tutorial-quant-library.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantlib - Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from src import quantlib as ql\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Financial prices time series \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example tickers and dates\n",
    "tickers   = [\"MSFT\",\"AAPL\",\"AMZN\",\"GOOG\",\"KO\",\"GE\",\"IBM\"]\n",
    "startdate = \"2011-12-31\" \n",
    "enddate   = \"2022-12-31\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get \"Adj Close\" prices from **yahoo finance**"
   ]
  },
  {
   "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>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>12.500196</td>\n",
       "      <td>8.951500</td>\n",
       "      <td>86.152100</td>\n",
       "      <td>16.573130</td>\n",
       "      <td>117.096077</td>\n",
       "      <td>24.712465</td>\n",
       "      <td>21.413067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>12.567373</td>\n",
       "      <td>8.875500</td>\n",
       "      <td>87.090561</td>\n",
       "      <td>16.644611</td>\n",
       "      <td>116.618393</td>\n",
       "      <td>24.557446</td>\n",
       "      <td>21.916992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>12.706896</td>\n",
       "      <td>8.880500</td>\n",
       "      <td>87.043640</td>\n",
       "      <td>16.413727</td>\n",
       "      <td>116.065269</td>\n",
       "      <td>24.441177</td>\n",
       "      <td>22.140968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>12.839730</td>\n",
       "      <td>9.130500</td>\n",
       "      <td>87.512894</td>\n",
       "      <td>16.189817</td>\n",
       "      <td>114.732773</td>\n",
       "      <td>24.286154</td>\n",
       "      <td>22.484926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>12.819363</td>\n",
       "      <td>8.928000</td>\n",
       "      <td>88.498276</td>\n",
       "      <td>15.503389</td>\n",
       "      <td>114.135666</td>\n",
       "      <td>24.286154</td>\n",
       "      <td>22.188959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>131.658981</td>\n",
       "      <td>85.250000</td>\n",
       "      <td>63.789425</td>\n",
       "      <td>89.809998</td>\n",
       "      <td>139.931198</td>\n",
       "      <td>63.334194</td>\n",
       "      <td>238.133545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>129.831772</td>\n",
       "      <td>83.040001</td>\n",
       "      <td>64.608337</td>\n",
       "      <td>87.930000</td>\n",
       "      <td>140.691849</td>\n",
       "      <td>63.721226</td>\n",
       "      <td>236.367981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>125.847855</td>\n",
       "      <td>81.820000</td>\n",
       "      <td>63.929810</td>\n",
       "      <td>86.459999</td>\n",
       "      <td>138.320984</td>\n",
       "      <td>63.086098</td>\n",
       "      <td>233.944031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>129.412415</td>\n",
       "      <td>84.180000</td>\n",
       "      <td>65.318062</td>\n",
       "      <td>88.949997</td>\n",
       "      <td>139.348358</td>\n",
       "      <td>63.463207</td>\n",
       "      <td>240.407837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>129.731918</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>65.349258</td>\n",
       "      <td>88.730003</td>\n",
       "      <td>139.180420</td>\n",
       "      <td>63.125793</td>\n",
       "      <td>239.220825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2768 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL       AMZN         GE       GOOG         IBM  \\\n",
       "Date                                                                  \n",
       "2012-01-03   12.500196   8.951500  86.152100  16.573130  117.096077   \n",
       "2012-01-04   12.567373   8.875500  87.090561  16.644611  116.618393   \n",
       "2012-01-05   12.706896   8.880500  87.043640  16.413727  116.065269   \n",
       "2012-01-06   12.839730   9.130500  87.512894  16.189817  114.732773   \n",
       "2012-01-09   12.819363   8.928000  88.498276  15.503389  114.135666   \n",
       "...                ...        ...        ...        ...         ...   \n",
       "2022-12-23  131.658981  85.250000  63.789425  89.809998  139.931198   \n",
       "2022-12-27  129.831772  83.040001  64.608337  87.930000  140.691849   \n",
       "2022-12-28  125.847855  81.820000  63.929810  86.459999  138.320984   \n",
       "2022-12-29  129.412415  84.180000  65.318062  88.949997  139.348358   \n",
       "2022-12-30  129.731918  84.000000  65.349258  88.730003  139.180420   \n",
       "\n",
       "                   KO        MSFT  \n",
       "Date                               \n",
       "2012-01-03  24.712465   21.413067  \n",
       "2012-01-04  24.557446   21.916992  \n",
       "2012-01-05  24.441177   22.140968  \n",
       "2012-01-06  24.286154   22.484926  \n",
       "2012-01-09  24.286154   22.188959  \n",
       "...               ...         ...  \n",
       "2022-12-23  63.334194  238.133545  \n",
       "2022-12-27  63.721226  236.367981  \n",
       "2022-12-28  63.086098  233.944031  \n",
       "2022-12-29  63.463207  240.407837  \n",
       "2022-12-30  63.125793  239.220825  \n",
       "\n",
       "[2768 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\")\n",
    "assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Returns \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Percentage returns"
   ]
  },
  {
   "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>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>0.005374</td>\n",
       "      <td>-0.008490</td>\n",
       "      <td>0.010893</td>\n",
       "      <td>0.004313</td>\n",
       "      <td>-0.004079</td>\n",
       "      <td>-0.006273</td>\n",
       "      <td>0.023534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>0.011102</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>-0.000539</td>\n",
       "      <td>-0.013871</td>\n",
       "      <td>-0.004743</td>\n",
       "      <td>-0.004735</td>\n",
       "      <td>0.010219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>0.010454</td>\n",
       "      <td>0.028152</td>\n",
       "      <td>0.005391</td>\n",
       "      <td>-0.013642</td>\n",
       "      <td>-0.011481</td>\n",
       "      <td>-0.006343</td>\n",
       "      <td>0.015535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>-0.001586</td>\n",
       "      <td>-0.022178</td>\n",
       "      <td>0.011260</td>\n",
       "      <td>-0.042399</td>\n",
       "      <td>-0.005204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.013163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>0.003580</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>-0.007423</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>-0.001542</td>\n",
       "      <td>0.005948</td>\n",
       "      <td>0.003605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>-0.002798</td>\n",
       "      <td>0.017425</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.017562</td>\n",
       "      <td>0.005466</td>\n",
       "      <td>0.007578</td>\n",
       "      <td>0.002267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>-0.013878</td>\n",
       "      <td>-0.025924</td>\n",
       "      <td>0.012838</td>\n",
       "      <td>-0.020933</td>\n",
       "      <td>0.005436</td>\n",
       "      <td>0.006111</td>\n",
       "      <td>-0.007414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>-0.030685</td>\n",
       "      <td>-0.014692</td>\n",
       "      <td>-0.010502</td>\n",
       "      <td>-0.016718</td>\n",
       "      <td>-0.016851</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>-0.010255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>0.028324</td>\n",
       "      <td>0.028844</td>\n",
       "      <td>0.021715</td>\n",
       "      <td>0.028799</td>\n",
       "      <td>0.007427</td>\n",
       "      <td>0.005978</td>\n",
       "      <td>0.027630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>0.002469</td>\n",
       "      <td>-0.002138</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>-0.002473</td>\n",
       "      <td>-0.001205</td>\n",
       "      <td>-0.005317</td>\n",
       "      <td>-0.004937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2767 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      AMZN        GE      GOOG       IBM        KO  \\\n",
       "Date                                                                     \n",
       "2012-01-04  0.005374 -0.008490  0.010893  0.004313 -0.004079 -0.006273   \n",
       "2012-01-05  0.011102  0.000563 -0.000539 -0.013871 -0.004743 -0.004735   \n",
       "2012-01-06  0.010454  0.028152  0.005391 -0.013642 -0.011481 -0.006343   \n",
       "2012-01-09 -0.001586 -0.022178  0.011260 -0.042399 -0.005204  0.000000   \n",
       "2012-01-10  0.003580  0.004368 -0.007423  0.001092 -0.001542  0.005948   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2022-12-23 -0.002798  0.017425  0.000245  0.017562  0.005466  0.007578   \n",
       "2022-12-27 -0.013878 -0.025924  0.012838 -0.020933  0.005436  0.006111   \n",
       "2022-12-28 -0.030685 -0.014692 -0.010502 -0.016718 -0.016851 -0.009967   \n",
       "2022-12-29  0.028324  0.028844  0.021715  0.028799  0.007427  0.005978   \n",
       "2022-12-30  0.002469 -0.002138  0.000478 -0.002473 -0.001205 -0.005317   \n",
       "\n",
       "                MSFT  \n",
       "Date                  \n",
       "2012-01-04  0.023534  \n",
       "2012-01-05  0.010219  \n",
       "2012-01-06  0.015535  \n",
       "2012-01-09 -0.013163  \n",
       "2012-01-10  0.003605  \n",
       "...              ...  \n",
       "2022-12-23  0.002267  \n",
       "2022-12-27 -0.007414  \n",
       "2022-12-28 -0.010255  \n",
       "2022-12-29  0.027630  \n",
       "2022-12-30 -0.004937  \n",
       "\n",
       "[2767 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)\n",
    "rets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Log-returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>0.005360</td>\n",
       "      <td>-0.008526</td>\n",
       "      <td>0.010834</td>\n",
       "      <td>0.004304</td>\n",
       "      <td>-0.004088</td>\n",
       "      <td>-0.006293</td>\n",
       "      <td>0.023261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>0.011041</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>-0.000539</td>\n",
       "      <td>-0.013969</td>\n",
       "      <td>-0.004754</td>\n",
       "      <td>-0.004746</td>\n",
       "      <td>0.010167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>0.010400</td>\n",
       "      <td>0.027763</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>-0.013735</td>\n",
       "      <td>-0.011547</td>\n",
       "      <td>-0.006363</td>\n",
       "      <td>0.015415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>-0.001587</td>\n",
       "      <td>-0.022428</td>\n",
       "      <td>0.011197</td>\n",
       "      <td>-0.043324</td>\n",
       "      <td>-0.005218</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.013250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>0.003574</td>\n",
       "      <td>0.004359</td>\n",
       "      <td>-0.007451</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>-0.001544</td>\n",
       "      <td>0.005931</td>\n",
       "      <td>0.003599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>-0.002802</td>\n",
       "      <td>0.017274</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>0.005451</td>\n",
       "      <td>0.007550</td>\n",
       "      <td>0.002265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>-0.013976</td>\n",
       "      <td>-0.026266</td>\n",
       "      <td>0.012756</td>\n",
       "      <td>-0.021155</td>\n",
       "      <td>0.005421</td>\n",
       "      <td>0.006092</td>\n",
       "      <td>-0.007442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>-0.031166</td>\n",
       "      <td>-0.014801</td>\n",
       "      <td>-0.010558</td>\n",
       "      <td>-0.016859</td>\n",
       "      <td>-0.016995</td>\n",
       "      <td>-0.010017</td>\n",
       "      <td>-0.010308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>0.027931</td>\n",
       "      <td>0.028436</td>\n",
       "      <td>0.021483</td>\n",
       "      <td>0.028393</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>0.005960</td>\n",
       "      <td>0.027255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>0.002466</td>\n",
       "      <td>-0.002141</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>-0.002476</td>\n",
       "      <td>-0.001206</td>\n",
       "      <td>-0.005331</td>\n",
       "      <td>-0.004950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2767 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      AMZN        GE      GOOG       IBM        KO  \\\n",
       "Date                                                                     \n",
       "2012-01-04  0.005360 -0.008526  0.010834  0.004304 -0.004088 -0.006293   \n",
       "2012-01-05  0.011041  0.000563 -0.000539 -0.013969 -0.004754 -0.004746   \n",
       "2012-01-06  0.010400  0.027763  0.005377 -0.013735 -0.011547 -0.006363   \n",
       "2012-01-09 -0.001587 -0.022428  0.011197 -0.043324 -0.005218  0.000000   \n",
       "2012-01-10  0.003574  0.004359 -0.007451  0.001092 -0.001544  0.005931   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2022-12-23 -0.002802  0.017274  0.000245  0.017409  0.005451  0.007550   \n",
       "2022-12-27 -0.013976 -0.026266  0.012756 -0.021155  0.005421  0.006092   \n",
       "2022-12-28 -0.031166 -0.014801 -0.010558 -0.016859 -0.016995 -0.010017   \n",
       "2022-12-29  0.027931  0.028436  0.021483  0.028393  0.007400  0.005960   \n",
       "2022-12-30  0.002466 -0.002141  0.000477 -0.002476 -0.001206 -0.005331   \n",
       "\n",
       "                MSFT  \n",
       "Date                  \n",
       "2012-01-04  0.023261  \n",
       "2012-01-05  0.010167  \n",
       "2012-01-06  0.015415  \n",
       "2012-01-09 -0.013250  \n",
       "2012-01-10  0.003599  \n",
       "...              ...  \n",
       "2022-12-23  0.002265  \n",
       "2022-12-27 -0.007442  \n",
       "2022-12-28 -0.010308  \n",
       "2022-12-29  0.027255  \n",
       "2022-12-30 -0.004950  \n",
       "\n",
       "[2767 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrets = ql.compute_logreturns(assets, mpor=1, ascending=True, dropna=True)\n",
    "lrets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Single compounding of returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     9.378394\n",
       "AMZN     8.383902\n",
       "GE      -0.241466\n",
       "GOOG     4.353847\n",
       "IBM      0.188600\n",
       "KO       1.554410\n",
       "MSFT    10.171723\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.compound(rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is, for example, all together the overall returns of AAPL have been about 938%. Or, for GE, it has been about -24%.\n",
    "\n",
    "This is equivalent to compute the return between the last and the first corresponding price series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL overall compounded return:\t938%\n",
      "GE overall compounded return:\t-24%\n"
     ]
    }
   ],
   "source": [
    "tt = \"AAPL\"\n",
    "rr = assets.loc[ assets[tt].index[-1] , tt] / assets.loc[ assets[tt].index[0] , tt] - 1\n",
    "print(f\"{tt} overall compounded return:\\t{rr*100:.0f}%\")\n",
    "\n",
    "tt = \"GE\"\n",
    "rr = assets.loc[ assets[tt].index[-1] , tt] / assets.loc[ assets[tt].index[0] , tt] - 1\n",
    "print(f\"{tt} overall compounded return:\\t{rr*100:.0f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Compounding returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative compounding returns. To see what an initial (e.g., investment) price/amount would have resulted in using all available returns history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>1.005374</td>\n",
       "      <td>0.991510</td>\n",
       "      <td>1.010893</td>\n",
       "      <td>1.004313</td>\n",
       "      <td>0.995921</td>\n",
       "      <td>0.993727</td>\n",
       "      <td>1.023534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>1.016536</td>\n",
       "      <td>0.992068</td>\n",
       "      <td>1.010348</td>\n",
       "      <td>0.990382</td>\n",
       "      <td>0.991197</td>\n",
       "      <td>0.989022</td>\n",
       "      <td>1.033994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>1.027162</td>\n",
       "      <td>1.019997</td>\n",
       "      <td>1.015795</td>\n",
       "      <td>0.976871</td>\n",
       "      <td>0.979818</td>\n",
       "      <td>0.982748</td>\n",
       "      <td>1.050056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>1.025533</td>\n",
       "      <td>0.997375</td>\n",
       "      <td>1.027233</td>\n",
       "      <td>0.935453</td>\n",
       "      <td>0.974718</td>\n",
       "      <td>0.982748</td>\n",
       "      <td>1.036234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>10.532557</td>\n",
       "      <td>9.523544</td>\n",
       "      <td>0.740428</td>\n",
       "      <td>5.419013</td>\n",
       "      <td>1.195012</td>\n",
       "      <td>2.562843</td>\n",
       "      <td>11.120946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>10.386382</td>\n",
       "      <td>9.276658</td>\n",
       "      <td>0.749933</td>\n",
       "      <td>5.305576</td>\n",
       "      <td>1.201508</td>\n",
       "      <td>2.578505</td>\n",
       "      <td>11.038494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>10.067674</td>\n",
       "      <td>9.140368</td>\n",
       "      <td>0.742057</td>\n",
       "      <td>5.216878</td>\n",
       "      <td>1.181261</td>\n",
       "      <td>2.552804</td>\n",
       "      <td>10.925294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>10.352834</td>\n",
       "      <td>9.404011</td>\n",
       "      <td>0.758171</td>\n",
       "      <td>5.367121</td>\n",
       "      <td>1.190035</td>\n",
       "      <td>2.568064</td>\n",
       "      <td>11.227157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>10.378394</td>\n",
       "      <td>9.383902</td>\n",
       "      <td>0.758534</td>\n",
       "      <td>5.353847</td>\n",
       "      <td>1.188600</td>\n",
       "      <td>2.554410</td>\n",
       "      <td>11.171723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2768 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL      AMZN        GE      GOOG       IBM        KO  \\\n",
       "Date                                                                      \n",
       "2012-01-03   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "2012-01-04   1.005374  0.991510  1.010893  1.004313  0.995921  0.993727   \n",
       "2012-01-05   1.016536  0.992068  1.010348  0.990382  0.991197  0.989022   \n",
       "2012-01-06   1.027162  1.019997  1.015795  0.976871  0.979818  0.982748   \n",
       "2012-01-09   1.025533  0.997375  1.027233  0.935453  0.974718  0.982748   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "2022-12-23  10.532557  9.523544  0.740428  5.419013  1.195012  2.562843   \n",
       "2022-12-27  10.386382  9.276658  0.749933  5.305576  1.201508  2.578505   \n",
