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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
},
"tags": []
},
"outputs": [],
"source": [
"# better formatting for large floats\n",
"import pandas as pd\n",
"pd.options.display.float_format = \"{:,.2f}\".format\n",
"from ipywidgets import widgets\n",
"import os\n",
"os.environ[\"SERENITAS_APP_NAME\"] = \"notebook\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
},
"tags": []
},
"outputs": [],
"source": [
"from risk.swaptions import get_swaption_portfolio\n",
"import datetime\n",
"from serenitas.analytics.base import Trade\n",
"from serenitas.utils.pool import dawn_pool\n",
"from serenitas.analytics.option import BlackSwaption\n",
"value_date = datetime.date.today()\n",
"Trade.init_ontr(value_date)\n",
"BlackSwaption.clear_cache()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"with dawn_pool.connection() as conn:\n",
" portf = get_swaption_portfolio(value_date, conn, override={k: 0.8 for k in [430, 431, 432, 433, 422, 423, 424, 425, 426, 427, 428, 429, 430]})\n",
"portf.index_formatter = lambda t: (t[2], t[1])\n",
"df = portf._todf().dropna(axis=1, how=\"all\")\n",
"df.index.names = [\"fund\", \"trade_id\"]\n",
"df = df.reset_index()\n",
"df = df.set_index([\"Expiry\", \"Desc\", \"Strike\", \"fund\"]).sort_index()\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"greeks = df.assign(Delta=lambda df: df.Delta * df.Notional, Gamma=lambda df: df.Gamma * df.Notional).groupby([\"Desc\", \"fund\"], group_keys=False)[[\"Delta\", \"Gamma\", \"Vega\"]].sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jupyter": {
"source_hidden": true
},
"tags": []
},
"outputs": [],
"source": [
"hedges = {}\n",
"with dawn_pool.connection() as conn:\n",
" for fund in [\"SERCGMAST\", \"BOWDST\", \"ISOSEL\"]:\n",
" hedges[fund] = pd.read_sql_query(\"SELECT security_desc, notional FROM list_cds_positions_by_strat(%s, %s) \"\n",
" \"WHERE folder in ('IGOPTDEL', 'HYOPTDEL')\",\n",
" conn, params=(value_date, fund))\n",
"hedges = pd.concat(hedges, names=[\"fund\"])\n",
"hedges = hedges.groupby([\"security_desc\", \"fund\"]).sum()\n",
"def f(s):\n",
" l = s.split(\" \")\n",
" return f\"{l[1]}{l[3][1:]} {l[4].lower()}r\"\n",
"\n",
"hedges = hedges.reset_index(\"security_desc\")\n",
"hedges[\"security_desc\"] = hedges[\"security_desc\"].apply(f)\n",
"hedges = hedges.rename(columns={\"notional\": \"current hedge\", \"security_desc\": \"Desc\"}).set_index(\"Desc\", append=True).swaplevel()\n",
"risk = pd.concat([hedges, greeks], axis=1)\n",
"risk = risk.assign(net_delta=risk[\"current hedge\"] + risk.Delta)\n",
"risk.swaplevel().sort_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"portf._todf().groupby(level=0)[[\"Theta\"]].sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"sr = np.linspace(65, 80, 100)\n",
"rec= []\n",
"for s in sr:\n",
" delta = 0.\n",
" gamma = 0.\n",
" for t in portf.trades:\n",
" if t.index.index_type == \"IG\":\n",
" t.index.mark(ref=s)\n",
" delta += t.delta * t.notional\n",
" gamma += t.gamma\n",
" rec.append((s, gamma, delta))\n",
"df = pd.DataFrame.from_records(rec, columns=['spread', 'gamma', 'delta'])\n",
"df = df.set_index('spread')\n",
"df.delta += risk.loc['IG37 5yr', 'current hedge']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"def get_newaxes(num):\n",
" plt.close(num)\n",
" fix, axes = plt.subplots(figsize=(10,5), num=num)\n",
" return axes\n",
"\n",
"axes = get_newaxes(1)\n",
"df.delta.plot(ax=axes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"axes = get_newaxes(2)\n",
"df.gamma.plot(ax=axes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"portf.pv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
