time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Read Historical Daily Yen Futures Data\n",
    "In this notebook, you will load historical Dollar-Yen exchange rate futures data and apply time series analysis and modeling to determine whether there is any predictable behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-08-02</th>\n",
       "      <td>3398.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3398.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-03</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-04</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-05</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-06</th>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle  Volume  \\\n",
       "Date                                                                 \n",
       "1976-08-02  3398.0  3401.0  3398.0  3401.0     NaN  3401.0     2.0   \n",
       "1976-08-03  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-04  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-05  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-06  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1976-08-02                         1.0  \n",
       "1976-08-03                         1.0  \n",
       "1976-08-04                         1.0  \n",
       "1976-08-05                         1.0  \n",
       "1976-08-06                         1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Futures contract on the Yen-dollar exchange rate:\n",
    "# This is the continuous chain of the futures contracts that are 1 month to expiration\n",
    "yen_futures = pd.read_csv(\n",
    "    Path(\"yen.csv\"), index_col=\"Date\", infer_datetime_format=True, parse_dates=True\n",
    ")\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6954.0</td>\n",
       "      <td>6954.0</td>\n",
       "      <td>6835.0</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>48336.0</td>\n",
       "      <td>51473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6877.0</td>\n",
       "      <td>6910.0</td>\n",
       "      <td>6865.0</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>38206.0</td>\n",
       "      <td>53860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6937.0</td>\n",
       "      <td>7030.0</td>\n",
       "      <td>6924.0</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>49649.0</td>\n",
       "      <td>55699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6952.0</td>\n",
       "      <td>6985.0</td>\n",
       "      <td>6942.0</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>29944.0</td>\n",
       "      <td>53111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6936.0</td>\n",
       "      <td>6972.0</td>\n",
       "      <td>6936.0</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>19763.0</td>\n",
       "      <td>52072.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle   Volume  \\\n",
       "Date                                                                  \n",
       "1990-01-02  6954.0  6954.0  6835.0  6847.0     NaN  6847.0  48336.0   \n",
       "1990-01-03  6877.0  6910.0  6865.0  6887.0     NaN  6887.0  38206.0   \n",
       "1990-01-04  6937.0  7030.0  6924.0  7008.0     NaN  7008.0  49649.0   \n",
       "1990-01-05  6952.0  6985.0  6942.0  6950.0     NaN  6950.0  29944.0   \n",
       "1990-01-08  6936.0  6972.0  6936.0  6959.0     NaN  6959.0  19763.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1990-01-02                     51473.0  \n",
       "1990-01-03                     53860.0  \n",
       "1990-01-04                     55699.0  \n",
       "1990-01-05                     53111.0  \n",
       "1990-01-08                     52072.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim the dataset to begin on January 1st, 1990\n",
    "yen_futures = yen_futures.loc[\"1990-01-01\":, :]\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: Initial Time-Series Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Start by plotting the \"Settle\" price. Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x44CjdIb85eO4v5cSQbZTEXkZGAa0FpFiYALWCP5fQBtguogsMMaca4xZKiJTgGVABXCTMcaxW9yIFbHUCJhh/wE8DbwgIqvt1x0bo8/WYKisMn456zPtnUpVCIoH+46U++2ff3x73/bFT8zyO/bTUwt57ut1nq+TiNiP3ATMQpRqalQIxpjLQhx6K0T/icBEj/YioK9HewlwSU1yKKGpMoaMjOAZQqVOtBQPivcc9tt3ZpKBE/MZt54OEFIhJII1O6trfXy4bBtn926XNFkaAqp+04BAk5EzQ1DLm+JFVob/bT93neWyC5xRtmqcE3KW+caNp8RHuADeXlgdAnv980UJec+GjCqENKDK+JuMfDOExGQpVuoRVVWGJ79Y49f25SprpXLgjLJtQR59ji6gf8dmvDpuiN+xAZ1axFdQJSmoQkgDLIXgniFY/9VkpATy+aodvPFtsecx93IEJy2FiDD15tM4uav/0qDMBDiUAR6+7ISEvI9ioQohDagy/g4+jTJSQlEWorjNpr1HuG/6Mt9+eWVqXDsX9T/ab79o3W5Wb4//CumGiiqENKBKo4yUKPjPVdV5KYdO+iQop1Gq0K4g17d98ROzGPn3L8L0VuqCKoQ0oMoYvym8JrdTQuG+JMIFHTT2WK187dAuAPz23J4xlyscLfJzEvp+DZkaw06V1KfK+Of/cEJQVSEo4Qg3gTxUFpz25J4f9OaeH/SOo0TeJCKJnmKhM4Q0oLLKf4aQqVFGSgSs2Rk6i2jTPB0rNkRUIaQB5ZVVZLkyVGZolJESAf06NA95bOAxGlbaEFGFUA9Zt/MQheOnM3uNVfC8ssr45ZVxZgh/+N8Sz/MVBcJXPEtEnqLa0r9jM9/2LS/PZ39JeZjeSjSoQqhHLNy4F2MMVz4zB4CxT84GoGj9Hr7+fpevX2D2SkXxItwMMpWuoc9/OwyAAle67WkLN/Pop6vZcaA0SVKlJ6oQ6gnvLt7C6Edn8tSXa9m4+4ivvcLDUeDOaxSYyExpGGw/UMILs9Z5HKlWAt3ahC5NmaiFZ5FwTKvG9O/YjO37/R/+//58DSdN/ChJUqUnqhDqCSvsSIuJ737n177XfuBfM7TQ1+Ye3F0/WfO/NER+8eK3/GHqUta5ksMF0rYgL+SxSGsiJIqFxftYsc072mj6oi0JliZ9UYVQT3h7kXed28XF+wDo2a6pr61pbnWEyILivXGVS0lNnPKpgfWSI2VkPcoqetuUBckWIW3Q2LJ6wpod3iO9a577BvCvg+tOYxEqVYGS3ji29cDV6pEEni364zkU5NWfspV6jccOnSGkCc217qzi4kBpBQDvLdla63PrkzJQYkuNCkFEnhGR7SKyxNXWUkQ+FJFV9v8WrmN3ishqEVkhIue62geKyGL72MN2KU3scpuv2u1zRKQwxp8xLRgz4Oiwx9s0zQ157ICG5zVYAovhfL4yfHnMeXePjKc4SooTyQzhOWBUQNt44GNjTHfgY3sfEemNVQKzj33OYyLieKceB8Zh1Vnu7nrN64A9xphuwEPAA9F+mHRm9prdYY+3auKvEMad0dW3/ae3lwV2VxoIr83zT3X9yjcbw/YPvI7qAx2aN0q2CGlDjQrBGPMFVq1jN6OByfb2ZGCMq/0VY0ypMWYtsBoYLCLtgQJjzCxjZdR6PuAc57VeB0ZIIoq11jO27i8Je7xVY/8EYC1d+2/N3xQXmZT6z8zxZ/m2/35p/yRKEj2JzOpbXlnFvPW70zaTcLRO5XbGmC0AxpgtItLWbu8AzHb1K7bbyu3twHbnnI32a1WIyD6gFbAzStkaHL89tyd5AWGCLVw+hXS9eJXomfSj4wH/0fU5fY5Kljg10jw/m72HvU2fW/eXUBGQviUe7C8p54YX5vkWgb4ybghDAgoH1Xdi/Q16jexNmPZw5wS/uMg4ESkSkaIdO8LbQtONwGlxk9zwujwzoG7umyGqZKUKT3z+PYXjp/vWWyh1o6CG5HT9OzUPavNKeZ0qfPm74WGPb9kXfgYdC376zFy/jABfr06/MWu0CmGbbQbC/r/dbi8GOrn6dQQ22+0dPdr9zhGRLKAZwSYqAIwxTxpjBhljBrVp0yZK0VOPqirDQTsqJJC9h8v44WMz2X6g+oL/6PYzmPv7Eb79wlaNg84LHCzdPmVhSldQmzRjOQAfLtvKrO93MX/DniRLVL+57OTOYY97pUZPZUtt04DIp6Ob5fHo5Sfyz7EDAFi/6zBXPTM3rivzv92w128/O84zkmQQ7SeaBlxtb18NTHW1j7Ujh7pgOY/n2ualAyIyxPYPXBVwjvNaFwOfmHCVO9KQBz9YQd8J73tGA72zaAvzN+z1K2nYpkke+TlZrP3L+Uy9aSjnHx881RePidfGgIiTVMGdfqOsoorL/jObHz72dRIlSn/csfvPXXMS94/pm0Rpas8VQ47hgn7tKbU/xx1vLOKLlTvo/6cPeHnuBn7y1BwKx0/n9L9+EjcZ4m2iSgY1+hBE5GVgGNBaRIqBCcAkYIqIXAdsAC4BMMYsFZEpwDKgArjJGONU2rgRK2KpETDD/gN4GnhBRFZjzQzGxuST1SMcp++BkoqgkdDCjXt92307FDCsR1sKGlk/m4h4Tv2tY8FtoWYhyeaF2et9251a5idRktjxxOffk5+TyVWnFCbl/dfvDK/83dfZsJ5tw/RMPY7v0Iyf21F0by+0DA2b9lbn97rzzcW+7Y27j2CMicvsJ1y22PpKjQrBGHNZiEMjvBqNMROBiR7tRUDQMMQYU4KtUBoqjtPXnWGyssrwxaodfmGDAzu34DcRli/0ylZZmqIrOncerE5a1jrMeor6wqOfruZv768ASJpCeG+p94K0U49txXdb9tOtbejEdqnOpSd18o3ObzjzWL5cFd6W/9XqnZzePfYm5pys9JshpN8nqof4FILr1/jnx6u45tlv/Prl1+BIduOpEMpTUyFkuT54fbcWHimr9CmDVMEdZVZZZejuyntVHzm/b7WJNJJ6y1c+PTee4qQVqhBSgHKPFNbfrA32q9cmCsRrhlxaEVwnN5ks37qfwvHTfdN+gCrXV/HqNxsoHD+9XuWqeWnuhmSLEMSAez/wbQcWU6qPNHYNjPKyk/cIu2fq0qCV4PUdVQgpwP4Sy7bvTmCX4fHL5OdEPkPwuuVLUmyGcMML8wBY40rRXOZSjne8YdmCU7GmQ1lFlef6jsDw3le/2cARj4L1ieSAfX0ZYyhavyfqDKipgnuw07Kx9wzhk1+fyUe3nwn4V1qLNf/4aFXcXjsZqEJIIV53+Qu8TD75tZghlLhmA04U0vyNeyirqGJR8V6Wbt5XB0ljQ4bHSPXJL9YEtS1wOdZThR53z+Dy/8wOar90UCe//TveWMzv31oc1C/RGGN481sreGGux+yzPvDKuCH8ZEhncrOq74PmIUxGXds08flJFhbX/VoPZcr0Ct+tz6hCSCHcCsFrWt+oFgrBuVHGn9eLO887DrAqTPW4ewYXPTKTCx7+qo7S1p1MD6Xn9fCfsSQ1C6DM8Xiwej043py/iT8HFDaKN/0CRsUVVYb1u+u3eWNI11bcP+b4oPYcV/hnr6O8/SN19U2FCshwh4NHypvfFvvqVaQaqhBSDGfxmFcEQ0UtLr5hPdrw35+dzLjTu9IixLQ62Q7cSOv27jyYmjePF6HMMV4zn3gS6HcZ8+hMvtuyP6EyJIq+HQp821NvHsrSP/mSLHOOXeinrhF2h11mvwGuUO+DtcwkvP1ACbdPWch1k7+puXMSUIWQZALXBvzocWtBltdzZUMtRngiwtBurcnIkJBpLpK9LiHS6XaH5nkUjp8eVW7/RFNSnhqO+7KKKtoVVIfwLt28nw+XbUuiRPHDnU8oNyvTz+k8tFtrAA7V8Vo/XFZ9fkGjbJ9SiGaGAKELXoXjnx+t4tmZa6N6v0hRhZBE5q7dTd8J7/u1Ldi4l50HS/1W7zrEOnY82dbPSEdtL8+1Ujbf8OK8eIoTEYXjp1M4fnrI4w9+sDKB0nhz6ROzWLPzEIOOaZlsURLCce2tGcJfL+4XdMwZDNV18OOeIVRVGS4eaGXi6VHLEN6v7DUT0QRKPPTRyrinsleFkETmrt3l2X75f2b7LnKA/1w1iCd+ciLn9Y1tNsqKSsNf3v0uaaPaEzo399u/sF/7pMgRKe58UjVRlMRCM3PXWb6NpnlZPHL5CUmTI1Fc2K89b/7iVC4Z2DHomDNbePWbjXUKX3YrhK9W7+SywVauqGaNaldd7vYpC6N6//0u01Q8Z/aqEJJIWYjp5sptB/3C6Xq0a8Kovu1jnjvlzL99yr+/WMOwv30W09eNlJO7+KcObleQV+M54Ubn8ebBGhacuR84rZvksvCec/xKm7rNDvHC7RfKycqgf8fmQX0cu3q6ICKc2LmFZ3qKpnbW18c++56HP44uRHT7gRL