Answers/Code/time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Read Historical Daily Yen Futures Data\n",
    "In this notebook, you will load historical Dollar-Yen exchange rate futures data and apply time series analysis and modeling to determine whether there is any predictable behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle  Volume  \\\n",
       "Date                                                                 \n",
       "1976-08-02  3398.0  3401.0  3398.0  3401.0     NaN  3401.0     2.0   \n",
       "1976-08-03  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-04  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-05  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "1976-08-06  3401.0  3401.0  3401.0  3401.0     NaN  3401.0     0.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1976-08-02                         1.0  \n",
       "1976-08-03                         1.0  \n",
       "1976-08-04                         1.0  \n",
       "1976-08-05                         1.0  \n",
       "1976-08-06                         1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Futures contract on the Yen-dollar exchange rate:\n",
    "# This is the continuous chain of the futures contracts that are 1 month to expiration\n",
    "yen_futures = pd.read_csv(\n",
    "    Path(\"yen.csv\"), index_col=\"Date\", infer_datetime_format=True, parse_dates=True\n",
    ")\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "  </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",
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      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yen_futures.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6954.0</td>\n",
       "      <td>6954.0</td>\n",
       "      <td>6835.0</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6847.0</td>\n",
       "      <td>48336.0</td>\n",
       "      <td>51473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6877.0</td>\n",
       "      <td>6910.0</td>\n",
       "      <td>6865.0</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6887.0</td>\n",
       "      <td>38206.0</td>\n",
       "      <td>53860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6937.0</td>\n",
       "      <td>7030.0</td>\n",
       "      <td>6924.0</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>49649.0</td>\n",
       "      <td>55699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6952.0</td>\n",
       "      <td>6985.0</td>\n",
       "      <td>6942.0</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>29944.0</td>\n",
       "      <td>53111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6936.0</td>\n",
       "      <td>6972.0</td>\n",
       "      <td>6936.0</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>19763.0</td>\n",
       "      <td>52072.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "              Open    High     Low    Last  Change  Settle   Volume  \\\n",
       "Date                                                                  \n",
       "1990-01-02  6954.0  6954.0  6835.0  6847.0     NaN  6847.0  48336.0   \n",
       "1990-01-03  6877.0  6910.0  6865.0  6887.0     NaN  6887.0  38206.0   \n",
       "1990-01-04  6937.0  7030.0  6924.0  7008.0     NaN  7008.0  49649.0   \n",
       "1990-01-05  6952.0  6985.0  6942.0  6950.0     NaN  6950.0  29944.0   \n",
       "1990-01-08  6936.0  6972.0  6936.0  6959.0     NaN  6959.0  19763.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1990-01-02                     51473.0  \n",
       "1990-01-03                     53860.0  \n",
       "1990-01-04                     55699.0  \n",
       "1990-01-05                     53111.0  \n",
       "1990-01-08                     52072.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim the dataset to begin on January 1st, 1990\n",
    "yen_futures = yen_futures.loc[\"1990-01-01\":, :]\n",
    "yen_futures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: Initial Time-Series Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Start by plotting the \"Settle\" price. Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot just the \"Settle\" column from the dataframe:\n",
    "settle_plot = yen_futures.Settle.plot(title='Yen Futures Settlement Since 1990',\n",
    "                                        figsize=(12,7), rot=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.4151247020844737,\n",
       " 0.13751299771595055,\n",
       " 0,\n",
       " 7514,\n",
       " {'1%': -3.431220579634037,\n",
       "  '5%': -2.861924730395352,\n",
       "  '10%': -2.566974787574595},\n",
       " 83512.35174961216)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "adfuller(yen_futures.Settle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Answer**_\n",
    " * Based on the plot above, we can see a long-term depreciation of the Japanese Yen against the Dollar. It could serve to improve the balance of trade for Japan. There do seem to be some more medium, 1-3 year consistent trends, but on a daily basis, there are a lot of short-term ups and downs.\n",
    " * *Adfuller Test*\n",
    "* The yen futures settlement price is non-stationary as its p-value is 0.138, greater than 10%. \n",
    "We failed to reject the null hypothesis that the yen futures are non-stationary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "plot_acf(yen_futures.Settle, lags=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(yen_futures.Settle, zero=False, lags=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Results from ACF and PACF**_\n",
    "* According to the autocorrelation graph, potentially all lags need to be included in the model as all of them appear to be significant, i.e. outside the shaded zone around the x-axis for time.\n",
    "* Based on the figure of partial autocorrelation, on the other hand, only the first lag is helpful to be included in the time-series model. In other words, most of the trends can be explained by the first lag. \n",
    "    * It is possible for the second lag to be included as well since it is on the boarder of the shaded zone indicating statistical significance. "
   ]
  },
  {
   "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": 9,
   "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",
    "ts_noise, ts_trend = sm.tsa.filters.hpfilter(yen_futures.Settle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Settle</th>\n",
       "      <th>noise</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6847.0</td>\n",
       "      <td>-61.503967</td>\n",
       "      <td>6908.503967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6887.0</td>\n",
       "      <td>-21.799756</td>\n",
       "      <td>6908.799756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>7008.0</td>\n",
       "      <td>98.942896</td>\n",
       "      <td>6909.057104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6950.0</td>\n",
       "      <td>40.776052</td>\n",
       "      <td>6909.223948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6959.0</td>\n",
       "      <td>49.689938</td>\n",
       "      <td>6909.310062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Settle      noise        trend\n",
       "Date                                      \n",
       "1990-01-02  6847.0 -61.503967  6908.503967\n",
       "1990-01-03  6887.0 -21.799756  6908.799756\n",
       "1990-01-04  7008.0  98.942896  6909.057104\n",
       "1990-01-05  6950.0  40.776052  6909.223948\n",
       "1990-01-08  6959.0  49.689938  6909.310062"
      ]
     },
     "execution_count": 10,
     "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",
    "settle_df = yen_futures.Settle.to_frame()\n",
    "settle_df['noise'] = ts_noise\n",
    "settle_df['trend'] = ts_trend\n",
    "settle_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1da90c90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Price vs. the Trend\n",
    "settle_df.drop(columns='noise').plot(title='Yen Futures Settlement Price vs Trend Since 1990',\n",
