------Calc Scenarios\n",
+ "vol_surface = {}\n",
+ "for trade in portf.swaptions:\n",
+ " vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
+ " value_date=rundate, 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]\n",
+ "spread_shock = np.round(np.arange(-.2, 1, .05), 3)\n",
+ "scens = run_portfolio_scenarios(portf, [pd.Timestamp(rundate)], 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, level='vol_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",
+ "scenarios['options'] = scenarios[set(scenarios.columns).intersection(options)].sum(axis=1)"
]
},
{
@@ -285,7 +363,49 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "#Now say that it widens by X percentage, then rebalance, then do the shock again\n",
+ "rundate = datetime.date(2019,6,21)\n",
+ "x = 1.2\n",
+ "for t in portf.swaptions:\n",
+ " t.index.spread *= x\n",
+ " vs = vol_surface[(t.index.index_type, t.index.series)]\n",
+ " t.sigma = max(0.2, float(vs(t.T, math.log(t.moneyness))))\n",
+ "for t in portf.indices:\n",
+ " t.spread *= x\n",
+ "pnl = portf.pnl\n",
+ "\n",
+ "analytics.init_ontr(value_date=rundate)\n",
+ "rebal = analytics._ontr()\n",
+ "rebal.notional = portf.hy_equiv\n",
+ "rebal.direction = 'Seller'\n",
+ "\n",
+ "rebal.spread *= x\n",
+ "portf.add_trade(rebal, ('rebalance', 'HYOPTDEL'))\n",
+ "portf.reset_pv()\n",
+ "\n",
+ "swaptions_scens = portf.swaptions[0].shock(params=['pnl', 'pv'],\n",
+ " spread_shock=spread_shock,\n",
+ " vol_shock=vol_shock,\n",
+ " vol_surface=vol_surface)\n",
+ "\n",
+ "#------------------------Calc Scenarios\n",
+ "scens = run_portfolio_scenarios(portf, [pd.Timestamp(rundate)], params=['pnl', 'pv'],\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, level='vol_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', 'rebalance']\n",
+ "scenarios['options'] = scenarios[set(scenarios.columns).intersection(options)].sum(axis=1)"
+ ]
}
],
"metadata": {
@@ -308,5 +428,5 @@
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-22 06:40:54 +0000