RM_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.size'] = 12\n",
    "matplotlib.rcParams['legend.fontsize'] = 'medium'\n",
    "matplotlib.rcParams['figure.titlesize'] = 'large'\n",
    "plt.rcParams[\"figure.figsize\"] = (15,7.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stock_prices = pd.read_csv('stock_prices.csv', parse_dates=True, index_col='Date')\n",
    "day_1_returns = pd.read_csv('1_day_returns.csv', parse_dates=True, index_col='Date')\n",
    "day_10_returns = pd.read_csv('10_days_returns.csv', parse_dates=True, index_col='Date')\n",
    "daily_log_returns = pd.read_csv('log_returns.csv', parse_dates=True, index_col='Date')\n",
    "log_10_day_returns = pd.read_csv('log_10_day_returns.csv', parse_dates=True, index_col='Date')\n",
    "\n",
    "rates = pd.read_csv('rates_interpolated.csv', parse_dates=True, index_col='Date')\n",
    "rates.columns = rates.columns.astype(float)\n",
    "\n",
    "vanila_rates = pd.read_csv('rates.csv', parse_dates=True, index_col='Date')\n",
    "vanila_rates.columns = vanila_rates.columns.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAL.L</th>\n",
       "      <th>BARC.L</th>\n",
       "      <th>BP.L</th>\n",
       "      <th>HSBA.L</th>\n",
       "      <th>LLOY.L</th>\n",
       "      <th>RBS.L</th>\n",
       "      <th>RDSA.L</th>\n",
       "      <th>RIO.L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-07-14</th>\n",
       "      <td>-0.046112</td>\n",
       "      <td>0.033590</td>\n",
       "      <td>-0.033061</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>-0.035911</td>\n",
       "      <td>-0.063063</td>\n",
       "      <td>-0.055305</td>\n",
       "      <td>-0.064180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-15</th>\n",
       "      <td>0.042874</td>\n",
       "      <td>0.084083</td>\n",
       "      <td>0.017569</td>\n",
       "      <td>0.061363</td>\n",
       "      <td>0.042934</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>0.003324</td>\n",
       "      <td>0.034152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-16</th>\n",
       "      <td>0.026554</td>\n",
       "      <td>0.059934</td>\n",
       "      <td>0.025769</td>\n",
       "      <td>0.054224</td>\n",
       "      <td>0.036310</td>\n",
       "      <td>-0.016630</td>\n",
       "      <td>0.019411</td>\n",
       "      <td>0.026900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-17</th>\n",
       "      <td>0.109610</td>\n",
       "      <td>0.069516</td>\n",
       "      <td>0.059996</td>\n",
       "      <td>0.076118</td>\n",
       "      <td>0.021175</td>\n",
       "      <td>0.019244</td>\n",
       "      <td>0.049897</td>\n",
       "      <td>0.142706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-20</th>\n",
       "      <td>0.099232</td>\n",
       "      <td>0.057434</td>\n",
       "      <td>0.075673</td>\n",
       "      <td>0.111995</td>\n",
       "      <td>0.091039</td>\n",
       "      <td>0.085354</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.163021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAL.L    BARC.L      BP.L    HSBA.L    LLOY.L     RBS.L  \\\n",
       "Date                                                                     \n",
       "2009-07-14 -0.046112  0.033590 -0.033061  0.002551 -0.035911 -0.063063   \n",
       "2009-07-15  0.042874  0.084083  0.017569  0.061363  0.042934  0.011609   \n",
       "2009-07-16  0.026554  0.059934  0.025769  0.054224  0.036310 -0.016630   \n",
       "2009-07-17  0.109610  0.069516  0.059996  0.076118  0.021175  0.019244   \n",
       "2009-07-20  0.099232  0.057434  0.075673  0.111995  0.091039  0.085354   \n",
       "\n",
       "              RDSA.L     RIO.L  \n",
       "Date                            \n",
       "2009-07-14 -0.055305 -0.064180  \n",
       "2009-07-15  0.003324  0.034152  \n",
       "2009-07-16  0.019411  0.026900  \n",
       "2009-07-17  0.049897  0.142706  \n",
       "2009-07-20  0.064516  0.163021  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_10_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAL.L</th>\n",
       "      <th>BARC.L</th>\n",
       "      <th>BP.L</th>\n",
       "      <th>HSBA.L</th>\n",
       "      <th>LLOY.L</th>\n",
       "      <th>RBS.L</th>\n",
       "      <th>RDSA.L</th>\n",
       "      <th>RIO.L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-07-02</th>\n",
       "      <td>-0.059875</td>\n",
       "      <td>-0.004317</td>\n",
       "      <td>-0.018642</td>\n",
       "      <td>-0.018222</td>\n",
       "      <td>-0.035043</td>\n",
       "      <td>-0.038942</td>\n",
       "      <td>-0.033347</td>\n",
       "      <td>-0.058686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-03</th>\n",
       "      <td>-0.007269</td>\n",
       "      <td>0.027306</td>\n",
       "      <td>-0.003541</td>\n",
       "      <td>0.017240</td>\n",
       "      <td>0.023980</td>\n",
       "      <td>0.023082</td>\n",
       "      <td>-0.006671</td>\n",
       "      <td>-0.004432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-06</th>\n",
       "      <td>-0.050879</td>\n",
       "      <td>-0.014413</td>\n",
       "      <td>-0.026536</td>\n",
       "      <td>-0.005022</td>\n",
       "      <td>-0.020954</td>\n",
       "      <td>-0.029037</td>\n",
       "      <td>-0.020968</td>\n",
       "      <td>-0.072136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-07</th>\n",
       "      <td>-0.000614</td>\n",
       "      <td>0.011040</td>\n",
       "      <td>0.002354</td>\n",
       "      <td>-0.007432</td>\n",
       "      <td>-0.001501</td>\n",
       "      <td>-0.009601</td>\n",
       "      <td>-0.004110</td>\n",
       "      <td>0.018397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-08</th>\n",
       "      <td>-0.040118</td>\n",
       "      <td>-0.030875</td>\n",
       "      <td>-0.006003</td>\n",
       "      <td>-0.019484</td>\n",
       "      <td>-0.035316</td>\n",
       "      <td>-0.045646</td>\n",
       "      <td>-0.008270</td>\n",
       "      <td>-0.012843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAL.L    BARC.L      BP.L    HSBA.L    LLOY.L     RBS.L  \\\n",
       "Date                                                                     \n",
       "2009-07-02 -0.059875 -0.004317 -0.018642 -0.018222 -0.035043 -0.038942   \n",
       "2009-07-03 -0.007269  0.027306 -0.003541  0.017240  0.023980  0.023082   \n",
       "2009-07-06 -0.050879 -0.014413 -0.026536 -0.005022 -0.020954 -0.029037   \n",
       "2009-07-07 -0.000614  0.011040  0.002354 -0.007432 -0.001501 -0.009601   \n",
       "2009-07-08 -0.040118 -0.030875 -0.006003 -0.019484 -0.035316 -0.045646   \n",
       "\n",
       "              RDSA.L     RIO.L  \n",
       "Date                            \n",
       "2009-07-02 -0.033347 -0.058686  \n",
       "2009-07-03 -0.006671 -0.004432  \n",
       "2009-07-06 -0.020968 -0.072136  \n",
       "2009-07-07 -0.004110  0.018397  \n",
       "2009-07-08 -0.008270 -0.012843  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_log_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.5</th>\n",
       "      <th>1.0</th>\n",
       "      <th>1.5</th>\n",
       "      <th>2.0</th>\n",
       "      <th>2.5</th>\n",
       "      <th>3.0</th>\n",
       "      <th>3.5</th>\n",
       "      <th>4.0</th>\n",
       "      <th>4.5</th>\n",
       "      <th>5.0</th>\n",
       "      <th>...</th>\n",
       "      <th>13.5</th>\n",
       "      <th>14.0</th>\n",
       "      <th>14.5</th>\n",
       "      <th>15.0</th>\n",
       "      <th>15.5</th>\n",
       "      <th>16.0</th>\n",
       "      <th>16.5</th>\n",
       "      <th>17.0</th>\n",
       "      <th>17.5</th>\n",
       "      <th>18.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>9.18</td>\n",
       "      <td>10.56</td>\n",
       "      <td>11.135</td>\n",
       "      <td>11.71</td>\n",
       "      <td>11.815</td>\n",
       "      <td>11.92</td>\n",
       "      <td>11.8825</td>\n",
       "      <td>11.845</td>\n",
       "      <td>11.8075</td>\n",
       "      <td>11.77</td>\n",
       "      <td>...</td>\n",
       "      <td>11.623</td>\n",
       "      <td>11.622</td>\n",
       "      <td>11.621</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-02</th>\n",
       "      <td>8.93</td>\n",
       "      <td>10.29</td>\n",
       "      <td>10.920</td>\n",
       "      <td>11.55</td>\n",
       "      <td>11.730</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.9075</td>\n",
       "      <td>11.905</td>\n",
       "      <td>11.9025</td>\n",
       "      <td>11.90</td>\n",
       "      <td>...</td>\n",
       "      <td>11.455</td>\n",
       "      <td>11.440</td>\n",
       "      <td>11.425</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-03</th>\n",
       "      <td>8.91</td>\n",
       "      <td>10.33</td>\n",
       "      <td>11.005</td>\n",
       "      <td>11.68</td>\n",
       "      <td>11.845</td>\n",
       "      <td>12.01</td>\n",
       "      <td>11.9725</td>\n",
       "      <td>11.935</td>\n",
       "      <td>11.8975</td>\n",
       "      <td>11.86</td>\n",
       "      <td>...</td>\n",
       "      <td>11.411</td>\n",
       "      <td>11.404</td>\n",
       "      <td>11.397</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.39</td>\n",
       "      <td>11.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-06</th>\n",
       "      <td>8.84</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.925</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.900</td>\n",
       "      <td>12.15</td>\n",
       "      <td>12.1725</td>\n",
       "      <td>12.195</td>\n",
       "      <td>12.2175</td>\n",
       "      <td>12.24</td>\n",
       "      <td>...</td>\n",
       "      <td>11.757</td>\n",
       "      <td>11.738</td>\n",
       "      <td>11.719</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-07</th>\n",
       "      <td>9.09</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.035</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.765</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.8975</td>\n",
       "      <td>11.885</td>\n",
       "      <td>11.8725</td>\n",
       "      <td>11.86</td>\n",
       "      <td>...</td>\n",
       "      <td>11.614</td>\n",
       "      <td>11.606</td>\n",
       "      <td>11.598</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0.5    1.0     1.5    2.0     2.5    3.0      3.5     4.0   \\\n",
       "Date                                                                     \n",
       "2009-07-01  9.18  10.56  11.135  11.71  11.815  11.92  11.8825  11.845   \n",
       "2009-07-02  8.93  10.29  10.920  11.55  11.730  11.91  11.9075  11.905   \n",
       "2009-07-03  8.91  10.33  11.005  11.68  11.845  12.01  11.9725  11.935   \n",
       "2009-07-06  8.84  10.20  10.925  11.65  11.900  12.15  12.1725  12.195   \n",
       "2009-07-07  9.09  10.45  11.035  11.62  11.765  11.91  11.8975  11.885   \n",
       "\n",
       "               4.5    5.0   ...      13.5    14.0    14.5   15.0   15.5  \\\n",
       "Date                        ...                                           \n",
       "2009-07-01  11.8075  11.77  ...    11.623  11.622  11.621  11.62  11.62   \n",
       "2009-07-02  11.9025  11.90  ...    11.455  11.440  11.425  11.41  11.41   \n",
       "2009-07-03  11.8975  11.86  ...    11.411  11.404  11.397  11.39  11.39   \n",
       "2009-07-06  12.2175  12.24  ...    11.757  11.738  11.719  11.70  11.70   \n",
       "2009-07-07  11.8725  11.86  ...    11.614  11.606  11.598  11.59  11.59   \n",
       "\n",
       "             16.0   16.5   17.0   17.5   18.0  \n",
       "Date                                           \n",
       "2009-07-01  11.62  11.62  11.62  11.62  11.62  \n",
       "2009-07-02  11.41  11.41  11.41  11.41  11.41  \n",
       "2009-07-03  11.39  11.39  11.39  11.39  11.39  \n",
       "2009-07-06  11.70  11.70  11.70  11.70  11.70  \n",
       "2009-07-07  11.59  11.59  11.59  11.59  11.59  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Метод подсчета доходностей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Логарифмичские"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "r_{t+1} = \\log\\frac{P_{t+1}}{P_{t}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Арифметические"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "r_{t+1} = \\frac{P_{t+1} - P_{t}}{P_{t}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Описание данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Цены закрытия акций выружены с Yahoo Finance.\n",
    "- Ставки взяты с сайта ЦБ РФ.\n",
    "- Кросс-валютный курс выгружен с сайта stooq.com, который берет информацию с Google Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведим описательные статистики, skew, kurtosis и автокорреляцию по первому лагу арифметических 10-дневных доходностей акций."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import skew\n",
    "from scipy.stats import kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def describe(df):\n",
    "    \"\"\"\n",
    "    Print description of datafame with autocorrelation, skew and kurtosis\n",
    "    \"\"\"\n",
    "    description = df.describe()\n",
    "    \n",
    "    colnames = list(df.columns)\n",
    "    autocorrelation = []\n",
    "    \n",
    "    for colname in colnames:\n",
    "        autocorrelation.append(df[colname].autocorr(lag = 1))\n",
    "        \n",
    "    autocorr = pd.DataFrame([autocorrelation], columns = colnames, index=['autocorr'])\n",
    "    skews = pd.DataFrame([skew(df, axis = 0).tolist()], columns = colnames, index=['skew'])\n",
    "    kurt = pd.DataFrame([kurtosis(df, axis = 0).tolist()], columns = colnames, index=['kurtosis'])\n",
    "    \n",
    "    description = description.append(autocorr)\n",
    "    description = description.append(skews)\n",
    "    description = description.append(kurt)\n",
    "    \n",
    "    return description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAL.L</th>\n",
       "      <th>BARC.L</th>\n",
       "      <th>BP.L</th>\n",
       "      <th>HSBA.L</th>\n",
       "      <th>LLOY.L</th>\n",
       "      <th>RBS.L</th>\n",
       "      <th>RDSA.L</th>\n",
       "      <th>RIO.L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000173</td>\n",
       "      <td>-0.000764</td>\n",
       "      <td>-0.000198</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>-0.000800</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.000444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.023887</td>\n",
