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-rw-r--r--python/notebooks/Option Trades.ipynb194
1 files changed, 100 insertions, 94 deletions
diff --git a/python/notebooks/Option Trades.ipynb b/python/notebooks/Option Trades.ipynb
index 6a28003f..32cd5b04 100644
--- a/python/notebooks/Option Trades.ipynb
+++ b/python/notebooks/Option Trades.ipynb
@@ -14,7 +14,13 @@
"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"
+ "from db import dbengine\n",
+ "\n",
+ "from db import dbconn\n",
+ "from analytics import init_ontr\n",
+ "conn = dbconn('dawndb')\n",
+ "init_ontr()\n",
+ "pd.options.display.float_format = \"{:,.2f}\".format"
]
},
{
@@ -32,6 +38,38 @@
"metadata": {},
"outputs": [],
"source": [
+ "#Trade Analysis - see if the trade is net positive gamma\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",
+ "option1.direction = 'Long' \n",
+ "option2.direction = 'Short' \n",
+ "option_delta.notional = option1.delta * option1.notional + option2.delta * option2.notional\n",
+ "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "portf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"def plot_trade_scenarios(portf, shock_min=-.15, shock_max=.2, vol_time_roll=True):\n",
" portf.reset_pv()\n",
" end_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n",
@@ -43,8 +81,8 @@
" vol_surface = {}\n",
" for trade in portf.swaptions:\n",
" vs = BlackSwaptionVolSurface(trade.index.index_type, trade.index.series, \n",
- " value_date=date)\n",
- " vol_surface[trade.index.index_type + trade.index.series] = vs[vs.list(option_type='payer')[-1]]\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\",\"delta\"],\n",
" spread_shock = spread_shock,\n",
@@ -84,73 +122,26 @@
"metadata": {},
"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",
- "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",
- "option1.notional = 50_000_000\n",
- "option2.notional = 100_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"
+ "def calc_simple_scenario(portf, shock_min=-.15, shock_max=.2):\n",
+ " portf.reset_pv()\n",
+ " end_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date\n",
+ " date_range = pd.bdate_range(portf.value_date,\n",
+ " end_date - pd.tseries.offsets.BDay(), freq='3B')\n",
+ " vol_shock = [0]\n",
+ " spread_shock = np.arange(shock_min, shock_max, 0.01)\n",
+ " index = portf.indices[0].index_type\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",
+ "\n",
+ " return df.xs('pnl', axis=1, level=1).sum(axis=1) "
]
},
{
@@ -159,6 +150,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "plot_trade_scenarios(portf)\n",
"plot_trade_scenarios(portf, -.15, .8, vol_time_roll=False)"
]
},
@@ -207,7 +199,14 @@
"option1 = BlackSwaption.from_tradeid(10, option_delta)\n",
"option2 = BlackSwaption.from_tradeid(11, option_delta)\n",
"portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
- "#plot_trade_scenarios(portf)"
+ "#plot_trade_scenarios(portf)\n",
+ "\n",
+ "#March 2019: May Bull Risky\n",
+ "option_delta = CreditIndex.from_tradeid(1063)\n",
+ "option1 = BlackSwaption.from_tradeid(41, option_delta)\n",
+ "option2 = BlackSwaption.from_tradeid(40, option_delta)\n",
+ "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
+ "results = calc_simple_scenario(portf, shock_min=-.3, shock_max=.3)"
]
},
{
@@ -250,21 +249,35 @@
"outputs": [],
"source": [
"#Current Positions\n",
- "#option_delta = CreditIndex.from_tradeid(945)\n",
- "index = 'HY'\n",
- "series = 30\n",
- "option_delta = CreditIndex(index, series, '5yr', value_date=datetime.date(2018, 6, 7))\n",
- "#option_delta.spread = 66\n",
- "option_delta.price = 106.75\n",
- "option_delta.notional = 1\n",
- "option1 = BlackSwaption.from_tradeid(14, option_delta)\n",
- "option2 = BlackSwaption.from_tradeid(15, option_delta)\n",
- "#option3 = BlackSwaption.from_tradeid(16, option_delta)\n",
- "#option4 = BlackSwaption.from_tradeid(17, option_delta)\n",
- "#portf = Portfolio([option1, option2, option3, option4, option_delta])\n",
- "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
- "portf.mark()\n",
- "plot_trade_scenarios(portf, shock_min=-.15, shock_max=.4)"
+ "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",
+ "\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",
+ "\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)"
]
},
{
@@ -273,13 +286,6 @@
"metadata": {},
"outputs": [],
"source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -298,7 +304,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.1"
+ "version": "3.7.3"
}
},
"nbformat": 4,
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-22 05:31:06 +0000