{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from analytics.basket_index import MarkitBasketIndex\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from db import dbengine\n",
    "from ipywidgets import widgets\n",
    "engine = dbengine('serenitasdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = widgets.Dropdown(\n",
    "    options=['IG', 'HY', 'EU'],\n",
    "    value='IG',\n",
    "    description='Index:',\n",
    "    disabled=False,\n",
    ")\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_1 = widgets.IntSlider(value=30, min=22, max=30, description = 'Series')\n",
    "w_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_date = (pd.datetime.today() - pd.offsets.BDay(1)).date()\n",
    "index_type = w.value\n",
    "series = w_1.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_string = \"select * from index_members(%s, %s)\"\n",
    "df = pd.read_sql_query(sql_string, engine, params=(index_type + str(series), trade_date), index_col=['markit_ticker'])\n",
    "df1 = pd.read_sql_query(sql_string, engine, params=(index_type + str(series-1), trade_date), index_col=['markit_ticker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removals\n",
    "df1.loc[df1.index.difference(df.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Additions\n",
    "df.loc[df.index.difference(df1.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_range = pd.bdate_range(trade_date - 52 * pd.offsets.Week(), trade_date, freq='5B')\n",
    "index = MarkitBasketIndex(index_type, series, ['5yr'])\n",
    "default_prob = {}\n",
    "maturity = np.array([np.datetime64(index.maturities[0]).view('int') + 134774])\n",
    "for d in date_range:\n",
    "    index.value_date = d\n",
    "    surv_prob, tickers = index.survival_matrix(maturity)\n",
    "    default_prob[d] = pd.Series(1 - np.ravel(surv_prob), index=tickers)\n",
    "default_prob = pd.concat(default_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Top 20 highest cumulative\n",
    "top20 = default_prob.unstack(-1)[default_prob[trade_date].nlargest(20).index]\n",
    "top20.index.name='date'\n",
    "top20.columns.name='tickers'\n",
    "top20.plot(title=f'market implied default probabilities to {index.maturities[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'market implied default probabilities to ' + str(temp.maturities[0])\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(dp_top.T)\n",
    "ax.set(xlabel='date', ylabel='probability',\n",
    "       title=title)\n",
    "ax.legend(dp_top.index, loc='upper center', bbox_to_anchor=(1.3, 1), ncol=1)\n",
    "fig.set_size_inches(10, 6)\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}