time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2019-10-09</th>\n",
       "      <td>9381.0</td>\n",
       "      <td>9391.5</td>\n",
       "      <td>9330.5</td>\n",
       "      <td>9343.5</td>\n",
       "      <td>38.5</td>\n",
       "      <td>9338.0</td>\n",
       "      <td>99153.0</td>\n",
       "      <td>145470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>9343.5</td>\n",
       "      <td>9380.5</td>\n",
       "      <td>9293.5</td>\n",
       "      <td>9301.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>9303.5</td>\n",
       "      <td>159397.0</td>\n",
       "      <td>144474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>9308.5</td>\n",
       "      <td>9309.0</td>\n",
       "      <td>9240.0</td>\n",
       "      <td>9267.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>9251.0</td>\n",
       "      <td>158810.0</td>\n",
       "      <td>147471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-14</th>\n",
       "      <td>9259.0</td>\n",
       "      <td>9292.0</td>\n",
       "      <td>9250.5</td>\n",
       "      <td>9261.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9265.0</td>\n",
       "      <td>69457.0</td>\n",
       "      <td>153902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>9264.5</td>\n",
       "      <td>9280.0</td>\n",
       "      <td>9216.5</td>\n",
       "      <td>9220.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>9221.5</td>\n",
       "      <td>108342.0</td>\n",
       "      <td>151564.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle    Volume  \\\n",
       "Date                                                                   \n",
       "2019-10-09  9381.0  9391.5  9330.5  9343.5    38.5  9338.0   99153.0   \n",
       "2019-10-10  9343.5  9380.5  9293.5  9301.0    34.5  9303.5  159397.0   \n",
       "2019-10-11  9308.5  9309.0  9240.0  9267.0    52.5  9251.0  158810.0   \n",
       "2019-10-14  9259.0  9292.0  9250.5  9261.0    14.0  9265.0   69457.0   \n",
       "2019-10-15  9264.5  9280.0  9216.5  9220.0    43.5  9221.5  108342.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "2019-10-09                    145470.0  \n",
       "2019-10-10                    144474.0  \n",
       "2019-10-11                    147471.0  \n",
       "2019-10-14                    153902.0  \n",
       "2019-10-15                    151564.0  "
      ]
     },
     "execution_count": 24,
     "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.tail()"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf0c8c8550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 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(figsize= (10, 5), title = 'Yen Futures Settle Price')"
   ]
  },
  {
   "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": 26,
   "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": 27,
   "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>2019-10-09</th>\n",
       "      <td>9338.0</td>\n",
       "      <td>24.939247</td>\n",
       "      <td>9313.060753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>9303.5</td>\n",
       "      <td>-5.868559</td>\n",
       "      <td>9309.368559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>9251.0</td>\n",
       "      <td>-54.454594</td>\n",
       "      <td>9305.454594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-14</th>\n",
       "      <td>9265.0</td>\n",
       "      <td>-36.423060</td>\n",
       "      <td>9301.423060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-15</th>\n",
       "      <td>9221.5</td>\n",
       "      <td>-75.844123</td>\n",
       "      <td>9297.344123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Settle      noise        trend\n",
       "Date                                      \n",
       "2019-10-09  9338.0  24.939247  9313.060753\n",
       "2019-10-10  9303.5  -5.868559  9309.368559\n",
       "2019-10-11  9251.0 -54.454594  9305.454594\n",
       "2019-10-14  9265.0 -36.423060  9301.423060\n",
       "2019-10-15  9221.5 -75.844123  9297.344123"
      ]
     },
     "execution_count": 27,
     "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['noise'] = noise\n",
    "df['trend'] = trend\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf0ee24210>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lFFdeeWWn9FEyY0JEkZ1FlWTEu8k9XMPAjHh2FVfhaSJQqgvGNJNtW1hgTOLS+AwAihJH8mt9K/PK74VvXoIJ11hNC7cwfOFtvOFeBh8BX/cjbtIrgARjQohO0kIGq6vEx8eHbmut+dvf/sbs2bPrtVm0aBFutzt0bLfb8futEkNKqU7vo2TGhIgSVV4/p//5c255cRUFFZ5Q+YqaJqrxB4cp+6sCUlQVa/Xg0H1Ou2KxOQr6TIAFd7D5qes5/OGD8N9zSC7bwiP+izDPnwel+xi570Wgrv6YEEJ0d4mJiVRUVDR53+zZs/n73/+Oz2ctctq2bRtVVVXNXmv48OHs3r2bnTuthU4vvfRSx3cYyYwJETU+3VIIwLKdJWgNgzMTWLipoNHk+rX7S5m/IR+AcWoXAOvMgaH7rTpjmrIznyRuyX3kbHmb2NxaimJyeKrfPbx3MJGfjjsN1j5P/wPvAVMkMyaE+NZIT0/npJNOYvTo0cTGxtKzZ8/Qfddddx179uzh+OOPR2tNjx49ePvtt5u9VkxMDPPmzeOss84iIyODadOmsWHDhg7vswRjQkSJ+RvyAKs6fklVLYN7WJmxhnPGzntiaej2WNtOarSLp37xvdC5OaN7MW/xLm5fVMWdZ/2TGd8sJBYv5Z54TkhNo3dKYIhz1AUkvHcbZ9uW4/Wf3MmvTgghus6LL77Y5Hmbzcb999/P/fffX+/89OnTmT59euj48ccfD92eM2cOW7Zs6ZR+hvrVqVcXQrTZ7mKrnEVJlbVf5ODMYDDWdNbKjsEM21rW6YH0Tk8KnR/dJ5m5Y3qxLreMshofPhyUY82ZOFhWQ++UWKvh+CsoyzieB53/wudtPk0vhBCic0kwJkSUCN8Q3G5T5KRbAVTDzNjx/VIAON/+BYNseTztP7PRtWJdDrx+s9FWR/sP1ZCVHAjGHC4Ojr6FBOXBnb+mI1+KEEKIdpBgTIgoYYYFY72SYoh3W3W/GmbGeiZZ2xidYVvNXjOTj8yJja7ldtio9RuU1/gb3dcnJSZ025M1EVMr4vJXdMhrEEII0X4SjAkRJcK3k4x12XHYbTjtqtHelMGg7ZS0w2zW/YHGy65dDhu1hkl5E5uAhzJjgD0+la06m4TClR3zIoQQgvqZ/mPNkbx2CcaEiBLhmbFDgXljMQ57o2FKU8PoXnHEVezB1XMYF03IbnQtl91GbRPDlACZSXW1dFwOGyvNYSQXrwZTtkQSQhy9mJgYSkpKjsmATGtNSUkJMTExrTcOI6sphYgS4e9bcS5riNLttDcaptRa01vng+nntGnTOG38uEbXcjtsmLpuMUC4/ml1BRBddhtfm8P5nv9j2PI+jPxOB70aIcSxKjs7m9zcXIqKiiLdlYiIiYkhO7vxl+SWSDAmRJQwm/gW6XbY8DaRGRtg5loHPYY2ea3gNkdFFfW3Q7pu2gCS45yhY6fdxgJzMoeThpP6/s9gyCxwtu8bnRBChHM6naHth0TbyDClEFHC1JqBPeLrnXPaFUaDIM0wNX2N/dZBev2NwINCwVhl/WAsNd5V79jtsOHDwdpBN0NVERxYdTQvQQghxBGQYEyIKGGaEO+qn6y221S9TcHBCtqyzQOQmAUxSTQlPDOWHu/i+pMHMGVgGpdO6ttku7tWJ1on9n7Z6Fp+w6SkQVAnhBCi40gwJkSU0FoTG5grFkyG2W0Kw2gwfGkajPRtgMwRzV7LZa8Lxkb2TuLOs0by8g1TSU9w128XCMYOeGM4nDgE9i5tdK0H5m9hwr0fU13buEyGEEKIoyfBmBBRwtTWsGQ4u83WaJhyYvUSehn5MOGaZq8VDOqKK70kxTqbbRcM2gB2xY2D/V+DUX8F5ocbrX0wt+Q3vfGuEEKIoyPBmBBRwtSa9Hgrc3XNiTkAOGwKo8Ew5dSaRRTZM2H42c1eKymmLgBLbiEYc4QFY+vso8FXBZveqdcmMXCt/Yeq2/ZChBBCtIsEY0JECVNDQoyDPXM2cP26S+E/Z5NMeaM5Y319e9jpHAo2e7PXCs+GhQdmLbl/10D8WRMx/vcTduceDJ33G1ZpjQqPDFMKIURnkGBMiChhak1vz3ZYdL8VaO1dyg+qnsE0wuqM1VbT08gj19nysvGkmLqFAC1lxsL5cPCfuKuw+yq598mnQudVYORUgjEhhOgcEowJESVMrRlZ9gWg4Jr3YcotnOZZyNzyV+oaFW3GhuaAM6fFa4VnxtoajAE8tDEJr3Yyxba50X3lnsbV/IUQQhw9CcaEiBKmqRlYuQp6j4e4NJj5B9a5j2dW1f+suhcAudYekrtjhrd4rfSwemLtCca8uFhtDmGqbVPoXHAHgAoJxoQQolNIMCZElBis99Gvch0MnmmdsNlYEj+bDLMY9i2zzu1bTrEtg1JnZovXUkrRK8mqpN+eYAxgmTmSkWov1BwGCO2NWV4jw5RCCNEZJBgTIsJ8hknO7e9zk/kSXns8TLk5dN838VPx4IaVT8O612DjW6x0TsSmVAtXtPgD2bT2BmOLzbHYlCb/w78AUBMIxiQzJoQQnUOCMSEi7IsdxQBMtG1lS9oMa4gywLDH8aF7Fmx4A968Dvoczz/jrsfWeiyGL1AsNim2fVvQrtWDecOYRubaJ6AiH29gmLJcJvALIUSnkGBMiAh7d+1BUiknTVVSElt/laTdpvh7zPVwyfNWXbHz/oEHN6otmTHjyDJjqXFOnvfPxIaBkbuSWkPmjAkhRGeSYEyICCr3+HhvfR79VSEApbH194502BWGBkacA5e+AD2GYpq6TZmx339nFEkxjlDR1uY8eMEYLpyQHTq+86yR9B95An5sGPtX1/VV5owJIUSnkGBMiAg6VFlLrd+kbyAYq4jpXe9+w9RsL6ysV4Xf1Bp7G6Kxiyb2Zd3vZ7fa9tLJ/Xj4onGh4wsnZDOiXybbzT4Ub/8KsLZpksyYEEJ0DgnGhIigYHX9vqoIgE01qfXu/3BjAQDvrauriG9q3aZhyvZ67NLxvH7TVADOP74PG8wBJB3aAGgyE2OoqjVCQ59CHIk9xVUUVXgj3Q0hoo4EY0JEUHDFY19VSJFOotJ0N9mu1l8XBGlNm1ZTtte54/swMcdaPJCZGMMuxyASjFJ6UEaPRKtflV4ZqhRHbvrDi5h038eR7oYQUUeCMSEiyG8EM2OF5OrMZocUY13WPpTFlV52FVe1ac7Y0SqMHQjAeNuOUDAm88ZER9Bat95IiGOIBGNCRFD4MOV+3aPe3LBwLrv1p3rpvOUAdEEsxr6EsRToVK6xf0hmMBiTeWOiAxworYl0F4SIKhKMCRFBhmmSQgV9bcXs0lmh4KxxO+v8jsJKAHzNtOtIFT4bz/jncJJ9I4OdVi00CcZER/hmf1mkuyBEVJFgTIgI8hma2faV2DBZaEzEbBBkvXLDFIBQra+g8DlknWV7YSWrzCEA5ChrIUGFFH4VRyh8aHLDQQnGhAgnwZgQEWSYmrNsyznkzmaj7o/RYC5N75RYALyB4Cs4b78rgjG7TXFA9wCgl2kFY+U1khkTR8Yb9jsrKyqFqE+CMSEiyGeYDLPt52DycYBqNGfM5bCF2gE4AjP3uyIYM01NAakY2MlxlACSGRNHLrjhPFgLUYQQdY4qGFNK/UQptUEptVEpdVvg3O+VUgeUUmsD/+aGtb9DKbVDKbVVKTU77PwEpdT6wH1/VZ1RREmIKGT4/WRQhic20zpuGIwFJu4Hg69gSYuGw5adwW9qTGyQnI27MheQ0hbiyNWEBWMllbUR7In4Nth0sJyPNxVEuhsd5oiDMaXUaOB6YDIwDjhbKTUkcPcjWuvxgX8fBNqPBC4FRgFzgCeVUvZA+78DNwBDAv/mHGm/hOhObNXF2JXGG9sTaCIYc9QPxoKlL7x+gy6T0h9beS52m+ra5xXfKlXeut+d8MBMiCMx969LuO7ZlYD1fhhc3NRdHU1mbASwXGtdrbX2A58D57fQ/lzgZa21V2u9G9gBTFZKZQFJWutl2prh+Sxw3lH0S4huw1FtfbPzNROMORtkxuyBzFj4B1tnufEUq86YLbUvlO4jxmHD45MK/OLIBLfTSo51hobdhTgSZQ3mrt73/mbO+MvnFJR7ItSjo3c0wdgG4BSlVLpSKg6YCwR3Of6RUmqdUuoZpVRwf5c+wP6wx+cGzvUJ3G54XohvPWcgGPPHW8OUDUtbOO2KpBgH6w9Yq8+CE/pnj+rV6X27Y+4I9jx4FiqlP1TkkeAwJTMmjlh5YL5herwLv6HZV1It+52KI7J4W1G942/2lwLdu37dEQdjWuvNwB+BhcAC4BvAjzXkOAgYD+QBfw48pKl5YLqF840opW5QSq1USq0sKipqqokQ3Yqr2togXCdkAda+k+GUUswc2Yu1gTebtHgXfdNiue2MIXSZJKtvfRxlkhkTRyy4Ejct3oXHZ3DKQ59x8/OrI9wr0R1tyisHINZpzXRKjnMBUFrdfeciHtUEfq3101rr47XWpwCHgO1a6wKttaG1NoF/Yc0pAyvj1Tfs4dnAwcD57CbON/V887TWE7XWE3v06HE0XRciKrg8BZhaEZ9mBTz90+MbtRmcmUBhhZcKjw+fYdI3NY4Yp71Ru06TaPWtl7283oo4IZqTc/v73PX2