{
 "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",
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