       "2022-12-28  10.067674  9.140368  0.742057  5.216878  1.181261  2.552804   \n",
       "2022-12-29  10.352834  9.404011  0.758171  5.367121  1.190035  2.568064   \n",
       "2022-12-30  10.378394  9.383902  0.758534  5.353847  1.188600  2.554410   \n",
       "\n",
       "                 MSFT  \n",
       "Date                   \n",
       "2012-01-03   1.000000  \n",
       "2012-01-04   1.023534  \n",
       "2012-01-05   1.033994  \n",
       "2012-01-06   1.050056  \n",
       "2012-01-09   1.036234  \n",
       "...               ...  \n",
       "2022-12-23  11.120946  \n",
       "2022-12-27  11.038494  \n",
       "2022-12-28  10.925294  \n",
       "2022-12-29  11.227157  \n",
       "2022-12-30  11.171723  \n",
       "\n",
       "[2768 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute returns leaving the first mpor row of nans\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=False)\n",
    "\n",
    "ival = 1\n",
    "ql.compound_returns(rets, start=ival).fillna(ival)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is, 1$ invested in AAPL trasnformed into 10.37 after 10 years. \n",
    "This is equivalent to the compounded return of about 938% seen previously\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Annualizing returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computation of the return-per-year, or **annualized return**.\n",
    "\n",
    "Let $R$ be the series of, e.g., daily returns. The total length series is $n$.\n",
    "Also, let $R_{c}$ be the compounded (total) return of the series (see above **.compound_returns**).\n",
    "Then, the annualized return, $R_{y}$, is calculated as:\n",
    "$$\n",
    "R_{y} = (1 + R_{c})^{p/n} - 1,\n",
    "\\quad\\text{where}\\quad\n",
    "p =\n",
    "\\begin{cases}\n",
    "252  \t& \\text{for daily returns},\\\\\n",
    "52   \t& \\text{for weekly returns},\\\\\n",
    "12  \t& \\text{for monthly returns},\\\\\n",
    "\\text{and so on}\n",
    "\\end{cases}\n",
    "$$\n",
    "The variable $p$ is the **periods-per-year** and denotes the number of **trading** days in a year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using daily prices/returns, 1d\n",
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"1d\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)\n",
    "drs = ql.annualize_returns(rets, ppy=252)\n",
    "\n",
    "# Using weekly prices/returns, 5d\n",
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"5d\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)\n",
    "wrs = ql.annualize_returns(rets, ppy=52)\n",
    "\n",
    "# using monthly prices/returns, 1mo\n",
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"1mo\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)\n",
    "mrs = ql.annualize_returns(rets, ppy=12)\n",
    "\n",
    "# Annualized volatilities\n",
    "rrs = pd.DataFrame({\"Using daily rets\"  : drs,\n",
    "                    \"Using weekly rets\" : wrs,\n",
    "                    \"Using monthly rets\": mrs})\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(1,1,figsize=(15,4))\n",
    "(rrs* 100).plot.bar(ax=ax,grid=True)\n",
    "fig.suptitle(\"Comparison of annualized returns\", fontsize=15)\n",
    "ax.set_xlabel(\"\",fontsize=18)\n",
    "ax.set_ylabel(\"Percent (%)\",fontsize=15)\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Drawndown\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"1d\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the absolute (*percent=False*) and relative (*percent=True*) **drawdown** series (*maxd=False*) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxd = False\n",
    "dd = ql.drawdown(assets, rets=False, maxd=maxd, percent=False)  # Absolute drawdowns\n",
    "dd_p = ql.drawdown(assets, rets=False, maxd=maxd, percent=True) # Relative drawdowns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(20,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(\"Drawdowns\", fontsize=15)\n",
    "\n",
    "n = 0\n",
    "dd.plot(ax=ax[n],grid=True)\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].set_ylabel(\"Dollars ($)\",fontsize=12)\n",
    "ax[n].legend(fontsize=10)\n",
    "\n",
    "n = 1\n",
    "(dd_p * 100).plot(ax=ax[n],grid=True)\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].set_ylabel(\"Percent (%)\",fontsize=12)\n",
    "ax[n].legend(fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the absolute (*percent=False*) and relative (*percent=True*) **maximum drawdowns** (*maxd=True*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxd = True\n",
    "mdd = ql.drawdown(assets, rets=False, maxd=maxd, percent=False)  # Maximum absolute drawdowns\n",
    "mdd_p = ql.drawdown(assets, rets=False, maxd=maxd, percent=True) # Maximum relative drawdowns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    -54.836014\n",
       "AMZN   -104.750496\n",
       "GE     -146.171562\n",
       "GOOG    -67.219002\n",
       "IBM     -60.552956\n",
       "KO      -20.094265\n",
       "MSFT   -125.961411\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.437972\n",
       "AMZN   -0.561453\n",
       "GE     -0.811914\n",
       "GOOG   -0.446018\n",
       "IBM    -0.437200\n",
       "KO     -0.369875\n",
       "MSFT   -0.371485\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdd_p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that drawdowns can be computed also if the input data are not prices but returns (*rets=True*). In this case *percent* returns are returns by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.437972\n",
       "AMZN   -0.561453\n",
       "GE     -0.811914\n",
       "GOOG   -0.446018\n",
       "IBM    -0.437200\n",
       "KO     -0.369875\n",
       "MSFT   -0.371485\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.drawdown(rets, rets=True, maxd=True, percent=True) # the same for percent = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The opposite of a drawdown is the (not so interesting) **drawup**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxd = False\n",
    "du = ql.drawup(assets, maxd=maxd, percent=False)  # Absolute drawup\n",
    "du_p = ql.drawup(assets, maxd=maxd, percent=True) # Relative drawnup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(22,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(\"Drawups\", fontsize=15)\n",
    "\n",
    "n = 0\n",
    "du.plot(ax=ax[n],grid=True)\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].set_ylabel(\"Dollars ($)\",fontsize=12)\n",
    "ax[n].legend(fontsize=10)\n",
    "\n",
    "n = 1\n",
    "(du_p * 100).plot(ax=ax[n],grid=True)\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].set_ylabel(\"Percent (%)\",fontsize=12)\n",
    "ax[n].legend(fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Volatility\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Classic Volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"1d\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.018335\n",
       "AMZN    0.020491\n",
       "GE      0.020440\n",
       "GOOG    0.016851\n",
       "IBM     0.014436\n",
       "KO      0.011169\n",
       "MSFT    0.016714\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Volatility over the entire period\n",
    "rets.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Semi-Volatility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computation of the semi-volatility, i.e., the volatility of negative returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.013329\n",
       "AMZN    0.014430\n",
       "GE      0.014990\n",
       "GOOG    0.012151\n",
       "IBM     0.011583\n",
       "KO      0.008977\n",
       "MSFT    0.012224\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Negative) Semi-Volatility \n",
    "ql.semistd(rets, negative=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If *negative=False* is passed, then it computes the semi-volatility of positive returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Annualizing the volatility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the annualized returns, the annualized volatility can be computed too (required if one need to \n",
    "compare volatilities of different stocks obtained from different types of returns).\n",
    "\n",
    "The annualized volatility, $\\sigma_y$, is a function of the square root of the time interval used to compute returns:\n",
    "$$\n",
    "\\sigma_{y} = \\sigma_p\\sqrt{p},\n",
    "\\quad\\text{where}\\quad\n",
    "p =\n",
    "\\begin{cases}\n",
    "252  \t& \\text{for daily returns},\\\\\n",
    "52   \t& \\text{for weekly returns},\\\\\n",
    "12  \t& \\text{for monthly returns},\\\\\n",
    "\\text{and so on}\n",
    "\\end{cases}\n",
    "$$\n",
    "Note that this annualization is *justifie* only if the returns used to compute the volatility are log-returns. \n",
    "This is because it is typically assumed that stock prices are log-normally distributed, which, in turns, implies that log-returns are normally distributed with mean $(\\mu-\\sigma^2/2)dt$ and variance $\\sigma^2 dt$. Hence, the volatility is given by and one can approximate the volatility for larger period of time by $\\sigma\\sqrt{dt}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using daily prices/returns, 1d\n",
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"1d\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)\n",
    "dvol = ql.annualize_std(rets, ppy=252)\n",
    "\n",
    "# Using weekly prices/returns, 5d\n",
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"5d\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)\n",
    "wvol = ql.annualize_std(rets, ppy=52)\n",
    "\n",
    "# using monthly prices/returns, 1mo\n",
    "assets = ql.getassets(tickers, startdate=startdate, enddate=enddate, datatype=\"Adj Close\", interval=\"1mo\")\n",
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)\n",
    "mvol = ql.annualize_std(rets, ppy=12)\n",
    "\n",
    "# Annualized volatilities\n",
    "vols = pd.DataFrame({\"Using daily rets\"  : dvol,\n",
    "                     \"Using weekly rets\" : wvol,\n",
    "                     \"Using monthly rets\": mvol})\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(1,1,figsize=(20,4))\n",
    "(vols* 100).plot.bar(ax=ax,grid=True)\n",
    "fig.suptitle(\"Comparison of annualized volatilities\", fontsize=14)\n",
    "ax.set_xlabel(\"\",fontsize=16)\n",
    "ax.set_ylabel(\"Percent (%)\",fontsize=14)\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EWMA Volatility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Distributions\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Normal distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Simple PDF and CDF evaluation at a given input point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x   = 0\n",
    "mu  = 0\n",
    "std = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3989422804014327"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PDF evaluation\n",
    "ql.dist_normal(x, mu=mu, std=std, cum=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CDF evaluation\n",
    "ql.dist_normal(x, mu=mu, std=std,cum=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Generation of the normal PDF and CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generation\n",
    "npdf = ql.gen_pdf_normal(mu=mu, std=std, dx=0.01)\n",
    "ncdf = ql.gen_cdf_normal(mu=mu, std=std, dx=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(20,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(\"Standard Normal distribution\", fontsize=15)\n",
    "\n",
    "n = 0\n",
    "npdf.plot(ax=ax[n],grid=True)\n",
    "ax[n].set_xlabel(\"\",fontsize=14)\n",
    "ax[n].set_ylabel(\"PDF\",fontsize=12)\n",
    "#ax[n].legend(fontsize=12)\n",
    "\n",
    "n = 1\n",
    "ncdf.plot(ax=ax[n],grid=True)\n",
    "ax[n].set_xlabel(\"\",fontsize=14)\n",
    "ax[n].set_ylabel(\"CDF\",fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Test for normality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computes the **Jarque-Bera** test for normality for a given input significance level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    False\n",
       "AMZN    False\n",
       "GE      False\n",
       "GOOG    False\n",
       "IBM     False\n",
       "KO      False\n",
       "MSFT    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.is_normal(rets, siglev=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Generation of normally distributed RVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "nnc = ql.gen_rvs_normal(mu=mu, std=std, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "plt.title(\"Normal RVs\")\n",
    "nnc.hist(grid=True, bins=60, density=False, edgecolor=(0.77,0.77,0.77))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evidently, the normality test is passed here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.is_normal(nnc, siglev=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### t-Student distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the Standardized and the Non-Standardized t-Student distibutions with $\\nu$ degrees of freedom where:\n",
    "  * the **Standardized** t-Student has mean zero and unit variance\n",
    "  * the **Non-Standardized** t-Student has mean equal to $\\mu$ and variance equal to $\\nu/(\\nu - 2)$, for $\\nu>2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Simple PDF and CDF evaluation at given input point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "x     = 0\n",
    "df    = 6  # t-Student degrees of freedom\n",
    "mu    = 0\n",
    "scale = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3827327723098717, 0.5)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Non-Standardized\" t-Student \n",
    "pdf = ql.dist_tstudent(x, df=df, mu=mu, scale=scale, stdz=False, cum=False)\n",
    "cdf = ql.dist_tstudent(x, df=df, mu=mu, scale=scale, stdz=False, cum=True)\n",
    "pdf, cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4687500000000001, 0.5)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Standardized\" t-Student \n",
    "pdf = ql.dist_tstudent(x, df=df, mu=mu, scale=scale, stdz=True, cum=False)\n",
    "cdf = ql.dist_tstudent(x, df=df, mu=mu, scale=scale, stdz=True, cum=True)\n",
    "pdf, cdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Generation of the t-Student PDF and CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generation of \"Non-Standardized\" t-Student, with (true) variance equal to (scale**2)*df/(df-2)\n",
    "tpdf = ql.gen_pdf_tstudent(df=df, mu=mu, scale=scale, stdz=False, dx=0.01)\n",
    "tcdf = ql.gen_cdf_tstudent(df=df, mu=mu, scale=scale, stdz=False, dx=0.01)\n",
    "t_var = scale**2*df/(df-2)\n",
    "tpdf.name = tpdf.name + f\" (Var: {t_var:.2f})\"\n",
    "\n",
    "# Generation of \"Standardized\" t-Student, with (true) unit variance\n",
    "tpdf_stdz = ql.gen_pdf_tstudent(df=df, mu=mu, scale=scale, stdz=True, dx=0.01)\n",
    "tcdf_stdz = ql.gen_cdf_tstudent(df=df, mu=mu, scale=scale, stdz=True, dx=0.01)\n",
    "t_stdz_var = 1\n",
    "tpdf_stdz.name = tpdf_stdz.name + f\" (Var: 1)\"\n",
    "\n",
    "# Normal distribution (for comparison), with variance equal to scale**2\n",
    "npdf = ql.gen_pdf_normal(mu=mu, std=scale, dx=0.01)\n",
    "ncdf = ql.gen_cdf_normal(mu=mu, std=scale, dx=0.01)\n",
    "n_var = scale**2\n",
    "npdf.name = npdf.name + f\" (Var: {n_var:.2f})\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(20,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(f\"t-Student distributions (df={df:.0f})\", fontsize=15)\n",
    "\n",
    "n = 0\n",
    "tpdf.plot(ax=ax[n],grid=True)\n",
    "tpdf_stdz.plot(ax=ax[n],grid=True)\n",
    "npdf.plot(ax=ax[n], grid=True, alpha=0.8, style=\"-.\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].legend(fontsize=12)\n",
    "\n",
    "n = 1\n",
    "tcdf.plot(ax=ax[n],grid=True)\n",
    "tcdf_stdz.plot(ax=ax[n],grid=True)\n",
    "ncdf.plot(ax=ax[n], grid=True, alpha=0.8, style=\"-.\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By putting *scale = 1*, observe how the **leptokurtic** character of \n",
    "the t-Student distribution appears by comparing, in particular, \n",
    "the Standardized t-Student distribution with the normal distribution both having the same (unit) variance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Generation of t-Student distributed RVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Non-Standardized\" t-Student, with (true) variance equal to (scale**2)*df/(df-2)\n",
    "trvs = ql.gen_rvs_tstudent(df=df, mu=mu, scale=scale, stdz=False, size=10000)\n",
    "trvs.name = \"Non-Standardized\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Standardized\" t-Student, with (true) unit variance\n",
    "trvs_std = ql.gen_rvs_tstudent(df=df, mu=mu, scale=scale, stdz=True, size=10000)\n",
    "trvs_std.name = \"Standardized\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(20,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(f\"t-Student distributions (df={df:.0f})\", fontsize=15)\n",
    "\n",
    "# Non-Standardized\n",
    "n = 0\n",
    "trvs.hist(ax=ax[n], grid=True, bins=60, density=False, edgecolor=(0.77,0.77,0.77), legend=True)\n",
    "ax[n].set_title(f\"Sample Var: {trvs.std()**2:.2f} - True Var: {scale**2*df/(df-2):.2f}\", fontsize=14)\n",
    "ax[n].legend(fontsize=12)\n",
    "\n",
    "# Standardized\n",
    "n = 1\n",
    "trvs_std.hist(ax=ax[n], grid=True, bins=60, density=False, edgecolor=(0.77,0.77,0.77), legend=True)\n",
    "ax[n].set_title(f\"Sample Var: {trvs_std.std()**2:.2f} - True Var: 1\", fontsize=14)\n",
    "ax[n].legend(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that the normality test is not passed here\n",
    "ql.is_normal(trvs_std, siglev=0.05), ql.is_normal(trvs, siglev=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### General distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a generic PDF is defined by the user, then random variables can be generated. \n",
    "\n",
    "For example, suppose that the following method, *some_pdf*, returns the normal distribution with mean $\\mu$ and standard deviation $\\sigma$ (evaluated at zero):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3989422804014327"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.some_pdf(0, mu=0, std=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, random variables with this distribution can be obtained by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.063703\n",
       "1     -0.566772\n",
       "2     -1.016666\n",
       "3      0.424346\n",
       "4      1.465896\n",
       "         ...   \n",
       "995    1.181810\n",
       "996    1.245621\n",
       "997    0.871018\n",
       "998    2.855671\n",
       "999    1.028223\n",
       "Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnrv = ql.gen_rvs_from_pdf(ql.some_pdf, size=1000, mu=0, std=1)\n",
    "nnrv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double check of the normality\n",
    "ql.is_normal(nnrv, siglev=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Distributions fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computation of the distribution that best fit a certain input sample distribution data via **Maximum-Likelihood-Estimation** (MLE) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock = \"GE\"\n",