+OG2pX1tmhpCZIZRVRj6Yqkv49Jcrq1djn/nXT6N+nZpQhZBEwo1YnPt64YRzOMYjm2ltCRyNQ3V8erLSGFS6Hl7rJl3Aym2pnfp6nUd+oL2Hy1hshzXuPeLv/G4WkMO/9z3+5sF44H6/TXuOkOuxcOtXI3vEXY5Uwe0/i3YR2R+nLfWLfrvqlGMAK7qpNj6EQD/Gnggjjf44bSk3vfStb3/XobK43SuqEJLIE59/79s+o4d/rhXH4RqrBFqv/fwU/vrjfp7T6q+SlNfdiaiae5eVFqumnDQOyYiOOlJW6TPFuBn75Gx+8IgVwnuoNPkO5UOuWcjHy7f7xew7JGKmkio0zq3+/LuiDPV0L+j80QkdfGHc2ZlSKzNU4ATGKwy4orKKK56azTeua23V9uCH//44LdZUhZAinBgwgnfCFCMNzayJrMwMLj2pEwUhbJ7baijRGS2VVYZXv9ng6SSvDBNiG45kJOn7fsdBz/bldkGf8soq3wjwssGdPPsCrIrjLOjTFdu56hn/vD1eqR0Cq+ulM61dNaKjHUfMWVPt67vrguN864FysjLDXos7D5b6ZSsODBv3irLbvLeEmat3cckTs3xtXhaCeN0DqhBSBHfGT6gezeTE2G/ws9O7eKbMPvnPH8f0fRxe+WYDd7yxmOe+Xhd0zLkhnBXLD14SWU3fRIfLllVUceG/wi/k6/77GT65Lux3dMh+0Y5SI+GaZ78JCmf0un7aFtT/jLKR4l7JHO2q4kMuh7JbweRmZfjlIVu9/aBfXq6L/vUVox+d6dt3BkCFrawU7/e/syzoWs70sAi8NCc4P5ZX7ZRYoAohibRxpXoOVffYK71DXWjfrBFPXTUopq8Zjt32orI9h4MfhM5ndlYsRzr9TnSZzT4T3vNtH9MqdL0GZ4bgjoOffstp3HJWN9/+jgOlQefFi9l3jvB0tLZtWrPzPh2JRiGEq0eek5Xhd82e/dDn/PLl+b79zXZZT8fEOezBzwArrQZYFdgmTPV3VrsVeLiHfrzye6lCSCKNXFP3a4d24b7RfRLyvi3yaxcqVxec+8nL9PUXu2xmpq30WjcJXlH9+BUnAlZN6Ycvs0Ioa5PTKRa4HYcTftA7ZL/rJhcB0MRlt+5zdDNuP6e6hsUvX57PXWFyGwXOFKPlsStO5Khm1oN/+X2j+MWwY2PyuvWZaCYI4TIEB/oQnNc3xvCiq+hToOP5uPbVaxfe+LbYb5bgfr+3F24JcoQvuOdsrjrlGJ9SiTWqEFKEds3yuPKUQs+HYqxJZOk/Z1QWrmKVoyzOdoVDzr5zBJOvHcx5x7fnpetP5n83DaW1HYqbzEI/kVTeapIbXuG+NGeD50xh5uqdDLr/I96aX+xxVu0Y1ad6zUpediZF67RGdTQKIVyG4JysDL/svA6nPfApE1xhqiUBSuXs3v7riQ67FII7SuyutxZz2gPVIabrJl1A8/wc7h3dl5MK47PosE5PBhG5VUSWiMhSEfmV3dZSRD4UkVX2/xau/neKyGoRWSEi57raB4rIYvvYw5LK1b5jxDuLNvulonBmCx/fPszXNmZAaFt0XXnyyoFxe2031dXgQvdxZgjun/2oZnmcaUdenXpsa9o0zSXbdj7/5rXoFvfEghM7taixTzsPG/3rN5zit3/980VBfa54ag4AD75f99XOgaZGrwiphoZXSvmaOBJm0aYVdhqsEDbtPeJnaioJSH/urI1wePqrtSzfauWZumfqEs/36tEuMaHhUSsEEekLXA8MBvoDF4pId2A88LExpjvwsb2PiPTGqpfcBxgFPCYiztz6cWAc0N3+GxWtXPWFm1+a77efbY/am7nMOfEMrjynz1HcMaqXX3H1wvHTYx7S+cinq4HwtvNI3SROmGrxniM19Iwd7u+jQ/NGfr9PKLzGM4GRIl5ZXS/qbw0Azu1T+xXpNf1u1wwtBJK35iSZfPm74YB/8aVICbeKPycrwzdbDefXCpxldGrh74f69xdrGPWPLwHLr+DFad1iXwLUi7rMEI4DZhtjDhtjKoDPgR8Co4HJdp/JwBh7ezTwijGm1BizFlgNDBaR9kCBMWaWsa7q513nNAiah3jITF2w2bM9Vtw47Fgut5fgOzzwnv9q3H2Hy/kgRH3e2rA3wAnmfoBFOiF0+/diZWuvCbd5qi41dCNJXV7QyBo5boxiAVVNZrQ7zzuOG848ljduPLXWr13f6dQyn6HdWvkthIyUcMWNcrIyfT6EcPVF1uw86BcOHOo6Chc9d9PwxPiA6qIQlgBniEgrEckHzgc6Ae2MMVsA7P9t7f4dgI2u84vttg72dmB7g+HJKxMX9RNIRobw9s2n+fYDUyT//MUixr0wj8176zYqLwowWRwIcfGf0rVVyDj+3kdX53damaBII/f38aMTor8sIyl/6ii8aLKShouGAeshNP68XrXOvZMuZIjU+B154Q43XvPn8/2O5bicyuFMSz999hu+WLkDgNPs7KtTbxrK4C7+foC+E973zRIDadUkMaHCkddkDMAY852IPAB8CBwEFgLhAsS9hoEmTHvwC4iMwzIt0blzZ68u9ZLAC8PhguMTk+zt2LbV5oyh3fzzCzlx7adO+oR1ky6I+j227fcf0VfakRfDevpPhV8eNyTka7gfZtGM9qLBWT3dNC+Lm13ho158dPsZdGvrnf0ykllQh+aNai+gTaK+j/pKZobUOux02kL/GXqgXybHXocwYeoSJs9aTyQM6Wrd6/07NWfKz08Jys0V+J6Jpk5OZWPM08aYE40xZwC7gVXANtsMhP1/u929GGsG4dAR2Gy3d/Ro93q/J40xg4wxg9q0SYxNLd6Em8Kf2SMxn9Fdq/nP7y735VipqjJsj1PcvFNIZsRx0SVaW74lMTMEpwLX3y8d4Huon1To71i+dFBHWjfJDakMHJyEhZ1aWg/+QIfkup21z5HvUFXP6yTHm6yM2s8Q7naFB7943clBx99dvJVV2w+GVAZdWwevMJ652jvDcSBOuPWxbRonNGNtXaOM2tr/OwM/Al4GpgFX212uBqba29OAsSKSKyJdsJzHc22z0gERGWJHF13lOictcdvPBx4THLVSYEchnJ2krJRPf2UV4YiFMgiVsrvC9vB5lQoNh1MwfWKCSlI69t5WrnDgl68fwvL7quMefjeqV0TprmfdeRaL/ngOG3db5rfABGWvuUqoTl2wKSL5phRtpHD8dPYcjs9CpXQhGpPR/pJqg0c0cY8XDwrOGxa4sPFHJ3Tg52d0Dep33vHtWTfpAj7+9bCwK99jTV0D0t8QkWXA28BNxpg9wCTgbBFZBZxt72OMWQpMAZYB79n9HcPbjcBTWI7m74EZdZQrpTkUxlEF8NX4s5h289CQpS/jjRMOG3gTRBOB5L9wp/p8Z6VlZi0Vwr2j+9bcKYY4+WfciisrM8MvH1CoinSB5GZlUpBXbfZ6b8lWXpi93rPe8q2vLIjoNZ+buQ6A4fYqWMWbw2WVdYpO69wy9Ar1UJRVVAXV+LhjVC+//b//vwHcef5xUcsVa6L2IQAYY073aNsFjAjRfyIw0aO9CEjsnZ5EFhXvDXu8IC+bfh557BOFs5I58FG9+1BZxM6tv8z4jme/WscVQ6p9PRVVxpe99Zd22G1tB159bMfy8J5tqKisivsiO8e0lRUmiD23ltFHYwYczf8WbObzlTtYZKfOvmVEd07p2opZrkRqz3y1lmtP6xL2tbI8ct9E8/BKd+qa0beTx3fa66imvuSGXjTJzfJboX9C5+ZJG+RFiq5UTgKrtlmZMxOdgiEci/94jm/bZxMNeNYMvP+jiF/v35+voayyimftESz4h0au2m59B+GiM7xwFMCnK3YwyU59EU98pq0wachru47y13YqC0cZAEya8R0LAwYK975jlUssr6wKmbsm8L07NG/EF3bcvVJNNA57R9F7+QKAkMogO1OY+MO+XH1qoa+s5wXHt+fl60MHTEy9aSi3jexB/47N+OC2M2ota6yo0wxBiY6OLayLM9wFkmia5mVzfIdmLN5U/ZCSWo/fw1NSXhlkXjlcg/ksHE99tZa7LwydWygWVFYFm4wcjm3TmO+jKJbe0mOU+OLs4IyWAG8v3MxH321j6oLNrP3L+UEKIDAv/q0ju9danobAJjtsuqrKRJwwcnCXlmzee4QPbjvT8/g/xw4IMu29dP3JdGndmPbNrHs8OxOW/ulc8nMyww4c+ndqTv9OzZP+++kMIQk4dvXa2s/jzbSbh/rth1uUEw1ei6caxTg3/3Mz1/LkF9+HPH6gpJw/TlsacR3p8srQJqOPfz0sqlDc2nzmX74837dAsbzSsGnvEXa5FuUFpkH48YnBjkyleoYQav2LF5VVhhb5OSHv08KA1ee/OacHpx7b2qcMHBrnZtV6FpksVCEkgRv/a5XDC2eGSAYiwnHtqxd/HYpxZa0/TVsaFOlx+cmxXU/yx7eX8ed3Q5uSzn3oC577eh33T18Wss85D33O459ZSmWmbXv2ylMfLdGmND9cVsHQSZ8w8P6PWGuHqLbI959tpNogI1W4abi1hqQ2g5yKKhP2HnWvPn/2pydx81n1f3amCiHBzHQ5t2obcpkInALslVUmqpWd4fhg2TZmLNni22/ZOMeXwylaahv55OSoX7jRO9XA2p2HWLntIA+8ZymVt+Zb4Z/ZMf6tTu/e2rM9cGGgG3c+qNW2DybUokbFH6eU5ucrt/Na0cYaelts2HU4bDBBnqs8aW0DC1KV9PgU9Yh7364embrjnFMFZ9RTUl7pi7BxU5sasg6ndK1+yN380nzfIqrdMageVpsi51Bd3nLxpn2s23mIz1fuYL9diKT/nz5gnEcWUoj9yDtU/WhBfMnYAnHXUfj6e+t894MoFQcYqcJRBVZtiDveWMxvX1/kmaXUzcMfr2Lr/pKw0Ul5OdXffaJSS8QbVQgJZvuB6trFx3dolkRJvHHs2yXllb46yH+zIyUA38OzNjhJ2xyiSd4Wih11SHI37MHPuPqZufzixW/pcfcM9h0p90U/AX5pBWJV27omlm3ZT6eW+fzHo6rdN66aBkts57+zZuTT3wxjzl2e0d4KVtCEm0c+WR22/98/rDkFuXstSmHr9Aj1VYWQYNz5eOpqLokHziDziGuG4I4MckJma0NgQrUz//ZZ1PKBv1O2spYzhFKPgidfrd4ZduaTk5WRsKRwT11tKYKaVqk7yuF5O0S4c8v8tBmlxoPAbLOvzysO0bMWr5ntNhmlTgh5XUi9J1Ka484blIo4D5hX5m70+RDcD5p7pi5hy77arfgMFVp6TpSpOWbfOYLfnmvF8h8pr6S8soryyqqIZi+lNZgKvPjduT1jXtv6Uo+0Bq2b5HBi5+pUJkc3C1/72O0/UWdyeJoHKPRNe4/w1JdrWGjXpZjyjZUC5N3FW9i6r8TjFYLJzszgmqGFvHHjKTV3rieoQkgA7hvXSeE8/rxeobonlf6dmgOWbdqZIbgfNqu2H+SUv3xS4+u4c7671za48crjFAnN8rN99RCK1u+m590zGP3ITG58cZ6vTyhn8/RFWzzbwxEPX88lg6rzPP7oRCutdrsCfwXw2W+Hc3SzPCb8oLevj5t4FVpPR7xWCN8//TtGPzqTPYfKfFFnv/jvt4x5dCbHtvFejBbIhB/0YeAx6ePYV4UQZ16Zu4Eud77ry1fTpmku2ZnCDWemZtHzq08pBGDP4XKfDyErQ4JGQTVl12wegWmsLmb5E+yR9M4DZVQZy/buziTpXiFdV3q2C5/FNBoGumYCD17cn+tP78KkH/Xz65OTlcHXd47gmqFdPCudBRYzUqLj9XnFfkp/6/4SmuendoqJeKEKIc6Mf9OKDHnyizWAFQcd68VYscSJsntm5lqum2xF3GRmSJCNtKYFPm518csQdQQOlUa/8K3Qzhr5fohqbve+s4w5ayJLNeyFu+b0+cfXvqRlTbhNUBkZwu8v6M3xHUMHGbR2me1OPdaK2nIW1108UBej1YXdh4Oj3eat3+PRM/1RhZBgDpdVpLQfwcvSkp2ZERRnfbiGRWvu1zm2jXcd35pC/8JxlG1fXxZQ4c2NV1hrpLb2M3q0YXBhS1o3yU2JVaZuE9jwnlYRQmeNhNvvoNQeZxGiogoh4Rwuq0yppHaB7PEYLWVmSFDaiZpH99UPsFD1hOtS5ctLqTYNyJNUFqBwKiqrqKwytG4S3hzw5e+Gk5edySvjhjDrzrOilrEm/vzD47l5ePgqbA5jXOU7uwQkW5tZx0yeDY2pNw2tuZNNtH6u+ooqhARzuKyS/NzUVQhOzVc3WRlC3w7N+L0rb3ttZgh5IUxkl50UfdoKr0VYB0or/KJ3Akf2joK4/vTggiQPX3YCb9x4Kv+5apAv1XFGhsQ1NPjykzvzGztaqiZyszL5v0v6M7xnG0YGRGfddnaPeIiXdvz67B78blRPeh7VlCa5Wdx9QXAdArdp7owebfjvz4IrpaUzqhDiTLYrF8o363ZbJqPs1DUZeZlHHDPL9Wd09WVoPVRayaLivSEre7nH/l4O6BevO5nCEGmFIyHUg9q9ujowgZ2zBiHHI81AfnYmA49pkbQqdZHw44EdefaawYCVadNB6x9Exi9HdOcXw7qRl53Jkj+dy09PLfQ7npuVwW/OqVauLfOzQw5m0hVVCHHk85U7/FIrXPLELGav2U12VvJt0rXB/fB1csIcLqvgokdmhqzsVVOOodNC5PKJlFCugA+WbuO2kdZN/bvXF/kdcwqY52ZlkhOgUOpbHH8PV+STl4JTaiawuFJZZRVTXHmOrrQj7hoSda2pfJuILBWRJSLysojkiUhLEflQRFbZ/1u4+t8pIqtFZIWInOtqHygii+1jD0sqePFqQeH46RSOnx6UDO6tb71XQ0ZaaDtVcD8sHdt9TWVADVa92H9fOZBOLfP52WlduHVE7LJBhrpEDpZW8Ivh3iG9E6YtBawH6M9O969EFmqtRKrirJwe0jV9YuCTjTFw2eBqM+YAe01OQyJqhSAiHYBbgEHGmL5AJjAWGA98bIzpDnxs7yMive3jfYBRwGMi4szHHgfGAd3tv1EkifLKKuZviC7kbNX26gpKz89ax3x7FWR9x22v980Qago7NVau/nP7WCGbd1/Ym9vO7hGy+lRdeHVcdaGhkce1q9Hu3yg7k1+N9Le7n9C5eczliidHN2/EP8cO4JHLT0y2KGnFJYM68eSVA5l954h6N2uMBXWda2YBjUQkC8gHNgOjgcn28cnAGHt7NPCKMabUGLMWWA0MFpH2QIExZpax7AzPu85JOJNmLOeHj33Nym2ha6W6qXBFsjR2Rb7cM3Up63fFLolbIim6e6RfnQJ3LQBnhuBOE7HHI7zTGOM5in/nltOYd/fIWIrLSYXVo+QxJxwNhDejlJRXBh0PTH5WHxg9oIOfE1SJDef0OcoX1tzQiFohGGM2AQ8CG4AtwD5jzAdAO2PMFrvPFqCtfUoHwJ2IvNhu62BvB7YHISLjRKRIRIp27NgRrehhcUwHuw5Glpr5gGuFo7OYJTAC58Zhx/rVdE31NMWtm+TSyrXUP8vPZGTNENxFaK6b/E3Qa4TyIOTnZMUsCdvI46xLy1nk1bpJLhf2sxTCtUO7kJOZ4enLcPTUMfbitqHdWtHrqNivRlaU+kbU4S62b2A00AXYC7wmIj8Jd4pHmwnTHtxozJPAkwCDBg2KbfWWgHc2IR9p/rhHyr96dQFjTujgq2blcMeoXtwxqpcvnfJHt3vXaE0lfjLkGP5lpwh2O2C9zDHfbtgb/AKmbqkpIuHRK05k72Hr+1836QK/h3/TvCzKKqs4Ul5Jfk6WX6STE1r76a+HYah/DmUldvz23J6s2HqAv1/aP2EpzlOZusQ/jgTWGmN2AIjIm8CpwDYRaW+M2WKbg7bb/YuBTq7zO2KZmIrt7cD2pODk7p8wdSkfRvDg9kowdsHDX3n2ffMXp/Lwx6vo0KKR5/FUol1BHv93SX8Wb9oXFI2RKuRmZdKuoDos0G2iamPPQnYcKOWLVZt8+78+uwdt7SRysc5gqtQ/bopwYWBDoS53+gZgiIjk21FBI4DvgGnA1Xafq4Gp9vY0YKyI5IpIFyzn8VzbrHRARIbYr3OV65yE44wWV20/GDTS9+KT5dt922MGHB2274mdW/DcNYNTsg6CFz8e2JE/XtQnor5LN/tH6VhTv+Q9cJ1CRJ+t2MEf/reEG+xMqF+sio+pUVHSgbr4EOYArwPfAovt13oSmAScLSKrgLPtfYwxS4EpwDLgPeAmY4wTu3gj8BSWo/l7YEa0ctUV90Psm3W76fWHGXyzbrdn39KKSv7x0SrffnmMaxCnKi09UgkHprKwnMqJkiiYRrbzuzigOpu76piiKP7UacmsMWYCMCGguRRrtuDVfyIw0aO9COhbF1liRZYrombhxr2UlFfx9Jdr/SJZHJZv8Y9E2rDrsM+OnZedQUl5FdcMLYyrvMmgY4tGQYnjxr1QxIJ7zvHth3IOJYoz7IVvgYn1vNJIK4pikbo5FJJEC1cedOP7HzzyLymv5F+frPJrW7xpHzOWbLWPV7Fu0gVxkzOZeGURdZy7DiYBTuVw5LlqQ7t5/1dnJEMcRakXqEIIwF1bN99+qHhZgnr94T3P8//z5Zq4yJVKFO+puYSmwXsdQqJwMqweDlAIGlGkKKGpH97NBOJOjrZ0c+hc+4FcYS/kKqiHC5zqgjtjpKNM73tnGSXlVTVWVYsnThGiv2pVMUWJGFUIAbhz6M+yK255pbK4oF973/ZF/Y/mzB5tgGob9fRbTounmEnlo9urzS5N86onmc99vRaAp7+y/geOzhNJQ8tSqSixQBVCAGUVwQ+xnR6rlo9yFUT/68X9fIVlnIdhOs8UurWtXtXr/pyrth3061deEX1FtLqipiFFqT2qEAIorzR+NQxCsWF3dThjXnYmx7Uv8DveUBY9udNQvDbPP7vrXo9Fe8nkH/9vQLJFUJSURhVCAGUVVT77czg+XLYNgA9us8wngbH5qZ6vqK58+bvhvBRQTeqWs/xXfTrfUbL4dUAlsb4dCkL0VBQFVCEEUVZRFbIGsMPyrdXOZqdQSVWAdcRr8VY60allPqd2a00TVx3jNTsP+UVpJRv373jfmL5+pi5FUYJRhRBAWWUVWRnBX0vh+Ols32+lQ/CKw2/d1F8B1Jf0FHWl99EFPHvNSQC8s2gLv3p1fpIlqsZdWjJPq4opSo3oXRJAWUUVPUOkQl66ZT/GGF6ea2Xxvuv8Xr5j+TlZPPtT68F47dAunuenK8N7tvVtv7t4q2974DEtvLonjHP6HMW4M7py1/m9uHhgx5pPUJQGji5Mc7H3cBnLtuz35ckPpLLS8Oa3m3jbrs07oJP/A294r7Zpuzo5Gp65+qRki8Bd5x9XcydFUQBVCH78/AUrI+aMJVu5dFBHphT5R8387Pkiv/2GWHM1FJ1aNmLjbv8VzM3y0zf0VlHSETUZudhxsNS3fe/omnPthSvT2NDodZR/BE9Bno41FKW+oU80Fyd2rjYB5WVnsm7SBaR59GjMCAwxvaiG2hCKoqQeqhBc9OvYDIBLXA7IN2481bPv+PN6ebYrFplajlBR6h2qEFzcM3UpANeeVh0ldEJn70iZG848NiEy1VcaykptRUknolYIItJTRBa4/vaLyK9EpKWIfCgiq+z/LVzn3Ckiq0VkhYic62ofKCKL7WMPSzLzJgONc8LbvzWnfjC9A1J36AxBUeofdSmhucIYM8AYMwAYCBwG3gLGAx8bY7oDH9v7iEhvYCzQBxgFPCYizlLSx4FxWHWWu9vHE85Nw61Rf6eWjcL2C7VOoSFz3Wn+ay9ys3XyqSj1jVjdtSOA740x64HRwGS7fTIwxt4eDbxijCk1xqzFqp88WETaAwXGmFnGqj/5vOucmGOMoWjdbr7bElzroKLSkJuV4VnYpb/tX1C8KWjkH2LaNI2zvSpKuhKr2MCxwMv2djtjzBYAY8wWEXGWsXYAZrvOKbbbyu3twPa48Pys9UyYZvkKAheRlVZUkesRSvrdvaPIyhQyRJJaJziVCUzmt6h4b3IEURQlauo8QxCRHOAi4LWaunq0harF7llqS0TGiUiRiBTt2LGjdoLazFiyxbcdmJPoua/Xsb+kIuicRjmZZGdmkJkh6iwNQWD9gUjKbCqKklrEwmR0HvCtMcYJRN9mm4Gw/2+324uBTq7zOgKb7faOHu1BGGOeNMYMMsYMatOmTVTCNsmtNmX846OVUb2GEkygQujU0jv9h6IoqUssFMJlVJuLAKYBV9vbVwNTXe1jRSRXRLpgOY/n2ualAyIyxI4uusp1Tsxxl8h0l388UFJOdqZwUX9dUBUNgQrBqTGtKEr9oU4KQUTygbOBN13Nk4CzRWSVfWwSgDFmKTAFWAa8B9xkjHHqVd4IPIXlaP4emFEXucIxwOUcbt6oOmX16EdnUl5pmLbQc3Ki1ECgD6G71h5QlHpHnZzKxpjDQKuAtl1YUUde/ScCEz3ai4CakwfFgNN7tOHVoo1s219KiasI/JodhxLx9mnLgE7NGXtSJxZs3MvyrQdo0zS35pMURUkpGlwGspMKWzLnrpH0+P0MDpVZCmHDrur6yMN6RuebaOhkZWYw6cf9KKuo4kBJatVSVhQlMhrs6qGyyir+O3u9b9tBC6nUjZysDFo10dmBotRHGqxCADhQWsHXq3fy//49y9emKRcURWmoNGiFAHD5U3PY5VqPMGvNriRKoyiKkjwarEIY3rMN+TmZQe1aclFRlIZKg1UI3+84xOGySr+2T38zjLzsYCWhKIrSEGiwCmFot1ZBbRoqqShKQ6bBKoRG2cERt/k6O1AUpQHTYBXCrkOlQW2auE5RlIZMg1UI487o6rc/olfbED0VRVEaBg1WIbjLZI4ecDRP//SkJEqjKIqSfBqsQnBn59xzWFMtKIqiNFiFkOOqjNa+IC+JkiiKoqQGDVYhtHMpgVtGdk+iJIqiKKlBg8t26mbF/aPYd6Sctk11hqAoitKgFUJuViZtm+raA0VRFGjAJiNFURTFn7qW0GwuIq+LyHIR+U5EThGRliLyoYissv+3cPW/U0RWi8gKETnX1T5QRBbbxx62aysriqIoCaSuM4R/Au8ZY3oB/YHvgPHAx8aY7sDH9j4i0hsYC/QBRgGPiYhjr3kcGAd0t/9G1VEuRVEUpZZErRBEpAA4A3gawBhTZozZC4wGJtvdJgNj7O3RwCvGmFJjzFpgNTBYRNoDBcaYWcYYAzzvOkdRFEVJEHWZIXQFdgDPish8EXlKRBoD7YwxWwDs/05OiA7ARtf5xXZbB3s7sD0IERknIkUiUrRjx446iK4oiqIEUheFkAWcCDxujDkBOIRtHgqBl1/AhGkPbjTmSWPMIGPMoDZt2tRWXkVRFCUMdQk7LQaKjTFz7P3XsRTCNhFpb4zZYpuDtrv6d3Kd3xHYbLd39GgPy7x583aKyPo6yB9rWgM7ky1EDaS6jKkuH6iMsUJljA3RyHhMqANRKwRjzFYR2SgiPY0xK4ARwDL772pgkv1/qn3KNOAlEfk7cDSW83iuMaZSRA6IyBBgDnAV8K8I3j+lpggiUmSMGZRsOcKR6jKmunygMsYKlTE2xFrGui5M+yXwXxHJAdYA12CZoaaIyHXABuASAGPMUhGZgqUwKoCbjDFODcsbgeeARsAM+09RFEVJIHVSCMaYBYCXdhoRov9EYKJHexHQty6yKIqiKHVDVyrHjieTLUAEpLqMqS4fqIyxQmWMDTGVUazQf0VRFKWhozMERVEUBVCFoCiKotioQlASiiYubDjobx0bEvk9qkKoJal+kYtIqte4yE62ADUhIq3t/ylbLENEmrm2U/WaTPnni4gMEpG2NfdMKgm7Z1L+B0sFROQ4ETkFwKSoF95OPf4f4KRky+KFLd9rwIMi0jvVHrZikS8iL2MvpnStk0kZRORkEZkKPCUi14pIbqpdkyIyWEReBP4iIseLSMo9Z0Skj4h8DUwAmidZHE+Scc+k3A+VSohIM/sh+wpwn4hMFJFuyZYrEBG5Hiv87Ftgfgo+bNsCjwDvYi2zvxW41j6WEqNbY3HY3m0tIjcCpNLDTET6AY9ipYl5DTgLSJnrUUQyRGQC8BTW4tIs4Cas1Pipxq3AW8aYHxhjVkLqXIuQvHsmZS72FOW3WKG5/YGfA62AwqRK5E1n4PfGmMeNMSUpOLLtD6w0xjwL/B/wJjBaRHoYY0wq3IgikmXn3toGXAfcKCLNjTFVKaQUBgOrjTEvAB8CeVjZAIDkP9CMMVXAeuCnxpj/Yi1CPQZImQGKiGSKSEusBJqP2G0/FJGOWJkSkv492iTlnkmVCz1lEJEuItLI3v0PcA+AMeZ7rKnl8UkSzYctY6693RJrlfdcETlLRN4XkbtE5Ef28YRf3CJymYj8SUQuspvmA4NE5FhjzCHgG6AIS8kmxQznkvEHtgwVdrr2LsA64HNgvC1zVaLlC5BxtN30NvBDEZkILMZKBPmwiNwBSfsezxSRk11NrwALbFPWLuAA0D7Rcrlxy2gPlg5j1XI5yzZt/Ry4H/iH3ScZ3+MY+769wG5aQBLuGVUINiJSKCIzsKa7L4qVtG+9MWaznasJ4AjwfYrI+JKIHGeM2Q3sAv6LVVjoMWALcI+I9E/kxW3b4W8Afof1UP2biPwMOIhV+OhWu+te4CMg3x6VJwwPGR8UkWtEpLGIHAOsNcYUY43AfwG8JiK5IpIwx56HjH8VkXHGmG1ALywn413GmCFYOcBOE9vHlUAZm4rIm8BbwM/tgQlAqTGmyhhTan9nHYEViZQtjIwtAIwxJcCzWOa3940xo4DfA31F5LwEy9hGRP4H3A7sxqovc7ExZgfwBla+OEjQPdOgFULA6Pk3wBxjzAjgUyyfQR/7mGOC8RX5SZQZIYyMnwD3i0gXLMfY8cBmY8xUe5r5LlaVuoRhK59TgEm2DDcBw7ByW70LdBORkfaIexfW97kvBWQcCZwO7AG6iMjbwN+wZgnr7Sp/5UmW8UwROc+uNtiN6qJS87BSzJcmSj6bMqxr8CdY6eovdsnucBywzRiz0n44D06yjJe4jj2GZSJqA2CM2QR8BSR6NngsMNMYc4Yx5gng18Bt9rGXgV4iMiJR90yDVghYNlh3qOZSAGPMI1j22stFpK2dorsbsNsYM18sh+MfRKR5EmV8FBiIVYt6B9as4WLXeW2Br+MtnIhcZU/JnRHid0AHEckyxnwELAGG2DK+BPzD/i5HYBVHyvF63QTLuAg4DegBbMLK3DvQGPMDoJOIDEwRGYeJ5Wx8H5hgDxacOuW7Eihjc2NMKdY19xGwEsu80cPu51yrLYHDIvJTrGvx+HibMCOV0RhzEGv0fbWIDLDv6ZFYM7K4Yss4TETysRT683Z7JtUlBMAyC74C/DNR90yqx6zHBRE5G2s6vkJEvjDGTBGR3cAJIrLS7rYEyyHWCmsE1hU4SUQ+BUqAX9k1pJMp41Ish3JnY8xdItJLRCZhjco328fjIZsAR2E94KuwzGiN7ZtqI9ZspRuwHOuCfghoZYx5UUQ6YRVS6gVcH6/vsJYyTsFy3L2G9buWuV5qhDEmLiOyWsr4Ktb3eLQx5t8icibVkTzXGmPiUiwqhIzjRORWY8xOu88sLGV6KXC/MabCPv1c4DKs2csVxphFqSIjgH1Pid3WB7jSru2SKBmvB241xmwTkUx74Hkc0MyWrwp4zh4ExP2ewX7TBvWHdYPNwTKnnIA1LfsF0BT4A/AO1tRxkP3j3WKfdwWWjW9kCsp4m31egX3RnBNH2TLt/z2AF+3tLKwp+GQs+/YzwJVAM/v4c8BE12vkxPn7i0bGycC99rYAGSkq4332djZwVJJk/BfwRkDfH9qydwPy7bZTgf+XojI2BrKd3zuJMr4Z0Od54FJ7+yjXa8T1nnH+GsQMwbH3G0vjngzMM8ZMtY99hD06NMbcJyJdjTFr7GMzqbbNvmKsULpUlvGAMWY51ogy1vJlAfcCmSLyLpbyqbRlrhCRm7Gc2b2xlNQYLIfiX7BGRD7zlfEfgaeKjJVYShhj3YFxccbHQMbZdt9yYGuSZLwF2CwiZxpjPrfb37JHt+8BTURkuDEmbibLWMgIDAe+s3/vlJARKwBjrYjcC/xIREYZY4rjdc8EkvY+BBG5BssBd5/dtBi4TEQK7f0srOnbQ/b+Wvu8cVjx6N9CfFetxlDGeF3YZ2LZOlsAq205y4HhjqPQVmT3Ag8Yy+b9JFb0yxz7vM/iIZvKmBQZjS3jH13nXYIVqfMp0M8Y853KWDsZbR/CtVgLDwuA4caKeEsciZiGJOsPaxTwP6xwx2+BXnb7P7DMMDOBF7FstdOBdvbxX2HF/Z6kMhqwInCudO0/hlX29KdYMxmwBhdHYdnhC+225kCHBP3WKmPiZZwCdHGdd7rKGLWMx2BFHP0DODERMnrKnaw3TtgHtByuAJOAV+3tTKwIiNPs/U5Ydu5cez9fZfSTLx/IpdrOeQXwF3t7AfBLe3sQ8HKSfmeVUWWsrzK+kgwZvf7S3mRkjHGW9v8DK8b8XGOZf/YZY76yj92AtXqxwj7ncNALNWAZjTGHjRWL75jNzsYKIwW4BjhORN7BmtF8myi5VEaVMU1knAcpkjIj2RopwVr758Dnrv3BWJkt3yXOERvpICPWrCUDK9yxm93WDcukcRoJMmuojCqjyhifvwZTU1lEMoyVqOx1rCiOUqwFK6uMlaco6aS6jPYIJgdrsc9bWA6wXVjT3/3JlM1BZYwNKmNsqA8y+pFsjZRgbZ0PfIGVTvaWZMtTH2XEWnVchbUO4rpky6MyqowqY+z+GswMAUBEfoMV032HsZa1pxypLqNYaYKvBP6eivKByhgrVMbYUB9kdGhoCiHDJCmVcaTUBxkVRUlPGpRCUBRFUUKT9mGniqIoSmSoQlAURVEAVQiKoiiKjSoERYkQEakUkQUislREForI7VJD5Tyxyp5enigZFaUuqEJQlMg5YowZYIzpg5WK4Hys8qXhKARUISj1Ao0yUpQIEZGDxpgmrv2uWBlnW2Nlq3wBq/AKwM3GmK9FZDZWbeG1WAVuHsZKYjgMK/nZo8aYfyfsQyhKGFQhKEqEBCoEu20PVpW6A0CVMaZERLpjZdkcJCLDgN8YYy60+48D2hpj7heRXKz05pcYY9Ym8rMoihcNomKaosQRJ0NlNvCIiAzAqorVI0T/c4B+InKxvd8M6I5d9EhRkokqBEWJEttkVAlsx/IlbAP6Y/nmSkKdhpXY7P2ECKkotUCdyooSBSLSBngCeMRYdtdmwBY77ciVWGmPwTIlNXWd+j5wo4hk26/TQ0QaoygpgM4QFCVyGonIAizzUAWWE/nv9rHHgDfsur2fAofs9kVAhYgsxKp490+syKNv7dTIO4AxiRFfUcKjTmVFURQFUJORoiiKYqMKQVEURQFUISiKoig2qhAURVEUQBWCoiiKYqMKQVEURQFUISiKoig2qhAURVEUAP4/z6eIwNPmNCwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot just the \"Settle\" column from the dataframe:\n",
    "yen_futures.Settle.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decomposition Using a Hodrick-Prescott Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Using a Hodrick-Prescott Filter, decompose the Settle price into a trend and noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Apply the Hodrick-Prescott Filter by decomposing the \"Settle\" price into two separate series:\n",
    "noise, trend = sm.tsa.filters.hpfilter(yen_futures[\"Settle\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Settle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>7008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6959.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Settle\n",
       "Date              \n",
       "1990-01-02  6847.0\n",
       "1990-01-03  6887.0\n",
       "1990-01-04  7008.0\n",
       "1990-01-05  6950.0\n",
       "1990-01-08  6959.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of just the settle price, and add columns for \"noise\" and \"trend\" series from above:\n",
    "df = yen_futures[\"Settle\"].to_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Settle</th>\n",
       "      <th>noise</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6847.0</td>\n",
       "      <td>-61.503967</td>\n",
       "      <td>6908.503967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6887.0</td>\n",
       "      <td>-21.799756</td>\n",