    "                                        figsize=(12,7), rot=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smoothing with the HP Filter and plotting the resulting trend against the actual futures returns, we can see that there's a lot of short term fluctuations that deviate around this trend. Perhaps these would represent profitable trading opportunities: For example, when the blue line deviates far below the orange, we can see this as a sign that the Yen is temporarily more undervalued than it should be (and, therefore, we'd see this as a short-term buying opportunity)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20a1e310>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Settle Noise\n",
    "settle_df.noise.plot(title='Yen Futures Settlement Noise Since 1990',\n",
    "                                        figsize=(12,7), rot=45)"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 13,
     "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARMA\n",
    "\n",
    "# Estimate and ARMA model using statsmodels (use order=(2, 1))\n",
    "model_arma = ARMA(returns.values, order=(2,1))\n",
    "\n",
    "# Fit the model and assign it to a variable called results\n",
    "results_arma = model_arma.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</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>Sat, 07 Mar 2020</td> <th>  AIC                </th> <td>15798.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>20:50:32</td>     <th>  BIC                </th> <td>15832.765</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>15810.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>    0.0063</td> <td>    0.008</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.009</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.y</th> <td>   -0.3062</td> <td>    1.277</td> <td>   -0.240</td> <td> 0.811</td> <td>   -2.810</td> <td>    2.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.y</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.y</th> <td>    0.2946</td> <td>    1.277</td> <td>    0.231</td> <td> 0.818</td> <td>   -2.209</td> <td>    2.798</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -3.3353</td> <td>          +0.0000j</td> <td>           3.3353</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>        -157.2118</td> <td>          +0.0000j</td> <td>         157.2118</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>          -3.3942</td> <td>          +0.0000j</td> <td>           3.3942</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:                      y   No. Observations:                 7514\n",
       "Model:                     ARMA(2, 1)   Log Likelihood               -7894.071\n",
       "Method:                       css-mle   S.D. of innovations              0.692\n",
       "Date:                Sat, 07 Mar 2020   AIC                          15798.142\n",
       "Time:                        20:50:32   BIC                          15832.765\n",
       "Sample:                             0   HQIC                         15810.030\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0063      0.008      0.804      0.422      -0.009       0.022\n",
       "ar.L1.y       -0.3062      1.277     -0.240      0.811      -2.810       2.197\n",
       "ar.L2.y       -0.0019      0.019     -0.099      0.921      -0.040       0.036\n",
       "ma.L1.y        0.2946      1.277      0.231      0.818      -2.209       2.798\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -3.3353           +0.0000j            3.3353            0.5000\n",
       "AR.2         -157.2118           +0.0000j          157.2118            0.5000\n",
       "MA.1           -3.3942           +0.0000j            3.3942            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results_arma.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1df05310>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Returns Forecast\n",
    "pd.DataFrame(results_arma.forecast(steps=5)[0]).plot(title=\"5-Day Returns Forecast on Yen Futures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: Based on the p-value, is the model a good fit?\n",
    "\n",
    "Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since p-values for constant, AR(1), AR(2), MA(1) are 0.421, 0.810, 0.921, 0.818, respectively, none of those are less than 0.05. The model is not statistically significant enough to be a good fit for returns on yen futures based on the data."
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/mlenv/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",
      "/Applications/anaconda3/envs/mlenv/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",
    "model_arima = ARIMA(yen_futures.Settle, order=(5, 1, 1))\n",
    "\n",
    "# Fit the model\n",
    "results_arima = model_arima.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Sat, 07 Mar 2020</td> <th>  AIC                </th>  <td>83905.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>20:50:34</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>    0.3161</td> <td>    0.700</td> <td>    0.452</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>    0.2822</td> <td>    0.699</td> <td>    0.404</td> <td> 0.686</td> <td>   -1.088</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>   -0.0126</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>   -0.064</td> <td> 0.949</td> <td>   -0.036</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Settle</th> <td>   -0.2973</td> <td>    0.699</td> <td>   -0.425</td> <td> 0.671</td> <td>   -1.667</td> <td>    1.073</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>           1.8918</td> <td>          -1.3786j</td> <td>           2.3408</td> <td>          -0.1002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           1.8918</td> <td>          +1.3786j</td> <td>           2.3408</td> <td>           0.1002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>          -2.2703</td> <td>          -3.0205j</td> <td>           3.7786</td> <td>          -0.3526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th> <td>          -2.2703</td> <td>          +3.0205j</td> <td>           3.7786</td> <td>           0.3526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th> <td>         -11.0795</td> <td>          -0.0000j</td> <td>          11.0795</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           3.3640</td> <td>          +0.0000j</td> <td>           3.3640</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:                Sat, 07 Mar 2020   AIC                          83905.238\n",
       "Time:                        20:50:34   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              0.3161      0.700      0.452      0.652      -1.056       1.688\n",
       "ar.L1.D.Settle     0.2822      0.699      0.404      0.686      -1.088       1.653\n",
       "ar.L2.D.Settle     0.0007      0.016      0.043      0.966      -0.030       0.032\n",
       "ar.L3.D.Settle    -0.0126      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     -0.064      0.949      -0.036       0.034\n",
       "ma.L1.D.Settle    -0.2973      0.699     -0.425      0.671      -1.667       1.073\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.8918           -1.3786j            2.3408           -0.1002\n",
       "AR.2            1.8918           +1.3786j            2.3408            0.1002\n",
       "AR.3           -2.2703           -3.0205j            3.7786           -0.3526\n",
       "AR.4           -2.2703           +3.0205j            3.7786            0.3526\n",
       "AR.5          -11.0795           -0.0000j           11.0795           -0.5000\n",
       "MA.1            3.3640           +0.0000j            3.3640            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "results_arima.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Conclusion on ARIMA(5,1,1)**_\n",
    "* The p-values for all coefficients and the constant are greater than 0.3. Therefore, the ARIMA(5,1,1) model on raw Yen settlement prices is not statistically significant and hence not a good fit for the underlying data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e6a98d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 5 Day Price Forecast\n",