    "rr = rets[stock]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computation of the **Normal** and **t-Student** distribution that best fit the given returns data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndist = ql.dist_fit(rr, dtype=\"n\", pdf=True, dx=0.002)\n",
    "tdist = ql.dist_fit(rr, dtype=\"t\", pdf=True, dx=0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(20,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(f\"{stock} returns\", fontsize=15)\n",
    "\n",
    "n = 0\n",
    "(rr*100).plot(ax=ax[n], grid=True, label=\"\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].set_ylabel(\"Percent (%)\",fontsize=12)\n",
    "#ax[n].legend(fontsize=10)\n",
    "\n",
    "n = 1\n",
    "rr.hist(ax=ax[n], grid=True, density=True, bins=50, edgecolor=(0.77,0.77,0.77), alpha=0.6, label=\"Sample data\")\n",
    "ax[n].plot(ndist[\"x\"], ndist[\"pdf\"], label=\"Normal fit\", color=\"tab:orange\")\n",
    "ax[n].plot(tdist[\"x\"], tdist[\"pdf\"], label=f\"t-Student fit (df={tdist['df']:.1f})\", color=\"green\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be observed that stock returns are likely to be t-Student distributed as the t-Student distribution computed via the MLE provide a better fit of the sample data. \n",
    "\n",
    "Computation of the **Empirical CDF** (ECDF) (i.e., the CDF of the sample data) and of the Normal and t-Student data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Empirical CDF (CDF) of the sample data\n",
    "fe  = ql.empirical_cdf(rr)\n",
    "# Normal CDF of the data (via MLE)\n",
    "nfh = ql.hypothetical_cdf(rr, dtype=\"n\")\n",
    "# t-Student CDF of the data (via MLE)\n",
    "tfh = ql.hypothetical_cdf(rr, dtype=\"t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(20,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(f\"{stock} returns\", fontsize=15)\n",
    "\n",
    "n = 0\n",
    "rr.hist(ax=ax[n], grid=True, density=True, bins=50, edgecolor=(0.77,0.77,0.77), alpha=0.6, label=\"Sample data\", color=\"tab:blue\")\n",
    "ax[n].plot(ndist[\"x\"], ndist[\"pdf\"], label=\"Normal fit\", color=\"tab:orange\")\n",
    "ax[n].plot(tdist[\"x\"], tdist[\"pdf\"], label=f\"t-Student fit (df={tdist['df']:.1f})\", color=\"green\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].legend(fontsize=12)\n",
    "\n",
    "n = 1\n",
    "fe.plot(ax=ax[n], grid=True, label=f\"{stock} ECDF\", color=\"tab:blue\")\n",
    "nfh.plot(ax=ax[n], grid=True, label=f\"Normal fit\", color=\"tab:orange\")\n",
    "tfh.plot(ax=ax[n], grid=True, label=f\"t-Student fit\", color=\"green\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].legend(fontsize=12)\n",
    "ax[n].set_xlim([-0.04,0.04])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe how from the plot of the CDF it can be seen that the t-Student distribution matches much better the ECDF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Generalized Pareto** distribution can be also fit. This is used to fit *excesses over a certain griven threshold* (i.e., it is used to fit the distribution tails)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a threshold defining the tails\n",
    "threshold = 95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the corresponding quantile that define the tails\n",
    "\n",
    "ll = -rr # Get the symmetric series so that the left tails would be on the right\n",
    "qu_l = ll.quantile(q = threshold/100)\n",
    "excess_l = ll[ll >= qu_l] - qu_l\n",
    "\n",
    "qu_r = rr.quantile(q = threshold/100)\n",
    "excess_r = rr[rr >= qu_r] - qu_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GDP fit\n",
    "gdpdist = ql.dist_fit(excess_l, dtype=\"gdp\", pdf=True, dx=0.001)\n",
    "gdpdist = ql.dist_fit(excess_r, dtype=\"gdp\", pdf=True, dx=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//enayhh69+7LVVddz1133c706c+ybt1aANxud+X27QsQgI8+ep8xY8btsL2wsJC//e1G7rzzPrp1687Gjfmce+4UHnjgMf7xj7s5/fSTKnP80Ufvc9xxJ1Zr9ABce+1feeutN7nnnml07dqV5567kuuuu4FevXqzcWM+xx13JCecUHsBMWrUaGbP/kjNHhERaTQtpcaC+tVZ6eluzjnnHNVZ9ayzbrnlJmbOfNexOqtq3mfN+hCAn3/+iQcfvJfHHnuKzp278PjjDxONRiuPWbt2DQ8/PJ3c3FyuueaPzJjxGlOmnF2tXqzqvPOm8t577/DXv97C4MFDUvr5bjNq1D68+OJzavaIiLRVtm1XG3ZaWlrChReeA0BZWSkHHHAwp59+JgsXzqewsJBHH32o8rWlS39kyJCheDwe9t13LAC77DKYwsJCAD79dC7ff/8tZ599OgCxWJRQKFR5rT322Kvy8fXX38i8eZ/w738/zooVyykvL6esrLTW/0gVFBRwxRWXcN55U9lzz+GUlZXtNM4NG35h1KjRdOwYL7YmT/4Nn302b6fnPvPMKdi2QSwWxe9PZ+rUSys/tTn33Gxef/0V1qxZzYIFXxEIBCqPGzZsj8rHn346h6Ki4sqpTpFIBcFg7g7X+t//PuHccy/E44n/5/H440/immuu2GlsqVi5cjkHHnjIDtu//fYbNm3atMN1li1bktKncdsYhsFtt93Fp5/O4b333mHFip+xbbvyE8ud6datBx9+WPsaQiIiIq1Vfeost9uguLikTddZl1xyHi6Xu03WWVXzvs3nn89j1KjRdO7cpTKGxx9/uPL1kSNHkZsbj33gwF3YsmVzSnHU5+fbrVv3Wqen1ZeaPSLS7pUPPj6pT4Sa2tChu7Fy5XK2bi0gJydIIJBR+WnOtuG8sVgU27Z56KHH8fvj83oLCgrwer1s3VpAWloaLld87f14QRNfoC4Wi3Lqqb/n2GPj7zMcDlNUVFh57fT09MrHU6eew4ABg9hnn3054ICD+e67xTUudLdNKBTiyisvY9KkIzn44MMqr7ezOF9//RWqns7tdteal6pr9lT16adzufvuOzj55NMYP34Cffr0ZebMt6q8p18Lkmg0xqWX/rGyQCstLSUcDu9wzmg0BvxaCMZiNpFIpNb4kmcQi8VqvGafPn155JEnK7dt3JhPMJhLfv6GlK9SWlrKmWeeyn77TWTYsL044ojJzJkzu9afIYDH48HtbpX3bRARkRaqpdRYUL86KxgMsHz52jZdZ1Vds6eqtlBnVc37Nm63u1p+XK7q+dnWiNqmrvppe6n+fLddc9vvVWNSVSci0kJ06pTH8cefzJ//fDXr16+v3L5+/ToWLfoal8tNRkYmQ4fuznPPPQ2QmC9+JnPnzq713KNG7cubb75GSUkxAI8++hA33/yXHfYrKirihx++44ILLmbChAPYsOEX1qxZXWOTAuJ3i/rLX65m0KBd+N3vzqjcXluco0btw+ef/48NG34B4O2330whS7/64ovPGDt2PMceezyDB+/KnDmzdhrn6NH78sorL1BRUUEsFuO2225h2rT4EGC32000Gqnc77XXXiISiRCLxXjllRcYOXJ00jG53Z6dFi29e/dh7do1O2wfOnR3Vq9excKF8wFYssTi5JOPrbHRU9v53W43kUiElStXUFJSwjnnXMi4cfuxYMFXhMPhneZmm3Xr1tC7d9863qGIiEjrVJ86q7CwUHVWK6+ztjdq1L58+eXnlXXWm2++luS1f30fNb0WiURS/vlus3btWvr06ZtUHKnQyB4RkRbkvPOm8u67b3PjjdcRCpVRXFxCVlYWBx10CL/5TXyO8F//egt33XU7p59+EhUVFRx00KEccsikyjnNNTnqqGPYuDGf8847AzDo0qUr1113ww77ZWVlcdppUzjrrNPw+/3k5XVh9933YPXqVfTo0XOH/T/88D0+/XQugwcP4YwzTqn8pOSOO+7eaZwAF154CZdeegGBQEaNCxYn45hjjuOGG67l9NNPIhqNMnLkPomFEXf8D+qUKWdx3313c8YZpxKLRRk0aBcuuugyAPbZZwz33ntXtf2mTDmFaDTCkCFDueyyK5OOqV+/fni9Ps4553QefvjJasPFJ048kLvvvoOzzjqv2jG5ubnceuvt3H//3YTDYWw7xp//fBPdunXf4Wc6btx+TJt2P5FIhEmTjqz22sSJB3LRRedy9913M2bMOE455Xi83jT69x9I3779Wb16FWlpaTuN/bPP5nHccScl/V5FRERam1TrrFgsqjqrlddZ2+vduw8XX3w5f/jDRXi9PgYN2qVydFRtqr6Pqo03gAkT9uemm/7MFVdck9LPd5vPPpvH/vsfWGcMqTJSHZaUrPz8oqY5MbD45595Y2lFrfsc3tfFnoMGNlUIrUowGKCgoNTpMFoN5St1yllqlK/UtKV8/eEPF3H22eczZMhuTXaN+uRrzZrV9br1el5elu7T3gI1VQ2W7O/WwiVLeWt57Z9itsc6rS39LWtuyl3DKH/1p9w1THPnL5k6a+3aNbzzzn+ZMuVsXC4Xs2d/yNNPP1ltOn1zKikp5vzzz+TRR5/C5/t12YK6cpdMDaZpXCIiIs3kT3+6lunTH015/ndTe/jhB7j66utTavSIiIiItCTJ1FmdO3dh48Z8Tj/9ZH7/+9/ywgv/4Zprdpxy11wef/xhLrnkj9UaPY1F07hERESaSbdu3bn99n85HcYObrwx+duyioiIiLREydRZHo+HK6+8rnkCSsLFF/+hyc6tkT0iIiIiIiIiIm2Imj0iIiIiIiIiIm2Imj0iIiIiIiIiIm2Imj0iIiIiUidj+RyMcJHTYYiIiEgS6lyg2TTNKcCUxFM/sCcwDvgXYAOLgamWZdV+j00RERERabXcr5xBYNCxlIy/0elQREREpA51Nnssy5oOTAcwTfN+4HHgL8D1lmXNMk3zIeBo4NWmC1NEpGktW7WKolB5k50/y+9jQK9eTXZ+EZGmZvfYG9/y9ykZdwMYhtPhiEgroRpLxBlJ33rdNM29gaGWZU01TfOvwOzES28Dh6Bmj4i0YkWhct5a3nQDFA/vm1yRs3jxIqZNu4/Cwq3EYjE6d+7K1KmX0r//gCaLbZtx4/Zmxoz3CQaD9Tr+o4/e5+WXX+C++x6udb8nnniEgQMHMX78xHpdR0ScYQ88BPfSd3EXLCOaO9DpcESklWgpNRaozpL2JelmD3AtsG3crmFZlp14XATkNGpUIiLtUDgc5qqrLuPOO+/HNAcDMHPmW1xxxSW8+OIbuN1uhyNsHF999QV9+/ZzOgwRSVFs4CG4gY1fPseKvifvdL/SUAjwNltcIiLJUJ0l7U1SzR7TNIPAYMuyPkpsqtqazQIKtj8mM9OHx9M0/2AMw8DrrT10j8cgGAw0yfVbG7fbpVykQPlKXVvImcfjxuttujXrq/5N2lm+tm6toLi4GLc7Vvn6iSceR+fOHcjO9uF2e7jttr/zzTdfU1JSgm3DjTfexPDhw7nuumvx+/0sWfIjmzZtYuLE/QkGg8yaNYtNmzZy4403MXr0Plx33bX4fD4s6wc2bdrMmDFjuOaaa0lLSwMgJyedYDDAyy+/zPPP/4dYLEYwGOTaa6+nf//+O8R83333MmPGDILBIH369MbjcRMMBli+fDm33HIzpaUl5OfnM3jwYO64405eeeVlLOt7HnroXrKyAgwcOLDG/Xw+X+U12sLvV3NSvqTJ5PSkKLM/xqr/8RYn7nS38XlaxlFEWp5QKERxcTFlZaWV2w45ZBIZGRnEYjEMw+Cee+7k228XUVZWim3b3HzzLfTvP5hbb70Bn8/PTz8tZfPmzYwbtx85OTl88skcNm3axNVXX8+IESMT+/lYsuRHtmzZzKhR+3DZZX/C46n+/64zZrzGK6+8hG3HyM4O8oc/XEmfPn13iPnRRx/i3XffJicnh549e1duX7lyBXfeeRulpaVs2rSRQYN24cYb/48ZM17Hsr7ngQfuweVy069f/xr3q1pnSduV7Mie/YD3qzxfYJrmRMuyZgGTgI+2P6C4uOnmZdq2TTgcqXWfSMRFQUFprfu0F8FgQLlIgfKVuraQs0gkSjjcdP+DUvVv0s7zlcYFF1zMeeedQ4cOnRg2bBh77bU3Bx10KCUlERYvns+aNeu4//7HcLlcPPXUdB56aBq3334X4XCEJUsW88ADj7J1awFHH30Yl112Bfff/ygvvPAfHnpoGqY5jHA4wvfff8999z2Cx+PhD3+4iH//+2mOO+4kALZuLWPhwsW88sor3HPPw/j9fj7//H9cfPFFPPPMS9WinTNnFu+8M5PHH38ar9fHtddeQSQSpaCglGee+Q8HHzyJQw89nEgkwllnncbbb7/LpEnH8N//vsVxx53I3nuP4f77765xv4kTD6y8Tlv4/WpOzZmvvLysZrmOtBwb8/al/8//wR8tJuTOdDocEZGkZWdnc8EFF/PHP168Q52VlpbG4sXfsHFjPtOmPVFZZz322KPceusdAPz44w871FkPPfQ4L7zwH55+ejojRowE4LvvFlers15//eXKOgtgwYKvePvt//LAA49W1lnXXntFjXXWrFkfMn36s5V11jZvvvkakyYdWa1+mjdvLscddyIfffQ+xx13IhMm7M/9999d435V6yxpu5Jt9pjAT1We/xF4xDRNL/A98FKNR4mISEpOPvk0Jk8+lgUL5vP11/N55pkneeaZJ3nkkX+z227DOPfcbF5//RXWrFnNggVfEQj8OoJj7NjxeDweOnbsRHp6OqNHjwGgR4+eFBYWVu43adJRlccddtjhfPzx7GpFyLx5c1m9ehXnn39m5baioiIKC7eSnf3rrN0vv/ycCRP2JxDIAOCIIybz4ovPAXDBBRfzxRef8cwzT7Jq1Uo2bsynrKxsh/eb7H4i7ZFpmtcAk4nPiXqA+HqJ03Hwbqj5nfah38/PMLj0SxZmTWzOS4uINFiqdVZ29q8faqjOktYmqWaPZVn/2O75j8CEJolIRKSd+uabhSxe/A2nnHI6Y8eOZ+zY8Zx77lROP/0kvvjif/h8fu6++w5OPvk0xo+fQJ8+fZk5863K49PSqq+Rsf2Q4W2qzkmPxWzc7urT16LRGIceejgXXnhJYp8YGzfmk5WVvcO5bNuufFz1vDfccB3RaIQDDjiYffcdxy+/rK+2b6r7ibQ3pmlOBMYAY4EAcAVwJw7fDXVrcAglrmyGFM9Ts0dEWpX61FkffDCz8njVWdLaNN0CFSIikpJgMJcnn3yMr79eWLlt06aNlJQUM2DAQL744jPGjh3Psccez+DBuzJnzixisdQ/1P/ww/cIh8OUl5fz9tszGDt2fLXXR4/el/ffn8nGjRsBeO21l7n00gt2OM8++4zho4/ep6ioiFgsxjvv/Np4+vzzeUyZcg4HHngIEB/SHItFAXC7PUQikTr3E2nnDgUWEW/mvAnMAEZQ/W6oBzV7VIab7zNGsWvp5xi2/q2KSOtRvzor9b9zqrOkpUjlblwiIm1Wlt+X0q0763P+uvTu3Yf/+79/8vDD97NhwwZ8Pi8ZGZlcc81f6d27L8cccxw33HAtp59+EtFolJEj92H27A9Tbvj4fH6mTj2boqIiJk48kMMPn1zt9VGj9uHUU3/P5ZdfiMvlIhDI4NZb/4FhGNX223ffcSxbtpSzz/4dWVnZDBw4iIKCLQCce+5Urr32T6Sn+8nIyGTPPYezevVqAMaN249p0+4nEonUup9IO9cJ6AMcCfQD3gBcydwNtalukuF2u/B43PyYM5a9i95nQHQJKwO77bCfbqRRMy2eXn/KXcM4nb+OORlMHhhqsvPnZPiTen/B4K7ce+993H//ffzyyy94vV6ysrK49da/MWzYEDIz/Vx55RWcccZviUSijBkzhvfff4/sbD9er4f09LRq18nOjt/UIiPDV5ljr9dDZmaASy45j8LCrRx88CGccsrJuFzxMRY5OekccsgBbNq0niuuuAjDcJGZmcm9995Lbm5GtXgnTTqEtWtXcu65p5OdnYNpmhQXFxIMBrjsssv585+vJD09nczMLEaOHEV+/nqCwQAHH3wgjzzyAF6vq9b9moPTv3utWWPkzmiqYVz5+UVNNj5s8c8/88bSilr3Obyviz0HDWyqEFoVLW6aGuUrdcpZapzM16233kC/fgM45ZTfOXL9+tDvV2qaeYFmo+69pD5M0/w7kG9Z1j8Tz78GBlmWFUg8Pxo42LKsi7Y/tqlqsGAwwKwvvuGjZVu5edmxfNDht7zd6awd9hufF2JOvr/Wc7XHOk1/y+pPuWsY5a/+Us1da6yzmpJ+9+qvrtwlU4NpGpeIiIhIyzMXOMw0TcM0ze5ABvBBYi0fiN8NdY4TgZW5s/g5fTeGlvzPicuLiIhIEjSNS0SkHbnuuhucDkFEkmBZ1gzTNPcDPif+4dxU4GdayN1Qv8vYh8kbHyanIp+taXlOhSEi0qKozpKWRM0eERERkRbIsqwra9jcIu6Guq3ZM7RkHp8GJ9d9gIiIiDQrTeMSERERkZT84u1DfloPdiv+xOlQREREpAZq9oiIiIhIagyDbzLHs0vpfPzRYqejERERke2o2SMiIiIiKfsmczxuogwtmed0KCIiIrIdNXtEREREJGWr/CYFnk4MK3bkpmAiIiJSCzV7RERERCRltuFiUeY4Bpd8gTdW5nQ4IiIiUoWaPSIiIiJSL99kjsdrlzO45AunQxEREZEq1OwRERERkXr5KX0Yxe4cTeUSERFpYdTsEREREZF6iRluFmeMZUjJ/3DHwk6HIyIiIgmtr9kTDTNq9qkMKP3a6UhERERE2r1vMseRHithUNkCp0MRERGRhNbX7DFcpFVsZd+tM5yORERERKTd+zEwnJArwLAiTeUSERFpKVpfs8flYWPncRouLCIiItICRF1evs3Yh91LPsGwo06HIyIiIrTGZg+Q33WChguLiIiItBCLMseTGd1K/7JFTociIiIitNJmz5aOIzRcWERERKSF+D5jFGHDx55Fs50ORURERABPMjuZpnkNMBnwAg8As4HpgA0sBqZalhVrohh3YLt/HS78on05tuFurkuLiIiIyHbCrnS+zdiXPYpn837sLKfDERERaffqHNljmuZEYAwwFpgA9ALuBK63LGs8YABHN2GMNfp1uPDi5r60iIiIiGxnfvaBZEUL6Lx1odOhiIiItHvJTOM6FFgEvAq8CcwARhAf3QPwNnBQk0RXi/hwYS/DijWVS0RERMRp3wdGUurKpGe+pnKJiIg4LZlmTydgb+AE4HzgGcBlWZadeL0IyGma8HYu7ErHCoxk9+I5YNt1HyAiIiIiTSbq8vJN5ni6b/6UtFi50+GIiIi0a8ms2bMJ+MGyrDBgmaYZIj6Va5ssoGD7gzIzfXg8TbOWjmEYeL0evgtOYPc1n9A/upTVgV2r7ePxGASDgSa5fmvjdruUixQoX6lTzlKjfKVG+UqN8iVOWpB1APsUvs2uJZ/xTdZ+TocjIiLSbiXT7JkLXGqa5p1ANyAD+MA0zYmWZc0CJgEfbX9QcXHTfaJj2zbhcIRv/KM4Hje7bpnFT55B1faJRFwUFJQ2WQytSTAYUC5SoHylTjlLjfKVGuUrNc2Zr7y8rGa5jrQeSwJ7EkrLZXjRh2r2iIiIOKjOaVyWZc0AFgCfE1+zZyrwR+BG0zTnEb9D10tNGeTOlLqzWRrYM75uj6ZyiYiIiDjKNtys7jSeISXz8EVLnA5HRESk3Urq1uuWZV1Zw+YJjRxLvXyTOZ4TNvyLbuGfWOcb4HQ4IiIiIu3a6k4TGLjuDYYVz+WLnEOdDkdERKRdSmaB5hbtm8zxRHGxV9EOM8lEREREpJltzhrMJk9X9ir60OlQRERE2q2kRva0ZMWeXH4MjGB40Ue81fEsMAynQxIRERFpMNM0FwBbE09/Bm4FpgM2sBiYallWzJnoamEYzM8+gAM2P0dGpIAST9DpiERERNqdVj+yB+J3fuhYsY4+oe+dDkVERESkwUzT9ANYljUx8XUGcCdwvWVZ4wEDONrJGGuzIOsA3MQ08lpERMQhbaLZ803mOCqMNIZruLCIiIi0DXsAAdM03zVN80PTNPcBRgCzE6+/DRzkWHR1WOfrz2rfQEYVznQ6FBERkXap1U/jAih3Z/Bdxj7sWfQRr+VdgG24nQ5JREREpCFKgTuAR4FBxJs7hmVZ224/WgTk1HRgZqYPj6fxayG324XH48brrf2zQsMw8Ho9zM+dxOT199I7toL1/uo30fB4DILBQKPH2JK53a52954bi3LXMMpf/Sl3DaP81V9j5K5NNHsgPlx4j+I5DCpdyI8ZI5wOR0RERKQhfgSWJpo7P5qmuYn4yJ5tsoCCmg4sLi5vkoCCwQCRSJRwuPZlgmzbJhyO8HnGARzOg+y18b+83vnCavtEIi4KCkqbJM6WKhgMtLv33FiUu4ZR/upPuWsY5a/+6spdXl5WnedoE9O4AL7L2IeQK6A7P4iIiEhbcCbwTwDTNLsD2cC7pmlOTLw+CZjjTGjJKXHn8G3mvowoeh+XHXE6HBERkXalzTR7Klw+FmWOY4/ij3HHwk6HIyIiItIQjwFB0zTnAs8Tb/5cCtxomuY8wAu85GB8Sfki+1CyogUMKfnM6VBERETalTYzjQtgftYBjCx8l11LvwDGOx2OiIiISL1YlhUGTqnhpQnNHUtDfJ8xmkJ3LqO2vsPizLFOhyMiItJutJmRPQA/BoZT7M7RVC4RERGRFiBmuPky+2CGlPyPzMgWp8MRERFpN9pUsydmeFiYOYHdij/FHSlxOhwRERGRdu+L7ENxE2VE0ftOhyIiItJutKlmD8CX2Yfgtcvpuv4jp0MRERERaffW+/qxwmcyautMsO26DxAREZEGa3PNnhX+XfnF25vua99xOhQRERERAT7POYzu4Z/oWb7E6VBERETahTbX7MEw+Dz7MIIFi3FvWeZ0NCIiIiLt3oKsAwgbXvbdOsPpUERERNqFttfsAb7MPpiY4cL/w4tOhyIiIiLS7pW5s1iQtT8jCt/HF9W6iiIiIk2tTTZ7Cj0d2dRxFD7rJYhFnQ5HREREpN37NGcyPjvE3kXvOR2KiIhIm9cmmz0Aa3tMwl2ynrRVHzsdioiIiEi7t9I/mFW+QYwpeFMLNYuIiDSxNtvsyc8bQ8yfi/+HF5wORUREREQMg0+Ck+ke/plgwSKnoxEREWnT2myzx3alEdrlWHw/zcQIbXE6HBEREZF2b0HWAZS5Mui56g2nQxEREWnTPMnsZJrmAmBr4unPwK3AdMAGFgNTLcuKNUWADREafBKBbx7Ht+R1QrtPcTocERERkXYt7Erni+xDGPfLDDaXbcJO7+h0SCIiIm1SnSN7TNP0A1iWNTHxdQZwJ3C9ZVnjAQM4umnDrJ9o3lAqOg3F/91zmhsuIiIi0gJ8mnMULrsC//fPOR2KiIhIm5XMyJ49gIBpmu8m9r8WGAHMTrz+NnAI8GqTRFhP5eUhFi5ZSs9OB7PrD//ip69mUJiza7V9svw+BvTq5VCEIiIiIu3PL76+bM7dg5xvn6FsrwvAaLOrCoiIiDgmmWZPKXAH8CgwiHhzx7Asa9tQmSIgZ/uDMjN9eDzuxoqzGsMw8HprD70iWs6ctS58xqFc55qG23qD93sOrbbPYb0jBIOBJomxJXG7Xe3ifTYW5St1yllqlK/UKF+pUb6kNVjdczIdFt2Md+Uswn0OcDocERGRNieZZs+PwNJEc+dH0zQ3ER/Zs00WULD9QcXF5Y0SYE1s2yYcjiS1TxgfX2UdxN5bZ/Jqx/ModWdX7hOJuCgoKG2yOFuKYDDQLt5nY1G+UqecpUb5So3ylZrmzFdeXlazXEfang1d9iO6rAvpXz+mZo+IiEgTSGbc7JnAPwFM0+wOZAPvmqY5MfH6JGBOk0TXSD4JTsZrhxlZONPpUERERETaPduVRtmwM/Cumo170w9OhyMiItLmJNPseQwImqY5F3ieePPnUuBG0zTnAV7gpaYLseHW+gbws38IYwre1ELNIiIiIi1AaOip2B4/6V8/6nQoIiIibU6d07gsywoDp9Tw0oTGD6fpfBqczKnr/86gsgUsCQx3OhwRERGRds325xIafCL+75+nZJ+rsAN5TockIiLSZrSb2x8szJxIsSubsQVvOB2KiIiIiABlw87CiJaTvvjfTociIiLSprSbZk/E5eXznMPYrXgu2ZGNTocjIiIi0u5FcwdQ3vegeLMnEnI6HBERkTaj3TR7AOblHIWbGPtu/a/ToYiIiIgIULbHObjKNuH/8VWnQxEREWkz2lWzZ6O3B98FRjGm4A3csbDT4YiIiIi0exU9xhDpOCS+ULNupCEiItIo6lygua2ZnXsCF6z5E8OLPgQOdzocERERkZ0yTbMz8BVwMBABpgM2sBiYallWzLnoGolhULrnuWR/cBlpK2dR0Wd/pyMSERFp9drVyB6AHwPDWevtz8QtL+nTIxEREWmxTNNMA6YBZYlNdwLXW5Y1HjCAo52KrbGVD5pMNLMbgfn3OR2KiIhIm9Dumj0YBrNyj6d7+Cc6bP7K6WhEREREduYO4CFgbeL5CGB24vHbwEFOBNUk3F7K9jwf79rP8Kz93OloREREWr12N40LYH7WARy58RH6rHgRe5+TnQ5HREREpBrTNKcA+ZZlzTRN85rEZsOyrG3DkouAHEeCayJlQ04h8NU9ZHx1D1u7P530cctWraIoVF7rPll+HwN69WpoiCIiIq1Gu2z2RF1e5gaP4fCNT7B5849EO+zidEgiIiIiVZ0J2KZpHgTsCfwb6Fzl9SygoKYDMzN9eDzuRg/I7Xbh8bjxemsfGG4YBl5v7SWmx2MQDAa22xrA3mcq3o9uIlj2I3TbM6m4yn6O8P7a2mM6rHekhus1L7fb5XgMrZVy1zDKX/0pdw2j/NVfY+SuXTZ7AD4NTubQLc+Q/vWjFO9/u9PhiIiIiFSyLGu/bY9N05wFnA/8wzTNiZZlzQImAR/VdGxxce2jXOorGAwQiUQJh2tfE9q2bcLhSK37RCIuCgpKd9huDPwtHT69m9isf1A46ZGk4kompp1drzkFgwHHY2itlLuGUf7qT7lrGOWv/urKXV5eVp3naH9r9iSUuHNY1/1Q/NbLGGWbnA5HREREpC5/BG40TXMe4AVecjieRmd7syjbfQq+n97GvflHp8MRERFptdrtyB6AFX2Op+fqN0n/5nFKR//J6XBEREREdmBZ1sQqTyc4FUdzKdvjbAILHyHw1X0UHXyP0+GIiIi0Su12ZA9AaUYfyvsfRvqi6RjhIqfDEREREWn3bH8uZbv9Dt+S13FtXe50OCIiIq1Su272AJSOuARX+Vb8i550OhQRERERAcr2PBdcHjK+vNvpUERERFqldt/siXQeRrj3BAJfPwIVZU6HIyIiItLuxTK6xNfusV7GvXmJ0+GIiIi0Ou2+2QNQMuJSXGWbSP/uGadDERERERGgdPhUbE86gc//6XQoIiIirY6aPUCk+yjC3UeTvuBBiDbN7UpFREREJHl2egfK9jgb/7IZePIXOx2OiIhIq6JmT0LpiEtwl/yC/4cXnQ5FRERERICyPc8j5ssh8NntTociIiLSqrTrW69XVdFrPyo670Fg/gOEdj0ZXL+mZtmqVRSFah/xk+X3MaBXr6YOU0RERKTdsH3ZlO51AZn/+zuedV8S6ba30yGJiIi0Ckk1e0zT7Ax8BRwMRIDpgA0sBqZalhVrqgCbjWFQOuISct4+C5/1MuW7nlT5UlGonLeW1/4WD++r6V8iIiIija1s2JkEvn6MjM9uY+vRL4BhOB2SiIhIi1fnNC7TNNOAacC2W1XdCVxvWdZ4wACObrrwmle43yFU5A0j44u7tHaPiIiISEuQFqB0xEV418wjbdXHTkcjIiLSKiSzZs8dwEPA2sTzEcDsxOO3gYOaIC5nGAYl+1yFu2g1/m+fdToaEREREQHKdjuNaHZvMj+9GWJRp8MRERFp8WqdxmWa5hQg37KsmaZpXpPYbFiWZSceFwE5NR2bmenD43E3WqBVGYaB11v7DLRk9vF4DILBQPWNOYcR+3oMmfPvxb/PFPBm4PG48Xpr74vVeK4Wwu12tdjYWiLlK3XKWWqUr9QoX6lRvqRNcvso3vdacmaej/+H5wkNOcXpiERERFq0utbsOROwTdM8CNgT+DfQucrrWUBBTQcWFzfdNCjbtgmHIw3eJxJxUVBQusN2z4gryH31N5TPfYCy4VOJRKKEw7Wv2bOzc7UEwWCgxcbWEilfqVPOUqN8pUb5Sk1z5isvL6tZriMCEB5wBBVd9ybjf/+gfOBkbG+m0yGJiIi0WLUOV7Esaz/LsiZYljURWAicDrxtmubExC6TgDlNGaATIt1HUd7nAALzH8AoL3Q6HBERERExDIrH/gVXWT7pCx50OhoREZEWLZk1e7b3R+BG0zTnAV7gpcYNqWUoHX0lrvKtpC+c5nQoIiIiIgJEug4nNOgYAgsewlW0tu4DRERE2qmkbr0OkBjds82Exg+lZYnk7UZowJGkf/0o3jETgKDTIYmIiIi0eyX7XI3vp7fJ+Ow2ig66O6ljystDLFyytNZ9svw+BvTq1RghioiIOC7pZk97VLrPlfh+focBy56AwOVOhyMiIiLS7sWye1K2xzkE5t9H2e5TiC8hWbtwJMYH62pff/Hwvk233qSIiEhzq880rnYjGuxP2e5T6LH6v3Qr/8npcEREREQEKB1xEdGMLmTOvg5s3YpdRERke2r21KF070uJeDI4Ov9BsO26DxARERGRJmV7MykZ+xfS8r+h56o3nQ5HRESkxVGzpw62P5dlA36PWfoVQ0o+czocEREREQHKB04m3HMcA5c+SmZki9PhiIiItChq9iRhda+j2ZDWk8kbH8JlR5wOR0REREQMg+L9bsUdDXHUxoedjkZERKRFUbMnCbYrjTfyzqNLeCVjCjRUWERERKQliOYOYEXfkxhVOJP+pd84HY6IiEiLoWZPkr7NGMOS9D05bNN0MiIFTocjIiIiIsBP/U5js6czx224WyOwRUREEtr1rdfLy0MsXLK0zv1KQyEwvLzS+WKuWHEuR258hOe7/inl6y1btYqiUO239czy+xjQq1fK5xYRERFpj2KedF7tfDFnrf0z+295gQ86nOJ0SCIiIo5r182ecCTGB+tide43Pi++z3pfP2bnHs8BW57n85xJ/Jy+W0rXKwqV89by2q93eN/am0EiIiLS9pmm6QYeAUwgCpwBGMB0wAYWA1Mty6q7kGmlkvmQDOIfyi3OHMvXmftx6KYnWZQ5jg3e3s0QoYiISMulaVwpmtnxdLZ4OnP8L//CZUedDkdERETapqMALMsaC/wFuDPxdb1lWeOJN36Odi68prftQ7K6vqLReL/r5c6XEDbSOXn97Riq0UREpJ1TsydFYVc6r+ZNpXv4J8YXvOJ0OCIiItIGWZb1GnBu4mkf4BdgBDA7se1t4KDmj6zlKvJ04LXOF9Iv9B3jC15zOhwRERFHtetpXPW1KHMc32WM5rCN01mYOZGtaXlOhyQiIiJtjGVZEdM0nwSOBY4HjrQsy068XATk1HRcZqYPj8fd6PG43S48Hjdeb+2fFRqGgddbe4np8RgEg4E69qn7Wttf75uOkxhePIsjNj7Gj7nj2ezt3qgxNYTb7WrS87dlyl3DKH/1p9w1jPJXf42ROzV76sMweCXvYq5ccSbH5t/H9O43Oh2RiIiItEGWZf3eNM2rgM+A9CovZQEFNR1TXNw06/8FgwEikSjhcO3LBNm2TThc+12xIhEXBQWldexT97Vqut4LeZdxVemZHLvqNh7seQcYRqPF1BDBYKBJz9+WKXcNo/zVn3LXMMpf/dWVu7y8rDrPoWlc9bTJ2513O57OHsVz2KNoltPhiIiISBtimubvTNO8JvG0FIgBX5qmOTGxbRIwx4nYWrqCtM680el8dilbwNitrzsdjoiIiCPU7GmAj3JPYpVvF47bcA9p4QKnwxEREZG24xVgL9M0PwZmApcBU4EbTdOcB3iBl5wLr2Wbl3ME3wVGMTn/IbqUL3c6HBERkWanaVwNEDPc/KfrlfxhxfmYP9wLQ590OiQRERFpAyzLKgFOrOGlCc0dS6tkGDzX9UquXHEWp63/G190/yfgdzoqERGRZqORPQ20ztef9zqeRrf1H+D9aabT4YiIiIgI8btzPdflCnqWL2XIyqecDkdERKRZqdnTCD7o8FuKMgeQOftajFCB0+GIiIiICPBt5lg+zTmSXda8zIDShU6HIyIi0mzU7GkEUSONb3e7ClfZRjI/vs7pcEREREQk4fW8Cyj2d+PU9f9HIFrodDgiIiLNos41e0zTdAOPACYQBc4ADGA6YAOLgamWZdV9b8w2rCh7F0pHXk7G53cQ7nMg5eZvnA5JREREpN0Lu9L5cpcrGf/NFfx2/W083v1mbEOfd4qISNuWzH/pjgKwLGss8BfgzsTX9ZZljSfe+Dm6ySJsRUpHXERFt5FkfnwdrsJVTocjIiIiIsCWrF14I+98diuZx8QtLzgdjoiISJOrs9ljWdZrwLmJp32AX4ARwOzEtreBg5oiuFbH5aHwoHsAyH7/EohFHA5IRERERADmBI9lYeYEjtj4KP1Lv3E6HBERkSaV1K3XLcuKmKb5JHAscDxwpGVZduLlIiBn+2MyM314PO5GC7QqwzDwemsPvbH2SXY/j8cgGAxA0CQ26Q7SXj+PDt89TGzcFVX2ceP11t5fqzxPI3K7XY1+zrZM+UqdcpYa5Ss1yldqlC+RnTAMnutyBT3Kl/K7dTfzzz4PU+zJdToqERGRJpFUswfAsqzfm6Z5FfAZkF7lpSygYPv9i4vLGxzczti2TThc+6iZxton2f0iERcFBaXxJz2PIGvQ0fg+vo3CTqOJdB2R2CdKOFz70kbVztNIgsFAo5+zLVO+UqecpUb5So3ylZrmzFdeXlazXEeksZS7M5je/QYuXTmV09b/jWk9/o5tNM2HkyIiIk6qcxqXaZq/M03zmsTTUiAGfGma5sTEtknAnKYJr/UqnvA3Ylk9yZ55PkbZJqfDERERERFgrW8Ar3S+BLP0K47Y+JjT4YiIiDSJZBZofgXYyzTNj4GZwGXAVOBG0zTnAV7gpSaLsJWyfTkUHvYQrrLNZL93CcSiTockIiIiIsBnOYczN2cyB255juGF7zsdjoiISKOrcxqXZVklwIk1vDSh8cNpWyJ5u1M8/iayZl1F4Mu7IXey0yGJiIiICPBq54voEl7Jyb/8g3xvT2CI0yGJiIg0mqTX7JH6CQ05hbR1XxD44i46DO9G/EZmIiIiIuKkmOHhye5/5fIVF3Dmmj/zRfd7WLik9mOy/D4G9OrVPAGKiIg0gJo9Tc0wKJrwNzz5i9h90c3k9nyILWldnY5KREREpN0rcefwWI+buXTlxYxYfBN/7/ovKly+ne5/eN+muwGJiIhIY0pmzR5pqLQAhZMewbCjnLXmeryxMqcjEhERERFgnW8Az3S9htziHzl1/f9h2LXfOVVERKQ1ULOnmUSD/flm2A10Cy/n1HV/UyEhIiIi0kIsyhrPor5ns0fxxxyd/6DT4YiIiDSYpnE1kvLyEAuXLK11n9LM3VmZdwHH5t/PYZue4O1OZzVTdCIiIiJSm6U9jmVLwSYmFLzM5rQufJx7vNMhiYiI1JuaPY0kHInxwbraR+uMz4sxJ/gbupX/zCGbn2G9ty8Lsg9spghFREREpDav511AMLKRo/MfZKsnj6+zdPNZERFpnTSNq7kZBi91uZSf0nfnt7/cTr+yRU5HJCIiIiKAbbh5pus1rPAP4dT1f2Ng6QKnQxIREakXNXscEDXSeLz7TWzxdOGsNdfTuXyF0yGJiIiICFDh8vFoj1vYmNaDs9dcR5+y75wOSUREJGVq9jikxJ3DtJ63ETU8nLfmKrIjG50OSURERESAUncOD/X8B4Wejpy75mq6h2pfl1FERKSlUbPHQZvTuvFIj/8jEC3i3NVX44uWOB2SiIiIiACFno482PMflLvSOX/NleSFVzodkoiISNLU7HHYav8uTO9+A13DKzhr7Z9xRcudDklEREREgC1pXXmw5x2AwYWrryC9dLXTIYmIiCRFd+NqAayMkTzb9SpOXf9/BL/+Kwx6Ftxep8MSERERh5immQY8DvQFfMAtwHfAdMAGFgNTLcuq/Vag0mD53l482PN2Llz1R/b+4jJK+7xCNNjf6bBERERqpZE9LcT87IN4qfPl5G38H9nvXQSxiNMhiYiIiHNOAzZZljUemATcB9wJXJ/YZgBHOxhfu7LON4AHet2JKxYh59XjcW/RGj4iItKyqdnTgswLHollTsW37C2yPvwj2PqwTkREpJ16EfhzlecRYAQwO/H8beCg5g6qPVvn68+XI/+FYdsEXz0B9ybL6ZBERER2Ss2eFmZlnxMoGX0lfutlMj/6E8SiTockIiIizcyyrGLLsopM08wCXgKuBwzLsuzELkVAjmMBtlMlmX0pOPZFbMNF8LUT8Gz4xumQREREaqQ1e1qg0r0vgVgFGV/chRGLUHTAP8GlH5WIiEh7YppmL+BV4AHLsp41TfP2Ki9nAQU1HZeZ6cPjcTd6PG63C4/Hjddb+2eFhmHg9dZet3g8BsFgoI596r5WstdrrH08HoOsfkOJ/X4Gnmd/Q/D1E4me8DR23/3qjNPtdtX5nqVmyl3DKH/1p9w1jPJXf42RO3UQWqjSUX8El5eMz26DaJiig+4Bd5rTYYmIiEgzME2zC/AucJFlWR8kNi8wTXOiZVmziK/j81FNxxYXN82dPYPBAJFIlHC49mnmtm0TDte+9mAk4qKgoLSOfeq+VrLXa6x9KuN2dcN1zCvkvHka7v+cSOEh9xIecEStxwaDgTrfs9RMuWsY5a/+lLuGUf7qr67c5eVl1XkOTeNqwUr3vpjiMX/Gv/RNsmeeD7otu4iISHtxLZAL/Nk0zVmmac4iPpXrRtM05wFe4tO7xCGxzG4UHPsSkc7DyJ55Af5vn3Y6JBERkUq1juzRbT+dV7bXedjuNLLm/IWcGVMonPQItjfT6bBERESkCVmWdSlwaQ0vTWjuWGTnbH8uBZP/Q/bM88madTXuwlWU7HMVGPo8VUREnFXXf4l0288WIDTsTAoPvIu0NZ+S89qJGKX5TockIiIiIgBp6RQe/hhlQ08jMP9+smdeABVlTkclIiLtXF3NHt32s4UoH3wChYc/jmfLj+S+fAyurcudDklEREREAFweiif8H8Vj/4J32VsEXzsBo2SD01GJiEg7VmuzR7f9bFnCfQ+k4OjnMcq3kvvysXh+WeB0SCIiIiICYBiU7XkuhZMewbPZIvelI/HkL3I6KhERaafqvBtXS7vtJzTvLTab+3p13oo0OJ5op3fwPH8SwddOIHrU/dhDjq31nLrlXWqUr9QpZ6lRvlKjfKVG+RJxVrj/YRQc+zLZb59N8OVjKJp4G+WDj3c6LBERaWfqWqC5xd32E5r3FpvNfb1kbkWKpyfGb94g5+2zSXv1LErWfE/p3peCYdS4u255lxrlK3XKWWqUr9QoX6lpznwlc9tPkfYo0nkYW054i+x3LyD7g8so3fA1HPF3p8MSEZF2pK41e3TbzxbKTu9IwdHPETKPJ+PzO8h67yItBigiIiLSQtiBTmyd/B9K9ziHwKIncD9zNK7idU6HJSIi7UStI3t0288Wzu2j6MC7iOQOJON/t+HZvIStkx4hltPH6chERERExOWhZNxfiXQeRtasq8l9/lCKDryLcN8DnY5MRETauLpG9khLZxiUjbiIwiOfxFW8htwXD8e7/IO6jxMRERGRZlG+y7FEzvqQWEYXcv77ezI+vQWiFU6HJSIibZiaPW1EuM8BbDnhLaJZPcn+7xQCn90BsajTYYmIiIgIQMdBbDn+Dcp2O53AgocIvvob3AU/OR2ViIi0UXXfikqaVXl5iIVLlta5X5bfx4Bevapti+X0oeA3r5H18bVkfPkvoj/PZuHQa4hmdiMSqbnxU9N5RERERJpSMvVOaShEfHnIlqMhdRoAnnSKJ/yNcI8xZM26itznD6V47F8IDT1tpzfaEBERqQ81e1qYcCTGB+tide53eN+d3O0sLT0+F7zHWAKzrmbE3LN5sec1fO0fndp5RERERJpIMvXO+Ly666Hm1uA6bdt5Bh7Jlq4jyPrwj2TNvgbvz+9SfMAdxDK6NFaoIiLSzmkaVxtVPvh4PtvnEbakdWbKyms47pe78cZ0ty4RERGRliCW2Y2tRz1N0fib8a75lNz/HIDvhxfBtp0OTURE2gA1e9qw0oxe/KvXfczpeCLjtr7OFSvOpW/ZYqfDEhEREREAw0Vo2BlsOeldoh12IfuDy8l58zRchaucjkxERFo5NXvauKjLy4xuF3Ffzztx2VEuXnUZR+Y/jDsWdjo0EREREQGiuQMoOPZliva7Bc/6L+nwnwNJ//pRiEWcDk1ERFopNXvaiWWBPflH30f5LPswDtzyHH9aeS79yhY5HZaIiIiIQHyUz+5T2PLbDwl3H03m3BsIvngEnvVfOR2ZiIi0Qmr2tCPlrgAvdL2Ch3rcRlqsnEtWXcqu3/0To7zQ6dBEREREBIhl9aDwyH+z9dCHcIU2k/vy0WR++EeMsk1OhyYiIq2I7sbVDlkZI7mt7+NM2vgEE1a/QuzZzykZ9xfKB05uU7f9XLZqFUWhuu82ptvPi4iISItiGIQHHsnm3vuT8eW/SP/6EXw/vUPp3pdRtvvvwd2ybkkvIiItj5o97VTYlc7rnS8k3TyI4T/dT/a7Uwl/+zTF428m2nGw0+E1iqJQOW8tb/jtUUVEREQc4c2gZMx1hAafQOYnN5H5yY34Fz9JyZjrCPc7rE19SCciIo1L07jaucKcwRQcP4OiCX/Hs/F7cp8/lIy5N2CECpwOTURERESAaIdd2HrU0xQc+RS4vOS8fQ45rx2v9XxERGSnNLJHwOUmtNtplA88gox5fyf968fw//AipSMvp2y30zVUWERERKSRJTPd3I5WYLjTqmzphTHiAXqsnkH/n54k9+Wj2dx1PEz8a6OMzE4mpuae/r5s1SrKfo4QiURbTEwiIq2Bmj1SyfbnUrz/bZTt/nsyP72FzLk3kP7NExTvew3hAYeDoYFgIiIiIo0hmenm4/MqmJPv3m6rC5iMt/fB7LflZQ7Z+Dye5w6mfJdjKN37UqK5A5s0puae/l4UKuf9tS7C4Z3HpSn5IiI70v+9yw6inYawdfKzFBz1NLbHT87M8wm+cDje5R+AbTsdnoiIiEi7F3al837H05g7/j+U7XU+vp/eIffZ/cl6dyruzUucDk9ERBymZo/sVEXviWw56V0KD/oXrnAROf/9PcFXjiVt9Sdq+oiIiIi0AJG0bErGXMem382LN31+fo/c/xxA1swL8OQvcjo8ERFxiKZxSe1cbsrN4ykfeDT+758n8OVdBF8/iYque1O69yWEe++vO0GIiIg0EdM0RwO3WZY10TTNgcB0wAYWA1Mty6r7tpPSLtiBTpSMuY7SvS4gsHAa/sX/xr/0TcI9x1M6/AIqeo5XzSYi0o6o2SPJcacR2u00QoOPjzd95t9PzozTqei8B6XDL4zf/tO1/ZzytqMlLlgoIiJtm2maVwK/A0oSm+4Errcsa5Zpmg8BRwOvOhWftEx2egdK9r2G0uFT8X/7NOlfP0bwjVOo6DSUsmFnUT5oMnj8TocpIiJNTM0eSY3HT2j33xMa8lv81ssEvrqPnHfOI5LTl7I9zyVkngBp6U5H2eha4oKFIiLS5i0DfgM8lXg+ApidePw2cAhq9shO2L5syoZfSNkeZ+G3Xib968fI/vAPxObdStnQ0wjtdjqxjC5OhykiIk0kqWaPhhDLDtxeQkN+S2jwiXh/epvAgofImn0tGZ/dQdnQUwnt9jtimd2djlJERKTVsizrZdM0+1bZZFiWtW3RvCIgp6bjMjN9eDyNP9rW7Xbh8bjxemtf8tEwDLze2kvMxtqnua+XbEwej0EwGKi2ze12VdvWWLms6VrVBWDM2dj7nkVk+Wxcn08j8OU9BObfj73L4cSGn4HddzwYyf18675e4/J43LgMas1Dc8fU2mz/uyfJU+4aRvmrv8bIXZ3/tdIQYqmVy0144JGEBxxB2rrPSV/4MIGv7iMw/wHK+08iNGwKFd1Ga464iIhIw1X9cC0LKKhpp+LiphlpGgwGiESitd4CG8C2bcLhSLPs09zXSzamSMRFQUFptW3BYKDatsbKZU3X2qncUXDoKFyjfyb926fxf/88nh/eIJLTj9DQU3GlDSccDjbe9RpBJBIlZrtqzUNzx9TabP+7J8lT7hpG+au/unKXl5dV5zmSGdmjIcRSN8OgovtoKrqPxlW4kvRFT+L//jn8y2YQyd2F0NBTCJnHY/uDTkcqIiLSWi0wTXOiZVmzgEnARw7HIztRXh5i4ZKl1bZ5PG4ikWjl89JQCPA2Szw1rj3Y+be4Ov6GLr/Mpueq1wl+egvjDTcdAvvwWc5hfJ8xmpjROlZ8qCnf20tmbUWt0SgibUmdf8HrO4RY2q9Ydm9Kxv6ZklFX4F/yOv7vniFz7g1kzPs/ygccQWjXk6josS8YtQ8TFhERkWr+CDximqYX+B54yeF4ZCfCkRgfrKs+asfrdVUbyTM+r/lWQdj52oNpwEHQ5SA6B1dwXGQGfdZ/yO4ln1DkDrIga3++yjqIlf7BLXqUdk353l4yaytqjUYRaUvq065PaghxU80XB80Fh+TmJm+bd+2q5ZxNO8c5AHlnwpgzqVi/CNeCJ/F9+xL+H1/Bzu5JbPeTiA07GToMaJKrJzPvPL5f9RzUND+yJc5hb0k0Hzc1yldqlK/UKF9ti2VZy4F9Eo9/BCY4GpC0WRt8fVjc8yymBc5j15LP2bvwPfbdOoP9Cl5lQ1pP5mcdwMKsiUB/p0MVEZEk1KfZk9QQ4qaaLw6aCw7JzU3eNhfc6/Xs9JzNNsfZPwD2vQlGXoPv5/fw//ACaZ/ehfuTf1LReQ/KBx1D+aDJjXpXiGTmwsf3q56DmuZHJnOu9jxfXPNxU6N8pUb5Sk1z5iuZ+eIi0rrEDA/fZo7h28wx+KPFDCuew96F73HI5qc4bPO/KdrUD1fBcZQPOIJoh0FOhysiIjtRn2aPhhC3M8nMX7ajFRjutDrPleX3MWDQZMoHTcZVvA7fkjfwLXmNzE9uJOOTm6josQ9rOoxhZYcxhH0daz+P5kuLiIiINJmQO5PPcybxec4ksiObGFb0MQdUzCb4+T/J+PwOIsEBhPsfRnn/w4h03rNFT/USEWlvkmr2aAhx+5bM/OXxeRXMya972l7Vec6xzG6U7XUeZXudh3vLUnw/voZv2Qz6rfknfbiTn9J3Z1HmOBZnjmVzWrednkdEREREmlahpyNzc48lu+9xDO+Wgffnmfh+eof0BQ8RmH8/0YwuhPscSLjvQYR7joM0TScVEXFS61hiX9q8aO5ASkdfQenoK1iy4H0KrY8YVvQxx+Y/wLH5D7DW25/FmWP4NmNfVvlNQIs7i4iIiDghltmN0O5TCO0+BSO0Be/yD/Aufx/fkjdI/+5ZbLePih77Eu49kXDv/YkG+2vUj4hIM1OzR1qcksy+zOz4e2Z2/D0dw2vYreRTdiv+hIM2P8shm5+myB2kqHgUPo4m3Gs/bH+u0yGLiIiItEu2P5fywcdTPvh4iIZJW/s53uXv4V05i8y5NwA3EM3qSbjXflT0HEe451js9J1P1RcRkcahZo+0aJu8PZjtPYHZuScQiG7FLPmSISWfMWzj//C++y42BpHOwwj3mkBFr3FUdBkOHr/TYYuIiIi0P25vvB7rNY4SwFW4Cu/K2XhXfoRv6QzSv3sWgEjHIYR7jqGi+75UdB+lD+5ERJqAmj3SapS6c1iQfSALsg9kSx+bvbNL8K76GO+q2QTm34/x1T3xYcPdRlLRYyw5di/c9iCiRt0LR4uIiIi0RuXlIRYuWVrrPqWhEOBtnoCqiGX3IrTbaYR2Ow1iETz5i/Cumkva6rmkL36KwNePYmMQ7bgr4e6jiXQbSUW3kcQyu9V98hS15DyJiDQFNXukdTLcRLoOJ9J1OKUjL8MoLyRt7WekrfkE7+pPyPjsNkYBexo+Vvh3ZVn6MH5O340V6UMod2nBQBEREWkbwpEYH6yr60Yatb/eLFweIl32ItJlL9j7YoiWk/bLQtLW/o+0NfNI//45jEVPABDN6kVF1xFUdB1Odrgz7tggoGFr/rSaPImINBI1e6RNsH3ZhPsdTLjfwZQARtkm1nz1OgUrFjKg7BsO2fwULmxiuFjr689y/1BWpO/KCv+uYPd2OnwRERGR9sXto6L7aCq6j4a9L4VoBZ5N35G27ov419r/4V/yGqOB4YaXNb6BrPQPZqXfZKXfZGNaT2xDN+wQEdkZNXtaqcYaitrcQ1qb63p2ekc2dNmPt8rGAeCPFtMn9D19y76lX+hb9i58l3FbXwegYnUW9g/Dqei8B5HOu8PA0WDnNsldI5atWkVRqPbbxmf5fQzo1avRr70zLTEm0c9FRETaGXcakc57EOm8B2V7nA2Aq3gtqxe8ReGa7+hR+j2jt77FfgWvABByBVjtG8ga3yACdl+6hXflF29vYob+90ZEBNTsabUaayhqcw9pdWoIbcidiZUxEitjJACGHaVLeCV9Qt8zzvMDXUuXxdf9saMAdEzvSKTTbkTyhhLpNJRASSaG3R3bcDcojqJQOW8tr/39Hd639v/Bb2wtMSbRz0VERCSW2Z0NXSfyfuwAwuEILjtKl/AKeod+oEdoCT3Ll7Dv1hl4C8oZCUSMNNZ7+7DWN4C1vv6s8/Zjva8fhe4OTr8VEZFmp2aPtEu24Wa9L14AdOx7JL5BAyFShmfj92QXfU945Xw8GxeTvvARjFgFY4GRhpf13n6s9fVnva9vooDoS6G7Y5OMAhIRERGRX8UMN+t8/Vnn6w858W0uO8phmUtZv2413cuX0b18GYNLPmdU4czK44pd2ZT90psuRj/We/vyi68Pv3h7q4YTkTZNzR6RbTzpRLoOJzZ4HMWDSuPbomE8m39kzbezyF+7jO7lPzG0ZB77FL5deViJK4sN3t54S/qQXjyCaO5AIsH+xLJ7gUv/xERERESaSsxwUxTow/xsk/kcWLk9I1JAt/DPdCv/mW7hnxkU+5m9ij8iECuu3CfkCrAhrRcbvPGvjnSmZ6gf+d6euqGHiLR6+j9Rkdq4vUTydmNdDz9vVfw6pSYzsoWu4eV0LV9Ot/ByOodXkLfhU7xr3qrcx3Z5ieb0IRrsTzSnL9FgP3KLvAQrurHV06nBU8JEREREpGYlniBLPXuxNLAXAOPzQszZ4CM7upku4RV0Dq+ic3glXcIrGVD2DXsXvQ+bYHTi+EJ3LpvSurPR24ONad3ZlNaNTWnd8Jb3AHuARgSJSIunZo9IPRR7clnqya0sIAAO7+tir14dcW9ZimfLMtwFy3AX/IS74Ge8K2dhRMvZG9ib+JzyzZ4ubEzrzua0bmxK68rmtG5sTutKWrgb2LaKCBEREZHGZBgUejpS6OnIksDwai+lxUIckvkzq37JJ69iNZ3Ca+lYsZaBpQsYGXn31x1Xgf2Jn2hWT2JZPYlm9SKa1SPxuAexzB7EMjprdLeIOE5/hUQake3PJdJtJJFuI6u/EIviKl7H8u/msmxVvHjoVBH/3jf0XbUhxawEe24gUTj0IJrZ/dfvmd2JZXYjmtEV0jS8WERERKQxVLj8FGb045usXXd4LS1WTm7kFzpUrGO/rPX08YdwF63CVbga34avcYW2VNvfNtzEMjoTy+wer98yuhHL7EosoyvRjK7xx4HO4PE319sTkXZIzR6R5uByE8vuyeaOezOvaMc7LPmjxXSsWEeHinWMzc6nl78cd9FqXEVr8OUvwlW2aYdjYr4cYhldiWV0SRQPXYgFOseLi0CXxPc88KQ3xzsUERGRdqC8PMTCJUtr3ceOVmC40xq8D0BpKAQ4+wFXhcvHBm9vNnh707+3i06DBlbfIVyCu3gNrqI1ie9rcZesw1W0Fs/Gb3Evfw8jEtrxvGnZlHs7UO7vRLm3A2FfB8p9HQl7cwl7cyn35uLN7kqfAbuB4WqW97ps1SqKQrXf7TOZn12W38eAXr0afK3GPFdjxe3E9aRxJPNza8zfAad/vmr2SKuUTKERLw68KZ/L43ETiUTrda76CrkzWeMexBr/IDp3DbPS54cuv77uipbjK8/HH8rHF8rHX55PVsVmcl0luIrXk7ZlCb6SDZW3jq8q5s0ilt6JWKAzdqAjsfQ8YukdiQU6EfN3IHdziC7l2RS7g5S6s9rlWkKt4Y91fTX3e2vLuWyJasr39n/DlG8RaUzhSIwP1u34wVVV4/MqmJNfez2RzD7x/Wq/VovgzSDaYReiHXahoqbXbRujfCuukvW4StbjLl6Pq3QDm9f+yJYtG8ku2UT21lXkRTeTZu94BttwY/s7EAt0jNd06R2J+Ttgp3cklt4h8Tj+PebvgO3PhSQaaTUpCpXz1vKG/3wP71t3EyeZazXmuRorbieuJ40jmZ9bY/4OOP3zVbNHWqXkCo3kioPtz+X1ugiHqx/bnIVGze8tDeie+AIMOHwXF3tW/WQpFsUIbcZVmo+r5BdcpRvijyu/NuDevIS0snnVhhtvW0cIIIZBmSuLEnc2Je6cxPdsupTnkF7YH9sXxPblEPNX/47duocht4Y/1vXV3O+tLeeyJaop39v/DVO+RUQcZhjY/iBRf5Box8GVDaEfliyt/jfctvHHSsiObiYrsoWs6GZGZxfQKxNcZZsSXxtJ+2UhRmgLrnDhTi8ZS8vE9geJ+XNxZ3Yky521XR2XTcyX8+s2Xza2NxvsVtBcE5GkqNkj0la43NiBPKKBPKKdhtS+byyCUbYZV2gTPy/5hm/XbCEjupXM6NbE9wIC0UJyIxvoUb6U7OJC3Ct2/j+MtuGiozcrXix4s7F92diVz7MSj+PbKp97M6t/T8totiHKIiIiIi2OYRByZxJyZ7LB2xuA7n1cdNx+2tg20TCu0BaMsk3x76EtuEJbcIU2Vz42QltwhwvxlKzCVV6AESqocST4NgdhMM4VIOTKoMyVQciVQcgdf1ye2NY15IOyrPhrrgDlrkDiezohVwblrnSMmNaWFHGamj0i7ZHLg53RmWhGZzZvTmNBDesIVXV4Xxd79uuBq3wrRqggXjyUb40PSS7fSjollBdujr8eLsRVvhXX1uUY4aL4PhUlSYUVS8vATsvE9mZgp233tW2bJ4Cdtu0rAzwB7LR0bE96fFvl8wC2Jz2++KGr/U1NExERkTbO7SWW0QUyurDz9g0EgwEKCkrjT2wbo6KkSh1XkHhchCtcyC9rf2b15iL8sRLSo8Wkx0rIjmwiL7Yaf6wEf6yEtC0VDK0rtmVgf+SLf6iXllFZt1U+9gTYtbQCT4mfsJFO2OUjbPgJu/yEXemEDV/8seEnsygd11YPeNKxPf54fedK051rReqgZo+IJMeTTsyTDhlddygofMEAJduKiJrEohgVxRjlRRjhQoyKElzhIoxwcfx5uCT+ergEo6IIo6I0/lpFMa6SXzAipfHCJFwSf5ziEGPb7assDmyPv0qxEN82rCxCbpmXCpePCsNLheEjYnipcHmpMLxEDC/d1vjx0Tt+LrcXPD5stw8Sz223F9zexLbEc5dXhYiIiIi0HIaRGFWdCVk9dqjpfgosrXM69oSORXy5IYY/WoLPLot/j5X++tguY/fsEN2yfYnaLvEVKcMIF+Mq3YBRUUZeqJC8ijJ89o6LV1ezCphXfZNtuMDtj3/A5/YzJuZiSDRew8VruXhdFzG2bUujS7GbzFB6YpuXqJEWr/Nc3sS2NDpu9JEW2FBZz1GRjaskVlnvxWu8NE13k1ahXs0e0zRdwAPAHkA5cLZlWbWvlisi7ZfLje3Lia/v01C2DdEQRkVZongoTTSDSuNFREUZRMqqbAvFt0e2bQ9V2RbCKN1IRmkhfcPlpMXCpNnleOwwXjtc/bobgG/rEa5rWyMoLfHdhyvNRy6eREMo7dfXXdu+pzG0OERWiZuI4SFmeIgYHqKGhyjx7zHDQ28jDX+oW3yklisNXJ74+Vzu+CdeLg8dNm1gQKmLmOEhijtxrJsobmJG/MsXSsMoyU4c78Y24t9xecBwq2El0oKoBhMRp8VcaZS4/ZS4d17XZfR1kb2z6WcJC7etWWTbpNnleGMhvHYo8b2ctMTzfTqF6ZeXE6/bKuL1G9Hq9VxRQT6bi8rjx9lhfNGtpEXKE3VdBZ5YGH9RmIGxMC5qadSsBeZX39Sxht0OBg7ARdRII2KkJb7Ha7SI4SVqeEhf62bPSPxxtS9+fdyjNI3MdR0r67jq9ZwnURd6EjWZB9udeM34tdarfN3ljh9vuBPb3VX2rVrjeeJLJ2zbZ1vdp+UU2pz6juw5BvBblrWvaZr7AP8Ejm60qEREdsYwEiNz0rHTOzTKKRduv0AiYNgx3HaEtETRcHD3Cob06oYRLYdoeeJ7GCNS5XGV70a0Ir5fLJzYtu15BV5XlGioDGIVGNEwRjQcb0RFKzBiFRANk1teRqCiAg8R3HaVr6qfwW0ErNrf24jEV61+Bj7e+cuVRYLhihcUhrt6U8hwJ4oFN/tWRBla4SJmuIjhrvbdxkXMcNNxk5ssK+vXRpLLHb+G4apy7sRjw43L7yUjHKu8FoYRv6bhShzjqjx2x22uGrdB1e3GdscblftQZR8bY7vzxJ/bVD3GqHK++PPKc9awn111f379bnt82Ok1lZciqsFEpI0xDCoMPxUuPzVN/O/fxUX3OhpHi2qo5bY3Pi/EnHw/LjuKxw6TZodJiyWaQXYYj13B+C4V7NK9c2VNl+GH0qLieK22rWaLhvkl/xd+2hLGY4dx21HcdkXiPBWVNVteWphILIrLjuCPlSa2J15P1HeBUIS0/Fh8Pc1YJF4bOjRqyMaosd6L10fuKnWYq0qTyJWo4RJ1XOX+8brK7U0jJ8qv+7hqqte21YFGlWsa1WvDndZ+rl+P3Va3udxU1l8uN1Xrr+4bNjJyK9iGQQwXdmI/GwMbFzaQt8GN1/NT9XqtSl23rQYMbl5HvzIAg1jieKDyfFHDDXbtv7dNrb7NnnHAOwCWZf3PNM2969hfRKRVsQ0XEcNLBC9lQFnARbRD4/zBDgYDFNY27Y2aG1AQb0K5iOK2IxzWK8bufXvFG0SxSOX3qgXD0pUr+HzdtsIjiosorsR3tx3BZUfZs2OMXnkdfy0wqp7Djsan4dkRiEXBjibOHQU7ArFY4rUI2DFKCreysSSKm2gi1ljiejHcRPHEwqRVxHCVlmHEokAMYrH4dexo4vq/bjfsKAYx/LEo2LH4MXY0PsLLTmzDbpSfS0tTMPlZKnrt53QY0vKoBhMRaYCY4Y6vE0Q6bLes49ZcFxU9f633AsEA5TXUbD+l0FyqzeF9t7u7LsSniMUqIBrZrsaLJmq1xPdYBKIV8VrIjkC0SrOo8rjta7lI4nG0yvkSdVgsUqXOSjSgErVe9ZovUX8ljiFxfPx5lZouFoZwOUZF/Bo11nDbrr3tHLEY8RowCtiJWGONVvsNTXzVah2wsO5zjUx81ea7wB9glz8kEVnTMGw79USZpvko8LJlWW8nnq8E+luWFWnk+EREREQkQTWYiIiIJKO+E/MKgayq51GRISIiItLkVIOJiIhInerb7PkEOBwgMV98UaNFJCIiIiI7oxpMRERE6lTfNXteBQ42TfNTwADOaLyQRERERGQnVIOJiIhIneq1Zk9TquuWoqZpHgX8BYgAj1uW9Uh7vg1pPfOVBjwO9AV8wC2WZb3R3LE7oT75qvJaZ+Ar4GDLsn5o1sAdUt98maZ5DTAZ8AIPWJb1WHPH7oQG/Ht8kvi/xyhwTnv5/YLkbiNtmmYAeA84y7KsH/Q3P+V8tdu/+ZI81V/1p1qsYVSb1Z/qtIZR3VZ/qt8aprnqufpO42pKx5C4pShwNfFbigKQeIN3AYcAE4BzTdPsWtsx7cAxpJ6v04BNlmWNByYB9zV30A46htTzte21aUBZcwfssGNIMV+maU4ExgBjE9t7NXPMTjqG1H+/Dgc8lmWNAW4Cbm3uoB12DLX8/U7caehjYECyx7Rxx5B6vtrz33xJ3jGo/qqvY1At1hDHoNqsvo5BdVpDHIPqtvo6BtVvDXEMzVDPtcRmT7VbigJVbym6K7DUsqwtlmWFgbnA+DqOaevqk68XgT9X2a89LexYn3wB3AE8BKxtxlhbgvrk61Dia0i8CrwJzGjWiJ1Vn3z9CHgSHf5soKJ5Q3ZcXX+/fcCxwA8pHNOW1Sdf7flvviRP9Vf9qRZrGNVm9ac6rWFUt9Wf6reGaZZ6riU2e7KBrVWeR03T9OzktSIgp45j2rqU82VZVrFlWUWmaWYBLwHXN0+oLULK+TJNcwqQb1nWzOYJsUWpz7/HTsT/YJ0AnA88Y5qm0QyxtgT1yVcx8eGYPwCPAPc0fZgtSq1/vy3L+sSyrFWpHNPGpZyvdv43X5Kn+qv+VIs1jGqz+lOd1jCq2+pP9VvDNEs91xKbPbXdUnT717KAgjqOaevqky9M0+wFfAQ8ZVnWs80QZ0tRn3ydSXwxzFnAnsC/tw0hbgfqk69NwEzLssKWZVlACMhrhlhbgvrk63Li+dqF+LzdJ03T9DdDrC1Fff5+62/+r5J67+34b74kT/VX/akWaxjVZvWnOq1hVLfVn+q3hmmWeq4lNntqu6Xo98Ag0zQ7mKbpBfYD5tVxTFuXcr5M0+wCvAtcZVnW480dsMNSzpdlWftZljXBsqyJwELgdMuy1jdv2I6pz7/HucBhpmkapml2BzKIFxbtQX3ytYVfO/ubgTTA3WwRO68+f7/1N5/k33s7/5svyVP9VX+qxRpGtVn9qU5rGNVt9af6rWGapZ5ryXfjGsavtxQdDmRalvVwlVXRXcRXRb+/pmOsdrIqej3zdTdwEtXnAE6yLKvNL3BXn3xtd/ws4Hz9ftWeL9M0bwf2T2y/tr0Ms67nv8dM4ivrdyN+V4y729MnvHXlrMp+s0j829Pf/JTz1W7/5kvyVH/Vn2qxhlFtVn+q0xpGdVv9qX5rmOaq51pcs0dEREREREREROqvJU7jEhERERERERGRelKzR0RERERERESkDVGzR0RERERERESkDVGzR0RERERERESkDVGzR0RERERERESkDVGzR0RERERERESkDVGzR0RERERERESkDVGzR0RERERERESkDfl/nsUI/Wty2KwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,2,figsize=(20,4))\n",
    "ax = ax.flatten()\n",
    "fig.suptitle(f\"{stock} returns\", fontsize=15)\n",
    "\n",
    "n = 0\n",
    "excess_l.hist(ax=ax[n], grid=True, density=True, bins=50, edgecolor=(0.77,0.77,0.77), alpha=0.6, label=\"Sample data\", color=\"tab:blue\")\n",
    "ax[n].plot(gdpdist[\"x\"], gdpdist[\"pdf\"], label=\"Generalized Pareto fit (left tail)\", color=\"tab:orange\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].legend(fontsize=12)\n",
    "\n",
    "n = 1\n",
    "excess_r.hist(ax=ax[n], grid=True, density=True, bins=50, edgecolor=(0.77,0.77,0.77), alpha=0.6, label=\"Sample data\", color=\"tab:blue\")\n",
    "ax[n].plot(gdpdist[\"x\"], gdpdist[\"pdf\"], label=\"Generalized Pareto fit (right tail)\", color=\"tab:orange\")\n",
    "ax[n].set_xlabel(\"\",fontsize=18)\n",
    "ax[n].legend(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose our guess is that a given input series of returns is **t-Student** distributed. \n",
    "Once the fit is doen (e.g., via MLE) the **hypothesis testin** checks if the \n",
    "the continuous **hypothesized** t-Student distribution, $F_H$ \n",
    "and the **empirical** distribution (ECDF) of the sample, $F_E$\n",
    "are significantly different from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock = \"AAPL\"\n",
    "rr = rets[stock]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'df': 3.382455901361647,\n",
       " 'mu': 0.0011371125808884515,\n",
       " 'scale': 0.01227038541839803}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hypothesized distribution FH\n",
    "FH = ql.dist_fit(rr, dtype=\"t\", pdf=False)\n",
    "FH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.000361\n",
       "1       0.000723\n",
       "2       0.001084\n",
       "3       0.001446\n",
       "4       0.001807\n",
       "          ...   \n",
       "2762    0.998554\n",
       "2763    0.998916\n",
       "2764    0.999277\n",
       "2765    0.999639\n",
       "2766    1.000000\n",
       "Name: ECDF, Length: 2767, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Empirical CDF \n",
    "FE = ql.empirical_cdf(rr, index=False)\n",
    "FE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null and Alternative hypotheses\n",
    "\n",
    "There are two hypotheses:\n",
    "\n",
    " * **Null** Hypothesis $H_0 = $ the return series <b>is</b> t-Student distributed.\t\t\n",
    " * **Alternative** Hypotheses $H_1 = $ the return series <b>is not</b> t-Student distributed.\n",
    "\n",
    "A statistical test is then used to test the **goodness of fit** \n",
    "Once computed, one computes a <b>p-value</b>, \n",
    "which is used to make a decision about the hypothesis, which we can either reject or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Critical Regions\n",
    "\n",
    "A previous step is knowing the test stastic distribution in order to known \n",
    "the correspondig **critical region**. This is calculated using a Monte Carlo simulation.\n",
    "\n",
    "Let $n$ being the size of the sample return series. Then:\n",
    " 1. Simulate a random number $u$ between 0 and 1.\n",
    " 2. Apply the inverse hypothesized CDF to $u$ and set $x := F^{-1}_H(u)$.\n",
    " 3. Do $n$ repetitions of Steps 1 and 2 (i.e., simulate a new sample return series of size $n$).\n",
    " 4. Via the MLE, obtain a new hypothesized CDF, $\\widehat{F}_H(x)$, for the new sample.\n",
    " 5. Also, compute the ECDF, $\\widehat{F}_E(x)$, of the new simulated sample return series.\n",
    " 6. Compute the chosen test statistic. \n",
    " 7. Repeat Steps 1-6 for a large number of times, thus obtaining a distribution \n",
    "    of the test statistic **under the Null Hypothesis**.\n",
    "\n",
    "Given the distribution of test statistic, one defines the **Critical Region** for a two-side test:\n",
    "$$\n",
    "CR_\\alpha := (-\\infty, q_{\\alpha/2}] \\cup [q_{1-\\alpha/2}, \\infty),\n",
    "$$\n",
    "where $q_\\alpha/2$ and $q_{1-\\alpha/2}$ are the $\\alpha/2$ (left tail) and $1-\\alpha/2$ (right tail) quantiles of the test \n",
    "statistic distribution. \n",
    "\n",
    "To make a decision about the hypothesis, one computes the test stastic using our data.\n",
    "That is, we compute both $F_E$ and $F_H$, the test statistic, \n",
    "and see is the test falls in or outside the Critical Region \n",
    "(or if the corresponding **p-value** is above or below the $\\alpha$-significance level).\n",
    "\n",
    "If the test does not fall into $CR_\\alpha$, then the Null Hypothesis is accepted.\n",
    "Otherwise, it is rejected in favour of the Alternative Hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Statistics\n",
    "\n",
    "<b>Kolmogorov-Smirnoff</b> (KS) test statistic: \n",
    "$$\n",
    "KS := \\max_x |F_E(x) - F_H(x)|,\n",
    "$$\n",
    "That is, the KS is the maximum of the vertical distances between the \n",
    "two cumulative density functions (more sensitive to the fit around the mean) \n",
    "\n",
    "<b>Anderson-Darling</b> (AD) test statistic: \n",
    "$$\n",
    "AD := \\max_x \\frac{|F_E(x) - F_H(x)|}{\\sqrt{F_H(x) (1 - F_H(x))}}.\n",
    "$$\n",
    "More accurate in the tails of the distributions. \n",
    "It is likely to be the test statistic to trust the most in case \n",
    "of assessing the fit using **leptokurtic** distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following, is a naive implementation of the Critical Region via **for** loops which is pretty slow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.9s\n"
     ]
    }
   ],
   "source": [
    "# Calculating the KS test\n",
    "tic = time.time()\n",
    "KS_dist = ql.dist_test_stats_naive(FH, dtype=\"t\", size=1000, mcs=1000, htest=\"KS\")\n",
    "print(f\"{time.time() - tic:.1f}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the loop can be rewritten in form of pandas dataframes on which **parallel_apply** can be used.\n",