       "      <td>6908.799756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>7008.0</td>\n",
       "      <td>98.942896</td>\n",
       "      <td>6909.057104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6950.0</td>\n",
       "      <td>40.776052</td>\n",
       "      <td>6909.223948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6959.0</td>\n",
       "      <td>49.689938</td>\n",
       "      <td>6909.310062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Settle      noise        trend\n",
       "Date                                      \n",
       "1990-01-02  6847.0 -61.503967  6908.503967\n",
       "1990-01-03  6887.0 -21.799756  6908.799756\n",
       "1990-01-04  7008.0  98.942896  6909.057104\n",
       "1990-01-05  6950.0  40.776052  6909.223948\n",
       "1990-01-08  6959.0  49.689938  6909.310062"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['noise'] = noise\n",
    "df['trend'] = trend\n",
    "# Drop NaN values\n",
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Price vs. the Trend for 2015 to the present\n",
    "df[[\"Settle\",\"trend\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PvPNdxjz0ETe+9Bk/fWJWpX1zv9niufKcI8Oe/VVdre5tP37sU85+4MPA3MRvfLaWAbdNZe+B7Ds/Clr5j42qGGu27Oa1+WvYuns/u7MxBpBgFatU3CwL1mxj6frEM3NTwQA///f/KmVBTdRlf/bTb3jwneXcY6dxiHnudLr+HscmM1iZbfaXV/DIe8t9x+F7EavR7XbTGyxZl5wBkLTbRQ72AMv9hANXGO6d8SVbdnlnAE3l0Vz6+CyG/uFNz33b4xga+9NMQT7PHp8zBn753Ly008Hf9toXfLdrP5t2ZCA7agIKWvlP/aJyquIz7puZ1fTOfn2r/W87mEvfT9U97+HkImmWb0ijt5BAoDVbrMlemVgtKl74q1v5V1QYvt580GoNe+ziiQ+/YvykhTyaIJ79jc/WUjZuEhs9wijdCnvy5wfdEvtSaFCSvhvGatQBV36g2Ly3dCN/nbqEm/77Of/+5Ouqp0vheUxfuC7uRMJk8SuBO/ndC3NWBaYvwqiRmtLZxfrt4USDeEUa3DVtSaWBpmQ4kKQCON5nbHUqL+kD7ywD0gsFTIU2jWpHPt8z40ue/riq0vFNwHnld9sTiLYmmJz22AcrASs0tlm9mpX2uZX/5U/NDlQ+P2xLMFnRzV67Fz1p/lomebgSs6n4MrhEQN5RUJZ/RYXhj28sZM2W3azfnprVsH7bHi59fBbbk4z9nfP1d1W2xauIf3/zS8o9+vZ+FHCmXqaX/rfa41r+rpbItfDel8HkPxrZ0xoUbNu4DgDfbt1TRfEn04YZY3w/6+dmfcOg8dP5v7eXsS6OVVpk90rue2sZZeMmxbm29b9XPQk2nULIPrEccsn5JehGJIw2qaCU/9xVW3jwneVc8+xcBt/h7S9MxF3Tv2T6wnVJL2Jx1v0fxN6Z5cr/8tzVleKs4/Ffl8J3Z4J0XC4VPjsZqVbuSx77lIv+5X8ayEF3j3VTh/7xzUhIbSpcPXEuj85c6avsTS99xsYde/nT5EVc9cycmOWK/M7WjpNoLcjwSL/+ZmeM4tcvzI9ZxhjDjiifeyJJc2HSXbINoN/iFRX+jIcw7kBBKX/nASfrFvE6h98XOB6ZSjMQXTHdlW/lxp1cPXEuv5joz1eZzaRpXsxYtJ53llROprY+yqqet2ord0yyIlQOTj4L5vqvzKvcyD/x4Uo+tSclReN+njv2xnbZFSeZqsNrbCOo3wfwy//4WxPATyTZ/W8vo8+tUyqNU1QEJOyUBd8mLuSBn4H1TCnfP01eRN/fTeWDpRtzbhnIglL+Dn5znnjhWFyxpgDcOXUxnyXp317mc8B17BOz+PFjiUPe3FbMyo076esKW3VysLyzZAPfbE4uJNCLyT5fyHTuOcDOvQdYt20PA2+fxuA/vMmjM1dwxn0zI/sffs8aPHX05I49B7hz2pKE512wZit/m7o48n37nv1xXYKLvt3OOQ8cTJj25sJ1lI2bxOad+xK6Al6YvYopC76NuH0c5n2zhf3lFXyzeVelEELnKRZ5nDdXJ0Y5Pv1vtx68hwfiKP93l2zgS5/RSV6DxX74xb8TGzqZup2Oh+D8Rz5Oas3gbFBQA75BPGDHiIhl2d0zYyn3zFjKJzeOoEWDWnHP5SRT++3LC3xdOzo6yQ/LoyY3PfTuwQkuR//5Ld8ZNLPJfW8tZc2W3dxxZt/ItjteX8j+AxVsshcMfybGAK7TI5sweVHMxcXd9eDM+z9g34EKfn58V0pLijjxrndZu3WP7/viTBhatHZbJeW/cO02rntuLpcM6xiZbOZY2Ie1b1TpHKffN5MLhnbgyY++om+bhrz686OAgwre0+cffPBUIBTZRpH7Hke7gdxcmIRLL9X39w1XNFQ2fOvXPTeX1d/t5tnLjvBsuN2EOf5cUJZ/EI27Y1V7TYRxk6kVqvywbc8BvlizjYVrt7F8Q+V1UIOqbO8uyVxe+79MWVxlkPaZj79mrcuajNmVt3+g30lljkvCUbDua/jBcfUYqroCX5yz2nMlK69ekGMIfLZ6K49/sJIPl21yKTsvt09uWv7O6m9u+dKZz+CmvMLwx9cXVnH7BUKStzPeRLoX56zm4xWbmbVyM2uSrE/ZpKAs/3QYNmEG/ds38u2vDfvldHKMDIlK9RDdA48XbRI2i76tnGbgfdeKaLFcCY4Cjnf3F6zZSosGNVm6fkfkPNt276dpVDilH5y4+nhZSveXVyRMFeLO73TrK1ZPsF+7RoC35b8li2sYJIMToeZOVhjUHI/3l27k/aUbeWFO1agzP/zulQWR8NlooutaIkbe+W7CMpc/FXvQ34uNO/ZGQnq/2rSTs+7/gJevGhaJXAuawrL809DHq7fsZtL8tVUGAGNfKzcss11RcwWmL0zedRQWo+6OnSQrVs51p5sdT+Fc8fQcfvPC/EpjBs58BIdE0/edWdSzv6oawhvN0ykO9MULLhjzkP+FWsLAPUifbC6dRLgHk1du3BmnZGViKX5IPTVFPLwa7V0xFon5cNkmBo2fHklAOPHTb9i0c1/SUYXJUO2V/58mL6Js3CQeeW95VhVykNEYAN/F8F8n4rPV4bmfwsCvW+t/X2+J2zv7/avx3Xp+wz8B9qRo+VZElL/13V1/cz09tXuOSixZg8hDNfyvb6d1fHmFSVsvlI2b5KnUverisAkzPM+xYI31nn6wzOrdOu7IIKIKY1Htlf//vW1ZdMmmM/bD+Q9/RE87FfTWXfsrDWzt2lcec8AxFQbcPi1xISXl5S+jj0vWDRAPP67CeCu8OfXquVnfBCZTpvFj/AS1SMqbafRmg3LPvuOxtrNXxoDvYsyvibgrbXGcxnPzzsw18tVe+afDll37uOSxT2MutvLBsk3s3l/Odc/Opd9tU+lz65TIvuv/M4/DVGFnHb+q/+vNu+IqKK/Z1dnGGUw+387V5DXDOlfxo1SDusc/eXxW4kIxuPzJ2Z6ZPJMlVQPdMTrch5eNm8Qj71uhy04IcybQAd843PfWUmYsWp8wN/qLefRSVmfunLo45WcR/e6mm/0xCKJnvubT2sN+lL97vkRQ/NxHTL+bNxet590vg4hcC+bZBBUZ5YeCsvy/2pTcpKboyThKbnPPjKWBnWvh2m1cF2d2c5FIpWya8QgqfUE+JSULwppOhVd9BmS4+XSF94ztTLJ2625ue/WLyLwIJzW2VwLC37/qbx5QshSU5R8vJ4kXS5PMi67kLxUeCdzi9SKe/Ogr39P131+6ie1JZMF0iFag+aT8/URBZZOpcWaiOy6WdJi5NLnEhJc8NouFaw+OK8VzgT06cyW3ntY7ZdliUVDKP1nmfrMlbBGULPHweysy5l99d8mGlCbFuaNhlm/YUWlNAiU5xj6Z2bTXyebtcSt+gC0+Ey0GiSr/OGwKMFpHUdLB75oLhcrufeXULi0OWwxf/Mujp+F3/lCQFJTPX1GU6knPWyannPUz29yWIDVMtlDlryhKtWCeummTQpW/oijVgvvfXpa4kBJBlb+iKEoBospfURSlAFHlryiKUoCo8lcURSlAVPkriqIUIKEpfxEZJSKLRWSpiIwLSw5FUZRCJBTlLyLFwH3AyUAv4DwR6RWGLIqiKIVIWJb/YGCpMWa5MWYfMBE4PSRZFEVRCo6wlH8bwL0s0Sp7WyVEZKyIzBKRWRs2BJFzW1EURYHwlL9XctoqOU2NMQ8ZYwYZYwY1b948C2IpiqIUBmEp/1VAO9f3tkD209opiqIUKGEp/0+BriLSUURKgTHAKyHJoiiKUnCEks/fGHNARK4CpgDFwL+MMZlZq0xRFEWpQmiLuRhjXgdeD+v6iqIohYzO8FUURSlAVPkriqIUIKr8FUVRChBV/oqiKFmkfZM6YYsAqPJXFEXJKi3q1wxbBECVf8a4+9z+HNm5adhiKIoSg7qlxRk5b6fmdePun/XVd0mf05gqCRDSptor/9P6HRLKdU/vf4gqfyVClxb1Uj72hSuOpFm93LAWc5H6NUt4+/rhSR93oCJ4hQowvFuLwM9ZngFZq73yH9CuUSjXFRFEvFIYKYXI45cMTvnYgR0aU1zt39T0KGsW39r2oiID1jRAiwaVG+oPbzg+4TF/+n7fuHUkE+1Uta9SFx9ZxiMXDqJxnRpZv3YmWmslPylO0xAoUkMiLbq1rMelR3WstO1AheG5y44I7Bqd7AYo+jqtG9ZOeOy5h7fn2G6xk1eaqnkv06baK/+iImFkr5aRl+eRCwcldfyx3ZqzePwonHevYxIWxoPvLEvqWkr1pSjNNy2flX+m3Z9+1OLxPVrSLirKxhgY3LFJYHJMufYYPv/9SZQUF9GmUWyF36NV/Zj73rp+OI9efHilbc/8dAg1S4Ifn6j2yt+hfVPrwQ8qa5zUcfVrlVCzpJjLjukMwBBXZblgaIfI51+d1J3OUQM9nX36eU/q3TIpmfzy0AUDM3LeXOP1XxzNJzeN4LJjOwV63oEdkqsr8UjX8j+0bcOAJKm+3Dy6Z9w6UFSU2Qa0RnER9WpaGXNe+tmRMcv1bN0g5r6OzepyXI/KYwYl6VoOMSgY5f/Piw7nwQsG0qhOaVLHdW9ptdLR727rhrW4/Yw+ke9XHteF207vU6nM+YPbJzz/jF8eyzkD2yUslwpDOlXfAefXf3F05PPeA+W0qF+LG07uGdlWJ4VIjpollV+Hq47rkrqANr8Y0ZU/nNmXYp+Kp3Pzup4uyr/9oB8Txw71fd2zDquyNlK14oUrDrprnEiYS4/uxPUndvcsbzBkWPdXokWDWjH3JStGrRqq/NOiSd1STurdqtK2MYfHV7ovXHEkP7MVgFNxoseInh07lOnXHQtUfah+LI1OzesxsldmLP/SPBwlbNu4NqUlieXudchB68mr25/KWN7rVx9d6Xun5nXTmpDTt01DrjuhG+cPae/b6qwwlvvg6UuHVNpep7SEAe0b+b523dLQcjZmhRb1a/HUT4ZU2R7PPRZrXzKu3CCIZ/lHc+MpPejbJjO9vvzTDgHRqVld6taM/4JYURZWhZEY7fWQTk0jYXxOdM+QAP2IqTCiRwueu+wIamcojjmT/H3MAH9O3ASMGZx8b6pz88puOkFSHmh74YojKyknP26fHw5pz4MXDKRF/VoM69Ksyv5YddCLVg1jW57pUqM4Ods1yKCanx5tDabWr1VCv3ZVlWK8Nta974rhnXn+cqv38PJVwzi6a9X7nS6dmtVlZE/LhTPr5pHMveUEXv/F0Vw8rAwgsi8eY4/pnLGowYJU/v+9chjPX3FkUt0vZ77AyX2t3oNXhY7uHYQV7dOwdo2EA1lnDbDcAq1tJeHuRodJKu4aL351knf3PxlE4C9n9/Nd/kRXD25gh8Y0dLlv/AzY3nFmX7q1jD0YmIwOqF8rc5b/KX1bZ+zciRh3ck9m3zySRnVKPZViLEVZo6ioUuP5m1E9GFRmvSMNatWgbePgUy7MuH44j1xkDd42q1eTRnVK6XVIA2oUF/HJTSP46zn9bJkDv7QvClL592/XiCZ1S+Pe9NGHVq7g3VvVZ+WE0XSN+3JaJ3Tih4OcRNI/ifkK7hfAGYC68ZQeAAzv3pxFt4/i2O5WWNmgsiasnDCaAe28BzfjRSZkgpKi1K1tN3U83B4rJ4z2LPvOr4bz6U0jPffF66JHD/D//PiuAAzyGCgOcswum75rL2ok6U6Mfp63ntYr5WsXFwlN7Qlvzm2I95oN7NCYy47pxM+O65wgAMN/nYuOxz+kYS3uOte/kQCW28q5j9FjTdmiIJW/HzrF8AM6Fc5LQTkDT46VF+RLmox14L7uu78+jrevH07HZlbFLykSatUortJQxTr/U5cOiRu5EDQtGtTiltN6pzxe8efvH8oVwzsndUyHpnVpHiPfSrz7PuWaY6qUXTx+lOfArNvt457+/9AFA/n+YW258rjEMrtFiSWXkzdGIPDop6CoVaNy7+7yY5N7Xg7OPYhnLNQsKeKGU3pSp7Qk0lg3SLNXFF03rz2hG2cOaJv8eWylf+3IbmnJkyrVe1QoA8RTBhELxC5z9sC2fL15Fw++szzjcrlxy9ikbilN6pby9eZdADS2o50iRYxzjPcPa1i7Bk3aNop5rXm3nEi/26amKbHFraf1omHtGlwwtAMXDO3AiXe9w5J1O5I6xw8SDOIHidc9ixWP7Y72KbE//2ZUD07o1ZITowIRYhGpXiJgvFWeW6TWcSJO0iFZmyZ6rCLaZTru5B48kMKcGMfIOiROTL3XdeMNvl8yrCP/mrnCc9+fvt+X5Rt3JjXwHo8axUUxe6PTrzuWrzbtDOQ6sShoyz+VgRSnInv5/J3X0TlrzZLiSuGH0XRoetDPmMi6FqyK6Qcv//LRXZvxu9N6cev3elvni2M1OTMUR/ZsQY3iorgNXkmxRJRZuhzZufKg2zM/HcodZ/aJUTrziCSv6GKfyzrTOQPbRurQ8O7Nk6qDTsl44wLZQMRKWfDur46LWebH9qAmVK1jQc1WrVWjmPvOP4xnLo0dAuu+vc7nJnHCvZvVj73vkEa1ueHkntSqUcxVx3XJaGrmLi3qMaJnZqIAHQpb+adyTLyDTPwyXaN8ju4GZEB7yzcJ0KdNgypdYRHhltN6VZn951dGEeHiYR0jYwDxGrGrju/CWQPacPeYAZFjk71eKnSPGl9oVq8mPxzSIUbp5Ehllmmi/EzJ/uyFt41iwvcPPdjwJqkDS4qLePrSITx96ZCYciUTEZQOrRvW5pBG8WLZY8sR73dHh7gmYvShreNGNrnlaFSnlPFn9OFJz2skvm9uua8/qTuHl4Ub1ZcuBa3808Ezttz+P7riP3/5Ebz/m6pW0vFRM/k6NLV8kmOP6cy4k3tU2idVPsQjcaF4CqhRnVLuPLd/pKFIfLXUGge3dZgMf/p+X1/l3vv1cZGB7uhBtXm3nJjw+Fi/6u5z+zP5mqOrbE/UCNYuLfY92SsWw7o0o0ndUp6IkQSsR2urAS0tKQo8RLAkKuw5bsMY59JOz+VXJ3Xnjai5FV4hrmCFSs6+2XtQPprrT4ztQ//R0A5xUy/EoyQqxDUT+Xayifr8kySu4R/D8h8Uw0K4eXRll9B5g9tR1rQOR3hYqc45/bzOft75eAPXySBCQqFiWXruSKKLjvBv4Z97uDVzuk2j2qzesjtmuXZN6tCpWeU5GA4NfST6i+X2OcMOk43OsZ6s1Z3o3reoX5P12/d67vNSkp2a1+Vv5/TjgXeWccaANjz76TdJyROPswe25bD2jbnxpc+SqoteDO7YhJnjjk9KCSeT0vrCI8v469QlAHRt6TeVduVgjWh6tm7AETFmzOdr9t7CVv5pPDMvheZYdX5j1UuiogZEhCNjWD7bdh+IlEmEH+PSmeDWpG5y6S68SPU2Ovfw3EHt+P3piX37L/3syErT5gd2aMzqLbvj3m/nMaVqcMe73dHPwq8y86ss3v31cXHDhe86tx/XPjsv8v3P3z+UpvVqctPo1EMpYyHE7y16lY9Hqta3H2q44mrjjbl5EUvu/u0a5q2Sj0VhK/9UiPP8h3RswtUjunJhDCs2nbqzeN12wJ8S82OBHt21GePP6MOZA9LPAZPod916Wi/unLqE7XsPpHT+X53UneHdm9P7kMozOv94Vl9O6duKHq1ix+K3bWwpmYEdqva+Zvzy2LipJJKx5GNFbcQjkRKNDomMJsiJSUd3bcZ7X26Mud9w0LgpjxEe/NxlR/CDBz8MTKZUqV1azKtXHUWn5nV9pQrxg2N8VScKWvl7vdxF4nfhhKqFioqEa08ILmb3mUuHMG3hOh6duTKyzY9C8tVAiPCjocEMpsbDUYqn92/DYbdPq7TPr8PpyhgJ1urWLGFUn/izTXu2bsBb1w+ngx2Z8e6vjotMuOrUPL5LwHL7VL6ZseZ/JEOHJnVYuHZboGkPoOr9DNpOdXz+sWaut3QtYuJuGHq0asBHyzcHLE18+iadBTX+3Wpct6qbcGTPlrw4ZzV92vjP1ZNLFPSAb7TP9SdHdUw4QShb0RQAR3ZpxlDbz+j4G33587PYPRX7nxe1XZZrPPdSpsXt2KxuJLa7fdM6vi3m+rVKqsoWgKx/OedQxp/RJ22lEd14ZPI2DmjfKGL5O66o6Hrm9pe79914Sk9fGUkPTzLdejY5tE2jKttO6duaxeNHxe155jIFrfwroiyYnw3vnJFFE9LBsbIa1rYsj7Dcjl1a1KuUwtpNLJkS5cN3EqkdGmcSWVgsGX+yZ4qIK1Kcjeqmfq0a/Ghoh5zyIcfLPTTlmmM4f3D7SF75A+UVnuUqxdS7tpeWFEWSHZ490Hsm7Mc3juBJjyyd2SP+gG8sck1fJENayl9E/iIii0Rkvoi8JCKNXPtuEJGlIrJYRE5ybR8oIp/Z++6REN8Apw473dV6tUoisbtDOsaPC8/Q8p9VcJR/cXHlMLt4ZGLVp+nXHVtp8Ro3sa6WSIzBHZsw45fHcl4KGTgzScPaNTx9xSsnjOacQbkja3RuoSokUQ/iuQq7t6qPiERCHd2D0O6V8eLl/BERPvvdiUw4yztMt2WDWgnHOLJBDrXHGSddy38a0McYcyiwBLgBQER6AWOA3sAo4H4RcZ7s/wFjga7236g0ZUgZJ6/N5cd2ZuWE0dQsKeaIzk354raTOCpBitdsVRIn50z3JGZ1ZrsCp9N+d2peL6cs4Lm3nMDMcQcX3PYjWlDT/ZOlab2arJwwOpAVx/w8Ay+fv3stijqlxXFz49evVaNKhJsSHmk9CWPMVGOMMwz+EeD06U4HJhpj9hpjVgBLgcEi0hpoYIz50FhB0k8AZ6QjQzocqLBM/+j0BF7d/bAY2qkpz44dGnPQ04tsqlIvnfHojw+393lLcs3IrjyYo0tMNqpTWmlyW6Ke1pRrjok54SqXiV5k3B0Pf2KMxYWccZtYM3tFhB84PSOxcu9HXyd3SfDW5I59EhhBNsOXAG/Yn9sA7hkmq+xtbezP0dtDwXH7ZHptz3QZ0qnpwUVlfIga+u9xJrvF2H3NyG5VVlXLV7q3qk/9WoknjIVBvFpw86m9WPHHUyLfrz+xeySoIJZHc0D7xjx84SBujjGPQKgcRHHT6F7cfGrycw4+vnFE0scoyZNQ+YvIdBH53OPvdFeZm4ADwNPOJo9TmTjbY117rIjMEpFZGzZsSCRq0ji+/qZ1/c8ezAcy6UUZ1sVj9nHU90iCu9xuU32Rz78hkezunlmN4qJIZJl7POuhqB7aCb1a5oRvXkmfhP4NY0zchBoichFwKjDCHJzvvgpwj4y1BdbY29t6bI917YeAhwAGDRoU+BDrlcd1oXPzepzUO7PZ87JNvxSjZ/4+pj+tEqQBfuTCw9m8ax/DJsw4uNGlZCac1fdgmouUpMgt8uE3XH9id37+7zn0iFp4ZmTPltz00ucpnNF6gD1a1fedbhpIO29RLNxjMJmjcviqnTUbgFG9W3HqoeGtXpYp0o32GQX8BvieMWaXa9crwBgRqSkiHbEGdj8xxqwFtovIUDvK50Lg5XRkSIcaxUWc1u+QnBpwTEQiSadee0zKy+yd3r8NQ2LkL3GoXVpcZWq+W6bGdUtdOY7y574mImxPWjyO6NyUWTefUCURX8s08/kn+/zc0T5BzofJZCqIaByph7nSiz9wwcCcGgcMinR/0b1ATWCaXVE+MsZcboxZICLPAV9guYOuNMaU28dcATwG1MYaI3ijyllzmAa1rVt2dRqr7zx68eFV0henQ7eW9SKLnrTLwFqkiYhWElFr2uQ1zm/L14k8yeC461LtuRUXSSAh0GHWm5o1injlqmF0bl6P3rdOCVGSzJOW8jfGxAxBMcbcAdzhsX0WEN4KHWlSs6Q4pTwublo3quV79aF8INpANAmWhswniouEZ346JG+V/9/H9OfqiXN9lXUUt5PxtF8S60ZHE9Szf9g1jyCzHBQ4FycdZgINus0zvLri2Uw5UeXarkvXKS1mWJdmrhH8aqD9sVYYCyL7aRic3r8Nc357QqVt/RLkvWnbuA6vXnUUv/te8NlB/VDu6j6cECPsVEmf6ufIquZ088hP7g6vC8Padi750s+GUa9mSSQVhZNRUwmX6IbLz0Sr5BOjBcfarXtCu3YhoZZ/ntGoTinfP8w7P0pYRPdGhnZqygM/OowbTukR4wglFwlyrCadc0QvkpMd8ntVrlRQyz+LBOWeyTVfupc4iVItK7lLOvUrCMXdOM4C60pwqOVfDQjFULIRJOcaIyU1nLQOQfjZ06kTTohqUAux+KPwKrFa/koAFN6LU12Ydu0xLF1vhQn3adMw7Ui23m2ssYK+Hvnv/eIM+JaGnATuxlN68Mh7K0KVIZOo8s9jfjikPZcd05mfPP5p2KIAueeOUrxxz1bt2rI+XZPIGJuI47q3SHpx9mjq1yzhx8PKQh/bGntMZ8Yek/76DbmKKv88xNGx/do2on3T7E/qiiYZpV+WA/IWMovHj8q4RZ3ujFwR4dbTegckjRILVf5Z5K/n9OOu6UsiK1ilSi7FJSSj+D++cQR1a2qVC5N8XnlKCRZ9E7NI37YN+dfFhwd3QlvxuhuDTKzi5VOMhKSba0ZRlODQaJ9qRiiTvNTXr+Q5x3VvDqSeETcfUcs/n/Hw/4Rh+TtoG6DkKyf2bsXi8aMKyi2mln8eEk/JZlsBC+HmFlKUoCgkxQ+q/Ksd2VrC0d3BULePouQfqvyVlJAYnxVFyQ9U+VcDwkmEdZDqtGKXkjzN6lWvNbALBR3wzWOcVM6DOjRh2YadWb22uBc5VQqaadcew5bd+8MWQ0kStfyrAbedEd5sSLf61w5AYdK4bikdm9UNWwwlSVT55zFOlE0YUQrOIvFFolk9FSUfUbePkhJ/O6cfvz21J8VFqvwVJR9Ry19JidKSIlrUt9I1aJy/ouQfqvzzGJMjKd7U8leU/EOVfx6iylZRlHRR5V9NODGApffSR1slRckXdMC3mvDAjwZSEVLcvap8Rck/VPlXE4qKhKKQ1LDO8FWU/EPdPnnIgPaNAeiU5opgQaGqX1HyD7X885Axh7fjiE5NKcuVWZUR7Z8b0UeKoiQmEMtfRK4XESMizVzbbhCRpSKyWEROcm0fKCKf2fvuEfUZJI2I5I7iRy1/RclH0lb+ItIOOAH42rWtFzAG6A2MAu4XEScHwf8BY4Gu9t+odGVQcgPN86Yo+UMQlv9dwK+p3Oc/HZhojNlrjFkBLAUGi0hroIEx5kNj5SF+AjgjABmUEHE6b6r7FSV/SEv5i8j3gNXGmHlRu9oA37i+r7K3tbE/R2+Pdf6xIjJLRGZt2LAhHVGVDOK4fdTyV5T8IeGAr4hMB1p57LoJuBE40eswj20mznZPjDEPAQ8BDBo0SFVLjuKM2uRKuglFURKTUPkbY0Z6bReRvkBHYJ7d7W8LzBGRwVgWfTtX8bbAGnt7W4/tSh7jJHZTy19R8oeU3T7GmM+MMS2MMWXGmDIsxX6YMeZb4BVgjIjUFJGOWAO7nxhj1gLbRWSoHeVzIfBy+j9DCRON11KU/CMjcf7GmAUi8hzwBXAAuNIYU27vvgJ4DKgNvGH/KdUAtfwVJX8ITPnb1r/7+x3AHR7lZgF9grquoiiKkjya3kEJDB3wVZT8QZW/kjaROH/V/TnLAz86jNH2usuKAprbRwkAHe/NfUb1ac2oPqr8lYOo5a+kjRr8ipJ/qPJX0uZAeQVgLequKEp+oG+rkjb7beVfo1irk6LkC/q2KmkzvHsLABrXqRGyJIqi+EUHfJW0uXl0T64Y3plGdUrDFkVRFJ+o5a+kTUlxES0b1ApbDEVRkkCVv6IoSgGiyl9RFKUAUeWvKIpSgKjyVxRFKUBU+SuKohQgqvwVRVEKEDF5kopRRDYAX4Uth00zYGPYQiRAZQwGlTEYVMb0SUW+jQDGmFHRO/JG+ecSIjLLGDMobDnioTIGg8oYDCpj+gQtn7p9FEVRChBV/oqiKAWIKv/UeChsAXygMgaDyhgMKmP6BCqf+vwVRVEKELX8FUVRChBV/oqiKAWIKn8lo4iIru9eAOhzDoZs3kdV/nHI9QotIvmwGE/OL+8lIs3s/4vDlsULEWno+pyrdTLndYmIDBKRFmHLkYCsvS85/8CyjYj0FJEjAEyOjoaLyBEi8jBweNiyxMKW8T/AX0WkV64pVrGoIyL/Bl4GMMaUhyxWJURkiIi8DDwiIpeISM1cq5MiMlhEngL+KCJ9RSTndIqI9BaRD4BbgUYhi+NJGO9Lzj2osBCRhrZCnQjcLiJ3iEiXsOWKRkR+ihXyNQf4X64pVQDburoXeB1revnVwCX2vpywXI3FLvtrMxG5AiBXlJeIHArcBzwP/Ac4HsiZ+igiRSJyK/AI8AbWkrBXAv1CFcybq4GXjDGnGWOWQO7UQwjvfcmJip4j/Aor9LUfcBnQFCgLVSJv2gM3GWP+zxizJ9esVZt+wBJjzKPA34AXgdNFpJsxxuTCiyciJSLSGlgH/AS4QkQaGWMqcqQBGAwsNcY8CUwDagFfOzvDvofGmAqsXFsXG2OeBu4AOgA5Y4yISLGINAEMlnJFRM4UkbZAbft76HWRkN6XXKjkoSEiHUWktv31YeAWAGPMMqzuYd+QRItgy1jT/twE6AN8IiLHi8gUEblRRM6y94dSkUXkPBH5vYh8z970P2CQiHQ2xuwEPgVmYTWqobjTXDKeZstwwBizFugIrATeAcbZMleEKN/p9qZXgTNF5A7gM6AtcI+I/MaWP4x7eKyIDHFtmgjMtd1Rm4DtQOtsy+XGLaNtGO0CjgGOt91TlwHjgbvtMmHcxzPs93a0vWkuIbwvBan8RaRMRN7A6rI+JSLdjTFfGWPWiEipXWw3sCxHZHxGRHoaYzYDm4CngTOA+4G1wC0i0i/bFdn2m18O/BpLgf5FRC4FdgBPYHVfAbYA04E6trUdpox/FZEfi0hdEekArDDGrMKyrn8G/EdEaopIVgbePOT7s4iMNcasA3pgDQDeaIwZCjwGHCX2mFS2EJH6IvIi8BJwmW2EAOw1xlQYY/ba96stsDibssWRsTGAMWYP8CiWC22Knd3yJqCPiJycZRmbi8h/geuAzcCjInK2MWYD8ALwc7voFrLwvhSM8o+yiq8HPjbGjADewvLx97b3OW6UNsA39rFZuU9xZJwBjBeRjliDVn2BNcaYl+2u4uvA6VVOmGHsxuYIYIItx5XAcGCELVMXERlpW9KbsO7p1hyQcSRwNPAd0FFEXgX+gmX9f2WM2WuM2R+ifMeKyMnGmBVYfv5VdvHZwHpgbzZkc7EPqw7+CFgDnO2S3aEnsM4Ys8RWxINDlvEc1777sdw8zQGMMauB94Fs9/A6AzONMccYYx4Afglca+/7N9BDREZk630pGOWP5TN1h0cuADDG3IvlXz1fRFoYY8rFGujdbIz5n1gDgb8VkUYhyngfMBAYC2zA6g2c7TquBfBBFuRDRC60u9aO9bcQaCMiJcaY6cDnwFBbzmeAu+37OQIQoNTrvFmWcT5wFNANWA0sBwYaY04D2onIwByQb7hYA4FTgFttw2AM0BtLMWQUl4yNjDF7sercdGAJlouim13OqatNgF0icjFWXeybaTekXxmNMTuwrOqLRKS//U6PxOppZRRbxuEiUger8X7C3l4MfGH/geXamwj8PVvvSz7EiaeFiJyA1aVeLCLvGmOeE5HNwAARWWIX+xxrsKoplmXVCThcRN4C9gDXGGO2hCzjAqzB3vbGmBtFpIeITMCytNfY+zMlnwCtsJR5BZY7rK79En2D1RPpAizCqsB3AU2NMU+JSDtgHJYL46eZuo9Jyvgc1sDaf7Ce7T7XqUYYYwK3tpKU71mse3iIMeZBETmWgxE1lxhjMrKoUQwZx4rI1caYjXaZD7EazR8A440xB+zDTwLOw+qV/NAYMz9XZASw3ymxt/UGLjDGZMRFFUPGnwJXG2PWiUixbWT2BBra8lUAj9kNfsbfF+yLVts/rJfpYyyXyACsrtXPgPrAb4HXsLp/g+wH9Qv7uB9i+eRG5qCM19rHNbAryIkZlq/Y/r8b8JT9uQSrK/04lk/6X8AFQEN7/2PAHa5zlOagjI8Dt9mfBSjKQflutz/XAFqFdA//AbwQVfZMW/YuQB1725HAuTkqY12ghvOsQ5TxxagyTwA/sD+3cp0jo++L81ftLH/HP2+slnQIMNsY87K9bzq2xWeMuV1EOhljltv7ZnLQlzrRWOFruSzjdmPMIixLMRMylgC3AcUi8jpWY1Nuy31ARK7CGmzuhdUonYE14PdHLGsn4oYylS3rXJGxHKvRxVhvXOCD5QHI95Fddj/wbdDy+ZTxF8AaETnWGPOOvf0l22qdDNQTkeOMMRlzOwYhI3AcsNB+1jkhI1ZgxAoRuQ04S0RGGWNWZep9iaZa+fxF5MdYg2O325s+A84TkTL7ewlWF+wu+/sK+7ixWLHecyCzMz0DlDFjkT22m2E20BhYasu6HzjOGcizG67bgD8Zy0/9EFYkysf2cW9nSr58kDHX5UtCRmPL+DvXcedgRcy8BRxqjFmoMiYno+3zvwRrEl8D4DhjRZ1lj2x0L7Lxh9W6/xcrvHAO0MPefjeWK2Um8BSWb3US0NLefw1WXO3hKmNEzqOxfKLO9/uBK4CLsXopYBkOrbD85mX2tkZAG5Ux9+VLQcbngI6u445WGVOWsQNW5M/dwGHZkNFT7rAunKGH0N7+fwLwrP25GCsS4Sj7ezssn3RN+3sdlbGKjHWAmhz0Tf4Q+KP9eS7wc/vzIODfIT3rnJYx1+VTGUOTcWIYMnr9VSu3jzHGmf5+N1b89knGcuFsNca8b++7HGvW3wH7mF1VTqQy7jJWrLvj/joBK3QT4MdATxF5Dau3MiebsuWLjLkun8oYmoyzIUfSSoTd+mSwNb4MeMf1fTBW9sbXyXDkRHWREatHUoQVZtjF3tYFyzVxFFlyT+SzjLkun8pYWDK6/6rlGr4iUmSsBF3PY0VT7MWa/PGlsfL2hE6eyOhMMnkEa9r8JVgTjH5ujNkWpmwOuS5jrssHKmNQ5IOMlQi79clgK1wHeBcrReovwpYnj2UcihW6+T7wk7DlyUcZc10+lbGwZHT+qqXlDyAi12PFTP/GWFO/c448kbEt1uSjO1XG1Mh1+UBlDIp8kNGhOiv/IhNCat5kyAcZFUWpnlRb5a8oiqLEplqFeiqKoij+UOWvKIpSgKjyVxRFKUBU+SuKByJSLiJzRWSBiMwTkeskwYpuYi29eX62ZFSUdFDlryje7DbG9DfG9Maarn8K1hKa8SgDVPkreYFG+yiKByKywxhTz/W9E1Zm1WZYWRmfxFokBOAqY8wHIvIR1lq2K7AWY7kHK4HfcKzEX/cZYx7M2o9QlDio8lcUD6KVv73tO6zV07YDFcaYPSLSFSub5CARGQ5cb4w51S4/FmhhjBkvIjWxUnafY6yF2RUlVKrdSl6KkkGcTIw1gHtFpD/Wak3dYpQ/EThURM62vzcEumIv0KMoYaLKX1F8YLt9yoH1WL7/dUA/rHGzPbEOw0rqNSUrQipKEuiAr6IkQESaAw8A9xrLT9oQWGun5rgAK5UvWO6g+q5DpwBXiEgN+zzdRKQuipIDqOWvKN7UFpG5WC6eA1gDvHfa++4HXrDXiX0L2Glvnw8cEJF5WCux/R0rAmiOne53A9Yi7YoSOjrgqyiKUoCo20dRFKUAUeWvKIpSgKjyVxRFKUBU+SuKohQgqvwVRVEKEFX+iqIoBYgqf0VRlAJElb+iKEoB8v/b58twfO7/LAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Noise\n",
    "df['noise'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Returns using an ARMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using futures Settle *Returns*, estimate an ARMA model\n",