    "pd.DataFrame(results_arima.forecast(steps=5)[0]).plot(title=\"5-Day Yen Futures Settlement Price Forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**What does the model forecast will happen to the Japanese Yen in the near term?**_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to ARIMA (5,1,1), Japanese Yen settlement price would increase in the next five days. \n",
    "Japanese Yen is expected to depreciate relative to US dollar. Long USD and short JPY."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volatility Forecasting with GARCH\n",
    "\n",
    "Rather than predicting returns, let's forecast near-term **volatility** of Japanese Yen futures returns. Being able to accurately predict volatility will be extremely useful if we want to trade in derivatives or quantify our maximum loss.\n",
    " \n",
    "Using futures Settle *Returns*, estimate an GARCH model\n",
    "\n",
    "1. GARCH: Create an GARCH model and fit it to the returns data. Note: Set the parameters to p=2 and q=1: order=(2, 1).\n",
    "2. Output the GARCH summary table and take note of the p-values of the lags. Based on the p-values, is the model a good fit (p < 0.05)?\n",
    "3. Plot the 5-day forecast of the volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arch as arch\n",
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate a GARCH model:\n",
    "# 'p' and 'q'  are akin to the 'p' and 'q' of the previous ARMA model.\n",
    "# The 'mean=\"Zero\"' means that we're estimating a GARCH.\n",
    "model_garch = arch_model(returns, mean=\"Zero\", vol=\"GARCH\", p=2, q=1)\n",
    "\n",
    "# Fit the model\n",
    "res = model_garch.fit(disp=\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Zero Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Settle</td>       <th>  R-squared:         </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>         <td>Zero Mean</td>     <th>  Adj. R-squared:    </th>  <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -7461.93</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   14931.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   14959.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>7514</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Sat, Mar 07 2020</td>  <th>  Df Residuals:      </th>    <td>7510</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>20:50:36</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:                Sat, Mar 07 2020   Df Residuals:                     7510\n",
       "Time:                        20:50:36   Df Model:                            4\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      4.2896e-03  2.057e-03      2.085  3.708e-02  [2.571e-04,8.322e-03]\n",
       "alpha[1]       0.0381  1.282e-02      2.970  2.974e-03  [1.295e-02,6.321e-02]\n",
       "alpha[2]       0.0000  1.703e-02      0.000      1.000 [-3.338e-02,3.338e-02]\n",
       "beta[1]        0.9536  1.420e-02     67.135      0.000      [  0.926,  0.981]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the model results\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Conclusion on GARCH(2,1) on Yen Futures Returns**_\n",
    "* After taking heteroskedasticity into account, the GARCH(2,1) on returns of Yen futures settlement prices is a better model than ARMA(2,1) on returns and ARIMA(5,1,1) on settlement prices as the coefficient of baseline variance term (omega), coefficient for moving averages (beta), how much previous' day's volatility add to today's (alpha1) are statistically significant. Their p-values are less than 0.025.\n",
    "    * Omega is close to zero as we initialized it to be. The variance is zero if there is no information from the past variances being passed onto today.\n",
    "    * beta1 is the moving average term of this GARCH(2,1) model. Beta1=0.9536 means 95.36% of today' returns can be explained by that of the last settlement date on Yen futures. This term should be less than 1. \n",
    "    * alpha1 suggests 3.8% of the previous day's volatility would be passed onto today's volatility\n",
    "    * alpha2 is statistically insignificant so the second to the last day's volatility contribute little to that of today's.\n",
    "\n",
    "* Note: to ensure staionarity, the sum of alphas and beta(s) are less than one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Yen Futures Returns Garch(2,1)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the model estimate of annualized volatility\n",
    "fig_garch = res.plot(annualize='D')\n",
    "fig_garch.suptitle('Yen Futures Returns Garch(2,1)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-10-15'"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<arch.univariate.base.ARCHModelForecast at 0x1a1e6a38d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 5 day forecast of volatility\n",
    "forecast_horizon = 5\n",
    "# Start the forecast using the last_day calculated above\n",
    "forecasts_garch = res.forecast(start=last_day, horizon=forecast_horizon)\n",
    "forecasts_garch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualize the forecast\n",
    "intermediate = np.sqrt(forecasts_garch.variance.dropna() * 252)\n",
    "intermediate.head()"
   ]
  },
  {
   "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>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": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e8e1290>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the final forecast\n",
    "final.plot(title=\"Five-day GARCH Forecast of Returns Volatility on Yen Futures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**A Seasonal Decomposition**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1990-01-31    6911.727273\n",
       "1990-02-28    6871.947368\n",
       "1990-03-31    6533.681818\n",
       "1990-04-30    6325.400000\n",
       "1990-05-31    6504.409091\n",
       "Freq: M, Name: Settle, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yen_futures_monthly = yen_futures.Settle.resample('M').mean()\n",
    "yen_futures_monthly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fE48e8ThRckqqmPrRalKyS3n8/N54uTnzyPeb+WT5nhN2za+x2Ji/LYcx3YPxPY7nTEPYbIrlqQUMig3Au5GXaAAx47W2GeYc1C9HZNUtUEq9ICKPAEFKqYdE5DzgLmAShkPE60qpYaaTRBJQ59W3Dkg04/StAe7GiNM3F3hDKTW3KX2GDBmiHDXlu82mePG3FBZsyyG9sJJBsQGs2l3IwJgA7jurG0Pjgzjt+YW4uzrz5IV9mNAnvFUze/6yaT8zV+8j40AlewuMUIn+nq48OqknyVmlxAZ5ceOoeIfplUx+8y+255Ry0cAobh7dmc2ZRdw/ayMikNDJm7R6XlXxnbzILa2mosbK+J6h3H92d/pGOUaqBKUUP6zP5Jk5yRSW1xDp78EFAyPxcXPh7vHdDiubXljBWa/8yeldg3l9yiC27i/hu6QMftm0n/IaK56uzrx0WX8uGND+vZE1ewr5+9cbSC+s5I8HxhLg5caV765gZ24Zvh4u9IrwY8O+IlY+Ov6YAVtPRixWG5W11kYf6nfPXM8fyTk8cE4PbhgZ3+J7WynFbZ8n8eeOPF6+fCCT+oUjIlz7wSpSskv56+Ezmt2LKqmq5eaP17J6TyEhvu68e10ig2MDGy2/M6eU+GDvgy9cRRU1fLB0NxvSi/hrVz5B3m5E+Hsw994xSUqpIUee36YGyozHNw4IBnIwvPFmA18DscA+4HLT2AhGrL4JQAVGOvi1Zj3TgEfNap9VSs0w5UM4lA5+HnC3OkaDHNlAvb5wJ6/M30HnYG+uHh7LtFEJfLUmnfeWpLK3sIIRCZ1YkVbAd7efRmJcUJvqsjOnlMyiSv75/WayiqtwdRZqrYoLB0Ty0mX97T48kJZXxpkv/8lj5/Xi5tGdAeMBcMbLiykqr2X+38fyxaq97C2oYGz3EH7ZtB8/T1cSgr35ePkeai02frlnNAnB9u1Nfb02nWfnJFNcWcug2ACuHhbL9D9TDzq7TOoXzpId+YT4upMYF8jC5ByqLTYW/H3sYWt/0vLKWJ5awOz1mazbd+DgvOOY7iFNumFbrLYWx6irqLGQkl3Ko99vpriylgfO6cFlicbgRkFZNb9tzeGtRbuM39PEnnZZVNsR2J1fzu2fJ5GSXco71yYyoW/4Cde1bX8Jt36+lvTCSh6e0POw4LfLd+Vz9Qer+N+VA7h4kPF/yi2p4v2laVx/WjzFlbV4uDrz8u/bGdmlE1cNi8XV2QmrTXHrZ0ks3p7Lg+f24MvV+8grrWZU12AuT4wmyNuNvlH+B58Nv2zaz11frmdYQhDXjojDz8OFp3/Zxu78cvw8XbluRBxp+eVU1Vj56MZh7W+gHBFHNVBP/LiFT1bsZfLASF69cuBhvZTKGiuP/7iFb5Iy6BPpxy93n95uvZiKGgv7i6qIDPDg4+V7eOnX7fh6uDC2ewj/nNTrqOG/GosNoFUmuv9IyeFAeS2XJh7thfjqgh28tnAnKx4ZT7j/oajsu/PLqbZY6Rnu12i9mUWVnPf6UmICvfjhjpGtGkS0OZRXWxj5wh/EBHly48gELh4UhZOTUGu1kV9WzeQ3l5FbWs14M6ROSnYp/aL8eGhCT3pFNNy+8moLF7+9jB05ZQC4OTvx9W2nHbZQu47//JbC12szmPm3EXQNPfEwQXfPXM/PG43R9deuGsjkgUf7Ku3JL+ePlFymjozHuRV7/ScbtVYbiU/PZ0LfcF667OjhwuPhQHkNl72znNIqC/84pweXJkYf9je32RSjXvyDPpH+vHbVQL5NymDm6n2kZJfiJFDnEV/3UjooNoDhCZ3YklnMX7vyeeKC3tw4KoGckipemJfCitQCskuMZS2+Hi4MjQ+ib5Q/M5btJtjHndySKsprjHBWYX7uvH7VIIZ37nSYziKiDRQ4poFalVbAle+t5PrT4nj8/N6NPjA3phcR5O1GTJD98s8s3p7Lr1uymb0hk07e7sy9Z/TBCXqrTXHJ9OWkZJUwZVgsj5/f+4SHKdLyypj0+lJqLDZ+uuv0w4bjqmqtjP3PIrqG+vDFzSNOqP65m7O444t1TD0tjptHd7bL3/SdP1N5YV4KP9wxkkENDJMs3p7Lsl35PDzh+NJE1JFbUsWW/cV0C/VlyvsrsdkU8+4bw/p9B5izKYunJvdle04pl01fjsWm8HV34fRuwYzrEcJ5/SMbdaypj8Vq4/2lu1mYnMPavQc4u3cYXUJ8eOjcHq067HwqcvfM9axILWD1o+Ob/bfcmVPKlPdXUVRRwyfThjXqlfvUz9v4aNlugn3cyC+rwd/TlfvP6sZfuwoY0TmITRnF3HVmV1KyS3nyp62UVllwd3HiwQk9uP60+MPqqrHYWLYrnxqrjT+Sc1m1u4A9BRUMjAngjSmDCPf3YGdOGekHKji9a3CDc07aQJk4moGqtdq47J0VZBdXsvgfZ+Dp5vieNWBEqLj8neVM6BvBG1MGAUbW3oe+28TILp1YnlrAdSPieGpyn2b39qpqrVzx7gr25Jfj5uKEj7sLz1/Sn37R/vi4u/DRX7t56pdtfHHz8BN2i1dKceeX65i7OZsgbzf+fHDcUXMDu/PLKSyvITGu8TF2MAzJ2j0HuGZE7GGpwcuqLaTmluHu6oRSxovIhvQiBsYEGPOIP29lVJdgPrxh6Am14XjYmF7EJdOXMzwhiA3pRVTUWPHzcKGkyoKvhwvvXpfID+syWbYrn/3FVXi5OdM9zJfUvDKiAjyZfm0is9dnMntDJn8b3ZlrhsciIvzfT1v5ePkeeob74ufhysfThjaY3E/TfH7ckMm9X23g3+f3Jsjblc7BPvSP9j/mfVRVa+Wit5aRX1bNp9OG0zuy8VGEjelFTH5rGTFBnrx06QBGdA5qtVEZpRT5ZTUE+7gdd53aQJnY20ClZJcQ7ON+0JX0mV+28cFfu3l9yiAutMPEdkv472/beXPRLn65+3S6hfkw7j+LCff34PvbR/LCvBTeXZLGgJgALk+M5toRccdV5//9tJVZa9KprLXyzrWJhPi6Me3jtRRX1hLh78HAmADmbclmeEIQX90yokU3lc2mWLIzjxtmrOGm0xOYelo8mUWV9I/2JyW7hBtmrKG82sITF/RhyrDYBocty6otjH1pEQXlNXi6OvP4Bb0Z1yOEjenF/Pf37ezKLTusvK+7C6XVFgA6ebsx797RhLZx4sjpi1P57+/bievkxUUDo1iRWsCkfuGc2Svs4BCtUooN6UV8uWofO3PL6Bflz5zNWZRXW6g215dlFlUyoU84Y7qH8OgPm7lhZDz/d2GfNtX9VKTGYuPGj1ezbNehlDhdQrwprqzlggGRPHhuD7zcXNi2v4QXfk3BWeDvZ/fg3SWp/LIpixk3DOWMnqFNXMFgf1El4X4eDtHj1QbKxF4GqqrWyu2fJ7Foex5hfu7cNrYLrs5OPDZ7C9efFsdTk49KeeXwHCivYdSLf1BRYyXE15280mo+nTaMMd1DUErx+sJd/Lgxk9355fxwxygGxgRQ93tryLAopRj67ALyy2p4+fIBB+eeckuqWL2nkCd/3kZpVS23je3CrWO6tFpv8+FvNzFr7aG14IFerlTV2gj39yAywINluwroE+nH93eMPGqB47NztvH+0t28dfVgvlqzj6U783FzcaLGYsPdxYl/ndcLPw9XymssuLs4c/GgKArLayitqiXY1/24lgPYi7S8Mj5Zvgd/T1fuGd+Nj5bt5r+/7aDGaqNvlB9f33qa7jW1EaVVtXyzNoPEuEDWmUOzQd5uLEjOoU+ksch+2a4CXJ0FpaDADFn2yMSe3NYBnVAcxkCJSA9gVj1RZ+BxIAD4G1CXovLROpdxEfkncBNgBe5RSv1myicArwHOwAdKqWNm1W0rA5VZVMk9M9eTEOzNU5P7HLxxf9yQyc8bs9hTUM6u3DLuPrMr36/LJLOoEoDoQE9+v39Mh73R/0jJIWnvAT5dsZduoT58d/vIw4xPaVUtZ73yJz7uLnx203Du+nIdHq7OvHNd4lEP5/TCCka/tIinL+rLdQ30uArLa7Ap1eoLGW02xVdr0skrraZftB/vLUmjosbKB1OHEOztznfrMnjw203cO74b95/d/eB5nyzfwxM/bWXKsFiev6QfVpvixV9TSM4q4Y5xXYnt5OWwa8hOlL0F5ewpMOYStLND+/PTxv08NycZLzdn4oO9efz83ri6ODHjr92M6xHK6d06ZiQYhzFQh11cxBnIxFj3dCNQppT67xFlegMzMQLFRgILgLqnxA6MdO8ZwBpgilJqW1PXPJaBeunXFDakFzF1ZDzn9glnS2YxCcHejS4m25VbxvfrMvhs5V6sNkVlrZVzeofxzrWJzN6Qyf2zNhIb5EW4nwcXD45iyrBYaq02DlTUsH5fEV1CfFrkQeUoHCivwdlZGuwRrEgt4NoPV2G1KVydjYdaQrA3069NPCwUzuz1mdw3awNz7xnd5Pi5Pbj3q/XM3ZzFnHtG4+Puwrwt2Tw3N5kze4Yy/ZrBdvME1GhOBhozUPZ+bR8PpCql9jYxlzAZ+EopVQ3sFpFdGMYKYJdSKg1ARL4yyzZpoJoiOauEtxen4uvuwj0z13P18FhmLNvDef0ieOuawUeVX5SSy62fJ2Gx2jirVxgPT+zJgm05PD8vhVs+S2Jhcg7DE4L47Kbhh81fuDo7Eerrwbl9Tnydg6PRVFTp07p04oPrh7BqdyFn9QqlxmLjrpnrufydFXz5t+EHXcKT9h7A282ZHuH2S5/QGI+f35slO/K4+ZO1HCivobTaQq8IP/535UBtnDSaNsLeBuoqjN5RHXeJyPXAWuABpdQBjACwK+uVqR8U9sggssMbusjxxuKbuXofbi5OfH/HSG6YsYYZy/YQ4e/BnM1Z9Fi4kz6RfozsEkz6gQq+Tcrgg6Vp9Irw46MbhhJmTnTHj/YmJbuUBdtymNgvghcv7d8hg1+2Nmf0DD1s4vb720dy1Xsruenjtcy9dzR+Hi4s3pHLkPgghxw66uTjzutTBvGf3wxng0cn9aJbqI82ThpNG2K3IT4RccOInddHKZUjImFAPkbA16eBCKXUNBF5C1ihlPrcPO9DjLBGTsC5SqmbTfl1wDCl1N1NXbexIb7SqlpGPv8HZ/UO439XDqTGYqOyxoqTE4x/+U9yS6sB8HJzpqLGighcPDCKJyf3OeF4VKc6G9KLuHT6cgK9XBkQHcDClNzDnCM0Gs2pgSMO8U0E1imlcgDqvgFE5H3gF3O3sSCyNCFvlLzSaoorao+K/jxrTTql1RZuHBUPGJEQ6no+Sx46g8oaK5szi5mzKYu4YCNkfahv27oHn+wMjAngrasH8W1SJguSc/B0dW5ReBeNRnNyYU8DNYV6w3t1Ec7N3YuBujTxPwFfisgrGE4S3YDVGOk2uolIAoajxVXA1ce6aHZJFffNWs9HNwxFRLDaFK/M386ny/cyPCGI/tFHh4TxcHXGw9WZMd1DGNM95MRbrDmKCX0jmNA3ghnLduPq7NRkZGONRnNqYZengYh4YXjf3VpP/JKIDMQY4ttTd0wptVVEvsZwfrAAdyqlrGY9dwG/YbiZf6SU2nqsa0f4e7Boex5zNmdRVFHL/G05/Lkjj7N7h/HIRJ3x0l7cOCrB3ipoNBoH45RbqJs4ZIiKuuFVNmUUA0Zww9vGduHOkzRHjUaj0Tg6jjgHZRcEmHHDUO6btYEx3UK4eXSCw+Q30mg0Gs0hTjkDBYbL8Gc3NeiRrtFoNBoH4ZQb4hORPGBvG1UfjOEq31HR+tsXrb996ej6Q8dtQ5xS6igPtFPOQLUlIrK2oXHUjoLW375o/e1LR9cfTo421Ecvg9doNBqNQ6INlEaj0WgcEm2gWpf37K1AC9H62xetv33p6PrDydGGg+g5KI1Go9E4JLoHpdFoNBqHRBsojUaj0Tgk2kA1gYh8JCK5IrKlnmyAiKwQkc0i8rOI+JlyNxGZYco3isg4U+4lInNEJEVEtorIMdPSO5L+9Y69JyI7zHZc2k76x4jIIhFJNv9295ryIBGZLyI7ze9AUy4i8rqI7BKRTSIyuF5dU83yO0VkakfT3zzuJyKZIvJme+jf2m0QkZfMOpLNMm0awuUEdO9p3hvVIvKPY9XTHnI5+kYAACAASURBVLRWG8xjASLyrXkPJ4vIae3VjhNGKaU/jXyAMcBgYEs92RpgrLk9DXja3L4TmGFuhwJJGC8AXsAZptwNWApM7Cj6m/tPAs+Y205AcDvpHwEMNrd9gR1Ab+Al4BFT/gjwork9CZiHEdFqBLDKlAcBaeZ3oLkd2FH0r1ffa8CXwJvteA+01v9gJLAMI7CzM7ACGOdguocCQ4FngX8cqx4H/fs32Abz2CfAzea2GxDQXr+jE/3oHlQTKKWWAIVHiHsAS8zt+UBdb6I3sNA8LxcoAoYopSqUUotMeQ2wDiN3VZvTGvqbx6YBz5vHbEqpdlmprpTKUkqtM7dLgWSMbMqTMW42zO+LzO3JwKfKYCUQICIRwLnAfKVUoTKyNM8HJnQg/RGRRCAM+L2t9a5PK7ZBAR4YD0Z3wBU4mAPOEXRXSuUqpdYAtcdZT5vTWm0wR0rGAB+a5WqUUkXt0YaWoA1U89kCXGhuX86hpIkbgcki4iJGjqpEDk+oiIgEABdgGgI70Sz9TZ0BnhaRdSLyjRjZj9sVEYkHBgGrgDBl5g4zv+tyyUcB6fVOyzBljcnbjZboLyJOwMvAg+2lb0O0pA1KqRXAIiDL/PymlEpuH82PW/fm1tOutLANnYE8YIaIrBeRD0TEuw3VbRW0gWo+04A7RSQJo8tdY8o/wrgZ1wKvAssx8lcBICIuGAkaX1dKpbWrxofTXP1dMHp8y5RSgzGGZv7bngqLiA/wHXCfUqqkqaINyFQT8nahFfS/A5irlEpv4Hi70NI2iEhXoBfGbykKOFNExrS+pg0odPy6t0s9drq2C8Zw/3Sl1CCgHGNo0KE5JaOZtwSlVApwDoCIdAfOM+UW4P66ciKyHNhZ79T3gJ1KqVfbT9ujOQH9C4AK4Afz0DfATe2lr4i4YtyYXyilvjfFOWJmYDaHj3JNeQaH91qjgf2mfNwR8sVtqXcdraT/acBoEbkD8AHcRKRMKdUuD5hWasO1wEqlVJlZ5zyMOaoltCHN1L259bQLrdSGDCBDKVXX8/uWDmCgdA+qmYhIqPntBDwGvGPue9V1mUXkbMCilNpm7j8D+AP32UXpejRXf6WUAn7m0AN+PEZ24/bQVTDGzJOVUq/UO/QTUOeJNxX4sZ78etOTbARQbA5//AacIyKBprfTOaasQ+ivlLpGKRWrlIoH/oExx9Nexqm1/gf7gLHmELIrMBZjPsWRdG9uPW1Oa7VBKZUNpItID1PUbvdxi2hNj4uT7YMxJJeFMeGYgdFzuBfDk2YH8AKHonHEA9sxbroFGOHjwXiDVKZ8g/m5uaPobx6Lw3jT3YQxfxbbTvqfbv7tNtX7200COpl67DS/g8zyArwFpAKbMZxU6uqaBuwyPzd2NP3r1XkD7evF1yptwPDce9f8fW0DXnFA3cPN+6QEw0koA/BrrB4H/fs32Abz2ECMIfxNwGzawZO1pR8d6kij0Wg0Doke4tNoNBqNQ6INlEaj0WgcEm2gNBqNRuOQaAOl0Wg0GodEGyiNRqPROCTaQGk0Go3GIdEGSqPRaDQOiTZQGo1Go3FItIHSaDQajUOiDZRGo9FoHBJtoDQajUbjkLRZug0R+Qg4H8hVSvU1Zf/BSNhXgxFM8kalVJGZiCsZI1gpGGH5bzPPSQQ+BjyBucC9SiklIkHALIwgp3uAK5SRLbVJgoODVXx8fKu0UaPRdHwsNoXFasNiU9hsCmdnwdXJCTcX/f7eXiQlJeUrpUKOlLdZsFgzGVkZRmqAOgN1DvCHUsoiIi8CKKUeNg3UL3XljqhnNUYE7pUYBup1pdQ8EXkJKFRKvSAij2BE5n34WHoNGTJErV27tnUaqdFoOhRKKTZnFjNzdTqLUnLJK6vGamv4Gejt5kzfKH8uGBDJlUNjcHXWBqutEJEkpdSQI+Vt1oNSSi0xDU992e/1dlcClzVVh5mIy08Z6aIRkU+Bi4B5wGQO5Sj6BCMB3TENlEajOfWoqLHwXVIGM1ensy2rBA9XJ8b3CiOhkzehfu4E+7gT5O2Gl5szBypq2V9USUpWCSvTCnls9ha+XLWPd69LJCbIy95NOaWwZ0bdaRhDdHUkiMh6jDwmjymllmKkhs6oVybDlAGEKSMRGsrIKhna2IVE5BbgFoDY2NjWa4FGo3FoLFYbX61J59UFO8kvq6Z3hB9PT+7DhQOj8Pd0Pa46ft2SzUPfbuSS6cv5+Mah9In0b2OtNXXYxUCJyL8AC/CFKcrCSIJXYM45zRaRPhjJz46k2WOSSqn3MFKuM2TIEJ0AS6M5BUjOKuHBbzeyJbOEofGBTL92MEPiAjGS1B4/E/qG0znEm6kfrebKd1fy7nWJjOoa3EZaa+rT7oOqIjIVw3niGmVOgCmlqpVSBeZ2EoYDRXeMHlN0vdOjgf3mdo45BFg3FJjbPi3QaDSOTK3VxusLd3Lhm3+RVVTFW1cP5utbT2NofFCzjVMd3cN8+f6OkUQFeHLDjNUs35XfylprGqJRAyUifk19TuRiIjIBY57oQqVURT15iIg4m9udgW5AmjmEVyoiI8T4ZV0P/Gie9hMw1dyeWk+u0WhOUTamFzH5zWW8Mn8HE/tGMP/vYzmvf8QJG6b6RPh78vVtp5EQ7M1tnyeRllfWChprmqJRLz4RSccYThMgEig1t32ATKVUk5M5IjITw4khGMgBngD+CbgDBWaxlUqp20TkUuApjGE/K/CEUupns54hHHIznwfcbbqZdwK+BmKBfcDlSqnCYzVYe/FpNCcfWcWVvPL7Dr5JyiDE151nLurLuX3C2+Ra6YUVXPTWMvw8XfnhjpEEeLm1yXVOJRrz4jumm7mIvA38qpT6ydy/ABijlHqwTTRtY7SB0mhOHtILK3h7cSrfJqUDMO30BO46oyu+HsfnAHGirN1TyNXvryIxLpBPbxqmXdBbSEvczIcppe6o21FK/SwiT7SqdhqNRtMMMg5U8MbCXXy7LgNnEa4cGsOtY7q0mxv4kPggXrysH/fP2siL81J47Pze7XLdU43jMVCF5kLYzzGG/K4FjhmxQaPRaFqbrOJK3vxjF1+vTUdEuG5EHLeN7UK4v0e763LxoGg2phfzwV+7GRIfyIS+Ee2uw8nO8Rioq4EnMeZ/AJYAU9pMI02rU22x4iyCix6G0HRASqpqWbojn1+3ZvPb1myUUlw5NIY7z+hKhL+nXXV7dFIv1qcX8Y9vNtE11JeuoT521edko81CHTkqp9Ic1IJtObz4awo7c8twdhLC/TyICvAkPtiLwbGBTOgbrid4NQ7FvoIKUrJLqLLYyCqqZNH2XNbuOYDFpgj0cuWCAZH8bXRnh4rokFlUyYVv/IWfpyuz7xiFv1fbzn+djLTESaIr8HeMoKwHe1xKqXNaWcd24VQxUG8s3MnL83fQPcyH8/tHUmOxkVlUSeaBSlLzyigor8HPw4WnL+rL5IFRx65Qo2lD1u4p5Pl5KSTtPXz2oEeYL2f2CuXMnqEMiglw2FGANXsKufr9lYzo3IkZNwx1WD0dlZY4SXwLfIgxB2Vt5kUbimjeYBRyc53Ta8AkoAK4QSm1zjxnKvCYWe0zSqlPTHmDkc6bo+PJyDt/pvLy/B1cMiiK5y/th7uL82HHlVJsySzhyZ+3ct+sDQDaSGnsQlWtlZd/384Hf+0mws+Df03qxbCEILzdXfDzdCHUt/3nlk6EofFBPHNRXx7+bjNP/LSVZy7q2yprr051jsdA2ZRSb5xg/R8DbwKf1pM9AiysF4X8EYzFuxMxFuh2A4YD04HhpkF7AhiC4aSRJCI/mak1pmPE2KuLdD6BQ3NlpyRfrNrLC/NSOL9/BP+5fADOTkffJCJCv2h/PrtpODfMWM2D32wiJsgY9tNo2gOlFIu25/LsnGRS88q