       "      <td>0.030933</td>\n",
       "      <td>0.018482</td>\n",
       "      <td>0.016300</td>\n",
       "      <td>0.035221</td>\n",
       "      <td>0.033601</td>\n",
       "      <td>0.013476</td>\n",
       "      <td>0.024755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.076172</td>\n",
       "      <td>-0.168794</td>\n",
       "      <td>-0.140368</td>\n",
       "      <td>-0.061640</td>\n",
       "      <td>-0.283638</td>\n",
       "      <td>-0.195471</td>\n",
       "      <td>-0.053226</td>\n",
       "      <td>-0.114285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.014169</td>\n",
       "      <td>-0.017052</td>\n",
       "      <td>-0.009557</td>\n",
       "      <td>-0.008810</td>\n",
       "      <td>-0.018900</td>\n",
       "      <td>-0.018744</td>\n",
       "      <td>-0.007538</td>\n",
       "      <td>-0.013972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001184</td>\n",
       "      <td>-0.001252</td>\n",
       "      <td>-0.000051</td>\n",
       "      <td>-0.000159</td>\n",
       "      <td>-0.000306</td>\n",
       "      <td>-0.001931</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>0.000828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.014911</td>\n",
       "      <td>0.016339</td>\n",
       "      <td>0.009352</td>\n",
       "      <td>0.009139</td>\n",
       "      <td>0.018373</td>\n",
       "      <td>0.018677</td>\n",
       "      <td>0.008970</td>\n",
       "      <td>0.015609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.095437</td>\n",
       "      <td>0.161956</td>\n",
       "      <td>0.089487</td>\n",
       "      <td>0.064578</td>\n",
       "      <td>0.204678</td>\n",
       "      <td>0.120018</td>\n",
       "      <td>0.038298</td>\n",
       "      <td>0.076540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autocorr</th>\n",
       "      <td>0.003796</td>\n",
       "      <td>0.014173</td>\n",
       "      <td>0.030910</td>\n",
       "      <td>-0.026055</td>\n",
       "      <td>-0.023473</td>\n",
       "      <td>0.042604</td>\n",
       "      <td>0.056323</td>\n",
       "      <td>0.049168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>-0.111088</td>\n",
       "      <td>-0.028955</td>\n",
       "      <td>-0.561599</td>\n",
       "      <td>0.027504</td>\n",
       "      <td>-0.538963</td>\n",
       "      <td>-0.231325</td>\n",
       "      <td>-0.205137</td>\n",
       "      <td>-0.140894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>0.504464</td>\n",
       "      <td>3.163102</td>\n",
       "      <td>6.592283</td>\n",
       "      <td>1.379727</td>\n",
       "      <td>8.716491</td>\n",
       "      <td>2.301204</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>0.910341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAL.L      BARC.L        BP.L      HSBA.L      LLOY.L  \\\n",
       "count     756.000000  756.000000  756.000000  756.000000  756.000000   \n",
       "mean        0.000173   -0.000764   -0.000198    0.000128   -0.000502   \n",
       "std         0.023887    0.030933    0.018482    0.016300    0.035221   \n",
       "min        -0.076172   -0.168794   -0.140368   -0.061640   -0.283638   \n",
       "25%        -0.014169   -0.017052   -0.009557   -0.008810   -0.018900   \n",
       "50%         0.001184   -0.001252   -0.000051   -0.000159   -0.000306   \n",
       "75%         0.014911    0.016339    0.009352    0.009139    0.018373   \n",
       "max         0.095437    0.161956    0.089487    0.064578    0.204678   \n",
       "autocorr    0.003796    0.014173    0.030910   -0.026055   -0.023473   \n",
       "skew       -0.111088   -0.028955   -0.561599    0.027504   -0.538963   \n",
       "kurtosis    0.504464    3.163102    6.592283    1.379727    8.716491   \n",
       "\n",
       "               RBS.L      RDSA.L       RIO.L  \n",
       "count     756.000000  756.000000  756.000000  \n",
       "mean       -0.000800    0.000427    0.000444  \n",
       "std         0.033601    0.013476    0.024755  \n",
       "min        -0.195471   -0.053226   -0.114285  \n",
       "25%        -0.018744   -0.007538   -0.013972  \n",
       "50%        -0.001931    0.000693    0.000828  \n",
       "75%         0.018677    0.008970    0.015609  \n",
       "max         0.120018    0.038298    0.076540  \n",
       "autocorr    0.042604    0.056323    0.049168  \n",
       "skew       -0.231325   -0.205137   -0.140894  \n",
       "kurtosis    2.301204    0.703511    0.910341  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe(daily_log_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>15.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.096688</td>\n",
       "      <td>5.474746</td>\n",
       "      <td>5.806954</td>\n",
       "      <td>6.099099</td>\n",
       "      <td>6.964226</td>\n",
       "      <td>7.506485</td>\n",
       "      <td>8.079683</td>\n",
       "      <td>8.333617</td>\n",
       "      <td>8.494708</td>\n",
       "      <td>8.581954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.531540</td>\n",
       "      <td>1.509381</td>\n",
       "      <td>1.499777</td>\n",
       "      <td>1.494949</td>\n",
       "      <td>1.476348</td>\n",
       "      <td>1.435242</td>\n",
       "      <td>1.334353</td>\n",
       "      <td>1.249837</td>\n",
       "      <td>1.159669</td>\n",
       "      <td>1.071605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.860000</td>\n",
       "      <td>3.390000</td>\n",
       "      <td>3.830000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>5.440000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.560000</td>\n",
       "      <td>6.880000</td>\n",
       "      <td>7.140000</td>\n",
       "      <td>7.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.740000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>4.917500</td>\n",
       "      <td>5.977500</td>\n",
       "      <td>6.640000</td>\n",
       "      <td>7.410000</td>\n",
       "      <td>7.710000</td>\n",
       "      <td>7.890000</td>\n",
       "      <td>8.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.840000</td>\n",
       "      <td>5.335000</td>\n",
       "      <td>5.770000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>6.690000</td>\n",
       "      <td>7.050000</td>\n",
       "      <td>7.620000</td>\n",
       "      <td>7.940000</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>8.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.120000</td>\n",
       "      <td>6.232500</td>\n",
       "      <td>6.350000</td>\n",
       "      <td>6.535000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.740000</td>\n",
       "      <td>8.252500</td>\n",
       "      <td>8.482500</td>\n",
       "      <td>8.620000</td>\n",
       "      <td>8.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.740000</td>\n",
       "      <td>9.340000</td>\n",
       "      <td>9.970000</td>\n",
       "      <td>10.560000</td>\n",
       "      <td>11.710000</td>\n",
       "      <td>12.160000</td>\n",
       "      <td>12.430000</td>\n",
       "      <td>12.420000</td>\n",
       "      <td>12.230000</td>\n",
       "      <td>12.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autocorr</th>\n",
       "      <td>0.997385</td>\n",
       "      <td>0.997438</td>\n",
       "      <td>0.997304</td>\n",
       "      <td>0.997109</td>\n",
       "      <td>0.996775</td>\n",
       "      <td>0.996916</td>\n",
       "      <td>0.996711</td>\n",
       "      <td>0.995841</td>\n",
       "      <td>0.994423</td>\n",
       "      <td>0.993101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>0.599426</td>\n",
       "      <td>0.864714</td>\n",
       "      <td>1.121669</td>\n",
       "      <td>1.350469</td>\n",
       "      <td>1.925105</td>\n",
       "      <td>2.148619</td>\n",
       "      <td>2.241309</td>\n",
       "      <td>2.199967</td>\n",
       "      <td>2.112796</td>\n",
       "      <td>2.013835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-0.604079</td>\n",
       "      <td>0.042969</td>\n",
       "      <td>0.727963</td>\n",
       "      <td>1.378767</td>\n",
       "      <td>3.139438</td>\n",
       "      <td>3.854065</td>\n",
       "      <td>4.156805</td>\n",
       "      <td>4.043147</td>\n",
       "      <td>3.786662</td>\n",
       "      <td>3.488642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0.25        0.50        0.75        1.00        2.00   \\\n",
       "count     788.000000  788.000000  788.000000  788.000000  788.000000   \n",
       "mean        5.096688    5.474746    5.806954    6.099099    6.964226   \n",
       "std         1.531540    1.509381    1.499777    1.494949    1.476348   \n",
       "min         2.860000    3.390000    3.830000    4.200000    5.440000   \n",
       "25%         3.740000    4.170000    4.570000    4.917500    5.977500   \n",
       "50%         4.840000    5.335000    5.770000    6.090000    6.690000   \n",
       "75%         6.120000    6.232500    6.350000    6.535000    7.300000   \n",
       "max         8.740000    9.340000    9.970000   10.560000   11.710000   \n",
       "autocorr    0.997385    0.997438    0.997304    0.997109    0.996775   \n",
       "skew        0.599426    0.864714    1.121669    1.350469    1.925105   \n",
       "kurtosis   -0.604079    0.042969    0.727963    1.378767    3.139438   \n",
       "\n",
       "               3.00        5.00        7.00        10.00       15.00  \n",
       "count     788.000000  788.000000  788.000000  788.000000  788.000000  \n",
       "mean        7.506485    8.079683    8.333617    8.494708    8.581954  \n",
       "std         1.435242    1.334353    1.249837    1.159669    1.071605  \n",
       "min         6.000000    6.560000    6.880000    7.140000    7.310000  \n",
       "25%         6.640000    7.410000    7.710000    7.890000    8.020000  \n",
       "50%         7.050000    7.620000    7.940000    8.170000    8.290000  \n",
       "75%         7.740000    8.252500    8.482500    8.620000    8.662500  \n",
       "max        12.160000   12.430000   12.420000   12.230000   12.010000  \n",
       "autocorr    0.996916    0.996711    0.995841    0.994423    0.993101  \n",
       "skew        2.148619    2.241309    2.199967    2.112796    2.013835  \n",
       "kurtosis    3.854065    4.156805    4.043147    3.786662    3.488642  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe(vanila_rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. VaR и ES для акций"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Дельта-нормальный метод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим остатки на нормальность."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1atTQRx99JJPJpJo1a2rYsGEym+l2AFBe2B7rY3QEAABQDFctZWvWrJG/v7+e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\niIiIiIhYSEWZiIiIiIiIhf4/oOoCkT4c9U0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_weights = np.array([0.1, 0.1, 0.1, 0.1, 0.175, 0.175, 0.125, 0.125])\n",
    "returns_distribution = day_1_returns@p_weights\n",
    "\n",
    "plt.hist(returns_distribution, density = True, bins=50)\n",
    "mu = returns_distribution.mean()\n",
    "std = returns_distribution.std()\n",
    "x_axis = np.linspace(-0.08, 0.08, 100)\n",
    "plt.plot(x_axis, stats.norm.pdf(x_axis ,returns_distribution.mean(),returns_distribution.std()))\n",
    "plt.legend(['Normal Distribution', 'Portfolio returns'])\n",
    "plt.title('Returns distribution');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis of normality is rejected.\n"
     ]
    }
   ],
   "source": [
    "p_value = stats.shapiro(returns_distribution)[1]\n",
    "if p_value <= 0.01:\n",
    "    print(\"Null hypothesis of normality is rejected.\")\n",
    "else:\n",
    "    print(\"Null hypothesis of normality is accepted.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Остатки не нормальны, но для упрощения предположим, что они нормально распределены."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция использует следующие формулы для подсчета VaR и ES:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "VaR_{\\alpha}(X) = \\sigma_p \\cdot \\Phi^{-1}(\\alpha) \\cdot \\sqrt{t}\n",
    "$$\n",
    "\n",
    "$$\n",
    "ES_{\\alpha}(X) = \\sigma_p \\cdot \\frac{\\phi(\\Phi^{-1}(\\alpha))}{\\alpha} \\cdot \\sqrt{t}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>$t$ - holding period</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def delta_norm_stock_VaR_ES(start, end, p_weights, alpha, return_period, daily_log_returns = daily_log_returns):\n",
    "    cov = np.array(daily_log_returns[start:end].cov())\n",
    "    stdev_p = np.sqrt(p_weights@cov@p_weights)\n",
    "    mu_p = (daily_log_returns@p_weights.T).mean()\n",
    "    \n",
    "    VaR = stdev_p * st.norm.ppf(1 - alpha) * np.sqrt(return_period)\n",
    "    ES = stdev_p * st.norm.pdf(st.norm.ppf(1 - alpha))/alpha * np.sqrt(return_period)\n",
    "    # check how return peroid is included in ES\n",
    "\n",
    "    return VaR, ES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "future_log_returns = log_10_day_returns[log_10_day_returns.index[-1] - dt.timedelta(days = 364):]@p_weights.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = 0.01\n",
    "\n",
    "VaR_delta_norm_forecast = [ -delta_norm_stock_VaR_ES(daily_log_returns.index[i], \n",
    "                                                        daily_log_returns.index[i] + dt.timedelta(days = 728), \n",
    "                               p_weights, alpha, return_period = 10)[0] for i in range(future_log_returns.shape[0])];\n",
    "ES_delta_norm_forecast = [ -delta_norm_stock_VaR_ES(daily_log_returns.index[i], \n",
    "                                                        daily_log_returns.index[i] + dt.timedelta(days = 728), \n",
    "                               p_weights, alpha, return_period = 10)[1] for i in range(future_log_returns.shape[0])];\n",
    "\n",
    "VaR_delta_norm_forecast = pd.Series(VaR_delta_norm_forecast, pd.to_datetime(future_log_returns.index))\n",