    "\n",
    "1. ARMA: Create an ARMA model and fit it to the returns data. Note: Set the AR and MA (\"p\" and \"q\") parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the ARMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the forecasted returns (the results forecast from ARMA model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Settle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>-0.410601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>-0.369458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>-0.564304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-14</th>\n",
       "      <td>0.151335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>-0.469509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Settle\n",
       "Date                \n",
       "2019-10-09 -0.410601\n",
       "2019-10-10 -0.369458\n",
       "2019-10-11 -0.564304\n",
       "2019-10-14  0.151335\n",
       "2019-10-15 -0.469509"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series using \"Settle\" price percentage returns, drop any nan\"s, and check the results:\n",
    "# (Make sure to multiply the pct_change() results by 100)\n",
    "# In this case, you may have to replace inf, -inf values with np.nan\"s\n",
    "returns = (df[[\"Settle\"]].pct_change() * 100)\n",
    "returns = returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lwbak\\.conda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "# from statsmodels.tsa.arima_model import ARMA\n",
    "\n",
    "# Estimate and ARMA model using statsmodels (use order=(2, 1))\n",
    "model = sm.tsa.ARMA(returns, order=(2, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the model and assign it to a variable called results\n",
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Settle</td>      <th>  No. Observations:  </th>   <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(2, 1)</td>    <th>  Log Likelihood     </th> <td>-7894.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.692</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 03 Dec 2020</td> <th>  AIC                </th> <td>15798.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>22:30:48</td>     <th>  BIC                </th> <td>15832.765</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>15810.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>    0.0063</td> <td>    0.008</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.009</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Settle</th> <td>   -0.3062</td> <td>    1.277</td> <td>   -0.240</td> <td> 0.811</td> <td>   -2.810</td> <td>    2.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.Settle</th> <td>   -0.0019</td> <td>    0.019</td> <td>   -0.099</td> <td> 0.921</td> <td>   -0.040</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.Settle</th> <td>    0.2947</td> <td>    1.277</td> <td>    0.231</td> <td> 0.818</td> <td>   -2.209</td> <td>    2.798</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -3.3346</td> <td>          +0.0000j</td> <td>           3.3346</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>        -157.1656</td> <td>          +0.0000j</td> <td>         157.1656</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -3.3935</td> <td>          +0.0000j</td> <td>           3.3935</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                 Settle   No. Observations:                 7514\n",
       "Model:                     ARMA(2, 1)   Log Likelihood               -7894.071\n",
       "Method:                       css-mle   S.D. of innovations              0.692\n",
       "Date:                Thu, 03 Dec 2020   AIC                          15798.142\n",
       "Time:                        22:30:48   BIC                          15832.765\n",
       "Sample:                             0   HQIC                         15810.030\n",
       "                                                                              \n",
       "================================================================================\n",
       "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const            0.0063      0.008      0.804      0.422      -0.009       0.022\n",
       "ar.L1.Settle    -0.3062      1.277     -0.240      0.811      -2.810       2.197\n",
       "ar.L2.Settle    -0.0019      0.019     -0.099      0.921      -0.040       0.036\n",
       "ma.L1.Settle     0.2947      1.277      0.231      0.818      -2.209       2.798\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -3.3346           +0.0000j            3.3346            0.5000\n",
       "AR.2         -157.1656           +0.0000j          157.1656            0.5000\n",
       "MA.1           -3.3935           +0.0000j            3.3935            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'5 Day Returns Forecast'}>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Returns Forecast\n",
    "pd.DataFrame(result.forecast(steps=5)[0]).plot(title=\"5 Day Returns Forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting the Settle Price using an ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. Using the *raw* Yen **Settle Price**, estimate an ARIMA model.\n",
    "     1. Set P=5, D=1, and Q=1 in the model (e.g., ARIMA(df, order=(5,1,1))\n",
    "     2. P= # of Auto-Regressive Lags, D= # of Differences (this is usually =1), Q= # of Moving Average Lags\n",
    " 2. Output the ARIMA summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    " 3. Construct a 5 day forecast for the Settle Price. What does the model forecast will happen to the Japanese Yen in the near term?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lwbak\\.conda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n",
      "C:\\Users\\lwbak\\.conda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  warnings.warn('A date index has been provided, but it has no'\n",
      "C:\\Users\\lwbak\\.conda\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warn('Inverting hessian failed, no bse or cov_params '\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# Estimate and ARIMA Model:\n",
    "# Hint: ARIMA(df, order=(p, d, q))\n",
    "model = ARIMA(returns['Settle'], order=(2, 1, 2))\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lwbak\\.conda\\lib\\site-packages\\statsmodels\\tsa\\arima_model.py:1490: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.sqrt(np.diag(-inv(hess)))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>D.Settle</td>     <th>  No. Observations:  </th>   <td>7513</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(2, 1, 2)</td>  <th>  Log Likelihood     </th> <td>-7897.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.692</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 03 Dec 2020</td> <th>  AIC                </th> <td>15807.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>22:30:58</td>     <th>  BIC                </th> <td>15848.772</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th> <td>15821.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td>-3.204e-06</td> <td> 3.64e-06</td> <td>   -0.881</td> <td> 0.379</td> <td>-1.03e-05</td> <td> 3.93e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Settle</th> <td>   -0.4840</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.Settle</th> <td>   -0.0051</td> <td>    0.011</td> <td>   -0.468</td> <td> 0.640</td> <td>   -0.026</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Settle</th> <td>   -0.5276</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2.D.Settle</th> <td>   -0.4724</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -2.1132</td> <td>          +0.0000j</td> <td>           2.1132</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>         -92.8672</td> <td>          +0.0000j</td> <td>          92.8672</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.0000</td> <td>          +0.0000j</td> <td>           1.0000</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.2</th> <td>          -2.1167</td> <td>          +0.0000j</td> <td>           2.1167</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:               D.Settle   No. Observations:                 7513\n",
       "Model:                 ARIMA(2, 1, 2)   Log Likelihood               -7897.613\n",
       "Method:                       css-mle   S.D. of innovations              0.692\n",
       "Date:                Thu, 03 Dec 2020   AIC                          15807.226\n",