    "In this way, we can use substitute the classic **apply** function, which would use only one CPU core, with **parallel_apply**, which will use all cores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n",
      "8.3s\n"
     ]
    }
   ],
   "source": [
    "# Calculating the KS and the AS test\n",
    "tic = time.time()\n",
    "test_stats = ql.dist_test_stats(FH, dtype=\"t\", size=1000, mcs=1000, htest=[\"KS\",\"AD\"], verbose=2)\n",
    "print(f\"{time.time() - tic:.1f}s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Critical region KS: (-inf, 0.0110) and (0.0247, +inf)\n",
      "Critical region AD: (-inf, 0.0387) and (0.1867, +inf)\n"
     ]
    }
   ],
   "source": [
    "# Computing the Critical Region at 5% significance level \n",
    "slevel = 5\n",
    "\n",
    "CR_KS = ql.critical_region(test_stats[\"KS\"], htest=\"KS\", slevel=slevel)\n",
    "CR_AD = ql.critical_region(test_stats[\"AD\"], htest=\"AD\", slevel=slevel)\n",
    "\n",
    "CR = dict(CR_KS, **CR_AD)\n",
    "\n",
    "print(f\"Critical region KS: (-inf, {CR['KS']['left']:.4f}) and ({CR['KS']['right']:.4f}, +inf)\")\n",
    "print(f\"Critical region AD: (-inf, {CR['AD']['left']:.4f}) and ({CR['AD']['right']:.4f}, +inf)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ECDF and Hypothesized distribution\n",
    "FFE = ql.empirical_cdf(rr, index=True)\n",
    "FFH = ql.hypothetical_cdf(rr, dtype=\"t\")\n",
    "# Compute the KS and the AD Test statistic\n",
    "KStest = ql.test_statistics(FFE, FFH, test=\"KS\")\n",
    "ADtest = ql.test_statistics(FFE, FFH, test=\"AD\")\n",
    "# Corresponding p-value\n",
    "KSpv = ql.pvalues(test_stats[\"KS\"], KStest)\n",
    "ADpv = ql.pvalues(test_stats[\"AD\"], ADtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AmjV4NqzDu2EDDfM20bggi8b5mTTOzyw3kZOfmEJhajrBtAY4DRoS3yiD+IxGJDRuRFrTxvgaNcLboCGe9HQ8aWl40tLxpKTUi2SNrkGR2NI1WPOaNUsvcyxodQ31qvPGjh1XKklSZMyYB8jKyuSxx54mLS2NvLw87r33DkaNuo8777wPgMMO68cNN9wS2WbixE+57roree21tyPJnquuupZDDz2ixo9lS+66azi77bY7AH6/n0GDLmf8+Hc55ZQzYhyZiIiIyI5pU56fhetyWbN6AxvnLyR38WICK1aQtGEtDbPW0yxvA81yN9LOX/qPosLEZPwNGxFo3JhQozbkNmpCsHkzUlu2IK1VC+KaNsGb0QhPegM8cWrei4hIcbXym+HTv1fx8V8ra3Qfx+/RkmO6ttjmelasWM7kyZ/z0UcTIwmc5ORkhgy5jT//nFXudkceeQyTJn3G5MkTOfHEUyu9v88++4QJEz4iPz+P1NQ0Hn98HBMmfMgHH7yH44Ro0CCDG264mXbt2rNkyWIefngUubm5rFu3ls6dd+Wee0aSmJhY6f3Fx8fTvfteLF68iBUrlnPddVfSu/eB/PPPX2RlZXHllddyyCGHVro+ERERkboq5DgsWZ/H6pVryF+yhMB/S/GsWIZ3xTJ8K5bSMnstOxfmFNumIDGFvEZN8e/UCqfVPuTv1JpGbXcmoWULvE2b4W3cBE9ycoyOSERE6oJamfipja699opiQ71atWrNyJFjsPZfOnToWGqIVpMmTenb9/AK6+zUaVfmz58fef/UU2N55ZUXI++vuOIqevfuU2q7hQsX8N57H5OamsbMmb/y+eef8tRTz5OUlMTPP09j6NAhvPHGe3zyyYccddSxDBhwNIFAgEsuOZepU6dsMa5oa9eu4ccff+Cyy64EYPnyZfTq1ZvBg2/m22+/YuzYh5T4ERERkXrHcRxWrFiHnfEnG2fPwbN4Iemr/qPNppV0LMgqVnZdSgYFzVtBt4PJa9uGRh07kNSuDd6WrfGmpZWzBxERkepRKxM/x3RtUS29capTeUO9PB4vodDWTVvk8UBSUlLkfWWHeu2yS6dIomnq1CksXfofAwdeHFmflZVFZuYmrrzyGmbMmM4bb7zCf/8tYe3aNeTl5W2x/nvuuYPExCQcJ4TPF8exx55I376Hs2LFcuLi4ujd+0AAdt11NzIzM6t62CIiIiI7lMw8P4tmL2D9rD8J2NkkL5lPkzXLaJq3kT3DZQrjEshsvjOFe/dkQ8eOJLVvT3qHdjRo35amSeqxIyIisVMrEz87kq5d92Dx4oXk5uaQkpIaWb5mzWpGj76f4cNHlbvtv//+w9FHH1flfaakpEReB4MhBgw4mquuuhaAUCjE2rVrSE9vwF13DSUYDHDYYf3o3bsPq1atpDKTeUfP8VNSfHx8ZJJnj8cD7HBzdYuIiIiUqzAQYvqvc1j360w88ywNly6gzboltC7MpTUQ8HhZ07g1Gzt3JbN9R5rvsRs7dduduFataF0PJkgWEZEdjxI/26hp02b063cUI0bcy2233Ulqaho5Odk89NADNGjQkMTEpDK3mzDhQ5YvX8Zhh/Xbpv3vt19vHnjgPk4//WyaNm3Khx++z7vvvsn//vc+P/88lccff5bOnXdlwYL5/PPPXxx++LbtT0RERKQucYJBNv5jWT3jF7J/m0nq3H/okrsBgKDHy7rmO7Npr97k79aFJnvtQfM9d6dlUtntOxERkdpIiZ9KKjnHD2yeg+fGG2/hlVdeYODAi/H54vD7CznooL5ccskVkbJffz2ZP/6YhccDoZBD27btePzxcVWaaLksvXrtzznnXMDgwVfh9XpJSUnl/vsfxOPxcPnlVzN06E0kJyeRmprGXnvtw9KlS7dpfxWZPn0q/fodFHmflpbO+PGf1dj+RERERKoi5Dis3JjHfzP/IvjrzyT/8ztNF88h2Z9PE4CkBqzcuTPB/XrQsU8vkjrvSottbKuJiIjEmqcyQ3+q05o1WRobVI9lZKSwcWPpx5SKyPaha1AkdnT9bX+O47BgXS6zZlqypk6j4exZ7LlqDg3DT9b6r0ELVrfrgnePPWm4777sumcnMlISYhy11BRdgyKxpWuw5jVrlu4pa7l6/IiIiIhInREIhvh1yXr++noa3p9/ovt/f3Bw1moActIyyNqrJ6Ge+9HkwN7stXOr8JyFIiIidZcSPyIiIiKyQ8vM9zPdruC/r74jfeZ09ln+NycU5hD0+sjadQ+CB51Ok4P60KR9ByV6RESk3lHiR0RERER2OCHH4Zf5q5n1yVdk/Pwd+6/4m72DhRQkpZC3z34k9j+M1N4H0iItLdahioiIxJQSPyIiIiKywwj4A/w64WvWTPiUrgt/5yR/HgXJaQT69iPtmKNosvc+eOLUxBURESmib0URERERqfUyFy7h79ffpsmPX7JLzgZaxyeRve8BNDvhWJrsv7+SPSIiIuXQN6SIiIiI1EpOYSHzP/qMTR99SLvF/7ALHua06ULmuVew1ylHE5ecFOsQRUREaj0lfkRERESkVlm5cCl/PP8au/z8JRn5WRSkNOaXvqfS4YyT6bNHp1iHJyIiskNR4qcS+vTpQceOu+D1+ootHzlyDK1atSYYDPLuu28yefIkgsEggYCfAw44iEsvHUhCQgIvvDCO8ePfpWnT5gAEAgF22aUTl146kDZt2gIwaNDlrFq1ktTU4hMQvvzy/4q9z87OZujQIYwd+0yZsa5du5axYx9i0aIFeDweEhISOf/8izjooL4AvPTSc3Tq1DnyvrL69TuIV199m1atWldpuyKffPIhfr+fiy++oNS6V155gY8++oAePXrh9Xo5/PD+9Oy5H6NGDeeEE05ht926bNU+RUREZMfhOA4rfv6VhS++yi7//sxejsPcjt1ZdNSJ7HPc4XRNTYx1iCIiIjskJX4qaezYcWRkZJS5bsyYB8jKyuSxx54mLS2NvLw87r33DkaNuo8777wPgMMO68cNN9wS2WbixE+57roree21tyPJnquuupZDDz2iwjiysjL599+/y10/atR99OjRi3vvHQnAwoULuPLKS3jmmXa0b9+BX3+dQfv2Hapy6NXijz9+p0OHXcpcN2HCx9x11/10775XseUzZkznhBNO3g7RiYiISKwEg0H++mgSvPUaO62Yz85xSczZ/0h2ufg8Dtyt7LaDiIiIVF6tTPzkT/yU/E8/qdF9JB1zHElHHrPN9axYsZzJkz/no48mRhI4ycnJDBlyG3/+Oavc7Y488hgmTfqMyZMncuKJp1Z6fyNG3ENBQQEXXng2L7zwGj5f8V5I69atpaCggFAohNfrpUOHjowa9TDp6em8//47WPsvTz01Fq/Xx5Qp39Ghwy6cffZ5ANx//92R97NmzeSRRx7E44HddutKKBSK7GPKlO955ZUXCAT8JCUlcfXV17PHHt144YVxrFy5gnXr1rJy5QqaNWvOnXfex99//8mUKd8zY8Z0GjVK56ijTozUNWzYbaxZs4oHHriXSy8dyPjx73HKKaczZ45l7do13HPPHdxxx7107bpHpc+RiIiI1H7+gkK+f+V9Gn/yNm02Lmd1amN+Oe4i9r7wTA5u3ijW4YmIiNQZtTLxUxtde+0VxYZ6tWrVmpEjx2Dtv3To0LHUEK0mTZrSt+/hFdbZqdOuzJ8/P/L+qafG8sorL0beX3HFVfTu3afYNkOH3sX5559RaghYkauvvp57772Tt99+gz337E63bnvRr9+RNGnSlFNOOZ1vvvmSU045nUMOOZQpU74rsw6/38+dd97KsGFu76HJkyfyySfjAfjvvyU8++yTPP74OBo2zGDBgvkMHnwVb731IQCzZs3kpZfeIDU1jVtuGcxHH73PJZdcEUkynXXW2WzcmBvZ1733juTUU4/jrruGs9tuuzN+/HvhY7+ayZMnRpaLiIhI3eAEAix6530K3niV7plrWNW4FYsuuZFuZ53E7okJsQ5PRESkzqmViZ+kI4+plt441am8oV4ej5dQyNmqOj0eSEra/DSKygz12pJ99+3J++9P4O+//2LWrN/48ccfeOml5xk79mm6dOlaqTrmz59HXFwcPXr0AqBfvyN58EF36NiMGdNZt24t1113VdRxeFm69D8A9t5730gSbNdddyMzc9M2HY+IiIjUDU4oxOrPPmfTs8/QeMMqVjduw9rLh3LgOcfj8XpjHZ6IiEidVSsTPzuSrl33YPHiheTm5pCSkhpZvmbNakaPvp/hw0eVu+2///7D0Ucft9X7njLlO55/fhwATZs25fbb7+aFF55l8OCb6N59L7p334vzz7+YBx64j88/n1Aq8ePxeIDNSSu/3x957TjFk1lFQ8pCoSD77rt5DiGAVatW0rRpM77//hsSE4tPvFiyHhEREalfHMche8oU1j31BGlLF5LVsBV/nnsT/S84kfSk+FiHJyIiUufp9so2atq0Gf36HcWIEfeSk5MNQE5ONg899AANGjQkMTGpzO0mTPiQ5cuXcdhh/aq0P5/PRzAYwnEc+vQ5hJdf/h8vv/w/xowZS3p6A375ZTrvvvtmJOGSn5/PsmVL2XXX3cLbxxEIBADIyMhg9ux/AVi7dg2///4bAJ06dcZxHKZOnQK4CaasrEwA9t23Fz//PI3FixcBMHXqFC644CwKCgoqEXegysdaFKuIiIjseAL2X1ZfeTkFQ28ka30mrx56EU1fep1TrjhNSR8REZHtpNI9fowxXuApoDtQAFxqrZ0Xtf4c4EYgCLxorX26mmONqZJz/MDmOXhuvPEWXnnlBQYOvBifLw6/v5CDDurLJZdcESn79deT+eOPWXg8EAo5tG3bjscfH1eqh8yWNGnSlN1378p5553Ok08+R8OGGZF1cXFxPPzwEzz11Fjee+94kpOT8Xg8HHXUcRx77AkA9OlzMOPGPUkgEOCUU87g3nvv5KyzTqZVq9bsu2+PSD0jR47hwQdHMm7cU3TuvCuNGjUGoEOHjtx88+3cdddQHMfB5/MxatTDpKSkVBj3/vsfwOOPP0JSUjynnXZupY71kEMO5d5772TIkNvo1Wv/Kp0nERERiZ3Q+nUsfWwsyV9PJCsxlfd7nEbPy87l+i4t8Xo8sQ5PRESkXvFUdiiOMeZk4Hhr7YXGmP2B26y1J0StXwF0BbKBf4Ce1toNJetZsyZLY3/qsYyMlGKTO4vI9qVrUCR26sP15/j9bHrrTXJfeQFPYSGTdjuEdcefxTkHG1o2KLsXtMj2Uh+uQZHaTNdgzWvWLL3MuytVmeOnDzARwFo7zRjTo8T6P4CGQAAoPnlMlLS0ROLifGWtknrA5/OSkVFx7yARqTm6BkVip65ff7k//8zSYXfhXfYfM1t0YfEZlzHo3ENpkKwhXVI71PVrUKS20zUYO1VJ/DQAoh/RFDTGxFlriyZh+Qv4FcgBPrDWbiyrkuzsiueCkbpNWV6R2NI1KBI7dfX6C23ayKbHHyU46TNWpjbhpYMv55RLTuKqjk0IFfjZWODfciUi20FdvQZFdhS6Bmtes2bpZS6vSuInE4iuxVuU9DHGdAOOATrgDvV63RhzmrX23a0LV0RERERqM8dxKPhiIpljH8bJyuK9XQ8j99TzuPdQQ3qSHhwrIiJSW1TlW/lH4DjgnfAcP39GrdsE5AF51tqgMWY10Kj6whQRERGR2iK4ahXZo4bjnzGdOY3b8Wy/y7ninMM5sGPjWIcmIiIiJVQl8TMe6GeM+Ql3Dp+LjDFnA2nW2meNMeOAKcaYQmA+8HK1RysiIiIiMeM4DgWTPifr0TEUFvh5vttJrO97NI8d04VGKQmxDk9ERETKUOmnelUXPdWrftO4TpHY0jUoEjs7+vUX2rCB7AdHUvjDt8xu1pHHe57FGcf04viuLYjzeWMdnsgW7ejXoMiOTtdgzauOp3qJiIiISD2U9/23bHxgBJ6cbF7uegx/7Hc0I47bnV2apsY6NBEREdkCJX5EREREpExOQQH/PTiGlEkfsaRha1459koO778fN3drhddT5k1FERERqWWU+BERERGRUvIXLWL5rbfQYNlCJnY5lJ0GX8/jpiU+rxI+IiIiOxIlfkRERESkmNlvvk/as4+AJ45XjhnEhdecSZNUTd4sIiKyI1LiR0REREQACObn8dttd9Phl2+wzToSf/s93LDPrng0rEtERGSHpcSPiIiISD0XCDlM+v5PWj56Dx3W/ceUXsdw+H23kJ6SFOvQREREZBsp8SMiIiJSj2UXBHhs7HucOXEcCU6Q+VffyQlnHKtePiIiInWEEj8iIiIi9dSspRuZ+sg4Lp0xnvwWrWk15mHatGsf67BERESkGinxIyIiIlIPTfhtMfkPjuDUpTPJ6XkgbYYPx5uSGuuwREREpJop8SMiIiJSjwRDDu9/8yc7PXoPu25ciu/igbS98CIN7RIREamjlPgRERERqSfy/UGefu1LBrz1EBmBPFKGjyL1kL6xDktERERqkBI/IiIiIvWAXZ3N28+8y4XfvIgnJZVmjz9HvNkt1mGJiIhIDVPiR0RERKSOW5mZz+cPPMXAX96noE1HWj/6GL5mzWMdloiIiGwHSvyIiIiI1GHTF65lzn0jOXfuD+T2OIA2I0biSU6OdVgiIiKynSjxIyIiIlJHTfpjKbn3381Ry/+g8PjTaXPjDXi83liHJSIiItuREj8iIiIiddCkXxcQN/x2Dlo7n4SB19D0nPNiHZKIiIjEgBI/IiIiInXM9zPm0Oiem2mXtZLEoXeTftTRsQ5JREREYkSJHxEREZE65Kuvf6PFqNtoVJhL/PAxpB/UJ9YhiYiISAwp8SMiIiJSBziOw/efT6HDw8PweDw0eOxJ0rt1i3VYIiIiEmOa3U9ERERkB5ddEGDc0+PpOPo2CuOTCD6opI+IiIi41ONHREREZAe2bFMeTz78Jld/+zz5TVvQ8elxJDRvHuuwREREpJZQ4kdERERkBzX+jxXM+N+HXDP1VWjbgQ5jn8TbqFGswxIREZFapNKJH2OMF3gK6A4UAJdaa+dFre8JPAx4gJXAudba/OoNV0RERESy8gOM/HIuwckTuXHmW4R27ULzRx7Hm54e69BERESklqnKHD8nAknW2t7ArcBDRSuMMR7gOeAia20fYCLQrhrjFBEREREgM9/PZW//TtzEjxny21sk7L0vLcY+paSPiIiIlKkqQ72KEjpYa6cZY3pErdsVWAdcb4zZE/jUWmvLqiQtLZG4ON/Wxis7OJ/PS0ZGSqzDEKm3dA2KxE51XH8zFq1nyHt/0Pu3L7j0z49JOfhgWj78CN7ExGqKUqTu0negSGzpGoydqiR+GgCbot4HjTFx1toA0BQ4ALgGmAtMMMb8aq39qmQl2dkF2xKv7OAyMlLYuDE31mGI1Fu6BkViZ1uvvy9mr+beSXM4f+5XnPznpyQcejjJd95LZl4Q8nRdi2yJvgNFYkvXYM1r1qzs3r9VGeqVCUTX4g0nfcDt7TPPWvuPtdaP2zNo360JVERERESKm7JgHcM+t1y96GtOnvUpiUceTfqw+/DEx8c6NBEREanlqpL4+RE4GsAYsz/wZ9S6BUCaMaZT+P1BwN/VEqGIiIhIPfbBHyu46aN/uGbx1/T7ZQKJRx9H2m3D8MTp4awiIiKyZVVpMYwH+hljfsJ9ctdFxpizgTRr7bPGmEuA/4Unev7JWvtpDcQrIiIiUi/4gyFGfTWPj/5cyQ3LvqPfr5+6SZ9bbsfjrcq9OxEREanPKp34sdaGgIElFs+OWv810Kua4hIRERGpt4Ihh2GfzebLOWt5MGsae8z4hMSjjlXSR0RERKpMfYRFREREapGVmfnc8elsZi3PZGzeDDp/9Z6SPiIiIrLVlPgRERERqSXW5hRy+duz2JDr56nCX+kw6W0SjzzGTfr4fLEOT0RERHZASvyIiIiI1AL5/iBXvjOLdTmFvOb9gwafvekmfW69Q0kfERER2WpK/IiIiIjEWCDkcPPH/7BofR6ven6nwfuvK+kjIiIi1UKJHxEREZEYe/u3ZUxdtIGHcn6m2eR3SDzyaCV9REREpFoo8SMiIiISQ1MXreex7xZw6+op7P7Th+Gkz51K+oiIiEi1UOJHREREJEbG/7GC0V/NY+CSbznktwkkDjhKSR8RERGpVkr8iIiIiMTAlAXrGPXlXG5YOYXDipI+tw1T0kdERESqlRI/IiIiItvZd/PWcvuns7l00bccNlNJHxEREak5SvyIiIiIbEfjflzE89OWcOH8rznxz8+U9BEREZEapcSPiIiIyHbywR8reH7aEm5fN5U+f35GYn8lfURERKRmKfEjIiIish1M/mcVo7+cy02rf6TPT+PdpM9QJX1ERESkZinxIyIiIlLDZq/KYsj7f3DV0u85bMbHJPY7UkkfERER2S6U+BERERGpQZvy/Ayd8C+n2684etYEN+lz+11K+oiIiMh2ocSPiIiISA3JKQww5KO/6fPzp5zxz+dK+oiIiMh2p8SPiIiISA3I9we56aN/2PWb8Zz/z+ekHXMMiTfdoaSPiIiIbFfeWAcgIiIiUtfk+YNc8c4fdPjyfS7653MS+w2gxf0jlPQRERGR7U6JHxEREZFq9syPi9jj+w8jSZ+02+9W0kdERERiQokfERERkWr00Z8r8L/5mpI+IiIiUiso8SMiIiJSTb6du5Z5T47j4n8+I/6I/qQN1UTOIiIiEltK/IiIiIhUgz+XZ/L7w09wwd+f4T2sHw1uvxtPnJ6jISIiIrGl1oiIiIjINpqzKoup9z3IuX9NItT3CJrceY+SPiIiIlIrVLpFYozxAk8B3YEC4FJr7bwyyj0LrLfW3lptUYqIiIjUUln5fqYOG8HJ/3xF4eFH0erOYRreJSIiIrVGVYZ6nQgkWWt7A7cCD5UsYIy5AtizekITERERqd1yC/xMuu52jvnnK7L7HUerYZrTR0RERGqXqiR++gATAay104Ae0SuNMb2B/YFx1RadiIiISC3lhELMvuMu+v7zLav7n0S7O+/A49X0iSIiIlK7VGXweQNgU9T7oDEmzlobMMa0Au4GTgJOr6iStLRE4uJ0J6y+8vm8ZGSkxDoMkXpL16BI9XACAf4afDNtp33JD/sfx4Vj7sXj8VS4ja4/kdjSNSgSW7oGY6cqiZ9MID3qvddaGwi/Pg1oCnwGtARSjDGzrbUvl6wkO7tgK0OVuiAjI4WNG3NjHYZIvaVrUGTbOYEA8266mUa/TOGTfY/n+LtvZtOmvC1up+tPJLZ0DYrElq7BmtesWXqZy6uS+PkROA54xxizP/Bn0Qpr7VhgLIAx5kJgt7KSPiIiIiI7Mic/n8VDhtBo1s98euBpnHr3YNKT9PQuERERqb2q0lIZD/QzxvwEeICLjDFnA2nW2mdrJDoRERGRWiKUlcX6IdeT8s9fvNXnbC699xqS4jV8XURERGq3Sid+rLUhYGCJxbPLKPfyNsYkIiIiUquE1q5l/Y3XEli0kIf3P4+BQy5S0kdERER2CHr0hIiIiEgFgsuWsuGqS8lfsoRh+1/CQRecSscmqbEOS0RERKRSNChdREREpByBeXPYdMO1ZOcWMPTAgQy86CgO2qVJrMMSERERqTT1+BEREREpg3/WTDYMGsj6QofBfa7i4vMHKOkjIiIiOxwlfkRERERKKPhqMhuvH8RKXwpDD72Wq87qy+G7Not1WCIiIiJVpqFeIiIiImGO45D3v1fJfeZJ5rXsxD29LuChc3vTtVWDWIcmIiIislWU+BEREREBnECAnEcfJP+j8fxp9uMOcxIjTuqmpI+IiIjs0JT4ERERkXovlJtD1rCh+KdPZep+x3Bfy0O4fYDhkE5NYx2aiIiIyDZR4kdERETqteCqVWTediOB+fN4rfeZvNmiB1ce2J4T9mwV69BEREREtpkSPyIiIlJv+f+YReYdtxDMz2d470uw7fbg0QGGAzs2jnVoIiIiItVCiR8RERGpl/I/+ZDsh0fjb9qc63pcin+ntrxxRneapiXGOjQRERGRaqPEj4iIiNQrTiBAzhOPkv/+OwT37sngXU+lICmVV8/em4bJ8bEOT0RERKRaKfEjIiIi9UZo40ay7hqK/7dfWH74CdzQ+GAKgh4eHbCrkj4iIiJSJynxIyIiIvWC/68/yLprKKENG5h0zGU8Gm/onJHC8GN2o2OT1FiHJyIiIlIjlPgRERGROs1xHPLfe5ucJx/D27wFL5w+lPdy0rl4vzZcvH87EuO8sQ5RREREpMYo8SMiIiJ1Vig7m+wHhlP43deE9u/D7budxG8bHW44dBfO2menWIcnIiIiUuOU+BEREZE6KWBnk3n37YSWL+ffY8/nntS9yc0Kcf8xu9F/t+axDk9ERERku1DiR0REROoUJxgk763XyX1+HE6DDEb1G8R3cW3Yt3kagw/ZBdMiLdYhioiIiGw3SvyIiIhInRFctZKs4XcT+P03cvc7iMFtjmadN4nRRxoO7dw01uGJiIiIbHdK/IiIiEidUPDlF2Q/9ABOMMSfZw3iltx2ZMQn8Nxpe9K5mXr5iIiISP2kxI+IiIjs0EIbNpD92EMUfvUF/l1357H9zuGrrEQO6NiI+4/pQlqimjsiIiJSf6klJCIiIjskx3Eo/Goy2Y+OIZSdzbQ+JzG88f40CCRx02FtOXWv1ng9nliHKSIiIhJTSvyIiIjIDie4dg05D42icMr35HbYlZt7XsrKxjtxUpfmXN2nA+lJauKIiIiIQBUSP8YYL/AU0B0oAC611s6LWn8WcD0QBP4ArrLWhqo1WhEREanXnGCQ/I8+IPe5p3EKC/n72PO41bcH7Zs14MPTu5GRHB/rEEVERERqFW8Vyp4IJFlrewO3Ag8VrTDGJAPDgUOttQcADYFjqzFOERERqef8f//JxssuJOeRB1nTqgPXHnEjQ+K606tjU547s7uSPiIiIiJlqEo/6D7ARABr7TRjTI+odQXAAdba3Kh686snRBEREanPQhs2kDPuSQo+/ZjstEY80es8vmvVjX3aZPDSwR3Yo1WDWIcoIiIiUmtVJfHTANgU9T5ojImz1gbCQ7pWARhjrgHSgMllVZKWlkhcnG9r45UdnM/nJSMjJdZhiNRbugZlR+IUFrLxzTdZ98zThHLzGN/pEP5n+nHMfrswvldb9tipYaxDrBJdfyKxpWtQJLZ0DcZOVRI/mUB61HuvtTZQ9CY8B9BoYFfgFGutU1Yl2dkFWxOn1BEZGSls3Ji75YIiUiN0DcqOoOhpXZueeRLvqhX82tzw+oEn0PuQfflgn51ompoAsMP9W9b1JxJbugZFYkvXYM1r1iy9zOVVSfz8CBwHvGOM2R/4s8T6cbhDvk7UpM4iIiKyNfy//8amxx+DOf+yqGFrXjnoCvY85nCe6bEzaYl6UpeIiIhIVVWlBTUe6GeM+QnwABcZY87GHdb1C3AJ8APwtTEG4DFr7fhqjldERETqoJyZv7HqmXE0+Gcma5Ib8uo+ZzC/Wx8eOrkbbRolxzo8ERERkR1WpRM/4V48A0ssnh31uipPCBMRERFh4Q/TyXnxOVrO+4NgYhpv7XsiySefypm7tGCPVg3weT2xDlFERERkh6Y+0yIiIrJdLd+Yxy8ff0mjz96ly7LZBBNS+fLg09n5nLO4crfWSvaIiIiIVCMlfkRERGS7yMzO57sX36HFFx9wwKblZCY3wB5/PrtedC5nNs2IdXgiIiIidZISPyIiIlJj1mYX8NYPlvSvP2O/37+id/4mVjVqRf6gm+lw4nF0TEyMdYgiIiIidZoSPyIiIlKtCgMhflmygWlf/ETLKZM4celMkoJ+VnToytrTb2b3ow/H49XUgCIiIiLbgxI/IiIiss0CIYeZSzfy2S8LifvuS46Y/xPnb1qOPyERDh9Axhln0HRXE+swRUREROodJX5ERESkyhzHYW1OITOWbOSneWvInj6d3gtmcPGKv0gOFpLftiOJF91E4yOPwpuaFutwRUREROotJX5ERESkUuzqbN78bRkL1uawZH0uLdcs4fD/fuXCZb+TkZ9FICWVxP4DSDvhJJrs3hWPR0/nEhEREYk1JX5ERESklJWZ+Sxan8vyTfks3pDHjCUbmbs6G7NpKcdtsuy7ZBYZ61bgxMWT0PtAkgYcRULvA/EkJMQ6dBERERGJosSPiIiI4DgOKzIL+H7+On5cuJ5pizYA4A0F6b5xEWdtmsNei2eRvHEt+HzEd9+bxIsvJKHvYXgbNIxx9CIiIiJSHiV+RERE6qHVWQXMWp7Jr/9txK7OZuG6XHIKgwDs7svlbs9S9lg+m5S/fsOTkw0JiST02o+Eg/uScEAfvA0zYnsAIiIiIlIpSvyIiIjUE+tyCpmyYB1TFqxnyoL1BEIOAJ3TvFySsIqua/5lp/l/4FuyCABv02bEH3IoCfsfQMJ+vfGkpMQwehERERHZGkr8iIiI1DEFgRB/LN/EqqwClm3MZ0VmPnZ1DvPX5uAA7RMCXJO0jgNzlpA+5y9Cc2ZDMAjx8cR335v4Y48nYb/e+Dp01ATNIiIiIjs4JX5ERER2YIGQw1/LM1mwPpc/lmcyb00OC9flUBh0e/N4nBDdgxs4tGAlg7OX03blfOIWzQfHgbg4vF26knjWecTvtTfx3fbCk5wc4yMSERERkeqkxI+IiMgOIhAM8dvSTfy9MotlG92nbs1enU1BIARAcpyHg9MDnBS/ij3y/yPjv3n45lnIzQXAk5JK3G5diD/0UuK670181z3wJCbF8pBEREREpIYp8SMiIlKLLd+Uzx/LM5m2aD0/LFhPZn4AgJ3ig+wbWMfxhWvolL2SpmuW4l00P5LkIS6OuE6diRtwNHFddidut93xtW2Hx+eL4dGIiIiIyPamxI+IiEgt4DgOSzbkMW9tDmuyC1mRmc/sFZtYNn8prbJW0yZnLbfEZdG5YD3pa5fDyhWRbT1pafh26UTckccQt0snfJ12Ja5TZzwJCTE8IhERERGpDZT4ERER2U7y/UEWrMtl0fpcNub52ZQfICs7j03/LSdryX+kb1xLy5x17JS9hj45azgjZy0JAf/mCpKT8e3cFl/XPYk7/iQ3ybNLZ7zNm2sSZhEREREpkxI/IiIi2ygQctiU52d9biHrc/38uzKLRRvy2JRbQHD9Rrwb1hK/cR1JmRtolruRFrnraZ23gb1z19MkLxMvTqQux+vF06o18bt3xNf2UHxt2ro/bdvhbdJUCR4RERERqRIlfkRERLYg5DhsyPWzMquApetzWbxiHSuXriZ3zTpCGzcQl7mJxvmZNM7fRJP8TDrlZ7J/QRaN8jbhc0LF6nK8XpwmzfDt1Jr41rvja9UKX8vWeFu1wteiJd7mLfDE6etZRERERKqHWpYiIlLvFAZCZBYEyMrJJ3v9JnI3bCRvYyYFGzMJZGaSHU7oxGdnkpiTRXJuJukF2TQozGHPghz2DQXKrDeQkkaocRN87VuQ0rIb3qZN8TZt5v5u0sx93aSJEjsiIiIist2o5SkiIjsUJxQikJPDunWZ5GbmkJOZRV5WDgVZ2eRkZlOQlYOTlwcFefjyconLyyUuNxtfXg6J+bkkFeSS4s8j1Z9PeqCA9Ar2lZ+QTH5qOv7UBtCsNXGNGhFq2pikls2Ia9QYb8OGeDIa4W3UyE3o6NHoIiIiIlLLKPEjIiLbxHEcCAYJFeRTkJNHXm4eBbkFFOTmUZhXgD8vj8K8fAL5BQTy8gkWFBDMLyBYUECooACnoIBAQQGewkK8hYXE+fOJL8wnzl9IQmE+8f4CEvwFJAYKSPAXkhQsBNwvsAbhn/KEPB7yElLIT0zBn5xCMDWVYLMm5KWmkZeaTlwD9yehYQOSMzJIadSQlEYNiGvUCG/DDD0VS0RERER2eJVO/BhjvMBTQHegALjUWjsvav1xwDAgALxorX2ummMVEdnhFCVFCIUgFMIJBtzfgSDBYBAnGCTgDxDwB/AH3NfBQJCAP0jA7ycUDLo/4fKhQBAnGCAUDBEKBAmFQjjBIKFAAH9hgFDAjxMI4AkEcAIB8AcgGIBAAE8wQBwhgvmFeIJuGU8wAMEg3qAfTzCINxDAEwriDQbwBgPusqBbzhcM4g0F8AaD+ELuT1wwQHzQjy9qcmKAhPBPZfm9cQR8cQTi4imMT8Qfn4Q/IRF/cgrZDRsRTEgikJCEk5SEJykZb0oKiWmpJKalkpCWSmJ6KikN0kjPSCe1YTqepCQ8ySmQmIjH663Wz1REREREZEdSlR4/JwJJ1trexpj9gYeAEwCMMfHAI0BPIAf40RjzibV2ZclKstesw4n6+8AJv3HCfzQ4zuZlOFHLI+WjKguFSi+PKlDylVNsx2UcoeOUscopvs9wuVKLQmVWWHpRqGTtpYs5OJTcqRMVX/GqyzjeYpsWnUsn+lexdWUVL7mw1BE7xc+Lp8xzV3pBSkoCOTn5pesq8b7YiQm/L7YHp/RvT1n1EP3ZOFHHV/xAi5dxih1TpLoyypQ66UUxOM7mZSX/bRSLvUQ9Tol6cDbHEam7jGMosawwECTouENiQuHfkf2FnPDn5+BxQuHDCeFxAKd0uaIfjxMKryo61w4et/LN5QjXESqKO1wPJesq/tqtKxTZvlgs4dceNi/bvH3UuvB+Nh+T4/4f4UTFGHLC/w9E1REqHacnOs5I2RDeUAivE8JT9Ntx8IWCeBwHrxOK+nHwhoLu7zL/sylfXPgnsUpbVZ7f6yPg8RH0+gh4fYS8PgK+OILh18Hwj98XR8jrw4lPIpQUh+Pz4fjiceJ84IuHOB/ExeHEJ+DEJ0CC++NLSMSblIgvKZG4pCTikhJJSE4iPjmJhPBPYkoSSSlJJKYkE5ecBPEJSs6IiIiIiNSQqiR++gATAay104wxPaLWdQHmWWs3ABhjpgAHAe+WrCT/5KO2PtoyeEr8ltpPAyfqphAeHI/HzcV4PDh4CIUfO+1E1m1e5v52y4c83mLLQuF6iJQvYzsPOB5vsf05JdZ5cH/jATxe8HjAU7TM4y7zhreL8+DxhF9H/XjCdeP1uU9jCv/gCb/2+dzXPi8ULffFgXfzNh6vF8frA58XvD63nM/97fF68fh8eOLi8Pm8eON8eH1x+OJ8eH0+932cD6/XhyfOizcuDq/XV3q9L46kxDjiExLwJsTji4/DFx+PLyEBX0I83vh48Plo1CiVjRtzt9u/CxERERERia2qJH4aAJui3geNMXHW2kAZ67KAhmVVMufUS/H4vFEJG/ePNPf1ZtF3fyPLPZ7Sy7ye0kmfsspR1k7KSheVUa70281/GBYrU0Z9ZdzE9pRc6CndU8ZTRnwej/sHdKmYoo83/LKictHVOmXFXEa5MoX/KC4RdYkyxevxeLxuT5MSZR1P0eZF598T3i7q84hUtLmM+2vze8dTouYSZZzwH/7Fz2/4tzfqvaf4PiLVeaOXlygTVZ+njOMp/jsqhmJxeCKbRJ8Dp+R2Jc+Bt3g8SfE+4uO8eMFNEITPn8fni5wPj88LeN1tvR48Hm84F+LB4/G5+/J63XPn84aPK7ydx/0sPV43geIt8Xl5ih9G5PP2RB930REX+4iLL/eUOP+y7Xw+LxkZKbEOQ6Re0vUnElu6BkViS9dg7FQl8ZMJxR5+4g0nfcpalw5sLKuSA667vCrxSR2TkZGi3gY7usgINceduwYIxTIeqRJdgyKxo+tPJLZ0DYrElq7BmtesWdnPq63KpAo/AkcDhOf4+TNq3b9AZ2NMY2NMAnAwMHXrQhURERERERERkepQlR4/44F+xpifcEdiXGSMORtIs9Y+a4y5AZiEm0x60Vq7rPrDFRERERERERGRyqp04sdaGwIGllg8O2r9J8An1RSXiIiIiIiIiIhsIz0/V0RERERERESkjlLiR0RERERERESkjvI4TskHiYuIiIiIiIiISF2gHj8iIiIiIiIiInWUEj8iIiIiIiIiInWUEj8iIiIiIiIiInWUEj8iIiIiIiIiInVUXKwDkLrHGJMMvA40B7KAC6y1a8oo1wz4CdjTWptf2e1EpGKVuZaMMZcBVwABYLi1doIxxgMsBeaGi0211t62/SIX2XEZY7zAU0B3oAC41Fo7L2r9ccAw3GvuRWvtc1vaRkQqb2uuwfDymcCmcLGF1tqLtmvgInVAZb7PjDEpwGTgEmvtbH0Hbl/q8SM14UrgT2vtQcCrwB0lCxhjBgBfAC2qsp2IVEqF15IxpiVwLXAgMAAYaYxJBHYBfrPW9g3/KOkjUnknAknW2t7ArcBDRSuMMfHAI0B/4BDg8vB1WO42IlJlJ1LFa9AYkwQQ9b2npI/I1jmRCr7PjDE9gO9x25qV2kaqlxI/UhP6ABPDrz8HjiijTCi8fH0VtxORLdvStdQL+NFaW2Ct3QTMA7oB+wI7GWO+McZ8Zowx2y1ikR1f5Lqz1k4DekSt6wLMs9ZusNYWAlOAg7awjYhUzdZcg92BFGPMF8aYr40x+2/voEXqiC19nyUCJwGzq7CNVCMN9ZJtYoy5BBhcYvEqNneZzQIaltzOWjs5vH304gZb2k5EitvKazD6WosuswIYaa191xjTB3e4WM9qD1qkbip5XQWNMXHW2kAZ64quuYq2EZGq2ZprMBcYAzwPdAY+N8YYXYMiVVbh95m19keo8G+/UttI9VLiR7aJtfYF4IXoZcaYD4D08Nt0YGMlq8vcyu1E6q2tvAajr7XoMv/gzn2AtXaKMWYnY4zHWutUf+QidU7J68ob1Xgt75qraBsRqZqtuQbn4PYEcoA5xph1QCvgv5oPV6RO2ZrvM30Hbkca6iU14Ufg6PDro4Afang7ESluS9fSz8BBxpgkY0xD3C7wfwF3AdcDGGO6A0uU9BGptMh1Fx4u8mfUun+BzsaYxsaYBOBgYOoWthGRqtmaa/BiwvOKGGNa4/ZAWLE9gxapI7bm+0zfgduRevxITXgaeMUYMwUoBM4GMMbcgHtX5eOqbCciVbbFa9AYMxY3IeQFbg8/We8B4HVjzDG4PX8ujEn0Ijum8UA/Y8xPgAe4yBhzNpBmrX02fP1Nwr3mXrTWLjPGlNomVsGL1AFbcw2+ALwc/r50gIvV40Bkq1R4/VV2m+0Tav3kcRzdzBURERERERERqYs01EtEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI5S4kdEREREREREpI6Ki3UAInWZMcYB+llrv4xadhDwBfCctfba8LLTgJuBPYB8YAowzFo7s4w6LwReqmC3r1hrL9zKeDsAu1trP92KbQ8F1lhr/6pE2QTgImvtuPD7b4Ep1to7amK7SsTTCZgLdLDWLirrcytnu+bAodbat8tZfyEw3Fq7szGmL/ANEG+tDWxFjGnAqdbal8PvF4Xrfr6qdYmIiNQEY8xZwP+AIdbah6KW3w3cFVXUDywFXgfus9b6y6ir5DYl3WOtvXsr49wLSLfW/rAV256K2/ZYWYmyW/XdXVPf+caYI4DJ1lqPMaY9sBDobK2dt4XtKmwfhj+rI6y1faLbPlsZY7G2VWXbZCJSMfX4EdmOjDHdgE+At4DrwsuOAV4AHsNN/BwCrAW+Nca0LaOat4FW4Z+iL9VTopZdtw0hvgj03sptvwZaVrLsWcCdUe9PBh6owe2qqhXwfSXKjQKOq2D928De1RIR3AhcGvW+J/BGNdUtIiJSHc4C5gEXlLHuZza3VQxwG3A58Fw5dY2JKl/UNukVtWzMNsQ5PhxDlRhj2gHvAmmV3GRrv7u3x3f+f7jncWElym6pfTgGOL46gqJ026qybTIRqYB6/IhsJ+G7JROBL4FLrbVOeNXFwKvW2tejyl4C9AXOpkRiw1qbB+SFyxVdw+src+epEjzVUEeV92OtXV/D21VJFc5lhecr+rOqBiWPfU011SsiIrLNjDGNgAG47ZrXjTF7l+i57C/x/brQGLMW+NIY87i19tfo+qy12UB2uO6iRMuaGLd3qrrd1n531/h3vrU2CFRXeyd72yMqe1/V9HmL1HtK/IhsB+Fuq18AfwBnh79sizjAfsaYBtbaTABrbSg8dCpzK/blAW4HrsS9IzUVuMZaOze8/lTgXqAj7t2eEdbal4wxL+P2NjrEGNPHWtu3jLrvBS4BmgAzgRustVPDXZABJhtj7rHW3m2MuQh3+Nou4eN4F7gG6EN4qFq4+24H4GXCQ7aMMW2AZ4EDgQDwUXi7HhVtF15+DTAYaI57Z/Eqa+3sMo4jHngEOBfIwr27FL0+0q04PETrIWB3YA3wtLV2ZLhb8wXh8n2ste3D2w0HBgKzcLuwl+zufLUx5g7chs0j1tr7w3XcTbibdFQci8L1BQh3dzfGOOEu2ovCdT9vjPHi3h0cCLQOH/u11tpZUcdzATAE93OfgZt8nF/y3IiIiGylU4EC3N6uw4ALcdsK5bLWfmWMmQ+cBPxaUdmSjDGJuN/f5+COYvgKt72zKrz+KtzvvdbAHGCotXZCeJh4O+C58Pf3hSXqjQfG4vamTgN+BAZZay2be8fMNcZcZK192RhzC3AFbi/sdbhD+YeFhzxV9N29J/AksC9uW+QN4BbctklF2/lwz++lQDruMPKB1toVZZyjBsA44FhgOW4P86J17Yka6lXZ9iHu57owHMMNuL2nllC6DTMcuBY3eXentfaF8PKXgThr7blRZR2gH24bsay2VVGbLAm4G/czb4zb23yQtXZx1PGcivvvohnwHXCxtXZtyXMjUt9oqJdIzWuA29OnDW7Sp7DE+ieBvYBlxpj3jDFXGmPaWWsXbWWPlkHA+cB5wH64Xa6/MsakhBNQ/8NNehhgBPC8MWY33CFiU4FHcYdQFWOMOSlc9zlAF+A34L1w0qFnuNjpwJhww+Ap3ARUZ9yExEXhen8CrgdW4Hbf/a/Erp7AHfvfA7cR0DtcT4XbhXtJ3R8uuxfu3AEfhRNhJd2D2wg6PhzzNWWUIdy4eh93eF4X4GpgmDFmAG635nfC63tGbXYCbsOlvCF3ZwP9ce+IDjHGXFpOuWhv4yafirrJlzQMt3E7GNgHt+Ez0RiTHlXmrvD6vrhD8kZUYr8iIiKVdTbweXgeu4+As8NJlC35F/fmSlWNwG0jHIubmPACE4wxHmPM3rjtmcG47Z23gXeMMRm4bZGluDdMyvquHoT7PX0M0A03KfNyeF2v8O/ewNvGmHNxv38vA3bFbV/caYzpxZa/u18HZgN74rZFzsO9ubal7e4O7++ycDzJwKtlnyKeAXYLn59rcRM1pWxl+/Bg3LZaWUPud8Jti/XGHdL3VHh+oS0pr20VfTyn4LZz98ftxPBxuL1W5DbctupxuO3gmyqxX5E6T4kfkZr3ZPh3AXBryZXW2m+AA3CTQwNwEyYLjTFvhO9sVNXNwC3W2q/DvV2uwe0xcgruF3E8sMxau9ha+xJucmWVtXYTUAjklJNwao+bkFlsrV0YPpbzAW9UF+QN4e6+ecAl1toPwvt5D/euX9dw4msTELLWrizR+6loP5uAReFu3yfjDoXb0nYDgbHW2jfDkxReg5uwaRBdKJwIuhS421r7vbV2Km7jrywNce8orQon4j4BDgdmRR1nfoku2M9a19/l1HmptXamtfZj3EbUwHLKRYSHjGVTupt80fFcEz6ej621/+I2Bv24n0+RR621X1lrfwGepuwGlYiISJUZY1rjJgI+DC/6AGgKHF2JzTfh9lypyv5ScBM0A6210637YInzgK64N1/a4/aoXmytXQyMBE4ECsNtnCCQGW77lNQe9/t9Ubg9cSWbkwdF3/drw9/Ny3AfOvFVuJ3wDO7wqa4VfXdH7WdtOMbvgaOASZX4zr8C9wEgn4XbeVcCM8I34qLLNsRNKA221v5mrZ2E24u4LFvTPnzMWjvfWjunjPoKgQuttX9ba1/BTSpVpr1TXtuqaCjhebg9mr+x1v6Jm+DpBBwZVfSe8L+J73F7Uam9I4ISPyLbw3rcL8/bgBuNMfuXLGCtnWGtPQ13CNVhwPPAmbgNlUoLj4HfGXjDGJNtjMnGvVPVFvdO1O+4X76fGmPmGWMeAdZZazdUovo3cBsz840x03HvHP1ty3hCVThh87sx5p5wLyaLe9fFV7JsGYYBpwFrjTHvAd0BW4ntdieqm7i1dpO1dkgZjbqmuN1/Z0Ut+6WsCsMNnAdw71QtM8aMw010VTTefFEF6wrCDZUiv+HeidsWzXGTU9OLFlj36Si/4PZSKhI9rCsTt4EnIiJSHc7ETaZ8Fn4/HXdoUVmTPJfUgKoPbe8IJAA/RLV31gBJuO2dSbhPSJ1pjPkTuA+YZ63NrUTdT4RjWm6M+QZ3wuo/yyoYvnm3xhgz0hjzoTFmMW6v2sq0d27DHdq1yhjzGtDSWrtoC9sUtWGi2zvzrbVDrbWhEmV3DcexxfYOW9c+rCjWhSWGV/1G8TbJ1tgV92/X6PbOetw2oto7IlugxI9IzbvRWrsOt5fFVODlop48xpg0Y8zj4XHJWGsLw3cxLsftbtuvivsqmrfrTNwutkU/u+HemXGstefgjid/Hbfr78/GmP5bqthauzq83VG4X7pXAL+F7/IVEx4K9RtuF+WJuOOtf6zMAVhrP8IdFncj7v9RL+E+TWJLSg6h25LoIWClHiMbFc9thM8fbnLpu/D8ReXJr2BdyUaZN2rfDqVVZh628vbno3jDs+T52V4TeYuISN13Fu4f2OuMMQHc77ZWwLHGmKZb2HZP4K8q7q/o+/EQird3dgXeCyd4jsDthfQZbjtkpnGfrloh686JuCtub5n5uEPIpxpjkkuWDQ8z/wp3uNUHuL2Cl1bmAKy1T+HOV3gPbjLno/B8fxWpalsHKtHe2cr2YVXbO0WxF2vvmM0PKtkStXdEtoESPyI1LwDulyruEJz2uHeewO3Oei5uoqakjWzuUlwp1tqNwGqglbV2XriL8kLcsdrdjTG7GWMeDnf5vdtauw/wA+6kilB28gGIPHb+CmvtJGvttbhjwNOBg8oofhnwirX2cmvt87jj93dh85dvRfsZDuxsrX3OWnsy7rCsM7a0HTCXqEenG2NSjTErjTF7lCi3FlhF8a6/ZT5y3RjT0hjzFG437NHW2oNwE1GnVyKesiQbY3aJet8L99yA21CJDEszxqTi9uYpUua+wj2aVuD2qCraNh638VaZnlIiIiJbzRjTGXeul8EUT8Ici5sMOquCbQ/DbRe9V8XdzsftYdQ0qr2zBngYaGeM6Q3cYa39wVp7C26PkFW4N6+g4nbI+cCJ1trx1tpLcdsIXXDn+ym53ZXA/dba6621r+K2MVqwhfaOMSbJGPMY4FhrH7fWHok7d0+F7Z3wd/4aird3OhtjVhljGpcsjpvoqUx7Z6vbh+XoYDY/iQ0qaO/g9t6KVt6+5uG2qaPbO01w55JUe0dkC/RUL5HtyFr7rzFmBHCXMeYD6z4RazgwIvzHelHDpw/uZIHnbMVuHgbuM8aswr2DdhNuz6Hrce/AXGGMyQRewW1sdQPeCm+bDXQyxjQP9/CJ5gUeDNf7C+5drSTc7sFF23Y1xszAfapF7/CdtSBud+ZWQGJU2YbGmF2BBSX20wV4whgzCMjFnZvo10ps9xjwpDFmFu7T04aF4/gnupC11jHGPAncbYxZgJtge6jUWXStx230+IwxD+IOqToI9wllRfHsZYzZyVq7rJw6ooVwe3xdi5sIuxb36RjgPmlrRHiiyOm4kzFHz2OUDbQyxnSw7hxL0R4KH88y3CeX3IJ79/HNSsQkIiKyLc7C/S59xlob3SvjL2PMT7jDvSYA8caYluF1ybhtnQeB50sMg94ia22WMeY53PbCFbjDyh7AbdPMxb05NcwYsxp32NdeuL2Jo9sTuxljGtvS8xo2BO4wxqzH/U49L1x+Dptvmnc3xqzEbWccboz5APcJYCNwk13R7Z1S393W2vzwgzDaGWNuw/2b7KgS8ZX3nf8YcI8x5j/c3kWPAb+VPA5rbWZ4CNljxn3CWDJwZzmndCOVbB+Ws31JCcArxpi7cJ/SehrufJbgtneeDPcmWobbbi2I2rbMtpW1NscY8www1hhzOW6SbVS4jom4Q+xEpBzq8SOy/Y3EvevxkjEm2Vr7EHA57tMjfsb90r8YOM9a++lW1D8G96kHT+ImQPYABlhrl4fnpjkZ98lT/+B2532azY/3HIf7JIvPS1Zq3YmN78BtpFncoVhnWWuL7rI8gtvougv3rtUK3KFtX+Le3XmSzXeavsZ9ksUfuHP4RLsS90v8K9zhYnG4TwqpcDtr7Ru4Da7HcCeSbg4cX8aYd3Cf/vUyboNmAu7j40ux7oTSx+FOFvk78Ckwmc09tl7FTeDMMmU/PaykDcDH4eN4EndC5vfD677CPbeP45632RQfHvc+buLo7zIaXo/gfubjcM9ZW+AQG36krYiISA06C/hfiaRPkadxe6DugdvrY0X450/cG1OjqMSkv+W4ATep8zZuMiEZ6G+tzbPW/o57Y+V63O/Th3GH3n8Z3vYJ3CHrz5VR75O4vXtfCm97AnCstXZDeOj+y7jz4VyC+8SrFNx2x/jwcb3P5vZORd/dZ+DeQJuGOx/RQjY/ZbSi7Ubhtl/+F952I+XPpTQoXPcX4eN5vKxC29I+LMfvwOJwfENxJ8Auml/otXDsH4Tjepfiw+MqalvdjPuZv4f7tNcC4LBy/u2JSBSP41S1556IiIiIiIiIiOwI1ONHRERERERERKSOUuJHRERERERERKSOUuJHRERERERERKSOUuJHRERERERERKSO2u6Pc//rr3+rfTbp+Hgffn9wywW3QYsWO9do/VsjLS2R7OyCLResg2r7seeNd5/KnnzSqdVed20/9ppSX48b6v6xr1q1tNx12+P/99qovh431Nyx77FHl8o8ea9aFLV1WrTYuUa/D6qqomtNiqvP12Bdp8+2btPnW3fps62c8to7daLHj8ez3dpytUpcnC/WIcRMbT/2gq+/pODrL7dccCvU9mOvKfX1uKF+H3t9/f+9vh431L1jr8nvA6k5de3foWymz7Zu0+dbd+mz3TbbvcePiIiISG1hjNkPGGWt7WuMeQtoGV7VHphmrT2zRPmZwKbw24XW2ou2W7AiIiIiW6FSiZ/oRlHUsrOBa6y1vcPvLwOuAALAcGvthOoPV0RERKR6GGNuBs4DcgCKkjzGmEbAN8DgEuWTwuX6btdARURERLbBFhM/JRtF4WV7AZcAnvD7lsC1QA8gCZhijJlsra27k1KIiIjIjm4+cDLwWonl9wCPW2tXlFjeHUgxxnyB24Yaaq2dVrLS+HgfHo+HjIwUsuPcUfUZGSnVHnxVbdigjt6V5fF4SEjQ+aqL9NnWbfp86y59ttumMmeuWKPIGNMEeAC4HnguXKYX8GM40VNgjJkHdANmVHfAIiIiItXBWvu+MaZ99DJjTHPgcEr09gnLBcYAzwOdgc+NMcZaG4guVDT55MaNuQQCocjrWCssDGy5kACQkBCn81VH6bOt2/T51l36bLfNFhM/0Y0iY4wPeAG3MZQXVawBm8e7A2QBDcuqr+guWHXaHtm/2nCnriSfz1sr49oeavuxZ7z2ao3VXduPvabU1+OGun/sFfVCqK93d+rrcUOtOPZTgf9Za8t6dMgcYJ611gHmGGPWAa2A/8qrLOPxZ2omShEREZFKqmrLal/cO1xP4w7p2t0Y8yjwNZAeVS4d2FhWBTXxCLbtkf2rDXfqSsrISKmVcW0POvb6d+z19bih7h97Rf9/19e7O/X1uKFWHPsRwPBy1l0M7AlcZYxpjXvjq+RwMBEREZFapUqJH2vtz0BXgHAvoLestdeH5/i5PzzpYSLQBfirmmMVAWDOooXkB/wEg065ZVITE9i1fYftGFVxuW++DkDKWefGLAYREdkqBlhQbIExrwJ34PZ6ftkYMwVwgItLDvMC2JSVhT8YZPnGHNInfQpA1oBjipWJ9feUiIiI1B/V0pfaWrvSGDMW+AHwArdba/Oro26RknIKCnlzbqjC3mPn774dAypD4U9TACV+RERqO2vtImD/qPddyyhzftTbs7dUpz8YZM6GEN+v8HPu1JkAvN6mf7Eysf6eEhERkfqjUomfko2ispZZa59j82TPIiIiIiIiIiISY95YByAiIiIiIiIiIjWjfj4yREREap3Vq1cxePB1vPHGW5FlP/zwPU8//TQ33DCEbt26M2eO5fXXXyMrKwvHcWjatCnnn38hbdu23WL9v/76C6+//hqBgJ927dpz1VWDSEkp/bS0LZVbu3YNt912Cw899CgNGjQAICsrixdeeI6lS/+joKCQU045lb59DwXg77//5rXXXqGwsJCUlBQGDbqWli1blhnjY489wkknnUynTrtU6dxVRVnnuTrNmPEzTzwxlqZNm0aWDR8+kuTkZD7++EO++uorfD4fDRo0YODAK2nZshVz5szh8ccfA+Cyyy6lW7e9AXj33bdp1KgxRxzRL1LXY489wimnnMbOO+9cI/GLiIhsD4FAgCuuuIwOHTpwxx3DAPc7+uqrr4y0a0Ihh+TkJI455jgOPLDPFusMBoO88spLzJw5k1AoyPHHn8iAAUduVblVq1Zx8803cuedd9OpUyc++OB9fvzxh8j6zMxM8vLyeP31N9m0aRPjxj3NihUrCIWC7LNPD84773y83tL9TNatW8ezzz7DrbcOrfanfUd7++03yczM4rLLLq+xffj9fkaMGE7//gPo3fsAAIYOvYWCgoJImeXLl3PEEf245JLLePPNN5gy5QcaNWrMbbcNJTU1jUAgwLBht3PrrbdH2pVr167hxRdfYMiQm8s8h1tDiR+RGuBJTIx1CCI7vC++mMR7773DXXfdQ4cOHSNfrsOG3U3Hjm5i5LvvvuX+++/lqafG4fP5yq1r06ZNPPHE49x//0hat27Na6+9wuuvv8rllw+sUrlvv/2Gt99+k/Xr1xfb7oknxrLzzjtz/fU3sG7dWgYPvo4999wTgNGjH+Cuu9yYJ0z4hOeeG8edd95VKsYff5xCSkoqbdu226bzFmvWWo4//gROOeW0YstnzZrFV199yciRo0lJSWHixM944onHGT58BB9++AGDBl1D8+YteOCB++nWbW/WrFnDn3/+wT33FH/A1hlnnMWjjz7MyJGjarTBWF0CcfGxDkFERGqh6dOn0aFDB+bPn8fSpf+x885tAEhISOChhx6NlFu9ejX33DMMr9cbSS6UZ/LkSSxfvpxHHx1LXl4et912Cx07dqRz512rVK6wsJDHHnuEQGDz8wtOPvkUTj75FABycrK55ZabufLKqwF4+eUX2XnnNtx8860UFhZy77138803X3P44UeUivHpp5/kzDPP2iG+wyti7Wyee+5Zli1bSv/+AyLLR4wYFXk9Y8bPvP76q5x55tnk5OQwbdpUnnjiacaP/4DvvvuOo48+hgkTPqFPn4MjSR+Apk2b0b59ByZO/Jyjjy7+cIitpcSPbDdzFi0kp6CwwjJ15SknDcc8FusQRCpt4zUDSy1LPOwIkk86FSc/n003XV9qfdJRx5J09LGENm4k885bS61PPvEUEg/vR3DVKnwtWlQ5pg8+eJ9vvvmaUaNG06hREwAKCgrIyckhL2/zswMOPvgQUlJSCIVC/PXXX7z66kul6jr33AvIysqkU6dOtG7dGoABA47kxhsHc9llVxRreMya9Xu55TZs2MDPP0/nzjvv4pprro5sk5WVxR9/zOKGG4YA0KRJUx54YDRpaelMnjyJffbZJ5Ko6t9/AHvttXeZx/z2228xZMhNAHz55Zf88MMPOI7DmjWrady4Cddccx2NGzcuts3y5csYOvRWnnvuReLj4wkGgwwceBl33XUPubm5vPrqKwQCfjZs2EC3bt25+uprSuyz+N2w6Pc5OTm8+OLzLFmymEAgSLdu3Tj//AvJz89j2LA7SsXfu/eBnHrqaVg7G5/Px48//khKSjJnnXUuXbt2pVGjDC6/fGCk99Quu3Ri/PjxAMTFxZOfn09ubi5xcW7T5JVXXuK88y4o1TBs2bIlqampzJjxM7167VfmuaxN3jr5hliHICJS73mH3VlqmXPAAThHHgUFBXjvH156/aGH4hx6GGRm4h3zYOn1AwbgHNgH1q6FqF6ulTVp0ucceOBBtGjRkk8/ncAVV1xZZrnmzZtz5pln8dFHH9K79wG88MJz/PPP38XKxMfH88ADDzJ9+nT69euPz+cjLS2NPn368P3335VK/Gyp3HPPjePQQw/j/fffLTOmV155mb333od99tkXgF699mO33boAbuKqbdu2rFmzptR2c+bMITNzE506dQZg2LDb6dBhF2bP/pfMzEwOOaQvZ555Vqnt3njjNQoLC7jooksB+O23X3nnnbd44IEHef/9d5kx42cKCgopKMjnggsuYr/9ik1PzMCBlzFkyC106tSp1PvZs2fz+uuvkJ9fgNfr5fTTz6BHj558++03fPLJR6Viufba62nXrj2ffvop5557Hh988F6Z5ygrK4tx457m1ltvJzU1lYKCAkIhh8LCQvLycklLS2PDhvXMmPEz995b+t/fEUf045ZbhtCvX3/i47f9JpISP7Ld5BQU8uo//grL6CknIvLqqy/z0Ucfctlll9OiRQsKC927TWlpaZx33gUMH34PGRmN2G233dhjjz3p0+cg4uPj6d69e7E7ZNE++OD9YkOPmjRpSm5uLnl5eSWGca0tt1zjxo25+ebSSa6VK1eQkdGITz75iN9++41AwM/xx59I69Y7sXz5chITE3n44TEsX76Mpk2bceGFF5eqY8mSxRQWFhTr7fPPP3/z4IMP0br1Trz22qu88MJz3HTTLcW2a916J9q0acsvv8ygd+8DmDXrd5o3b8HOO7fh4Ycf4swzz2KPPfYkLy+Pq666gvnz55Genl6pz+Hll19kl1124ZprriMYDPLEE2P55JOPOPHEk8s9zwDp6ekcdNDB7L9/b2bP/pcHHhjJww8/UuzY/H4/r7/+Ggcc4N65PO2003n66ScIBIJcccUVzJo1i5SUlFIN1SLdu+/F9OnTdojEj4iISEn//fcf1lpuuukWdtllF+6883bOPvuccsu3a9eBJUsWA3DJJZeVW66sdszixYurVO7LLycTDAbp169/mYmf//77j59/ns6TTz4TWRbdE2nBggX88MP3ZSYzpk79kX337VFs2fLly7j//pEUFORz661uMqZHj57Fyhx+eD9uu+1mzj33AuLj4/nmm6854oj+rF69mj/+cHsHJyYmMmXKD7z11v9KJX7Kk52dzZNPjuXOO++iefMWrF+/nltvvYl27drTt++hkWH7ZbnhhhsByk38fPjhB+yzz76RZFNiYiJHH30MN988hDZt2nDqqafzzDNPcfbZ55bZa71x48akpaUze/bsSC/ybaHEj0gNyH35BQBSLrwkxpGIbFnG48+Uu86TlFThem9GRoXrq9rbJz8/nyVLlnD77Xfy8MNj6Nq1K23abE4YHH/8CfTr15+///6Lf/75mw8//IAPP/yABx54kHnz5pXb48dxQkDpLsUlx01Xtly0YDDI6tWrSE5OYcSIB1ixYgV33DGUVq1aEQwG+eWXGdx33what27Np59O4MEHHyiVOFm2bBktW7Yqtqx7971o3XonAPr168eNNw4uc/+HH34E33zzNb17H8DXX38VmQ/nmmuu5bfffuX9999l2bJlFBYWkp+fX+nEzy+//MLcuXP56qsvAbfbN7jduyvq8ROdHOvSZXeMMcyaNYvDDjsccIfTjRnjDvc6++xzAWjTpk2ka7TXC7fccgu33jqUL7+czLRpU2nSpAmXXnp55I5XixYt+OmnHyt1HLHWZ9rHAEzZ//gYRyIiUn+F7r2v/JWJiRWvb9Cg4vVb2dtn3317kJ7egPT0BjRv3oLJk7+gT5+Dyizv8XhIDE8lUVGPH8dxiG7HOI5TZhumvHILFszniy8mct99I8qNfcKEjznqqKNJTU0ttW7mzJmMHfsIl1xyGR06dCy1ftmyZaXmKurffwBxcXHExaVxwAEH8PvvM0slflq2bEn79u355ZcZ7LlnN/7660+uumoQycnJXHvtdfzww/esWLGCuXMt+fn5VJa1s9mwYQOjRo2MLPN4PCxevIi///6rwh4/FSksLGTy5C8YPfqhYsuPOupojjrqaIDIZ9ipUyeefPJx1q9fzyGH9OXggw+JlG/RogXLly9T4kektir8dQagxI9IVSUkJHDrrUOJi4vj5JNPYcSIEYwePYb09HRmz/6X2bNnc+KJJ9GjR0969OjJOeecx/XXX8sff8yid+8Dyu2Jkpm5iblz50ber1u3jrS0NJKSkoqVa9q0WaXKRWvUyB1+VZTYaNWqFV26dGHu3Lk0atQYY3aLDB07/PAjePHF5ykoKIg04Iq4jbDNohtqoZDbIFu/fj33339vZPnttw/jgAMO5JVXXmLp0v/455+/ueaa6wC4887badeuHXvvvQ8HHNCHuXPnlNqHO4xq87LosfyhUIghQ26KzDmQk5MNeEhNTS33POfkZDNx4uecfPKpxYZoFd3JWrRoEQ88cD/77bc/559/YZl3uD7++CP69DmIhIQEPvnkYx555DE++OA9vvvu20hSy+fzVdtkhzWt/ZJ/ASV+RETElZ+fz3fffUt8fDwDB7q9d/Ly8vj888/Yf/+y5/CZN29upOdsRT1+mjZtyoYNm+ch3LBhPU2aNKl0uW+//Ybc3DyGDr0lvHwDjz32MOeffyE9e/YiGAwybdo0HnxwTKk6P/74I8aP/4Drr7+R7t27lxtjZdo7kyZN5IsvJgLu0PCrrhrEgAED+Oabb9i4cSO9eu1HcnIyCxbM54EHRnDsscez11570bVrV559tvQNyfLaO6FQiJ133pkHHtg8lG/9+vU0aNCAuLi4Cnv8VGTmzN9o375DuQ/zCAaDvPHG69x440189923NG/enIEDr2LIkMHst9/+kTZidbZ3doxWk4iI1Aterzcyx8tJJ51C27ZteOSRhwiFQjRo0ID333+Xf//9J1J+w4b15ObmbnFC5L322os5cyzLly8H3Imje/bstdXlorVo0YKOHTvyzTdfA7Bx40asnU2nTp3Yb7/9sHY2q1atAmDatKm0adO2VNJnp512YuXKlcWW/fnnn6xbty4cx0R69OhJ48aNeeihRyM/jRs3JiEhgQMP7MMTT4xl//17k5iYSE5ONvPnz+O88y5g//17s27dWlauXEkoFCq2jwYNGjB//nwcxyEvL49ffvml2Ln45JOPcRwHv9/PyJEj+Pzzzyo8F0lJyUyc+DnTpk0F3O7ec+fOZe+992HdurXcffednHbaGVx00SVlJn02bFjPtGnTOPLIo8INQwePx4PH46GwcPMTMlavXsVOO+1UYSwiIiK10ffff0d6ejrPPfcizzzzHM888xxPPfUM+fn5TJ1aujfr8uXLeO+9dzj++BO2WHevXr346qsvCQaD5ORkM2XKlDKHRZdX7uKLL+WJJ56KtDMaNWrEddfdEGkLLVmymLS0VJo3L96j+/PPP2PixM8YOXJUhUmfsto733//HaFQiOzsbH766Ud69OjJgAFHRmK46qpBgDucbMGC+Xz55ReRG0H//PM3u+zSieOPP4Hdd+/Kzz9PL9XWAWjQoCHz5s0D4K+//mTDhg0A7LqrYcWKFfz9t9v7ZuHCBQwadCXr16/b4rmuyN9//0W3bt3KXT9p0uf07Om26wIBfyTBEwyGCAaDkXLV2d5Rjx8REamVPB4PgwffwHXXXcubb77BOeecx80338Ybb7zOunXrSEiIJyUllUGDrtnil2LDhhlcffU1jBkzmkAgQMuWLSM9Y+bNm8fTTz/BQw89WmG5itx8820899w4Jk2aiOM4nHbaGZGJCy+77ApGjx5JIBAkLS0tMoFztLZt25GQkFDsqR5NmjRh7NhH2bBhAzvvvDNXXnlVufs/4oj+fP75Z5Gnj6WmpnHSSadw0003kJiYRJMmTTBmN1auXFns7tNBBx3Cb7/9xqBBV9K4cRO6du1K0Y24Sy65jBdffJ7Bg68jGAzQrVt3TjzxpArPg8/n45ZbhvLCC8/x9ttv4fN5ueGGITRo0IBnnnmKgoICPvtsAp99NgHY3DW9yKuvvsJ5552Hz+cjJSWFXr324+qrB9K4cRNuvXVopNzMmTOr7SkXIiIi29MXX0zkuONOKHYDJDU1LfKEp8LCQm688XoAPB4vCQnxnHPOeaXmxinLgAFHsXLlSm644XoCgQD9+w+ga9c9AHjzzf8BcNZZZ1dYriIrVqygWbPmxZa58/a9SkpKCg8++EBkedEQ8Gi9ex/Aiy8+X2wC58LCQm699Sby8vI48sij6Nat7MRRfHw8BxzQhz/+mBWZA7BPn4OZNm0q1147CMcJse++PcjOziYvL6/Ytueeez7PPvsMkydPomPHXdhlF/ehGw0bNmTIkFt47bWXKSz04zghrr32+lKJrapasWIFu+zSqcx1mZmZfP/999x33/2RYxgxYjhfffUlffseGpl7cuPGjWzatCkyafa28pTsalXT/vrr32rfYUJCXGTyz5rSosXONVr/1sjISGHjxtxYh1FpM62txOTO8extzBbreXNuCL8/WG6ZytRTk4qeklTR3Cdba0f73KtLfT1uqPvHvmrV0nLXbY//32uLH374jn///ZfLLx/I999/y5QpUxg6tPRcOnXdlj7zlStX8Oijj1T5ce577NFluz039tspU505G0J8v8LLue+4cxe9fnrxiblj8T1V0bUmxdWn/3vqG322dZs+39rv3nvv5qyzzqZz510ZNux2jjrqmC0+ph7q32f79ttv0qBBw8icQJVVXntHQ71EaoC3YUO8DRvGOgwR2YEcdNAhZGdns3jxoliHUqv973//46qrrq5S0ieW8pLSyEtKi3UYIiIitcLAgVfy7rvvlJrrRzZbu3YNCxYsoH//AdVWp4Z6idSABsNHxToEEdkB3XDDEAA6d+7EwQf3jW0wtVTR41N3FO8ff3WsQxAREak1mjdvEenRfO+998c4mtqpadNm3Hbb7dVap3r8iIiIiIiIiIjUUUr8iNSAnGeeJOeZJ2MdhoiIxFjfH96j7w/vxToMERERqcc01EukBvj//jPWIYiISCUYY/YDRllr+xpj9gE+AeaGVz9trX07qqwXeAroDhQAl1pr51VU/84r5tdM4CIiIiKVVKnET4lG0V7A40AQt9FzvrV2lTHmMuAKIAAMt9ZOqKGYRURERLaZMeZm4DwgJ7xoH+Bha+1D5WxyIpBkre1tjNkfeAg4ocYDFREREdkGW0z8lNEoegy4xlr7uzHmCuAWY8xo4FqgB5AETDHGTLbWFtRQ3CIiUk1q+hHPLVrsXKP1i2yD+cDJwGvh9/sCxhhzAm6vn+uttVlR5fsAEwGstdOMMT3KqtTj8eD1eomP90WePhYf7ytWxufzkJGRUp3HskUbNqijd2V5PB4SEnS+6iJ9tnVbeZ9vVlZmje43Pb1BjdYvuna3VWXOXMlG0ZnW2hVR2+cDvYAfw4meAmPMPKAbMKNkZdGNoOqyPf4RbO/GWWX4fN5aGVd5fD5PqYZvWWW2dEw+nwePp+K6YtGgjpYd506fVRMx7Gife3Wpr8cNNX/sNf3H4JZij95/MBjkk08+5rvvviMYDBIIBOjVqxfnnHMu8fHxfPnllzz33LO0aNECAMdxyM3No2vX3Rk06BoSEhIAWLBgAR9//DHXX389P/88neeff574+Hiuv34wnTt3BmDs2LEcfPDB7LXXXpF9Dx8+nOuuu46MjIxScT7yyCP8/vtMevXqxdVXD2Ly5C8YP348gUCAvfbai8svv4K4uNLn8ptvvuGDD97H4/GQmJjI5ZdfEYkh2gsvPM+UKT+Snu4++nunnXbmlltuYerUqSxevIgzzzyr3HP42Wef4fV6OfLII/nhhx9488038fm8NG3alCuvvIrmzZszbtw4/v77r8g269ato3Hjxjz++BOl6rv++usoLCyMHE/fvn05+eRTyty33+/n3nvv4cgjj+TAA/sAkJ+fz+OPP86CBfNxHIcLLriQ3r17A2Ct5ZlnniE/P4/GjRtz441DaNiwIYMHX8+KFSu4/vrBHHjggeUea3Wy1r5vjGkftehn4Hlr7a/GmNuBu4AhUesbAJui3geNMXHW2kB0vY7jEAqF8PuJPK7W7w8W23cw6GXjxtzqO5hKKCwMbLmQAJCQEKfzVUfps63byvt8Q6GafXR4Vf5NBYNBPv10AlOmfB9p6/To0ZMzzzyb+Ph4vv76K1566QWaN28OgONAXl4uXbrszsCBV0XaOgsXLuDTTycwaNC1zJjxMy+//BLx8fEMGnQtnTp1AuCpp57gwAMPonv37pF9jxo1kquvHkTDhhmlYnv88cf4449Z9OjRkyuuuJKvvvqSjz/+kEAgSLdu3bnkkkvLbOt89923fPTRh3g8kJiYyMUXXxaJIdrLL7/I1Kk/kZbmtnVat96JG2+8ienTp7FkyWJOO+2Mcs/bl19+QSjk0L//AH78cQrvvPM2Pp+PJk2acNllV9C8eXNeeOE5/vnn78g269evJyOjEY888lip+m666YZibZ2DDjqEE088qcx9+/1+RowYTv/+A+jd+wAACgoKeOqpJ1i4cCGOE+Lcc89nv/32B2DOnDk8//yz5Ofn07hxI667bjANGjTk5ptvZOXKlQwadG2knu1li639ko2ioqSPMeYAYBBwMDCA4g2hLKBhWfWVbPhUh+3xH/j2bpxVRkZGSq2MqzzBoLPFz78yDeFg0MFxKq4rFg3qaE6jpkDN/LvZ0T736lJfjxtq/thj/f9n9P6ffvpJcnJyGDbsHlJTUwmFAowePZpHH32U664bTCAQpEuX3SOPAXW3L+SOO27jiy8m07//AEKhEI899ii33no7hYUBXn/9De67737WrFnDO++8w0033cLcuXPIzs5m9933KLb/448/kSeffJKbbrqlVJyhUIhjjz2eE044kXnz5vPGG2/w4IMPk56ezqOPPsIHH3zAiSeeXGybZcuW8eKLLzJmzEM0atSYX3/9hREj7mfcuOdL1f/PP/8yePCN7LbbbpHvtcLCAPvu25MJEyaw995z6NChY6ntVq9ezZdfTmbkyNEsWrSEJ598gvvuu5927drz999/M2LECEaPHsNFF10Stc0q7rhjKIMGXVvq88/Pz2fFihW89NKrxRp3Zf07sXY2zz33LMuWLeWII/pHyrz++uskJCTw2GNPsGbNGoYOvYX27TvQoEFDRo4cwQ03DGG33bowceLnPProo9xxxzDGjHmEYcNuB5xY/lE23lq7seg17tD2aJlAetR7b8mkT0mZaY2qLzoREdnhPfvsM+Tk5HDXXfeSmppKfn4+jz76ME899QTXXTcYoNy2zrfffhNp6zz11BPceqv7yO+3336L4cPdts748e9H2jq5ubmRpA+Az+fjhBNO4tlnx5XZ1gEibZ0lSxbz9ttvFmvrTJjwcZltnVdffaVYW+fBBx8os61jrWXw4CHstttuxZbvt9/+TJz4OQsXLii3rfPVV18yYsQoli9fzrhxTxdr64wZM5rRo8dwySWXRW3jtnWuvfa6UvXl5+ezcuXKUm2dskS3dfr3HxBZ/vbbb5KUlMTYsZvbOp06daJBg4aMGTOqWFvnySef4I47hvHQQ4+G2zrb31Y91csYcwbwDHCMtXYNpRtC6cDGbY5OZAeVPuxe0ofdG5N9z1m0kJnWVvgzZ9HCmMQmUpHVq1fxww/fc9VVg0hNTQUgKSmJK664kl699it3u6ysLHJyciN3j3766UeaN29BkyZNAIiPjycvL5+cnFzi4uJwHIfXXnuFCy64qFRdXbt2ZenS/1i4cEGFsf7888/07NmLhg0b4vV66d+/P999912pcvHxcVx11dU0atQYgE6dOrFx40b8fn+xcn6/n4ULF/Dhhx8wePC1jBgxgjVr1kTWH374EbzzztuUZfz49zn44L54PB4WL15E+/btadeufeR41qxZzerVq4pt8/TTT3HccSeU2biaO3cuSUnJDB9+D4MHX8tLL71AQUHZI7c//fRTzj33vFI9mKZPn06/fv0BaNasGd26defHH39k/vx5JCensNtuXSLH9eeff9R4F/wqmGSM6RV+fTjwa4n1PwJHA4Tn+NniTP4fH305Hx99ebUGKSIiOya1dTa3dUaPfqBKbZ1DDz1UbZ1tUOX+/caYc3Ence5rrV0fXvwzcL8xJglIBLoAf5VThYjUoJyCQl79x19hmfN3307BiFTB/PnzadOmLSkpxYeGNWrUqFh32H///Ycbb7yewkI/2dlZtGrVmhNOOJEDDnCHB02d+hP77rt56pXzzjufRx4ZQ0JCIldeeTVffjmZPffsRrNmzcqMo1u37kyfPr3MhkKRdevW0qxZ88j7Jk2asn79ulLlmjdvQfPmm4elvfzyS/To0ZP4+Phi5davX88ee+zJ2WefQ5s2bfn0048ZNWoEDz74MB6Ph+7d9+KJJ8ZSUFBAYmJiZDvHcZg2bSonneTefevQoQNLliyJ3DGbMeNnsrKy2LBhQySO3377lbVr13D00ceUeWz5+XnsscceXHzxpSQmJvLoow/zxhuvcfHFl5Yqe8MNNwLwwQfFH1e+bt1amjRpWuz8rFu3jrVr19K06ebl8fHxNGjQkHXr1teW+RGuBJ4wxhQCK4HLAYwxrwJ34PYC6meM+QnwAKVb1CIiIuVQW2dzW+ejjz6sUlvn9NNPB9TW2VpVSvwYY3zAWGAJ8IExBuA7a+1dxpixwA+4vYhut9bmV3ewIjuK7LEPA5B27Q0xjkRkx+H1enGc0BbLFXV/DoVCvPfeu/zww/fFGkvLli3j2GOPi7zfffeuPPige01mZ2fz1VeTuffe+3n//Xf5999/adeuHeedd0GkfPPmLZg7d06FMYRCoWLz1TmOg9dbfifa/Px8nnhiLGvXruXOO4eVWt+iRQvuuGPz8pNOOpm33nqL1atX06JFC9LT04mPj2fNmjXsvPPmybLdO4A5kYZOy5atuPrqaxg37hkCAT89e/aiffv2xboxT5jwMSeddAo+X9nzpPXs2YuePXtF3p9yyqmMHj2qzMZQeRzHKfP8OE6ojHn+Kj53Nc1auwjYP/z6N6DUoHtr7flRbwdWpf5+3/wPgMmHnr3VMYqISN2gts7m5SeccCLvvfdOpds6LVq0oLAwoLbOVqpU4ie6UQQ0LqfMc8Bz1ROWSO0wZ9FCcgoKKyyTmpjAru07FFsW2MJ/pNuyr157da1y3SI7gs6dd2Xp0qXk5eWRnJwcWb5u3TqeeeYphgy5uVh5r9fL6aefgbWzeeKJsdx2mztm2uMpfxLHN998g1NOOY21a9fw559/cPfd9/HUU0/wxx+z6NbNHQPv8/m2+OXcrFkz1q9fH3m/YcP6SHfrktasWcPIkfez8847c8899xW7i1Vk0aJFLFq0kL59D40scxyHuLjNDZay4vJ4Nk8k7PV68fv9tGzZigceGA243aonTPgkkhjatGkTc+fO5eabbyv32GbM+JmUlFS6du0ajoNicVRG06ZNwxMqZgDu+WnfvgNNmxY/b4FAgKysLJo0KbNpUSe0WPNfrEMQEZFaQm2dbWvrAGrrbKXYpp1EarmiYVMV/WwpWVMb9yVSGzVu3JiDDjqYJ598nNxcd0Lo3NxcnntuHOnp6WU2IgAuu+xyZs36nZ9/ng64T4hYtWplqXKLFi1kzZo19OzZC7/fj9frfsF7PJ5i47pXr17FTjvtVGGsPXr04pdffmbTpo04jsPkyV+UOTY/Ly+PYcPuYP/99+eGG4aUewxer4cXX3yeVavc8emfffYZ7dq1j3QhzsnJwe/3l+qynZ7egLS0tMgYeb/fz+2338rate77CRM+oUuX3UlPd6fhmz37X3bZpRNJSUnlHtu6det49dWXKSgoCD9l7SMOOKBPheejpJ49ezF58hfh+tYyc+ZMevToQefOu5KVlcXs2bMB+Prrr9h1V0NqalqV6hcREdkRqa2zua0zadLnVWrrrF69GlBbZ2vV7DN8RUSk1mvRYuctF9pOLr98IO+++w5Dh96Kz+clEAjQs+d+nHHGmeVu07JlK0488WReeulF9tprb3r3PoDp06dx2GGHFyv38ssvcfnlVwDQrl17MjIyuPrqgbRp05a9994nUm7WrN+58cabKoyzffv2nHbaGdx11zCCwQCdO+8aecrFjBk/M2nSRO64Yxiff/4pa9euYfr0aUyfPi2y/d1338uqVat5+ukneOihR2nbth2XXHIZI0feTygUolmzpgwefGOxmPbdtwfx8fHMmzcvsh3A/vv3ZubM3zjyyKNISUnhyiuvZvjw+wiFQuy8884MGnRtpJ4VK1ZEHg8bbdKkicyfP4+rrhpE//4DWLVqFTfddCPBYJA99tiD008/o1S5ipxxxlk8++wzXHfdNYRCIc4//wJatmwFwM0338Lzzz9Hfn4+6enpZT5tQ0REpDolJ6fGOoSIkm0dvz9Ar171r63TpEmTKrV1fvvtN444or/aOlvJ4zhldxGrKX/99W+173B7PM69Nv1hVGRHe7z1TGsrMelwPHu7c0dVWM+bc0MVPs69MvVUxtbGvPEadwqIjMefqfZ9Hbrf3hV+7tV1nmubHe3fe3Wq68e+atXSctdtzf/vwWCQm2++kaFD7yy3S3J5/vrrTyZO/LxUV2uAxx9/jLZt23HCCSducf+PPvrwFhtUFSl53HfddScXXXQJ7du3B2D06Ae4+eZbAVi1ahVjxoxi9OiHyhhTXr3y8vJ4/vlnueaammnADBt2O8cddxw9e+6/5cJVtMceXWr25ET5dspUZ86GEN+v8HLuO6MAeP304o/NjcX/wxVda1Lc9mhbSmzos63b6svnWxfaOiVtqa3z0EOjGTVqTJ1o6xx11DHF5myqTuW1dzTUS6QG+Nq0xdembazDEKmXfD4fV155Nf/73+tV2i4YDPLhh+O55JLyJ/abMOFjxo17usJ6li79j+OOO75K+67I9OnT6NJl90hDaN26dRx++BGR9S1atKBv38P44otJ1bbP8ixcuICTTjql2usNBoPceOP1zJ8/v9rrjrX1jVqwvlGLWIchIiJ1SH1s6xx22OFq62wD