+nrngitx0+JdlFRZH5or9x7q8r6J7u9w4PcnWAooNc4JQHE3XhhytKspMwP/7QdcALwUmAMWdD7WcCbA/4BLlVJupdQArIn6X2ut84AKpdSUwCrKq4B3jqZfQnQX+bl7KCaZwb1S+Pf3J/HgBWMatRnYwwrQNh0sx+M3QvPIukySNWtggMqrVytKiJY8v3xfveOSQDmLnkkxoXPDeiV1aZ/Et8OhQDBW6zcp9/hIibWCsReW7+22+54e7bv6G0qpTcC7wA+11oeBPwXKVKwDZgA/BdBabwReBTZhDWv+UGsd/Jp9M/AU1qT+ncD8o+yXEN2CrTKfAp1CnNvBjGGZxLsdjdoMzLCCsUvmLWfDgfLQpt1dpscwiE1jgrFOMmOiVU1Ozi87wLhtj/J1zA+5fuePmKS2ANAjoYt/l0W3t3hbER+FlbS45pmvCVbD+ia3jDdWH4hU145K43f+dtBan9zEue+10P4+4L4mzq8ERh9NX4TojnLc5eTrDBKaCMKC+qXH1TueMSyzs7tVn80Og2Zw3KZP+YcEY6IV1bV1vyNaa1T5QfjHNE6pOcRK+3EM9R7kH65HmO39E15/RgR7Krqjq575ut7x6n2l9TKsu4u7Z4kLqcAvRASlGoeoDZS1aI7bUX9+2ClDI/ABNnAGqeZhMjx7u/65RbdSExaMlZSWwbs/AV8Nv0j7G3/p9SCvDfsLyVTxnvs39K1c38KVhGhesAYj1M/Gltf4eWftgW5XPkWCMSEipfoQqbqUcnfLwRjAGSOsbJjLbiMxxtnZPWssNQeABF9x1z+3iHrh23NV11oT9W2Y5D9yKuxYCDPv5itPNj0S3WzT/bio9ncAXFTxbET6K7qn8D1Nk2KCowkav69u4v5zy/fyk5fXNip/Ee0kGBMiUnZ8AsCuxEmtNh3YIwGAn88a2qldala8lY2L85dG5vlF1PpmfylD75rPku3Wh19wmHKu7StG2/ZQfPpfuHjtWPYfqiEz0U1lrZ81egivGacy1r8eKrvXh6aInG0FFaHbNuAOxwtsd1/FH7fO4aeO13BRV7euYWHYaCfBmBCRsv1DDpFEYeKoVpsGi/FFbJ1QXDoA8f6ySPVARKngB+TTX+wGoMrrx00tP3S8zQ6zN897TuLr3VYJi7PG9qYqsL/p7sxZ2DGpWf92ZDouup2t+XXB2HdZyI2O91loTuBzcxw/cbzF+67f4MbKkgVXXHYXEowJ0UG01vz+fxtZ2NbNa/cuY5keQ4yrDcOOgWisYVHYLhNrbaSRYEowJuoLbl4f3Pz7cFUtTzkfZoRtP3/2X8Sjn+wE4L0fT2N83xT8hvU7HN93LPvNHuz95CkWbWqytKQQ9eQHtjuaN3oTv/b/k2XGSH7ku5UbfD/j1tofMcR2gCvtC4HWg7HaKCvTI8GYEB1k6Y4S/vPlHh6Yv7n1xqaBrshjj9mDGGfrf4bBD7yIldCxO6mxJ5JoSDAm6gtu+l1S6cUwNZFzylUAACAASURBVGb+Bk62byBv0u3MN08ItRvdJxmABy4Yw9VT+3Py0B484v8uw/1bOPjBHyPSdxF5fsNsc8mcmlqD1DgnsyreJjdmGFf5bsfEBih2Z53JMmMkP3AsYGhcFeUNttryGyb3vb+JgnIPr63cz9C75rM+N3rezyQYE6KDvPuN9e2+Z2JMKy2BqiKUNsgzU0P7TbYkNEwZwYKGNc4UknR5ty2qKDpH8IP0YJmHQb/5AO+6NzC0In7y1U2275sWx93njiYhxsGb5imsMocwvPwLNh4swzTld+tYUlju4cbnVjH8twva1L7GZzDEXgAFG0g/8UpuOm04C247mWtOzOHZH0zmMeMCMijnDfMnxJXvqffYL3YU868lu/ndOxv5eLM1erF0Z/QsSJJgTIgO4DdM5m/IA6C6lW952woqKC2wqpMX6FSmDW69VEVwBWWXboPUgMeZQioV3XrLEdHx6mc1NOPKPuMrPYrEjLrNWMb1TWn0uDiXtRrua3M4o9VuLvzbp5zxl887u7siSvgNk8n3f8InWwrb/Jgan8EZyqozFjv2PH4+axjDeyXx+++MIjXexU1XX83i098ihlomFb9V77HBhSUaTU1gj90H528h93B1B72ioyPBmBAdoNzjpzyw915ZK5vVznpkMXc9Z81rMBN60b9BUdem/GBaDr+cPYyrpuYcdV+PlMeZQrqq4JPNbZwTJ44JNWHB2C32/zHQls+bxkmhqugA7/zwpEaPC27yvMIchksZjGEnu4qr8PqlsPCxoKq2/v/ntszhSq/cwZW1r0H/kyClX6P7pw/LZOYpJ7Mi5kSmVC6EmsOh+4K/VzFOO4WBuWcAd7wZHbXuJBgTogMECww67YrD1a0vqU72W+nxQYOG1vvQao7bYeeHMwbXK3TY1Xpl9SFVVVDbzYopis7lCWQZEqjmp47Xed+YzBuGtTnLKzdMYcmvZjT5uGAwtlpb5Vom2rYCkF/mabK9+HapaRCMNTxuyqzSlzGVHb77VIvtnvTMxG1UYTw1G/zewPWt39MYh53ysLIXXl90vJ9JMCZEBwh+q8tMjKHc48NoZe5LT3UIQytmHN96WYto4UrKJI0KKrpZ/R7RuYKZsRNsm3Eqg+eNmejAR8sJA9Ppm9Z05jf4HSQ1vSfbzD7MsK8FNLuLq7qi2yLCqgLFgU8clF7vuFmGn3E1X7Emdgok9W6x6TantcrSXrIVvnkZgE+3WBn9hBgHFR4/5463rjGkZ8LRvIwOI8GYEB0gmBlLiXOiNby/Pq/F9j0ppYgUJg3sPnvz2eMzcCsfvprySHdFRBFPrUGflFim2TZQo12sNoe06XF9UmL5wUkD+NdVE1iUdA6TbNs4x7asXi0p8e1V7bWC+IzAZvHVTWTGRv7fAm59aY11sP8rEnUl6xMaD3k39MnPT2Wp4wR2uYbCogc5sHEpH2+25qb5DJPKWj/90+Ppnx5HpbeVILCLSDAmRAfwBWonBbMEd7yxrsX2vdQh8nUqDnv3+RNUcWkAGNWHW2kpjiU1PoMc5yHm2r9ihTkML642Pc5mU/zfOSMZnJnItbfdh04bxOXupWyRYKxNJt67kLmPLYl0N45YMBOWEmctTrr3/U2NVmpX1xr8L7BKnQ1vUIuDHYmTW712YoyTy0/oz+1Vl6NrK+j92lk84PgX19o/IHP3/5jIFpJdkBTjrDdkGUnd55NAiCgWzIxdf/JAAM4ck9VSc3qqwxTotE7vV4cKFH6l6lBk+yGiiqfWxwPVdxOLlz/6Lzuia9gdDlS/KYxSu9lRIMFYWxRX1rIpr/tmqYN7mE4eYL0PLtpaxMDffMCuokoKKzxsOhj22lY+AyufZqHjVExnfJuun5MRz9fGUAquXc2W3udzvv0Lfut8nh+V/pHX3Pdw0dcXcaHxAeXV3tYv1gUkGBOiAwSDsV7JMQzIiG+1iGFPdZgiUruiax0nkBlTHsmMiTop1XvpZ+zjAf/lbNQ5XDwxm18cyR6qWeNJMkuhQqrxHwvKa6xgbERWEreebg1ta21teXTPu5uY+1cr6+fCR+2nD0L/k3jQdlObRxMyA/UeC7wu3uj9S44zn+PDs5czw/tnflT7Y5QzhqtLn+D8sujYrN7RehMhRGuCw5Quu43kWGezm9SapiaVclJVJXNOmtiVXTx6sVYw5vSURLgjIpr092wCrBIVAH/87tg2rRBuJGscAH1qtqK1PrJriG4j+B6Zfngt5xx8iThHGevMAVSU9qM0sCK9FyXc7nwJV3UBnPoval704WxzMGbNRSus8DJ/Qz4ZiW5mTRjOja/vYrfO4voLfk7tWz/kysOvwoIeMPNusLdha7pOIsGYEB2grrSFjZQ4Z7P7ohlaM9duFS3MGH9Wl/WvQ6QNwI+DPt4dke6JiCKDvFuoscWzU1ur0444iOo1BhMbI9hFWY2PlLi2zT0TdMvgtazGx8POf5Dy4mKS7G762/24HAZ88lemOvqz3ZnKRNs2YvHyouM8Lh94Kn7zI1z2tr3OnklWZuyTzQUcKK0BrN/NM0f3Yv6GfHomx/LcwF+Su6KC85c/YWX+T/lFp73e1sgwpRDtpLVm5Z7686Zqw+qMJcc6Q9/sGjJMzXfsX3IobgD0HN3pfe1QDje57sEMqN0a6Z6IKDLUv439McO5efqQo6uD54qjMnEgx6kdFFdGxzye7uAP723iO48v5eQ/fRrprrRLasGXXGhfDJNvpOjmzYzw/oeLvb/lk6wbKFQZZKpSPjfHcl38X3nAfzkAPr/Z5mHKjAQXSsGOwsp65x+79Dheu2kqvZJjiEtI5Ke1N+E77fcw9pKOfontIpkxIdrp75/v5E8LtvLKDVM4YaBVI8fnD8uMtTBMaRgG49VOtqZfQlo3+yYLkJcwgjElC8A0wSbf5TpScCugj392aoR70g611QzSe1iScBK/mjOcX80ZflSXK+s3k1Mq/s663Ssg87QO6uS329Nf7A7d7jYZsvKDnLH3UQpIp+fMe+hhd2Ng52s9gp5Jp7Ol6my2V1hB1KkZPajYVsSGA2X4DN3mYUqH3UZanIudRdZ1HrxgDAAuh41JOdaUi6RYa1jy8HE3h+aYRYq8mwrRTu+vs2qIecK27wjNGXPYSI5zNVv41Sg7iFv5KI9rvJVHd1CcNIoEaqBEhio72o7Cykbf4qPeqn/jwCQ3ZVKHXM5zwq0U6yQyvvlHh1zvWFPTysKhSHpqyS6WbC+yDl69irTag/zZfQs4Y7DZ6gLIjQfK2B72d3DyEKsW4/JdJfhMs83DlABp8a7QjiinDuvR6P6kGCsf9ZePtrVaqLuzSTAmRDtUev1sDCy59oa98QXnjDls1jCl1lDhaSI7dsj6FlsV37fzO9sJPAlWEGmU5ka4JyLifDXozx5gkTGOwvQTOuSSaWnpLDVHk1yytkOud6xpbq5qpBWWe7j3/c187+mvIW8d5K7grbRr+SamrmbYkl/NYFx2MrsCOzDMGNaDrffO4dLJ1nuOz9BoTZszYwCp8da8Q4dNkdrEHMTkQGbs5RX72RzhMiESjAnRDve+tyl0O7xidPBbld2mSAn8gTc1b0wdtoKx6oT+ndnNTmOLSwHAU9F9ao15/QZPf7Ebv+yp2bHy16NqK3jROI0YV8fMeEmJc7FBDyDeWwAVLW9Iv6uokp+9ujb0RehYY1OQGMjs3HGmNTzc3FzVSPtiR3Ho9uKXHwa7m8UxpxHjsofO902L48IJ2aHjO+aOwO2wE+e0oxQcrrYCzfYUyo4J7H964YTs0O1wwWAMoMIT2Ur8EowJ0Q7hE5TD91IzApWjbUqFKko3NW9MHd6NT9vxxLZcFDZaOROsuRa13ajw69Nf7OYP723ilZX7I92VNmlYhTxqHVgNwDfmoNCm30fLblPsdgfmnR1c3WLbW19ew5urD7Al79grEmuYGlPDddMGsvP+uUzob9UsjNbM2JLtVjB2mm01J5R+gHfYuRSb8cQ0WPARzIJBXWV+m02R4HLw4cZ8gHZ9qZo+tAdZyTH86LTBTd4/LjuFG0+xCnWXNzWS0YUkGBOiHcKX2wf3VoO6D1B7YJgSYOnOYhqyle4mV2dgd0Suns3RcMZbCxb8ld0nGAsWlzwcpR9UDTW1R1+0+HJHcaigsT6wikO2NApII9bVMcEYAL3GYmALBXvNCWaBusN89Y4WKqXjUNhtitR4F25qQ9mjaLPpYDkKk/udT7NT9+aE1afz9e5DjbJVTruNARlWhf16WSuvn70l1QBsbccODT+YNoBld5xOdmrTm9XbbIorp1ijFJHeFkmCMSHawWeY2AOTTetlxgJf1myqLhj704IGJSBK9xOzbzGbdf/QNbqb2Lh4vNrRrfandAYm/Nb6o3c4ywybPBzp4ZLmFFZ4uPypr5h838cUVngwclezyjcAoEN/n0f0z2K72Qedu7LFdsGfU3Mrl7/N/IHfF6fNBrsWkfPcFDa7v0//bx4Bv1UWxOMzomYTbI/f4Mqsg/RSh/mH/xxKSQQgxtk4BHn9pqn89weTcTvqArXLJtfNsQ1uLN5RkmKs9+tyGaYUovvw+U3cgdT6ox9v5+Wv9wFgBjJjjqqDJNTUTW7/YntYduydH4Lp50H/Zd03GHM5KCcearpPMJZf5gGie6XZ2tzS0O1ID5c0Jxj8lHv8XD/vUxyHd/CNOZB4l52pgRIvHSHe7eBT8zjUrk9h52ct9Mf6OV3x1Fcd9tzdRbCUjksZ8O5PsNkdLNZjGb/7X/DoWDi0m9G/+5Crno6On02t32QOX4IjlouuuD50PjzgCkpPcHPq0PorHx+4YCzL7zide84dxa/mDOvQviXEODhrbBb905rOnnUVCcaEaAe/Wb/Oze1vrqew3IPNV8mrrrvJmHccvf57Ir93/Id4argy+GZYfQh2f87h8TexT/fsxsGYnTIdj/KURborbVJW4+O9QCmSqiga/ttVVElVWNbigie/DN2O9HBJc2rCfn4nHXoLgOXmCF68fgp9O/CDLN5l56/+86mK6wuf/6nZdhGuRBBRweHInOr1cHgP6vT/46f2O/nvwIeg5jD+JY/iNzWr95W2cqWukek7wHHln8DQWZw8Koc/XTiW/ulxTMxp+/68vZJjuGpqDnEdtFgkyG5TPHH58ZwxsmeHXre9JBgToh1qDbPR0ur1B8oYu/3vTFTbqDnpdoxRF/I9+0Jud7wEBIag9i0DoKqXVQKg2wZjTjulJGDzRsebfGteW7k/lBGrivCQzea8cgrLPVTX+jntz5/zy9e/abJdtGbGvvP4FwAkUcVtzrfYnzWLlXo4afEdu21RnMuBBzfPlo9H537Nrf9dwvlPLq03lNuWSdxb8yt4dUX3WLTRXvsOWfOnRhYvALsbhswiNcHNFxzPzj7fwb7uJU6yrY9wL+vcbzyCVnaYcRcAF0/sy+e/nMFVU3Mi27EoIsGYEO3g8zcuOpi04TlG7n+Rl43p1J70C2wXzONZYxZX2D9hmm09m/LKYesH4E6moucEwJpb1h3FOK3MmKM2sjV52mrR1iJGZiUxMisp4sHYmY8tYcbDi1i91wpkP1if32ShyWidMxbs6szkXFz4WJ/1XaBuzk1HiXdbQ1dLzdEo00/51sWs2VdKddgwc1HYdkmDMxOavM7sRxfzqzfWdWjfosX+Q9WcoDbTY/srMOFqcCeQFudi4aYCLt02gyJXH/7pfIQ4PJFfnXtoF6PULpb1vhp6DI1sX6KYBGNCtIPPMHGGLceeatvIpI1/IC9lAg/6L8Nms1boPOK/kIOk87zrAZLfuBTWvgTDzsRrWh80TU1c7Q5iXXbKiEd5ukdmrMLjo0eim0155Xy8uTBiE5qDiweqag2W7yoJnZ/72BI+2Vy/nla0DlMGnejejakVu53WB2tCTMcOG8W7reutMIfh1U5OtVsB1fawVXQHS615gHFtWMUZzQs3jtTekmrOdy5Du5PgjLuBugKnRaRwp/cqEpSH6ba1eLv49a/ae6je35ne/B4AuzNmdGk/upvu+YkgRIT4DI0jbIjxJ443qXBmMH/MY5STEBp+LCeeWd4/8YL/dPocWk5lvxn4ZtyFJzDvpqkChN1BbCAzZusmwVil10+Cuy5Y2HQwMhm9gnJP6PZXu+uCsa0FFfzt0/pbS0V6VVdrhhvb2K778NDn1ly8jh5yn9A/lTNGZOLFxVJzFBfYlzJY5XL+k1+GsjyLthYCkJMeHyq1ES68jElNFM0V7Cj7DlUzzrkPlTUOXNZ8vbSwsjufVA+mWCdxpv3rLs0IF1V4+e7fl3F7ICOpN74Nn93PGnMwnvjsVh59bJNgTIh2CM4Ze/2mqTzW/wum2DbzacYV1NqsN8Lw4cdqYnip50+Zm/QGo7f+gD99WRGav9RRRTK7WozTTjnxJKlqMKP/Q67KaxDvtvP01RMB2NaOGkUdKbwY54o99VeiNlyqH92ZMc3g2i2sNZsuotkR4lwOnrp6EpNyUrnXfyXa5uBhp7VXZXDPwmAAO6BHPB5f48zPj19aE7pd7Yvu4PZI5JZUMNDcC73Ghs6lhs3dM7GxPfVUTrOtobi4sMv6tb3Q+vt6b10e+L1Uv34Lu2z9uKH2p/UKZovG5KcjRDv4DROXw8ZE1z7OLXiSxfYp/KvqVILTMoLB2AMXjOHF608g1mlna1ENAGv3l4Y+ODq0SGYXstsUZdoqykg3WFFZVuMjzuXgtOGZJLodEQvGwodtRqk93OP4Nx+7fsHDzn/gKN5Ur220TuAf1TuJKwd5cftKWaOtYOzW04d06nPu0r2Zn3o54227GKH2smxnSb37MxPd9faIBasAc/j2O1Xe6P/S0B5aa1IOr8OtvdDn+ND5nklWUD9tcAZDMhPoO/NmXPhJfO8GaMe8MZ9hcudb6/nt2xvanVV88at9odsFaz4gXlfxh8pzKSI14nM2o50EY0K0g88IlLbY/C4oOz+u+gEbCj3kHrZWNwVHbC6b3I8TB2UQ47SH3gcT3I5unxkDGDM4x7oR5UOVa/eXUuMzyCurQSnF0F6JbMlvXzBWVOHtkD0tgzWxslUhr7nu5mL7IvJ0GnNsX/NQ+a/IooT7zx/DgIx4KqM0eKj1m3y39BkMewyfG+OA5ifPd4Tg382alFl4tZNrHfNZHtjVIt5lZ+6YXsQ67dT4DD7bWsgNz67ENDXvr8+rd53q2m9XEFBSVctp5jIM5YQhs0LnL5rYl9dumspz107mw9tOoc/IE/mH/TJ6F3/JytUr2nz93cVVvPDVPp5bvpc1+9teT3DJ9qJQGRkA3/q3KNXxrLBZ2bvibrIDRqRIMCZEO3j9Bi67DXZ+CtmTKMP6MApmvBrOnwkvahgfFox11zljAEZMsnWjJrqDsd3F1pDW5AFWQdKhPRPqTQJvTU2twaT7Pub3727klRX7eHbZniPuizUPTHO742UUmlenvkXFxa9zZ89/4MDg765HOC6ukDiXPWozCBm+gxxX9QV5Y24mD+tnmp0a22