5Zngsj07qhbe7PcODtowrh8ayO7+Cd/5MxdlJ+Pf5vbX7eQs5nl/Dj2aw1R+A6jqhUqrkWCc2FNGcxqOQT8ZIzaGAlSISYIYwGgfMr1uEKyLzgQkispjGI52fclhtig//SuO5uSmc0SOEV64Y2KBxqo+nmzPvXJvIRW8v45ZPk/j57lF2n3TWnLxUW6wk7T3Alsxifli/n+SsEqIDPfnspmGM7nZUKqAOycMTemBTiveWpLEitYBbx3bh7N5hxx2YVnM4x2Ogbja//11PpjAiOJwIjUUhjwLS65Wri1zelLyxSOenDBarjTmbs5i+OJWU7FLO7RPG61MGHXeytUBvNz64fggXv72cWz5N4utbT8PTzfnYJ2o0zeCnjft58qetFJTXANArwo//Xj6AyQMjT6pehojw6KReDI0P4sVfU/jHNxsRgehAT7qE+NAlxIceYb6M7RFCmF/HGL60J8c0UEqpmPZQhMYjlzdXfnTFJ2G6jZKqWr5PyuDDZbtJL6ykS4g3r08ZxAUnEHesW5gvr101kJs/Xcs9X63nzasHHTVvpdGcCDab4tWFO3l94U4Gxwbw4qX96R/j32Hmlk6Us3uHcVavUNbtK2LZrnx25paRmlvGyrQCqmptOIlR5uEJPekcol3TG+OYBkpEPDEy2sYppW43vfq6KaVOdCgtR0QizN5T/SjkGUB9Y1gXuTyDQ0OCdfLFNB3p/DBOlnQbxZW1LErJ5c8defy6JZvKWiuDYwP493m9OatXGE7HGNJrivG9wnjywj48/uNWJr+5jLvP7MbEvuEtqtNeVFusKAUertrI2pOKGgsPfrOJOZuzuDwxmmcv7ndKpVEXERLjAkmMOzS3a7MpduaW8eOGTD5bsZcJry7ltrGdueOMrvr32gDH42Y+E9gMXK2U6isiXsAypdSg47rAEencReQ/QEE9J4kgpdRDInIecBeGF99wjNTuw0wniSRgsFnlOiBRKVUoImuAu4FVGE4Sbyil5jalT0dzM88vq+a3rdn8uiWbFakFB9eDnN07jGtHxNE/OqBVr/fb1mxe/DWFtLxyuob6cOcZXbigf6TDu81W1Vr5ctU+vlqzjx05RpRpXw8XuoT4MKlfOJcOjqaTj7udtTxxlFLklVazv7iKgrJqCspqyC+vpqrGip+nK/6ersQGeTE0PsjuLxU2m2J+cg7Pzkkm/UAFj07sxc2jE7RX2xHkllbx3JxkZm/YT2yQF89f0u+UzTPVknVQa5VSQ0RkfZ1REpENSqmBx3HRhiKaz6aBKOSmm/mbGJ54FcCNSqm1Zj3TgEfNap9VSs0w5Q1GOm9Kp45ioDIOVPDun2nMWptOjcVGfCcvzu0bzrl9whkYHdCmDyGrTTFvSxZv/rGLlOxSEoK9+fvZ3TmvX4TdH35HYrHa+G5dBq8t2Mn+4ioS4wIZ3S0YV2cn8kqrWb/vABszinFzdmJiv3CmjUpgQEzrGvXWxGZTbM4sZv2+A+wpqGBfofFJL6yg2mI75vmxQV7cd1Y3Jg+MOqaTTGtTWF7DR3/tZtbadPJKq+kc4s1zF/djROdO7apHR2N5aj7/+mELu/PLmTIsln+d1wufVvBmVEqxanchv23NZm9BBVnFVdRYjJVC4f4exHXyZmz3EMZ2D2nz3ptSiv3FVQR5uTU4x90SA7UcOBNYrpQaLCIJwCyl1LBW0r1dcXQDlV9WzZt/7OKLVXsBuHRwNFNHxtMz3Lfd30BtNsXv23L43/wdbM8pZUC0P89d0s8hUl6XVVuYtzmL6X+mkpZXzoCYAB4+twcjG3gD3ZlTyher9vFdUgal1RauGBLNIxN7EeTtGFE0qmqtLNmRx4LkHP5IySO/zHCW9XJzJjbIi9ggL+I6eRET5EWkvyfBvu508najk48bHi7OlFZbKKmsZd2+A7y3JI2t+0voHubD38/uzjm9236YtsZi490/U5n+ZyqVtVbO6hXGBQMimdQ33OF73o5CZY2V/y3YwftL04gN8uLVKwcyqAXLPtILK3j4u00sTy3A09WZhGBvIgM8cHdxxqYUWcVVpOaVUVplwdvNmXP6hHPhgEhON1/uqmqt7MotY2NGEVsyi8kurqKgvIYaiw2bUjiJ4ObihLOTYLUpaq0KpRTh/h7Ed/ImOtCTaouNgrIatueUsHV/CUUVtXwybRhjux/tsdkSAzUBY61Sb4xeyljgJqXUwhP+69kRRzVQZdUW3l+SxgdL06istXLFkBjuGd+NyAD7u31bbYofN2Ty3NxkDlTUctPpCdx3Vje83Np/zUp2cRWvLtjB7A2ZVNXa6B7mwwPn9OCc3mHHNODl1RZe/2MnHy7djbe7Cw9N6MGUobF26xVuySzmi1V7+WVjFqXVFnw9XBjbPYTxvUIZ2SWYUF/3Zr+U2GyKeVuyefn37aTll5MQ7M11I+IY0z2YLiE+rf6Ss3p3IY/+sJlduWVM7BvO38/uTrcw31a9xqnE6t2F3D9rA9klVTw8oQd/G9252f+zX7dk8eC3mwD4xzk9uGJITIO9llqrjZVpBczZlMW8LdkUV9bi6+6Cj4cL2SVV1JmGQC9XogI96eTtjrtplCw2hcVqw2JTODsJLk7Gi8j+okr2FJRTUWP01DxdnekW5kOfSD96R/gxvldYg8+0EzJQ5rBbOEYiwZEYnnPLlVIdNr26Ixqohck5PPL9ZvJKq5nYN5wHzunhkEEniypqeGFeCl+tScfX3YXxvUIZ2yOEM3qEtnlMv7JqC+/+mcr7S9Ow2eCyIdFcOjiawbEBzb6Bd+SU8u/ZW1i1u5ABMQE8PblPq8/lNcWaPYW8tWgXi7fn4enqzMR+4VwyKJrhnYNazeXaYrUxb0s2HyxNY2NGMWA8aHqbD4peEX4kBHvj6+GCl5sL3m4ueLk7H/f19xVU8Mr87czesJ+oAE+euagvZ/QMPfaJmmNSUlXLQ99s4tet2UzoE85Ll/fH7zjyW9VYbDw/L5kZy/YwICaAN6cMOu6YhTUWG0t25LFoey5VtTZigjzpGurDgOgAogM9m3WPKaUoqqjF0835uIcOW9KDSlJKJR63dg6OIxmosmoLT/60lW+SMugZ7svzl/RrUbe+vUjae4CZq/exKCWXgvIa3JydOLtPGFcMieH0rsGtPvexK7eMaR+vYV9hBRcMiOShc3u0OFioUorZGzJ5dk4K+WXVnNsnjPvO6k6vCL9W0vpwrDbF4u25vPtnGqv3FBLk7cZNpydw7Yi4Nl3EqZRib0EFq3YXsG5vEcnZJaRkl1LTyHyWm7MTwT5uDEsIYlhCJ/pE+hEV6ElFtZXskiqSs0pYtbuA+dtycHYSpo1K4K4zu9qlN30yo5Tig6W7eeHXFGKDvJh+7WB6hjf+29yZU8rD321i3b4ibhwVzz8n9upQHpMtMVDTgffr4uJ1dBzFQG1ML+Ler9azr7CCO8Z15Z7x3TrUDwoOTejP3pDJ7PWZHKioJcLfg8sSo7ksMZq4Tt4tvsaK1AJu/Wwtbi5OvH1NIsMSglpB80OUVNUy4689fPBXGqVVFib1C+e+s7rTvQXDVEUVNSxPLWB7dimlVRZKq2pZubuA9MJKIvw9uGVMZ64aGmu3BdEWq43d+eXsK6ygvMZKRbXlsO+MAxWsTCsgv6ymwfNDfd2Z1C+C28d10YtN25hVaQXcNXM9JZW1XD08losGRtE9zBc3FyeyS6pYviuf+dtyWJCcg7e7C89f0o/z+0faW+1m02wDJSIuSimLiGwGegGpQDnGMJ9SSg1u8EQHx94GymK18d7SNF75fQehvu68etWgVn/o2oNqi5WFybl8vTadJTvysCnoGe5Lj3BfBscGcsGAyGY5JVhtis9X7uWZOduI7+TNRzcMbdMUC8UVtXz4VxofLdtDeY2F8/pFcO/4bsc9n1JjsfH7tmxmrt7H8tSCg+P33m7O+Hq40jnEm2uGx3FOn7AOETmhrueVnFVCTkkVPh6udPJ2o1eEH2F+zZ8b05w4uSVVvDAvhR837sdqO/p5HezjzmWJ0dwyprPDOP40lxMxUOtMr70uDR1XSqW2so7tgj0N1KaMIh79YTNbMkuY1C+c5y/uf1LmjskqruT7dZms2VPIjuxS9hdX4evhwr/P683lQ6KbfLgVV9Qyb0sWM5btYXtOKWO6h/DGlEHtFsvsQHkN7y9N45Ple6iotTKuewgjOneiT6Q/Hq5OHKio5UBFDRarwsfDBZtNsX7fAX7ZlEVBeQ1RAZ5cMjiKcT1C6B8d0CGMkaZjUFhew1+78kkvrKDGYqOTjxtD4oLoGe7rcMs/msuJGKiD655aWZEeGOk26ugMPA4EAH8D8kz5o3WLbkXkn8BNGOk+7lFK/WbKJ2Ck6HAGPlBKvXCs67eHgbKaD611+w6wdX8JmQcqySquIrOokmAfN56a3JeJfcNPmbfQbfuN1B6rdhdyXr8Inruk32EGx2K18fu2HL5LymDJzjxqrYruYT7cfWY3zj+B0E2tQWF5DR8sTWPelmx255c3WdbD1Ylx3UO5algMo7uFtPv6I42mo3MiBioDeKWxCpVSjR5rhlLOQCZG5IgbgTKl1H+PKNMbmAkMAyKBBUB38/AO4GyMsEdrgClKqW1NXbOtDJTFamP1nkJ+3ZLNvC3Z5JUaa1kizQVxIb7uDIoN4JJB0Sdlr+lY2GyKd5ek8fLv2wnz8+CRiT0J9HJj3b4DzFqTTmaRMT9zfv8ILhgQSb8of4cx4Hml1aTmlVFtsRHo5UqglxsuzkJ5tQURISrAU4ep0WhawIkkLHQGfGg4KGtrMR5IVUrtbeJhNBn4SilVDewWkV0Yxgpgl1IqDUBEvjLLNmmgWopSitzSarZnl7K3sIKMA8Yq/+WpBRRV1OLh6sQZPUKZ1C+CkV06dejwOq2Jk5Nw+7gujOgcxL1fbeDumesPHhueEMTjFxjxBB2x9xHi606Ir/4/ajTtTVMGKksp9VQbX/8qjN5RHXeJyPXAWuABMylhFEZCwjrqp9U4Mg3H8IYuciLRzHNKqliRWsCq3YUk7S2ksLyWWquNaouVqtpDLrpuzk5EBnhwRo9Qzu0TxpjuIdrltgkGxQay4O9j2ZZVQlWtlS4hPvrhr9FoGqSpJ2mbvsqKiBtwIfBPUzQdeBojZcbTwMvAtEb0UEBDs88NjlcebzTzaouV+dty+HptBkt35qGUEXB0SFwgiXFBuDkLrs5ORAd60iPcjy4h3gT7uHf4Ccr2xs3FiYEOHA9Po9E4Bk0ZqPFtfO2JwDqlVA5A3TeAiLwP/GLuNpaGgybkx43NZgRU/GljJnM3G+E+Iv09uPvMbpzbJ4ye4X4OOeyk0Wg0JzuNGqi6FOttyBTqDe/V5Ygydy8GtpjbPwFfisgrGE4S3YDVGD2rbmbw2kyM4cKrm6tElcXKtI/XIALn9gnn4kFRjGqDaAgajUajaR52mSwxc0qdDdxaT/ySiAzEGKbbU3dMKbVVRL7GcH6wAHcqpaxmPXcBv2E4dHyklNraXF283Fz4/Obh9I7w06nONRqNxoE4Zqijkw0RyQP2tlH1wUB+G9XdHmj97YvW3750dP2h47YhTil1VB6OU85AtSV1yR3trceJovW3L1p/+9LR9YeTow310XFYNBqNRuOQaAOl0Wg0GodEG6jW5T17K9BCtP72RetvXzq6/nBytOEgeg5Ko9FoNA6J7kFpNBqNxiHRBkqj0Wg0Dok2UE0gIh+JSK6IbKknGyAiK0Rks4j8LCJ+ptxNRGaY8o0iMs6Ue4nIHBFJEZGtInLMnFWOpH+9Y++JyA6zHZe2k/4xIrJIRJLNv929pjxIROaLyE7zO9CUi4i8LiK7RGSTiAyuV9dUs/xOEZna0fQ3j/uJSKaIvNke+rd2G0TkJbOOZLNMW8f7bK7uPc17o1pE/nGsetqD1mqDeSxARL417+FkETmtvdpxwiil9KeRDzAGGAxsqSdbA4w1t6cBT5vbdwIzzO1QIAnjBcALOMOUuwFLgYkdRX9z/0ngGXPbCQhuJ/0jgMHmti9G/q/ewEvAI6b8EeBFc3sSMA8jDNYIYJUpDwLSzO9Aczuwo+hfr77XgC+BN9vxHmit/8FIYBlG1BdnYAUwzsF0DwWGAs8C/zhWPQ7692+wDeaxT4CbzW03IKC9fkcn+tE9qCZQSi0BjoxJ2ANYYm7PB+p6E72BheZ5uUARMEQpVaGUWmTKa4B1GIFt25zW0N88Ng143jxmU0q1y0p1pVSWUmqduV0KJGOkWpmMcbNhfl9kbk8GPlUGK4EAEYkAzgXmK6UKlZHCZT4woQPpj4gkAmHA722td31asQ0K8MB4MLoDrsDBANGOoLtSKlcptQaoPc562pzWaoM5UjIG+NAsV6OUKmqPNrQEbaCazxaMNCEAl3MoovpGYLKIuIgRwDaRw6OtIyIBwAWYhsBONEt/U2eAp0VknYh8IyJh7asyiEg8MAhYBYQpM7Cw+R1qFovi6BxhUU3I242W6C8iThjpZx5sL30boiVtUEqtABYBWebnN6VUcvtofty6N7eedqWFbegM5AEzRGS9iHwgIt5tqG6roA1U85kG3CkiSRhd7hpT/hHGzbgWeBVYjhHcFgARccGI3v66MrMA24nm6u+C0eNbppQajDE089/2VFhEfIDvgPuUUiVNFW1AppqQtwutoP8dwFylVHoDx9uFlrZBRLoCvTB+S1HAmSIypvU1bUCh49e9Xeqx07VdMIb7pyulBgHlGEODDo1O/dpMlFIpwDkAItIdOM+UW4D768qJyHJgZ71T3wN2KqVebT9tj+YE9C8AKoAfzEPfADe1l74i4opxY36hlPreFOeImZ7FHD7KNeWN5Q7LAMYdIV/clnrX0Ur6nwaMFpE7AB/ATUTKlFLt8oBppTZcC6xUSpWZdc7DmKNaQhvSTN2bW0+70EptyAAylFJ1Pb9v6QAGSvegmomIhJrfTsBjwDvm/v+3d+bxbVVn3v8+kiXvju14yY4TEkICJBDCTiFsgUJZCi28ndLSUqbttDPTdqadYd6XttPpRmFg6Aplylo6XYCyTvofsQAAIABJREFUDpRSIECTANn3OKuT2Fm8x/si67x/3HvlK1nSPYplW4bz/Xz0sXz06NFzz733/M5+85wms4hcBoSUUlvt/78HTAC+OiZBu0g1fqWUAl5gsIC/BOvRJ6MRq2D1mW9TSt3r+uh5wJmJdwvwnCv90/ZMsrOBo3b3xyvAUhEpsWc7LbXTxkX8SqlPKqVmKKWqgK9jjfGMljil6xzsBy60u5ADwIVY4ymZFHuqfkacdB2DUuowcEBE5tpJo3YfD4t0zrh4v72wuuQOYQ041mK1HL6CNZNmB3Ang7txVAHVWDfdX7C2jwerBqns9PX267bxEr/92XFYNd2NWONnM0Yp/vPtvNvoyrsrgYl2HDvtv6W2vQA/B3YDm7AmqTi+bgV22a/Pjrf4XT4/w+jO4kvLMWDN3PulfX1tBe7NwNgn2fdJG9YkoVqgKJGfDM3/uMdgf3YqVhf+RuBZRmEm63BfZqsjg8FgMGQkpovPYDAYDBmJESiDwWAwZCRGoAwGg8GQkRiBMhgMBkNGYgTKYDAYDBmJESiDwWAwZCRGoAwGg8GQkRiBMhgMBkNGYgTKYDAYDBmJESiDwWAwZCTD3s1cYh5LHYuyH7ZlMBgMBkMqDHsvPhF5I8nHSil18bB+IM2UlZWpqqqqsQ7DYDAYDFg74a5ds6ZRKVU+9MMM2LF2NF+nn366SsYnHlypfvPOvqQ2bj710Lvq8RV7te2/+OvV6v5lu7Ttv/ybNeqnr+3Qtv/q79ap/3q1Wtv+n/+wXt39p+3a9t9+brP6zvNbtO2/8/wW9c1nN2nb/+B/t6rbn96obf+jl7epbzy5Xtv+Z6/vVF/6zRpt+1++uUt9/vFV2vb//dZu9blH39O2f3T5XvXph97Vtn9y9QH18ftXaNs/s7ZWXffzv6pwOKxl//z6OnX1T9/Wtn9p40F15Y/fUqEBPfs3q+vVZfcuU919IS375bsa1MX/+Ybq6tWzf2d3o7ro7jdUR0+/lv3qmmZ14V2vq6PdfVr2Ww8eVefd+ZpqaO/Rst9xuE2dd+dr6sjRbi37XfXt6twfvqYOtnZp2dc0dqhzf/iaOtDcqWV/+Gi3OveHr6mdR9q07OvbetR5d76mth/Ss29s71Hn/+g1tbmuVct+X2OnOu5fX1TAahWnvE7rGJSInCwiN4rIp51XOv2PNP0DYVbsbmLNvhYt+3BYsXxXI6s17QH+uquR1TXN2vbLdzWyqiY1/6tS8L9iVyPvpWK/u5F39zZp26/c08S7e/T9r9zTlJL/d/Y08U4K/t/d28w7u/X9v7unmZUp2L+3t5kVKdqv3N3kPFrBk1V7m3mvppm+UFjPvqaZdftb6e4f0LJfXdPMxtqjtPWEvI2B1fta2HKwjdauPm9jYM2+FnYc6aC+rVfLfu2+FnY3dHLwaLee/f5W9jR2UtuiZ79ufws1TV3sb+rSst9woJXalm5213fo2dcepbalm52a9ptqj1LX2k314XY9+zrLfvshPftth9qoa+1my0G9h/LuONJObUs3m+qOatnvrO/gQHM3m2r17Bs6kl8HaRMoEfk28FP7dRFwF3BNuvyPBs2d1k2me7O1dPUxEFa0dPVr2ff0D9DeE9K27x8I09LVrx1POKxo7uyjpVPPv1KKxo4+bf+Aba/n37LvpSUV/+29KfrvS91/dz/hsJ4gNHb00tYTIjSgJwiNHb109Q3QoykIDR299A2E6erTs2+0b2jdc+bY615zjR2p3QOp+3fsdf0fazx69g2R/Ew1/pE63hTjaT+2/GzpTDWekb0eEpHOFtTHsJ7SeFgp9VlgIZCdRv8jToN9sptH6GQ3daZm7wimbjyDgqln39YTom8gTLOmoIUGwrR09UXi8iIimF19Wi0Et2AOpCAg7T0h+lMQkIGwol2zhRApILtTLeBTu0F18zRiP0LXaMMxxpNqAawb/2A8KQqCbvztqd1jkfzUtW9PLZ7U8z/FeCL5r5efkTJROz9TrCC0j55AdSulwkBIRIqAemBWGv2POClf3KleTCkKYEPk4k6tcGzu1BMEd+GiY2/5he7+Abo1avyOYPYPKDp6vQXBEcywgjYNQejsDUVaHjo3RDisIpUEnXOglIoUGCN9TaRaA05Z0EZKcNpTjSfFGnyKBXzqAj7SApKiwLYfo+Bo+081P1ON59iuz0SkU6BWi0gx8N/AGmAt8J7Xl0TkYRGpF5HNCT4XEfmJiOwSkY3uae0i8icRaRWRF9NxAKnWFhqOsbZwtLtfq8vIuRk6ekP0hrwFwfHfGwprjTk49gNhpTXmUO+q7egcs3Oxgp7IumtTOgWM++LW8d/iapnpnOO2nlBkrEfHPlXB7OkfiOS7jn+lVMqVlmO9RnULvFQFPOV7LNUWV6otlmO951OMP3XBGdl4Us3PlCs4KdiX5AUSfp42gVJKfUkp1aqUegC4DLjF7urz4lHgiiSffxiYY78+D9zv+uxu4FPHFvFQnAJVd8zBORm6Yw6OvVKWSHnauwpsnS4jd4Gtc0FFF/Cp2ev4H57gpD+eaMFMMZ4U49eJp8llo+PfaWGCXn529Q0Kpk48zhgp6OWP0+WrGw8cSwvt2Gr8KXcJjlAXZaotzFQrFCPdYhy0T7FLMIX8KStIPBKU7ll8U0XkXGAGUCwiF3h9Ryn1FpBsGta1wOP2rMR3bL+T7e++BuhNX9HAXcDojDm4Z6CkKiA6g4hRBWqKBaRWPMNoEaV+vKkKgk6Lyx3PCAhOe6rny53/KfrXKACi/OsIbLv7+vH2n6pgOl2+oHc9RAumt31vaLCFqROPWzB18t/d5as9qH+MXY6pCk6q3frakxhS7pId2UkeDR29SQVq2DtJOIjIj4CbgK2A05xQwFvDdD0VOOD6v9ZOO5RCbJ/Han0xY8aMhHaxNfhkGQfRBUBzZx+TJuQktx+G4Gi1WIbTokg1npFoEbWnaD+ceFIUZL0Wo9t+BAQ8xRZpw3DyP0X/WtebWzB1WpgdqeWnWzB18qe1uz/S5asTj1swdSYZhAbCkThSHSPVsXePkY604IzkGNSCacUJP0+bQAHXAXOVUnoLHPSROGkpbX+hlHoQeBBg8eLFCb87rC6ykbihUxxjia4xpyqAKba4RlgwR0IAo7ocUxWQDOgSHE4XZar5ORLxNAwjf1K9v0aii7gpqgdBo4XZNSiYOvevWzB14mnvHRwj1WkBRgtmP0opROIVrxbhsIocc0uXtTTD50ts7xZMZ5w9y5+8k66xvZeygmDCz9PZxbcHSDzadezUAtNd/08DDo7A79DQ3su0klxA84Zw2euOyUT8p2ivW2Me0Xg6eplanIuIfo15UlEOfp9o52dZQTbZWT7t/CnOC1CQnaUlOA0dvQT9PioKs7X9+wQmT8jRy39bAKcW56Y0RjetJFczf3oi9jo1+Cj/qcaTwqQWK56RON5jj3+k40n1/KZSIXXi8ZpZ6/avszTDaWFOK8mlL+S99q61u59QWDGtJFdraYYjmE6Z4jVM0t03QGffAOWFozMG1QWsF5Ff2rPufiIiP0mD3+eBT9uz+c4GjiqltLv3UqGxo4+5lYWAfg3bsdetEZ7g+E/RXreL5vjyAnyiX4OcWZZPlk+046ksymZCbkD7eCuKsinJC2rnZ3lhNqX5Qe0burwgm5J8zXja+ygrCFr+NeOfWJBNWYGeoDkzkiqKsrW7EAuzs5gyQbcA68PvE2aVF6TUQphTUZBSAXlCZWFKLa65lYUpxTO3sjClFuxc7Xj6XPGk6F9jaYbbXmftnTsenaUZTg/F3MpCraUZbnudpRkNrvjBu5LZGGvvcQ7c8YB3GeT4H61JEs8D3wVWYE0zd15JEZHfAiuBuSJSKyKfE5EvisgXbZOXsFpnu7CmsH/J9d23gSeBS+zvXn6swTsDrHM0BcfpL55dWQDotih6mV6SS27Ar3lDW+NahTlZ2gVMZVE2xXm6BXAf5YXZlOQHNQXQGpcrzdMXkLKCbErzA9oFWFlBkJK8oHYBn3I8tgDqx2Plj06LJXK8mvE02PGU5Ae0C9TS/CBlBfrHW5IXoKIwJyXBnDwhR7tClBPwpdxCmF1RoNVCcAr4OZWFHHV1f3n5n1NZSIer+ysRTgtkTmWh1tIMt3/wLiMa21Ozb4j173FNuPMHNAQkxj7V+L0FLSYeD3tn2Ur5aEySUEo9JiJB4AQ7qVop5XnXKaU+4fG5Ar6c4LMPpRxoApzm79TiHPKC3gLi9BdPKsqhKCfLc9aKM8BqFdhBzz5jRzDLCqwWiFeftyOYZQXZFOcFPP07/cXldoGq2yI6vaqEpk697Y4a2ns5aUoRHb0h7QLy+PICwkpv+yhngLWt26c9i6+yKIfcoJ/th7z3ImvosFtceQH2NXVqxW/lf5DtGnupOf3vJXlB1nW1asXvCKDeLMG+iMDqjDlEBDMvGNkOKtmYg1vAnaUZycYcHMEsL8yOrL2bkJt4VKChvTcimM7SjNL8xOMVbsEEa5yooijxxKXGjj4CfqFqYh5gjbPkBRMXiU4BfIJdKW3p7KeiMJn/3mj7rj6mFOcmtHcE020/w45Nx7/XNdEY499LQBpS9D80fi+BHcUWlIgsAXYCPwd+AezQmWaeKTS4MqtEowXiztwSjS4pZ7CxrNAWEA97RzDLC4JaNXhHMJ0CzMu/018cETSP2pozwGrlT8DzeN2CqSOAjmA6BaRuDb7cFnzdLilLELwF3O2/OOUWmnf+DMbjCIjGmIMtmCX5QTo11t5F/OcFtMYcIoKZH9Qac4i0YG3R8BpziAhmnmWv0wXk3C+g1yXljkfnHp6YP2jvFY8jmJNs0dOJxxJMWwA1WkQBv3DcxHzt+H0Cs8odwdEThBN0W1ARQU6tS3BOha5/uwwtHJ1JEvcAS5VSFyqlLgAuB/4rjf5HFEf9dbuAIvb2DefZfHfZ6xSo9W57DUFzx68jmIP2emMyUYKpcbxuwbTiSX7zOLs26I4RdfWF6OwboKzQjsfDfzisoroEvQaV3YJZmh/UGnNocBXwOmMOzqSQ0ryg9phDuauA92rFDnYh2gW2Ro3Z6ZIF7wLSiV9XcBpSFBAnPyMCotFF5o5H5x5wrn8t+5j81LnnU8lPRzAnpiCYTpevrn1OwMfUYmcilcf1095rC6bTwtQTzOMrbIH1EjR71vHE/NEZgwoopaqdf5RSOxiZWX0jglNbKNccc3DUX3dQ322v00KIsk/Fv2aLpTHS/5ujNQbVEOd4k9X4o/Mn4NlCiM0fr+2gnIvbKQC8toNyBNPJT69BZaeF6VwPkPwGdRahlhda+e9l73T5lrsLvCQFRqRLttBV4HkWAINduF7xROwL9Qt496SWVOyd4/XskupIMZ72vqh4vFssvdrn1/I/2GLXsnfGeFNoMZanUKE4lhZjeaE1ycknevlfVpBNQXYWQb9Pq4VWmp9NXjBLa5ikoaOH4rwAwazEMpTuvfgeEpEl9utXaEySyBQGm5tWl4hui8Wp0erPWAlqCtpgAazTYhks4IMU24PuyQXB6XIMRgQt2SMoouLJD3oOKjfGtDC99vuLtg94bgfV4BLkYo0WhbtLVueGbnS3MPO8Czy3YBZrFKhNUefXu4bt7pIt0RAcZwpvWWFQq8Bzj5HqFKhOl295QTDSBadTwJe5BNO7wIvuQtTqEtTMz2j7FASh0HW8mvFMyA1oLc1wuqCLcrK0lmY02AKYG/AT1Fia4eSnzyf2zFq9+EXEnsjjVSb2RVpzWsMkHtscQXoF6u+ALcA/Al+x338x6TcyCGeANT/o1xSEPoJ+H0W5WXYLQW/GjXNDeO335xbM0vyg535/UV2IeUHPZwzFjqGFFUnHHBpj/EPyQdAGl2AOdkklEYQYAfTyH9uCtew1BCeqi0w3Hu8C2C2YkTEZLcEMatXgG2O6ZD3t4whysnjcgqnj31mEWhaV/4n9RwumdwHfFwpztLs/RpAT+48WTPt4k/h3FqGWFQ4KiM49bK3T82utvXMK+Cy/jwm5AY1JDH2DgqAxTuoIvojo9Zp0DG4r5Ix7etsPCo6OvbOmqSQ/4NkF7fafiHRuFturlLpXKXU98DngtRHYVWLEcNcWdMYcrDUyQbt24T3m4Ayw5gT8kS6aZIPKsYIJXgWeNcA6ITegVWN2+otL8oJafeRRLUyNGm1jnALPK55Ye90CWKcG7BbMwXhSa3ElP163fWr5WarRYokr4DrHG9Vi0YlfVzCjK1z6/q0uo4A/+dq7ps5B+9yg31qaoSmYwSwfhdlZSf0ftRehlhVk4/cJxbnJWwi9oYGIYAKea+8G7GehldsFcGle8mEDa1KRNcYFePbKWM9OcwmIxjivW6B0lkK4Wzil2oI2GL9uCy0Z6ZzFt0xEikSkFFgPPCIi96bL/0jTEFO7AI8ac3v0yfC07xi8+Io1CqSGKMH0HnNoaLcGWJ3alE78pfn2zalTwEcJpl48sYLpFY9PrBtBS3DsFsVE9yB6khvU3cLU6aKJnQQDyQXHPelEp4ssqotYQ8Cj4s918l/veAtzsqzF21rxZ5Mf9HuOObhnveYE/J5jDu41LyLWNaeX/04NPvnMSPfxAp7jqg0dMf49Jua4W5hWPMkL4KbOXsK2YAKeM3ePdvfTP6Ci4k/mv703RK/d5QtExnkTERoI0+QSTK+Zu9akomjBTBaP8ygYJz/1BG10u/gmKKXagOuBR5RSpwOXen1pmM+DukVEdtqvW4YTfFRtQWfMIab5Cx41yJiT52nfkZoAOv3jgHYLqsxVuwPvFsJgf7RePLGC6dViKc0P4veJdoulOC9AwO+LdBklb7EMtjC1xqA6BgVTR9CcFsLEfL0xB3eLSGfMwd0CyfL7KMrJ0rMvDGqNObgn2eiMOTQOEZDkBXzsmhevGry7hQnegtMYKyAeE50aXYLpxKPXQtbr8nK3MAHPcedY/15ddo2xguwRv7uFGYlHs4UJTosxcX52xAim1/XW3TdAR28o6TZHkF6ByrIfg3EjkMoDBB/lGJ4HZbfUvg2cBZwJfFtESlIP21L/+vYeygsHa2uQuICx7HujLj5Ib4urvm1oiy7ZCa+P8u89ZlLfPthfrFNgRx2vhiDXtw8KZrHGmEN9nPxJGo8rf4pzvQWtvr0nIpi5Ab/nfn/1bYOCqTPmUN/ey4Rca0aSJSDJa7T1bT3kB/3kBv2RMYfk/nsiggneBV5926BggveYg2MfVcB4XA/gFpDkglbvamFG7DXiKS/QK1Dr23rs+AfvYb14Bns1dPIzqoD3uN6seFwCopGf5S5B8Lre3PF4nq/Y82sLfqKJVPXtQwUz2dKM+jiCmWyYxMmfZLtIQHoF6j+AV4BdSqlVIjILa+FuUobxPKjLgVeVUs1KqRbgVZILXUI21h6lpas/su27Vwth66E2Gtp7WTBtAoBnF9y+pk72NHZG/HuNgRxo7mJnfQcLbf9egnboaDfbD7dp+69v72Fz3VFOmToh+ngTxN/W08/qfS2ReIrsaaqJ7Dt7Q6za2xyJpzA7K+l+f919A7y7pymSn7lBPzmBxALS0z/Ayj1NLLDjj4w5JLAPDYR5e2djxL8zzpjIPhxWvLWzIeoxAMkK1HBY8WZ1Q8Q/JC/AlFIs2xHj36MGvKy6gZOnTsBv7+zgJTjLdjQwb3JRZAqvV4tl2Y4G5lQUkBv0a8ZTz8yyfAqzsyL2yVosb1bXU1mUTaW984LXbirLqhsoKwgy2X6EjWc8OxooyQswvdRas+N5vNX1FOVkRdb4eHWRLdtRT07AF1m0au3ukjz+YJaPuZMKbf/e8QT8wrzJRS7/iQVkWXUDfp9w0hTbPj+YdDuoN3c0AETu+dK8IKFw4rV3y6rrAVjoKhOTLc1YVm35Xzg9ukxMlKeO/SmueyYe6Zwk8aRSaoFS6kv2/3uUUjekwXWi50ElSh+CiHxeRFaLyOqGhoYhnz+zro5glo8rT5kMeLeInllbR8AvfGTBFGBQQBJdsM+sq0MErjvNsvealvvc+joArjttarR9AsF5bv1BlILrbfuinOTrHJ5ff5CwgusXWfbOmEOiAuPlTYfoC4X56KJpAJFxq0T2f9p8mO7+gUg8Trdgonj+vPUwnX0DXG/7B6eLI77/17bV094TirJP5n/57iYa2nsjxwtOjTa+/3f2NHHoaA8fPW3QPlmXy6qaZupau7nBHU9e4llMa/e3sq+pKzqeJIJTfbidLQfbtOPZ3dDBhgOtkfy3/CeOZ19TJ2v2tUTnf5J4alu6eHdvM9efNjWydVJpkvxv6uhlWXUD1506NbJ1UjLBae3q4/Xt9VyzcGpk66Rkgna0u59Xtx7hmoVTCNj2ya6Hjt4Qr2w5wkcWTiE7yx+xd7aDiqU3NMCLGw+xdP4kCmxBTrb2rn8gzAsbDnLpvIrIVk6RpRlxJlKFBsI8u/4gS+ZWRCqLpfmWgLTHEZCBsOK59XVceEK5q1cj8dIMpRR/XFvLmVWlEQFPtvbOsq/jtBnFVJXlR+KBJGXiulpOmTqB2fYuEoNr3eKfsz+uq2Pe5KKIICcinZMkckTkyyLyC3tc6WEReTgdruOkqSTpQxOVelAptVgptbhkYlnUZ00dvTy7vo7L5lVGLiZHEHYe6RhywbZ29fHMujoucl1Mzvd21rcPse/oDfGHVQc4Z9ZEJk+wVnDnBKxZSbvqO4asPerqC/G7VQc4c+bgxRTwWy2EnfXtQ+y7+wb47Xv7WeS6mHw+a6xl55GOITWqnv4B/ufd/SyYNngxWYPWAXbVtw+x7x8I8/jKfcwqy4+0oJw82lXfMWSqfGggzOMra5hRmsfpxw32uJbY9rFN/oGw4tEVNUwtzuXMqtJB+/wguxuG2ofDisdW1DCpKIdzjp8YY985ZINQpRSPLN/LhNwAF51YEUkvzQ+yt7FjSAGjlOLh5TUUZmdx2fzKKP97Gzvj2j+yvIb8oJ+lJw3al9r28ZYGPLJ8LzkBHx+2K0RgCU5NU3z7R1fsxe8Trl44JSqefY1dcQu8R5fX4BO49tRB+9L8IPua4ts/srwmqgJl+Q+wv7mLrr6hBeRjK2qAwQoUWIKzrym+/RPv7CcUVnzUJcil+UGaOvto7xlagP3m3f30DYSHVCiOdvfHLYB/997+qAqU47+zbyBuq+X3qw5EVaDAyv++UDjqmWEOz607SGtXf1T8zr1/oLlriP0LGw7S1