9fipJPX42Xb1qcdPTe5LPX7qn7p+7NXd46cuqK/HDTV37LHq8VMe9fip3erzNVjX6bOt2/T51l36bCunvPaO5viRalGZJ1K5E4rVnk5mO2LMIiIiIiIiIlWhxI9Ui6InUlXktI7bt3fZltRkzFmjRwCQfvPQrdpeRETqhqMnvwzAZ/0ujGkcIiIiUn8p8SNSA4L/LYl1CCIiUgs03rAq1iGIiIhIPacxLCIiIiIiIiIidZQSPyIiIiIiIiIidZQSPyIiIiIiIiIidZTm+BGpAXGdd411CCIiUgusatYm1iGIiIhIPafEj0gNSLv2hliHICIitcDkQ8+OdQgiIiJSz2mol4iIiIiIiIhIHaUePyI1IOveYQCkD7s3xpGIiEhFjDH7AaOstX2NMXsBjwNBoAA431q7qkT5mcCm8NuF1tqLKqr/+M+eBeDjoy+v5shFREREKqdSiZ8SjaJOwMuAA/wFXG2tDRljLgOuAALw//buPVqyqj7w+Pfc6rqNtA0tk/jKGBsM/IKsKIJRo4g9xEfwEchDQlzYE03UjOSpExkIJitrmZWVFz4SHUYMQ8ARMiLMKEo0MUAEXwnTTMLDXzcqY2JIQrQbmgZuV9et+aPq4u3LrbrVdavqVJ36fta6a1XV2fec3659du1du84+m3dl5nUjilmaeM37/rXsECRJa4iIdwCvB/Z1Xnov8IuZeVtEvAU4D3jbsvSHAWTmtn6PccSDu4cWryRJ0iDWHPhZpVN0EXBhZt4YERcDZ0TEF4BfAp4LHAbcHBF/kZkLI4pbkiRpvb4K/DhwRef52Zl5b+fxBuCRFemfDRweEZ/pbL8gM7+4cqdFUTA3N0e9XqMoCgDq9dpBaWq1gi1bDh9aRvqxe7cXeverKArm532/qsiyrTbLt7os2/Xp551b2Sk6Gbip8/h64OW0L4m+pTPQsxARdwPPAv5m5c6Wd4KGZRwnwbg7Z/2o1eYmJq5arXhMp/YxiuaaafrpCNdqBUXR+3j97mdUMT+4oX37rEMpn37iqdWKNcu93/1MyrnTr0k638et6nnv9WV0Vhv5Wc03jDfvmfmxiNi67Pm9ABHxQuAXgFNX/MtDwB8AHwKOBa6PiMjMA8sTtVotFhcXaTTajwEajeZBO2o259iz56HhZmgN+/cfWDuRAJif3+D7VVGWbbVZvtVl2a7Pmj2rlZ0ioMjMVufxXuBI4Ai+M999+euPsbLjMwzjOAnG3Tnrx5Yth09MXM1ma+2yba1d/v10hJvNFq1W7+P1u59hxPzIIw1u+NKOg1777gceBGBX5/VNG+c5buvR646n2Zyj2Vzsmbd+9zMp506/Jul8H7eq573X5/esNvKzmm8oP+8R8VPArwOvysz7VmzeCdzd6QftjIhvAU8B/mHMYUqSJPVtkJ/UFpc93gzsAR7oPF75ulR5B5otrtzVOOi1bUceA8CNd7Zf3/7MsYclSTpEEXEO7fsVbsvMb6+S5I3ADwBvjYin0v7h695V0j3qH5/yjKHHKUmSdCgGGfjZERHbMvNG4HTgBuDLwG93bnq4ETie9o2fpZl044t/suwQJEmHICJqwPuAbwDXRATATZn5mxFxOXAh8CfAZRFxM+1FLt64cprXSrYHkiSpbIMM/LwduCQi5oG7gKszsxkR7wM+B8wBv56ZK2+IKI1No7HAjsyeaRYWFmifrpKkWZWZ9wAv6Dw9qkua7cuevm7UMUmSJA1TXwM/yztFmbkTeMkqaS4BLhlmcNKgVpt+tdJrj2n13L4eP/Hx9wPwsR89d2THkCRNPtsDSZJUttlcMkQascc98mDZIUiSJoDtgSRJKpsDP5oo/U/Rqo8nIEmSJEmSppgDP5ooZU/RkiRJkiSpSryzrSRJkiRJUkV5xY80Avd87/EHPXeVMUmaTSvbA0mSpHFz4EcagZtf8KMHPXcKmyTNppXtgSRJ0rh5eYEkSZIkSVJFOfAjjcDZ11zE2ddcVHYYkqSS2R5IkqSyOdVLGoENB3pP65IkzQbbA0mSVDav+JEkSZIkSaooB34kSZIkSZIqyqleU2jnPV9n38J+arWCZnP1laBazQZFrd5zP5s2znPc1qP7OtZaXIq8evot+37OI0mSJElSORz4mUL7FvZz+Z0N6vUajUZz1TSvPabJR7P3frY/s/9jrcWlyA+265hnlx3CuvVb9v2cR5I0q6rQHkiSpOnmwI80Al967o+UHYIkaQLYHkiSpLINNPATEXXgT4GtQBN4E3AAuAxoAbcD52bm4lCi1Eg0GgvsyN6XBTmFa7I0Ggv89d/e1nWKH1hmknQoIuL5wO9m5raI+D569GUiYg74APBsYAH4ucy8e/xRS5Ik9W/QK35eCWzIzBdGxMuA3wbqwIWZeWNEXAycAVw7pDg1AgeaLa7c1Xsqj1O4BnPO//xdAD581nlD3e+BZosPf6XZdYofWGaS1K+IeAfwemBf56WL6N2XORM4LDN/KCJeAPxhJ01Xo2oPJEmS+jXowM9OYEPnl68jgAbwAuCmzvbrgZfjwI8kSZpcXwV+HLii8/xkevdlTgH+HCAzvxgRz11tp0VRMDc3R71eoygKAOr12kFparWCLVsOH1I2+rN7tzP8+1UUBfPzvl9VZNlWm+VbXZbt+gz6zj1Ie5rXV4DvAl4NnJqZS5ca7AWOXO0fl3eChmUcJ8G4O2e91GrFo+/jyo7ko4pm923DTjPu4xXN3nkvIZ6VaR7T0Z+wvPfzhWPpPFvLsL687LjjK+x9+JGu2wsKHv+4jTznhO9f97GmTa02N1GfQcPW68vorDbys5pvGG/eM/NjEbF1+eHX6MscAdy/7HkzIjZk5oHliVqtFouLizQa7cfAY67UbDbn2LPnoSHkon/79x9YO5EAmJ/f4PtVUZZttVm+1WXZrs+gPatfBT6dmedHxNOAvwLml23fDOxZ7R97TVEZ1DhOgnF3znppNls0Gs2eq3rR6uO9HlaacR+v1e5I90w35nhWpnlMR3/C8t7PF46l82wtw/rysufBh3uuIlav1/jpYxcnqi6Oy5Yth1c6370+v2e1kZ/VfEPpeV9+b8LV+jIPdF5fMrdy0EeSJGnSDHoH2N185xevb9O+v8+OiNjWee104HPrC02SJGms1urL3EL7Pod07vHz9+MLTZIkaTCDXvHzbuDSiPgc7St9LgD+FrgkIuaBu4CrhxOiNH3uOu4Hyw5BknTo3s4qfZmIuBy4kPb9fl4WEZ8HCuANa+3Q9kCSJJVtoIGfzHwQOGuVTS9ZXzhSNdx64mllhyBJ6kNm3kN7gQoycyer9GUyc/uypz9/KPu3PZAkSWUbdKqXpB42NBbY0FgoOwxJUslsDyRJUtkc+JFG4Oxr38PZ176n7DAkSSWzPZAkSWVz4EeSJEmSJKmiHPiRJEmSJEmqqEFX9ZIkABqNBXZk9kyzaeM8x209ekwRSZIkSZKWOPAjaV0ONFtcuavRM832Z44pGEmSJEnSQRz4kUbg7054UdkhSJImgO2BJEkqmwM/0gj83QmnlB2CJGkC2B5IkqSyOfAjjcDjHt4LwMOP21xyJKvr5748CwsLeP93SVqfSW8PJElS9TnwI43AT3ziAwB8+KzzSo5kdf3cl+e1x7TGFI0kVdektweSJKn6/DlfkiRJkiSpohz4kSRJkiRJqigHfiRJkiRJkirKgR9JkiRJkqSK8ubO0gjc+uz/UHYIE8VVxCTNKtsDSZJUtoEHfiLifOBHgXngA8BNwGVAC7gdODczF4cQozR17ornlR3CRHEVMUnTIiJ+BviZztPDgBOBJ2fmns72twE/C9zXSfOWzO4j27YHkiSpbAMN/ETENuCFwIuAw4H/DFwEXJiZN0bExcAZwLVDilOaKpv3fhuAvZuPKjkSSdKhyMzLaP+QRUS8H7h0adCn4yRge2be2s/+bA8kSVLZBr3i5xXA39Me2DkC+DXgTbSv+gG4Hng5DvxoRp1x/SUAfPis80qOpFr6mTK2aeM8x209ekwRSaqqiHgucEJmnrti08nA+RHxZOCTmfk7K/+3KArm5uao12uc+ecfAuCq151/UJparWDLlsNHE3wXu3c7w79fRVEwP+/7VUWWbbVZvtVl2a7PoO/cdwFPB14NHA18HJjLzKW5GnuBI1f7x3q9RlEUAx52deM4CcbdOeulVisefR/r9drqiYpm923DTjPu4xXN3nkvIZ6VaZbO8Udfr2rex3i8oig4sNji6l29Z5Ce8/2Niaqvw1CrzVUuT8v1+jI6q438rOYbJirvFwC/tcrrVwHvBx4Aro2IV2fmdcsTtFotFhcXaTTajwEajeZBO2k259iz56GRBN7N/v0Hxnq8aTY/v8H3q6Is22qzfKvLsl2fQXtW3wK+kpn7gYyIR4CnLdu+Gdiz2j+u7PgMwzhOgnF3znppNls0Gu0vyV3fz1Yf7/Ww0oz7eK12R7pnujHHszLNYzr6Vc37GI9Xr9f62k8ZX6ZGbcuWwyuXp+V6fX7PaiM/q/mGych7RGwBvj8zb1jxegG8JzPv7zz/JPAc4LrH7ESSJGlCDDrwczPwyxFxEfAUYBPw2YjYlpk3AqcDN/T4f3Wx856vs29hf880rn4kded0MElDcCrwl6u8fgRwe0QcD+wDTgMuHWdgkiRJh2qggZ/MvC4iTgW+THsE4lzg68AlETEP3AVcPbQoZ8i+hf1cfqerH0mD6mcFse3PHFMwkqZVAF979EnE64DHZ+YHI+IC2j9uLQCfzcxPlRSjJElSXwaeRJ+Z71jl5ZesIxapMr508ivKDkGSNKDM/P0Vzz+y7PEVwBX97sv2QJIklW0i7p4oVc2uZ5xYdgiSpAnQrT1wWqokSRoXB36kETjq2/cC8O2jnlJyJJKkMnVrD5yWKkmSxsU7BEsj8Mq/vJxX/uXlZYchSSqZ7YEkSSqbV/xI0gj1s1Kf0zkkSZIkjYoDP5I0Qv2s1Od0DkmSJEmj4lQvSZIkSZKkinLgR5IkSZIkqaKc6iWNwM3Pf3XZIUiSJoDtgSRJKpsDP9II3PP0E8oOQZI0AWwPJElS2ZzqJY3Ak/71GzzpX79RdhiSpJLZHkiSpLJ5xY80Ai+78UoAPnzWeSVHotU0GgvsyOyZxiXWJQ2D7YEkSSqbAz+SZs6BZosrd7nEuiRJkqTqc6qXJEmSJElSRXnFz5DsvOfr7FvY3zONU0ekaumn3i8sLOAYuyRJkqSyOPAzJPsW9nP5nU4dkWZJP/X+tce0xhSNJEmSJD3WugZ+IuKJwK3Ay4ADwGVAC7gdODczF9cboDSNbjjlJ8oOQZI0oIjYAdzfefr1zHzDsm2vAX6Ddr/n0sy8pNe+bA8kSVLZBh74iYg68N+AhzsvXQRcmJk3RsTFwBnAtesPUZo+33zq95UdgiRpABFxGEBmbltlWx14N/CDwD7gloj4RGb+c7f92R5IkqSyreeKnz8ALgbO7zw/Gbip8/h64OU48KMZ9T3/dDdgh1+SptCzgcMj4jO0+0kXZOYXO9uOB+7OzN0AEXEz8GLgo8t3UBQFc3Nz1Os1nvqPuwD4p39/7MFHKZrU67WegdRqBVu2HL7uDC3ZvdsZ/v0qioL5ed+vKrJsq83yrS7Ldn0Geuci4meA+zLz0xGxNPBTZObSzSz2Akeu9r/1eo2iKAY5bFfjOAnW6njVasVQOnD97Geps1gUPdL20aEcWppxH69o9s57CfGsTHPaLdcAcNXrzh/6sSYq72M8XlEUUDC2vA27vq7nWLXa3FC//E2aXl9GZ7WRn9V8w0Tk/SHaP259CDgWuD4iIjMPAEfwnSlg0KW/02q1WFxcpNGAF9/UHhP68FnnrUgEjUazZyDN5hx79jw0eE5W2L//wND2VXXz8xt8vyrKsq02y7e6LNv1GbRn9UagFREvBU4ELgeeuGz7ZmDPav+4VidnEOM4CdbqeDWbraF04PrZz1JnsV6vdU/bR4dyaGnGfbxWu1PdM92Y41mZptVqj4E++npV8z7G49XrtbHmbdj1dT3H2rLlcL582x2VXTmw1+f3rDbys5pvmIi876R9VU8L2BkR3wKeAvwD8ADtPs6Srv0dSZKkSTHQwE9mnrr0OCJuBH4e+P2I2JaZNwKnAzcMI0BJkisHSmP0RuAHgLdGxFNpX+Vzb2fbXcCxEXEU8CBwKu2rgyRJkibW3BD39XbgtyLiC8A8cPUQ9y1JkjQOfwJs6dy/589oDwSdFRFvzswG8Dbg08AXaK/q9c3yQpUkSVrbuifRr1j14iXr3Z8kSVJZMnM/8LoVL39+2fZPAJ8Ya1CSJEnrMJt3jpRG7C+2/XTZIUiSJoDtgSRJKpsDP9II/MsTv7fsECRJE8D2QJIklc2BnzFqNBbYkdkzzcLCAsO99ZLKsPX/3QHAPU8/oeRINCjrq6RhsD2QJEllc+BnjA40W1y5q/eqPK89pjWmaDRKp3zpOsCO/jSzvkoaBtsDSZJUNn+qliRJkiRJqigHfiRJkiRJkirKqV59uH/vXv5pj/f6kCRJkiRJ08WBnz40mk0uv7PZM433+pAkSZIkSZPGgR9pBD710u1lhyBJmgC2B5IkqWwzP/Bz/969NJq9r+ZpNhdxGpcOxbePekrZIUiSJoDtgSRJKtvMD/w0mk127l7smeaYI5zGpUNz7FdvA2DXM04sNQ5JUrlsDyRJUtlmfuBHGoXn3/ppwI6+JM062wNJklQ25y9JkiRJkiRVlAM/kiRJkiRJFeXAjyRJkiRJUkUNdI+fiKgDlwJbgY3Au4A7gcuAFnA7cG5m9r5rsiSJRmOBHZldt9dqBQsLC6w1Vr/WfgA2bZznuK1HDxKmNBNW6+Nk5seXbX8b8LPAfZ2X3pK5RsWTJEkq0aA3dz4H+FZmvj4i/h2wA7gNuDAzb4yIi4EzgGuHE6Y0Xf736W8qOwRNkQPNFlfuanTdXq/XOPNpa68uuNZ+ALY/85DDk2bNan2cjy/bfhKwPTNv7WdntgeSJKlsgw78fBS4etnzA8DJwE2d59cDL2eVgZ96vUZRFAMednVFUTA/P1hWiqJgbm6tGW9N6vXaGjsaf5qiKLqnHWc84z5e0eyd9xLiWZnmkaO+G4D6CI41UXkf4/GKooCCiayLo04zzLzXagVbthzeez9jtnt398/v9Xy+T7NZzTdMRN5X6+MsdzJwfkQ8GfhkZv7Oyh0s9S3q9dpj24NHE42/vvaqazrYBJyHGhHLttos3+qybNdnoHcuMx8EiIjNtDtHFwJ/kJlLP0nvBY5c7X8bjeYgh+xpfn4D+/ev7Jf1p9Vqsbi49oy0NeNujT9NvV7rnnac8Yz7eK12ufVMV0J5LHd8fhmAu+J5Qz/WROV9jMer12ull2tZaYaZ90ceaXDDl3b03g/jnRLW6/N7PZ/v02xW8w3l571LH2e5q4D3Aw8A10bEqzPzuuUJlvoWjcYq7cGjidaur83mHHv2PDR4ZlaY1XNqEGWfhxody7baLN/qsmzXZ+Ahs4h4Gu0rej6QmR+JiN9btnkzsGedsUlT6+T/ewOwSkdfKlk/08HAKWGabSv7OMteL4D3ZOb9neefBJ4DXLfqjrA9kCRJ5RtoVa+IeBLwGeC8zLy08/KOiNjWeXw68Ln1hydJkjQ+Xfo4S44Abo+Ix3cGgU4D+rrXjyRJUlkGveLnAuAJwDsj4p2d134ZeF9EzAN3cfD8eEnSFHGFMM2w1fo4lwCbMvODEXEBcAOwAHw2Mz9VUpySJEl9GfQeP79Me6BnpZesLxxJ0iRwhTDNqh59nKXtVwBXjC8iSZKk9RloqpckSZIkSZImn+uhSSPwsde8tewQJEkTwPZAkiSVbSIHfu7fu5dGs/cSp/VajSM3bx5TRNKhefhxnpsSwM57vs6+hf0902yqLfp5rsqyPZAkSWWbyIGfRrPJzt2LPdMc94QxBSMN4Fl33AzA351wSsmRSOXat7Cfy+/sfa+gn/NeQaow2wNJklQ27/EjjcCz7riFZ91xS9lhSJJKZnsgSZLKNvYrfvqZxtVs9r7aR5JUvn6WfF9YWKCKvzE4JVmSJEnTYuwDP/1M4zrmiBZQjCcgSdJA+lny/bXHtMYUzXg5JVnj0M/g6qaN8xy39egxRSRJkqbRRN7jR5Ikadb1M7i63XtkSZKkNUztwM/i4iL/tmcPAEVR0Gqt8qtyaxGK3lMMnFYmSeVa/nm+0qOf7318nveTpp/pV+OcktztWMvbNaeMSZIkaT2md+Cn1eLuzmX2c3NzLC4+thN+zBEtvvaA08o0flf92K+UHYI0NZZ/nq+09Pne7+f5Wmn6mX41zinJ3Y61vF1zyth0sz2QJEllm9qBH2mSHahvLDsESdIEsD2QJEllc+BHGoGTb/srAG498bSSI5G0XK9pZUtmfQqwK5YNl+2BJEkqmwM/0ggcv/NvADv60qTpNa1syaxPAXbFsuGyPZAkSWVb406ZkiRJkiRJmlZe8SNJ0ggMa1pZP/uZ9alX/UxPq6pGY4EdmT3TbNo4z3Fbjx5TRJIkadIMdeAnIuaADwDPBhaAn8vMu4d5DEmSpsGwppX1s59Zn3rVz/S0bX3ua62+TES8BvgN4ABwaWZeMkjMw3Kg2eLKXY2eabY/c0zBSJKkiTTsK37OBA7LzB+KiBcAfwicMeRjSJIkjcqZdOnLREQdeDfwg8A+4JaI+ERm/nNZwfZj6aqgeXoPENFahGKNuwAMKc2sX6UmSdI4Fa1Wa2g7i4iLgC9n5lWd59/MzO8Z2gEkSZJGqFdfJiKeBfxeZv5I5/m7gc9n5kdLC1iSJGkNw7658xHA/cueNyPC+whJkqRp0asvs3LbXuDIcQUmSZI0iGEP/DwALL9udy4zDwz5GJIkSaPSqy+zcttmYM+Y4pIkSRrIsAd+bgFeCdCZF//3Q96/JEnSKPXqy9wFHBsRR0XEPHAq8IXxhyhJktS/Yd/jZ2kljGfRXqbkDZn5laEdQJIkaYRW68sAJwGPz8wPLlvVa472ql7vLy1YSZKkPgx14GdQ61k6NSKeD/xuZm7rPP8+4DKgBdwOnJuZixHxJuAtnX28KzOvG0PW1jTkvJ8I/BHQ7Oxre2b+S0S8D3gR7XsRAJyRmcvvUVCKIef9JOATwK5Okv+amX82ieU+5HxfBTy5s3kr8MXMPLtKZd5ZRedS2vnbSLscPz4Ldb1H3k+k4nW9R94rXdd75HsW6noNuAQI2uf2GzLzq9NW1zU9+jhP3wb8LHBf56W3ZGaOPVANbGW/adnrXftamh49yte6O8W69YWWbbf+DmDYU70GdSadpVOB/0J76VTgoKVTXw68BHhzRDy5s+0dwIeAw5bt6yLgwsx8Me1f6s7opP8l2p3jVwC/ExEbR52pPp3J8PL+XuAXOx9+1wDndV4/CXhFZm7r/JX+paDjTIaX95OAi5bl8c8muNzPZEj5zsyzO+X9Y7TvM/GrnU1VKvNzgG916vTpwB93/mUW6nq3vM9CXe+W96rX9VXzPSN1/TUAmfki2h26izr/Mm11XdPjTLqcpx0n0R5YX6pffnGcIl36iz37Wpoe3cq3w7o73br1Aa2/6zApAz+nAH8OkJlfBJ67bNvxwN2ZuTsz9wM3Ay/ubPsq8OMr9nUycFPn8fXAS4HnAbdk5kKnU3w37Uu4J8Ew8352Zt7WebwBeKTza9axwAcj4paIeONosjGQYZf7qyLiryPiTyJiM5Nb7sPM95LfAv4oM++tYJl/FHjnsnRLN1mdhbreLe+zUNd7lXuV63q3fC+pbF3PzP8FvLmT5unAv3QeT1td1/TodZ5C+9w7PyJujojzxx2c1q1bv6lXX0vTo1e/2Lo73Xr1hay/A5qUgZ+Blk7NzI8BjRX7KjKztSLtJC+/OrS8Z+a9ABHxQuAXaI+GbqI9JeQc4EeAt0bEpHSOh1nuXwZ+LTNPBb4G/GavfZRsmPkmIp4I/DDtqRBQsTLPzAczc2/nC/7VwIWd7ZWv693yPgt1vUe5V7qu98h35es6QGYeiIg/pZ2vqzvbp62ua3r0Ok8BrgJ+HjgNOCUiXj3O4LQ+3fpN+PlRCT3KF6y7U61XXwjr78AmZeBnmEunLq6SdpKXXx3qsrER8VPAxcCrMvM+4CHgvZn5UGbuBf6K9lz2STDMvF+bmbcuPQaeM8A+xmXYSwX/JPCRzGx2nleuzCPiacANwBWZ+ZHO9pmo613yPhN1vUveK1/Xu5U5M1DXATLzPwLHAZdExCamr65renQ9TyOiAN6Tmf/W+VX5k7Q/bzT9/PyoMOtuNfToC1l/BzQpAz/DXDp1R0Rs6zw+Hfgc7V+IXxwRh0XEkbQvEbt9uFkY2NDyHhHn0P71f1tmfq3z8nHAzRFR68yJPAX4P8PPxkCGWe6fjojndR7/MHArk1vuw14q+KW0pz8sqVSZR8STgM8A52XmpcvSV76ud8v7LNT1HuVe6breI99Q/br++mWX5D9Ee8CnyfTVdU2PXufpEcDtEfH4zhfJ02h/3mj6DdLX0vSw7k65NfpC1t8BbVg7yVhcC7wsIj5PZ+nUiHgd31k69W3Ap/nO0qnf7LGvt9P+lXCe9olxdWY2o73yyec6+/j1zHxklBk6BEPJe7RXQ3kf8A3gmogAuCkzfzMi/gfwRdqXQ16emXeMPFf9GWa5/yfgjyNiP/DPwJsz84EJLfdh5hvaK+AsffknM++qUplHxHuBJwDvjIil+b6nMwN1vUveX8UM1PUe5V7put4t35n5MNWv69cA/z0i/hqoA7+SmY9ExLTVdU2Ptc7TC2j/4rwAfDYzP1VirFqndfa1NOGsu5VyAY/tC10CbLL+Dm4ilnOXJEmSJEnS8E3KVC9JkiRJkiQNmQM/kiRJkiRJFeXAjyRJkiRJUkU58CNJkiRJklRRDvxIkiRJkiRVlAM/kiRJkiRJFeXAjyRJkiRJUkX9f9Uyl6gfQPgwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig = plt.figure(figsize=(20, 7))\n",
    "ax0 = plt.subplot2grid(shape=(2, 2), loc=(0, 0), colspan=2)\n",
    "ax1 = plt.subplot2grid(shape=(2, 2), loc=(1, 0), colspan=1)\n",
    "ax2 = plt.subplot2grid(shape=(2, 2), loc=(1, 1), colspan=1)\n",
    "fig.subplots_adjust(wspace=0.05, hspace=0.3)\n",
    "fig.suptitle(f\"{stock} returns\", fontsize=15)\n",
    "\n",
    "FFE.plot(ax=ax0, grid=True, color=\"tab:blue\", label=\"ECDF real PnL\")\n",
    "FFH .plot(ax=ax0, grid=True, color=\"tab:red\", label=f\"ECDF t-Student fit\")\n",
    "ax0.set_xlim([FFH.index.min(), FFH.index.max()])\n",
    "ax0.legend(loc=\"best\", fontsize=12)\n",
    "\n",
    "\n",
    "if KStest > 1e4:\n",
    "    kslabel = f\"KS={KStest:.4} (p-value={(KSpv*100):.0f}%)\"\n",
    "else:\n",
    "    kslabel = f\"KS={KStest:.4f} (p-value={(KSpv*100):.0f}%)\"\n",
    "ax1.axvline(x = KStest if KStest < test_stats[\"KS\"].max() else test_stats[\"KS\"].max(), \n",
    "            linestyle = \"--\", \n",
    "            color = \"tab:red\", \n",
    "            label = kslabel)\n",
    "\n",
    "if ADtest > 1e4:\n",
    "    adlabel = f\"AD={ADtest:.4} (p-value={(ADpv*100):.0f}%)\"\n",
    "else:\n",
    "    adlabel = f\"AD={ADtest:.4f} (p-value={(ADpv*100):.0f}%)\"\n",
    "ax2.axvline(x = ADtest if ADtest < test_stats[\"AD\"].max() else test_stats[\"AD\"].max(), \n",
    "            linestyle = \"--\", \n",
    "            color = \"tab:red\", \n",
    "            label = adlabel)\n",
    "\n",
    "crlabel = \"CR(%) [0,{:.1f}),({:.1f},100]\".format(0.5*slevel, 100 - 0.5*slevel)\n",
    "\n",
    "test_stats[\"KS\"].hist(ax=ax1, grid=True, density=True, bins=60, edgecolor=(0.77,0.77,0.77), alpha=0.7, label=\"\", color=\"tab:blue\")\n",
    "ax1.axvspan(xmin = 0, xmax = CR['KS']['left'], facecolor = (0.77,0.77,0.77), alpha = 0.6, label = \"\")\n",
    "ax1.axvspan(xmin = CR['KS']['right'], xmax = test_stats[\"KS\"].max(), facecolor = (0.77,0.77,0.77), alpha = 0.6, label = crlabel)\n",
    "ax1.set_xlim([test_stats[\"KS\"].min(), test_stats[\"KS\"].max()])\n",
    "ax1.set_title(\"KS Test statistic distribution\",fontsize=14)\n",
    "ax1.set_xlabel(\"\",fontsize=18)\n",
    "ax1.legend(fontsize=12)\n",
    "\n",
    "test_stats[\"AD\"].hist(ax=ax2, grid=True, density=True, bins=60, edgecolor=(0.77,0.77,0.77), alpha=0.7, label=\"\", color=\"tab:blue\")\n",
    "ax2.axvspan(xmin = 0, xmax = CR['AD']['left'], facecolor = (0.77,0.77,0.77), alpha = 0.6, label = \"\")\n",
    "ax2.axvspan(xmin = CR['AD']['right'], xmax = test_stats[\"AD\"].max(), facecolor = (0.77,0.77,0.77), alpha = 0.6, label = crlabel)\n",
    "ax2.set_xlim([test_stats[\"AD\"].min(), test_stats[\"AD\"].max()])\n",
    "ax2.set_title(\"AD Test statistic distribution\",fontsize=14)\n",
    "ax2.set_xlabel(\"\",fontsize=18)\n",
    "ax2.legend(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is, the Null Hypothesis (being the return series being t-Student distributed) cannot be rejected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Value-at-Risk estimators\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methods returning the Value-at-Risk-based estimators for a fixed CL **Confidence Level**. \n",
    "\n",
    "By default, the computed $VaR_{CL}$ is defined as the (1-CL)% quantile of an input distribution of returns (left tail). Quantile of the right tail are computed by passing *left=False*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "rets = ql.compute_returns(assets, mpor=1, ascending=True, dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confidence level\n",
    "CL = 99/100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Value-at-Risk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Simple (1-CL)% VaR figure corresponding to CL.\n",
    "It uses the *pd.quantile* method (interpolation between scenarios will be used by default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.048387\n",
       "AMZN   -0.056431\n",
       "GE     -0.059087\n",
       "GOOG   -0.047631\n",
       "IBM    -0.043399\n",
       "KO     -0.031608\n",
       "MSFT   -0.042652\n",
       "dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Left tail\n",
    "ql.var(rets, CL=CL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.050662\n",
       "AMZN    0.057416\n",
       "GE      0.061956\n",
       "GOOG    0.044122\n",
       "IBM     0.037885\n",
       "KO      0.029773\n",
       "MSFT    0.047053\n",
       "dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Right tail\n",
    "ql.var(rets, CL=CL, left=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Value-at-Risk (no interpolation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple (1-CL)% VaR figure corresponding to CL without interpolation. That is, for series of total $n$ returns, it computes\n",
    "$$\n",
    "s := \\text{round}(n \\cdot (1 - CL))\n",
    "$$\n",
    "and the result is rounded to zero decimal places. Hence, returns the return corresponding to the $s$-th scenario \n",
    "(of the sorted series). For very large $n$, there will be barely difference with previous *.var* method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.049119\n",
       "AMZN   -0.056840\n",
       "GE     -0.059480\n",
       "GOOG   -0.047786\n",
       "IBM    -0.044075\n",
       "KO     -0.031675\n",
       "MSFT   -0.043115\n",
       "dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.VaR(rets, CL=CL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Value-at-Risk under Normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Returns the VaR figure corresponding to the input Confidence Level by assuming normality of \n",
    "the return series.\n",
    "\n",
    "Let $\\mu$ and $\\sigma$ be the mean and the volatility of the returns $R$ \n",
    "and assume that $R\\sim N(\\mu,\\sigma^2)$. \n",
    "If $X\\sim N(0,1)$, then $R = \\mu + X \\sigma$.\n",
    "In this way, to compute the (1-CL)-quantile of $R$, it is suffices to compute \n",
    "the (1-CL)-quantile of the stardard normal distribution. \n",
    "\n",
    "By definition, the (1-CL)-quantile is the number $z_{CL}$ such that \n",
    "$\\mathbb{P}(R \\leq z_{CL}) = 1-CL$.\n",
    "Hence: \n",
    "\n",
    "$$\n",
    "1-CL \n",
    "= \\mathbb{P}(R \\leq z_{CL}) \n",
    "= \\mathbb{P}(\\mu+ X\\sigma \\leq z_{CL}) \n",
    "= \\mathbb{P}\\left(X \\leq \\frac{z_{CL}-\\mu}{\\sigma}\\right) \n",
    "= \\Phi\\left( \\frac{z_{CL}-\\mu}{\\sigma} \\right)\n",
    "\\quad\\Longrightarrow\\quad\n",
    "z_{CL} = \\mu + \\Phi^{-1}(1-{CL})\\sigma \n",
    "$$\n",
    "\n",
    "where $\\Phi^{-1}(1-{CL})$ is the (1-CL)-quantile of the standard normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.041640\n",
       "AMZN   -0.046651\n",
       "GE     -0.047442\n",
       "GOOG   -0.038453\n",
       "IBM    -0.033415\n",
       "KO     -0.025581\n",
       "MSFT   -0.037870\n",
       "dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.var_normal(rets, CL=CL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Value-at-Risk with Cornish-Fisher method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a modifed version of the parametric normal method for quantile computation (semi-parametric method).\n",
    "The method relates quantilies of non-normal distributions with the quantiles of the standard normal distribution as follows: \n",
    "$$\n",
    "\\tilde{z}_{CL} \n",
    "= z_{CL} + \\frac{1}{6}(z_{CL}^2 - 1)S \n",
    "+ \\frac{1}{24}(z_{CL}^3 - 3 z_{CL})(K-3) \n",
    "- \\frac{1}{36}(2z_{CL}^3 - 5 z_{CL})S^2\n",
    "$$\n",
    "where $\\tilde{z}_{CL}$, $S$, and $K$ denote the (1-CL)-quantile, \n",
    "the skewness, and the kurtosis of the input non-normal distribution \n",
    "(e.g., the input returns series), respectively, \n",
    "and $z_{CL}$ is the (1-CL)-quantile of the standard normal distribution. \n",
    "If the input return distribution is, indeed, normal, \n",
    "then $S=0$ and $K=3$ and so $\\tilde{z}_{CL}$ would be equal to $z_{CL}$.\n",
    "\n",
    "Then, the comprauted VaR in this case will be equal to $\\mu + \\tilde{z}_{CL}\\sigma$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.066104\n",
       "AMZN   -0.075270\n",
       "GE     -0.082082\n",
       "GOOG   -0.066826\n",
       "IBM    -0.072159\n",
       "KO     -0.054817\n",
       "MSFT   -0.071164\n",
       "dtype: float64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.var_normal(rets, CL=CL, cf=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Value-at-Risk under t-Student distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns the VaR figure corresponding to the input Confidence Level by assuming the return series being t-Student distributed.\n",