nPd95xfQC4cc5E1mWew4X2xVyUez9ojaE1fVPjmDwgDb+p+f6/V/DRpgL2HaputFdlXpmnqct3Sx6fwfbcAi60L6akz2kQkxS6L8HtYFJOGkopbDaFUorCfnMAeO/N59iS37b5kuG/f2Xt2Hi8qKJuhetwtY/MAwv5yJjI4987gd+dM5KfzZSVlC2RYEyIdqiuNehhr4SDa2DQaaHzwRIFqkHJivDhyI82FoQyJN11NSUA7hQAfFXRXYVfYf2/mDHMquadFu9q1+T44HZXL329n1+/sZ7/e2fjEfel0uPnbsd/ONu+nHnG2WRmD2HumCx8yf24w3cdA1U+QxZcQaJLRW0wNsi3HYCanNND54b2TOy057t8cj923j+XQT0SmHTzUyxIuYzTvJ+g37ieq813mJn7N07d/iB/iX+WH9jn04PDbMmvqLenodthY9Xe6P49bY/pDy3i3eceJUVVUTvp5lbbjx41jp1mFqfavmFbQWWbniN8b9Q3Vue20LK+4HugDZOnXA9TreJ53DiPeJeD7580oMO3Mfq26cafCEJ0vRqfwczq9wANQ87gssn9AGsIp6lVZTFhk1ZrDZNHF1ofaN05M0ZsIBirKGmlYWQFSxoEJw677HYMUzdZ26spwVV6bW3fnMNVtVC6n6sdC3nBfzqP+i8gNc4KGOJdDt42p/FL3404qvKZaK6L2o3Chxg78SsHZI4MnQtfqdrRbDZV9zdls/Nx1g28YZ8Dm9/hDudLjM9/HbXpbeaqL/k/53OsiPkhx80/l9El80mlnEk5qaTHu+otnuju8ss9HKe2k69TyRhxcqvtv3t8NjX9pjPVtom9ecWttof6mbGPN7d98n9w4/avr4ghWxVzV/Wl7NM9u/WUjK4kwZgQ7ZDgLeLMQ8/B6O9CnwmcPCQDsCa9NrXCf83++kN51mpM1aiKf7eSlAWA/9CeyPajFbWGiY26Ir1Oh/VfXxvngDW1Sq+9Vu87zHF/WEjV2jcA+KdxNhpbaOVbsKbWInMc2pXAVO8XUZcZK6n04jNMhuqdFMYMIiam84YmWxLrdnGvvpbDP81ljOcpnj9tOfxqF6svW8N07595yHcxhq+Gqd/8hsXun/LPqYdJjnNRWu3DZ5j16rt1Z/1tBRxQvYhpw7ZADruN0TOvJkb5mLzxnjZd/6evrK133FTpkKZsK6ggJc5J+q7/UaXdfGxaiwu662KlrtaNPxGE6HqDfFtwaD9MuQWomyNWa5hNVtUf0ye50blunRUDnPGp7DF7Ystf03rjCMrMX8wa9w30eGIovP1DXIF3u9o2B2NHl6HyG2Zoz8mptV+yjRz2aWv/u5RAZixYxsKLCzXqfKaULWCYZ23TF4wAr99gwr0fc8//NjJC76IgYTjuQKYxI6Fjt0FqTZzbzuFqH5VegwriiA0EsqlxLvboLJ4wzuMq5yO8O+EZ9utMUt+5iht9z7HvUBVD7pzPpfOWtysgq6k1eGrJrjYH750tmOntrwoocPRp+wP7T+X9pEs5oWIhlOxstXlwD9e7zhoBwJ6SqjY9zeb8ck7p5UdtfoeSvjPxYA1LSjDWNhKMCdEOA43dmNhCQzXBArDNDVP+Zu6IRue6ezAW67SzXg/AVRDFW82seZ5Za39EiU7C6HcirH2eQYeXANaWVm3h9dcPxtobfKwNZEVn275mom0bS1zTQvelxFrXqhcYznmASmc6l/jeadfzdKYdgbpemzZvJEVVUZI4nB6Jbq6dNoCXb5jSpX1ZvusQAE99sQuo+zsKHyrdW+plT9xYLq/9Debwcziv8hVSC+tWEq7cc6jNz/fox9u49/3NvL8ur/XGXaCwwkMsHnqqUvLtvdr12N39LwTA3LqgzY+ZmJMGwM7CtgVjlTU+flXyWzAN+p3169D5uG7+ftdVJBgToo0MUzNU76U0tm+o6rWtlcxYemA4ql9aHL+eMxzo3mUtwCrKuc4ciKsyF6raNg+lK/zslbX88IXV1sGyJyiKH8qZtQ/CRc9CfCaDD74LWOVJ2iJ8mDIz0d2eUk0ALN1Rgg2TB51PsdYcyIKk74buC85jq1fHyZ3IvtQTGMc2dJQU1N0aKAVyavV8AA6lT0ApxW/PHsngzM6bvN+UmSMygbpVe8G/oz4psVw1tT/fm9Ifn6HZf7iacpWI7bwnqXBm8BPHG6FrfLW77cHYpjxr9WF+eXSsxswv8zBa7QFgn6N/ux7bd9BItpt9WP3xy/VWPTZlcGYCZ43JYlhgccbOorZN/O/t3Um2dwfM+gNk1RWjDV9QIZonwZgQbeTxGYxQezmcWLdEO5gZa27OmM2mePG6E3j9pqmMzbaGLLt7MBbrsrNR51gHhZtabNtVDpTW8OaaA7y/Po+y/ZuhcBPfZJxNrXJhd7pg5LlkFX9JLJ4271UYHKZ865YTOWtsVpuHN4OW7ihmlNpDqqrkGf9c4uMb1+Q6Y6Q1bPnqjVMBKE49jlRViTd/a7ueq7Nsza8ghQputL/P28aJeNOHR6wv1508ECC012Jw+MtmU9xz7mhOCwRrO4uqcDvsKFccOwdfw4n2Tdxqf5MLJ2SzYs+hFhdk+A2Tfy/dzasr9rNku/VFY1s7a9N1lltfWsM0+wYMrdjkHNOux47uk8wn5nGMMzbyt7cXY5iaBz7YzMHSmkZta2oNYpx2Yl12+qTEsisQjHl8BmUt7A4xzbfUGjUY8Z16520dvF3Wt5UEY0K0kafyMP1sRZQn130gBYcmfX7d7JvOiYMzyEyKYUSWVRMoppvPoYhz2SnRgfpG1W3PNHSmp5bsCt3+8t2nAPjt1gG47Dar3MjYi7EbHu5yvNCmoGr++jyu/e9KwJpk77Lb2r3h9PoDZZzssILVZeZIeiXFNGpz6aS+bLh7NpMHWENC5T0mAODbs6xdz9VZtuRXMN62E7fy8ZL/dBLckfvdDQ5LfrrFWuHX8EtNdoq1sGD1vsOhQG3Eeb9kT9aZ/Mz5OlOTivH4zFB5GYA3V+fyxfa67O7Gg+Xc/e4mfvVG3RB8WzNDne1gWQ2zbCtYqwfjcbQvK5kc6+RtYxoGNm7ZcT2/feYd/rl4Fz97tfH8RI/PIDYwwXJQZgI7i6xhyjMfW8K4uz9q8vp+v8FMvuZA8vEQn9HOVyZAgjEh2sw4aL1BV6bUzQNz2Kw/oVrDxN7EMGW4tHgXvZJiiO3ONcawyjGU6kCWpybyNZyKKrz8e+keAAarXE4rep7FxhjySK/bD6/vZPYNuoxL7Z/i97T+4XpzYLjz9OGZDO6RgNNua9dEbo/PoMZncEnGHnaSTREp9EyKYeFPT+GDW+tKEiil6s15qk0eSIlOpHDj521+rs60Nb+CUYGhsU26P3FtWMHXVRrOvcxOtaYOaA1zx1hzqtzuGHIufwzcyZyy7QGAetmdn736DVc+/VXouKSq/hBeVnIMFVGyunWs2sUI237eer/OVgAAIABJREFUME5pckpES5JjnWzR/Ti/9h5SqWTInheBpjdRr/EZZJrFsP51zrJ/zYGiQ2it2V3c/Nyx2k/uY4jtAHuy5oTOLbvjNJb8aka7+nks696fCkJ0oZhv/kuVdlPd87jQOXvYBP6GBV+bcu20AZw9tnen9bEr9EqOocoe+GYeBcFY+IfrlfaPMbXm576bAKzdEgJKs07BrjSOwvUtXk+HTQ67L/F1bA/05nvrr6YPBW2uOVZW4yMOD73L15A88gxuPW0wF07IZkjPREb2Tmr2cTV+ky/MMfTKXQCH97TpuTpL7uFq8ss9jLLtYY/ZkwriIl5244RABhEar9KLddn50YzBJLod3DI9bCPzxJ5w6i/pUbKCi+2fhX5fzLD/l3MeXYzHZ/D8cmtvxSSqGKN2cXxqTbv3Z+ws59iX4cXJu8ZUvnt8O1ZTQqiUzmbdn4XmBM61L8WJH6PBREitNdOM5fxo3QXwxrVcsvtOPlM3UrhxUfMXL88jdtlfeNs4kdRp14VOZyXH0jctrl39PJYdVTCmlPqJUmqDUmqjUuq2wLk0pdRCpdT2wH9Tw9rfoZTaoZTaqpSaHXZ+glJqfeC+v6q2fKoJ0ZUO7SZpxzv825iDMyE9dNoRNoG/LaXDrj9lIFdOad/k22jjctjI6ZVOLa6oCMbCM1bH2XawxhhEEdbbznH9UkL3eXpY82xchS2vAj1Y5gE0P3e8Sq/1/4D+J5LiyeUh5zw8PoOiCm+9D/KGfv36Ou5+dyO/dTyHw/CQMfVKfjZrWJs+mC6ckM0ffZdit9ngswdabd+Z3ll7EIDRajcbdX++e3w2MwNz3CLlr5fVfRFqau7lL2YPY8VdZ9A7pUEttEnXUdb7ZB5wPIW3aC8AxWFZsC35FeSXedi2dSPPO+9jtftG3nXfxRP5V3Crdx4EFlTkldVwx5vrjrrsyZEYbMujNC6HFX+44IjfQ6YMTGPKhbeRpir5t/OPKKP+HLDanUt4yPFPihOGwo2L2T7nBUp0EnFvfZ/ByqrG3+gLyZrnUWhW5dzI6OzU/2fvrsPjrrIGjn/vaFybNNbUvaVeSg1Ki2wpLU5xWGSxxZZlkQUWXV7cpYvDYgsUt1KkCqXuLmkqSZo0noz97vvHSCaNSxs7n+fp08n8JDedNHNy77nnIBqn0cGYUmoQcCUwGhgCTFNK9QZuB+ZqrXsDc30fo5QaAMwEBgInAy8qpfz/m14CrgJ6+/6cjBCtSaY3f+hLzzGVlkfMQQn8dS1TtieDUqLJJxzdCoIx/xvjGxcMYoDaxUrdi8gQ73LaTVOC+uFFJrNfxxKSU3swtnRnHpNNy/mr5TOK+p0D533Ayh5/YYxpA9c98CSjHvqR95ZkVHutYWg+XLqbX9fs8DYD73MhdBlV768lzGYhvUcfltpGwc4F9b6uLhm5pXyxam+Drtl5oIRLwheTbsph8NipPHHOECJDWnZnXOegvLuoGnbpVVs6xhpK9nGPoYDYTe8DkFVQEYwpDLb89BYf2e5jkGknL3mmcxO3srzzmZzHd7ieHsbTH37LX99bwftLdvP9uv3N+nXVRWtNGtkUhqQSYjXXaxb+UBvuP5l3Lj+aTkP+xJ2uyxlvXscp5V9XnLDxa+zvnkoJIcwb8igkD6H3mGm8kvIguMv5znY7J5iWBvrrAlCcg7H0deZ5BpPUY1AzfKUdV1NmxvoDv2mtS7XWbuBX4HRgBvCW75y3gNN8j2cAH2itHVrrHcBWYLRSKhmI0lov1t71gbeDrhGidchag2Gysk2nVMqbqUjgr98yZXsxMDWaPCOCgrz6t0s5XPwlKDoVb8SqPKwwevHVX8fz+XXjGBRUdLdTpJ01Rndcmcur3OOCV3/jvi/X4fYYLNmRx+nmheTqSEpOfBLMVgafej3bjGReND9OmspmSQ0lEvw7/YaZtnqXRPtPbfDX0zMhgh+Ku0NhJuTvbvD1h1q/t5CJj/3MDe+vqHVG71CleXu50zMLuk0g/cS/NnkczeW6ST05c3hag1sx2eLT+cUYQpftH0DutkDJigndw3nQ8gYnrL8Dl7bwdt8XeMJ9DnuTp7Cw9+1c5byZkoIDTFh3N8t3eYvG7j/Czcddbg9dVDZFoV0afY9QmzmwXGkadRnzPIO5tPwdHF/9g++fupKyD68g096bSY4ncUd3D1x31LDRHO94nPW6K09YX8a96UcwDCjcBy+PwyjK5iXP9CNeBLi9aUowthaYqJSKV0qFAVOBLkBnrfU+AN/fib7zU4HgnyyZvudSfY8PfV6IFlPqdLMneNv3/jUURPTCjSUw6wIVy5QlTg8dKBZjYu9OFBDeKoIxf3HW2IPeGa+VRi+6xoczpEtMpfOSokJYY/Qg2bm7yozewq25vLFwJ73u+pYD639limkZX3nGEBrqnYkJi4zlgej7CVMOppp+r3GLv3+WbpRpEx6tiO83rsFfT//kKJYafQHYv/aXyt+HjTD12fmBx/7m5/XRvWQldhxwwn1gbj2J+38/qR9PnDOkwdeF2sw86z4Ds6cM/dI41n/5DFdavuY/B6/kAstcXnafynHOJ9GJg/jvFUcz66IRZBU7+MEYxb9clzDCtIX3bQ/SS2Xy1qKdRzR/zlWwlxDloiQ8rVnud9/0QbwYcT2bLH2xLn+dyfkfs8rTlbMKbsCBrVI+3tiencghlmtdN+LAQsyn58IzQ+CF0eAoYobjfhYbA4kLl0bgTdHoYExrvQH4P2AO8B2wCqjtu7O6typdy/NVb6DUVUqppUqppTk5OQ0csRD199f3VjDukZ8q8iP2r2W7uTuRdgvd48MD5wVX3c882LQ3zbaka3w4xaZIQl35dZ98mPlnxmL2/Eym7kQOMdWeF2638JMxFJPSFMx5tNpzeqo9POe8h306jlnuaYQE73yN68YGI51JplWByvRVxuILDEeoTWzQXTGHVm2HVZcBKVFs1OkU6VDmfPcZxz32c4Pv4XfoTFhRef0DiB7OTbiwQueG1bRqrcJsFlbqXnx49GxKwlK5sewF7rL8F090Ouc57+IR93lM7NOZU45KYlyvTsSE2bjg6K5MH5LCZ8Y4HnBdSB+VyRPWl9lbUMbAe78/YmP35O4AoCyi8TNjwcwmRVLXvlxvu4/vZyxluOMVZjrvZj/efNjgpd7uncJ547JR/P3cE5ngeIaMCY9CyhBKe5zE38IeCtQc7J1YtY6eqL8mJfBrrV/TWg/XWk8E8oAtQJZv6RHf3/5fnTPxzpz5pQF7fc+nVfN8dZ9vltZ6pNZ6ZEJCQlOGLkSt/JW6t2QXQVEWlGTzR1kKQ9NjKtUT85e2APjLsT2O+DhbUpZKJMa5z7tk0YIcbg8TTauI2LOQ+XFnMn1IzbtVH7vhEj72TCRy5X/AWXWr/jTTb1gw+GXsW5w8fnSl3ZiDU6P5yRjKKPMmCvNzq50ZKXN6sOJmmGkrf/hmtxqqX1IkBiaWGn04ybyUSE9Bo+4DVftwFjdgNqe3azO77b3A0j6Wn/wJ/zmmTvw86VOmOR5kxwWL2HfGbBYbAwF46cLhlToL9E+O8m0aULzmmcrD7vMZYtrOZebvqGHOIKDU6eblX7fVWhKivozcnQCURzTf5p/oUCsFpS7yyqGQ8ErHDt0cMalvIulxYZRjZ1vq6XDuu1xZdAWfZHk3dJhNim6dKt9DNExTd1Mm+v5OB84A3ge+AC7xnXIJ4G+09gUwUyllV0p1x5uov8S3lFmklBrj20V5cdA1QrSIkd28u4K+WLkXMr297X4tTGJ4euXdQmZzRWB2x5+q9qFszzLNqdiMcihq2d59DpfBn0xLMEJiOe+6ByvtuDtU78QI5jAGs+GCPRW5YxaTwmpWHG9ewQrdi7FDB3H3tAGV8gCvP74XPcediQUPM8wL2Z5T9U127d5CbrB8Srhy8LMxtFFfjz8n8XH3uURTwj+t7zbqPlA1GAsueForj5tenq1khA5o9Odubcwmhc1ioszpocxjYq3ugSW+O2ZzReBRV3eM2Z7xzPMM5l7rO5xi+h23x6h2Z+Xe/DIG3PM9j3y7kbcW7QTgnFcWc9MHKxo3+IM78GiFK7L5yuJEhVopcrg5WOIEYMbQintX19w73rcMedmbf9Dt9q9ZuNWbPzeiayzf3zSx2cbVUTW1ztgnSqn1wJfAdVrrg8AjwAlKqS3ACb6P0VqvAz4C1uNd1rxOa+3/Lr4GeBVvUv824NsmjkuIevlsxR42Z1VtdxIX5p0N+OWPVeivb8EZkcYKo2eVGlFW3yzZ6KD6Rx3FXrMvtTN3a4uOo8zloZ9pN56EgXXO4ljMJpzJ3ir3ZC4JPB9qM3NCF80Q03bmeoYTG151p57dYubkk6ZTmjqWuy3vsn9H1Xpld7y/iKvNXzLbM455RsPzmvzunjaAdbobn3rGc6J5GbidgWPfrd3Pv75YV6/7HNoUPafIWcOZh8jZQCgOMsPb1y8YYTYzpU4PDl8AFWI1kxITwrD0GD68akydm3Dm3DqFsMtms81I5irLV1z19lL63V21+XZwyoJ/aXjJjjw+W9mwHa1+7tzt7NWdiI1svtmn6FArWsNbi3cRE2ZlZ9AMXlx41f9HCZH2avNiX7pgOL1kibLJmrpMOUFrPUBrPURrPdf3XK7WerLWurfv77yg8x/SWvfUWvfVWn8b9PxSrfUg37HrtW5oS14hGk5rzU0fruRPz8yvcsw/o3C24xN0WT4bJv2HcuyVkvcBEqNCePGC4bx+af3LF7QX+y2+YOzA5ma97zkvL+a5uVvqfX5xuZM+ajcqqX6zOD3T09muUzAyKoIxt8fgfMf/AJhrDCMmtIagzmTCetZ/sCsXq757s0pV/tEm747Ojz0TSW9CwUv/zrQfjeFEUAa7FgaOXf3uMt5ctJPdeaV13sf/fXznVG8Lr5259Vgy0xp+fRQ3ZjIjGze711pFhVgpLHcFyjOE2szYLWZmXzuOo3vE13jd7GvH8vqlI+neKZyRPRJYkjSTIabtuLbMBaiyoSN4CfuT5ZmVZiQbXKPM7SAscyEbdDoDUxqeg1iTKF+ZkpwiR6B+HsBz5w2jZ0LV4CrUZmb+bZOYf9skvvrr+MDz4Q3c1SqqJxX4RYflz5+prqq6vw/h0aaNFCeOJC/cW9G7unYwUwcnN3ibfXuQb0mkwBwbqMHWXJbszOOJOfUP8MyFuwlXDixJ9atzNKRLDH94+qB3LQKP93tgol7G+IOz2dj1AroNGFXRRqka1tg0Vho9mWJezoZ9hZWOTTavpFTbeerWa5hzS+OXbvwJ1AuNQZRrK3rTN4FjiZHe5aL61Lryfx/Hh3t/kahPSYblC7+DDV/wAudSFprcmOG3WrFhVj5fuZen5niD/ZBaXudgw9JjOb5fRcHb0BEXsNtI4FXrEwxXm6t8HxzaQumRbzcGHm9saOPxtZ8Q5jzA254Tia6htlpjBP9i2bdzJLec2BeLSXHSwKQar0mLDaNLXFilkjFhbbzXbmshwZjosHKLa16ycXoMoiimn8ogJ34kpb6WKHXllHQkVouZrbYBsPu3ZrtnaQ2lF+asz+LtxTurPRZV4AvcOg+s1+fokRDOHGMEZmchfHkjaM1Z6ieKrfH0u/hZXrloZJ33mOMZwVDTNnRRVsWTWnOcaSW7Y0eTGBeN3dL47xX/91k5duYZR8HK9yDD20PRn8/z4Ncbam3Vo7UOLJHZLCbCbGZW7q599+uazAJ+++49DGXhv8YJgbpU7cWqTO9mCP/MmKWRX19EZBQznA9QQDg3WT7hhZ+3VmqjVXzIrtXCoI/X7W3ghozN31NoT2KBMahZXw9/Ad/0uDBevWQUZ41IY+vDU2v9RSRYSrS37EtHqq94OLWv/2lCNECuL3E1vJrf7Fweg2nhGzEpTUbE0MAPb/ktsILVrNhoG+jtoRgclDTBgaCcpq3ZFTMIV769lHs+rz5PKqbYt6SZ0K9enyPSbuVHYzgbel0FK9/FmP0XTjAvY13SafWup3XsqRcBEL/8Oe+yHmDMf5I0lcOu+KYnMydHV1Sav9d1KQd0NHx2DT9v2M+u3IrlyYOlNf9C8cbCnUx7zlvF32YxkVXoYOXufNbuqTkY+O/vuzjOtIpd4UeR5w6p9xtzW5F6aJukRhqcFk0eUbzm/hMTzWso2bqIRdtyA8eLHd5lSX/D8i+Duh9Ut/GjVgc2kx3WG7PJVKmUTlON6xXPx1cfw69/P46EyIbXCPvyr+MrLVeKpmlf/9OEaAB/Xoe1mjecMMcBbjdeYwcpfFfQhTLfjE21rVY6KKvZxEazt91QacayZrlncL/AwmpqYv3zs8pJ8yUON3EHV7PPlAT2+iURR4RYAMWSrldB8lBMqz9kpdGDVekX13uc8T2G8Zb7BNK2vANf3QyZyzD9dD9feI5hV5fp9b5PTdJiK/LN9hHPAyWnQ942Ni6YDcD5R6cDNc8kAvywvmIZMzioWu2bHVq7p4BPllXU2y4sLWPoqvsYYNrFH7ajcRu63c2M/e/qY7h+Uq+6T6xD56gQXr90JBddfx86LIH3bA/xwuuvBY4XlLmwmBRPTu9OX5VBCBXf14fOmtXK7YADm8kK6V6pzEpzUEoxsltco2e24iPslZYrRdO0r/9pQtRhdWY+f+z07inxLz1W11NydOmvROkCXul8L5sOOLnbNysjM2MVbBYTP+/zziR9On9ls9zzQFHFm5bDVbV+2bu/ZVTK8fvkm28Z4/6Dol71D4DC7d7X8N6vN8Plcyi9fi2nOR9E2yLruLJCdJiNe92XsqbbZbDsDfRrUzhINHe4riAstOmzL6E2Mw+dPojzRnuDrm+M0RRaO3HigbfoG+Vicj9vY5MSR83LlN2D6j4Fv5HfOXsNz/+0hWnPLeBv/1vFrtwSPIYmc97bzDT9yFt6Gm95TvRe185mxlJiQhnfu1Oz3Ov4fp1JS+6M+suvZOoEHrO+gj64C7QmNGcN39huJ+SJ7nxvv5119j9zl+VdQFdb6211Zn71GzKy14PhZk9In3b3WojK5NUVHcr05xdy9suLAShzeX8oVjf1P8axiD3WrpA4oNI2dckZq2A1m8jV3lIfquRAg6/3GLrK5gn/0jFUrZHlF7yDsWfGx5QSQp/T76z35w3O5XJgxhXuXUpqyBJQXJgNm9nMVwlXwSlP4hp5FRc4/kEJofRPjqr7BvVwwdFd+ctEbyFhZbbyr5Kz6Fq+ief1I4T5vg9f/KXmsiL+TSUKg/iDq7jM/C2nmhYRRyGP/1CxQeLYx37hi18Wk7T6ZbYYqczvfhPrsrzf8zU1427L/InrzdZLMTqVdzr/nUTyUc8cBQ+ncP3WK4ilCKbcR84Jz/GZMY4rLd/wROQH/LRmB9+trajNV+7yMP35hZz50qKq9963CoCdtt7tbpZSVNbxtoAJAazcnc/NH3p/0FV5Ey7cx0D3Or6Ju5geCeEc+MM7W3PTlN6Vqu93dFazoowQSrWdJEsDd4gB//xsLTlF5bx6ibcsSLnLU6lauTOoRpZSgdQsXB4jsFycVLqZreZeHBVauRhvfW3JKg7kZzXkzc5kUqTGhpJZUA6nXM7BwnLWz5/Lg6cNqlIYuCm6dQpn5yOn0OOOr/lUT8TmdvOIehVL1i9AKN+vqzlXr9xlYMfJu7aH6ff1Zu71xVVl2sZ97ov5xjOaQiI41bSIU+e9iNKaf7hvZmBqND9u9DZO8dfba0/6do5kxtAUjuvbfF1ceo+YzCmfP8wnJ5Rhy9/Gw8tMfO4Zy8rxM0kA0lNOwbHiX5y5+m2wHuSlXzpz8iDvTtWffP/W2UGzwuAtHBubsZxQezRZpiTsluqb04v2QUJt0SF9tLSiZ73Jt0yZebCUvrfPJufD6zGh2Zp4YqU31sTIkCr36cj8tbhydRTRRsNb9mzOKiIjaGlmxvMLmTVve+Dj4GDMXxMJwOXxRWUeF2nObWTYG54DNP+2SQD8sTMvMDtnMTcs0E6NCQ3MmvqXng5XiZO/nehtrXTapX+H2O6k/P4gPdWeWq9RZbm8ZH2aUabN6JP+zcFr1vBS71msNHrxiPVVVodcxUr7lTxne54VRi8mOJ5hjjGSlJiK7/Pqit+2dRaziWdmDuP0Yc3TdBu8s22bdReyB1/J+pEP8JbnJC6ePDxwfFT3eOxnPMdrejqnmxbSV1X8/Pl6TcUs2dKdFQHX+c98TeHKLyB1OC5DyzJlOyczY6JD6hZfkSBdUObi503ZFJe7ucg8h4Q9P/KA60JiE/tWSlBtzho/7UGy7007lyjC3Q3/rf1giROP1mitUUqx6ZBOCE5PRT5UcGAWWKbM3oBdO8gMq19Ji2Bd4sJIjQnlj515nOirq2Rp4KxnWmwoP27wzkyV+nK3DlcBzOsm9eLa43p6k62nP4fto4t5w/oo18TOqvZ8rTX91z3FePMaXCf+G+sx1xILXDkzjZOe7MQU+wbIWksXlc0u3Zl3PCdw9pg+9EmKJDm6IuctJbp5dh+2d/4gvLjcHdjtOn1I1Rpt4cffSvFPP3Jzwb8hZwQk9KlU++2slxez85FTmDVvG1e5/kuMuYjSY+/BOc/A2sBfFkTbIqG26JCCk8OLHW4ue+MPPGUFXGv5nJXWobzmmUpiVEil3ZMSjFXmX97L1VGEu2uvX1Wdg6VOyl0eut/xDXd/trbKcafb4KeNWXywJCNQWsT/PAB7vDs4D0TXr9jroUZ2i2XJjoO4fcFdcNP3+kiLDeVAsZM1mQWBmbHqyqQ0l8Cut+4TUKe9SLoph8keb/cIh9tTaWdleW4GZ5rn8YHneKxjrw08bzGbSI2P5DfzMN5UM9g99iH+45lGOXYeOG0QF43pypC0GMb2jOfxs4dI8+d6io/wlobYV1DOrtxSlKq8I9Zv5rFDuJWbsTnyKP/oz2AYgY1EflprHvlmPSeZ/+BbYzQb6I7TY8jMWDsnr67okMrdHswmFSgR0E3tY9K8c4lTxbxq9/a5j/Il+vrb2oTZJXk/mH8GJVdHEek+2KBrPYYmv8xFYZk3gHjnt12BYw+dPohwynC6Df785lJu/9RbzmJMD2//T//MWPmOxeTpCJK69m3U+PslRXGg2EF+qbcmVHUlTmrjf7M99fkFPOtr3xQddoQC9j4nk2nryYUlb8PeFcx4fiED7vkegL2ZO3B/9GcU8EfKRVUuDbWaKHO6cbgN7FYzn1wzlrl/OzZwPDrMyntXjuGsEc23jNfe9UuKxGJSrM7MZ1duCcmH/CIXLDPuGO53XURIzhpY9galTnelTR+5JU5GmzYSr4qY6xnOpv1FFDvcTSoiLFo/CcZEh1RY5sZuMWG3mFAYPGqdhd15kEudf+erA962J/7WR/6+fl2b0GuwPfI3E84lighPQUWGfT0UlLnQmkozXsPSY7gtaRkzfz+DdSGXM33eNC40zwkcT4ryzsS5DQ3bf8G+/n/86BlBUiMLedp9wVeebwdnZAOXGNODlrr3Fnhzx/onNc9OyjopxScpt3r/zd8+jdz9vhwkrVn3yp+xZK3mNtdV/GnC6CqXhlrNgV6KdouJEV1jq+1FKOovxGpmYGo0L/6yjU9X7Kn0vXGo588fxhfGWJYafeDrW7ix9HmGpkXz6sXezg/79+ziZsvH5Gtvp4jlGQdZmZHP0C4xR+rLES1AgjHRIWUVlhNiNRNqMrjH8g6jTZv4Kf0GfjGGBc7x16M6eVAyOx85JbAUIbz6dI5kcr9EDugorLjAUVj3RT6HVo63mU10Lt/OX/KfQtkieNp9BsXWTjxofYPnrc9ynGkFSb6ZOKfbgJ8fxhWRxr/clzS6GKZ/2WezL1ctIqRhwdjQtBguH98d8C5PHZUWfUR32+bEDOZq0z1oVxlv2B7lWvPn8Pn1nGBezlPuM5ltTCCmmqX16FArWYXenXt2WfpqNv6ZW4Bu8TUv7/ZIiOD64/twofMOdva9nDP0j1y+4yYG7f2Q003z6f7FmQw3beMR93mUY+fjZZk4PQYnDOhc4z1F2yf/E0WHtGzXQUIsJkYf+ITLLN/ztvsEvjYfX+mcw5WM3V7YLCZeu3QUkfG+ROUG1Bo7WFI5GAu1mTmj9GNcyo66eDafRl3MX23385L7VCaYVvOm7TGm7X+BNJWDJWsV7P6d7D7nUUrjW/b4r/u3r4lzeDVN4GtjMilOH5YKeAPEyAYGc02VHhfG8tIEbi67nBhVzG3WD9HrPuU99yRe9ZwCQGJU1V8gOgX9UmGXunnNZkTQzuvR3eNqORPG9+pEOXZuyj2DR1wzCSnKIGnB3TxlewlncR7nOu5iTefTKl0zsmvzlUwRrY8EY6JDyi1xYreaOWr/ZywzenOP+zK+XrO/0jkNfXPuqErMvuWTkpx6X3PQl6fld7rzS050/8JvcdNRYXGcPSKN5ZlF/J/7PEY4XuYTzwQG7XqbX2030e+LUyGiM/t6nQs0vkr8obNCjQmmgjsyBJffOBLOHenNd/zMGM94x7MML3+ZnKvXcqf7SgxMRIda6dGp6vJjp6A+hDIz1nzG9IwPPO5ex8YH/y96W7KLedkznc0zF8BNa5nseIyxjmdZrvsQajXz8oUjAtc0tqm5aBvk1RUdSvAqUm/LfuLKdvC5Z2y15x7pN9e2qtji/Y1dF2fX+5rgmTELbm62fMw8z2B+SrsGqOi9CBAdHsaD1htYfsZ8ZnmmkTHkZrjsW8rM3vysxgYUh17XmNc7ePY0NvzIFkiNDrPyz1P6Bz7OI4rP13nrvcWEWfn46mOqXTY9Kq2iXEtJNa15RONEhVgD5Sdi6yiW6y+FUeLbSXl8/ySI6cI2nUoZ3tzIA8UOTh6UdBhHLFoTCcZEh+H2GAR335lqXQHAz8bQKud2iw87cjvj2rg8vG/uL3y1uNq+e9VeE5QzNkxtJVqV8p5nMlFh3sTn+Ah7YNbp1pP68s2NEyCmC//nPo9tA66F+J6BEhc2c+Olf2hyAAAgAElEQVSW2oJn1N7+8+hGvd7Bs2ktkQR/xYQelT5+6JsNAPzj5H707lx9r82BKdFsfOBkbji+FzOGph72MXYk/oLEdQXmwTuz/35SxW5gf8/Ri4/pypW+VlgXH9OVJ84e0txDFa2MrMOIDsPf6zA1JhQjP5M/5b3DxpCh7C6vSIw95ahkFmw5wLPnDavpNuIQm4pDKNRhdCpcz43vr+C1S0fVeU1wAv948xo8WrHIGMiooDcxu8VEqdNDVIiV5OhQDpZ4lzYdvh2Y/tez0TljQUHcxD6Na40TZrPwzMyhvLloZ7O212mI5OgQ8kqcOIIK4/pz2WoSYjVzy4mNKwkianbhmHTe/S2jzp258eF2Th2SwqVjuzKia0V+2YsXDqegzFWp28f9MxpXR0+0LRKMiQ7DP5Ny/tHpDNv9C/btJXDms/B2ZuCcEemxvHD+8JpuIaqxr8jNPDWYSeaV3L4xC7fHqDO/JXiZcrxpLat1TwoJJy6o/Y63rpIrsJzonym74f2VXDjmYGC5rakJ/E01Y2hqi84wLfzH8Ww/UMyUJ+cB8N4VR9dY40ocXvdPH8Q/TxlQ565as0nxXDW/8NktZhIj5bXriGSZUnQY/pmUqFArYyP2Q1Qq/QYM4aULKoKv+Ij21xj5cHO4DX4xhtJZ5TNQ7WRnbmmd1+T5ZrkmmFYzwrSFOR7vaxCca2O3en88+YOmUF8w5vQYvL5wR6BGWUsHYy3NZFJEBZWwGC677lqMyaQkEBaNIjNjosPwz4zZzSbIWgedvT0NTxqYxG0n9yW/1MXUwVX7yYm6/WF4l7z6mzLqlTeWX+rEYlLcaXmPrUYKr3mmAhWFZKEiH8vwFZMNPaTVkL+nX2PrjB2upt4tITEyhJcvHM7o7vESDAjRBrWfn0ZC1CEQjJnckLMJep8AeH+bvfa4Xi05tDYtOTqE/QXevJdkcgM5XbXJLXHSPVLTt3w3L6mzceANwoJnxh4/ewi3f7Im0Kw99JAgY0++t+q9fwatoXomhHPKUckc27tlcr2a28mD5BcJIdoqCcZEh+Ffpowr3QGGCzpLYmxz+OaGCRQ73LhmxZPszg38O9fE4faQkVfKVen7MO3XZNj7gHeSq9IutH5JUXx23bjAx9ZDZsC25RRjNikiGlkPTikl+YFCiFahfSRNCFEP/pmx3ptmgSUEulZfX0w0TGy4jS5xYbjDk0lWeThctQdju/PK8Bia4eYd3o9D+wWOhdvqv8S2NauY2DDrEW1BJIQQh4MEY6LDcLoNjlYbSMr8FibcClEpLT2kdsUTmeoNxty1B2PenDLNwLI/yNSdGNy3YolYqdoDq0+vHcu3N04AoMjhrrO4phBCtAUSjIkOw+kxOMa8Do2Csde39HDaHSMyhRSVi9NTe85YqcPNReY5pOT9jn3sNdx2Ur9azw82PD2WvkHFTPvUUNhUCCHaEgnGRIfhdBt0Udk4w1PAGtrSw2l/olOJUqV4yopqPa3Y4eZ6y2cUJ48l4YRbMDdwmTF4WfK+GQMbNVQhhGhNJIFfdBglDg/pKht3dDr2uk8XDWSK6QJA3r4daN2vxiVHT2EWnVU+uT1OIMLk/X3wmZlDG1Rq4tNrx9I5KoROEfJKCiHaPgnGRIdxsNTJSJWNKXZESw+lXTLHpAGwYNlKUnoPZdpR1efkWXO9/RNNyYMDzzW0gv3wdClsKoRoP2SZUnQYRYWFdFb5WBN61H2yaDBbnHdmLFnlsXDrgSrHtdZ8tXovtgPrAAjtIs2PhRACJBgTHYiRvwsAS7wEY4eDOToFbQ3nuNDtFJZXrcL/y+Ycrn9vBTnblpNNLCHRiS0wSiGEaH0kGBMdhrXQG4wR261Fx9Fuma2oo85mkns+FkdBlcP5pd7m4ANUBrusEhALIYSfBGOiwwgryfQ+kGDs8Bl+CSE4GVC8qMohk1LEU0BPtYecsD4tMDghhGidJBgTHUZ82U7KVCiExbf0UNqvpKNwYSGlfEeVQ0opbrV8hAK2pZx65McmhBCtlARjomNwOxlTvoDNEaOhjirvognMFvZa00l1VQ3GzNrFaeaF/M8zEWtS/Qu9CiFEeyfBmOgYtnxPLIWs6zyjpUfS7u21diPNvavK89H5mwhVThYYg0mJkaK7QgjhJ8GY6BDcu//Aqc0cTJbm4IdbVkg3Eo0cKC+s9Hxc3goAlhu9SY0JaYmhCSFEqyTBmOgQ3Ad2kKkTiIkIa+mhtHtZoT19D7z1xMpdHh7/bgPRe38hU3diP/EkR8vMmBBC+EkwVk8eQ1PmrL0Bsmi9dN4OdutEYsNsLT2Udm9v+CDvg/WfA7AiI59uC28j9cAiPnIfB0BipLQxEkIIPwnG6unmD1fS/57vWnoYojG0xpa/ne06WYKxI8AZEs9najL8/hJs/AZX1kbOMs/jP+6pPOs5HQCLWX70CCGEn/xErKcvVu0FvC1dROs3b3NOxUxmcRZmdwnbdTJx4RKMHW52i4n79eUQ3xu+upnBi26gVNuZ5Z4GKJkVE0KIQ0gw1kAOt9HSQxB12J5TzMWvL+Gfn631PpG7FYAdOpnYMGsLjqxjsFlMlHpMcPK/KXO6KHYa3Om6nBxiAOgaL3l7QggRzNLSA2hrypweQqzmlh6GqMHyjIO89Ms2ALZkF3mf9AdjRhLxETIrc7jZzCacboMvSwfy18Lnqhy/YXLvFhiVEEK0XhKMNVCZy0NsSw9C1OimD1aSkVcKeCu+A3BgC06sDBs8CLNJCr4ebjaLCUPDX99fUe3xQSnRR3hEQgjRuskyZQOVuSp2VLo8BnPWZ0keWSvSIyE88FgBlBfAqvdZoQYSYpMlyiPBZqn8Y2VE11geOn1Q4GOzWQJiIYQIJsFYA5UHBWP3frGOK99eyvtLdrfgiATAjgMlZBeWExlSEXApBSx9A0pzeZrzCJXl5SPCdshOyafPHcoFR3cNfGw1yY8dIYQI1qSfikqpm5VS65RSa5VS7yulQpRS/1JK7VFKrfT9mRp0/h1Kqa1KqU1KqZOCnh+hlFrjO/asUq23eaA/GNNa897vGQDcOXtNSw5JAJMe/4XRD8/lS9+uV8AbfO2cDwn9WenuRohVgoAj4dCZsS5x3oT9qYOTSIkOwW6R10EIIYI1OmdMKZUK3AAM0FqXKaU+Amb6Dj+ltX78kPMH+I4PBFKAH5VSfbTWHuAl4CrgN+Ab4GTg28aOrbkFz4bll7oA2JpdXOkcw9CYJB+pVejeKRxDa0ItCnb/gR54OmW7PTIzdoQEB2Pf3zQx8Pi584ZjUkG5fEIIIYCmL1NagFCllAUIA/bWcu4M4AOttUNrvQPYCoxWSiUDUVrrxdqbfPU2cFoTx9Wsisrdgcf7Csr5eWM2Jzw1r9I5heWuIz0sUYMdB0roFGGnS+l6cBTg6uLtR2mXYOyICJ756psUGXhsNikJxIQQohqNDsa01nuAx4EMYB9QoLX+wXf4eqXUaqXU60op/+bDVCA4uSrT91yq7/Ghz1ehlLpKKbVUKbU0JyensUNvsKKgQOu93zO47M0/Ah/PGJoCgNMj9cdaisfwbqC4cEx64LkQMxxX9CWYrDh7TAGq5jKJw8P/7xwZIpu1hRCiPhr97uQLsmYA3fEuO4YrpS7Eu+TYExiKN0h7wn9JNbfRtTxf9UmtZ2mtR2qtRyYkJDR26A1WGDQztn5fIankMFxtJpGDxIR6E8ZdHtlR2VL8y8ipMRXFRM8o+ZDjyufC6KvwWKMApKzFEeJfpoyRArtCCFEvTfnVdQqwQ2udA6CU+hQYq7V+13+CUuo/wFe+DzOBLkHXp+Fd1sz0PT70+VajsMw/M6a51vw5t1k/AsCpzWzYO50PmIFLKvO3mFJf26MIu3cZMoJSTi76mEWWoxl70kO4S5wAWKSkwhHhT9gPt8nMmBBC1EdTflpmAGOUUmFAGTAZWKqUStZa7/Odczrg60nDF8B7Sqkn8c6k9QaWaK09SqkipdQY4HfgYqBq2e4WVFjuIoUDfBD2KOlGJt95RvGB5zgmm1ZwUdZsrrWAyzOlpYfZYflnxkJtFp4f7+T4Df8ipKSEt+znMFapwDKmzIwdGb0TI7hpSm+m9O/c0kMRQog2odHBmNb6d6XUx8BywA2sAGYBryqlhuJdatwJ/MV3/jrfjsv1vvOv8+2kBLgGeBMIxbuLstXspATIKXJwneVzUoz93Or6C7M94/Fg5hdjGMemW7lizzfsKrwHOkfWfTPR7Eqc3mXkMJuZqXueBpy80vVx1uzvTmG5C7cvGLNIMHZEKKW4aUqflh6GEEK0GU3KaNZa36u17qe1HqS1vsi3U/IirfVgrfVRWuvpQbNkaK0f0lr31Fr31Vp/G/T8Ut89emqtr9etqKS9YWg++20jZ1gWsi35FD72HIuHil15uwddhw031nemsXH7rhYc6eG1cX8h57yymDKnp+6TjzD/bteksi2wbyVMvJVdMUezt6CcYffPIfNgGQBmKTYqhBCiFZJ3p1q4PAZnvDCPi/JfJJRysvpeCECvxAgm90tkw/0noxP682fX3+lt2oN72TstPOLD54Gv1rNkRx7LMw629FCq8Of0pW//EMw2GHw2Tl8On8fQrNqdD8jMmBBCiNZJgrFa/LEzjy77fuAs8zyyh92IOW04APHhNl67dBShNjNWs2K+cRTLjN50y/iU/BIH+wvKm/R552/JYe2egub4ElqF7MJyHvt+42GbVSssd3GqaRGdNr4Lwy6CsDh2HCgJHF+8PReQnDEhhBCtk2x3qoXdYuZ08wL26HjKxtxKsm+ZKy22ooSC1beN/0PPcTxa9B+ufuhhvjNG88TZQ3B6DM4bnV7tvWuy80AJF722BJvZxJPnDmFFRj69EyOY2cD7NDf/wnFjwpm/f7yaXzfnMK5XJ8b27NSs4wIoLHNzrvlnPHG9MP/p/wC4b/pAft2cww/rs1jjC2xlZkwIIURrJDNjtTCKsphoWs0XnrGE2Cz0SIjgnctHc/+MgYFz/AUuv/IcwyYjjZdtT3O2+Rf+9r9V3PFp7T0rf9+ey578skrP/bQxG/AWkb3+vRW8tmAHt3+6hoKy1lPh3+H2cOfsNWQX1m8G0D/2wsP0NeQVFjPCtAV6TQazt7bVoNRorpvUC7vFhMO321JmxoQQQrRGEozVxO0k7Y+HsCiDzzzjsFu8SfsTeicQbq+YUHT5Ku+XEsJ054OsNrpzhfkbQGOtpa5VucvDubN+47xZv1V6vqagy7/sll1UzpVvL+VAsaMpX12D+WfGPFqzeFsu7/2eUWew6ecPWAvL3HWc2Tjm/SsIVU7M3SdUOWYxKcp9+WNSZ0wIIURrJMFYTdZ+QvKuL3jKdSabdDoh1ur/qfwFLgEc2PjIcxx9TZncNdyFy6NxuKvPk9q4vwiAjLxSAJbuzKOg1EVBmQszHvqo3Uw1/UZ/i3cz6oEiB/mlTuZuyGbO+ixu/6R+gVBzc7qNQDDqH3td/BXZD9fsXsIBX3uqruOqHDObVCCZX3ZTCiGEaI3k3akmQ2Yy95h3eMZzJgAhNTSZ7hRhZ/vDUwMff+UZQ5kpnJk7/kk8BTUGIPsLKpYnN2cVcdbLi3nku42YCjP5OfQ2frD/gxdtz/Kt5W+8b32Q9xZuYuj9c9iWXQzAjxuymusrrRft61DlcBs4XN7gZl89NyqYfMuD87ceCBRobbZxOYoYVfwT+0J6QlhclePBeWKSMyaEEKI1kmCsJkqxO2Jw4MPa3shNQcfm/vN0zBd9TGRZJmeZ55FfWlMwVhHInPz0PACG5n7L9TuuJZZC7jddw6mOB1nY/a8cY17PjIx/E4KD33bkBq5zHsEWTP5lymv/u5wyX0BV7Kh+2dHh9mAYFaXi/Dlb8zbn8OWq5u10Vf7xtXTXmazrf3O1x4NnwyRnTAghRGskwVgtsooq8rKUqt8beXyEHVv3sRR3Gsp086IqCfp+P27IDjw2NBxnWsm5ex4iT8XySPwjzOZ41ugeHBhyDc+4z2CGeREPWN6gOKhpeUZeSXW3PuyCZ7ecbgOtNa8v2EFusQPD0PT953fc9dnaivPdBsf0iAfqP5tWL84SQrZ+xWueqUQMnlrtKcF5ezIzJoQQojWS0ha12JXrDXaGp8c0+Fo96AwG/nIPN7z5GR8MGM4rF40MHDtQ7GDRtgOkRIdwsCCff1neZrp5EftCenKj9WG6RMej87wzYMnRodxhOpcYdxEzzT/zcO75QBQA23JK6JV4ZFowuYNmuhZtOxB4nJFXiqE193+1noUbMniwx1r+aZlP4op8nAdN2Dr1oF9pX/I7jyEyxEKer2l3s9i3CqUNfjP6c05S9f8OwbNhMjMmhBCiNZKZsVrsOFDK8f0S+fTaqonhdYkYfjZuzPzb+irz11VukzR3QxaGhouP6cpdlv9ytvlXvjDGcWb+jeSUm4gOtXLp2G4ApMWGEmYz86FnEnbl5iTz0qDxHbmZsZhQb8mIEKuJ95fsDjy/ZEceV876mX9Z3uTfmReTPP9OzjP/xGC1nbW7sihb8T/+r+SfXFb4EvHhNrblFDffoPYsB2BfeH9iwmzVnlI5Z0y+3YUQQrQ+MjNWA601u3JLGNOjalJ4faioFF5LvIO/ZD/IheY5lLtOC2wC+H5dFunRNi7aejPhlnm8zSnc47rAe2GRg+gwKzdO7s25o7qQHB1KqM3M+uKu7DISOdf8C//zHEtsRBjbmzOwqYPTV8IjzGah3FUxu/Xk7AW8YHuGEebN/GQMY5b7FJbqfoHjYZTzD8v7XHLgE87vOo7/25KM1rrey761yvyDbJVAQnLNBXElZ0wIIURrJ1MFNcguclDq9NC9U3ij71Hcazq/G/04z/wTB0sqcqV+357LbZ0WEJ45j4dd57F+4N8rXTftqGSUUiRHhwLg9mhAMT/1CoaZtvJU8o8kR4dwoLgZl/zq4N9BGVy4tbc5i1/sNzNCbeZm17Vc5fpbIBA7Y1gq4K2/9oj7PHRcDy7PuI0Rej2lzdAW6YGnn8Oz/gt+dA2mZ0LNr1HwzJjNIsGYEEKI1keCsRrs9C0BdotvfDB2/tHp/Nc9me6mLNTiF0Br3B6DMzzfcuK+V6DHJC64+XHuP31I4JqhXWI4Kq1yjpo/6X3w1L/gTh/PNNtyIuyWSsn8h5u/Xpo/d8yEwV2291BoTnL+H18aYyud3zcoh+vMMX1QV8zFZY3kYssPzJq3ndkrMhs9FsPj4eqDj7HZSONB94VEhlhrPDe40GtN5UmEEEKIliTLlDXwGJr+yVH1nhn75JqxlBxS6iE5OpSLLr+R3976iTG/PwSOXbhDE3jA+iZ7okeTOuN5ukZHAN7ZsK9W7wsUSA32zMyhON0GQ7rEQLexMP9x4ru62HYEe4k7gspoRFDK3E5P0Ll4A4+6z2WbTq10rt1iIjXWO6t3zsg07p8+CEyKval/Ysr2/3H73FUUE8bpw9IaNZbifZtJUAW8G34JpXkhhNtqDrKCZ8YkGBNCCNEaycxYDcb26sS3N06oVGG/NiO6xjKxT0KV52MjwzjPeRdbe14CK98lZPFTfOMZzYKjX4HoimDknJFdgIrWQcFmDE3lbN9xuowGbTDA2EKxo+EV7bXWPPjVelZn5jfouuBg7BTz73Qu3sATYTfzomd64PkLjvbmbvVLiuTkgUncemIf7jplQKAOW16P6YQoF5NNyxs8boAr317K6wt24Njlrbg/cNQkHj3rKC4d163Ga4JzxkIlGBNCCNEKyczYYRYXbkdjYn6Pm+k17RaOe/RHduokHjVXXlrrHBUCwDE942u/YZq3REYf5waKyxs+s+T0GLy6YAdvLNrJtoerr81VHUdQbbEppuWUhaexOu5PkHeAGyf3ZlK/RIZ2iaFfUiQnDUrCYjZx/fG9K93DnTKS3UYC/7B+wCLHIJ75cQvXHNez2tnAQy3POMic9VnMWZ/FtOHLKNF2zIn9OGdgSq3XyTKlEEKI1k6CscMsJtRKmM3sLUMRO4idOhmAvNLKyfd9kyL58ZZj6VHXsmhoLMT1IN2xmaLySRiGrtQBoC7ezQDeZdiG8M+M2XAx1rSWgtSzeHL6UHbmljCsS2xgDBcd063Ge0SE2LjadTNf2O7iQsuPPPVjDDFhVi4ZW/M1fu/+5i0PMrJLJCUbf2KH7k7PztF1XuffQWkzm2Q3pRBCiFZJlikPM5NJMbRLDMt2HQRgYIq3YOu0o5KrnNsrMaJ+gVXXsXQv+I1Y42CDe1S6PI1roeRwG4TbzBxrWkW4clCcfjzxEXZGdI2rdzAYGWJhne7GGt2DcSZvhX5/w/S67Dno7WQweO9HdDcymBN2Cl3j615CtvrGZq+h0bsQQgjR0uQd6ggY2TWWdXsLWbozj7hwG8PSY0iLrV8uWrXG34JZu7na8iVXvbOsQZc6Gx2MeUgLd3O/9U22Gck40yc0+B4Rdu9E7EJjIEPUNsIpY3deab2u9ZfDOMM8n7WqD/+49a561Sqz+5Ymm6OchhBCCHE4SDB2BAz1tVM66+XF5Je6AkFJo8X3JL/z0YwybWzwpf5lyobwGBqXRzPRuolklce97ktJiG14i6hw39e9wBiMVXmYGrWd3Hq0R1qyI481ewoIp4wBahfrQoZjtdQv/+uCo9O54fhe3DNtQIPHK4QQQhwJEowdAWN7dgo8XrOngCRfsn5TxHUbQi+1l/E9Yxt0XWOWKf01xvqzHUMrdoQMJCHS3uD72H2J+suN3rhNds4P+Y2D9QjGfli3H4Bhpq2YlWZLyKB6f86YMBu3nNi3XnlpQgghREuQBP4jIHgX333TB3LuqC5Nv2lCP8KUA1vRngZd1qhgzFd9f4hlF3utXXj9iuMafA8ApRSXju3G+F6dsOy/kWHzHmW4ZyhaH1/rkmOUry/mSNMmPFqxwdyvxnOFEEKItkZmxo6QO6f2IykqhIuP6do8JRYS+wMQU7K1QZe5GrFM6d9JmVyyibQBx1Sqrt9Q/5o+kCkDOsOx/6DUFs+JLGZrdu09NqN9wdgxpvVs1OmsyJb8LyGEEO2HBGNHyFUTe/LbnZObp0E2QKc+ACQ5duJuwGxXY5cpE8gnzJENyUPqvqA+zBbc6eM4xrSeDfsKaz011GrmdNN8jjZt5GvP0Y3KexNCCCFaKwnG2qrQGErtifRSmQ1qGN64YMzgKNM27wfNFYwBri4T6KzyCcnfVvt5hsFVlq8oih3IK55TMbQEY0IIIdoPCcbasLKYPgxVW8nKr32ZL1ijlildBpeZv8Npj4WU4Q2+viaebuMBiM35vdbzrKXZ9DftpqDHKXgw45FgTAghRDsiwVgbVjLwPHqY9hO69MU6z/UYmiU78ho1M2bOWsV48zp2D7wGbE2oj3bofeN6sNPoTN8db0NpXo3nJWXPA8DdYwoAEosJIYRoTyQYa8NChp7Jt55R9Fj3PLjKaz337s/Xcs4ri5m9vGG7LwFsWSsBKOr+p0aNsyZWq5lbXNcQUbYX5j9R5bjWmke/20hs5k/s1XHYUgY36+cXQgghWgMJxtqw+IgQvjLGYTEckLOh1nO/XLkXgDnrK9on6XpOMYXkbaBQh6Gim6EkRxCb2cRy3YddcWNh3WwwvLN2X6zay3Nzt5BX4uTg/FkMLlrA154xRIRa67ijEEII0fZIMNaGmU2K/WHeXZXsW13l+As/b6X7HV+jtabM5S0HUeRwB46769ksPOzgJjbodOy2ZijJEcRq9n77bYybAoV7KNyyAIAb3l/BE3M24ywv5h+WD1joGcij7pmE26QsnhBCiPZHgrE2zhPVhTIVBvu9wdj+gnK63f41X6/ex2Pfb0Jr2FdQXinw6uerE+Z01yN/TGsiCzax0eiCvZ4tiOrLbFKYTYrPy4dSqEMp+uhqyNkUOO5c8iYxqoRn3GeA2YrZ1/T7LxN7NOs4hBBCiJYkwVgblxAdxlZTd9jrzevakl0EwH9/34XV7A1eNmUVVbpmSv/OABwsrUdJjPwMrO5iNur0QDuj5mQzm9iQZ3C96wbs7iL2vTyDmeafeMX6JF1+f4D5nkEs0f0Cs2g7HzmFO6b2b/ZxCCGEEC1FgrE2rnOUnZ+NIbBnKWz4MlDd3+E2iAzx5lht3FdENMXcbXmHV62PcWnuE6RwgLV7ai+2CsD+Nd57GIcnGLOaFbtyS5lnDOFq502Eugt5xPoqE6wbed4zg+tdNwAKi6mZiuUKIYQQrYwEY21cpwg7z5adjE4cAD//O7CU53B7KPHlh4Wsfpsl9mu5xPw9/UILiN82m0/s/6J43XeB+xwscbJo64HKNzc8sOBJyqyxrNddsTdHG6dDFJZX5LAt1f0Y53iW+VPnUnz9Wp50n0MBEd6v4TB8biGEEKI1kGCsjYuwW3BjoXzAOZC9DlOht3TF2j2FONweTjIt4aLcZ1li9OPbsR+Qcvty1OU/4MDO9PW3cDDTm6N17qzFnP/q75S7gvo+bp0Le5axoMdNOLAdnmXKQ+5ZQijWTt1JjIvl5il9As+HNvPmASGEEKK1kGCsjQu3e3cYFqZ7C6LGbP3Md0TziPV1XrE9zTadwjWum+jcZxQmk4KUYZzj+CcGJkq+fwCAzVneKv7ZhY6Km++cD2Yb62MmYVIclqXCubccy5ybJzL/tkmB5yJ8X9ONU3qz5K7J9EuK5JyRzVtWQwghhGgtpFZAGxcIxsK70bnHJNJXP80s61FEWAzG6hUs6TyTi3adjAMbnSJsgeteufYU3pj1Hdfs/hLHV7cBEwHF/sJy0uN9VfZ3LoDUkewvMxETZmu+JudBusRVVPTvnRjBluxiwoJmwRIjQ/juponN/nmFEEKI1kJmxtq4CLs3cClxeuC0l9jdYyajTRsZFbYPJt7Gb73/hgNvEBYfYQ9cNyg1mqfdZ7I66QzsS1/hBNMyAHbmlnhPKNwLe5dDr+PZlVtCt/jma4NUkzf/PJprj+tJtypxhh4AAA5ISURBVPjww/65hBBCiNZCgrE2zl8IdVt2MUQls+aoOxnqmMWOi5fC8XdRFlRLLCqkYiLUajYRGRHBhwl/ZbORyr22dxlo2smynQfZvjcbvv6b98T+M9hfWE5yTOhh/1pSY0K57eR+3qVUIYQQooOQZco2rnuCdxbp+3X7Oa5vgq8RuMLmq8v153HdCbeZ+cuxPassM4bazPz3j31sVpfzmu0xPrPezSMrzmP1qu30MC+Ck/4NCX0oLMsgWloRCSGEEIeFzIy1cYmRIcwYmsIP67MY8eCP5Je6ALD6dikmRNq5/vjegaKpwdJivEuPf+h+vHjUp6zWPbjb+i6nmRfxCmfyeOFk5qzPorDcRVSIBGNCCCHE4SAzY+1An86Rgcfbcry7Iv3V92vzxDlDGPvITwDcetoYTs14HJ29gSIdyh4S4OetgXOjQuVbRQghhDgc5B22HeiVGBF4/NXqfQCBZcrapMSEsuTOyWzJLsZiNvHRNeNYu2cw5/3ntyrnxoTaqrmDEEIIIZpKgrF2oGdCRTCWX+oixGqqd45XYlQIiVEhAESGWANNxINdNKYr04YkN89ghRBCCFFJk3LGlFI3K6XWKaXWKqXeV0qFKKXilFJzlFJbfH/HBp1/h1Jqq1Jqk1LqpKDnRyil1viOPasOR0GrdqzrIWUn0uPCGl0TLOqQIG7mqC48cNogyRkTQgghDpNGB2NKqVTgBmCk1noQYAZmArcDc7XWvYG5vo9RSg3wHR8InAy8qJTyV/d8CbgK6O37c3Jjx9URHZqcnx7X+Dpd5kPKSjiCSmMIIYQQovk1dTelBQhVSlmAMGAvMAN4y3f8LeA03+MZwAdaa4fWegewFRitlEoGorTWi7XWGng76BpRT0+fOzTwOD2uaQVa75k2gHNGpgFU7lUphBBCiGbX6GBMa70HeBzIAPYBBVrrH4DOWut9vnP2AYm+S1KB3UG3yPQ9l+p7fOjzVSilrlJKLVVKLc3JyWns0Nul04alBvK9Dl22bKg/j+/OiQOSAHB5dJPHJoQQQoiaNWWZMhbvbFd3IAUIV0pdWNsl1Tyna3m+6pNaz9Jaj9Raj0xISGjokNu9rMJygIrekk0woU8nzhqRxr2nDmjyvYQQQghRs6bsppwC7NBa5wAopT4FxgJZSqlkrfU+3xJktu/8TKBL0PVpeJc1M32PD31eNNBBX8HXpi5TAtgtZh4/e0iT7yOEEEKI2jUlZywDGKOUCvPtfpwMbAC+AC7xnXMJ8Lnv8RfATKWUXSnVHW+i/hLfUmaRUmqM7z4XB10jGiEt9vD3kRRCCCFE82j0zJjW+nel1MfAcsANrABmARHAR0qpy/EGbGf7zl+nlPoIWO87/zqttT87/BrgTSAU+Nb3RzTQ1cf25KOlu7FbzHWfLIQQQohWQXk3MLY9I0eO1EuXLm3pYQghhBBC1EkptUxrPbK6Y9IoXAghhBCiBUkwJoQQQgjRgiQYE0IIIYRoQRKMCSGEEEK0IAnGhBBCCCFakARjQgghhBAtSIIxIYQQQogWJMGYEEIIIUQLkmBMCCGEEKIFtdkK/EqpHGDXYf40nYADh/lziKaT16ltkNepbZDXqW2Q16ltCH6dumqtE6o7qc0GY0eCUmppTa0LROshr1PbIK9T2yCvU9sgr1PbUN/XSZYphRBCCCFakARjQgghhBAtSIKx2s1q6QGIepHXqW2Q16ltkNepbZDXqW2o1+skOWNCCCGEEC1IZsaEEEIIIVqQBGNCCCGEEC1IgjEhhBCiFVJKqZYeg6hbc7xOHT4YU0p1a+kxiLoppUYqpRJbehyidkqpKUqpES09DlE7pVR00GN5w2+9LC09AFEv1qbeoMMGY0qp4UqpH4H7lVLmlh6PqJ5SaqBSahFwLxDT0uMR1VNKDVNKfQvMBnq19HhE9ZRSRyulPgdeVUr9+f/bu/cYO8o6jOPfh7ZCWy5CLIJUbYQ2VAg0KdGScCtFggoCpkWjCIEECISCgkICIRDAxCiiIBCKmBYQiyKXGERRIgGBoBUCGC4pkKICykXUUGiL7D7+8b6La+1tb2d29jyfpGl3Zk7Pe/Y5M/Obd96ZkbS5cxXXqCNpjqQbKfun6dlHjU6S9pZ0M3CJpI8OJaeuK8ZUnAssBW6yfYztnr55zbYu1uF04Dbbh9leDslpNJE0TtI1wPeBRcCPgJl1XtdtX0YzSXsAVwI/BW4GDiSF86gjaXfge8AdwMvAicAxdV62faNEPVNzBXAn5XFHpwPH13kDzqnrNpb1KHACcL/ta+Hdo/rxOUIcPepOfjvAlC88ko6UNBWYWH/Ohqlh9UDml8C+tm8HbgHmStrCdm+zrYu1zAaetX0D8GtgC+DPfTOzPo0ac4CnbS+lHOS8BXxR0jTbTk6jxp7ActuLgW8DtwKHS5oxmJy6ohiTtL+kj/eb9C1gJ0mXSFoGXARcJ2l+My0M+N+c6k7+LWA/4EBJPwROAi4GvluXSfHcgLXXJ9u32l5VNz69wHJgUmMNDGCd272fA0dK+jrwR2AqcLmksyHrU1PWkdMy4IOSdrb9JmWd+hdwAiSnpkg6QtI5kj5dJz0K7NUvp2XAHyj7qQHnNKaLMUlbSbqVMo7lJEnbAtRf3A3ALOBM24cC9wGHSJrRWIO71AZyWg0sppxaucv2IcC5wO6SPtlYg7vU+nKqp/5VNz5PA/MovS7pbWnABtanVyhH8+OBc2zPAZYA+0jau6n2dqt15LRdnfUc8HtgsaTbgb0op5XHS9qimdZ2L0lTag5nAK9Tcplv+1XKmYCFddF/AncDkyTtOND3GdPFGPA28BvgaOAlYEHfDNs3AkfZvq9OuhuYAqzsdCNj/TkBV1FOS04BsP0icD/laDE6a505uZK0me0XgN8B8/vmNdXYLrah7d7TwK7AX+qkh4FXgDUdbmOsf31aafss4FRgie3DgGeBPeoBanTWzsADtvezfTVwJvCVOm8psKukeXVYxt+BnSg9mQMy5ooxScfUbt/32l4DXEsptJZTuhRn1OVk+/V+L/0EZXxSirEO2NScbK+kHHkcK2mWpJOBg4DnG2p6VxnA+rSZ7V5J44FngDeba3X32dScql8BF9Rey88Du1F2IjHCNpLT7P452X68jsOEcrHFQ+lp7oya0wGSJlEOWK6v08cBT9Y/UE733wRcJmkXylkBAe8Z8HuOhQPX+gXdgXIlVy+lm3cycLrt1+oy04FjgdW2L67TNgP2AS6jDGQ9ux45xggYbE51+ucop1h2o5xieaLDze8aQ1mfakH2HWCl7fMa+QBdYoA5rbF9UZ02kfLw4u2BccBptp/8/3eI4TDE7d5syuDwHuBE2891uPldY2M5SRpnu0fS0cBnbB/V77VnATMovc4n2H5qoO/f+p6x+gsysBXwou15wCmUc7uL+paz/Qylwv2ApF3quXcDLwLn2z48hdjIGUJOkyVNsP1j4NyaUwqxETLE9WlinX1GCrGRNYicdlS5X9Uk26uA44BjbR+UQmzkDGF96luXnqfsn+alEBs5G8npmrUWP5hyexgk7QBg+5vAKbb3GUwhBi2+u289HXIhME7SncDWlKMHbL8j6TTgJUn72763Tr9N0kzKpfhbAgfWDVG+5CNkmHKaCzyV8UcjJzm1wxBz+gWwpaS5dYfxt2Y+xdg3HOuTpL79073NfIqxbzA5UYYyrZB0IfBZSYfYfsH220NpSyt7xiTtTzmK2JYysPEi4N+U+xt9DN4dOHwhcEG/1y2gXI13D2UwZI4IR9Aw5jSoI43YNMmpHZJTO2T/1A6DyamOGTue0jO2NTDX5aKlobenjQexkvYFprncvBBJV1EG0q0CFtqeXceDbQ9cThkLtqK+Dtu/bajpXSU5tUNyaofk1A7JqR0GkdPXKGcTFwLX235kONvTyp4xSjX7E/33OVAPAB+yvYTS3bjQ5TLTqUCP7RVQvuT5ondUcmqH5NQOyakdklM7DCSnXtt/sv2c7S8PdyEGLS3GbL9le43rMyUpt6V4tf77OGCmpDso9wAZ9l9abJrk1A7JqR2SUzskp3YYYE4Pw8jexLq1A/jh3fO3Bt4P/KxOfgM4B9gdWOFyk9BoUHJqh+TUDsmpHZJTOwwkp5G8OKmVPWP99FIe+v0asEetYs+jdCneny/6qJGc2iE5tUNyaofk1A6jIqdWDuDvT9Ic4MH6Z7HtHzTcpFiH5NQOyakdklM7JKd2GA05jYVibCrwJeBSl8dLxCiUnNohObVDcmqH5NQOoyGn1hdjEREREW3W9jFjEREREa2WYiwiIiKiQSnGIiIiIhqUYiwiIiKiQSnGImLMk9Qj6VFJT0h6TNIZ9blzG3rNNElf6FQbI6J7pRiLiG6wyvYs27tRHnvyKeD8jbxmGpBiLCJGXG5tERFjnqSVtrfs9/NHgGXA+4APAzcAk+vsU20/KOkhYCawArgOuBz4BnAAsDlwpe1FHfsQETFmpRiLiDFv7WKsTvsHsCvlOXS9tldLmg4stb2XpAOAr9o+tC5/IrC97YslbQ48ACywvaKjHyYixpxWPyg8ImIIVP+eAFwhaRbQA8xYz/IHU55dN7/+vA0wndJzFhExaCnGIqLr1NOUPcArlLFjLwN7UsbRrl7fy4CFtu/qSCMjomtkAH9EdBVJU4CrgStcxmlsA/zVdi/l+XTj6qJvAFv1e+ldwMmSJtT/Z4akyUREDFF6xiKiG0yU9CjllOQ7lAH7l9Z5VwG3SFoA3AO8Wac/Drwj6TFgCXAZ5QrLRyQJeBU4olMfICLGrgzgj4iIiGhQTlNGRERENCjFWERERESDUoxFRERENCjFWERERESDUoxFRERENCjFWERERESDUoxFRERENCjFWERERESD/gPzq1Z7eTZGXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 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.loc['2015':].plot(y=['Settle', 'trend'], figsize= (10, 5), title = 'Settle vs. Trend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf0ed89c90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Noise\n",
    "df.noise.plot(figsize= (10, 5), title = 'Noise')"
   ]
  },
  {
   "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": 9,
   "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": 9,
     "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 = (yen_futures[[\"Settle\"]].pct_change() * 100)\n",
    "returns = returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/dev/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:218: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Estimate and ARMA model using statsmodels (use order=(2, 1))\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "arma_model = ARMA(returns['Settle'], order=(2,1))\n",
    "\n",
    "# Fit the model and assign it to a variable called results\n",
    "results = arma_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Sun, 07 Feb 2021</td> <th>  AIC                </th> <td>15798.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>15:56:26</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> 0.421</td> <td>   -0.009</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Settle</th> <td>   -0.3064</td> <td>    1.277</td> <td>   -0.240</td> <td> 0.810</td> <td>   -2.809</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>    0.2948</td> <td>    1.277</td> <td>    0.231</td> <td> 0.817</td> <td>   -2.208</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>          -3.3328</td> <td>          +0.0000j</td> <td>           3.3328</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>        -157.1207</td> <td>          +0.0000j</td> <td>         157.1207</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -3.3916</td> <td>          +0.0000j</td> <td>           3.3916</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:                Sun, 07 Feb 2021   AIC                          15798.142\n",
       "Time:                        15:56:26   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      0.421      -0.009       0.022\n",
       "ar.L1.Settle    -0.3064      1.277     -0.240      0.810      -2.809       2.197\n",
       "ar.L2.Settle    -0.0019      0.019     -0.099      0.921      -0.040       0.036\n",
       "ma.L1.Settle     0.2948      1.277      0.231      0.817      -2.208       2.798\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -3.3328           +0.0000j            3.3328            0.5000\n",
       "AR.2         -157.1207           +0.0000j          157.1207            0.5000\n",
       "MA.1           -3.3916           +0.0000j            3.3916            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f91d8c35890>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Returns Forecast\n",
    "pd.DataFrame(results.forecast(steps=5)[0]).plot(figsize= (10, 5), title='5 Day Returns Forcast')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Arma model is suitable for this forecast because the predicted data is stationary with p-value less than 5%*"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/dev/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:218: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/opt/anaconda3/envs/dev/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:218: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# Estimate and ARIMA Model:\n",
    "# Hint: ARIMA(df, order=(p, d, q))\n",
    "arima_model = ARIMA(yen_futures['Settle'], order=(5,1,1))\n",
    "\n",
    "# Fit the model\n",
    "arima_results = arima_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(5, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-41944.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>64.281</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 07 Feb 2021</td> <th>  AIC                </th>  <td>83905.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>15:56:29</td>     <th>  BIC                </th>  <td>83960.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th>  <td>83924.259</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>    0.3159</td> <td>    0.700</td> <td>    0.451</td> <td> 0.652</td> <td>   -1.056</td> <td>    1.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Settle</th> <td>    0.2817</td> <td>    0.700</td> <td>    0.403</td> <td> 0.687</td> <td>   -1.090</td> <td>    1.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.Settle</th> <td>    0.0007</td> <td>    0.016</td> <td>    0.043</td> <td> 0.966</td> <td>   -0.030</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.D.Settle</th> <td>   -0.0127</td> <td>    0.012</td> <td>   -1.032</td> <td> 0.302</td> <td>   -0.037</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.D.Settle</th> <td>   -0.0137</td> <td>    0.015</td> <td>   -0.889</td> <td> 0.374</td> <td>   -0.044</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.D.Settle</th> <td>   -0.0012</td> <td>    0.018</td> <td>   -0.065</td> <td> 0.948</td> <td>   -0.036</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Settle</th> <td>   -0.2967</td> <td>    0.700</td> <td>   -0.424</td> <td> 0.671</td> <td>   -1.668</td> <td>    1.074</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>           1.8912</td> <td>          -1.3791j</td> <td>           2.3406</td> <td>          -0.1003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           1.8912</td> <td>          +1.3791j</td> <td>           2.3406</td> <td>           0.1003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>          -2.2676</td> <td>          -3.0221j</td> <td>           3.7783</td> <td>          -0.3524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th> <td>          -2.2676</td> <td>          +3.0221j</td> <td>           3.7783</td> <td>           0.3524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th> <td>         -10.9882</td> <td>          -0.0000j</td> <td>          10.9882</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           3.3699</td> <td>          +0.0000j</td> <td>           3.3699</td> <td>           0.0000</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:                 7514\n",