    "ES_delta_norm_forecast = pd.Series(ES_delta_norm_forecast, pd.to_datetime(future_log_returns.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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cwQ0AAACwoWuvvVYDBgzQoEGDPD9Tpky54HCPPfaYTp48WeLlW758uf75z3/m\n6nbffffpnnvu0dSpU5WZmVmocfXv318bNmwoiWLm68yZMxo9enSpTrM4eBwAAAAAYFNTp05V1apV\nizTM+vXrS6g0llOnTik+Pl4rV65Uy5YtPd137dql+fPn6/XXX1d4eLieffZZvf/++7Z9kPeZM2e0\ndevWsi5GoRHcAAAAgHLm2muv1ccff+wJdVmv58yZI0kaNWqUXnjhBY0YMUKTJk1S48aNJUl9+vTR\npEmTVLVqVY0YMUJ169bVoUOHNG3aNB08eFCvv/66UlNT5XA4dO+996pDhw65pv3NN9+oZs2aGjp0\nqH788UdP9++//15xcXGqVq2aJOlvf/ubXnnllTyD2+7duzV58mSlpaUpJiZGqampnvfefvttff/9\n90pPT1dqaqqGDh2qTp066d5779WIESPUtm1bSdKUKVNUv3599erVyzPsoUOHCv25XnzxRaWnp2vQ\noEFaunRpvvN0165deuWVVxQcHKyUlBQNGTJECxcuVHR0tHbt2qXMzEyNHj1azZs316ZNmzR79my5\nXC4ZhqG+ffuqc+fOxVrWWQhuAAAAwDkl/YzENWsSi9T/qFGj5HD8eXXTlClTVL169Xz7HzNmjFas\nWFGomrojR45o/PjxatGihU6fPq3Jkydr8uTJioqK0tGjRzVs2DBdeumluuiii7IN16NHD0nSihUr\nsnVPTExUVFSU53VERISOHDmS57SfffZZ3XLLLbrpppu0adMmjRw5UpIVvH7++WdNmzZNgYGB+vrr\nrzVv3jxdffXV6tGjh5YtW6a2bdsqOTlZP/zwgx544AGvP9eYMWPUv39/xcfHy+l0Fjivdu/erX//\n+9+KiorShg0btHXrVj388MNq2LChFi9erPj4eE2fPl3z5s3THXfcoa5du2rnzp1aunQpwQ0AAACo\n6LxpKllYTqdTTZs2lSRt3rxZx44d0/jx4z3vG4ahP/74I1dwy4/b7ZZhGJ7XpmlmC51ZTp48qT/+\n+EPdunWTJDVv3lz169eXJEVFRenxxx/XypUrdeDAAW3ZskUpKSmSpO7du2vBggU6ceKEvv32W7Vv\n315hYWFef66saRZGREREtlAaFRWlhg0bSpIuu+wyT4jt0qWLpk+frh9++EFt2rTRoEGDCj2NCyG4\nAQAAAOcUtUasLJmmKUnKyMjItx/DMDz9Scp2sxB/f39PTZPb7VadOnX06quvet4/evSop9ljYVx0\n0UU6evRotuEjIiJ09OhRjR071tP9+eefz1Z+SZ5y/P777xo/frx69eqltm3bqmXLlpo6daokKSws\nTJ07d9aXX36pr776ylNLl1OQsLP1AAAgAElEQVRhP9f5Zc2S3zwNDg7O9jogIMDz//nzuEePHoqL\ni9P69eu1du1aLViwQAsXLszWv7e4qyQAAABQzlSrVk2//fabJOmrr77K9p7D4fAEtPP727Bhg44d\nO5bn+Jo0aaIDBw5o48aNkqQdO3bo73//e57hJj9xcXH64YcfdPz4cZmmqWXLlqlTp06qVauW4uPj\nPT8RERFq1KiRPv30U0lWWPvjjz8kSb/++qsaN26s3r17q2XLlvruu+/kdrs907jlllv00Ucfye12\n64orrrhgmQr6XE6nU2632xO6CpqnhfXQQw9p+/bt6t69ux555BGdOXNGSUlJXo0rJ2rcAAAAgHJm\n+PDhmj59usLCwtSmTRvVrFnT816XLl308MMP66mnntLgwYM1depULV26VI0aNVKjRo3yHF+1atU0\nadIkzZkzR+np6TJNU48//ni25oEX0qBBA/Xr10+jR4+Wy+XS5Zdfnu8dJcePH6/Jkyfrk08+Ue3a\ntVW3bl1JUteuXbVq1Srde++9Mk1T7du31+nTp5WcnKyQkBA1bNhQVapU8VxndyEFfa6sMvbv31+L\nFi0qcJ4W1pAhQ/TKK6/ozTfflGEY6tevX5HmYUEM8/w6yjKWkJBQ1kXIJTo62pblQuliPai8WPZ5\ny+/i/fLUxKgw7Lr8s+Z/RZvfdmDXZY7SwfK3vwMHDmjUqFFauHChgoKCfDZeuyz76OjofN+jxg0A\nAABAmSjKiag333xTy5Yt0+jRo30a2soLghsAAOVESd+mHADsbMCAARowYEBZF6PMcHMSAAAAALA5\nghsAAAAA2BzBDQCACiQ2NpImlQBQARHcAAAAAMDmCG4AbI3aAwAAAIIbAAAAANgewQ0AAAAAbI7g\nBgAAAAA2R3ADAAAAAJsjuAEAAACAzRHcAAAAAMDmCG4AAAAAbIXHAeVGcAMAAAAAm/MquLndbr3+\n+usaN26cJk6cqEOHDuXq59SpUxoxYoTS09OLXUgAAAAAqMy8Cm7r1q1TRkaGnn32WfXt21cLFy7M\n9v6GDRv0zDPP6OTJkz4pJFCWYmMjVa9eWZcCAAAAlZlXwW3btm1q1aqVJKlRo0bauXNn9pE6HHri\niScUFhZW/BICAAAAQCXn581AKSkpCgkJ8bx2OBxyuVxyOp2SpBYtWvimdAAAAAAA74JbcHCwUlJS\nPK9N0/SEtuKIjo4u9jhKgl3LhdKRtWqzHpQNO8x/ln1u+W3yK+K8stNnyjnf8yqbHb4z5R3zrnJj\n+Zeu/LZZZbEts/uy9yq4NW7cWD/99JPi4uL0+++/q06dOj4pTEJCgk/G40vR0dG2LBdKj8sVKafT\nj/WgjLhc1q2AExISy2T6bAPylrVcciqr5VRS7Lb8c873vOZ3WX9nyju7LXOULpZ/6ctvm1Xa2zK7\nLPuCwqNXwe2qq67Sr7/+qvHjx8s0TQ0bNkzLli1TVFSU2rZt63VBAQAAAAC5eRXcHA6HBg8enK1b\n7dq1c/U3a9Ys70oFAAAAAPDgAdwAAAAAYHMENwAAAACwOYIbUEixsZGKjc37hgwoX1iWAACgvCG4\nAQBQRkryJAInKACgYiG4AQDyxcE/AAD2QHADbIyDZgAAAEhePg4AAACUHk7gAACocQMAAAAAmyO4\nAQBQBDRhBgCUBYIbAMBnCDUAAJQMghsAAAAAW+KE4J+4OQmASitrR7BmTWIZlwSVDQchACo7toNF\nR40bgDLH2TQAAICCEdwAAKgEOEECAOUbwQ0AAAAAbI5r3HyMa2YAwML2MDdqvAAA3qLGDQAAAABs\njho3AABsiho6AEAWatwAAAAAwOYIbgAAAABgcwQ3AAAAALA5ghsAAAAA2Bw3JwEAoBC4UUjJ4LER\nAAqDbQU1bgAAAABgewQ3AAAAALA5ghsAAJVIbGxkiTX7LMlxA0BlxzVuAABUYAQpAKgYqHErQGxs\npOrVK+tSAAAAAKjsCG4AABQDzQMBAKWB4FZC2JEDAAAA8BWucQMAwAucnENp4flVwJ8q8/eBGjcA\npY4aaQAAgKKhxg0AAJvhxAYAICdq3ACUC9TSAQBQPrEP9w1q3AAAQLFwQAYAJY8aNwAAUKI42w5A\nYltQXAQ3AMXGhhgAAKBkEdx8gINWAAAAACWJ4AYAAAAANkdwAwCgBNEqAwDgC9xVEgAAlAgCKwD4\nDsENQJnhoA4AAKBwaCoJAAAAwGs0CS8dBDfAx0pj48UGEgAAoHIhuAHFRIgCAAAoXZXx+IvgBgAA\nAAA2x81JAACAT1W2s+AAUBoIbl5ghwRUbFnf8TVrEsu4JChpLGsAWWJjI+V0Sj/8UNYlAfJGcAMq\nKQ5YgT/xfSgc5hOAoqCyw7e4xg0AAAAAbI4aNwClhjNvkKi1AQDAGwQ3wEcIJQAAACgpNJUESkhl\nfL4IAAAASgbBDZIIGQAAoOLiOAcVAcENAACgHCB8AJUbwQ0AAAAAbI6bkwCoNDhTDXiH7w4AlD1q\n3AAAAM3wgHP4LsCuCG4AAAAACo1wWzYIbgB8hg05UP7xPQay4zsBuyC4AQAAAIDNEdwAAADKGLU6\nAC6E4AYAAACgwqooJ0YIbgAAoMKoKAdoRVEZPzNQGfEctwoka6O9Zk1ioboDAEoOB9IAAF+ixg2o\noDgDCwAFYzsJVEwV9btNjRuAcoUaZBSE9QOVAes57KIihiM7I7gB8Fpl2WBzkITKiPUeAOyF4AbY\nUGUJRACQhaAIAAXjGrdKJGd734ra/rcyYRnmj3kDAAAqEoJbKeNgEgAAAEBR0VQSAABUKpxARWGw\nnsBuqHEDAAAAAJsjuAEAAACAzRHcgAqAaycBwHtsQwGUB1zjBuSDnTgqI9Z7AADsyevg5na7FR8f\nrz179sjf319Dhw5VVFSU5/2VK1dq5cqVcjqduu2229SmTRufFBhA8XBgDgCoLNjnVW4Vbfl73VRy\n3bp1ysjI0LPPPqu+fftq4cKFnvdOnDihzz77TE8//bTGjRund955RxkZGT4pMFDe0AQHAHyPbSuA\nysbr4LZt2za1atVKktSoUSPt3LnT896OHTvUuHFj+fv7KyQkRFFRUdqzZ0/xSwsAAFBIlT3cVfbP\nD1Q0hmmapjcDzpkzR7GxsWrdurUk6YEHHtDMmTPldDq1atUq7d27V/fcc48kaebMmbrmmmvUokUL\n35W8nKtXr+D3d+/2zTAFTbsw/V9omvlN+/xpFLXc3k6zuMOeP3xh52Vx5Vdeb6fvTfkLW4a8lmlJ\nrHOFlbPcJbFMcw5bmGkW9zuTV38Xmq4vvzOlte7nNe2cvNmulOU6Cd8o6rL15Tpc2O9aSezbfLGN\nulB/50+ruNsVXxxDeDPN/D6zt9sEX2wLSuKYLr9hC7MsC5puYaZdkbeLZbnP8wWvr3ELDg5WSkqK\n57VpmnI6nZKkkJAQpaamet5LSUlRaGjoBceZkJDgbXFKTHR0dImUy+Uq+AxYQkKiT4YpaNqF6f9C\n08xv2udPo6jl9naaxR32/OFzvl/a60Fhl2V+4yvK8IUtQ17LtLDT+eGHrHEWulgXlLPcRV2mhZ2G\n0+nnWfaFmWZxvzN59Xeh6fryO+PtuucNb7cNBW1XfL0dLKnvPvJX1GXry3U46zvvcmXmOa6S3Ld5\nu40qzLB5zaPibld8cQzhzTTz+8zebhNyjs+b73xJHNPlN2xhlmVB0y3MtEtzP1DaClrv7bK9j46O\nzvc9r5tKNm7cWL/88osk6ffff1edOnU87zVs2FBbt25Venq6kpOTdeDAAcXExHg7KQAAUEmsWZOo\nNWsq7oEjAHjL6xq3q666Sr/++qvGjx8v0zQ1bNgwLVu2TFFRUWrbtq3++te/6sknn5Tb7VafPn0U\nEBDgy3IDAAD41Jo1iYqOjlZ5OddMwAUqF6+Dm8Ph0ODBg7N1q127tuf/66+/Xtdff733JYMtZO0U\nuLgZAAAAKDteN5UEAAAoazStBFBZeF3jBgBliQM1AABQmVDjBgA2UBlrDSrjZwbKAt81oGKgxg0A\nAACVEoEW5QnBrRJiI4XKhPUdeWG9ACombqqGiozgBgCFxAEBANgTJ2OKj3lofwQ3AABgWxX1YLKi\nfi4AJYfgBgAAcB5CFQA7IriVEZpcAQDgO4QtABUdwa0CYGcFAAAAVGwENwAAgFLCyVYA3uIB3AAA\nAABgcwQ3AAAAALA5ghsAAAAA2BzXuAEVCNdOAAAAVEwENwAAgELg5FjZYxnAF8rrekRTSQDIx5o1\nidq9u6xLAQCwszVrEsttEED5Qo0bgGzY+QCwA7ZFvsc8Bco3atwAwEY4cwsAAPJCjRsAAICPcOIF\nQEmhxg2o5KjhQXnG+guUDL5bgP0Q3AAAAADA5mgqCQAAgAqF2kJURAQ3ALChynTQkfVZY2Mjy7gk\nAOyoMm0PgYLQVBIAAACoJLh+sfyixg0ASgC1SKWLgxAA8A7bz/KDGjcAAAAAsDmCGwAAAADYHE0l\nAUiiqQQAAICdUeMGAAAAFBM3/UBJo8YNOA8bXJQ01jEAAOANghsAFBHhC0DOO8eyXQBQ0mgqCQAA\ngDzR/K/omGcoKQQ3AAAAALA5ghsKhbNHAAAAQNnhGjcAAIASxslPAMVFcCtjOS9uBgAAqMgKG2IJ\nu0B2BDcAAACUOAIbUDxc4wYAAAAANkeNGwAAAFCOUCtZOVHjBgAAgAJxd2mg7BHcAAAAAMDmaCoJ\nAD5UkmekOdsN2A/fSwClheAGAAAAVBCcTKi4CG4AAACAjxGg4GsENwCwOXb+AACA4FaOcTAHAAAA\nb3AcWf4Q3FAh5dwYxcZGllFJAACoODjYB8oOjwMAAAAAAJsjuAEAAACAzdFUEgBgC1lNsGjaDAAo\nCeW9qS/BDQAAoJIq7weyQGVCcLMJzjQDKAtse4DKicAGlD8ENwCFwk4eAACg7BDcbIaD48qF2g4A\nAAAUBsENAAAAZY6T10DBeBwAYANr1iSywwIAAEC+qHEDAHDiAAAAm6PGDbZHbRQAAAAqO2rcABsh\noAIAACAvBLdyiIN7AJUd20EAQGVDU0kAAAAAsDlq3AAAAIByjpYIFR81bgAAAABgcwQ3AAAAALA5\nmkoCAGyF5j4AAORGjRsqBZ4FBwAAgPKM4AYAAAAANkdwAwAAAACb4xo3AAAAlBguVQB8gxo3AAAA\nALA5ghsAAABQirhpGrxBcAMAAAAAmyO4AQAAAIDNEdwAAAAAwOYIbgAAAABgc149DiA9PV0zZszQ\nqVOnFBwcrAcffFDh4eG5+jt06JCmTJmil156qdgFBbIu4o2NjSzjkgAAAACly6saty+++EJ16tTR\nU089pWuuuUYffvhhrn5WrVqladOm6dSpU8UuJAAAAABUZl4Ft23btqlVq1aSpNatW2vTpk25+gkN\nDdXEiROLVTjA17j9LgAAKK84jqncLthU8uuvv9ann36arVvVqlUVEhIiSQoKClJycnKu4dq0aVPk\nwkRHRxd5mNJgl3I5ndbfsixPVhlyylmm88ua3zAXGja/9/ft87yTb/lKYh7ZZT2wAzusi6WpOJ/z\nQuuzL1zoO1aS0y5NBX33S1J5n28oOpa5b5TXfUVplreox0gFjaO8zWc7svs8vGBw69q1q7p27Zqt\n27/+9S+lpqZKklJTUxUaGuqTwiQkJPhkPL4UHR1tm3K5XNa1XQkJZXemJasMOeUs0/llzW+YCw2b\n3/uFKZ+v55Gd1gM7sMO6WFqKu+yLsz57O438VIbl5Wt89ysflrnvlMd9RWkv/6IeI+Xlhx+y+vVF\niSovu3z3CwqPXt2cpHHjxvr555/VsGFD/fLLL7r88su9LhwAAAAqHpr0Ab7l1TVuf/nLX7R//349\n8cQTWrlype644w5J0ttvv60dO3b4tIAAAAAAUNl5VeMWGBio0aNH5+p+zz335Oo2d+5cbyaBPNjh\nzBW35AcAAABKHw/gBoAKgruNAQBQcRHcAAAAAMDmvGoqCaByo1YHAACgdFHjBgAAAAA2R3ADAAAA\nyhDXKKMwCG4AAAAAYHMENwAAAACwOYIbAAAAANgcwQ0AAAAAbI7gBgAAAAA2x3PcUKFwRyYAAABU\nRAQ3ACjnOGEBAEDFR1NJAAAAALA5atwAoIKjRg4AgPKP4Abb4mATAAAAsNBUEgAAAABsjuAGAAAA\nADZHcAMAAAAAm+MaN5sxk89IRw5JpikZhiTDesOQ5PSXLoqW4ee7xWa6XZLhkGEYMk1TcrmkzAzJ\nlSllnPsbECiFhEmpKdKxw5LDoUAjXGlmkM/KAQAAACB/BLcCmC6XMo8cknlov5Se9udPWprMrP/P\nnpYSD8o8dkRyOKywdTJJOpEknQtFVneH5DCsv2HhMiIvlkJCpfSscaVLxxKlhL0FFyooWGpwuRWw\nTh6XgkOk6jVlVK8lVa8pyZCSz1g/Z89aQTD5jDX+rMCXkiylnJWSz1ohTZKcflZIK6T/ay5tTb5C\nrxwcrg1nr/RuBgMoEVk39omNjSzjkgAAAF8huBXA/cpTOrj5l6IPGBAoVaspOZ1WzZnbLZnuc/+7\npAO7Ze7ZkXu4wCDp8hYyate1wp4pnftlDZuWKnP7FimrTCFhUmKC9MdvWX3lzeGwyuRyWa+DQ6Tg\nUKlGhDVNt8sqo9NpBTg/f8nPT4afv+T0k5meKp09IwUGyagZIbnc+mn5YV0Z9otmNximDWdbyjUz\n8M9h/Pz0WO1wpbkD5f4sQDdXv0QhjhSFOM/qSEaE9qfFaG96jI5n1pCnRhEAAABAvghuBTBiuyg4\nIkopbrcVfPL4MYKDpYhoqdZFVgZxu6TAYBlG/oHEdLuk48ektFQrOGWNzz9AhuPClx2aZ09LAUEy\n/P1lut3S6ZPS8aPWOE1TCg2zQl1ImBQaesHyeOOhmZG6InizHo6eplahG6WN58p27v1ba557/ZH0\nz5i8x3HWFaLjmTXkejrAqkkMDJYRFKwxtWso3QyQ+52z0pnTVo3niSSrNjO8mhx3D5XRsIlPPw8A\nAEBZo8UECkJwK4Cjw7WqefvdSkhI8Ol4DYdTqun9F9IIrXLeuBxS1erWT73LfFG8Qtua0lRDds5V\noJGqb79OkDIzraaXmZm6s1e4gh2pmj/jD036h5TiClaKO0QR/omqE7hXlwTuU0zAfoX7nZQOH5PS\nUiRZwa9nVuj7v3MTCgiQqtWyglvCXrn/NV5G7wEyrmgpBQRJgYFWAPbz9wRU0zSlo4elfX9YobBe\nIxkhoaU6f4Dz8VxCAABQHAQ3FFuaGSQjJCxbtz1pVjA1WtTUZ8cLDqlr1iRaNYfpqVJKiu7sGSx/\nI0Nvv3PCCl3Va3lqIs2tG+V+fbLMd1/P3Tw0q0moKcllBUgPw5DCws8FPD/rWsOsYbJuApN1DWJg\noIzmbWW06Wj1n5EhV2iwzMwMqykoAAAAUMoIbrAFw+GQgkKkoJA/Q1/t3DUUxhUt5Rj3ssxVn1vX\n3aWnykxLtZqdpqVKaWlWAHP6yah1kVTnUiklWebObdZNY9LTpLOp1shMt+Q2JZnnrj889396uszf\nN8v8cIFnup4617Aq1rWBNSJknPurGhEyGlwuo0atEp1HAAAAqLwIbih3jFoXybitX4mN3zx7WuZP\n38vc/ItkGDL8AxTkMJSSdMy61u7QAWnvH9lq/Ez/ABk97pJxwy2eu4n68rENAAAAqNw4sgRyMEKr\nyLimu3RNd0+3WtHRnmsdTdO0HgORdERKOiLzcILMz5fI/HCBzCVvWXfoDAyS0bqD1LyNVZvocEhV\na1h3G61anVAHAACAIuHoESgiI+t6ubBwqU4DGZLMTjfI/PhtmXt2Wo9aSEyQufr/pNX/l/tavHN3\nx1S1mlK1GtZNUwKDrefwRUTJqBUlRVwkhVbx+d1AAQCAfXDjKhQFwQ3wASO0ioy7H/C8Nk1T2rlV\n5u4dVm2b22U1szyeJPPEMevxDQl7pT07cgU7z+ugYKlWlPV4B8+EjD//hoTKaNJKRqNm1vP3sp4X\nGBBoXX8HAACACoPgBpQAwzCkhk0KfN6caZpS8lkp5ayUkiwlHZV59LB09JDMI4esxxkcOSjtT81/\nHD/9kOfD140uf5Vx5/00yQQAAKggOKoDyohhGFZtWlaNWkx95WwYaZrn7nhpvcr2R8cSZW76Sdq7\n03r4u2FdS2fu2Crzm89kHk6QcdU1Vm2cn58Mp1Ny+ktOp/VIBD8/6aJLZFQJL/kPCwAACo0mlMgL\nwQ2wMcMw/mwemVPkxTKuuzlXZzM1Re74l6SNa2Vu3fhn97zG4R8go+P1MjpeZz3aICTUanLpdlvN\nOz3/n3vtckkZ6dYz8iKiZASH+OaDAgAAoEAEN6CCMYKC5Rj2uLT5F5mnT0muzHM/LitwuTKtv+lp\nMtd/J/Ob5TK/We7dxCKipJhLZcTUl9EmTsbFMb79MBUAZ00BAIAvENyACshwOKXmbXM1vczJvOUe\nmT99L+3cJvNkknWtncMhOZzn/lo/xvmvAwKtJpmHDkj7/pB+/kHmzz/I/OQdq2lmszbZb6JiGJIM\nGUFB0kXRVs2ew2k9I4+7ZgIAABQKwa0EuNymTqRmyuU+d6xrGHIYkkPW/8H+DjkduQ9YUzPd2pKY\nrP2n0mWa5455z3vfz2Hoiohg1a0WKMMwlOEy5edQkQ5+TdNUusvUmXSXTqe5lJLplp/DkJ/DUKbb\nVIbLej/r/wy3qUA/Q+EBTp3NcOvg6XQ5DEMBlwQoIzFMZjqrUHlm+PnJiO0sxXb2anjTNKXjx2Tu\n3Cpz+Qcy13wrrfk2737zLICR48fx519Hzm6G9YXydJMUUkXGxZdItS6yHsMQHCwFhVhNOINCPK/l\n5y8dP/rnTV9OJslo3tYKt4RHAIDNmOlp0tkzktMhGU7rryPn/45c+zDT7ZaOJUrJZ6S0VCktzWph\nk5YqpadJ6X92U1qqJFMKDpMCAqTMDOtyiKzr67N+3O5z/7utnbl57hKK9DSZyWesbv7+1r43vJr1\nU6WqjKz/w6tJYVW5YZoPMAcLkJbp1tZDp7T/cLJSMt1Ky3Sf+2t6Xp9Kc2nviTQdOpNhHWdKOp6a\nqUx3/uMNcBqqHR6gsACnUjPd1k+G+4LDZakR7CeXaepkqkuBTkORYf6KDLV+HIZ0Ks0KZafTXTqV\nav1NyzTl5zBkSsp053kIXSQ1bjssd6qfTq1qoNRtkcUeH8onwzCkGrVk1LhaZpuO0pZfZCYdObdh\nN5XthipnT0uHD8g8eTzHTuC8HYOU/3vn7zCyuiUmyNy/K1e5CrOGm998JjVuLqN+I2sHd/aMtQNK\nPmu9Dq8mo901ct18u29mFgCgQjN/3yxz1QqZmRnZrxE33TmuGc/RzeGwbhhmmlJGhnTqhPXYoAtx\nOqWLY2RE15WZmS6dPC4d2COlppT8h81Dvo83yhIcYp14zdmHed5rM49ukvXs3EvqyYi42HpcUlCw\ndXI2IMhqGeR0em7Spqz/nU5rDElHrJO26enZji2MKztYj1QqRwhuBZj0f/u0OfHCK7/DkGqF+EuS\nXKapBjWCFBHqLz+HIbcpuU3TOs40TblM6VhyhvafTFeay1SA01Cgn0NBTkOXVg9Ss4tC1LBmkJyG\nFbL+XKdNJWe49cvBs/rf4WSF+jsUUzVQZ9NdSjyboX0n03OVK9BpqEqgU9FVAhTo5/AEtrAAp8IC\nHAoLcCrY3yG3aYW5rJq3AKchf4chf6f1OvVcQA32dyi6SoAy3aaeecWp4OYJqvaX35TR8oCe+j9r\neH+nNXz4tSdkZjr10ZZ0BV1uyhHgkhGQKdfpQGUmhSozKVhyOX21qGADhsMhNWtzweaZvmS63dbO\n7fhRKSVFZmqy1dwzNVlKSTn3N9k6s1i9llTrIhkRF0kBQXKv+FDatF7mb5uyjzQgwDr7+MfvMndu\nU8J7r0t1G8qo19DawWZkSJkZMjMzrR2Ay5VjJ3zeTV1Skq3AGl5NRsylUtVqVnc/PykkTAoJkxFa\nxbopTGiYtSPKGU5NtyRDCq9qnbF0OLJ//qyQ6/TzqvbQdLuzjRMAUHTmzz/KPXeKdQ35hXhakJwL\nGm63VdtlOCR/Pyk03DqxGF5NMk2ZWfsVl+vPfY3LZdWYJeyRuX+3NV6HQ7qotoyY+lJ4dSkw0Lq8\nITDI+hsQKCMw0Ao7Wd0NScnn9pP+AZK/f/6tXnK+DgyyWrs4HFJGmrXPO3VCOnVC5rm/On3yz9dn\nT593p+zz5oX1z59/cnaTpJNJ1k3X8pv/hVlIOZ08TnCrSG65ooZaxDjkSktWkJ/j3I+hID+HAv0c\nCvZzKDTAoYvPBaOiyApzeTWZLMj1Darl2f1MuktHzmZIkqoEOlUlwFnkMhXFmO8jlfxrtMKv+12B\ndU7op4Ts74c0PyVJWvCLVO0vR3INb5qS62SQ3GcD9Mw3boX6OxQS4FCIv1OhV6bJdDn0ybbTOpnq\n0p4TqTpyNlOGIVUP8tPgdhfp4ioBJfbZUH4YDodUM9L6kYoUGp2Nmlq1dampVmjKClL+1kkY8+Rx\nmev+q4BtG5X2v19k7tlR+JFnXQ8YFGKN9+hhmQf25NlrkXY2WTv6rB33+arVlNGinRRV29pRmqbu\nqFlHJ1zVZf6cLLndMpOOSklHrLOP5/7XmVPWdYe161pfzJSzMmrXldGqvTUuh+PczjnEutYRgHXS\n5PABq2YjI906k5+ZLjPjXFOznN9PHzKCQ6UqVa3ahMxM6yDXP8BqPpfXVjCvEzp5biwLMazDYTU9\nd/pZB/bKOpjXn//L+PM9h0MKCi53TdLNA3usZoqmKfP4ESnxoHUyMOtGX+f9NV0uK3Rt+kkKCJBj\n2D+lug2zXScuI8d143nMD9M0vTz55pKOHbH2N6GhZbed9ve39nde7I8Ly8yqiUxNkVKSZaamWE0/\n87obtifkmlL1mjIioqyTo+dde69yeEM1wzRzRt+yk5CQcOGeSll0dLQty1XWYmPPax5pmPr2u8Oe\na+IyXG7d2quGDH+XXuuSwG4AACAASURBVH41UWMmhMhMd8rMcMoZnia/GmflVzNZfjWS5QjOKNT0\nQvytEJqc4Vb1IKcmdo1RvepB/9/evQfJVdd9Hv+cS9+n55LLZJhcgRhCEu6PXPbhCRCzSCnFCuKK\nK+UKbqkFIlWI1EqgygLiloLUSplFURQXHmBdU0JtoWxYso8IxaJIooTHEAghAZJJMsncenr6ds7Z\nP845PT2ZmVzm1ieZ96tqqk+fPn36nP71dP++v8v3HPF5ZceTaRx7gHwoPgfTV3t7uz56b7s/zMK2\n/UpLLO4vh0MxalpOR+q98lwn+OEPkr+US1J/fzA00x+mqXzO/zEya1s3g2XPC1ovuwaH1RhGNcmL\nDEPa9Z7fmnm07JgfsGUb/WPr6zn89obhD3NJN0hBL6HfW9ggJZP+ORUG5HUdkPI5Ge0L/MpLJisj\nHpfsoCU3Fvd7Nee0y0hG/3IS/O8fG8/z/EqsJL8ir5rlEe5r+Dxtz3WkvXv8hpV8f9CD4Q0+L7wZ\nqaX+kH2PuK66yhhxH83ZrLo79w/tOamtKvV0+UmdDpAx9qg0z5BOWSqjeYb/vWPH/F4lK7gN19m2\njOpyzP9eaW3350qNI/DzwhEYsWC/I/XmhIudexX/X0+p8Porx/5CTS0yb17jD7/HcSkq3/ft7e2j\nPkaPG8bPM4LeyMFVTpdfIbtg3oAG3jrMHDjD04svdShfcpUvO+ovu8qXXJVdV6ZhKB0ztbA5oaZg\n58+93aVHXt+rO/73Tp2UjSsV83s+UzGzuuxJKjmu3u8q6r2ughK2qeWtaS1oSihh+8NATcOo1iHC\n5DGG4S8nbVPntWeUidO7AJ+RTEnzFo39+aYltc0bvn4cx3Qoz3Gk7Vv9HrRsk7/zni7/khCVsp/F\ns2WWNGNWELANVoY8z/ODPtuWYglp+9/l/fXPQc+dK69QCALMPr8ivecDf1L6aAcTi8vb9Z70//7F\n3/9I29i2tPRMGW3z/S0yDTJOWuBX8sLKf75/cN7hQL9/WxgYvIi8VVMBtG2/YmbF/MC2+gYf0iMg\njZD1NNxu+Pb9LS1yu7uHb9+5V9q5XV5fd9Cy6w1mYw2D+Zr7hu0Hqzppnox4YmhgXttzMSQBzyHD\nk8IvqmRKmtU2dNhssegH9j1d1R5XWbbfC2PZwTFZ/nMzWf+9ch2p4gSt1I6qGbUasn4Fd+9HUtdB\nqWWG34oeiwc9LSMnRPD+9JK8Z//Zf2/GonafdWxT7jqajZIpP4tu80y/IcKuaZSwY/57PSm8weFo\n4bDrcF6UcxRD9KSR39sR3+8R1jnBkL4wmA6f57ry5Kk6x8MLlivlwezDR3d2wwWZjIdsYNb+v5g1\nt8bQ+wP9R26UOkRB8ocpLj7dX9E0Q8ack/zhi5Y1+P805NaUEil/nhUwiQjcUF+eEcy5syTFjrj5\np09rUTZh6Z//ul/7+ssqVFyNlmvFMqSTW5LKlRz96cOc/vRh7qgPK2EZumh+VqmYqZLjaWZTTioP\naFY6prZsTG0NMc1Kx8bdkwdMFMOypCXLh68/mucahj/xO7RkxRHH/Xvl8mAwVyz6gUAi6Qdelu0n\njvlgx+BQsnLZnwNRLkvFgrxtW6Qtb8jb8sbgPo/2ZKfQwaPZyLIkGTVzEofzRlkes3TGbwzoz/lz\nP6Y6GcGhQaHr+Z8H25ZOP8uvNFcnadfc1gYIYQKjIckI/GVjVpu04BR/fmgYCI+QwMCrXTfSa9S+\nTs1mI64LFlpmzFB3f94/tyH/QMGdeFxavMwPwHFEnuf5Q7PzucGgr1IOMhiW5TmV6tzhIY/nc/L2\n7vaHdIeptgd3OpjI6tBkH2Eg63n+/KsFp8jIZOWVS8Pnn1U/K8GtZWvmp69V16LTjrvhnZgeCNxw\n3Fm5qFErF/mVTM/zVHQ8DZRdDZT9DJ2G4V86oTUTq87z299fVmd/WQXHH8rpeZIbPN9PHON/bbue\np339Zf2f7T36l/d7a151eIudZUizM34Q9w9zG/Tp01pk8kWPacKIxaSmFv9vJG3zZIzQy1jLO9gp\n9QW9Wb098nbv8lvHwwxr6YyUapCRzgRzEDN+cBhO5A8rfLW3lbI8tyYgqA0a/AWNmPV0lO2bm5rU\n3d1dE2T42xpNLf5Q0Bmzh1TwvGpFsiaZgBskEdjzgV8RdSoa/OKpSTBTe9yHPlabmruvR96Od6Qd\n7/i9Y7PapKZmGY1BeWSb/J4Hx/Vfy3UG5+UUBqRcnzzHGQyITGuwR8515OV6pVLJ72VomeVf8uPg\nfv89d93B+T2VyuD5OY6Msz4u46r/IGPWnGP4JI3PZHzjZtrb1ROB4VInCsMwpJmz/b+RHp/i4zmS\ndHu7uil/RBSBG45rhmFUE8a0pEbfbnYmptmZI/foha5dPlM7u4v+NessQ9mWmXrvww515ivqyJXU\n0VdWR66svbmSNnfktbkjrz99lNN/PLtVZddVJm5pQROtscDhGDOCoZvh/TPOm5j9TshefA3t7eo9\nhkqcYRjB8KlDhkw1NEozW2WsmJhzlMaezAAAcHwicANGYBqGTq5JftLemlVDZeTED90DFf34tQ79\n+aOcvvX8+9X1S2YmdfHCRv86mYahWWlbrUEAyfw5AONF0AYA0wuBGzBOzSlbay6Zqxff69G2zoIa\n4qZ2dhf1l9392nagMOJzMjFTrcE8uWzCVCZmqbUhprnZuGY3xDQjaSsTHzllMAAAAKYfAjdgAhiG\nodWnNmv1qYPr9uZK2tZZkGlKFcdTZ76i/f1l7Qv+9vSVtKOrOOo+mxKWTpmR1My0XR36NZjh2lBD\n3NQF87NaMjNJgAcAAHCCI3ADJsmchrjmNIx+oXDP85QrueovOcqVXHXkSvqot6QD+YoODpS1s7uk\nTXv6D/sa6//1oBripizTCPIZeErYpv7Tea36NwsaD/tcAAAAHD8I3IA6MQxD2YSlbMKf77Z45vAL\nivcWHeWKziGpxP17e3rLemVXr97uHJDkX4vOMgx15Eq6/+Xd+uaFni47pWkKzgQAAACTjcANiLDG\nhKXGxMiJTOY1JvTxeQ3D1m/rHNB3/+8H+q+v7tFjm/bJNg3FLMO/NY0h9z82M6WrT5+hhlFeAwAA\nANFA4Ia6ee21ffU+hBPSklkprV29QI/8ea+6C44qrqdixVPOdVVxPJVdT5XgquV/7cjr+Xe69IlT\nmjSnIa5swpLreXI9yXE9OZ4nx1V1XcX1VHRclR1P8xrjWjIrpbmNca5fBwAAMMkI3IAT0MktSf2X\nyxeO+nh44fLfb+vS/3zrgJ7d2jXm18rETC2emdSqU5p0yaJGEqUAAABMAgI3YBoKL1x+9bKZ+uTH\nmrWzq6j9+Yr6S44sc3C+nGlIlmn4y6a/LmmbMg3p/e6i3u4c0LbOgv7akddfO/L63bZu/dPCbPAa\nfvbLcDkdM7WwOaE5DbHqvo3wVlyTCgAA4HAI3IBpLh2zdHprWqcf4/OWtab1qSUtkvxLH/xq0369\nsqsvSJZy7PzgTTIN/4LlYTDnB3j+OtfzVHE8mYahhG2oJWVrYXNCc7NxZeKWMnFTDcFtJm4pE/Pv\n26ahff1lfdQbZu4s64L5WS1vTY/pWAEAmGye542pUbNYcdVbdNRfctRfdpUvueovO8rXLJccf8qE\nbfqXF0rapkqOp5LjyvUkz5M8+VMlwqzVroL1wfSJgYpbTaAWtwxl4pZmpGz/L21Xl5uTtiyTxtmJ\nQOAGYNzmNMR1xz/N1faDBe3LleWFuS+DdJiepJ6Co53dRXXmy6P+CLienzVz8EdjcL3rebIMQ5bp\nB3DFiqePeg9/LbzDeXZrl/7tqU06sy2jvqKjvqKj3lJwW3Q0I2Xr8sVNOukk78g7AwBMe/v7y3rp\n/V6VHX+OeDhf3PU8OcHvWDhvPLzvupJpSjHTkCep7Hg6kC9rV09JvUWnOgLGCka9mKYhK1gXswyd\n3JLU4hlJFR1Xnfmyth8oaldPUVH65TINqTlpqylpqTZ+82rqCLXrqve9wUzaTUlbH5uZ1PymhNIx\nU+mY30CbtE3Zpt+4a9W8N+EoIUnqyJW1u7ekYjUo9fd6xpz0YS/bFEUEbgAmzKkzkjp1xvDLGkwW\nx/XUkfMvaN5fctRfvS6e38oYXiOvWHHVmolpbmNccxvjStim/vvm/Xphe49e2N4z6v43vtejk//S\nqY+flNLy1rRcz1PZ8VQKkrz4P8gKkrgMLoc/1rnSYBC4ZFZKs9K2XM//gc4GGUMTtjll7xcAYHLs\ny5X1n1/YqQP5yrj3ZUia0xDTvMZ4teFyMPAbXM6XXL36QZ9e/aCv+tyEZWhZa0qz0zGl46bSMX/0\nSe1yJm4pbhkyDD9QzJUcDVRcJSxTccufHmHUjHYZHBHjL4dTHVK2qYaEKdMwVKq46iu5OjhQ1sF8\nRQcHav6C+x195WEBZRjHGcG0iXCFUfOYJH3QU9Kbe/Pjfm9rXTi/Qd9ZOW9C9znZCNwAHLcs06gG\nY8fq7JMyeun9Xg2UXTUG19MLb7MJS9s6B/T8O916fXe/fn0gL+nAxJ+A/OEl4WtnYmbQ6zjYy+h5\n/g9ac9LWrLQtO+hxDHsinWCb9sa4Ll6QVVs2rkLFlet5SseGXubB8zz1FBx15MrqyJW0N1fWwYGK\n2rNxnTojKdfz1F9ydXJLQm3Z46sVEgAmS9lxlS+7qgRZmSuun2XZcf1GvGLF1X/7U4cO5Cv69ytm\n6ow56epcbnOEeeOmEfQMBY85rlR2PZmGFLMMZeNH16jneZ7291e0o7ugdMzUjFRMbQ2xugxLTNqm\nGpMa0+/x0ciXHb17oKC9ubLy5WDoZ8nVQMXvRXPDTNi1vZxB7+bsTEzzmuJKx6wh8+rPmHP8TZcg\ncAMwLdmmoVWHuUD5mW0ZndmWUfOsVj33l3e1s6eouGkqZhmKW/4QlaE/xOEPsz+kxTQMZeKmGhOW\n9ubK2tZZUF/JH/ZScrzqkMzeYJjmnr6yChVXUjjPz0/uYhj+0JGyWzjiOT2+eb+StqFCxW/TzMRM\nNSVtJWxDjutpX3+5+tiRLJ2V0qKWhCzD/0FuStrVALP6l7SUss3qHIyy42p/f0W9RUcLmuPDAkfg\nRFBxPb3+UU6vfdin/pKrouOpVPFvixVXJcevNE6WhrillpTfiFN2XBnBnF/LMA4Zbl4zBD0Ymj7i\n+kOGpdc+noqZmpm21RC3hgzZC3teZEimBpNLmbW9JcH346KWhM5oTasxeXxUOd8/0K8fv7JbfUVH\nrudpb39Ze3NluUdRpNcun6kvnjV78g8yYBiGWhtiam2ITdlr1ks6ZunMtky9D6Pujo//IkROeA22\nCy5orfORAJMrHbf1jwsb9Y/j2MechvhR/eCMNhHd8zzlSq4O5P3KQ5jAJbyVpH/dn9fLO/vUXaio\nOWnLNKTO/oq6ixX1FPwax0nZuOY0xNTWEFdbQ0xzGmJqSdn6oKekHV0FxSw/a+jmPf36W0deW48i\n0YxtGkrZhl95dQZrNqbhX5aiKWEpYRuKW+bgrWXo1JlJ/UN7w3E9VDSs4FZcf76EVdOaPl3t6inq\ng+5g3mnYsh0sVIdEHfJY7dtlyA8Y3u8u6J0DBfUVner7GzZmhAmLDMMIgobB5SHZag/Z3lTt8K/B\n1w7/j8qO57fem/vUlRtQ2XGrx1VbZz84UFFPwRly3ob83vOEHQw1m6QeD8+T9vWX9X732Ob2hmp7\nHWobikxzMBAzDH/O1vaDR240OhrJ4L2JWYZiZtgAZtYsB7dBA1k6Zmp+U0LtjbHqXKWwHEwN9mTV\nfhcOvZV6i44+6i2pv+QqYRuyzcHEV7XCoYAf9pa0/q1tKtWUfTZh6fTZKTUlbdmGIdvyv/csw5Ad\nnItlGJrXFNclixon5L0CRkPgBgARMVr2MMMwqkM4R9PeGNfqU5vH9LontyS1sqbCcc2ymeopVNRT\ndOS6ngbKbrV3sHdIT6HfuzZQdhW3TKVipmZnYsrETW3rLOjdAwNyDtNKnbJNzWvyh9Vk45ZObklo\ndiZWnaDfW/TnK/YVHfWVHOWKflY0y/QrS2EFMKzw2aZfMQwr6sG7V62U+feGXqpC1fuHLAc9Cg0N\nOeX7+4dV9nb3lfT2/gH1FIdW4MPXCHslwh6KhGVqfnNCJzcnlLTNIfNHwuDDDOeVaOiyZQ6dY5KO\nmVo6K6XmlC3X89RbcNSZr+jAQFkH8hV1DVTkeUHl0lT1/bJM/7lNSVtxy6jOlXGC4V+O58mUoaak\npZhl6IOekvb1lzU7beukbDxIAuDvxzb9/Yf3HdfT/9hyQL97u2vCkyLYQQW9NqnA0fR+jIdpSHHL\nHDLHJiz+hGXo00uatfrUZrVmYorb/vs7lZc0CYdDx0y/l63oeHKDoXZh0FWboTcMZsLP1NEea9ho\nlC871bm74fyqMIlEmEiqNqlEmHSq5Hp6u3NAb+3Nq7foqOwOzhPuL7sqO+Wgh3KS3qgxmJGO66vn\nzda57X5jmz8XbPo2xiBaCNwAAMM0JW01jXNoU5jMpRgOI3NclSqe8mVXb+zO6eVdfdoZ9ByUHE9v\n7Ok/7P6StqlMzFSh4qocJIgpHS4ynBCjX5x+dtrWmXPSfiu+oSGB0OCtnzWuv+xo855+bT7COR6L\nlqSlvpKjinvkbafK3Ma4rvhYsyzDqFbmQ2El31/2RsgeN3h/bmNcS2YmNSNlj1ppHhzmd5jMtDXZ\na90g6PMOCTzC+aS2aSgVM3XK/Lk6sK8j0pX15CE91YlJqs0dTaPRkZzVlpFWHH6bcK5YyfFUdtxq\nw82unmI1oUVtaYRp6msTQrmuVy3L8PFUzNLcbFzNSUtFx/8+CrMUHvrZlPxGgqs//jH1d+0f8/kC\nk4nADQAwKcLr7Y00HHLFnLS+dM7gUOveoqMdXQV1D1RkBmmuwwpjNm6pIe73Ah3K8/xEAWXXrbbk\n184vGtYLoMH00kOChZoAILgrz5Nmz56tffv3D+tVmJW2NTN9bPNKckVHH/QWg8rj0ADC9SQ33H/N\ncm2SmrAy2jVQ0Vv7BvRRb0mnzohpRioWHI9/TC0pS6ZhVHvS/CQKfvKDfNlRb8Hv+ahNMW4FvWcV\n11NPoaKi42leoz+0tjNfUUdfSSXXv46i4w1P0FBxPS2bnda/O71FMWtqhr8aQYKH4N6E7TcZsyId\ntJ2IrKDn1m8r8oPEtqy0ZFZqyo+lKRVT/+jtNUBdEbgBAOq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      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(VaR_delta_norm_forecast)\n",
    "plt.plot(ES_delta_norm_forecast)\n",
    "plt.vlines(future_log_returns.index, ymax=list(future_log_returns), ymin = 0, colors='b')\n",
    "plt.legend(['VaR_1y_forecast', 'ES_1y_forecast', 'Future 10-day returns'])\n",
    "plt.title('Delta-normal Forecast');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Historical simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VaR и ES для Historical simulation считались так:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "VaR_\\alpha(X) = - inf{[x|P(X \\leq x) > \\alpha]}\n",
    "$$\n",
    "\n",
    "$$\n",
    "ES_\\alpha(X) = \\frac{\\Sigma [x|P(X \\leq x) > \\alpha]}{\\#x}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def historical_stock_VaR_ES(start, end, p_weights, alpha, returns, plot=False):\n",
    "    \n",
    "    #Calculation\n",
    "    p_returns = returns[start:end]@p_weights.T\n",
    "    p_returns = p_returns.sort_values()\n",
    "    \n",
    "    VaR_location = int(np.floor(p_returns.shape[0] * alpha))\n",
    "    VaR = - p_returns[VaR_location]\n",
    "    ES = - p_returns[:VaR_location].mean()\n",
    "    \n",
    "    #Plot    \n",
    "    if plot == True:\n",
    "#        plt.figure(figsize=(15,7.5))\n",
    "        plt.tick_params(labelsize=14)\n",
    "        plt.title('Historical Simulation')\n",
    "        plt.legend(['Empirical PMF', 'VaR', 'ES'])  \n",
    "        \n",
    "        plt.scatter(p_returns, np.linspace(0, 1, p_returns.shape[0]), s = 20)\n",
    "        plt.scatter(p_returns[VaR_location], np.linspace(0, 1, p_returns.shape[0])[VaR_location], s = 300)\n",
    "        plt.scatter(p_returns[:VaR_location].mean(), np.linspace(0, 1, p_returns.shape[0])[:VaR_location].mean(), s = 300)\n",
    "        plt.xlabel('P\\L')\n",
    "        plt.legend(['Empirical PMF', 'VaR', 'ES'])\n",
    "    \n",
    "    return VaR, ES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "future_stock_10_day_returns = day_10_returns[day_10_returns.index[-1] - dt.timedelta(days = 364):]@p_weights.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = 0.01\n",
    "\n",
    "VaR_historical_forecast = [ -historical_stock_VaR_ES(day_10_returns.index[i], \n",
    "                                                        day_10_returns.index[i] + dt.timedelta(days = 716), \n",
    "                               p_weights, alpha, returns = day_10_returns)[0] \n",
    "                           for i in range(future_stock_10_day_returns.shape[0])]\n",
    "ES_historical_forecast = [ -historical_stock_VaR_ES(day_10_returns.index[i], \n",
    "                                                        day_10_returns.index[i] + dt.timedelta(days = 716), \n",
    "                               p_weights, alpha, returns = day_10_returns)[1] \n",
    "                          for i in range(future_stock_10_day_returns.shape[0])]\n",
    "\n",
    "VaR_historical_forecast = pd.Series(VaR_historical_forecast, pd.to_datetime(future_stock_10_day_returns.index))\n",
    "ES_historical_forecast = pd.Series(ES_historical_forecast, pd.to_datetime(future_stock_10_day_returns.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0aZLuuOMO9erVS6mpqYqLi1O7du20atUqjRs3TldccYWOHz+u+++/X71791Zi\nYqIyMzOVkJAgq9Wq+Ph4/f777xo7dqy++OKLYmsqqzOCGwAAAGBQJQWQvn376q677tIPP/ygH374\nQStXrtTKlSs1b948BQYGlntdvr6+uuOOO/TZZ59p2LBh+vLLLzVz5swSl9m6dau6dOmikJAQSdJt\nt92mOXPm6NixY5Kkli1bOubdsmWLMjIyHE0Zc3NzFRISojNnzmj//v268847JUn16tXTihUrJEkv\nv/yyvvvuO61fv16HDx+WJGVlZally5ZKSEjQyJEjFRUVpd69e6t+/frl/szVCcENAAAAkPRU56t1\nX0SAu4tRJrt379aePXvUr18/dejQQR06dNDgwYPVv39/bd++XZ06dXLqfXv06KFhw4apdevWatSo\nkS6//PIS57fZbDKZTAWm2e125ebmSpL8/f0d061Wq5544glFR0dLygtg2dnZjmadF77PkSNHFBoa\nqsGDB6tjx45q1aqVbr/9dm3atElSXs3j8uXLtXPnTu3YsUNjxozR6NGjFRMT49Tnrg54HAAAAABQ\nzYSEhGj58uXavXu3Y9qJEyd09uzZMo8qWZR69eqpefPmmjt3rnr06FHq/O3atdPXX3+t06dPS5I+\n//xzBQcHF1n71bZtW3300UfKycmRzWbTjBkz9Oabb6pmzZqKiIjQl19+KSlv1Monn3xSBw8eVGZm\npgYOHKiYmBjt3LlTOTk5slqt+vjjjzV16lS1bdtWQ4cOVdu2bfXLL79IksxmsyM4ehJq3AAAAACD\nuriPmyQNGjRI7du316RJk5SQkKA///xTfn5+qlmzpsaOHauGDRtWaJ233XabXn/9dUfNWEmioqLU\np08fjRo1SjabTSEhIXr55ZcLlVmSHnnkEc2fP1+DBw+WzWbT1Vdfrccff1ySNH78eM2cOVMffvih\nTCaTnn76aTVr1kzt27fXo48+Kl9fXzVq1EhXXnmlkpOTdeutt2rnzp167LHHVKNGDV122WW69957\nJeX11Rs5cqQmTpxYoRBrNCa73W53dyHypaRUbmdQZ4SHhxuyXKhaHAfei31fsujovA72iYmpbi5J\n5WD/ex/2uXdj/+c1fZw1a5bCwsL0wAMPuLs4VcYo+z48PLzY16hxAwAAADzIkSNHihw1UpIaNGig\nF154ocjXMjMzdf/996tFixYaPnx4ZRYRTiC4AQBQzXh6TSeAimnYsKESEhLKvVxAQIBWr15dCSWC\nKzA4CQAAAAAYHMENAFBh0dH1HLVAAADA9QhuAAAAAGBwBDcAAAAAMDgGJwEAlBvNIgEAqFrUuAEA\nAAAG1KVLFw0YMECDBg1y/Dea9ggSAAAgAElEQVR9+vRSl3v66aeVlpZW6eX77LPP9Oyzzxaa9thj\nj+mhhx5SfHy8cnNzy/Re/fv3186dOyujmMXKyMjQqFGjqnSdFUGNGwAAAGBQ8fHxqlWrVrmW2bZt\nWyWVJs+ZM2eUkJCgdevWqXXr1o7pBw8e1JIlS7Rw4UIFBwdr8uTJ+ve//23YB3lnZGToxx9/dHcx\nyozgBgAAAFQzXbp00X/+8x9HqMv/e8GCBZKkuLg4vfLKK4qNjdWLL76oyMhISVK/fv304osvqlat\nWoqNjdWVV16pY8eOaebMmfr999+1cOFCnTt3TmazWY8++qg6dOhQaN3//e9/VadOHQ0bNkzfffed\nY/q3336rmJgYhYSESJLuvvtuzZ49u8jgdujQIU2bNk3nz59XgwYNdO7cOcdry5cv17fffqvs7Gyd\nO3dOw4YNU8eOHfXoo48qNjZWUVFRkqTp06erUaNG6tOnj2PZY8eOlflzTZ06VdnZ2Ro0aJBWr15d\n7DY9ePCgZs+eLX9/f2VlZWno0KFatmyZwsPDdfDgQeXm5mrUqFFq2bKldu/erXnz5slqtcpkMunB\nBx9Up06dKrSv8xHcAAAAgL9Udh/exMTUcs0fFxcns/nv3k3Tp0/XpZdeWuz8Y8eO1RdffFGmmro/\n//xT48ePV6tWrZSenq5p06Zp2rRpCgsL0/HjxzV8+HA1btxYl112WYHlevToIUn64osvCkxPTU1V\nWFiY4+/Q0FD9+eefRa578uTJ6tWrl+68807t3r1bTz31lKS84PX9999r5syZqlGjhr7++mstXrxY\nN910k3r06KE1a9YoKipKmZmZ2rx5sx5//HGnP9fYsWPVv39/JSQkyGKxlLitDh06pBUrVigsLEw7\nd+7Ujz/+qJEjR+rqq6/WqlWrlJCQoFmzZmnx4sW677771LVrV+3fv1+rV68muAEAAACezpmmkmVl\nsVjUvHlzSdKePXt04sQJjR8/3vG6yWTSgQMHCgW34thsNplMJsffdru9QOjMl5aWpgMHDqh79+6S\npJYtW6pRo0aSpLCwMD3zzDNat26dkpOTtXfvXmVlZUmSbrvtNi1dulSnT5/Whg0b1L59ewUGBjr9\nufLXWRahoaEFQmlYWJiuvvpqSVLTpk0dIbZz586aNWuWNm/erBtuuEGDBg0q8zpKQ3ADAAAA/lLe\nGjF3stvtkqScnJxi5zGZTI75JBUYLMTX19dR02Sz2dSwYUPNnz/f8frx48cdzR7L4rLLLtPx48cL\nLB8aGqrjx49r3LhxjulTpkwpUH5JjnLs27dP48ePV58+fRQVFaXWrVsrPj5ekhQYGKhOnTpp7dq1\nWr9+vaOW7mJl/VwXljVfcdvU39+/wN9+fn6Of1+4jXv06KGYmBht27ZNW7du1dKlS7Vs2bIC8zuL\nUSUBAACAaiYkJEQ///yzJGn9+vUFXjObzY6AduF8O3fu1IkTJ4p8v2uvvVbJycn64YcfJEm//vqr\nHn744SLDTXFiYmK0efNmnTp1Sna7XWvWrFHHjh1Vt25dJSQkOP4LDQ1VRESEPv30U0l5Ye3AgQOS\npF27dikyMlJ9+/ZV69attWnTJtlsNsc6evXqpQ8//FA2m03XXHNNqWUq6XNZLBbZbDZH6Cppm5bV\nE088oV9++UW33XabRo8erYyMDJ08edKp97oYNW4AAABANfPkk09q1qxZCgwM1A033KA6deo4Xuvc\nubNGjhypiRMnasiQIYqPj9fq1asVERGhiIiIIt8vJCREL774ohYsWKDs7GzZ7XY988wzBZoHlqZJ\nkyZ65JFHNGrUKFmtVjVr1qzYESXHjx+vadOm6ZNPPlH9+vV15ZVXSpK6du2qjRs36tFHH5Xdblf7\n9u2Vnp6uzMxMBQQE6Oqrr1ZQUJCjn11pSvpc+WXs37+/3nvvvRK3aVkNHTpUs2fP1qJFi2QymfTI\nI4+UaxuWxGS/sI7SzVJSUtxdhELCw8MNWS5ULY4D78W+L1pxnferUxOjsjDq/s/f/p62vY3AqPsc\nVYP9b3zJycmKi4vTsmXLdMkll7jsfY2y78PDw4t9jRo3AAAAAIa3aNEirVmzRqNGjXJpaKsuCG4A\nAAAADG/AgAEaMGCAu4vhNgxOAgAAAAAGR3ADAAAAAIMjuAEAAACAwRHcAAAAAMDgGJwEAIBqorjH\nMAAAPB81bgAAAADcIjq6HjelyojgBgAAAAAGR3ADAMCDcPcaADwTwQ0AAAAADI7gBgAAAAAGR3AD\nAAAAAIMjuAEAAACAwRHcAAAAAMDgCG4AAAAAYHAENwCGxtDmAAAABDcAAAAAMDyCGwAAAAAYHMEN\nAAAAAAyO4AYAAAAABkdwAwAAAACDI7gBAAAAgMER3AAAAADA4AhuAAAAAAyF57gWRnADAAAAAIMj\nuAEAAACAwRHcgFJER9fTVVe5uxQAAADwZgQ3AAAAADA4ghsAAAAAGBzBDQAAAAAMjuAGAAAAAAZH\ncAMAAAAAg3MquNlsNi1cuFDPPfecJkyYoGPHjhWa58yZM4qNjVV2dnaFCwkAAAAA3syp4JaUlKSc\nnBxNnjxZDz74oJYtW1bg9Z07d+qll15SWlqaSwoJAAAAAN7MqeD2008/qU2bNpKkiIgI7d+/v+Cb\nms16/vnnFRgYWPESAgAAAICX83FmoaysLAUEBDj+NpvNslqtslgskqRWrVo5VZjw8HCnlqtsRi0X\nqsZfhzXHgZsYYfuz7wvL3y8X88RtZaTPdPF2L6psRvjOVHdsO+/G/q9axZ2z3HEuM/q+dyq4+fv7\nKysry/G33W53hLaKSElJqfB7uFp4eLghy4WqY7XWk8Xiw3HgJlZrPUlSSkqqW9bPOaBo+fvlYu7a\nT5XFaPv/4u1e1PZ293emujPaPkfVYv9XveLOWVV9LjPKvi8pPDrVVDIyMlI7duyQJO3bt08NGzZ0\nrmRANRIdXU/R0UVfrAKAMyrzvMI5CwA8i1M1bu3atdOuXbs0fvx42e12DR8+XGvWrFFYWJiioqJc\nXUYAcKn8i9nERGokAABA9eBUcDObzRoyZEiBafXr1y8039y5c50rFQAAAADAwangBgDwDtROGgNN\nHgEATvVxA1A16KMCAAAAieAGAAAAAIZHcAMAAAAAgyO4AQAAAIDBEdwAACgH+p4CANyB4AYAcBlC\nDQDAlfhd+RuPAwAAoIpxEQIAKC+CGwAAAIAqxQ2s8qOpJAC3c1czCJpfAACA6oLgBgCAF+BGBQBU\nbwQ3AAAAADA4+rgBAFBFqPECADiL4OZi+T/KiYmpbi4JALgX58OKI+gBAPLRVBIAAAAADI7gBgAA\nAAAGR3ADAAAAAIMjuAEAAACAwRHcAAAAAMDgCG4AAAAAYHAENwAA4DbR0fV47AGAUnGu4DluAACU\nibdfMAAA3IsaNwAAAAAwOIIbAABepDKbG9GUCQAqD00lAQDwYAQpAPAM1LgBAAAAgMER3EoQHV1P\nV13l7lIAAAAA8HYENwAAKoB+XQCAqkBwAwAAAACDI7hVEu7AAgAAV+CaAoDEqJIAADilMi+kuUgH\ngKLlnx8TE1PdXJKqR40bgCrH3WMAAIDyIbgBqBYIewAAwJsR3AAAAABUGm6+ugZ93AAAQIVwQQag\nLLy5f5orUOMGAAAqFXfbAaDiCG4AKoyLMgAAgMpFcAMAAAAAgyO4uQC1DQAAAAAqE8ENAIBKxM09\nAIArENwAAAAAwOB4HAAAt6EWAvBsfMcBwHWocQMAAAAAgyO4AS5GfxYAAAC4GsENqCB3BDXCIQAA\nMAquS6oGwQ0AAABAteKNYZHgBgAAAAAGx6iSAADApbztLjgAVAVq3JzgjVWz8Dwcx8Vj2wCA94mO\nrqerrnJ3KYDiUeNWAVzYAUD1dvF5PDEx1U0lAQCgZAQ3AABQJvlBl4ALoCyo5HAtghuAKsMJHAAA\nwDn0cQMAVCn6EAIAUH7UuAEuwoUoAAAAKgs1bkAloVYBAAAArkJwgyRCBgAA8Fxc58ATENwAAACq\nAcIH4N0IbgAAAABgcAxOAsBrcKcacA7fHQBwP2rcAAAAzfAAwOAIbgAAAMBfuIkBoyK4AXAZfuwA\nAJ6G37bC2CbuQXADAAAOXJABgDER3AAAAADA4AhuAAAAbkZNJ1B5POX7RXADPJSnnKQAACXjfA94\nB4IbAADwGIQYAJ6KB3B7kPwfqsTEVDeXBABAeAAA9/DUa2Jq3LwAdx/hSTieAXg7zoOAd6LGDQBK\n4al37jwR+wrlwfECVAw3EKoWwQ2A0zhhA56LUAMAxkJwAwyIQFRxXHQC1QvfWQAoGX3cvAht4j0P\n+xQAAMA7ENy8GBf9AAAAQPVAU8kqRlMQAAAA4+PmNoyG4AYAALwKF+QAqiOCG+BluGABAACofujj\nBngA+isCAAB4NmrcAAAO3ACAN6L/OYDqgOAGFIMLWAAAqjd+y72bp+1/p4ObzWZTQkKCDh8+LF9f\nXw0bNkxhYWGO19etW6d169bJYrHo3nvv1Q033OCSAgPVDXdyAQAAUFFO93FLSkpSTk6OJk+erAcf\nfFDLli1zvHb69Gl9/vnnmjRpkp577jm98847ysnJcUmBAQAAytK319v7/3r75wc8jdPB7aefflKb\nNm0kSREREdq/f7/jtV9//VWRkZHy9fVVQECAwsLCdPjw4YqXFgAAAAC8kNPBLSsrSwEBAX+/kdks\nq9UqScrMzCzwmr+/vzIzMytQTAAAAADwXia73W53ZsGlS5eqadOmiomJkSQNGzZMCxYskCRt27ZN\nO3fu1KBBgyRJ06dP17333qsmTZq4qNjV31VXlfz6oUOuWaakdZdl/tLWWdy6L1xHecvt7DoruuyF\ny5d1W1ZUceV1dv3OlL+sZShqn1bVdirKxeWujH168bJlWWdFvzNFzVfael35nanKfersuaGk80pl\nnAdRtcq7b115DJf1u1YZv22uOEeVNt+F66roecUV1xDOrLO4z+zsOcEV54LKuKYrbtmy7MuS1luW\ndXvyedGdv3mu4PTgJJGRkdq+fbtiYmK0b98+NWzY0PHa1VdfrXfffVfZ2dnKzc1VcnKyGjRoUOp7\npqSkOFucShMeHl4p5bJaS25znpJSeCALZ5Ypad1lmb+0dRa37gvXUd5yO7vOii574fIXv17Vx0FZ\n92Vx71ee5ctahqL2qbPldIWLy13efVrWdVgsPo59X5Z1VvQ7U9R8pa3Xld+Zqtynzp4bSjqvuPo8\nWFnffRRv8+a8/1/cN6u073hp85VF/nfeas0t8r0q87fN2XNUWZYtahtV9LziimsIZ9ZZ3Gd29pxw\n8fs5852vjGu64pYty74sab1lWbc7f9srW0nHvVHO9+Hh4cW+5nRwa9eunXbt2qXx48fLbrdr+PDh\nWrNmjcLCwhQVFaXbb79dL7zwgmw2m/r16yc/Pz9nVwUAAFDpEhNTFR4erjLcazYERisGvIvTwc1s\nNmvIkCEFptWvX9/x727duqlbt27OlwyGkP+jwKhUAAAAgPs4PTgJALhTYmIqd5sBcC4A4DWcrnED\nACPwlAs2arcBAEBJqHEDAADwYNRKAp6BGjcAgFtQywjA3Qi0qE4Ibl6IkxS8iSuPd4IGABgb52l4\nMoIbAMDrcAML8Cx8p+ENCG4AAMCwPPWCnJohGI2nftc8CcENAADgAlV5AcvFMoCyIrgBAIBqjwAE\nwNMR3NzElU0k+LECAAAAPBvBDQAAoIpwsxWAs3gANwAAAAAYHMENAAAAAAyO4AYAAAAABkcfN8CD\n0HcCAADAMxHcAAAAAHiN6nqjm+AGoIDqejKrDImJqQoPD1dKirtLAsAIOD+6H/sA3ozgBgAADIcL\ndNdjm1YOVz6bFygJg5MAgIEkJqZycQUAAAohuAEAAACAwdFUEvByNPEAANehxhxAZaHGDQBQbdG0\nFKgcfLcA46HGDQAAAB6F0AlPRHADgEpQ0SaoXHQAAIALEdwAAG5FP0sAJeFGFpCHPm4AAACAl6D/\nYvVFjRsAoNrjIgQAnMP5s/oguAGQxIkbAADAyGgqCQAAAAAGR3ADAAAAKoi+Y6hsNJUEgHKqyA8z\nP+oAAMAZBDfgAlxUAwAAwIgIbgAAAOV08fMHPfXGH89ZBIyDPm4AAACAi9DXDZWF4AYAAAAABkdw\nQ5lw9wgAAABwH/q4uRltxwEA8Hzc/ARQUQQ3AAAAVLryhlfCLlAQwQ0AAACGQWADikYfNwAAAAAw\nOGrcAMCFuFMMAKhs/NZ4J2rcAAAAUCJGlwbcjxo3AKgmuGgCAMB7EdwAAACcxA0VAFWF4AYAAAB4\nCG4meC6CGwAYHD/CAFD9cO6GqzE4CTwSnagBAADgSahxq8YIJqXL30bR0fXcXBIAAKo/rj08B/uy\n+iG4AQAAAPB41T2s0lQSAAAAAAyO4AYAMAT6pgIAUDyCGwAAAAAYHH3cDIJBNAAAQFWjlhuoPghu\nAMqEH3fPxE0jwDtxTgeqH4KbwXAiBQAAAHAxghvgRtR2AACQh5vXQMkYnAQAAAAADI4aNxieN9RK\necNnhLFxpxsAAGOjxg0AAAAADI4aN8BAqPUAAABAUQhu1RAX9wAAAIB3IbgBAKodbmABALwNwQ0A\nAACo5rih5fkYnAQAAAAADI7gBgAAAAAGR1NJAICh0NwHAIDCqHEDAAAAAIMjuMErJCamchcfAAAA\n1RbBDQAAAAAMjj5uAAAAqDS0eAFcgxo3AAAAADA4ghsAAABQheh7D2cQ3AAAAADA4AhuAAAAAGBw\nBDcAAAAAMDiCG6oN2oMDAADAW/E4AFQ7hDcAAAB4G4IbvAqhDwAAANURwQ0AUC1w4wWAt+M86N3o\n4wYAAAAABkeNGwAAAFyO2qGqwXb2Hk4Ft+zsbL3++us6c+aM/P39NWLECAUHBxea79ixY5o+fbpe\nffXVChcUfDEBAAAAb+VUU8mvvvpKDRs21MSJE3XzzTfrgw8+KDTPxo0bNXPmTJ05c6bChYRxMCQ/\nAAAAUPWcCm4//fST2rRpI0m67rrrtHv37kLz1KxZUxMmTKhQ4QAAZceNFQAAPFepTSW//vprffrp\npwWm1apVSwEBAZKkSy65RJmZmYWWu+GGG8pdmPDw8HIvUxWMWi53slgK/n3xNsp/PTw8vNC8Fytu\n2eJeL0u5KmOfcRz8rTK3sxFV5HNW5Hgu7zry37u475y37C9XY7t5H/a5d6vK/V/eayRULqNv71KD\nW9euXdW1a9cC02bMmKFz585Jks6dO6eaNWu6pDApKSkueR9XCg8PN2S53M1qrVfg75SU1CJfT0lJ\nLTTvxYpbtrjXy1Ku8ixTFhwHBW3enPd/b9gkFd33FTmey7uO/Pcu7jtXGev2dHz3vQ/73LtV9f4v\n7zUSKo9RvvslhUenmkpGRkbq+++/lyTt2LFDzZo1c65kAAAAAIBSORXcbr31Vv322296/vnntW7d\nOt13332SpOXLl+vXX391aQEBAAAAwNs59TiAGjVqaNSoUYWmP/TQQ4Wmvfnmm86sAgAAAPAK+QNL\nRUeX3HQS3s2pGjcAAAAAQNVxqsYNMCqGQgcAAIAnosYNAAAAAAyO4AYAAAAABkdTSQCo5mgiDACA\n5yO4lcC2eqWO/d82WXNy3F0Uw1nS1FdHzzfQoj8G6uD5xu4uDgAAAODRCG4lOXVcuceSJbvd3SUx\nnPp+UoT/PnWu9Y1Wn+wh+7kHZLrE36XroBYBAAAAyENwK4H5kScUPu5lpaSkuLsohvOP6FDFBG3W\n8Mvnqled/8j28naZHx8n0+UN3F00ABfhJggAANUfg5PASSZtTr9Rj/2yVO/9eb/0+1HZJo+WLel/\n7i4YAAAA4HEIbqiQXLuvZv0eJ9OQ/yfJJPvC6bK9u1A+JvoFAgAAAK5CcINLmNt2lPm5V6XwhrJ/\nvUbzGj+uer5/uLtYAAAAgEegjxtcxnT5FTI/O0P2t+eqReIGrYh4QNbnaum9SIskKcfup6m/jdXu\nzNZuLing2fL7tEVH13NzSQAAgKtQ4waXMtW4RKaBozTtt/+n07mXStnndYn5vAItGWp8yQF1CPrO\n3UUEAAAAqh1q3OByJpNJ/zl5r/5z8l4lLktVz+h6anzJr1oe8ZACLRnuLh7gFozsCAAAKoIaN1SJ\nDGuQJCnIku7mkgAAABhTYmIqN/pQLIIbqkSGNVCSqHEDAAAAnEBwQ5XItAXIajcrkBo3AAAAoNwI\nbqgiJmVYAxVEjRsAAABQbgxOUoKPfzypHRuOKTv7vLuLYji1e/+u7ORaythyVZmXybAGKdBMjRsA\nAABQXgS3EvxyIks7fiNoFMWvfpZ8w9N09vsrZM8u22GUbg1UwxpHKrlkAAAA1QMDkaA8CG4lGNOx\nvl7rG66UlBR3F8Vwbhl5VoFtj8r3snRlH720TMtkWIMUYMmSRbmycujBS/CjDAAAXIE+bnBKzrFg\nSZJv2JkyL5POIwEAAAAAp1DtAafkHMsLYb5hZQ9h6bb8RwKk67S1bLV0AAAA3obWGigKNW5wii3L\nT7lpl8gv7Iwke5mW+fsh3IwsCQAAAJQHwQ1OyzkWJLN/riy1zpVp/r8fwk1TSQAAAKA8CG5wWnn7\nueX3cQukxg0AAAAoF4IbnFbefm75NW4MTgIAAACUD8ENTsv5M1D2XNNf/dxKR40bAAAA4ByCG5xn\nMyvnzyD5hGbozHlrqbM7atzM1LgBAAAA5cHjAFAh536pK7/Lz2jdr6d1b/M6Jc6bUcYaN4bABVyD\n7xIAAJ6DGjdUSNbeMNmyzfr8l1Oy2kp+LEC6LT+4UeMGAAAAlAfBDRViz/bRuZ8uU+rZXCUll1yT\n9vfgJPRxAwAAAMqD4IYKy9wVLkn6dN+pEufLsvkr126hxg0AAAAoJ4IbKiz3ZE21vCxAu45l6kja\n+RLmNCnDGsiokgAAAEA5EdzgEndGXipJ+uznkmvdMqxBNJUEAAAAyongBpdoVz9QdQN89M3BNJ3N\nLv7RAOnWQJpKAgAAAOVEcINLWMwm3R5xqc7l2vX1gbRi58uwBsrffE4+ppwqLB0AAABQvRHc4DK3\nNqklX7Ppr0FKin40gONZbmaaSwIAAABlRXCDywRf4qObrgrW7+k58ruy6L5u6ba8RwLQXBIAAAAo\nO4IbXOrOiLxBSgJapRT5en6NW5AlQ4mJqUpMTK2ysgEAAADVFcENLnV1nUsUWddfNa46KUutrEKv\n5z+Emxo3AAAAoOwIbnC5OyNCZDIVXeuWnt/HjUcCAAAAAGVGcIPLxTQMlvWsr/yvPabVP51UQKtk\nBbRKln/z35VmzqtxC6LGDQAAACgzH3cXAJ7H12JS5u5wBbU/rITtqQru/Hc/tpSkQOkswQ0AAAAo\nD4IbKsXZbQ2UmxqoV6af0rhxteQTkqWgGw8py89POktTSQDlZ8/MkH3DF1J29t8TfXxk6vgPmWpd\n6r6CAQBQBQhucEr+aJDR0fWKnsFm1vlDdRTT0Krz+0NlrZOhoBsPKduSd8gFW85UVVEBeAj7N5/J\n/p/lhV/IzZGp5z+rvkAAAFQhghuqhD3HIknKtvhKkkIsp91ZHADVkH3fHkmSOfZfkt8lsp86Lvtb\nr0lpRT83EgAAT0JwQ5XID245Pnn/r+VDcANQdnarVdr/oxR2hUwto/ImZpyRXZI9Pc2tZQMAoCow\nqiSqhD03L7DJx6603GCFENwAlMeRA9L5czJFtPh7WkCgZDZLGTS9BgB4PoIbqoQ9J+9QM/tZdTr3\nUl1KcANQDvZf/i/vHxHNHdNMZrNUM0hKJ7gBADwfwQ1VxCRbjlkmH6tOW2sp2JImu83m7kIBqCby\n+7eZml5b8IXAYImmkgAAL0BwQ5Wx51hk8s2rcbOYbNJZHgkAoHR2m036ZY9U9zKZaocWfDGolpSZ\nIXturnsKBwBAFSG4ocr8HdxC8ibQLwVAWaQcljLPFuzfli8oOO//melVWyYAAKoYwQ1Vxp5jlsnX\n9ndwo3kTgDLIbyZ5Yf+2fKagWnn/oJ8bAMDDEdxQZey5f9W4WfNr3AhuAEpn35c3MImpaeHgpsD8\n4Mb5BADg2QhuqDL2bItMFrtO/RXc7NwhB1AKu90u7dsjhdSWQsMKzxCY11SS8wkAwNMR3FBl8p/l\ndspEHzcAZfRHspSeJlNEC5lMpsKv5/dx43wCAPBwBDdUmfxnuZ0y/XWhRdMmAKVw9G8rqpmkLuzj\nxvkEAODZCG6oMvacvBq3M+b84MYdcgClyO/fVsTAJJIuqHEjuAEAPBvBDVUmP7ilmwLy/uZCC0AJ\n7HZ7Xo1bYLB0eYOiZwrkRhAAwDsQ3FBl8ptK5vr4KNPqT58UACU7kSqdOi5FNC+6f5t0weAk3AgC\nAHg2ghuqTP7gJI5HAnCHHEAJ8vu3FfkYgL+YfHwl/5rcCAIAeDyCG6qMPfuC4JZ7qZSeljfUNwAU\npbT+bfmCggluAACPR3BDlXHUuPlYdTq3lpSbI53PcnOpABiV/Zc9kn+AdMVVJc8YmBfcuBEEAPBk\nBDdUmfw+bmY/W16Nm0RzSQBFsp8+IaX+Ll19rUxmS8kzB9WSrFYp82zVFA4AADcguKHK5I8qafL5\nq4+bRPMmAEWy/7JXUhmaSUoyBfIQbgCA5yO4oco4gpuvVadz/wpujAQHoCj5/dtKGJjEgYdwAwC8\nAMENVcbmCG42R3Cz01QSQBHs+/ZIfjWkK5uUPnMgD+EGAHg+ghuqTIEaN0dTSS60ABRkTz8jpRyR\nmjTLG+6/NEH5z3L7/+3de2yU9Z7H8c9cOjOdXrQHuThA6Z6w0IWKXM4iZLsFscHGwyERMZJIjJes\nGhRNKpJI6YYAZaMgORIbPBgUA0uIkQg5wSU1JfHCcRs9ltUaiyCEc3RaysXa9vQ205n9o7bYlEJv\n8/ye9nm/Ev+g83Se76/fZ34+n3lufBEEABi9CG6wTNfNSbofByBxcxIAvZ3t//VtkuTqOlWSa9wA\nAKOY13QBcI5fPw7gp77qugAAAA0dSURBVK5TJT//WB3hv5ksq18u+f3qaGszXQYMoPcG1NVIklz/\nnNO/5VM7g1v8kzJ1/HJTk+FC/52Hnjsb/XcO18K75f7XfzddxoAQ3DAkFRV1/V62+1RJX4fqIuOl\nMeOkK3XS1cuJKm/YtJouAMbQe0PGjJN+O61/y44Pdd6g5FJt53/DiP47Dz13NvrvIGPGSgQ3oA8d\nLsVjkssbUyTuk3vbn6RIxHRV/XL77berpqbGdBkwgN4bkpR08+e3/cIVTJF7+z4pOvzzCf13Hnru\nbPTfOVz+gOkSBozgBgu5FI945Erq6PyX2yP5+7djZpo7kDwiP+AYOno/Mrg8Hskz/PMJ/Xceeu5s\n9B92xs1JYKlfBzcAAAAA/UNwg6U6g1vMdBkAAADAiEJwg6XiUTdH3AAAAIABIrjBUtdOlYybLgUA\nAAAYMQhuSLiKirruxwbEIx65XJI8nC4JAAAA9BfBDZbqfpYb17kBAAAA/TaoxwG0t7dr165damho\nUHJysp555hmlp6f3WGb//v2qrq5WLBbTPffco/z8/GEpGCNbPNL5XcHRYxcl+cwWAwAAAIwQgzri\nVlZWpszMTG3evFl5eXk6fPhwj9erqqpUW1urkpISbd68WUePHlVTU9OwFIyRreuIW1uUa9wAAACA\n/hrUEbfq6motX75ckjRnzpxewW3atGnKysqSJLlcLsViMXm9POsb14Jba5RTJQEAAID+ummaOnHi\nhI4dO9bjZ7fccouCwaAkKRAIqLm5ucfrPp9PPp9P0WhUr7/+uvLz8xUI3Pwp9KFQaCC1W8auddmZ\npzOf9fjbeTySOjo3uZRbMhQK/cZAZYPHduBc9N7Z6L/z0HNno//OZffe3zS4LVmyREuWLOnxsx07\ndqi1tVWS1NraqpSUlF6/19TUpJ07d2rGjBm6//77+1VMOBzu13JWCoVCtqzL7jo6xkmSwuG6Hj/r\naHNJkn68eEkTk1qN1DYYbAfORe+djf47Dz13NvrvXHbp/Y3C46CucZs+fbq+/PJLSVJlZaWys7N7\nvN7e3q4tW7bo7rvv1sqVKwezCoxS8WjXqZJc4wYAAAD016AuPFu6dKlKS0tVXFwsr9er559/XpJ0\n4MABLViwQNXV1bp48aLKy8tVXl4uSVqzZo3GjRs3fJVjROIaNwAAAGDgBhXc/H6/CgsLe/189erV\nkqSpU6dq2bJlQ6sMo9K1u0oS3AAMTEcsrjNXWtXecW3+8LhdmjYmWUkel8HKAABIPG71CEt1PceN\nI24ABqKmsV1//EuNqi+39Hrt/n/5jR6dyxkdAIDRjeAGS107VZJr3ADcXDwe14ff/6y9f72o1mhc\nd01K1W8zrt2l+PjZev3PmZ+0YuYYpfs9BisFACCxCG6wVFdwO3e1VbF4XG4XpzcBuL76lqher6jV\n5z82KSXJrRf+7XblZaX3WCboc2vvX+v05+qrevjOsYYqBQAg8QZ1V0lgsKJXUjT9toC+rPmH/vT5\nRcXjHHkD0FvFD4167th5ff5jk2aND+q13/9Tr9AmSUun3qp0v0fHvvtJzZEOA5UCAGANjrjBWnGX\n/nPxZG0s/5uOn6nX//69UZ4RcNTN4zmvjg52Cp2I3lsvLulqS1RJbpeemDdOy6Zn9Hl0PuB16w/Z\nGfrv/7usp46eU5J7eOcT+u889NzZ6L9z3DctQytzxpguY0AIbrBcqt+jTUsm649/qVFNY7vpcvrF\n63UrKm6o4kT03oxZ44P6j9+NV+at/psu+/tpGaoM/0NXW6LDXgf9dx567mz03zm8I/CyaIIbjLg1\n4NWmJZNNl9FvoVBI4XDYdBkwgN7bX4rPo/9aOiUh703/nYeeOxv9h50R3JAQFRV1pksAAAAARg1u\nTgIAAAAANkdwAwAAAACbI7gBAAAAgM0R3AAAAADA5ghuAAAAAGBzBDcAAAAAsDmCGwAAAADYHMEN\nAAAAAGyO4AYAAAAANkdwAwAAAACbI7gBAAAAgM0R3AAAAADA5ghuAAAAAGBzBDcAAAAAsDmCGwAA\nAADYHMENAAAAAGyO4AYAAAAANkdwAwAAAACbI7gBAAAAgM0R3AAAAADA5ghuAAAAAGBzBDcAAAAA\nsDmv6QLgHBUVdaZLAAAAAEYkjrgBAAAAgM0R3AAAAADA5ghuAAAAAGBzBDcAAAAAsDmCGwAAAADY\nHMENAAAAAGyO4AYAAAAANkdwAwAAAACbI7gBAAAAgM0R3AAAAADA5ghuAAAAAGBzBDcAAAAAsDmC\nGwAAAADYHMENAAAAAGyO4AYAAAAANkdwAwAAAACbI7gBAAAAgM0R3AAAAADA5lzxeDxuuggAAAAA\nQN844gYAAAAANkdwAwAAAACbI7gBAAAAgM0R3AAAAADA5ghuAAAAAGBzBDcAAAAAsDmv6QISJRqN\navfu3bp06ZIikYgeeOABTZo0SaWlpXK5XJo8ebKeeOIJud2d2bW2tlbbt2/Xq6++2uN9jh07pvr6\nej388MO91tHQ0KBdu3apvb1dGRkZWrNmjWpqarRv377uZc6cOaMXX3xRs2fPTuh4cX2mtgO/368j\nR47o5MmTCgaDWr58uebNm2fJmNHJit7faJm2tjZt3bpVTz/9tCZOnJiYQeK6htr7y5cva/fu3ero\n6FA8HtdTTz2lUCjUYx19fe67Xtu4caN27Nghn89n7eAdzGTfjx8/ro8++kiStHLlSuZ7i5ns/Vtv\nvaXTp08rOTlZkrR+/XoFg0Fr/wAOZ6r/Jvb5R21w++STT5SWlqa1a9eqsbFR69evV1ZWllatWqWZ\nM2dqz549+uKLLzR//nx9/PHH+uCDD9TQ0ND9++3t7XrjjTd09uxZ3XXXXdddx3vvvafc3FwtXrxY\nR44c0Ycffqhly5Zp06ZNkqTPPvtMGRkZhDaDTG0Hs2bN0smTJ1VSUiJJKi4uVk5OTveOHRLPit73\ntcz333+vN998U1euXEn4ONHbUHt/6NAh3XvvvZo/f75OnTqlgwcPat26dT3W0df837X8zz//bPWw\nHc9U3/Py8lRWVqZXXnlFkUhEhYWFmjt3rlwul9V/Ascy+Zk/f/68ioqKlJ6ebvWw8QuT/bd6n3/U\nniq5cOFCPfTQQ93/9ng8OnfunGbMmCFJmjNnjr766itJUkpKSvcfvkt7e7sWLVqkFStW9LmO06dP\ndzdo9uzZ+vrrr7tfa21t1bvvvqvHHntsuIaEQTC1Hfzwww+aMWOGfD6ffD6fJkyYoAsXLgzz6HAj\nVvS+r2UikYjWrVvHkTZDhtr7Rx55RHPnzpUkxWIxJSUl9VpHX/O/2+1WcXGxUlNTh31cuDFTfU9P\nT9f27dvl9XpVX1+vYDBIaLOYqd7HYjHV1tZqz549Ki4u1okTJxIxPNyEyTlfsnaff9QGt0AgoOTk\nZLW0tGjnzp1atWqVJHVPpsnJyWpubpYkzZs3T4FAoMfvp6am6s4777zhOpqbm7sPh//6/STpxIkT\nWrhwId/AGGZqO8jMzNS3336rlpYWNTY26rvvvlNbW9twDw83YEXv+1omOztbt91223AMA4Mw1N6n\np6fL6/UqHA5r//79evDBB3uto6/5f9asWUpLS0vY2NA3k333eDw6fvy4ioqKtGDBgoSNEddnqvdt\nbW0qKCjQ2rVrtWHDBpWVlfElrQEmP/uStfv8o/ZUSanznNUdO3Zo6dKlys3N1YEDB7pfa2lpUUpK\nyoDer7q6WocOHZIkLV++XMFgUC0tLfL5fL3e79NPP1VhYeHwDARDYmI7mDRpkgoKCrRt2zZNmDBB\nU6dOZWfOgET3vusbOtjPUHtfVVWlvXv36tlnn1UoFBrQ/A9zTPa9oKBA+fn52rZtm6qqqpSTk5OY\nQeK6TPTe7/frvvvu674MIicnRxcuXNCUKVMSN1Bcl8nPvpX7/KM2uNXX16ukpESPP/647rjjDklS\nVlaWvvnmG82cOVOVlZUDnlSzs7N7HF49deqUKisrtXjxYp06dUrZ2dmSOlN5JBLhG3cbMLUdNDQ0\nqKGhQVu2bFFzc7O2bt2qzMzM4RwabsKK3sOehtr7qqoq7du3Txs2bNDYsWMl9X/+hzmm+h4Oh3Xw\n4EG98MIL8ng8SkpK6r4JAqxhsvevvfaaXn75ZcViMVVXV2vRokUJHSt6MznnW73PP2qD2/vvv6+m\npiYdPnxYhw8fliQ9+uijevvttxWNRjVx4sQhn86wYsUKlZaWqry8XGlpaXruueckSeFwuLvxMMvU\nduD3+1VXV6eXXnpJXq9Xq1ev5n/kFrOi97Cnofb+nXfeUTQaVWlpqSQpFArpySef7LFMX/M/zDHV\n90AgoClTpmjjxo2SOq+n6bq2BtYw2fvc3FwVFRXJ4/EoLy9PkydPTtxAcV0m53yr9/ld8Xg8btna\nAAAAAAADxiEAAAAAALA5ghsAAAAA2BzBDQAAAABsjuAGAAAAADZHcAMAAAAAmyO4AQAAAIDNEdwA\nAAAAwOYIbgAAAABgc/8PYDnSHV1XfCEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(VaR_historical_forecast)\n",
    "plt.plot(ES_historical_forecast)\n",
    "plt.vlines(future_stock_10_day_returns.index, ymax=list(future_stock_10_day_returns), ymin = 0, colors='b')\n",
    "plt.legend(['VaR_1y forecast', 'ES_1y forecast', 'Future 10-day returns'])\n",
    "plt.title('Historical Simulation Forecast');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VaR: C вероятностью 0.99 портфель не потеряет за 10-дневный срок больше чем 11.24% стоимости\n",
      "ES: Если портфель за 10 дней будет терять больше, чем 11.24% стоимости, то он в среднем потеряет 12.72%\n"
     ]
    },
    {
     "data": {
      "image/png": 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WLCUmJurRRx/VlClTlJKSom7duumKK67QLbfcori4uPOqFwAAoCF4bXk8K2i5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\n9pEtVe1Lvtt9pKwjoHWEst6CWKj70gwGyTZOso4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SRjabTWlpaSouLvZvKy4u1tixYwe0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\ngg==\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VaR_hist, ES_hist = historical_stock_VaR_ES(day_10_returns.index[0], \n",
    "                                            day_10_returns.index[0] + dt.timedelta(716),\n",
    "                                            p_weights, alpha, plot = True, returns = day_10_returns)\n",
    "\n",
    "print(f'VaR: C вероятностью {1-alpha} портфель не потеряет за 10-дневный срок больше чем {round(VaR_hist * 100, 2)}% стоимости')\n",
    "print(f'ES: Если портфель за 10 дней будет терять больше, чем {round(VaR_hist * 100, 2)}% стоимости, то он в среднем потеряет {round(ES_hist*100, 2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. VaR и ES для облигаций"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "VaR(\\alpha=1\\%) = \\dfrac{dP}{P}(\\alpha=1\\%)\n",
    "$$\n",
    "$$\n",
    "VaR(\\alpha=1\\%) = q_{\\Delta r}(\\alpha=1\\%)*Duration,\n",
    "$$\n",
    "$$\n",
    "Duration = \\dfrac{dP}{dr}/P = \\dfrac{dP}{Pdr} \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>$P$ - цена облигации<center>    \n",
    "<center>$\\Delta r$ - приращение ставки дисконтирования<center>\n",
    "<center>$q_{\\Delta r}(\\alpha=1\\%)$ - $1\\%$-квантиль распределения приращения ставки дисконтирования<center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найдем структуру купонных платежей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class bond():\n",
    "    \"\"\"\n",
    "    Class Bond is used to get structure of payments of bonds with given parameter.\n",
    "    Bond can be defined in two ways:\n",
    "        1. Coupon, frequency, maturity, amortization.\n",
    "        2. Dates and payments.\n",
    "    \n",
    "    Properties are:\n",
    "        .dates - dates as np.array\n",
    "        .payments - payments as np.array\n",
    "        .structure - dict of dates as keys ans payments as items     \n",
    "    \"\"\"\n",
    "    def __init__(s, coupon = False, frequency = False, maturity = False, amortization = False, \n",
    "                 dates = None, payments = None):\n",
    "\n",
    "        if dates is not None and payments is not None:\n",
    "            s.dates = dates\n",
    "            s.payments = payments           \n",
    "        else:           \n",
    "            dates = np.linspace(0, maturity, frequency * maturity + 1)[1:]\n",
    "            s.dates = dates\n",
    "            \n",
    "            if amortization == True:\n",
    "                amortization = np.ones(maturity*frequency) * (100/(maturity*frequency))\n",
    "                s.amortization = amortization\n",
    "                payments = np.linspace(1, 0, maturity*frequency + 1)[:-1] * coupon/frequency + amortization\n",
    "                s.payments = payments\n",
    "            else:\n",