    "\n",
    "Let $\\mu$ and $\\sigma$ be the mean and the sample volatility of the returns $R$.\n",
    "By using the MLE, fit a t-Student distribution with degrees of freedom $\\nu$, mean $\\mu$, \n",
    "and **scale** parameter $s$.\n",
    "\n",
    "Recall then that the relationship between the scale parameter $s$ \n",
    "and the sample volatility $\\sigma$ was $((\\nu-2)/\\nu)^{1/2}\\sigma = s$.\n",
    "From this, one had that $\\sigma^2 = s^2\\nu(\\nu-2)^{-1}$, where $\\sigma^2$ is the sample variance.\n",
    "The t-Student distribution found via MLE is thus \n",
    "$R\\sim t_\\nu\\left(\\mu, s^2 \\nu (\\nu-2)^{-1}\\right)$.\n",
    "\n",
    "However, given that \n",
    "$$\n",
    "((\\nu-2)/\\nu)^{1/2}\\sigma = s\n",
    "\\quad\\Longrightarrow\\quad\n",
    "((\\nu-2)/\\nu)^{1/2}\\sigma^2 = \\sigma s\n",
    "\\quad\\Longrightarrow\\quad\n",
    "\\sigma^2 = \\sigma s (\\nu(\\nu-2)^{-1})^{1/2}\n",
    "$$\n",
    "we rewrite \n",
    "$R\\sim t_\\nu\\left(\\mu, \\sigma s (\\nu(\\nu-2)^{-1})^{1/2} \\right)$.\n",
    "\n",
    "If now $T$ is a Standardized t-Student distribution with degrees of freedom $\\nu$ and (then) \n",
    "unit variance, i.e., $T\\sim t_\\nu(0,1)$, \n",
    "then $R = \\mu + T ( \\sigma s (\\nu(\\nu-2)^{-1})^{1/2}  )^{1/2}$.\n",
    "In fact note that\n",
    "$$\n",
    "\\mathbb{E}(R) = \\mu + \\mathbb{E}(T)( \\sigma s (\\nu(\\nu-2)^{-1})^{1/2} )^{1/2} = \\mu \n",
    "$$\n",
    "and\n",
    "$$\n",
    "Var(R) = Var\\left(T ( \\sigma s (\\nu(\\nu-2)^{-1})^{1/2}  )^{1/2} \\right) \n",
    "= Var(T) \\sigma s (\\nu(\\nu-2)^{-1})^{1/2}  \n",
    "= \\sigma s (\\nu(\\nu-2)^{-1})^{1/2}\n",
    "$$\n",
    "\n",
    "\n",
    "In this way, to compute the (1-CL)-quantile of $R$, it is suffices to compute \n",
    "the (1-CL)-quantile of the Standardized t-Student distribution with degrees of freedom $\\nu$.\n",
    "\n",
    "By definition, the (1-CL)-quantile is the number $z_{CL}$ such that \n",
    "$\\mathbb{P}(R \\leq z_{CL}) = 1-CL$.\n",
    "Hence: \n",
    "\n",
    "$$\n",
    "1-CL \n",
    "= \\mathbb{P}(R \\leq z_{CL}) \n",
    "= \\mathbb{P}\\left(\\mu + T ( \\sigma s (\\nu(\\nu-2)^{-1})^{1/2}  )^{1/2} \\leq z_{CL}\\right) \n",
    "= \\mathbb{P}\\left(T \\leq \\frac{z_{CL}-\\mu}{ (\\sigma s (\\nu(\\nu-2)^{-1})^{1/2})^{1/2}  }\\right) \n",
    "= t_\\nu\\left( \\frac{z_{CL}-\\mu}{ (\\sigma s (\\nu(\\nu-2)^{-1})^{1/2})^{1/2} } \\right)\n",
    "\\quad\\Longrightarrow\\quad\n",
    "z_{CL} = \\mu + \\sigma^{1/2} t_\\nu^{-1}(1-{CL}) \\left(s (\\nu(\\nu-2)^{-1})^{1/2}\\right)^{1/2} \n",
    "$$\n",
    "\n",
    "where $t_\\nu^{-1}(1-{CL})$ is the (1-CL)-quantile of the \n",
    "Standardized t-Student distribution with degrees of freedom $\\nu$.\n",
    "\n",
    "Note tha however, this would be equivalent to compute\n",
    "$$\n",
    "z_{CL} = \\mu + t_\\nu^{-1}(1-{CL}) \\left(s^2 \\nu (\\nu-2)^{-1}\\right)^{1/2}\n",
    "$$\n",
    "as well as\n",
    "$$\n",
    "z_{CL} = \\mu + t_\\nu^{-1}(1-{CL}) \\sigma.\n",
    "$$\n",
    "Differences come from the approximation due to the sample volatility with the scale parameter \n",
    "relationship $((\\nu-2)/\\nu)^{1/2}\\sigma \\approx s$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.048813\n",
       "AMZN   -0.054478\n",
       "GE     -0.053559\n",
       "GOOG   -0.045070\n",
       "IBM    -0.038580\n",
       "KO     -0.029591\n",
       "MSFT   -0.044309\n",
       "dtype: float64"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# t-Student via MLE\n",
    "ql.var_tstudent(rets, CL=CL, left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.046034\n",
       "AMZN   -0.051561\n",
       "GE     -0.052340\n",
       "GOOG   -0.042491\n",
       "IBM    -0.036874\n",
       "KO     -0.028257\n",
       "MSFT   -0.041875\n",
       "dtype: float64"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# t-Student with a fixed degrees of freedom (no MLE)\n",
    "ql.var_tstudent(rets, CL=CL, left=True, df=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Expected Shortfall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns the (1-CL)% Expected Shortfall figure corresponding to the input Confidence Level.\n",
    "This is defined as the average of distribution returns worst than the (1-CL)% VaR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.068166\n",
       "AMZN   -0.074504\n",
       "GE     -0.081151\n",
       "GOOG   -0.060446\n",
       "IBM    -0.065567\n",
       "KO     -0.049247\n",
       "MSFT   -0.061645\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.es(rets, CL=CL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Expected Shortfall (no interpolation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Returns the (1-CL)% Expected Shortfall figure corresponding to the input Confidence Level.\n",
    "Here, corresponding (1-CL)% VaR is computed with the *VaR* method that does not use interpolation (see above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.068166\n",
       "AMZN   -0.074504\n",
       "GE     -0.081151\n",
       "GOOG   -0.060446\n",
       "IBM    -0.065567\n",
       "KO     -0.049247\n",
       "MSFT   -0.061645\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.ES(rets, CL=CL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Estimators comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "VaRs = pd.DataFrame({\"VaR\": ql.var(rets, CL=CL),\n",
    "                     \"Normal VaR\": ql.var_normal(rets, CL=CL, cf=False),\n",
    "                     \"Normal CF VaR\": ql.var_normal(rets, CL=CL, cf=True),\n",
    "                     \"t-Student Var\": ql.var_tstudent(rets, CL=CL, left=True),\n",
    "                     \"ES\": ql.es(rets, CL=CL, left=True)})\n",
    "\n",
    "title = f\"Comparison of {CL*100:.0f}% VaR estimators\"\n",
    "(VaRs * 100).plot.bar(figsize=(18,4), grid=True, title=title)\n",
    "plt.ylabel(\"Percent (%)\", fontsize=15)\n",
    "plt.legend(fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Rolling estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple computation of rolling historical VaRs and Expected Shortfall estimators for a given rolling window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = rets[\"AAPL\"]\n",
    "window = 252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "rvars_l = df.rolling(window=window).apply(ql.var, kwargs={\"CL\":CL, \"left\":True})\n",
    "rvars_r = df.rolling(window=window).apply(ql.var, kwargs={\"CL\":CL, \"left\":False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "es_l = df.rolling(window=window).apply(ql.es, kwargs={\"CL\":CL, \"left\":True})\n",
    "es_r = df.rolling(window=window).apply(ql.es, kwargs={\"CL\":CL, \"left\":False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(1,1,figsize=(20,6))\n",
    "fig.suptitle(\"\", fontsize=15)\n",
    "\n",
    "df.plot(ax=ax, grid=True, alpha=0.7)\n",
    "rvars_l.plot(ax=ax, grid=True, color=\"tab:orange\", label=f\"{CL*100:.1f}% VaR\")\n",
    "rvars_r.plot(ax=ax, grid=True, color=\"tab:orange\", label=\"\")\n",
    "#\n",
    "es_l.plot(ax=ax, grid=True, color=\"tab:green\", alpha=0.8, label=f\"{CL*100:.1f}% ES\")\n",
    "es_r.plot(ax=ax, grid=True, color=\"tab:green\", alpha=0.8, label=\"\")\n",
    "#\n",
    "ax.set_xlabel(\"\",fontsize=14)\n",
    "ax.set_ylabel(\"Returns\",fontsize=14)\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following method returns a dataframe with summary statistics of an input pd.Series of pd.DataFrame of returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "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>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(Ann.) Return</th>\n",
       "      <td>0.237492</td>\n",
       "      <td>0.226191</td>\n",
       "      <td>-0.024856</td>\n",
       "      <td>0.165097</td>\n",
       "      <td>0.015860</td>\n",
       "      <td>0.089164</td>\n",
       "      <td>0.245822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Ann.) Std</th>\n",
       "      <td>0.291064</td>\n",
       "      <td>0.325284</td>\n",
       "      <td>0.324477</td>\n",
       "      <td>0.267496</td>\n",
       "      <td>0.229157</td>\n",
       "      <td>0.177299</td>\n",
       "      <td>0.265326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skewness</th>\n",
       "      <td>-0.072332</td>\n",
       "      <td>0.412764</td>\n",
       "      <td>0.166840</td>\n",
       "      <td>0.423256</td>\n",
       "      <td>-0.539958</td>\n",
       "      <td>-0.664706</td>\n",
       "      <td>0.034961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>8.487693</td>\n",
       "      <td>10.548673</td>\n",
       "      <td>10.819133</td>\n",
       "      <td>11.823880</td>\n",
       "      <td>13.250592</td>\n",
       "      <td>12.816816</td>\n",
       "      <td>11.632551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VaR 0.99</th>\n",
       "      <td>-0.048387</td>\n",
       "      <td>-0.056431</td>\n",
       "      <td>-0.059087</td>\n",
       "      <td>-0.047631</td>\n",
       "      <td>-0.043399</td>\n",
       "      <td>-0.031608</td>\n",
       "      <td>-0.042652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal VaR 0.99</th>\n",
       "      <td>-0.041640</td>\n",
       "      <td>-0.046651</td>\n",
       "      <td>-0.047442</td>\n",
       "      <td>-0.038453</td>\n",
       "      <td>-0.033415</td>\n",
       "      <td>-0.025581</td>\n",
       "      <td>-0.037870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal CF VaR 0.99</th>\n",
       "      <td>-0.066104</td>\n",
       "      <td>-0.075270</td>\n",
       "      <td>-0.082082</td>\n",
       "      <td>-0.066826</td>\n",
       "      <td>-0.072159</td>\n",
       "      <td>-0.054817</td>\n",
       "      <td>-0.071164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal tStudent 0.99</th>\n",
       "      <td>-0.048813</td>\n",
       "      <td>-0.054478</td>\n",
       "      <td>-0.053559</td>\n",
       "      <td>-0.045070</td>\n",
       "      <td>-0.038580</td>\n",
       "      <td>-0.029591</td>\n",
       "      <td>-0.044309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES 0.99</th>\n",
       "      <td>-0.068166</td>\n",
       "      <td>-0.074504</td>\n",
       "      <td>-0.081151</td>\n",
       "      <td>-0.060446</td>\n",
       "      <td>-0.065567</td>\n",
       "      <td>-0.049247</td>\n",
       "      <td>-0.061645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max Drawdown</th>\n",
       "      <td>-0.437971</td>\n",
       "      <td>-0.561453</td>\n",
       "      <td>-0.811914</td>\n",
       "      <td>-0.446018</td>\n",
       "      <td>-0.437200</td>\n",
       "      <td>-0.369875</td>\n",
       "      <td>-0.371485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minimum</th>\n",
       "      <td>-0.128647</td>\n",
       "      <td>-0.140494</td>\n",
       "      <td>-0.151592</td>\n",
       "      <td>-0.111008</td>\n",
       "      <td>-0.128507</td>\n",
       "      <td>-0.096725</td>\n",
       "      <td>-0.147390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum</th>\n",
       "      <td>0.119808</td>\n",
       "      <td>0.157457</td>\n",
       "      <td>0.147300</td>\n",
       "      <td>0.160524</td>\n",
       "      <td>0.113011</td>\n",
       "      <td>0.064796</td>\n",
       "      <td>0.142169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          AAPL       AMZN         GE       GOOG        IBM  \\\n",
       "(Ann.) Return         0.237492   0.226191  -0.024856   0.165097   0.015860   \n",
       "(Ann.) Std            0.291064   0.325284   0.324477   0.267496   0.229157   \n",
       "Skewness             -0.072332   0.412764   0.166840   0.423256  -0.539958   \n",
       "Kurtosis              8.487693  10.548673  10.819133  11.823880  13.250592   \n",
       "VaR 0.99             -0.048387  -0.056431  -0.059087  -0.047631  -0.043399   \n",
       "Normal VaR 0.99      -0.041640  -0.046651  -0.047442  -0.038453  -0.033415   \n",
       "Normal CF VaR 0.99   -0.066104  -0.075270  -0.082082  -0.066826  -0.072159   \n",
       "Normal tStudent 0.99 -0.048813  -0.054478  -0.053559  -0.045070  -0.038580   \n",
       "ES 0.99              -0.068166  -0.074504  -0.081151  -0.060446  -0.065567   \n",
       "Max Drawdown         -0.437971  -0.561453  -0.811914  -0.446018  -0.437200   \n",
       "Minimum              -0.128647  -0.140494  -0.151592  -0.111008  -0.128507   \n",
       "Maximum               0.119808   0.157457   0.147300   0.160524   0.113011   \n",
       "\n",
       "                             KO       MSFT  \n",
       "(Ann.) Return          0.089164   0.245822  \n",
       "(Ann.) Std             0.177299   0.265326  \n",
       "Skewness              -0.664706   0.034961  \n",
       "Kurtosis              12.816816  11.632551  \n",
       "VaR 0.99              -0.031608  -0.042652  \n",
       "Normal VaR 0.99       -0.025581  -0.037870  \n",
       "Normal CF VaR 0.99    -0.054817  -0.071164  \n",
       "Normal tStudent 0.99  -0.029591  -0.044309  \n",
       "ES 0.99               -0.049247  -0.061645  \n",
       "Max Drawdown          -0.369875  -0.371485  \n",
       "Minimum               -0.096725  -0.147390  \n",
       "Maximum                0.064796   0.142169  "
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.summary_stats(rets, CL=CL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Covariance Matrix estimations\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given $N$ assets, let $\\Sigma$ denote the $N$-by-$N$ symmetric square \n",
    "**Covariance** matrix, $\\Sigma := [c_{ij}]$, \n",
    "where each element $c_{ij} := \\rho_{ij} \\sigma_i \\sigma_j$ ($i,j=1,\\dots,N$) \n",
    "denotes the covariance between the returns of assets $i$ and asset $j$,\n",
    "with $\\sigma_i$, $\\sigma_j$, and $\\rho_{ij}$ being the volatilities \n",
    "and the **correlation** of assets $i$ and $j$, respectively. \n",
    "If $i=j$, then $c_{ii}=\\sigma_i^2$ (as $\\rho_{ij}=1$) \n",
    "is simply the **variance** of asset $i$ (diagonal entries are the variances)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Covariance matrix (no model risk - high sample risk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest covariance estimator is the **sample covariance matrix** \n",
    "$\\Sigma = [c_{ij}]$ for which there is no model risk (as we are just applying the definitions) \n",
    "but there is dependence on the sample size of the return series. \n",
    "A total of $N(N+1)/2$ parameters are to be estimated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      AMZN        GE      GOOG       IBM        KO      MSFT\n",
       "AAPL  0.000336  0.000182  0.000122  0.000172  0.000103  0.000071  0.000184\n",
       "AMZN  0.000182  0.000420  0.000102  0.000210  0.000086  0.000056  0.000196\n",
       "GE    0.000122  0.000102  0.000418  0.000115  0.000139  0.000088  0.000114\n",
       "GOOG  0.000172  0.000210  0.000115  0.000284  0.000099  0.000068  0.000188\n",
       "IBM   0.000103  0.000086  0.000139  0.000099  0.000208  0.000076  0.000111\n",
       "KO    0.000071  0.000056  0.000088  0.000068  0.000076  0.000125  0.000077\n",
       "MSFT  0.000184  0.000196  0.000114  0.000188  0.000111  0.000077  0.000279"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.covmat(rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constant Correlation Covariance matrix (high model risk - low sample risk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A second estimator is a **constant correlation** estimator, $\\hat{\\Sigma}$, in which \n",
    "we assume a constant correlation coefficient $\\hat{\\rho}$ for all assets. \n",
    "That is, the entries of the covariance matrix are defined as\n",
    "$$\n",
    "c_{ij} := \\hat{\\rho}\\, \\sigma_i \\sigma_j\n",
    "\\qquad i,j=1,\\dots,N.\n",
    "$$\n",
    "In this way, one avoids to compute all $\\rho_{ij}$ (hence reducing the sample risk, \n",
    "since they rely on the return series) and only single volatilities should be computed. \n",
    "Thus, instead of $N(N+1)/2$ parameters, only $N+1$ will be computed. \n",
    "However, it is introduced a risk model (as correlations, in general, will not be the same). \n",
    "\n",
    "The choice of the constant $\\hat{\\rho}$ can be the average of 'true' correlations:\n",
    "$$\n",
    "\\hat{\\rho} := \\frac{1}{N(N-1)} \\sum_{i\\neq j}^N \\rho_{ij}.\n",
    "$$\n",
    "Notice that in this way, one should compute all $\\rho_{ij}$ again.\n",
    "Indeed, the adavantage can be purely about reducing the sample risk rather \n",
    "than making computation faster as, for example, two assets can show a positive or a \n",
    "negative correlation when sampled at different frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      AMZN        GE      GOOG       IBM        KO      MSFT\n",
       "AAPL  0.000336  0.000161  0.000160  0.000132  0.000113  0.000088  0.000131\n",
       "AMZN  0.000161  0.000420  0.000179  0.000148  0.000126  0.000098  0.000146\n",
       "GE    0.000160  0.000179  0.000418  0.000147  0.000126  0.000098  0.000146\n",
       "GOOG  0.000132  0.000148  0.000147  0.000284  0.000104  0.000080  0.000120\n",
       "IBM   0.000113  0.000126  0.000126  0.000104  0.000208  0.000069  0.000103\n",
       "KO    0.000088  0.000098  0.000098  0.000080  0.000069  0.000125  0.000080\n",
       "MSFT  0.000131  0.000146  0.000146  0.000120  0.000103  0.000080  0.000279"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.covmat_cc(rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Shrinkage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Statistical Shrinkage** allow one to find the optimal trade-off \n",
    "between **sample risk** and **model risk** \n",
    "as one commputes the **weigthed average** average of two covariance matrix estimates, \n",
    "one with high sample risk and one with high model risk:\n",
    "$$\n",
    "S := \\delta \\Sigma + (1-\\delta)\\hat\\Sigma\n",
    "$$\n",
    "where $\\Sigma$ is the sample covariance matrix, $\\hat{\\Sigma}$ is the constant correlation \n",
    "covariance matrix, and $\\delta$ is the **shrinkage factor** (between $(0,1)$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GE</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>KO</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      AMZN        GE      GOOG       IBM        KO      MSFT\n",
       "AAPL  0.000336  0.000171  0.000141  0.000152  0.000108  0.000079  0.000158\n",
       "AMZN  0.000171  0.000420  0.000140  0.000179  0.000106  0.000077  0.000171\n",
       "GE    0.000141  0.000140  0.000418  0.000131  0.000132  0.000093  0.000130\n",
       "GOOG  0.000152  0.000179  0.000131  0.000284  0.000102  0.000074  0.000154\n",
       "IBM   0.000108  0.000106  0.000132  0.000102  0.000208  0.000072  0.000107\n",
       "KO    0.000079  0.000077  0.000093  0.000074  0.000072  0.000125  0.000079\n",
       "MSFT  0.000158  0.000171  0.000130  0.000154  0.000107  0.000079  0.000279"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql.covmat_shrinkage(rets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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