{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Delta Chart: Red = Long Risk, Blue = Short Risk"
   ]
  },
  {
   "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",
    "    date_range = pd.bdate_range(portf.value_date,\n",
    "                                end_date - pd.tseries.offsets.BDay(), freq='3B')\n",
    "    vol_shock = np.arange(-.15, .31, 0.01)\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=date)\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",
    "                                 vol_shock = vol_shock,\n",
    "                                 vol_surface = vol_surface)\n",
    "    df = df.reset_index()\n",
    "    df.vol_shock = df.vol_shock.round(2)\n",
    "\n",
    "    if index == 'HY':\n",
    "        df['price'] = 100 + (500 - portf.indices[0].spread * (1 + df.spread_shock)) \\\n",
    "                    * abs(portf.indices[0].DV01) / portf.indices[0].notional * 100\n",
    "        df = df.set_index(['date', 'price', 'vol_shock'])\n",
    "        sort_order = [True, False]\n",
    "    else:\n",
    "        df['spread'] = portf.indices[0].spread * (1 +  df.spread_shock)\n",
    "        df = df.set_index(['date', 'spread', 'vol_shock'])\n",
    "        sort_order = [True, True]\n",
    "     \n",
    "    pnl = df.xs('pnl', axis=1, level=1).sum(axis=1)\n",
    "    for trade_id, t in portf.items():\n",
    "        if isinstance(t, BlackSwaption):\n",
    "            df[(trade_id, 'delta')] *= -t.notional \n",
    "    delta = df.xs('delta', axis=1, level=1).sum(axis=1).xs(0, level='vol_shock')\n",
    "    delta += sum([x.notional * -1 if x.direction == 'Buyer' else 1 for x in portf.indices])\n",
    "\n",
    "    pnl.name = 'pnl'\n",
    "    delta.name = 'delta'\n",
    "\n",
    "    plot_color_map(pnl.xs(0, level='vol_shock'), sort_order)\n",
    "    plot_color_map(delta, sort_order)\n",
    "    plot_color_map(pnl.loc[date_range[-1]], sort_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_trade_scenarios(portf, -.15, .8, vol_time_roll=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dec Jan 2017 Trade\n",
    "option_delta = CreditIndex.from_tradeid(864)\n",
    "option1 = BlackSwaption.from_tradeid(3, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(4, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#Feb 2017: Sell May Buy April Calendar Trade\n",
    "option_delta = CreditIndex.from_tradeid(870)\n",
    "option1 = BlackSwaption.from_tradeid(5, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(6, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#April 2017: Sell May Buy June Calendar Trade\n",
    "option_delta = CreditIndex.from_tradeid(874)\n",
    "option1 = BlackSwaption.from_tradeid(7, option_delta)\n",
    "option2 = BlackSwaption.from_tradeid(8, option_delta)\n",
    "portf = Portfolio([option1, option2, option_delta], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#June July 2017 Calendar Trade\n",
    "option_delta_pf = CreditIndex.from_tradeid(874)\n",
    "option_delta2_pf = CreditIndex.from_tradeid(879)\n",
    "\n",
    "option1_pf = BlackSwaption.from_tradeid(7, option_delta_pf)\n",
    "option2_pf = BlackSwaption.from_tradeid(9, option_delta_pf)\n",
    "option_delta_pf.notional = 50_335_169\n",
    "\n",
    "portf = Portfolio([option1_pf, option2_pf, option_delta_pf], trade_ids=['opt1', 'opt2', 'delta'])\n",
    "portf.value_date = datetime.date(2017, 5, 17)\n",
    "portf.mark()\n",
    "#plot_trade_scenarios(portf)\n",
    "\n",
    "#July 2017: Buy Sept HY payer spread\n",
    "option_delta = CreditIndex.from_tradeid(891)\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Look at steepness of volatility - 30 days, .85 vs .15 payer deltas on HY\n",
    "days = 30\n",
    "delta1 = .85\n",
    "delta2 = .15\n",
    "index = 'HY'\n",
    "\n",
    "sql_str = \"select b.quotedate, b.ref, b.ref_id, b.expiry, a.delta_pay, a.vol from \" \\\n",
    "          \"swaption_quotes a join swaption_ref_quotes b on a.ref_id = b.ref_id and index = %s\"\n",
    "df = pd.read_sql_query(sql_str, dbengine('serenitasdb'), \n",
    "     index_col=['quotedate'], parse_dates={'quotedate': {'utc': True}}, params=[index])\n",
    "df['days_expiry'] =  (df.expiry - df.index.date).dt.days\n",
    "r_1 = []\n",
    "for i, g in df.groupby(pd.Grouper(freq='D', level='quotedate')):\n",
    "    r = []\n",
    "    for i_1, g_1 in g.groupby(['days_expiry', 'delta_pay']):\n",
    "        r.append([i_1[0], i_1[1], g_1['vol'].mean()])\n",
    "    if len(r) > 0:\n",
    "        temp = np.dstack(r)\n",
    "        f = SmoothBivariateSpline(temp[0][0], temp[0][1], temp[0][2])\n",
    "        r = (f(days, delta1) - f(days, delta2))[0][0]\n",
    "        r_1.append([i, r])\n",
    "    else:\n",
    "        pass\n",
    "df_1 = pd.DataFrame(r_1, columns=['date', 'steepness'])\n",
    "df_1.set_index('date').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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