    "                principal = np.zeros(maturity*frequency)\n",
    "                principal[-1] = 100\n",
    "                payments = np.ones(frequency * maturity) * coupon/frequency + principal\n",
    "                s.payments = payments\n",
    "        \n",
    "        s.structure = {date: payment for (date, payment) in zip(dates, payments)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bond_portfolio(*bonds):     \n",
    "    \"\"\"\n",
    "    A function to get the structure of payments of a portfolio of bonds from a number of bonds.\n",
    "    Output is a class bond object.\n",
    "    \"\"\"\n",
    "    portfolio = bonds[0].structure\n",
    "    \n",
    "    for Bond in bonds[1:]:\n",
    "        Bond = Bond.structure\n",
    "        portfolio = {k: portfolio.get(k, 0) + Bond.get(k, 0) for k in set(portfolio) | set(Bond)}   \n",
    "        \n",
    "    dates = np.array(list(rates.columns))\n",
    "    portfolio = {k: portfolio.get(k, 0) + 0 for k in set(portfolio) | set(dates)}\n",
    "    portfolio = {k: portfolio.get(k, 0) for k in sorted(portfolio)}\n",
    "    \n",
    "    payments = np.array(list(portfolio.values()))\n",
    "    structured_portfolio = bond(dates = dates, payments = payments)\n",
    "    \n",
    "    return structured_portfolio\n",
    "\n",
    "def dcf(bond_portfolio, rates):\n",
    "    \"\"\"\n",
    "    Get DCF of a bond portfolio.\n",
    "    Input: bond_portfolio as bond class object\n",
    "           rates as a dataframe of rates on a required period\n",
    "    Output: Dataframe of DCF\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    dcf = np.ones([rates.shape[0], rates.shape[1]]) * bond_portfolio.payments    \n",
    "    dcf = dcf * np.exp(-rates/100 * np.array(list(rates.columns)))\n",
    "    \n",
    "    return dcf\n",
    "\n",
    "def duration(bond_portfolio, rates):\n",
    "    \"\"\"\n",
    "    Get duration of a bond portfolio.\n",
    "    Input: bond_portfolio as bond class object\n",
    "           rates as a dataframe of rates on a required period\n",
    "    Output: Series of duration\n",
    "    \n",
    "    \"\"\"\n",
    "    DCF = dcf(bond_portfolio, rates)\n",
    "    dates = np.ones([DCF.shape[0], DCF.shape[1]]) * np.array(list(rates.columns))    \n",
    "    return (DCF * dates).sum(axis = 1) / DCF.sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bond1 = bond(coupon = 4.84, frequency = 2, maturity = 2)\n",
    "bond2 = bond(coupon = 6.14, frequency = 2, maturity = 3)\n",
    "bond3 = bond(coupon = 5.7, frequency = 2, maturity = 6)\n",
    "bond4 = bond(coupon = 4.34, frequency = 2, maturity = 10, amortization = True)\n",
    "bond5 = bond(coupon = 2.28, frequency = 1, maturity = 13, amortization = True)\n",
    "bond6 = bond(coupon = 4.73, frequency = 1, maturity = 18, amortization = True)\n",
    "\n",
    "portfolio = bond_portfolio(bond1, bond2, bond3, bond4, bond5, bond6)\n",
    "\n",
    "DCF = dcf(portfolio, rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KpKwBAAAUSFkDAAAokLIGAABQIGUNAACgQMoaAABAgZQ1AACAAilrAAAABVLW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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=portfolio.dates, height=portfolio.payments, width=0.4)\n",
    "#ticks = portfolio.dates[((portfolio.dates < 10) | (portfolio.dates % 1 == 0))]\n",
    "ticks = portfolio.dates[(portfolio.dates % 1 == 0)]\n",
    "plt.xticks(ticks)\n",
    "plt.title('Superbond payments');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Дельта-нормальный метод"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "today_date = '2011-07-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F9+inwNp17Y4HAECeee3UTokSJdzDLcLCwpSeni6n05nja/L6oIBIHi5QYDi2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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.plot(rates.T[today_date])\n",
    "plt.ylabel('Yield, %', fontsize=14)\n",
    "plt.xlabel('Duration, years', fontsize=14)\n",
    "plt.title('Yield Curve (interpolated) for ' + str(today_date), fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff_rates = rates.diff()[1:]\n",
    "diff_rates_history_today = diff_rates[diff_rates.index <= today_date]\n",
    "\n",
    "max_increments = diff_rates.max(axis=1)\n",
    "max_increments_history_today = diff_rates_history_today.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UKgZIREQOOOqoo/D000/j6KOPjvmYP//5z7jllltwxRVXoKysDPvvvz8ee+wxnHPOOVi9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\nPwcwmu+R6BJDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "max_increments_history_today.plot()\n",
    "plt.title('Maximum Increment dynamics', fontsize=16)\n",
    "plt.xlabel('Date', fontsize=14)\n",
    "plt.ylabel('Increment', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте проверим максимальные приращения доходностей на нормальность."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis of normality is rejected.\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "p_value = stats.shapiro(max_increments_history_today)[1]\n",
    "if p_value <= 0.01:\n",
    "    print(\"Null hypothesis of normality is rejected.\")\n",
    "else:\n",
    "    print(\"Null hypothesis of normality is accepted.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = 0.01\n",
    "z_crit = stats.norm.ppf(1-alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "min_obs = len(max_increments[max_increments.index <= today_date])\n",
    "\n",
    "max_increment_today_future_means = max_increments.expanding(min_obs).mean().dropna()[:-1]\n",
    "max_increment_today_future_means.index = rates[rates.index > today_date].index # дата - дата прогнозирования бонда\n",
    "\n",
    "max_increment_today_future_stds = max_increments.expanding(min_obs).std().dropna()[:-1]\n",
    "max_increment_today_future_stds.index = rates[rates.index > today_date].index\n",
    "\n",
    "quantiles_today_future = max_increment_today_future_means + max_increment_today_future_stds*z_crit\n",
    "\n",
    "durations_today_future = duration(portfolio, rates)[duration(portfolio, rates).index >=\\\n",
    "                                                    quantiles_today_future.index[0]]\n",
    "\n",
    "n_days = 10\n",
    "VaRs_10days = -(durations_today_future*quantiles_today_future/100)*np.sqrt(n_days)\n",
    "# Date - дата построения прогноза!\n",
    "\n",
    "bond_prices = np.sum(DCF, axis=1)\n",
    "\n",
    "bond_prices_today_future = bond_prices[bond_prices.index >= today_date]\n",
    "bond_prices_tf_pct_change = bond_prices_today_future.pct_change(periods=10).dropna()\n",
    "\n",
    "VaRs_10days = VaRs_10days[:-(VaRs_10days.shape[0] - bond_prices_tf_pct_change.shape[0])]\n",
    "\n",
    "VaRs_10days.index = bond_prices_tf_pct_change.index\n",
    "\n",
    "ES_10days = -durations_today_future *(quantiles_today_future \\\n",
    "* stats.norm.pdf(stats.norm.ppf(1 - alpha))/alpha*np.sqrt(n_days))/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZstlsiomJUceOHbV582ZJUocOHSRJJ510kr788kv973//U9u2bRUbGytJyszM\nrHZZA2HwzDPP1LRp0yRJJ598spxOZ8jrtmzZogEDBoS8Z9SoUVqzZo3+/e9/S5IOHjxY5T0dO3aU\nJKWkpOjee+/V22+/rXbt2gXL5XA4JEktWrTQM888I5fLpT179ujMM8+UZIZNt9sdnEZJSYl++ukn\nXXzxxSHraP/+/frll1/0yCOPSJKKi4v1q1/9qtplrivumQMQdfhScACA1XXo0CH4sJBPPvlEZ511\nlq644gpNmTJFU6ZMUWpqqgoKCrR//35J0saNGyVJixcvVkJCgsaMGaNBgwapuLhYhmGoZcuWSkhI\n0Jw5c4KBaNGiRcrJydHTTz8twzD00UcfVSlHYLpffPGF2rVrpzZt2gS7WJaVlemrr77SKaecIik0\ngEpmCPrpp59UXFwsn8+nb7/9ttpl3bRpkyRp/fr1atu2bbXTkqQ2bdoEX7tu3TrNmDFDrVu31lVX\nXaUpU6bo4YcfVv/+/UPek56eHnzPoUOHNHjw4BqnP2nSJI0cOVKjRo1S8+bNZRhGja9t166dvvrq\nK0nmw2skKTk5WampqXr00Uc1ZcoU3XDDDcrOzq52meuKljkAaACERUSyxugCS7dboH794Q9/0KRJ\nk1RWVqb09HT16dMnZLzD4dADDzygBx54QImJicH7ss455xyNHz9e69evV1xcnFq1aqU9e/YoNTVV\nl1xyiZ555hk9+OCDksyWsvvuu0/JyclyuVw699xzq5Rj+fLlev311xUfH69Ro0YpMTFRa9eu1d13\n363S0lL9+te/1umnn17tMjRt2lQ5OTm655571LRpU7lcrmpf98knn+jjjz+Wz+fTqFGjalwn119/\nvZ544gktWbJENptN999/v+Lj4/Xkk09q0aJFKiws1M033xzynl69emn16tUaOnSofD6f7r333mq7\nrErShRdeqCFDhighIUEpKSn65ZdfaizL73//e02cOFH/+c9/1Lx5c8XExMhut+uee+7R6NGjZRiG\n3G53sPtruGxGIFJGsO3btzfKfD0eT6PNG5GD7eDEc3jQOvzksvJ4hyNGH3+8PWR4XU5Ga5uH1VVe\nF7UFVysv+4n6+a/rtlxT3db0mQlne4jEz8qJWu+om2iu/3//+9/68ccfdeuttzZ2UYIef/xx9e3b\nV926dTsu86uv+l+5cqWaNm2qzMxMff7555o7d64mT558TOWqDi1zAFCPaJEDjg0teEDjeO6557R+\n/Xo9+uijjV2UE8LJJ5+sJ554Qg6HQ36/X0OHDm2Q+RDmAAAAgCh3++23N3YRqr2Yc6RulZGsTZs2\nmjp1aoPPhwegAABgQTzIB8Cd4a1QAAAgAElEQVSJiv1b3dEyB8BSGvPBDYAV1MdnhG0e4aCLLHD8\nEeYAADVqyJOzSDvxi7TyNAZCHABYC2HOIjjJAABIBC4AQAXCHAiKAHACi8RH/wMA6gdhzmKOJXiF\n+97q3hfJATCSywYAR+tY990AEOnYX4WPMIca8cFCJGF7hJVxkQkAjh77ztoR5qIQHwwAqF/HY78a\nbRc0OFYBQO0IcxEm0g9e0XYygRML2y+ORX0+8j8wjUjf5+P4OtL2wLZy9LhfFNGAMAfA0jjBaRgE\n3/rBekQ4jhRCatqm2AcCR+dEOX8gzEUxTjJwIjtRdtInokjb90RaeQAAdRftx3vCnEVF+4YLAMeq\nMUMcARJoOA3x+eK8C5GKMAfguInmg2E0LzuA6hHqw0NX0+jFsbSqsMOc3+/X888/r59++klOp1N3\n3XWX0tLSguPff/99vf/++3I4HLryyivVpUsX5efnKy8vTyUlJWrWrJmGDBmiJk2a1MuCAA2JnUfk\n4SQIgFXRcnTiYL03Hs4DTGGHuU8//VSlpaWaMGGCvvnmG7388st64IEHJEn79+/XO++8o8cff1yl\npaUaO3aszj77bL3++uvq3bu3LrjgAv3jH//QkiVLdOmll9bbwqB6bOx1x04ZAHCsOJZEtqOpn8PP\noajb+sG5af0JO8xt3LhR2dnZkqTTTz9d33//fXDcd999p4yMDDmdTjmdTqWlpemnn37Spk2bdOWV\nV0qSsrOzNW/ePMJcPWmsnQs7tdqxjgDg2IS7H23I/W9dHnvPo/EjC8HsxBat9Rl2mPN6vXK73cG/\n7Xa7fD6fHA6HCgsLQ8a5XC4VFhaGDA8MqwuPxxNuMY/Z8Z73li3BOdcw/HCeI77vyO+tflo1v89T\nazkC2rat2xw3b646LNz3Bt5XeV3UNK26vDd0eEzI8OreW5Oa6ubw923eXPO0aipvbRpi/VYeH+76\nbdu2+s/Vkcpb+3Zc/fYbmNfmzbV/jg5/b4XDd5U17xdqK2fl8oQOV3B4bXV0PLaH6j7/1Y8/0n6h\ntvcGynD020Nt6rJ+67o9BI4DNW2/odtWXffdR55n5fIe6/qt7vX1Xd763B6qe/2RjnFSXbbjo993\nb94cehwIbFM1bz8V86j7Mbuu6rJeTTV9nmpSn8eeuhx/w1WXz25g2cNZv1XLe+RpHT6v6vbt9b0O\nAupynIj0aYZTR6Hvq/kzUdfPQHV1V/F5b7zscbTCDnMul0terzf4t2EYcjgckiS3262ioqLgOK/X\nq/j4eLndbnm9XsXGxgaH1cX27dvDLeYx8Xg8jTZvq/P56tZ8vn171asn4b438L7KwwPDDv+C3rq8\nNzDc4Yg54nZQU3kD86zprYe/b/v2XTVOq6by1qYh1m/l8Udb3sDwur6vuvcerWN9f33vB460rQWG\n11ZHDbk91LbdNoRwtofa1GX91mWbqK7+q/ss1Ldj3W6jzdFsQ7W9R1LIfv/jjwPTUrV/R5pA+QJq\n685Wn8ee6o6/9aUun91j+dxULq/DEaOPP95ePq0jv/7w41pDroOAuhwnIm2atZ2H1aejPU4cfnyo\n7byvsdTUwBR2mMvIyNDnn3+unj176ptvvlF6enpwXPv27TVv3jyVlJSorKxM27ZtU+vWrZWRkaE1\na9boggsu0Nq1a5WZmRnu7IGwRFvTO2AFhx/kwb7qWB3L+lu1ald5iK/HAkWAaPicHWu9BzRU/UdD\nHRyOfVnDCzvMdevWTV988YXGjBkjwzA0ZMgQLVq0SGlpaeratasGDBighx9+WH6/X9dee61iY2N1\n5ZVXaurUqfrggw+UmJioYcOG1eeyAEAVVj6QROOBv65YN6hvVt5XHEk4y1XTe/jcVTh8HR3NemY9\nNqzD6+Jo7l214sWcsMOc3W7XHXfcETKsVatWwd/79++v/v37h4xv2rSp/vSnP4U7SyAi1bbTwLE5\nUU+wAKAxVLdPZT/bOCI91DVE+djW6h9fGg4cQThXaNhRAdGBz7q1RfqJtJUcKSBGcxCwWnlPVLXV\ng9XriTAHoNFwMgUrsvqBHzhWfAZwJGwfxxdhDgAARC1OPIEj48JrZCPM4YRxLPcBcDBHJONAeuz4\njJ/Y+IyceKjTyEOdRCbCHBoEH/jasY6AUMfrM0GwA4CasY+0FsIcACCicCKBcLHtwIq4uItjQZgD\ngBNUNJwgRMMyAieimj67BHLg6BDmACACNPYJTGPPH8CJie9iBRoWYQ5oZJxEozGx/QE4ntjn1Iye\nBggHYQ4AjqO6nMhwsgMAsBqOXY2DMAdEEK7K4Ug4UAI4UbF/A8JDmAOiHAESJxJOCAFr4TN7fB3p\nmE9dWBNhDgAsrrZAzgEaAIATE2EOAAAAiHCRcmGOHj2RhTAHoNFFygHK6qLhAFvTtsI2BOBEVR/7\nN/aRJy57YxcA0WHVql3sSBoQ6xcAgBMDx3QcDVrmADQ4DkoAAAD1jzAHRCDCDwAAaCjR0C0/WhDm\nAAAAgAhzPC7scvHY+ghzAAAAAI4KQTAyEOZgOew8AAAAasc504mPMIeIx47o+DqW9U1dRQbqAcCJ\ngv0ZcGR8NQEAAAAAWBAtcwAkcfUTAACr4GmUCKBlDhGHL8sEAAAAakfLHI4rriTVr8NDL+sXAABw\nUTx6EObQKNjJHJvGWH8ERQAAgMhCN0sAAAAAsCBa5hCxaL2zDuoKAADg+CPMAVEmErpLEv4AAACO\nHd0sAQAAAMCCCHMAAAAAYEFhdbMsKSlRXl6e8vPz5XK5dPfddyspKSnkNfPnz9fq1avlcDiUk5Oj\n9u3ba/PmzXrhhRdkt9vldDp19913q2nTpvWyIAAAAEA04zaG6BNWy9x7772n9PR05ebm6vzzz9cb\nb7wRMv6HH37Qhg0bNHHiRI0YMUKzZs2SJL344ou69dZbNW7cOHXr1k0LFy489iVAROMLwAEAABoG\n51kIK8xt3LhR2dnZkqTOnTtr/fr1VcZnZWXJZrOpRYsW8vl8ys/P14gRI9S2bVtJks/nk9PpPLbS\nAwAAAECUqrWb5dKlS/XWW2+FDEtOTpbb7ZYkxcXFqbCwMGS81+tVYmJi8G+Xy6XCwkKlpaVJkjZt\n2qR3331XjzzySJ0K6fF46vS6htCY80bksNp2sGVL4Leay+1wlL/iKJet8vvCnYbVnOjLhyOj/qMT\n9R7drFb/0XI8Pl6stB5rDXN9+/ZV3759Q4ZNmjRJRUVFkqSioiLFx8eHjHe5XPJ6vcG/vV5vMPx9\n/PHHevPNNzVq1Kgq99nVZPv27XV6XX3zeDyNNm9EjhN1O/D5zK8m2L796LpnVH5fuNOwkhO1/lE3\n1H90ot6jmxXrPxqOx8dLpNZ/TQEzrG6WGRkZWr16tSRpzZo1yszMDBmfmZmpdevWye/3a8+ePTIM\nQ0lJSfrwww+1ePFijRs3Ti1btgxn1gAAAAAAhfk0y4suukhTp07V2LFjFRMTo+HDh0uS5syZox49\neqh9+/bKzMzUmDFjZBiGBg8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ukHZsMR8ccPavzNB5eLeyyv/HOM0TZ5e7/JHbLnM7dMSY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\n0wcefdJjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "VaRs_10days.plot(lw=2)\n",
    "ES_10days.plot(lw=2)\n",
    "plt.vlines(bond_prices_tf_pct_change.index, ymax=list(bond_prices_tf_pct_change), ymin = 0, colors='b')\n",
    "\n",
    "plt.title('Delta-normal Model')\n",
    "plt.legend(['10-days VaR', '10-days ES', '10-days bond price change'], loc=1, fontsize=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><center>$X_{250, 10}$</center>\n",
    "<center> &nbsp </center>\n",
    "<center>$\\Downarrow$</center>\n",
    "<center> &nbsp </center>\n",
    "<center>$X_{250, 3}^{PCA}$</center>\n",
    "<center> &nbsp </center>\n",
    "<center>$\\Downarrow$</center>\n",
    "<center> &nbsp </center>\n",
    "<center>$G_{1000, 3}$</center>\n",
    "<center> &nbsp </center>        \n",
    "<center>$\\Downarrow$</center>\n",
    "<center> &nbsp </center>\n",
    "<center>$\\widehat{X}_{1000, 10}^{PCA}$</center>\n",
    "<center> &nbsp </center>\n",
    "<center>$\\Downarrow$</center>\n",
    "<center> &nbsp </center>\n",
    "<center>$\\widehat{P}_{1000,1}$</center></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def interpolate_df_rates(dataframe):\n",
    "    \n",
    "    \"\"\"\n",
    "    Function for interpolation of rates. It works only for the current task.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    seq_1 = np.arange(1.5, 18.5, 1)\n",
    "    seq_2 = np.arange(4, 5, 1)\n",
    "    seq_3 = np.arange(6, 7, 1)\n",
    "    seq_4 = np.arange(8, 10, 1)\n",
    "    seq_5 = np.arange(11, 15, 1)\n",
    "    seq_6 = np.arange(16, 19, 1)\n",
    "\n",
    "    new_cols_float = sorted(np.concatenate([seq_1, seq_2, seq_3, seq_4, seq_5, seq_6], axis=0))\n",
    "\n",
    "    new_cols_str = ['{:.2f}'.format(x) for x in new_cols_float]\n",
    "\n",
    "    new_vals = [np.nan]*len(new_cols_str)\n",
    "\n",
    "    dataframe = dataframe.reindex(columns=dataframe.columns.tolist() + new_cols_str)   # add empty cols\n",
    "    dataframe[new_cols_str] = new_vals  # multi-column assignment works for existing cols\n",
    "\n",
    "    dataframe = dataframe.reindex(['{:.2f}'.format(x) for x in sorted(dataframe.columns.astype('float'))], \n",
    "                                            axis=1)\n",
    "\n",
    "    dataframe_interpolated = dataframe.interpolate(method = 'linear',\n",
    "                                             axis = 1)\n",
    "    \n",
    "    return(dataframe_interpolated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def PCA_transformation(historic_data, needed_variance=0.95, n_monte_carlo = 1000, rand_state=26):\n",
