{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "w = widgets.Dropdown(\n",
    "    options=['BOWDST', 'SERCGMAST'],\n",
    "    value='SERCGMAST',\n",
    "    description='fund',\n",
    "    disabled=False,\n",
    ")\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fund = w.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from risk.swaptions import get_swaption_portfolio\n",
    "import datetime\n",
    "from serenitas.utils.db import dbconn\n",
    "from serenitas.analytics import init_ontr\n",
    "conn = dbconn('dawndb')\n",
    "conn.autocommit = True\n",
    "value_date = datetime.date.today()\n",
    "init_ontr(value_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "portf = get_swaption_portfolio(value_date, conn, source_list=[\"MS\"], fund=fund)\n",
    "portf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = portf._todf()\n",
    "positions = df.set_index(\"Index\")[[\"Delta\", \"Notional\"]].prod(axis=1).groupby(level=\"Index\").sum()\n",
    "positions.name = 'current_delta'\n",
    "gamma = df.set_index(\"Index\")[[\"Gamma\", \"Notional\"]].prod(axis=1).groupby(level=\"Index\").sum()\n",
    "gamma.name = 'gamma'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hedges = 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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(s):\n",
    "    l = s.split(\" \")\n",
    "    return f\"{l[1]}{l[3][1:]} {l[4].lower()}r\"\n",
    "\n",
    "hedges[\"Index\"] = hedges[\"security_desc\"].apply(f)\n",
    "hedges = hedges.rename(columns={\"notional\": \"current hedge\"})\n",
    "hedges = hedges.set_index(\"Index\")[\"current hedge\"]\n",
    "hedges = hedges.reindex(positions.index, fill_value=0.)\n",
    "risk = pd.concat([hedges, positions, gamma], axis=1)\n",
    "risk['net_delta'] = risk[\"current hedge\"] + risk.current_delta\n",
    "risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "portf.theta"
   ]
  },
  {
   "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['IG34 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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