NnHR0+LrnAB7GseuqfjS5sPWxWomPwE2N801P8rWw4PqUA58dTE2TPy7Z2N7G7ojMl/q8zaG8d+5Z4mqo+0x40nnv9VNc1srhtagQLY0zDUfu3+liEVqESks4vv18AkrK63N7Ge25SOx7Eneh7UMT0nKvYCvOPZzXT1DvD3F8+OpGVn+SnMzuLRFTXc8+cdUfbffG4LbT39/OMlcyJpWX4fJXkBnnhnP3f+aXuU/ff/dyuH23r42mUnRKWXF2bzzLo6vvV89NyQH760nbrWbr52aYx9UTYvbjzE/31mU1T6Xa9sZ19TF/902dyo9IrCHP605TD/8tTGqPR7X93BnsbOuPH8ZVs9//SH9VHpv3hjN1sOtvHPS+dGbTRaXpDNyj1NfPGJ6LXYv3xrDxtqj/LPS0+Iti/MZlVNC7c9tjrK/qG/7mHd/la+dtkJURuTlhVks25/K5955L0o+0dX1PBeTTNfvXROpLvLiifIprqj3PzQu1H2T66uZVl1A/9w8exIbdnybwna9b9YEWX/zLo6/rLtCF++eDY5Abd9Nvuaurjmp8uj7F/YeIg/bTnM3y05Pmqj0bKCbOpau7nyJ29H2b+06RAvbjzEFy44PlIbB2ts5khbL0v/662oSs6bOxr47XsH+Oy5VVEznsoKsmnvDfGhu96IqlQs39XIr9/Zx6fPqYraKLWsIJvu/gHO+9HrUaL/7p4mHltZw9+cOSNSgXLs+wcU5975epQor9nXzK/+upebFk+PVKCAyHjmWd9/LUoEtxw8yk9f38lVCyZz4qSiKP8AC77zZ466atnVh9v58V92cvlJlZHuKyd/ABZ+5880dQzew7vq27nn1R1ccmJFVAXKGTtZ9N1XI+MdAHsaOrjrT9u58ITyqAqUE8+ZP3iNQ0e7I+kHW7v57otbObOqlAvmlA+xv/Tet9jT0BFJP9LWw78/v4VFM4q5aK7L3o7/ivveZueRwWKxvr2Hbz23mYXTJnCpq0LkjC195Kd/ZevBwY2Nmzp6uePZzZw0pYjLT5oUSXfGcq7/xQo21R6NpLf19PMvT21kVnk+1506KAhO/Lc8/B7v7GmKpHf0hvjGkxupmpjHDadPG2J/66Or+evOxkh6V1+If/rDeqaX5nLjGYPFsRP/F59Ywxt2dyFYFeR/+v16phbnctOZ7uI7PukUqNlKqW8CnUqpx4CrgFPS4DfR86BeAZaKSIk9OWKpnZaUXlcNtbM3xMubD3Pr+TOHNDXvv/l0ZpTmsaF2cJfpnv4BXtx4kFvOqeLkqdF9pz//m0XMLMtn3f5B+3DYairfdMZ0znC1DgDuuXEhcysL2XBg8GJSSvH02lpuWDQtqnUAcPfHFjBvchHrD7RG26+p5bpTp3D+nOiW4Z03nMJJU4qi4gd4ek0tVy2YzEVzK6LSf/DRU1g4bUJU/ABPr61lydxyrlowOSr921efxBlVJax3xe/Ynz+7jGtPja4dffMj8zlrZmmceOo4a2YpNyyKtv9/V83j3OMnsuHA0agC++m1tZx+XAk3nRF9cd/+4RP50JwyNhxojWplPrW2lhMnFXLreTOj7L966QlcOq+CrYfaogrgp9fWMqeigL/90Kwo+3+8eA5L51dSfaQ9qpXw9JpaZpXl83dLZkfZf/mi2Xz45EnsaeikzdVK+OPaWmaU5vEPF0fb/92S2Vy1YDL7m7uiav1/XFtLeWE237giugJy63lVXL9oKo0dvRxs7XbZ11GaH+T2D59mH9D5AAAgAElEQVQYZf+pc47jxsXTaO7so7Zl0P6ZdXUU5QS446r5UfY3n30cnzhzOq1d/VG1+GfW1VEQzOJbV0fb/58zpvPJs2bQ3htit6vAfn7DQXwi/OC66KLgutOm8rnzZ6IU7KgfLLBf2HCQAaX44fULoio4Vy+cwhcusM5JtauAf3HjIfoHwtx5Q7T9VQum8HdLjgdg+6FB+5c3H6Y3FOauj0XbX3HypMg52VI3KAh/3nKY9t4Qd95wSlSF6MITyvnG5dY52ewSkFe3HqGtJ8SdNyyI2tn9vNllkXOyqW7wnnljez2tXf384PpTIt2TAOfMmsj/u3Ke5d9lv6y6gebOPn7w0VOitgg6c2Yp3/qIdU7Wu+6xFbuaONzWw/euPTkyvghw8pQJ/PB665xsdNm/u6eJutZuvnPtyVEVrnmTC7nrYwsAou7hVTUtHGju5t+vPimqwjWnooB7Pr4QEdjoKiPW7muhpqmLb35kPkU53jvhpVOgnLuwVUROBiYAVV5fOtbnQSmlmrGeP7XKfv2HnZaUXlcX0N5Gq/m5MM5A3flzyjhtRnFUE3VfUxdKwYLpxUPsz51dxhlVJVH2B4920xsKRw2GO5xRVco5x09kb2NnpACub++lq28gbjynH1fKecdPpKapM1IAN3f20dYTiuv/tBklnD+njH1NnZEa9tGufpo6++L6Xzi9mAvnVlDb0hUpsHtDA9S2dMX1P39KEZfMq6SxozdSAIcGwuxv6oqaLOBw4qQiLptfSWtXf2TsIRxW7G3qZOH04iGPgTihspDLT5pER28osmZFKcXexk4WTJswxH52RSFXnjKZ3lCYOleB7djHPjaiqiyfqxdOQanobpS9DZ2c4pqM4DBjYl5EdGsaXfaNnZwUx356aV6kC6ymcfCa2NPYyUlTioY8lmJqcS4fs2use1z2exs7OXFSYVTrD6CiKIebFk+P2AzadzC3sjCq9QdWi/qmM2ZEbNzxnFBZEFV4gVVj/sSZM+LGc3xFAfnZ0Y+lmFiQzafOOW6ofUMnx03MY0JedGE0ITfALedURWzc/meU5g15rEZRToBbzq2Kc7ydTC3OHTJduSA7i8+eN9R+T0Mnk4pyqIx5DEd+dhafO3+mHf9g/uxt7KQwO4uZdve5Q07Az+fOn4nI0PhzA35m2zuMO2Rn+bn1vJn4fRIdT2MnQb8vqnUJ1sSfW8+fScAvQ/Lf7xPmT4m2z/L7+My5VeQF/VEtOue3Yicj+HzCJ86cwUS7KzrWPraMEBFuXDydyqLsaHv7t2LLCBHhhtOnMWVC7pDrDQYnU3iRToF60G7JfBOr1bMVuMvrS0qpTyilJiulAkqpaUqph5RSDyilHrA/V0qpLyuljldKnaKUWu367sNKqdn26xGdIPsGwpFxCiejZ5bnx7WdWZbPwaPdkXEBJ6NnlcW3n1VeEFVgR/wnsJ9Zlh9VADviNrOsIL59eT49/WEO21NqveKfVZZP/4Cizq4xO/3NifwfX55PWFlCDFb/elglPl7nuJwCuLalm1BYJTzeWXaczkV68Gg3faFw0vyBwQLAEfCE+V8W7b+9p5+G9t7E+Rlj3903wMGjPd7x2PaOgCc83hh7R8AT2R9vx+kUMEop9jZ0Js7/8mj/zvtk14PlP8Y+gf+qsjj+k8RTNTEfEYYUkIn8Ty3JHVIA70liP6koh5yAb4ggJLIvt3etiBXwRPbFeUEm5gej8mePnZ/xnqOVE/APKYD3NnZSVZYf9zlawSwf00ty4wp4bAUHrMlIx03MH+J/RmleVGvLwecTZpblx5zfDsoLsylM0FqJtd/T2ElJXiCyeD+efez1VpidlXDLolnlQ+1zA/7IbE4v0jmL71dKqRal1JtKqVlKqQpHZDKNAy1WAexkXNXExAWSchXYzoVV5VVA2ifc8e9VwO+NsU8mmG67PZ7+7QLPvsCdCz1xgRpdQA4Kpl4BHDnehPEXxLXXFQQvAXeei+PE77R0dAtgZwA4Uf5XleXZ9pZ/LwGfMTHPLrD1BHxqSS7BLF/EvrGjj/bekHYB3NrVR0tXf8J4SuxFx469l4AX5QQoK8iOXJ9eAp4T8DO1ODcS/0BYsa+pK2F+xhbASilqkgiOzydUTRws8LwEXMQusDUFHKxrV1fAHfvYAjhRPGAX8Cn4jxWEZAJuxVMwpAWYiv+9DV72BUPjSSDgjv89rl6iZAIej3TO4qu0dzN/2f5/voh8Ll3+04lbEKZMyBnSHeIwK1KgdkS+V16YHdXX6ub4Cst+d70jCJ3kB/0JV0vHFsA1TZ1kZ/mYnOApoLE1/prGTrJ8Enm+i5f/vQ2d+ARmlMZ/SqcjLLsbogvsRII8vTS6APYS/GkluXYXhyMgyQVqSnEuQb9viKA5QhGLsxO0MwYy2GKM798qgIOR66HGI/68oPWwuj2ReLrseOLbZ2dZBbauIPt9wsyJ+ZH4vfJfRKgqy3PFkzx+5zPHzql4zUyQn85nsQKeKB6A48sLIvEfbO2mbyDMzCTxuAvII229dPcPJPXvtm/qtAQ8mX1V2aAAtnRaAp4sHnf8vaEB6lq7k+anuwDuHwhzoLkr4fU5GI9lPxBW7GtO3KJ2/Nc0dTEQVhEBTxbP8eX51La4e326kud/eT717b2R2ZQ1TZ0e+Z9Hc2dfZAp/TVPyeKom5tPeE6LJ7ta3KiCJ8yeWdHbxPYo1ScGZq7oD+Goa/acNZ5DVUf9EOBea8/hur9rIjNI8snzC9sNtEfuqssS1C6cAdvzvabBOdqLaRWVhDrkBP9Uu/zMm5iV8zHZZQZDC7CyqDw8e77SSvITPX8m3nxa67dCg/4n5wYSP5XZqzNWu/CnKyUr4WO6A38eM0ryoePKCfioSCLhVw85z5X8HwSwfUybEF2QRYVZ5QWRQfG9DJyJEFmPGY2ZZPttd14OTlsy+2hUP4FkAVx+2lh/o+J9Vns/2w9ZyAkc4ZyVo4Vi+Ctjh2Hu0wC1f+eywlx8MxpPMfz4769sJDYQ9BdaJf0+DNV1ey94ugLv7BiI1f68WyP7mLjp7Q1r+Z5ZZBXZ7T79nhcWJv6mzj/r2HvbbY86JegQcX+09IQ4d7XG1kBPn56yyfLr7BzjQ3G0JeJIubsd/XyhMTVNnRMCTtwALrIknR9pp6+mnsaPX83oAy76rzzqO5PlvHVv14Xa7i7s7efz2b1cfbqd/IMx+D0GOJZ0CVaaU+gMQBlBKhYDkjxQdA/KCfh5bUcO2Q23sOtKeVP0LcwKcPauUJ97Zx7ZDbVQfbk9aGAX8PpbMreB3qw6w7VAbm+uOJq2N+H3CxSdW8OTqA2w92MbG2taktS+fT7hkXgVPr6ljy8GjrD/QmjQeEeHS+ZU8u76OzXVHWbe/NWk8AJfNr+RPmw+z4UAr7+1t9rS/dF4lr247wrr9Lby7t4mZSQTZsq/g9e31rNnXzDt7mu1xiyT28yt5a2cDq2qaWbmniaqJeUm7By6bX8nqfS38dWcjf93VwJQJuQlbyE78Gw60sqy6nrd3NlBRmD1kAkBsPFsOtvHatiO8taOR0vzgkAkAsfFUH2nn1a1HeHNHA4VJBNyxr23p5oWNB3nD3l1gakl8Qbbir+BwWw9/XFfH69vr8fuE6SWJr6FL51sTW55ac4A3ttd7Cvil8ypp6ern96sP8No2a7pwsmvisvmVdPcP8PjKGl7bdgRILgiXzq+kLxTm4eV7tfxfOr+SUFjxq7f38hcd//MqUAr+++29kXiS+b/4xAp8Ag8s28NfnHiS3GNL5lbg9wkPvLmbv2x14kmcn0vmVpDlE+5/c3ck/mTxXHBCOUG/jweW7eZV53iTxHPu8RPJC/p54M3dg8ebxP6smRMpyM7igTf3aOX/mTNLKcrJ4oE3d/PG9nqUSp7/i48roTgvwANv7ubN6gZCYZU0nlgk2UPtUkFElgE3YG0/tMieEv4jpdSFafmBNDFvwamq+8rvA9Zi0yc+d9aQKeNu1u5viayVKcrJ4onbzoo7q82h+nA7V/z4LZSydmh45LNncubM0oT2u+rbWfpfbxFWlng+dMsZQ6aYu9nb2Mll975JKKzICfj41afPGDLF3M2B5i4uvmcZ/QOK7Cwfv/zU6SyJmWLupr6thwvvXkZ3/wABv/DTT5zGFSdPTmjf2NHLhXe9QWffAFk+4b7/c2pkh414tHT2ccFdb9DeG8LvE+69ceGQKelujnb186G7XqetJ4RP4O6PLYxanxFLV1+IC+9eRkN7LyLwH9ecxKfs2WLx6OkfYMndyyITT7599Xw+GzMlPdb+knvejMwUvOOqedwWMyXdTV8ozKX3vsl+e0Hnv15xYmT6czwGwoorf/x2pJX/9xfN5uuXz01oHw4ra62M3er9woWz+LcPz0tor5Ti2p8vZ6O9Vua282dyx0fmJ7W/4f4VrLWXH3zm3Cr+/ZqTEtoDfOqhd3nbXitzw6Jp/OfHFySthNz66Cpe324VjteeOoX7bjo1qf3fPr6aV20xuGrBZH72idOS2n/pN2t4adNhAK44aRL337woqf2/PLWBP6yuBeBDc8p49LNnxp3E4PB/n9nE/7y7H7AE4rFbz4w7icHhW89t5vGV+wA4a2Ypv/7cWUmfKvsfL2zl4eV7ATijqoQnbjtryKxON/e+uoOfvGbtMnfK1An87vNnJ610/fS1ndzzqrXec/7kIn7/hbMTTqoAuH/Zbn5kr/c8cVIhv//COQl7WQD++609fP+lbQCcUFnAH75wzpBJGCKyRim1OPa76RSoRcBPgZOBzUA58DGl1MakXxxlFi9erP7lgWdoaO/lmoVTohYbJuKpNbUcaevhIwsmc5yG+j+/4SD7mzq54uTJzK5I3Nx3eHHjQWoaO7n8pEnMsff6SsbLmw6xq76DpSdNiuz1lYxXthxmx+F2Lp1f6bm1CMAb1fVsqj3KkrnlScXY4c0dDazf38qFc8s5Nc4U/Fje3tnA2n2tnD+nLGqxZCJW7GpkVU0L582eyOKqxGLvsLqmmeW7mjhjZgnnHp9YvB3W2C2uxVUlnDfb23693eJaNKOEC04o97TfWNvK69vrOXV6cdLKgcP2w238afNhTpoyIWo3i0TsONLOS5sOMW9y9OLNROyqb+fFjYeYW1nIFSdPSlpYg7V10gsbDjKnopArT/G2P9DcxdNra5lZls81C6d42te1dvPU6lqml+ZGbYeUiIOt3Ty5upZpJbl89DRv+yNtPfzuvQNMKc7h+kXTkooNWMs3nnhnH+WF2Xz89GkJu9AdWrv6eHzlPsoKsvn44mlJxQmsStdjK2sozQ9y4+LpScUJrMW2jy2voTg/yI2LpyUVJ7AqaY8sr6EgO4sbF08fsoQglp7+AR76617yg34+vnh6UjFz7B9evpecLD83njE94Zi8Q29ogEeW1xDw+7hx8bS44jdiAiUiZwAHlFKHRSQL+AJWS2or8C2dtUmjiYg0APtGyH0Z0OhplbmY+McWE//YMt7jh/F7DMcppYbU9tIhUGuBS5VSzSJyAfA74B+AU4F5SqmPDesHxhEisjpeLWC8YOIfW0z8Y8t4jx/eH8fgJnnbTA+/q5V0E/CgUupp4GkRWZ/kewaDwWAwJCQds/j8dtcewCXA667P0iGABoPBYPgAkg4B+S3wpog0At3A2wAiMhs4muyL70MeHOsAhomJf2wx8Y8t4z1+eH8cQ4S0zOKzp5RPBv6slOq0004ACpRSa4f9AwaDwWD4wJG2aeYGg8FgMKSTdO4kYTAYDAZD2jAClQT7sfX1IrLZlbZQRFaKyCYReUFEiuz0oIg8YqdvEJEldnqeiPyviGwXkS0icud4it/12YMissM+jhtGKf7pIvKGiGyz8+4rdnqpiLwqIjvtvyV2uojIT0Rkl4hstBePO75use13isgt4y1++/MiEakTkZ+NRvzpPgYRucv2sc220dvSevRiP9G+N3pF5OtefkaDdB2D/VmxiDxl38PbROSc0TqOY0YpZV4JXsAFwCJgsyttFXCh/f5W4Lv2+y8Dj9jvK4A1WBWAPOAiOz2INYnkw+Mlfvv/7wDfs9/7sPZdHI34JwOL7PeFWBsQz8d6ztjtdvrtWFtqAVwJvAwIcDbwrp1eivXQy1KgxH5fMl7id/n7MfA/wM9G8R5I1zk4F1gO+O3XSmBJhsVeAZwBfB/4upefDM3/uMdgf/YYcJv9PggUj9Z1dKwv04JKglLqLSB2J4y5wFv2+1exds0A66J5zf5ePdAKLFZKdSml3rDT+4C1QOLN5NJIOuK3P7sV+KH9WVgpNSor1ZVSh5Q9yUYp1Q5sA6YC12LdbNh/r7PfXws8rizeAYpFZDJwOdYekc1KqRas475iHMWPiJwOVAJ/Hum43aTxGBSQg1UwZgMB4Egmxa6UqldKrWLw6eBefkacdB2D3VNyAfCQbdenlGolwzEClTqbgWvs9x8HptvvNwDXikiWiMwETnd9BlhNbOBqbCEYI1KK344Z4LsislZEnhQR7w3i0oyIVAGnAe8ClUqpQ2DdwFi1RrBu3AOur9XaaYnSR43hxC8iPuAe4BujFW88hnMMSqmVwBvAIfv1ilJq2+hErh17qn5GlWEewyygAXhERNaJyK9ERH9b8THCCFTq3Ap8WUTWYDW5++z0h7FuxtXAfcAKIOR8SazFzL8FfqKU2jOqEUeTavxZWC2+5UqpRVhdM/85mgGLSAHwNPBVpVRbMtM4aSpJ+qiQhvi/BLyklDoQ5/NRYbjHINa6yHlY19JU4GKxtkYbcVKIfVT8jNFvZ2F199+vlDoN6MTqGsxozE4PKaKU2g4shchar6vs9BDwNcdORFYAO11ffRDYqZS6b/SiHcoxxN8EdAHP2B89CYzak5JFJIB1Y/5GKfVHO/mIiExWSh2yu4/q7fRaolut04CDdvqSmPRlIxm3Q5riPwf4kIh8CSgAgiLSoZQalQImTcdwM/COUqrD9vky1hjVW4wgKcaeqp9RIU3HUAvUKqWclt9TjAOBMi2oFBGRCvuvD7gDeMD+P89pMovIZUBIKbXV/v97wAQy4AnDqcavlFLACwwW8Jdg7VQ/GrEKVp/5NqXUva6PngecmXi3AM+50j9tzyQ7Gzhqd3+8AiwVkRJ7ttNSO21cxK+U+qRSaoZSqgr4OtYYz2iJU7rOwX7gQrsLOQBciDWekkmxp+pnxEnXMSilDgMHRMR5uNio3cfDIp0zLt5vL6wuuUNYA461WC2Hr2DNpNkB3MngYucqoBrrpvsL1vbxYNUglZ2+3n7dNl7itz87DqumuxFr/GzGKMV/vp13G115dyUw0Y5jp/231LYX4OfAbmAT1iQVx9etwC779dnxFr/L52cY3Vl8aTkGrJl7v7Svr63AvRkY+yT7PmnDmiRUCxQl8pOh+R/3GOzPTsXqwt8IPMsozGQd7svsJGEwGAyGjMR08RkMBoMhIzECZTAYDIaMxAiUwWAwGDISI1AGg8FgyEiMQBkMBoMhIzECZTAYDIaMxAiUwWAwGDISI1AGg8FgyEiMQBkMBoMhIzECZTAYDIaMxAiUwWAwGDKSD9zjNsrKylRVVdVYhzFqtHb1U9faTTjOnot+nzBlQi7FeYExiCw5rV39HG7roX8gTMDvY1JRTkbGaUid1q5+DrZ2M2Bfk5l8HRpGhzVr1jQqpcpj0z9wAlVVVcXq1avjfvbsujr+/fkttHZbT0vOC/jIDvhp7epnSnEu37h8LtedFv9BrM+uq+PuV6o52NrtaetFOn2dd+frhFq7E34ufuGOjy08Zv9uYvOvJC/At68+KWXfdzy7id+8s58yV1og4OeO60+J6yud+ZUqY/nbw2Gk4va6Bp5dV8c3ntxARTi6whQCrjh7Bt+77pRhxzASjGR+jcb1k6570/GV7phFZF/c9A/abuaLFy9W8QTqjmc38cQ7+5N+Nzfg54cxheSz6+r4tz9upLs/HGUrwCddN1yykxp78cSSzNeE3AAiJBTRqtv/1zNPphbnsvz2i6PSkv3GRSeW88b2hqhjAfjGkxvoDw+9nm62Y9cpvOLlpUNxboD1316qlWf5QT+dfQP4RRhQiuLcAH2hAbpifMe7URP5jrV1hNR9xM65WnxcKXe/Uk1da3ckhqkpnvdEeZToOz6BsGLI78Ri5fMmuvsHhuTZ9z869PrWKYy8zp1zDZz2H3+mpSvxdf5fN53KdadNjfxuXWs3wuDjjxOdL11bxz5Zvru/53VcXvEkO/c65cax4P59L9z3ZmzM8WKPd83D8K4dABFZo5RaPCR9vAuUiDwMfASoV0qd7GUfT6CeXVfH136/XvsZ4CV5Aa5aMJmn19QmvHB1yA/6OXX6BJbvbtayD/oFpRQ6P+nEGO9iikdxbiBpQZkMAYJZPnpDx5YX+UE/H100ld+/dyCuwMXappJnugT9QpZPhghYPG4+ewaAZ4UmGQEfWufRTV7AR08ojEcWDcG5Fl7ccOiYz7GbVPLKQYBzjy9N63kryQswf3IhK3Y3a9+7qeAXGNB0fLNdKUkmZmDlXZ+GU+ee0D1nTl6k+75wk0p+JLu+44n6+1mgLgA6sJ4ymrJAPbuujq/9YT3jPBsMBoNh3BDwC3e7hhYSCdS4n8WnlHoLOKZqg9MfbsTJYDAYRo/+AcXdr1R72o17gdJBRD4vIqtFZHVDQ0Mk/e5Xqj27lAwGg8GQfg5qjJF9IARKKfWgUmqxUmpxefngTEadDDIYDAZD+plSnOtp84EQqEToZND7jdzAB/qUGwwfaDLl/g/4JTL7NxljHq2I/FREfpLoNZK//Y3L5xLwyZD0gF+476ZTqbnzKu676VSKc70XEOYH/dx89oy4/lLh5rNnUHPnVZHfTucFJcAPr19AySgsiDzv+FKtfEvG1OJcrTy4+ewZtp0/7ufuM1KSF+C+m07V8uvYOrP2kjGnIp94Z15i/iZi6HmPfyxunOv0vptOZapd2Ur16ivJC3Dz2TPSdp3lB/2RmLzuheJc67eTWYnLNk8zRl1b59inFuciJC+8neNyzpHONaFzLlK5HlMlNuaaO69i23c/nPT+d06ZX5JH7z7Pqd7nJXmBqAkSyRjzWXwicov99jxgPvB7+/+PA2uUUl/z+P5vgSVAGXAE+LZS6qFE9vFm8ekuYIu3fsJrnYruOptUftfL1mt9RSrrYJKhexzx1kNA4nVTTrzOmhjd3zqWBYS6eauz/kPn93V/L3YdWuwaLp2FlsdynSWLP9WF7MnWELlncemsGxrJ40rmO9XyIN53huPTa23bsSzAjXf/J1p/lar/Y10QnPHTzEXkDWCpUqrf/j8A/FkpdVE6fyfRQt33GzqFzVjvgDCSixVHgkzIs/FIOncxMKSHTLuWx4NAVQPnKKWa7f9LgHeUUt4dlSnwQRGo8USm3SwGg2F0SSRQmbQX353AOrslBXAh8O9jF45htLjutKlGkAwGwxAyRqCUUo+IyMvAWXbS7Uqpw2MZk8FgMBjGjkyYxXei/XcRMAU4YL+m2GkGg8Fg+ACSCS2ofwI+D9wT5zMFXBwn3WAwGAzvc8ZcoJRSn7f/pnW2nsFgMBjGN2PexecgIh8XkUL7/R0i8kcROW2s4zIYDAbD2JAxAgV8UynVLiLnA5cDjwEPjHFMBoPBYBgjMkmgnGXNVwH3K6WeA4JjGI/BYDAYxpBMEqg6EfklcCPwkohkk1nxGQwGg2EUySQBuBF4BbhCKdUKlALfGNuQDAaDwTBWZIxAKaW6gHrgfDspBOwcu4gMBoPBMJZkjECJyLeBfwX+zU4KAE+MXUQGg8FgGEsyRqCAjwLXAJ0ASqmDQOGYRmQwGAyGMSOTBKpPWVurKwARyR/jeAwGg8EwhmSSQP3BnsVXLCJ/C/wF+NUYx2QwGAyGMWLMtzpyUEr9p4hcBrQBc4FvKaVeHeOwDAaDwTBGZIxAAdiC9CqAiPhF5JNKqd+McVgGg8FgGAPGvItPRIpE5N9E5GcislQs/h7Yg7U2ymAwGAwfQDKhBfVroAVYCdyGtTg3CFyrlFo/loEZDAaDYezIBIGapZQ6BUBEfgU0AjOUUu1jG5bBYDAYxpIx7+ID+p03SqkBYG8q4iQiV4hItYjsEpHbRyRCg8FgMIw6mdCCWigibfZ7AXLt/wVQSqmiRF8UET/wc+AyoBZYJSLPK6W2jnTQBoPBYBhZxlyglFL+YXz9TGCXUmoPgIj8DrgWSChQ1dXVLFmyZBg/aTAYDIbRIBO6+IbDVOCA6/9aOy0KEfm8iKwWkdX9/f2xHxsMBoMhAxnzFtQwkThpakiCUg8CDwIsXrxYLVu2bITDMhgMBoMuIvGK8vEvULXAdNf/04CDyb6wZs2aRhHZN0LxlGHNQhyvmPjHFhP/2DLe44fxewzHxUsUa3/W8YmIZAE7gEuAOmAV8DdKqS1jFM9qpdTisfjtdGDiH1tM/GPLeI8f3h/H4GZct6CUUiF714lXAD/w8FiJk8FgMBjSy7gWKACl1EvAS2Mdh8FgMBjSy3ifxZdpPDjWAQwTE//YYuIfW8Z7/PD+OIYI43oMymAwGAzvX0wLymAwGAwZiREog8FgMGQkRqCSICIPi0i9iGx2pS0UkZUisklEXhCRIjs9KCKP2OkbRGSJnZ4nIv8rIttFZIuI3Dme4nd99qCI7LCP44ZRin+6iLwhItvsvPuKnV4qIq+KyE77b4mdLiLyE3vj4I0issjl6xbbfqeI3DLe4rc/LxKROhH52WjEn+5jEJG7bB/bbJv4qzPHLvYT7XujV0S+7uVnNEjXMdifFYvIU/Y9vE1Ezhmt4zhmlFLmleAFXAAsAja70lYBF9rvbwW+a7//MvCI/b4CWINVAcgDLrLTg8DbwIfHS/z2/98Bvme/9wFloxT/ZGCR/b4Qa83bfOAu4HY7/XbgR/b7K4GXsXYYORt4104vxXoAZilQYr8vGS/xu/z9GMYxo6cAAAQOSURBVPgf4GejeA+k6xycCyzHWg7ix3r+25IMi70COAP4PvB1Lz8Zmv9xj8H+7DHgNvt9ECgerevoWF+mBZUEpdRbQHNM8lzgLfv9q4DTmpgPvGZ/rx5oBRYrpbqUUm/Y6X3AWqwdL0acdMRvf3Yr8EP7s7BSalRWqiulDiml1trv24FtWHstXot1s2H/vc5+fy3wuLJ4BygWkcnA5cCrSqlmpVQL1nFfMY7iR0ROByqBP4903G7SeAwKyMEqGLOBAHAkk2JXStUrpVbhegSQh58RJ13HYPeUXAA8ZNv1KaVaR+MYhoMRqNTZDFxjv/84g1stbQCuFZEsEZkJnE70NkyISDFwNbYQjBEpxW/HDPBdEVkrIk+KSOXohgwiUgWcBrwLVCqlDoF1A2PVGiHx5sFamwqPJMOJX0R8wD1YT5seM4ZzDEqplcAbwCH79YpSatvoRK4de6p+RpVhHsMsoAF4RETWicivRCR/BMNNC0agUudW4Msisgaryd1npz+MdTOuBu4DVgAh50tibcv0W+Anyn48yBiRavxZWC2+5UqpRVhdM/85mgGLSAHwNPBVpVRbMtM4aSpJ+qiQhvi/BLyklDoQ5/NRYbjHICKzgXlY19JU4GIRuSD9kcYJSD/2UfEzRr+dhdXdf79S6jSgE6trMKMZ9ztJjDZKqe3AUgAROQG4yk4PAV9z7ERkBbDT9dUHgZ1KqftGL9qhHEP8TUAX8Iz90ZPA50YrXhEJYN2Yv1FK/dFOPiIik5VSh+zuo3o7PdHmwbXAkpj0ZSMZt0Oa4j8H+JCIfAkoAIIi0qGUGpUCJk3HcDPwjlKqw/b5MtYY1VuMICnGnqqfUSFNx1AL1CqlnJbfU4wDgTItqBQRkQr7rw+4A3jA/j/PaTKLyGVASNlP9hWR7wETgK+OSdAuUo1fKaWAFxgs4C8hyQMh0xyrYPWZb1NK3ev66HnAmYl3C/CcK/3T9kyys4GjdvfHK8BSESmxZzsttdPGRfxKqU8qpWYopaqAr2ON8YyWOKXrHOwHLrS7kAPAhVjjKZkUe6p+Rpx0HYNS6jBwQETm2kmjdh8Pi3TOuHi/vbC65A5hDTjWYrUcvoI1k2YHcCeDu3FUAdVYN91fgOPs9GlY3TTbgPX267bxEr/92XFYNd2NWONnM0Yp/vPtvNvoyrsrgYl2HDvtv6W2vQA/B3YDm7AmqTi+bgV22a/Pjrf4XT4/w+jO4kvLMWDN3PulfX1tBe7NwNgn2fdJG9YkoVqgKJGfDM3/uMdgf3YqVhf+RuBZRmEm63BfZqsjg8FgMGQkpovPYDAYDBmJESiDwWAwZCRGoAwGg8GQkRiBMhgMBkNGYgTKYDAYDBmJESiDwWAwZCRGoAwGg8GQkfx/+RVomLsZBUQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposed = seasonal_decompose(yen_futures_monthly, model='multiplicative')\n",
    "decomposed.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on your time series analysis, would you buy the yen now?\n",
    "* According to the graph above on Garch(2,1) forecast, the returns on yen futures is expected to increase. That means we should buy Yen futures now and lock up the price to purchase Yen on a certain date in the future. It is supported by the forecast from ARIMA(5,1,1) model that the raw settlement price of Yen is expected to rise in the next five days, meaning one dollar can purchase more Yen in the next week. \n",
    "\n",
    "Is the risk of the yen expected to increase or decrease?\n",
    "* Based on Garch(2,1), the risk of Yen is expected to rise in the next five days while staying relatively quiet for a period of time in the long run. The volatility will keep quiet as the sum of beta1 and alpha1 is 0.99 < 1. If it were one, we have a random walk. It is consistent with the plots on residuals and conditional volatilities on Yen futures returns above.\n",
    "\n",
    "Based on the model evaluation, would you feel confident in using these models for trading?\n",
    "* I would trade based on Garch(2,1) model because of its statistically significant omega, beta1 and apha1 coefficients. Besides, it has the lowest AIC and BIC scores out of the three models. \n",
    "    * In comparison, I am not comfortable trading based on ARCH(2,1) and ARIMA(5,1,1) models as none of the parameters of the models are statistically significant for the training data. \n",
    "    \n",
    "* Based on seasonal decomposition, it is observed that Yen settlement price spike during the second and third quarters of a year. Therefore, seasonality will be taken into consideration for trading as well. \n",
    "    * Since the last record date was November 15, 2019, it may not be as profitable choosing to hold the Yen futures contracts for too long even though the Garch(2,1) forcasts an increase in returns. \n"
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