       "Model:                 ARIMA(5, 1, 1)   Log Likelihood              -41944.619\n",
       "Method:                       css-mle   S.D. of innovations             64.281\n",
       "Date:                Sun, 07 Feb 2021   AIC                          83905.238\n",
       "Time:                        15:56:29   BIC                          83960.635\n",
       "Sample:                             1   HQIC                         83924.259\n",
       "                                                                              \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const              0.3159      0.700      0.451      0.652      -1.056       1.688\n",
       "ar.L1.D.Settle     0.2817      0.700      0.403      0.687      -1.090       1.653\n",
       "ar.L2.D.Settle     0.0007      0.016      0.043      0.966      -0.030       0.032\n",
       "ar.L3.D.Settle    -0.0127      0.012     -1.032      0.302      -0.037       0.011\n",
       "ar.L4.D.Settle    -0.0137      0.015     -0.889      0.374      -0.044       0.016\n",
       "ar.L5.D.Settle    -0.0012      0.018     -0.065      0.948      -0.036       0.034\n",
       "ma.L1.D.Settle    -0.2967      0.700     -0.424      0.671      -1.668       1.074\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.8912           -1.3791j            2.3406           -0.1003\n",
       "AR.2            1.8912           +1.3791j            2.3406            0.1003\n",
       "AR.3           -2.2676           -3.0221j            3.7783           -0.3524\n",
       "AR.4           -2.2676           +3.0221j            3.7783            0.3524\n",
       "AR.5          -10.9882           -0.0000j           10.9882           -0.5000\n",
       "MA.1            3.3699           +0.0000j            3.3699            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "arima_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f91d9433f50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QrV06Y/p1ZmRuR0bldlLASmEKXCIiIvWw74DVafck9+yOrRSwBFDgEhER2St3Z2nJtq9Mcl+/9b8B6+i80O3BUX0VsGTPFLhERETC7C1gdW3XgqP6Zu6eg9U7UwFLIqPAJSIiKc3dWVaybXe4mrG0lOKwgDU6CFijFLDkAChwiYhISnF3lpdu3/2ncsIDVpe2LRgVdgUrRwFLGogCl4iIpIS5Kzfxlw+X82FhCeu2/Ddg1YSrkbkd6dOptQKWRIUCl4iIJLV5RZuZOG0Jby4upn3LNMYe1FkBSxqdApeIiCSl+as2M3FaPtMWraN9yzRuPrk/l4zOoU0L/eqTxqfvOhERSSoLV29h4rQl/GfhOtqlN+OHJx7EpUfl0DY9LdalSQpT4BIRkaSweO0WJk7N5/UFa2mb3owbTujH5Uf3oZ2ClsQBBS4REUloS9Zt5eFp+bw6bw1tWzTjB8f344qj+9C+pYKWxA8FLhERSUj567by8JuhoNW6eTO+f1weVxzdh4xWzWNdmsjXKHCJiEhCKSgu45E383n589W0SmvK947ty3ePzqVDawUtiV8KXCIikhCWrg8FrZfmriY9rSlXHdOXCcfk0lFBSxKAApeIiMS15SXbeGR6Pi/OXkWLZk25ckwuE47JJbNNi1iXJhIxBS4REYlLK0q38ej0Al6YvYq0psYVR/fhqrF96aSgJQlIgUtEROLKyg3beXR6PlM+W0WzJsalo3O4amwuXdqmx7o0kf2mwCUiInFh5Ybt/PatAp6bVUSTJsZFI3vzvWP70qWdgpYkPgUuERGJqVWbdjBpegHPzVqJYVxwZDbXHJtHt/YKWpI8FLhERCQm1mzewW/fKmDyp6Ggdd4R2XzvG33p3r5lrEsTaXAKXCIi0qjWbi7nd28X8M9PVuI45wzvxbXfyKNHhoKWJC8FLhERaRTrtpTz+7cL+ccnX1Jd7XxneC+u/UZfsjq0inVpIlGnwCUiIlFVvDUIWh9/SWW1c/ZhWVx3XB69OipoSepQ4BIRkahYv3Unf3ynkCdnrKCy2jnz0J58/7h+ZGcqaEnqUeASEZEGVVK2k8feXcrfPlrOrspqzjg0i+8fl0dOp9axLk0kZhS4RESkQWzYtos/vlvI3z5cwc7KKk4f1pPvH9+PPgpaIgpcIiJyYDZu28Vj7y3lrx8uZ0dFFacN7cH3j+9H385tYl2aSNxQ4BIRkf2yafsuHn9vKX/5YDnbK6o49ZAeXH98Hnld2sa6NJG4o8AlIiL1snl7BU+8v5Q/fbCcsp2VfOuQ7lx/fD8O6qqgJbInClwiIhKRzTsq+NP7y/jTB8vYWl7JuCHduP74g+jfTUFLZF8UuEREZK+2lFfw5/eX88T7S9lSXskpg7px/Qn9GNC9XaxLE0kYClwiIlKnreUV/PXD5Tz+3jI276jgxIFdueGEfgzq0T7WpYkkHAUuERH5irKdlUHQWsqm7RWcMKALN5xwEIN7KmiJ7C8FLhERAWDbzkr+9tEKHnu3kI3bKzju4C7ccEI/DsnKiHVpIglPgUtEJMVt31XJkx+t4I/vLmXDtl0c278zN5xwEMN6KWiJNBQFLhGRFLVjVxVPfbyCP7xTSEnZLsb068SNJx7EYdkdYl2aSNJR4BIRSTHlFVU89fGX/P7tQkrKdnJ0XiduPLEfh/fuGOvSRJKWApeISIoor6ji6U9CQat4605G983k9xcexhE5Cloi0abAJSKS5Morqpj86Up+93YB67bsZGRuRx49/1COzM2MdWkiKUOBS0QkSe2srOKZT1fy27cKWbulnBE5HXno3GGM7tsp1qWJpBwFLhGRJLOrsppnZ63kt9MLWL25nOG9O/Cbc4Yyum8mZhbr8kRSUkSBy8yuB64EDHjc3Sea2QPAeGAXUAhc5u6bzOxE4F6gebDvZnefbmZtgffCTpsF/N3db2i47oiIpK6Kqmqem1XEpOkFrNq0g8OyM7jv7EM4Oq+TgpZIjO0zcJnZYEJhawShAPW6mb0KTAVuc/dKM7sPuA24BSgBxrv76uDYN4Ce7r4VGBZ23lnA8w3dIRGRVFNRVc3znxXx6PQCijbuYFivDO45cwjH9FPQEokXkVzhGgDMcPftAGb2DnCGu98f1mYGcDaAu88O274ASDezFu6+s2ajmfUDuvDVK14iIlIPlVXVPD97FZOmF/Dlhu0cktWeu04bzLH9OytoicSZSALXfOCXZpYJ7ADGATNrtbkcmFzHsWcBs8PDVuB8YLK7ez3rFRFJeZVV1bw4ZzWPTs9nRel2hvRszxOXDOe4g7soaInEqX0GLndfFNwynAqUAXOBypr9ZnZ78Pyp8OPMbBBwH3BSHac9D7hoT69pZhOACQDZ2dn77ISISCqoqnZemruKR94sYFnJNgb1aMfjFw/nhAEKWiLxLqJJ8+7+BPAEgJndAxQFjy8BTgWOD79aZWZZwAvAxe5eGH4uMxsKNHP3WXt5vceAxwCGDx+uq2AiktKqqp1XPl/Nw2/ms3T9NgZ0b8cfLzqckwZ2VdASSRCRfkqxi7sXm1k2cCYwysxOITRJfmzN/K6gbQbwKqEJ9R/UcbrzgacPvHQRkeRWXe28Mm8Nj7yZT0FxGQd3a8sfLjyMkwZ2o0kTBS2RRBLpOlxTgjlcFcC17r7RzCYBLYCpwf9hzXD3q4HrgDzgDjO7Izj+JHcvDh6fQ2gemIiI1KG62vn3/DU8PC2f/OIyDuraht/+z2F8c7CClkiiivSW4pg6tuXtoe3dwN17OVduxNWJiKSQ6mrnjQVrmTgtny/WbSWvSxsePf9QvjWku4KWSILTSvMiIjHm7ryxYB0Tpy1h8dqt5HZuzcPnDePUQ3rQVEFLJCkocImIxIi7M3XhOiZOy2fhmi3kdmrNxHOHMX6ogpZIslHgEhFpZO7Om4uKmfjmEuav2kJOZisePGco3x7ag2ZNm8S6PBGJAgUuEZFG4u689UUxE6fl83nRZrI7tuKBsw/hjEN7KmiJJDkFLhGRKHN33lmynoem5TN35SayOrTk/rMO4YzDepKmoCWSEhS4RESixN15L7+Eh6YtYfaXm+iZ0ZJ7zxzCWYdnKWiJpBgFLhGRBubufFBQykPTljBrxUZ6tE/nnjOGcPbhWTRvpqAlkooUuEREGtCHhSVMnJrPJ8s30L19OnedPphzhmfRolnTWJcmIjGkwCUi0gBmLC3loalL+HjZBrq2a8H/njaIc4/opaAlIoACl4jIAflk2QYemrqEj5aW0qVtC+4cP5DzRmSTnqagJSL/pcAlIrIfZi7fwEPTlvBBQSmd27bgZ6cO5H+OVNASkbopcImI1MOsFRuZOG0J7+WX0KlNc376rQFccGRvWjZX0BKRPVPgEhGJwJyVm3ho6hLeWbKezNbN+cm4g7lwZG9aNdePURHZN/2kEBHZi8+LQkHrrS/W06FVGrd+82AuHqWgJSL1o58YIiJ1mFe0mYnTlvDm4mIyWqXx41P6c8moHFq30I9NEak//eQQEQkzf9VmJk7LZ9qidbRvmcbNJ/fnktE5tFHQEpEDoJ8gIiLAwtVbmDhtCf9ZuI526c246cSDuOyoHNqmp8W6NBFJAgpcIpLSFq/dwsPT8nlt/lrapjfjhhP6cdlRfWjfUkFLRBqOApeIpKQl67by8LR8Xp23hrYtmvGD4/txxdEKWiISHQpcIpJS8tdt5eE3Q0GrVVpTrvtGHt8d04eMVs1jXZqIJDEFLhFJCfnrtvLI9AJe+Xw1rdKacs3Yvlw5JpcOrRW0RCT6FLhEJKnVFbS+OyaXjgpaItKIFLhEJCkVFG/lkTcLeFlBS0TigAKXiCSV8KDVUkFLROKEApeIJIXaQevqYI6WgpaIxAMFLhFJaApaIpIIFLhEJCEpaIlIIlHgEpGEoqAlIolIgUtEEkJBcRmPTs/npbkKWiKSeBS4RCSu1Q5aVx3TlyvH9CGzTYtYlyYiEjEFLhGJSwpaIpJMFLhEJK4oaIlIMlLgEpG4oKAlIslMgUtEYqqguIxJQdBKV9ASkSSlwCUiMVG4voxH3wwFrRbNmnLlMblMGJOroCUiSUmBS0QalYKWiKQiBS4RaRQKWiKSyhS4RCSqFLRERBS4RCRKCteXMWl6Af+as0pBS0RSngKXiDSorwWtMblceUwunRS0RCSFKXCJSINQ0BIR2TMFLhE5IApaIiL7psAlIvtl6foyHlXQEhGJiAKXiNTL0uCK1osKWiIiEVPgEpGIhAet5s2a8N0xuUxQ0BIRiYgCl4jslYKWiMiBU+ASkTopaImINBwFLhH5irqC1pVjcuncVkFLRGR/KXCJCADLSrbx6PR8XpytoCUi0tAUuERSXO2gdcXRfZhwTF8FLRGRBhRR4DKz64ErAQMed/eJZvYAMB7YBRQCl7n7JjM7EbgXaB7su9ndpwfnaQ5MAo4FqoHb3X1Kw3ZJRCKhoCUi0nj2GbjMbDChsDWCUIB63cxeBaYCt7l7pZndB9wG3AKUAOPdfXVw7BtAz+B0twPF7n6QmTUBOjZ4j0RkrxS0REQaXyRXuAYAM9x9O4CZvQOc4e73h7WZAZwN4O6zw7YvANLNrIW77wQuBw4O2lUTCmci0ggUtEREYieSwDUf+KWZZQI7gHHAzFptLgcm13HsWcBsd99pZhnBtrvM7FhCtyGvc/d1+1W5iERkeck2Hg0+dZjW1BS0RERiYJ+By90XBbcMpwJlwFygsma/md0ePH8q/DgzGwTcB5wU9lpZwAfufpOZ3QT8Grio9mua2QRgAkB2dnb9eyUiXwtal43OYcLYXLq0TY91aSIiKcfcvX4HmN0DFLn778zsEuBq4PiaW45BmyxgOqGJ9B8E24xQYGvr7tVm1gt43d0H7e31hg8f7jNn1r6gJiJ7UjtoXXhkbwUtEZFGYmaz3H147e2Rfkqxi7sXm1k2cCYwysxOITRJfmytsJUBvEpoQv0HNdvd3c3sZUKfUJwOHA8sPIA+iUgYXdESEYlfka7DNSWYw1UBXOvuG81sEtACmBq6eMUMd78auA7IA+4wszuC409y92JCAe1JM5sIrAcua8C+iKQkBS0RkfhX71uKjU23FEXqtrxkG5PeKuCF2ato1sS4aKRuHYqIxNoB3VIUkfhRO2hdOjqHqxS0RETimgKXSIJQ0BIRSVwKXCJxTkFLRCTxKXCJxKkVpaHJ8ApaIiKJT4FLJM6sKN3GpOkFPK+gJSKSNBS4ROJE7aB1yagcrh6bS5d2CloiIolOgUskxhS0RESSnwKXSIwoaImIpA4FLpFGpqAlIpJ6FLhEGsmXpduZ9FY+Uz5T0BIRSTUKXCJRVjtoXTyqN9eM7augJSKSQhS4RKJEQUtERGoocIk0sPCg1VRBS0REUOASaTB1Ba2rx/alq4KWiEjKU+ASOUBflm7nt28VMOWzIpooaImISB0UuET2U+2gdZGCloiI7IECl0g9rdywnUnT/xu0LhzZm2uOVdASEZE9U+ASiZCCloiI7C8FLpF9UNASEZEDpcAlsgcrN4TmaD03S0FLREQOjAKXSC0KWiIi0tAUuEQCdQWtq8f2pVt7BS0RETkwClyS8hS0REQk2hS4JGUpaImISGNR4JKU4+78fcYKfvnvRVQ7CloiIhJ1ClySUtZtKefm5z7n3SXrOeagztx75hB6ZLSMdVkiIpLkFLgkZbz6+Rpuf3Ee5RVV3HXaIC4c2Rszi3VZIiKSAhS4JOlt3lHBz/81nxfnrGZorwwePGcofTu3iXVZIiKSQhS4JKl9UFDCj56dS/HWndx4wkFc+42+NGvaJNZliYhIilHgkqRUXlHFfa8v5s8fLCe3c2uev2Y0Q3tlxLosERFJUQpcknTmFW3mxmfmUFBcxqWjc7jllINp2bxprMsSEZEUpsAlSaOyqpo/vFPIxGn5ZLZpzpNXjGBMv86xLktERESBS5LD8pJt3PjMHGZ/uYnxQ3tw12mDyGjVPNZliYiIAApckuDcnX988iV3v7KItKbGI+cfyreH9oh1WSIiIl+hwCUJq3hLObdM+Zy3vljPmH6duP/sQ+jeXouYiohI/FHgkoT02rw1/OSFeWzfVcWd4wdy8agcmjTRIqYiIhKfFLgkoWwpr+DOfxPuGzEAABFJSURBVC3g+dmrOCSrPQ+eM4y8LlrEVERE4psClySMjwpL+dGzc1m7pZwfHN+P7x+XR5oWMRURkQSgwCVxr7yiil+/8QX/9/4y+nRqzXNXj+LQ7A6xLktERCRiClwS1+av2sxNz8xhyboyLhrZm9vGHUyr5vq2FRGRxKLfXBKXqqo9WMR0CR1aNecvlx3Bsf27xLosERGR/aLAJXFnRek2bnpmLrNWbORbQ7pz9+mD6