    "    np.random.seed(rand_state)\n",
    "\n",
    "    pca_model = PCA(random_state=rand_state)\n",
    "    \n",
    "    historic_data_centr = historic_data - historic_data.mean(axis=0)\n",
    "\n",
    "    pca_model.fit_transform(historic_data_centr)\n",
    "    best_n_components = np.sum(np.cumsum(pca_model.explained_variance_ratio_) < needed_variance)+1 \n",
    "    # число компонент для дисперсии больше 0.95\n",
    "\n",
    "    pca_model_best = PCA(n_components=best_n_components)\n",
    "    historic_data_transformed = pca_model_best.fit_transform(historic_data_centr)\n",
    "    historic_data_transformed = pd.DataFrame(historic_data_transformed,\n",
    "                                                      index=historic_data.index)\n",
    "\n",
    "    n_sample = n_monte_carlo\n",
    "    norm_random_factors = np.zeros((1000, best_n_components))\n",
    "\n",
    "    i = 0\n",
    "    for col in historic_data_transformed.columns:\n",
    "        norm_random_factors[:, i] = np.random.normal(historic_data_transformed.mean()[i],\n",
    "                                    historic_data_transformed.std()[i], n_sample)\n",
    "        i += 1\n",
    "\n",
    "    historic_mean_data_matrix = np.ones([n_monte_carlo, historic_data.shape[1]]).T@historic_data.mean(axis=0)\n",
    "    full_transformed_increments = pd.DataFrame(norm_random_factors@pca_model_best.components_) + np.ones([n_monte_carlo, historic_data.shape[1]]) * np.array(historic_data.mean())\n",
    "    full_transformed_increments.columns=list(historic_data.columns)\n",
    "    \n",
    "    return full_transformed_increments, best_n_components # обратно к доходностям!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bond_pca_VaRs(rates, transformed_increments, n_days, rates_names, bond_payments, percentile=0.01,\n",
    "                 include_ES = False):\n",
    "    \n",
    "    VaRs = []\n",
    "    \n",
    "    if include_ES:\n",
    "        ESs = []\n",
    "\n",
    "    for i in range(rates.shape[0]-n_days):\n",
    "\n",
    "        # цена бонда на сегодня\n",
    "        today_price = float((np.exp(-(np.array(rates.iloc[i])/100)*np.array(list(rates_names))))\\\n",
    "                                                                @ bond_payments)\n",
    "\n",
    "        # сценарии ставок на следующий день\n",
    "        rates_scenario = np.tile(rates.iloc[i],\n",
    "                                 (transformed_increments.shape[0],1))\\\n",
    "        + transformed_increments \n",
    "\n",
    "        scenario_bond_prices = np.exp(-(np.array(rates_scenario)/100)*np.array(list(rates_names)))\\\n",
    "                                                                                @ bond_payments\n",
    "\n",
    "        quantile_price = sorted(scenario_bond_prices)[int(len(scenario_bond_prices)*percentile - 1)] \n",
    "        quantile_loss = (quantile_price - today_price)/today_price\n",
    "        VaRs.append(quantile_loss)\n",
    "        \n",
    "        if include_ES:\n",
    "            quantile_expected_price = np.mean(sorted(scenario_bond_prices)[:int(1000*0.01)])\n",
    "            quantile_expected_loss = (quantile_expected_price - today_price)/today_price\n",
    "            ESs.append(quantile_expected_loss)\n",
    "    \n",
    "    if include_ES:\n",
    "        return VaRs, ESs\n",
    "\n",
    "    else:\n",
    "        return VaRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.50</th>\n",
       "      <th>1.00</th>\n",
       "      <th>2.00</th>\n",
       "      <th>3.00</th>\n",
       "      <th>5.00</th>\n",
       "      <th>7.00</th>\n",
       "      <th>10.00</th>\n",
       "      <th>15.00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>9.18</td>\n",
       "      <td>10.56</td>\n",
       "      <td>11.71</td>\n",
       "      <td>11.92</td>\n",
       "      <td>11.77</td>\n",
       "      <td>11.65</td>\n",
       "      <td>11.63</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-02</th>\n",
       "      <td>8.93</td>\n",
       "      <td>10.29</td>\n",
       "      <td>11.55</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.90</td>\n",
       "      <td>11.74</td>\n",
       "      <td>11.56</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-03</th>\n",
       "      <td>8.91</td>\n",
       "      <td>10.33</td>\n",
       "      <td>11.68</td>\n",
       "      <td>12.01</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.46</td>\n",
       "      <td>11.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-06</th>\n",
       "      <td>8.84</td>\n",
       "      <td>10.20</td>\n",
       "      <td>11.65</td>\n",
       "      <td>12.15</td>\n",
       "      <td>12.24</td>\n",
       "      <td>12.09</td>\n",
       "      <td>11.89</td>\n",
       "      <td>11.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-07</th>\n",
       "      <td>9.09</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.76</td>\n",
       "      <td>11.67</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0.50   1.00   2.00   3.00   5.00   7.00  10.00  15.00\n",
       "Date                                                             \n",
       "2009-07-01  9.18  10.56  11.71  11.92  11.77  11.65  11.63  11.62\n",
       "2009-07-02  8.93  10.29  11.55  11.91  11.90  11.74  11.56  11.41\n",
       "2009-07-03  8.91  10.33  11.68  12.01  11.86  11.62  11.46  11.39\n",
       "2009-07-06  8.84  10.20  11.65  12.15  12.24  12.09  11.89  11.70\n",
       "2009-07-07  9.09  10.45  11.62  11.91  11.86  11.76  11.67  11.59"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_rates = pd.read_csv('rates.csv', parse_dates=['Date'], index_col='Date').drop(['0.25', '0.75'], axis=1)\n",
    "pca_rates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_rates_history_today = pca_rates[pca_rates.index <= today_date]\n",
    "pca_rates_today_future = pca_rates[pca_rates.index >= today_date]\n",
    "\n",
    "pca_diff_rates = pca_rates.diff(periods=10).dropna()\n",
    "pca_diff_rates_history_today = pca_diff_rates[pca_diff_rates.index <= today_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_transformed_increments = PCA_transformation(pca_diff_rates_history_today)[0]\n",
    "\n",
    "full_interp_transformed_increments = interpolate_df_rates(pd.DataFrame(full_transformed_increments))\n",
    "# это матрица сценариев инкрементов на 10 дней, \n",
    "#которую применяем ко всем ставкам\n",
    "\n",
    "pca_rates_today_future_interpolated = interpolate_df_rates(pca_rates_today_future)\n",
    "\n",
    "pca_VaRs = bond_pca_VaRs(pca_rates_today_future_interpolated, full_interp_transformed_increments, n_days=10, \n",
    "              rates_names=rates.columns, bond_payments=portfolio.payments, percentile=0.01,\n",
    "             include_ES=True)[0]\n",
    "\n",
    "pca_ESs = bond_pca_VaRs(pca_rates_today_future_interpolated, full_interp_transformed_increments, n_days=10, \n",
    "              rates_names=rates.columns, bond_payments=portfolio.payments, percentile=0.01,\n",
    "             include_ES=True)[1]\n",
    "\n",
    "pca_VaRs = pd.Series(pca_VaRs)\n",
    "pca_VaRs.index = bond_prices_tf_pct_change.index\n",
    "\n",
    "pca_ESs = pd.Series(pca_ESs)\n",
    "pca_ESs.index = bond_prices_tf_pct_change.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pYmNj1bx58zLlFQBQNIthGEZVZwIAABTvX//6l1599VVt377d46mZvuSXX35R9+7dNWPG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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7.5))\n",
    "pca_VaRs.plot(lw=2)\n",
    "pca_ESs.plot(lw=2)\n",
    "plt.vlines(bond_prices_tf_pct_change.index, ymax=list(bond_prices_tf_pct_change), ymin = 0, colors='b')\n",
    "\n",
    "plt.title('Model Method (PCA)')\n",
    "plt.legend(['10-days VaR', '10-days ES', '10-days bond price change'], loc=1, fontsize=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Учет валютного риска"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RUBGBP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>1.9526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-02</th>\n",
       "      <td>1.9635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-03</th>\n",
       "      <td>1.9543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-06</th>\n",
       "      <td>1.9701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-07</th>\n",
       "      <td>1.9598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RUBGBP\n",
       "Date              \n",
       "2009-07-01  1.9526\n",
       "2009-07-02  1.9635\n",
       "2009-07-03  1.9543\n",
       "2009-07-06  1.9701\n",
       "2009-07-07  1.9598"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange = pd.read_csv('rubgbp.csv', parse_dates=['Date'], index_col='Date')\n",
    "exchange.columns = ['RUBGBP']\n",
    "exchange.head() # фунтов за 100 рублей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exchange_today_future = exchange[exchange.index.isin(pca_rates_today_future_interpolated.index)]\n",
    "# только совпадающие даты в будущем\n",
    "\n",
    "pca_exchange_rates = pd.concat([exchange, rates], axis=1).fillna(method='ffill') # заполним пропуски предыд. знач.\n",
    "\n",
    "pca_exchange_rates_history_today = pca_exchange_rates[pca_exchange_rates.index <= today_date]\n",
    "pca_exchange_rates_today_future = pca_exchange_rates[pca_exchange_rates.index >= today_date]\n",
    "\n",
    "pca_exchange_today_future = exchange[exchange.index >= today_date]\n",
    "\n",
    "exchange_log_returns = np.log(exchange/exchange.shift(n_days))[n_days:]\n",
    "\n",
    "pca_diff_exchange_rates = pd.concat([exchange_log_returns, pca_diff_rates],\n",
    "                                    axis=1).fillna(method='ffill') # заполним пропуски предыд. знач.\n",
    "\n",
    "pca_diff_exchange_rates_history_today = pca_diff_exchange_rates[pca_diff_exchange_rates.index <= today_date]\n",
    "pca_diff_exchange_rates_today_future = pca_diff_exchange_rates[pca_diff_exchange_rates.index >= today_date]\n",
    "\n",
    "full_transformed_increments_exchange = PCA_transformation(pca_diff_exchange_rates_history_today)[0]\n",
    "\n",
    "RUBGBP_transformed = full_transformed_increments_exchange['RUBGBP']\n",
    "\n",
    "full_interp_transformed_increments_only_rates = interpolate_df_rates(\n",
    "    pd.DataFrame(full_transformed_increments_exchange).drop('RUBGBP', axis=1))\n",
    "\n",
    "full_interp_transformed_increments_exchange = \\\n",
    "pd.concat([RUBGBP_transformed, full_interp_transformed_increments_only_rates], axis=1)\n",
    "# это матрица сценариев инкрементов на 10 дней, \n",
    "#которую применяем ко всем ставкам\n",
    "\n",
    "# берем только совпадающие даты\n",
    "pca_rates_tf_interp_forex = pca_rates_today_future_interpolated[pca_rates_today_future_interpolated.index.isin(\n",
    "    exchange_today_future.index)]\n",
    "\n",
    "bond_prices_tf_pct_change_forex = bond_prices_tf_pct_change[bond_prices_tf_pct_change.index.isin(\n",
    "    exchange_today_future.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_cur_VaRs = []\n",
    "pca_cur_ESs = []\n",
    "\n",
    "for i in range(pca_rates_tf_interp_forex.shape[0]-n_days):\n",
    "    \n",
    "    # сценарии валютного курса через 10 дней\n",
    "    \n",
    "    exchange_scenario = ((np.exp(np.array(RUBGBP_transformed)) * np.array(exchange_today_future.iloc[i]))/100)\n",
    "    \n",
    "    # цена бонда на сегодня\n",
    "    today_price = float((np.exp(-(np.array(pca_rates_tf_interp_forex.iloc[i])/100)\\\n",
    "                                                            *np.array(list(rates.columns))))\\\n",
    "                                                            @ portfolio.payments\\\n",
    "                                                            * np.array(exchange_today_future.iloc[i])/100)\n",
    "\n",
    "    # сценарии ставок через 10 дней\n",
    "    rates_scenario = np.tile(pca_rates_tf_interp_forex.iloc[i],\n",
    "                             (full_interp_transformed_increments_only_rates.shape[0],1))\\\n",
    "    + full_interp_transformed_increments_only_rates \n",
    "    \n",
    "    scenario_bond_prices = np.exp(-(np.array(rates_scenario)/100)*np.array(list(rates.columns)))\\\n",
    "                                                                            @ portfolio.payments\n",
    "    \n",
    "    scenario_bond_cur_prices = scenario_bond_prices * exchange_scenario\n",
    "    \n",
    "    quantile_price = float(sorted(scenario_bond_cur_prices)[int(1000*0.01 - 1)])\n",
    "    quantile_expected_price = float(np.mean(sorted(scenario_bond_cur_prices)[:int(1000*0.01)]))\n",
    "    \n",
    "    quantile_loss = (quantile_price - today_price)/today_price\n",
    "    pca_cur_VaRs.append(quantile_loss) # получаем VaR\n",
    "    \n",
    "    quantile_expected_loss = (quantile_expected_price - today_price)/today_price\n",
    "    pca_cur_ESs.append(quantile_expected_loss) # получаем ES\n",
    "\n",
    "pca_cur_VaRs = pd.Series(pca_cur_VaRs)\n",
    "pca_cur_VaRs.index = bond_prices_tf_pct_change_forex.index\n",
    "\n",
    "pca_cur_ESs = pd.Series(pca_cur_ESs)\n",
    "pca_cur_ESs.index = bond_prices_tf_pct_change_forex.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TyVRoGcMsAQCATYKC6luCHQDjWrFihbZs2aIBAwZUdFVQSgyzBAAAAKqwZ555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\nRERERFXo/wP2EGb14vr87QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7.5))\n",
    "pca_cur_VaRs.plot(lw=2)\n",
    "pca_cur_ESs.plot(lw=2)\n",
    "plt.vlines(bond_prices_tf_pct_change_forex.index, ymax=list(bond_prices_tf_pct_change_forex), ymin = 0, colors='b')\n",
    "\n",
    "plt.title('Model Method (PCA) with Currency Risk')\n",
    "plt.legend(['10-days VaR', '10-days ES', '10-days bond price change'], loc=1, fontsize=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Backtesting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подпортфель акций"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найдем доходности портфеля, на которых будем проводить бэктестинг."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_weights = np.array([0.1, 0.1, 0.1, 0.1, 0.175, 0.175, 0.125, 0.125])\n",
    "historic_stock_log_returns = (daily_log_returns@p_weights.T)[daily_log_returns.index[252]:daily_log_returns.index[504]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Дельта-нормальный метод"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_weights = np.array([0.1, 0.1, 0.1, 0.1, 0.175, 0.175, 0.125, 0.125])\n",
    "return_period = 10\n",