       "Time:                        22:30:58   BIC                          15848.772\n",
       "Sample:                             1   HQIC                         15821.491\n",
       "                                                                              \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const          -3.204e-06   3.64e-06     -0.881      0.379   -1.03e-05    3.93e-06\n",
       "ar.L1.D.Settle    -0.4840        nan        nan        nan         nan         nan\n",
       "ar.L2.D.Settle    -0.0051      0.011     -0.468      0.640      -0.026       0.016\n",
       "ma.L1.D.Settle    -0.5276        nan        nan        nan         nan         nan\n",
       "ma.L2.D.Settle    -0.4724        nan        nan        nan         nan         nan\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -2.1132           +0.0000j            2.1132            0.5000\n",
       "AR.2          -92.8672           +0.0000j           92.8672            0.5000\n",
       "MA.1            1.0000           +0.0000j            1.0000            0.0000\n",
       "MA.2           -2.1167           +0.0000j            2.1167            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'5 Day Futures Price Forecast'}>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bvzvsUEREBpySRJZmTRxNbNo4VjTuUtE/ERl2lCQGQEMsyo6Wk6x/83DYoYiIDCgliQFw02W1lJcUsmKtrjMhIsOLksQAqCgt4gOX1fKjF1X0T0SGFyWJAdJd9O/HL6ron4gMH0oSA+SqqSr6JyLDj5LEADEzlsaiNL5xmO37VfRPRIYHJYkBdNtVdRQWGCvXaWtCRIYHJYkBNLGyjPdcMpFH1+3mjIr+icgwoCQxwBpiUQ6cUNE/ERkelCQG2LsvrqGmspSH12rKSUTyn5LEACsqLOD2qyL8fKuK/olI/lOSGARLYhE6u5wfqOifiOQ5JYlBcFHNaOqnq+ifiOQ/JYlBsiQWZWfLSda9oaJ/IpK/lCQGyU2X1lJRUqgzsEUkrylJDJJ40b8p/OjFZk6o6J+I5CkliUHUUB+ltb2TH7+4J+xQREQuiJLEILpq6lguqqlgRaOuMyEi+UlJYhCZGUvro6x74zDb9x8POxwRkX5Tkhhkt10ZoajAWKmtCRHJQ1klCTMbb2ZPm9m24H5cinaLzGyrmW03s2Xp+pvZQjNbZ2YvBffvySbOMNVUlsaL/q1vUtE/Eck72W5JLAPWuPtsYE3w/BxmVgh8A7gBmAvcZWZz0/Q/ANzs7pcCdwPfyTLOUMWL/rXz81f2hx2KiEi/ZJskFgPLg8fLgVuTtLka2O7uO929HXgo6Jeyv7u/4O7dhwRtAsrMrDTLWENz3cU1TKws1TkTIpJ3sk0Sk9y9GSC4n5ikTR2QuHZsCl7LtP/twAvufjpZAGZ2j5k1mlljS0tulucuKizg9gURfr61hf3HVPRPRPJH2iRhZs+Y2ctJbovT9e1+iySvZVTQyMzmAX8D/FGqNu7+gLvH3D1WU1OTYUhDb8mCeNG/R1X0T0TySFG6Bu5+faplZrbPzGrdvdnMaoFkk+5NQDTheQTonkpK2d/MIsBjwIfcfUcG3yWnzawZzdXTx7OycRcfe9dMzJLlThGR3JLtdNPjxHcsE9yvStJmLTDbzGaYWQlwZ9AvZX8zGwv8GPisu/93ljHmjCWxCDsPnKRRRf9EJE9kmyTuBxaa2TZgYfAcM5tiZqsB3L0DuBd4CtgCrHD3TX31D9rPAv7KzDYEt2T7K/LKTZfFi/7pqnUiki9sOF3vIBaLeWNjY9hh9GnZoy+yasMe1v7l9YwuTTvbJyIy6MxsnbvHki3TGddDrKE+yqkznfxoo4r+iUjuU5IYYldGxzJr4midMyEieUFJYoiZGUtjUda/eURF/0Qk5ylJhOC2q+ooKjCVEBeRnKckEYLq0aW8d85EfqCifyKS45QkQrK0Pl7072cq+iciOUxJIiTvnB0U/dM5EyKSw5QkQlJUWMAdCyL8fOt+9qnon4jkKCWJEC2JRelyeHS9dmCLSG5SkgjRjOoKrp4xnpWNTQynM99FZPhQkghZQyzKawdOsvZ1Ff0TkdyjJBGyGy+dzOjSIhX9E5GcpCQRsvKSIm6+vJbVLzVzvO1M2OGIiJxDSSIHNMSCon8vNocdiojIOZQkcsAV0bHMVtE/EclBShI5wMxYWh/lhTePsG2fiv6JSO5QksgRt17ZXfRPWxMikjuUJHJE9ehSrp8ziR+s3017h4r+iUhuUJLIIUvroxw8qaJ/IpI7lCRyyLWzq5lUVaopJxHJGUoSOaS76N+zKvonIjlCSSLHLFkQL/r3yDoV/ROR8ClJ5Jjp1RW8dcZ4VjbuUtE/EQmdkkQOaohFef1gK8+/dijsUERkhFOSyEE3XlobL/qnHdgiErKskoSZjTezp81sW3A/LkW7RWa21cy2m9mydP3N7Goz2xDcNprZbdnEmW9GlRRy8+VTVPRPREKX7ZbEMmCNu88G1gTPz2FmhcA3gBuAucBdZjY3Tf+XgZi7XwEsAv7VzIqyjDWvLK2P0namiyc2quifiIQn2ySxGFgePF4O3JqkzdXAdnff6e7twENBv5T93b3V3TuC18uAEbcH9/LIGN4ySUX/RCRc2SaJSe7eDBDcT0zSpg5IXNM1Ba/12d/M3mpmm4CXgI8lJI1zmNk9ZtZoZo0tLS1Zfp3cYWY0xKJs2HWEV1X0T0RCkjZJmNkzZvZyktvidH273yLJa2m3DNz9OXefB9QDnzWzshTtHnD3mLvHampqMgwpP9x2ZR3FhcYKXbVOREKSNkm4+/XuPj/JbRWwz8xqAYL7ZEWHmoBowvMIsCd4nLa/u28BTgLz+/PFhoMJ3UX/XlDRPxEJR7bTTY8DdweP7wZWJWmzFphtZjPMrAS4M+iXsn/Qtih4PA24GHg9y1jzUkN9lEMn2/nZK/vCDkVERqBsk8T9wEIz2wYsDJ5jZlPMbDVAsC/hXuApYAuwwt039dUfeAew0cw2AI8BH3f3A1nGmpfeObuGyVVlPKwpJxEJQVaHlbr7QeC9SV7fA9yY8Hw1sLof/b8DfCeb2IaLwgLjjgUR/t+z29l7tI3JY5LumhERGRQ64zoPLIlF6HJ4dL2K/onI0FKSyAPTJlTwtpnjWdG4i66uEXfKiIiESEkiTzTEorxxsJXnX1fRPxEZOkoSeeKG+bVUlhbpnAkRGVJKEnliVEkhN18xhdUvN3NMRf9EZIgoSeSRpbHuon970jcWERkAShJ55LLIGC6eVMmKRh3lJCJDQ0kij5gZDfVRNu46wta9KvonIoNPSSLP9BT9UwlxERkCShJ5ZnxFCQvnTuIxFf0TkSGgJJGHGmLxon9rtqjon4gMLiWJPHTt7Bpqx5TxsKacRGSQKUnkoe6if798tYXmo6fCDkdEhjEliTy1ZEE0XvRvnQ6HFZHBoySRp6ZOKOftMyeworFJRf9EZNAoSeSxhvoIbx5q5bnXVPRPRAaHkkQeu2F+LZVlRTpnQkQGjZJEHisrLuSWy6ew+iUV/RORwaEkkeeW1kc53dHF4xtU9E9EBp6SRJ67tG4Ml0yuZKWmnERkEChJ5DkzoyEWZWPTUV7ZeyzscERkmFGSGAZuu7KOksICVqzVORMiMrCUJIaBcT1F/5o43dEZdjgiMowoSQwTDfVRDree4ZnN+8MORUSGESWJYeIds6qZMqZM50yIyIDKKkmY2Xgze9rMtgX341K0W2RmW81su5kty7S/mU01sxNm9qls4hwJeor+bWthzxEV/RORgZHtlsQyYI27zwbWBM/PYWaFwDeAG4C5wF1mNjfD/n8PPJlljCPGHQuiuIr+icgAyjZJLAaWB4+XA7cmaXM1sN3dd7p7O/BQ0K/P/mZ2K7AT2JRljCPG1Anl/M5FE1ixbpeK/onIgMg2SUxy92aA4H5ikjZ1QOJEeVPwWsr+ZlYBfAa4L10AZnaPmTWaWWNLS8sFf5HhoiEWZdehU/z2tYNhhyIiw0DaJGFmz5jZy0lui9P17X6LJK+l+5l7H/D37n4i3Zu7+wPuHnP3WE1NTYYhDV+L5k+OF/1bqx3YIpK9onQN3P36VMvMbJ+Z1bp7s5nVAsmOv2wCognPI0B3oaFU/d8K3GFmXwXGAl1m1ubuX0//lUa2suJCFl8xhZWNTdx36gxjRhWHHZKI5LFsp5seB+4OHt8NrErSZi0w28xmmFkJcGfQL2V/d7/W3ae7+3TgH4AvK0Fkbmlsarzo30YV/ROR7GSbJO4HFprZNmBh8Bwzm2JmqwHcvQO4F3gK2AKscPdNffWX7Myvq2JObZWK/olI1tJON/XF3Q8C703y+h7gxoTnq4HVmfbv1eYL2cQ4EsWL/kW474nNbGk+xpzaqrBDEpE8pTOuh6lbrwiK/mlrQkSyoCQxTI2rKGHhvEk89sJuFf0TkQumJDGMLY1FOdJ6hqc37ws7FBHJU0oSw9g1PUX/VKZDRC6MksQwVlhg3BGL8qttLexW0T8RuQBKEsPckgURFf0TkQumJDHMRceXc82sCaxoVNE/Eek/JYkRoCEWpenwKX67U0X/RKR/lCRGgPfPm0xVWREP65wJEeknJYkRIF70r44nX97L0dYzYYcjInlESWKEWFofpb2ji8c37g47FBHJI0oSI8T8ujHMra3SORMi0i9KEiNIQyzCS7uPsnnPsbBDEZE8oSQxgtx6pYr+iUj/KEmMIGPLS3hfUPSv7YyK/olIekoSI8zS+ihHT6non4hkRklihLnmomrqxo7SlJOIZERJYoQpKDDuWBDh19sP0HS4NexwRCTHKUmMQHcsiADw6DqdMyEifVOSGIGi48u55qJqVq5T0T8R6ZuSxAi1JBah6fApfqOifyLSByWJEaqn6N9a7cAWkdSUJEaosuJCbr2yjp9sUtE/EUlNSWIEa4jFi/6tUtE/EUlBSWIEm183hnlTqjTlJCIpZZUkzGy8mT1tZtuC+3Ep2i0ys61mtt3MlqXrb2bTzeyUmW0Ibt/MJk5JrSEWZdOeY7y8+2jYoYhIDsp2S2IZsMbdZwNrgufnMLNC4BvADcBc4C4zm5tB/x3ufkVw+1iWcUoKi6+YQklRASt1BraIJJFtklgMLA8eLwduTdLmamC7u+9093bgoaBfpv1lEI0tL+H98ybzww17VPRPRM6TbZKY5O7NAMH9xCRt6oDEn6lNwWvp+s8wsxfM7Bdmdm2qAMzsHjNrNLPGlpaWbL7LiLU0Fi/691MV/RORXtImCTN7xsxeTnJbnK5v91skeS3dab7NwFR3vxL4c+D7ZlaVrKG7P+DuMXeP1dTUZBiSJPqdiyZQN3aUppxE5DxF6Rq4+/WplpnZPjOrdfdmM6sF9idp1gREE55HgD3B46T93f00cDp4vM7MdgBvARoz+VLSPwUFxpJYhH9cs42mw61ExpWHHZKI5Ihsp5seB+4OHt8NrErSZi0w28xmmFkJcGfQL2V/M6sJdnhjZjOB2cDOLGOVPnQX/Xtkna6BLSJnZZsk7gcWmtk2YGHwHDObYmarAdy9A7gXeArYAqxw90199QfeCbxoZhuBR4CPufuhLGOVPkTGlfOOWdWsbGxS0T8R6WHuw2eFEIvFvLFRM1IX6vGNe/jTB1/gu3/4Vt4xuzrscERkiJjZOnePJVumM66lx/vmTmLMqGIe1g5sEQkoSUiPsuJCbruyjqc27eVIa3vY4YhIDlCSkHMsiUXiRf827EnfWESGPSUJOce8KWOYX6eifyISpyQh52mIRdncrKJ/IqIkIUksvryOkqICVmgHtsiIpyQh5xlTXsyieZP54Qu7VfRPZIRTkpCkltZHOdbWwVOb9oYdioiESElCknr7zAlExo1iZaPKdIiMZEoSklRBgbFkQZRfbz/ArkOtYYcjIiFRkpCU7ohFMFPRP5GRLG2pcBm56saO4h2zqnlkXRN/+t7ZFBYkuzSIJHOktZ3NzcfY0nycLc3H2LbvOEWFBYwrL2F8RTHjKkrij8tLGFcRvFZewviKEqrKiinQWEuOUJKQPjXEovzJgy/wPzsOcO1sXdSpt84u542DJ3uSQfdtz9G2njYTKkq4eHIlZrD7yCle3n2UQyfbae/sSvqeBQbjupNHeQnjKooZ351UEu7HlgevV5RQWVqEmRKLDDwlCenT++ZNYmx5MQ+v3TXik8SJ0x28EiSBzUFS2Lr3OKeCw4QLC4yZ1RXEpo9nTm0Vc2ormVtbRU1l6XkrcHentb2TQyfbOdzannB/hsMn2znU2s6R4PXXD7Sy/s0jHD7ZTkeKMu5FBZY2qSQuH1deQnlJoRKLpKUkIX0qLSrk1ivq+P5zb3L4ZDvjKkrCDmnQuTtNh08FWwXHg6RwjDcTduBXlRUxp7aKpfVR5tZWMae2itmTRlNWXJjRZ5gZFaVFVJQWER2f2ZUA3Z3jpzviSSRJUkl8/dV9JzgcPE51eZCSooKk013Jkkr365l+P+m/ri7ndEcXbWc6aevopO1MF6eD+7YzncGt+7XOs22D5RdPrmTxFXUDHpeShKTVEIvyrf95nVUbdvPha2aEHc6AajvTyda9iVNFx9my9xjH2zoAMIPpEyqYX1fFkgWR+BbClCqmjCkb8l/hZkZVWTFVZcVMm1CRUZ+uLudY25m0SeXQyXb2HDnGoZPtHD11JuX7lZcUBlNh6ZPK+PISxpaXUFKUf8fHuPt5K+G2jk5O9zw+u+I+faar17LEFfvZZd0r97Ze7U4H79fekXz6MRPFhcbNl01RkpBwzJ1SxaV1Y3i4sYm7f2d6Xk5RuDv7j58Odiaf3ULY2XKi55d2eUkhl0yu5JbLpwTTRVVcMrmSitL8/TMpKDDGBivrTHV0dnH01JmepHLOlFhigmk9wxsHWzl8sp3jpztSvl9laVF8R31FCePLi8/ub+lJMMXnJJqxo4opKjybWLpX2N0r43NWtIm/qIP70wnLe/8yT/w13tf7nc5ihV1YYJQVFVBWXEhZcSGlxQWUFZ29rx5d1LOsrLiA0qKgXU+fhPuis+8Rb3f2fbs/o7So4JzxGmj5+79fhlRDLMJfrdrEpj3HmF83Juxw+tTe0cX2/SfObh3sjSeFQyfPXiOjbuwo5tRWceP8yT0JYer4ch1VBBQVFjBhdCkTRpdm3Ke9oyu+D6W1ncMnz6RMKgdOBFNhre20tqcu+VJVFt8R373SvlAFxtmVdcJKtXslPL6iJFgRn7sSLu21ku69PHHl3tMneN/iQVxhh0FJQjJyyxV1fPHHW3h47a6cShKHTrb3JIPNe+L7Dna0nOBMZ3zzoKSogIsnVXL9nInMqa1ibm0Vl9RWMWZUcciRDy8lRQVMrCpjYlVZxn3aznQmJJMzPcnkcHDv0POLufSclfv5K/BzkkD3iryokOJCy8st31yiJCEZGTOqmEXzJ7Nqw24+d9OcId+B2dnlvHbgRM9RRd23fcdO97SZWFnKnNoqrrt4Ys+RRTOqKwZ1U1wuXFlxIbVjRlE7ZlTYoUgflCQkY0tjUVZt2MNTm/YOyg6ybsfazvBK83E27znasyN5697jPdMORQXGrImjueai6p6pojm1lf2aHhGRzChJSMbeNnMC0fGjWNG4a0CSRFeXs+tw6znnHWxpPkbT4VM9bcaVFzOntorfe9u0nmQwa+JoSot0KKbIUFCSkIx1F/372tOvsutQa8bH9wO0tncEh5oeZ3NzfAth697jnAiOiikwmFFdwRXRsdx19dSecw8mVZ1/IpqIDB0lCemX2xdE+PtnXmXluib+fOFbzlvu7jQfbTv3vIPmY7x28CQeHGpaWVrEJbWV3H5VXc900VsmVTKqRFsHIrlGSUL6pW7sKK6dXcMjjbv443ddxI6WEwnnHhzjlb3HOdJ69mSsqePLmVNbyS1XTOk5uigybpS2DkTyRFZJwszGAw8D04HXgQZ3P5yk3SLgH4FC4N/d/f50/c3sMuBfgSqgC6h397be7y1DryEW4d7vv8C8z/+k50S0UcWFXDy5khvm1zK3tpI5tVVcPLmSyjIdaiqSz8w9RWGXTDqbfRU45O73m9kyYJy7f6ZXm0LgVWAh0ASsBe5y982p+ptZEbAe+KC7bzSzCcARd+/zgsuxWMwbGxsv+PtIZto7uvjy6i1UlBYyt3YMc2ormTahQqXERfKUma1z91iyZdlONy0GrgseLweeBT7Tq83VwHZ33xkE81DQb3Mf/d8HvOjuGwHc/WCWccoAKikq4Au3zAs7DBEZAtmeZTTJ3ZsBgvuJSdrUAbsSnjcFr/XV/y2Am9lTZrbezD6dKgAzu8fMGs2ssaWlJcuvIyIiidJuSZjZM8DkJIs+l+FnJJuDSDfHVQS8A6gHWoE1webQmvPeyP0B4AGITzdlGJOIiGQgbZJw9+tTLTOzfWZW6+7NZlYL7E/SrAmIJjyPAHuCx6n6NwG/cPcDweesBq4CzksSIiIyeLKdbnocuDt4fDewKkmbtcBsM5thZiXAnUG/vvo/BVxmZuXBTux3Ed+HISIiQyjbJHE/sNDMthE/eqn70NYpwa9/3L0DuJf4in8LsMLdN/XVPzgM9mvEE8wGYL27/zjLWEVEpJ+yOgQ21+gQWBGR/uvrEFjVUBYRkZSUJEREJKVhNd1kZi3AG1m8RTVwYIDCGUiKq38UV/8orv4ZjnFNc/eaZAuGVZLIlpk1ppqXC5Pi6h/F1T+Kq39GWlyabhIRkZSUJEREJCUliXM9EHYAKSiu/lFc/aO4+mdExaV9EiIikpK2JEREJCUlCRERSWnEJQkzW2RmW81se3A1vN7Lzcz+KVj+opldlSNxXWdmR81sQ3D76yGK6z/NbL+ZvZxieVjjlS6uIR8vM4ua2c/NbIuZbTKzTyZpE9Z4ZRJbGGNWZmbPm9nGIK77krQZ8jHLMK6w/iYLzewFM/tRkmUDP1buPmJuxK+xvQOYCZQAG4G5vdrcCDxJ/DoYbwOey5G4rgN+FMKYvZN4mfaXUywf8vHKMK4hHy+gFrgqeFxJ/LK9of//6kdsYYyZAaODx8XAc8Dbwh6zDOMK62/yz4HvJ/vswRirkbYl0XMpVXdvB7ovpZpoMfBtj/stMDa41kXYcYXC3X8JHOqjSRjjlUlcQ87dm919ffD4OPGqx3W9moU1XpnENuSCcTgRPC0Obr2PphnyMcswriFnZhHgJuDfUzQZ8LEaaUmir0up9qdNGHEBvD3Y/H3SzHLlItNhjFemQhsvM5sOXEn8F2ii0Merj9gghDELpk82EL/o2NPunhNjlkFcMPTj9Q/Ap4GuFMsHfKxGWpLI5FKqF3K51Wxl8pnriddXuRz4Z+CHgxxTpsIYr0yENl5mNhp4FPgzdz/We3GSLkM2XmliC2XM3L3T3a8gftXKq81sfq8moYxZBnEN6XiZ2QeA/e6+rq9mSV7LaqxGWpLo61Kq/Wkz5HG5+7HuzV93Xw0Um1n1IMeViTDGK62wxsvMiomvhL/n7j9I0iS08UoXW9j/x9z9CPAssKjXolD/j6WKK4Txuga4xcxeJz4l/R4z+26vNgM+ViMtSfR1KdVujwMfCo4SeBtw1N2bw47LzCabmQWPryb+b3dwkOPKRBjjlVYY4xV83n8AW9z9aymahTJemcQW0pjVmNnY4PEo4HrglV7NhnzMMolrqMfL3T/r7hF3n058HfEzd/+9Xs0GfKyKsumcb9y9w8y6L6VaCPynu28ys48Fy78JrCZ+hMB2oBX4/RyJ6w7gj82sAzgF3OnB4QyDycweJH4UR7WZNQGfJ74TL7TxyjCuMMbrGuCDwEvBXDbAXwBTE+IKZbwyjC2MMasFlptZIfGV7Ap3/1HYf5MZxhXK32Rvgz1WKsshIiIpjbTpJhER6QclCRERSUlJQkREUlKSEBGRlJQkREQkJSUJERFJSUlCRERS+v80cCLSlU7HVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Price Forecast\n",
    "pd.DataFrame(results.forecast(steps=5)[0]).plot(title=\"5 Day Futures Price Forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volatility Forecasting with GARCH\n",
    "\n",
    "Rather than predicting returns, let's forecast near-term **volatility** of Japanese Yen futures returns. Being able to accurately predict volatility will be extremely useful if we want to trade in derivatives or quantify our maximum loss.\n",
    " \n",
    "Using futures Settle *Returns*, estimate an GARCH model\n",
    "\n",
    "1. GARCH: Create an GARCH model and fit it to the returns data. Note: Set the parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the GARCH summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arch \n",
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "model = arch_model(returns, mean=\"Zero\", vol=\"GARCH\", p=2, q=1)\n",
    "\n",
    "# Fit the model\n",
    "results_garch = model.fit(disp=\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Zero Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Settle</td>       <th>  R-squared:         </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>         <td>Zero Mean</td>     <th>  Adj. R-squared:    </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -7461.93</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   14931.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14959.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Thu, Dec 03 2020</td>  <th>  Df Residuals:      </th>    <td>7510</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>22:31:41</td>      <th>  Df Model:          </th>      <td>4</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Volatility Model</caption>\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>omega</th>    <td>4.2896e-03</td> <td>2.057e-03</td> <td>    2.085</td> <td>3.708e-02</td>  <td>[2.571e-04,8.322e-03]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th> <td>    0.0381</td> <td>1.282e-02</td> <td>    2.970</td> <td>2.974e-03</td>  <td>[1.295e-02,6.321e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th>   <td>0.0000</td>   <td>1.703e-02</td>   <td>0.000</td>   <td>    1.000</td> <td>[-3.338e-02,3.338e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beta[1]</th>  <td>    0.9536</td> <td>1.420e-02</td> <td>   67.135</td>   <td>0.000</td>      <td>[  0.926,  0.981]</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Covariance estimator: robust"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                       Zero Mean - GARCH Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                 Settle   R-squared:                       0.000\n",
       "Mean Model:                 Zero Mean   Adj. R-squared:                  0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:               -7461.93\n",
       "Distribution:                  Normal   AIC:                           14931.9\n",
       "Method:            Maximum Likelihood   BIC:                           14959.6\n",
       "                                        No. Observations:                 7514\n",
       "Date:                Thu, Dec 03 2020   Df Residuals:                     7510\n",
       "Time:                        22:31:41   Df Model:                            4\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      4.2896e-03  2.057e-03      2.085  3.708e-02  [2.571e-04,8.322e-03]\n",
       "alpha[1]       0.0381  1.282e-02      2.970  2.974e-03  [1.295e-02,6.321e-02]\n",
       "alpha[2]       0.0000  1.703e-02      0.000      1.000 [-3.338e-02,3.338e-02]\n",
       "beta[1]        0.9536  1.420e-02     67.135      0.000      [  0.926,  0.981]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "results_garch.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-10-15'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the last day of the dataset\n",
    "last_day = returns.index.max().strftime('%Y-%m-%d')\n",
    "last_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<arch.univariate.base.ARCHModelForecast at 0x262a9fb9f10>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "# Start the forecast using the last_day calculated above\n",
    "forecasts = results_garch.forecast(start='2019-10-15', horizon=forecast_horizon)\n",
    "forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>h.1</th>\n",
       "      <th>h.2</th>\n",
       "      <th>h.3</th>\n",
       "      <th>h.4</th>\n",
       "      <th>h.5</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>7.434048</td>\n",
       "      <td>7.475745</td>\n",
       "      <td>7.516867</td>\n",
       "      <td>7.557426</td>\n",
       "      <td>7.597434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 h.1       h.2       h.3       h.4       h.5\n",
       "Date                                                        \n",
       "2019-10-15  7.434048  7.475745  7.516867  7.557426  7.597434"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualize the forecast\n",
    "intermediate = np.sqrt(forecasts.variance.dropna() * 252)\n",
    "intermediate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2019-10-15</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>h.1</th>\n",
       "      <td>7.434048</td>\n",
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       "    <tr>\n",
       "      <th>h.2</th>\n",
       "      <td>7.475745</td>\n",
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      "text/plain": [
       "Date  2019-10-15\n",
       "h.1     7.434048\n",
       "h.2     7.475745\n",
       "h.3     7.516867\n",
       "h.4     7.557426\n",
       "h.5     7.597434"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the forecast so that it is easier to plot\n",
    "final = intermediate.dropna().T\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lwbak\\.conda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1235: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the final forecast\n",
    "final.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on your time series analysis, would you buy the yen now?\n",
    "\n",
    "Is the risk of the yen expected to increase or decrease?\n",
    "\n",
    "Based on the model evaluation, would you feel confident in using these models for trading?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Readme file"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}