dBai5iKiEjiUuCSuOHu/PPTldz1ykKaNjEmnjuM04b1wEzLPYiISGJT4JK4sH7rTm6d8jlvLi5mdN9Mfv2dofTI0CKmIiKSHBS4JOZen7+Wn7wwj207K/nZqQO5dLQWMRURkeSiwCUxs7W8gl+8vJDnZhUxuGc7HjpnGP26to11WSIiIg1OgUti4uOlpdz0zFzWbN7Bdd/I4wfH96N5My1iKiIiyUmBSxrVzsoqfvOfJTz+3lJ6d2zFs1eP5vDeWsRURESSmwKXNJpFa7Zw4+Q5LF67lf85Mpvbxw2gdQt9C4qISPLTbzuJuqpq5/H3lvKb/3xB+5bN+dOlwznu4K6xLktERKTRRBS4zOx64ErAgMfdfaKZPQCMB3YBhcBl7r7JzE4E7gWaB/tudvfpwXneBroDO4JTn+TuxQ3YH4kzKzds54fPzOWT5Rv45uBu/PKMIXTUIqYiIpJi9hm4zGwwobA1glCAet3MXgWmAre5e6WZ3QfcBtwClADj3X11cOwbQM+wU17g7jMbuB8SZ9ydZ2cW8YuXF9DEjAfPGcoZh/bUIqYiIpKSIrnCNQCY4e7bAczsHeAMd78/rM0M4GwAd58dtn0BkG5mLdx9ZwPVLHGupGwntz0/j6kL1zEytyO//s5Qsjq0inVZIiIiMRNJ4JoP/NLMMgndChwH1L5CdTkwuY5jzwJm1wpbfzazKmAKcLe7e/3Llng1deE6bnv+c7aUV/LTbw3g8qP6aBFTERFJefsMXO6+KLhlOBUoA+YClTX7zez24PlT4ceZ2SDgPuCksM0XuPsqM2tLKHBdBPyt9mua2QRgAkB2dnY9uySxULazkrteXsjkmSsZ0L0dT313GP27aRFTERERgIhWmnT3J9z9MHc/BtgA5AOY2SXAqYSC1O4rVWaWBbwAXOzuhWHnWRX8uxX4B6F5YXW93mPuPtzdh3fu3Hn/eiaN5tPlG/jmw+/y7KyVfO/Yvvzr2qMUtkRERMJE+inFLu5ebGbZwJnAKDM7hdAk+bE187uCthnAq4Qm1H8Qtr0ZkOHuJWaWRiioTWvAvkgj21lZxUNT8/nju4X06tCKZ64axfCcjrEuS0REJO5Eug7XlGAOVwVwrbtvNLNJQAtgavDJsxnufjVwHZAH3GFmdwTHnwRsA94IwlZTQmHr8YbrijSmL9Zu5YbJc1i0Zgvnj+jF7d8aSBstYioiIlKniH5DuvuYOrbl7aHt3cDdezjV4ZGXJvGoutp54v1lPPDGF7Rr2Yz/u3g4JwzUIqYiIiJ7o0sSErGijaFFTD9etoGTBnblV2cOIbNNi1iXJSIiEvcUuGSf3J0pn63izpcWAPDA2Ydw9uFZWsRUREQkQgpcslcbtu3iJ8/P4/UFaxnRpyO/+c5QenXUIqYiIiL1ocAlezR98Tp+/Nw8tuyo4CfjDuaKo3NpqkVMRURE6k2BS75m285K7n51EU9/8iUHd2vLk1eMYED3drEuS0REJGEpcMlXzFqxgZuemcuXG7Zz1dhcbjrxIFo0axrrskRERBKaApcAsKuymoffXMLv3y6kR0ZLJk8YxYg+WsRURESkIShwCUvWbeXGyXNYsHoL5wzP4o5TB9I2PS3WZYmIiCQNBa4UVl3t/PnD5dz3+mLatmjGYxcdzkmDusW6LBERkaSjwJWiVm3awc3PzuXDwlJOGNCVe88aQictYioiIhIVClwpxt15cc4qfvbiAqrdue+sIZwzvJcWMRUREYkiBa4UsnHbLm5/cR7/nreW4b078OA5w8jO1CKmIiIi0abAlSLe+qKYW577nI3bd3HLKQcz4RgtYioiItJYFLiS3PZdlfzy1UU89fGX9O/alj9fdgSDerSPdVkiIiIpRYEric3+ciM3PTOX5aXbuHJMH354Un/S07SIqYiISGNT4EpCFVXVPPpmPr99u5Bu7dJ5+sqRjMzNjHVZIiIiKUuBK8kUFG/lxslzmbdqM2cdlsXPvz2QdlrEVEREJKYUuJJEdbXz14+Wc+9ri2nVvCl/uPAwThncPdZliYiICApcSWHN5h3c/OznvF9QwnEHd+Hes4bQpW16rMsSERGRgAJXgvvXnFXc8eJ8Kqude84YwvkjtIipiIhIvFHgSlCbtu/ipy/O55XP13BYdgYPnjOMnE6tY12WiIiI1EGBKwG9s2Q9P35uLqVlu7j55P5cdUwuzZo2iXVZIiIisgcKXAlkx64qfvXaIv720Qr6dWnDE5ccweCeWsRUREQk3ilwJYi5Kzdx4+Q5LC3ZxhVH9+Hmk7WIqYiISKJQ4IpzFVXVTJpewKS3CujatgX/+O6RjM7rFOuyREREpB4UuOJY4foybpo8h7lFmznj0J7c+e1BtG+pRUxFREQSjQJXHHJ3npyxgnv+vYj0tKb87oLDGDdEi5iKiIgkKgWuOLN2czk3PzeX9/JLGHtQZx44+xC6tNMipiIiIolMgSuOvDx3NT99cT67Kqu5+/TBXHBkthYxFRERSQIKXHFg8/YKfvbSfP41ZzXDemXw0LnD6KNFTEVERJKGAleMvZ9fwo+enUtJ2U5uOvEgvndsXy1iKiIikmQUuGKkvKKKe19bzF8+XE7fzq157OLRHJKVEeuyREREJAoUuGLg86LQIqaF67dx6egcbv3mwVrEVEREJIkpcDWiyqpqfvd2IY+8mU+nNi34+xVHcnQ/LWIqIiKS7BS4Gsmykm3cOHkOc1Zu4rRhPfjfbw+mfSstYioiIpIKFLiizN35+8dfcs+ri2jerAmPnn8o44f2iHVZIiIi0ogUuKKoeEs5P57yOW9/sZ4x/TrxwNlD6dZei5iKiIikGgWuKPn3vDX85IV5lFdU8b+nDeKikb21iKmIiEiKUuBqYJt3VHDnSwt4YfYqhma158Fzh9G3c5tYlyUiIiIxpMDVgD4sCC1ium7rTm44oR/XfiOPNC1iKiIikvIUuBpAeUUVD7zxBU+8v4zcTq2Zcs1ohvXSIqYiIiISosB1gOav2syNk+eQX1zGJaN6c+s3B9CyuRYxFRERkf9S4NpPlVXV/PHdpTw0dQkdWzfnr5ePYOxBnWNdloiIiMQhBa79sKI0tIjpZ19u4tRDunP36YPJaNU81mWJiIhInFLgqgd35+lPVnL3qwtp1sR4+LxhnDasZ6zLEhERkTinwBWh4q3l3DplHtMXF3NUXia//s5QurdvGeuyREREJAEocEXg9flruO35eWzfVcXPxw/kklE5NGmiRUxFREQkMgpce7GlvIJfvLSQKZ8VMaRnex46dyh5XdrGuiwRERFJMApcezBjaSk/fGYuazbv4AfH5fH94/tpEVMRERHZLwpctZRXVPHg1CU8/t5SendsxXPXjOaw7A6xLktEREQSWESXbMzsejObb2YLzOyGYNsDZrbYzD43sxfMLCPYfqKZzTKzecG/x9VxvpfMbH7DduXALVy9hdMmfcBj7y7lgiOz+ff1YxS2RERE5IDt8wqXmQ0GrgRGALuA183sVWAqcJu7V5rZfcBtwC1ACTDe3VcHx74B9Aw735lAWYP35ABUVTuPvbuUB6d+QUar5vz5siP4Rv8usS5LREREkkQktxQHADPcfTuAmb0DnOHu94e1mQGcDeDus8O2LwDSzayFu+80szbATcAE4JmG6MCB+rJ0Oz98dg6fLt/IuCHduPv0IXRsrUVMRUREpOFEErjmA780s0xgBzAOmFmrzeXA5DqOPQuY7e47g+d3Ab8Btu9fuQ3vlimfs3jNVh46dyinD+uJmZZ7EBERkYa1z8Dl7ouCW4ZTCd0KnAtU1uw3s9uD50+FH2dmg4D7gJOC58OAPHe/0cxy9vaaZjaB0FUwsrOzI+/NfvjVmUNIa9aEnhlaxFRERESiI6JJ8+7+hLsf5u7HABuAfAAzuwQ4FbjA3b2mvZllAS8AF7t7YbB5FHC4mS0H3gcOMrO39/B6j7n7cHcf3rlzdP8gdE6n1gpbIiIiElWRfkqxS/BvNnAm8LSZnUJokvy3a+Z3BW0ygFcJTaj/oGa7u//e3Xu4ew5wNLDE3Y9tqI6IiIiIxKtIV/KcYmYLgZeBa919IzAJaAtMNbM5ZvaHoO11QB5wR7B9Tk1gExEREUlFFnYnMC4NHz7cZ86sPUdfREREJP6Y2Sx3H157u/5WjYiIiEiUKXCJiIiIRJkCl4iIiEiUKXCJiIiIRJkCl4iIiEiUKXCJiIiIRJkCl4iIiEiUxf06XGa2HlgR5ZfpBJRE+TXiVSr3HVK7/6ncd0jt/qdy3yG1+6++R19vd//a3yWM+8DVGMxsZl2LlKWCVO47pHb/U7nvkNr9T+W+Q2r3X32PXd91S1FEREQkyhS4RERERKJMgSvksVgXEEOp3HdI7f6nct8htfufyn2H1O6/+h4jmsMlIiIiEmW6wiUiIiISZSkVuMzsFDP7wswKzOzWOvabmT0S7P/czA6LRZ3REEHfjzWzzWY2J/j6WSzqjAYz+5OZFZvZ/D3sT+Zx31ffk3nce5nZW2a2yMwWmNn1dbRJ5rGPpP9JOf5mlm5mn5jZ3KDvv6ijTTKPfST9T8qxr2FmTc1stpm9Use+2Iy9u6fEF9AUKARygebAXGBgrTbjgNcAA0YCH8e67kbs+7HAK7GuNUr9PwY4DJi/h/1JOe4R9j2Zx707cFjwuC2wJFX+m69H/5Ny/IPxbBM8TgM+Bkam0NhH0v+kHPuw/t0E/KOuPsZq7FPpCtcIoMDdl7r7LuCfwGm12pwG/M1DZgAZZta9sQuNgkj6nrTc/V1gw16aJOu4R9L3pOXua9z9s+DxVmAR0LNWs2Qe+0j6n5SC8SwLnqYFX7UnLCfz2EfS/6RlZlnAt4D/20OTmIx9KgWunsDKsOdFfP2HTyRtElGk/RoVXIJ+zcwGNU5pcSFZxz1SST/uZpYDHEro//TDpcTY76X/kKTjH9xSmgMUA1PdPaXGPoL+Q5KOPTAR+DFQvYf9MRn7VApcVse22ok/kjaJKJJ+fUbozxEMBR4FXox6VfEjWcc9Ekk/7mbWBpgC3ODuW2rvruOQpBr7ffQ/acff3avcfRiQBYwws8G1miT12EfQ/6QcezM7FSh291l7a1bHtqiPfSoFriKgV9jzLGD1frRJRPvsl7tvqbkE7e7/BtLMrFPjlRhTyTru+5Ts425maYTCxlPu/nwdTZJ67PfV/2QffwB33wS8DZxSa1dSj32NPfU/icf+KODbZrac0PSZ48zs77XaxGTsUylwfQr0M7M+ZtYcOA94qVabl4CLg08wjAQ2u/uaxi40CvbZdzPrZmYWPB5B6HujtNErjY1kHfd9SuZxD/r1BLDI3R/cQ7OkHftI+p+s429mnc0sI3jcEjgBWFyrWTKP/T77n6xj7+63uXuWu+cQ+l033d0vrNUsJmPfLNovEC/cvdLMrgPeIPSpvT+5+wIzuzrY/wfg34Q+vVAAbAcui1W9DSnCvp8NXGNmlcAO4DwPPs6R6MzsaUKfyOlkZkXAzwlNIk3qcYeI+p60407o/3QvAuYFc1kAfgJkQ/KPPZH1P1nHvzvwVzNrSihIPOPur6TCz/tAJP1P1rGvUzyMvVaaFxEREYmyVLqlKCIiIhITClwiIiIiUabAJSIiIhJlClwiIiIiUabAJSIiIhJlClwiIiIiUabAJSIiIhJlClwiIiIiUfb/vtmQMRYkvQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Price Forecast\n",
    "pd.DataFrame(arima_results.forecast(steps=5)[0]).plot(figsize= (10, 5), title='5 Day Futures Price Forcast')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does the model forecast will happen to the Japanese Yen in the near term?\n",
    "\n",
    "*The line graph demonstrates that the Japanese Yen tends to grow over time with small changes in values. I think Arima model is appropriate becuase it fits the dataset, which is non-stationary (p-value is more than 5%)* "
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "garch_model = arch_model(returns, mean=\"Zero\", vol=\"GARCH\", p=2, q=1)\n",
    "\n",
    "# Fit the model\n",
    "garch_results = garch_model.fit(disp=\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Sun, Feb 07 2021</td>  <th>  Df Residuals:      </th>    <td>7510</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>15:56:33</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:                Sun, Feb 07 2021   Df Residuals:                     7510\n",
       "Time:                        15:56:33   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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "garch_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-10-15'"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "\n",
    "# Start the forecast using the last_day calculated above\n",
    "forecasts = garch_results.forecast(start=last_day, horizon=forecast_horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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": 21,
     "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": 22,
   "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>Date</th>\n",
       "      <th>2019-10-15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>h.1</th>\n",
       "      <td>7.434048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.2</th>\n",
       "      <td>7.475745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.3</th>\n",
       "      <td>7.516867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.4</th>\n",
       "      <td>7.557426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h.5</th>\n",
       "      <td>7.597434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f91d9427490>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the final forecast\n",
    "final.plot(figsize= (10, 5), title='5 Day Forecast of Volatility')"
   ]
  },
  {
   "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": [
    "*The graph illustrates volatility increasing steadily over time, which indicates the high exchange rate risk of the yen. Although I believe that these models seem to be effective in predicting future currency movements of the yen, I would not recommend investing in it because its future prices are growing higher with low returns.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python [conda env:dev]",
   "language": "python",
   "name": "conda-env-dev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "mimetype": "text/x-python",
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  "npconvert_exporter": "python",
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  "version": 3
 },
 "nbformat": 4,
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}