    "alpha = 0.01\n",
    "\n",
    "VaR_delta_norm_backtesting = [ -delta_norm_stock_VaR_ES(daily_log_returns.index[i], \n",
    "                                                        daily_log_returns.index[i] + dt.timedelta(days = 363), \n",
    "                               p_weights, alpha, return_period = 1)[0] for i in range(historic_stock_log_returns.shape[0])]\n",
    "ES_delta_norm_backtesting = [ -delta_norm_stock_VaR_ES(daily_log_returns.index[i], \n",
    "                                                        daily_log_returns.index[i] + dt.timedelta(days = 363), \n",
    "                               p_weights, alpha, return_period = 1)[1] for i in range(historic_stock_log_returns.shape[0])];\n",
    "\n",
    "VaR_delta_norm_backtesting = pd.Series(VaR_delta_norm_backtesting, pd.to_datetime(historic_stock_log_returns.index))\n",
    "ES_delta_norm_backtesting = pd.Series(ES_delta_norm_backtesting, pd.to_datetime(historic_stock_log_returns.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пробитий: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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TBx98IG9vb0erIl8cCnNms1kRERGZ3qtSpYr97w4dOuTaNLl06VJHZgsAAAAY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\nyQ8o+5rHDgzw3JEMFzTF+B9XNLEsGTrhHGxdmTI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cFv38U+jf/9oEHKlqM5lyJGoOXCoe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\nP9BRoXu6N1kZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.plot(VaR_delta_norm_backtesting);\n",
    "plt.plot(ES_delta_norm_backtesting);\n",
    "plt.vlines(historic_stock_log_returns.index, ymax=list(historic_stock_log_returns), ymin = 0, colors='b')\n",
    "plt.legend(['VaR_250d_history', 'ES_250d_history', '1-day returns'])\n",
    "plt.title('Delta-normal Backtesting');\n",
    "print(\"Количество пробитий:\", (historic_stock_log_returns < VaR_delta_norm_backtesting).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historical simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "historics_stock_1_day_returns = (day_1_returns@p_weights.T)[day_1_returns.index[252]:day_1_returns.index[504]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_weights = np.array([0.1, 0.1, 0.1, 0.1, 0.175, 0.175, 0.125, 0.125])\n",
    "alpha = 0.01\n",
    "\n",
    "VaR_historical_backtesting = [ -historical_stock_VaR_ES(day_1_returns.index[i], \n",
    "                                                        day_1_returns.index[i] + dt.timedelta(days = 363), \n",
    "                               p_weights, alpha, returns = day_1_returns)[0] for i in range(historics_stock_1_day_returns.shape[0])]\n",
    "ES_historical_backtesting = [ -historical_stock_VaR_ES(day_1_returns.index[i], \n",
    "                                                        day_1_returns.index[i] + dt.timedelta(days = 363), \n",
    "                               p_weights, alpha, returns = day_1_returns)[1] for i in range(historics_stock_1_day_returns.shape[0])]\n",
    "\n",
    "VaR_historical_backtesting = pd.Series(VaR_historical_backtesting, pd.to_datetime(historics_stock_1_day_returns.index))\n",
    "ES_historical_backtesting = pd.Series(ES_historical_backtesting, pd.to_datetime(historics_stock_1_day_returns.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пробитий: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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tUmpqql0XAgAAAIB1OnTooFq1asnVNWsnvenTp8vPz09HjhzRO++8o4SEBKWn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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7.5))\n",
    "\n",
    "plt.plot(VaR_historical_backtesting)\n",
    "plt.plot(ES_historical_backtesting)\n",
    "plt.vlines(historics_stock_1_day_returns.index, ymax=list(historics_stock_1_day_returns), ymin = 0, colors='b')\n",
    "plt.legend(['VaR_250d_history', 'ES_250d_history', '1-day returns'])\n",
    "plt.title('Historical Simulation Backtesting');\n",
    "print(\"Количество пробитий:\", (historics_stock_1_day_returns < VaR_historical_backtesting).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подпортфель облигаций"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Дельта-нормальный метод"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_today_date = '2010-07-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff_rates = rates.diff()[1:]\n",
    "historic_diff_rates = diff_rates[diff_rates.index <= new_today_date]\n",
    "\n",
    "max_increments = diff_rates.max(axis=1)\n",
    "historic_max_increments = historic_diff_rates.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis of normality is rejected.\n"
     ]
    }
   ],
   "source": [
    "p_value = stats.shapiro(max_increments_history_today)[1]\n",
    "if p_value <= 0.01:\n",
    "    print(\"Null hypothesis of normality is rejected.\")\n",
    "else:\n",
    "    print(\"Null hypothesis of normality is accepted.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "min_obs_backtest = len(max_increments[max_increments.index <= new_today_date])\n",
    "\n",
    "backtest_max_increment_means = max_increments[max_increments.index < today_date\n",
    "                                             ].expanding(min_obs_backtest).mean().dropna()[:-1]\n",
    "backtest_max_increment_means.index = rates[(rates.index > new_today_date)&\n",
    "                                           (rates.index < today_date)].index # дата - дата прогнозирования бонда\n",
    "\n",
    "backtest_max_increment_stds = max_increments[max_increments.index < today_date].expanding(min_obs_backtest).std().dropna()[:-1]\n",
    "backtest_max_increment_stds.index = rates[(rates.index > new_today_date) & (rates.index < today_date)].index\n",
    "\n",
    "backtest_quantiles = backtest_max_increment_means + backtest_max_increment_stds*z_crit\n",
    "\n",
    "backtest_durations = duration(portfolio, rates)[(duration(portfolio, rates).index >=backtest_quantiles.index[0])\n",
    "                                               & (duration(portfolio, rates).index < today_date)]\n",
    "\n",
    "VaRs_1day_backtest = -(backtest_durations*backtest_quantiles/100)\n",
    "# Date - дата построения прогноза!\n",
    "\n",
    "bond_prices = np.sum(DCF, axis=1)\n",
    "\n",
    "backtest_bond_prices = bond_prices[(bond_prices.index >= new_today_date) & (bond_prices.index < today_date)]\n",
    "backtest_bond_prices_pct_change = backtest_bond_prices.pct_change(periods=1).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XS9HR0ZoyZUqJ5bz22mv1P//zP5o4caJCQ0PVokWLYqcNCgrS2rVr9fbbbys4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\nQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "VaRs_1day_backtest.plot(lw=2)\n",
    "plt.vlines(backtest_bond_prices_pct_change.index, ymax=list(backtest_bond_prices_pct_change), ymin = 0, colors='b')\n",
    "\n",
    "plt.title('Delta-normal Backtesting')\n",
    "plt.legend(['1-day VaR', '1-day bond price change'], loc=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модельный VaR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "historic_pca_rates = pca_rates[pca_rates.index <= new_today_date]\n",
    "backtest_pca_rates = pca_rates[(pca_rates.index >= new_today_date) & (pca_rates.index < today_date)]\n",
    "\n",
    "pca_diff_rates_1d = pca_rates.diff(periods=1).dropna()\n",
    "historic_pca_diff_rates = pca_diff_rates_1d[pca_diff_rates_1d.index <= new_today_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50    -0.164255\n",
       "1.00    -0.173570\n",
       "2.00    -0.160601\n",
       "3.00    -0.147097\n",
       "5.00    -0.122729\n",
       "7.00    -0.107174\n",
       "10.00   -0.099407\n",
       "15.00   -0.096216\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historic_pca_diff_rates.mean()/historic_pca_diff_rates.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "full_transformed_increments_backtest = PCA_transformation(historic_pca_diff_rates)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "backtest_interp_transformed_increments = interpolate_df_rates(pd.DataFrame(full_transformed_increments_backtest))\n",
    "# это матрица сценариев инкрементов на 10 дней, \n",
    "#которую применяем ко всем ставкам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "backtest_pca_rates_interpolated = interpolate_df_rates(backtest_pca_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_VaRs_backtest = bond_pca_VaRs(backtest_pca_rates_interpolated, backtest_interp_transformed_increments,\n",
    "                                    n_days=1, rates_names=rates.columns, bond_payments=portfolio.payments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca_VaRs_backtest = pd.Series(pca_VaRs_backtest)\n",
    "pca_VaRs_backtest.index = backtest_bond_prices_pct_change.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kKCwsTDNnznS7TidOnFDTpk01evRobd++XS+88ILeffddx7i33npLZrNZixYt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\neCsjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "pca_VaRs_backtest.plot(lw=2)\n",
    "plt.vlines(backtest_bond_prices_pct_change.index, ymax=list(backtest_bond_prices_pct_change), ymin = 0, colors='b')\n",
    "\n",
    "plt.title('Model VaR (PCA) Backtesting')\n",
    "plt.legend(['1-day VaR', '1-day bond price change'], loc=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Портфель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_VaR = pd.concat([VaR_historical_backtesting, pca_VaRs_backtest], axis=1)\n",
    "portfolio_VaR = portfolio_VaR.interpolate(axis = 0)\n",
    "portfolio_VaR = portfolio_VaR[1:]\n",
    "\n",
    "portfolio_returns = pd.concat([backtest_bond_prices_pct_change, historics_stock_1_day_returns], axis=1)\n",
    "portfolio_returns = portfolio_returns.interpolate(axis = 0)\n",
    "portfolio_returns = portfolio_returns[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4+HitXbtWp06dsnux+MaNG9W9e/cCg1yO4OBgLVu2zPa/1WrViy++qOXLl2vK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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7.5))\n",
    "plt.vlines(portfolio_returns.index, ymax=list(portfolio_returns@np.array([0.4, 0.6])), ymin = 0, colors='b')\n",
    "plt.plot(portfolio_VaR@np.array([0.4, 0.6]))\n",
    "\n",
    "plt.title('Model VaR Portfolio Backtesting')\n",
    "plt.legend(['1-day price change', '1-day VaR'], loc=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Задача c лимитом"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Формулировка задачи"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "0.9 \\cdot VaR_t - q_\\alpha(max(\\Delta r_t)) \\cdot D_t^p = 0 \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>где $t - 13.02.2012$ <center>\n",
    "<center>$D_t^p = \\Sigma_{i = 1}^{6}w_i D_i$ - дюрация портфеля<center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ограничения"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>$\\Sigma_{i = 1}^{6}w_i = 1$<center>\n",
    "\n",
    "<center>$w_i \\in (0, 1)$<center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Что было вначале?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем дюрации бондов, VaR на дату и найдем 99% перцентиль ставки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_date = dt.date(year = 2012, month = 2, day = 13)\n",
    "\n",
    "bond_prices_limit = np.array([dcf(bond_portfolio(bond1), rates[check_date:check_date]).sum(axis=1)[0],\n",
    "                 dcf(bond_portfolio(bond2), rates[check_date:check_date]).sum(axis=1)[0],\n",
    "                 dcf(bond_portfolio(bond3), rates[check_date:check_date]).sum(axis=1)[0],\n",
    "                 dcf(bond_portfolio(bond4), rates[check_date:check_date]).sum(axis=1)[0],\n",
    "                 dcf(bond_portfolio(bond5), rates[check_date:check_date]).sum(axis=1)[0],\n",
    "                 dcf(bond_portfolio(bond6), rates[check_date:check_date]).sum(axis=1)[0]])\n",
    "\n",
    "durations = np.array([duration(bond_portfolio(bond1), rates[check_date:check_date])[0], \n",
    "                      duration(bond_portfolio(bond2), rates[check_date:check_date])[0],\n",
    "                      duration(bond_portfolio(bond3), rates[check_date:check_date])[0],\n",
    "                      duration(bond_portfolio(bond4), rates[check_date:check_date])[0],\n",
    "                      duration(bond_portfolio(bond5), rates[check_date:check_date])[0],\n",
    "                      duration(bond_portfolio(bond6), rates[check_date:check_date])[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_duration = duration(portfolio, rates[check_date:check_date])[0]\n",
    "diff_rates_to_limit = diff_rates[diff_rates.index <= '2012-02-13']\n",
    "max_increment_to_limit = diff_rates_to_limit.max(axis=1)\n",
    "quantile_to_limit = max_increment_to_limit.mean() + max_increment_to_limit.std()*z_crit\n",
    "var_check_date = p_duration*quantile_to_limit/100*np.sqrt(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Веса на 2012-02-13 : [0.18462519 0.18661749 0.17324746 0.16554183 0.13753522 0.15243281]\n",
      "Дюрация на 2012-02-13 : [1.92849592 2.78068056 5.10108088 4.29963298 5.59931594 6.58660059]\n",
      "VaR на 2012-02-13 : -0.048\n"
     ]
    }
   ],
   "source": [
    "print('Веса на', check_date, ':', bond_prices_limit/bond_prices_limit.sum())\n",
    "print('Дюрация на', check_date, ':', durations)\n",
    "print('VaR на',  check_date, ':', np.round(-var_check_date, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: -3.420153875388632e-09\n",
      "            Iterations: 2\n",
      "            Function evaluations: 16\n",
      "            Gradient evaluations: 2\n"
     ]
    }
   ],
   "source": [
    "def loss(w):\n",
    "    return var_check_date * 0.9 - quantile_to_limit * durations@w/100*np.sqrt(10)\n",
    "\n",
    "cons = ({'type': 'eq', 'fun' : lambda w: np.sum(w) - 1.0},\n",
    "        {'type': 'eq', 'fun' : loss})\n",
    "\n",
    "b = (0.01, 0.99)\n",
    "bounds = (b, b, b, b, b, b)\n",
    "\n",
    "w0 = np.random.rand(6)\n",
    "res = minimize(loss, w0, method='SLSQP', constraints=cons, bounds = bounds, options={'disp': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal weights: [0.15089661 0.43182161 0.01       0.01       0.38728178 0.01      ]\n"
     ]
    }
   ],
   "source": [
    "weights = res.x\n",
    "\n",
    "print('Optimal weights:', weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Решений бесконечное множество, но заметно, что оптимизатор снижает веса у бондов с более высокой дюрацией и наоборот."
   ]
  }
 ],
 "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",
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 },
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