final_capstone_Fred_Etter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Introduction - Sell in May and Go Away ?  \n",
    "\n",
    "Fred Etter - July, 2019\n",
    "\n",
    "This notebook captures 10 years of daily historical data from ST Louis Federal Reserve's database for the following 5 securities:  \n",
    "\n",
    "- S&P 500  \n",
    "- 10 Year Treaury Bond  \n",
    "- Gold  \n",
    "- Oil  \n",
    "- Bitcoin  \n",
    "\n",
    "The goal is to answer several questions that could help various firms in the financial industry capitalize on patterns in market data.  Another goal is to explain relationships among various securities and provide a better understanding of day to day movements in price.  Accurately predicting daily price change can be profitable for short term trading while predicting medium or long term price change can be very beneficial for wide ranging planning and stretegic purposes for many, many different businesses and industries.  \n",
    "\n",
    "This notebook will follow the outline:  \n",
    "\n",
    "1.  Weather Data  \n",
    "\n",
    "2.  Financial Data  \n",
    "    A.  Question:  Do different days of the week yield significant price change ?  \n",
    "    B.  Question:  Do 'winter' months perform better than 'summer' months ?  \n",
    "    \n",
    "    There has been much debate about wheather the winter months perform better than the summer.  Hence, the title of this notebook.  Many believe that the winter offers better returns overall for equities and one should consider buying equities in early November and sell in early May.  This notebook will answer the question.  \n",
    "\n",
    "    C.  Question:  Does the 3rd Presidential Year yield better results in the 'winter' months vs. 'summer' ?  \n",
    "\n",
    "    Similarly, there is at least one extensive study that makes the point that the winter of the 3rd Presidential Year performs better than any other season.  This notebook axamines this claim.  The link to the study is provided in this section.  \n",
    "    \n",
    "    D.  Question:  Does any month of the S&P 500 over the past 10 years perform significantly better than any other ?  \n",
    "    E.  Question:  Is it possible to build a supervised learning model to predict the daily price change of gold just by using day of the week as the only feature ?  \n",
    "    \n",
    "3.  Weather and Financial Data Combined  \n",
    "    A.  Question:  Does the daily temperature or amount of sunshine in NYC affect the price change of the S&P 500 ?  \n",
    "    B.  Question:  Is it possible to build a supervised learning model that uses the day of the week, month, day of month and weather data to predict daily price change of the S&P 500 ?  \n",
    "    C.  Question:  Is it possible to build an unsupervised learning model to identify relationships among the 5 securites previously analyzed in this study ?  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fist step -- Import necessary modules and libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "from datetime import datetime\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ttest_ind\n",
    "from scipy import stats\n",
    "import sklearn\n",
    "from sklearn import preprocessing\n",
    "from sklearn import linear_model\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.  Weather Data  \n",
    "\n",
    "Import weather data for New York City from meteoblue.com.  The weather data incorporated in this study includes the daily minutes of sunshine and the daily temperature in degrees Farenheit.  To measure if sunshine or temperature has an affect on daily price change of the S&P 500, different thresholds were used to measure the difference.  For sunshine, price change was analyzed for above and below 300, 500, 700 minutes per day.  For temperature, price change was analyzed above and below 35 degrees, and above 75 vs below 35 degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Sunshine Duration daily sum [sfc]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-726.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>418.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>743.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>436.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>848.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day  Hour  Minute  Sunshine Duration daily sum [sfc]\n",
       "0  2009      6   25     0       0                            -726.93\n",
       "1  2009      6   26     0       0                             418.70\n",
       "2  2009      6   27     0       0                             743.23\n",
       "3  2009      6   28     0       0                             436.08\n",
       "4  2009      6   29     0       0                             848.28"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use pandas read_csv command to import the local csv file\n",
    "dfw = pd.read_csv('nyc_daily.csv', skiprows=11, sep=';')\n",
    "dft = pd.read_csv('temp_and_snow.csv', skiprows=11, sep=';')\n",
    "dfw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Temperature daily mean [2 m above gnd]</th>\n",
       "      <th>Snowfall Amount (high resolution) daily sum [sfc]</th>\n",
       "      <th>Temperature daily max [2 m above gnd]</th>\n",
       "      <th>Temperature daily min [2 m above gnd]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.24</td>\n",
       "      <td>64.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.19</td>\n",
       "      <td>66.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.91</td>\n",
       "      <td>65.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.78</td>\n",
       "      <td>66.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.19</td>\n",
       "      <td>65.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day  Hour  Minute  Temperature daily mean [2 m above gnd]  \\\n",
       "0  2009      6   25     0       0                                   71.72   \n",
       "1  2009      6   26     0       0                                   76.91   \n",
       "2  2009      6   27     0       0                                   76.34   \n",
       "3  2009      6   28     0       0                                   72.35   \n",
       "4  2009      6   29     0       0                                   75.43   \n",
       "\n",
       "   Snowfall Amount (high resolution) daily sum [sfc]  \\\n",
       "0                                                0.0   \n",
       "1                                                0.0   \n",
       "2                                                0.0   \n",
       "3                                                0.0   \n",
       "4                                                0.0   \n",
       "\n",
       "   Temperature daily max [2 m above gnd]  \\\n",
       "0                                  81.24   \n",
       "1                                  88.19   \n",
       "2                                  83.91   \n",
       "3                                  78.78   \n",
       "4                                  84.19   \n",
       "\n",
       "   Temperature daily min [2 m above gnd]  \n",
       "0                                  64.02  \n",
       "1                                  66.18  \n",
       "2                                  65.85  \n",
       "3                                  66.54  \n",
       "4                                  65.20  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop some of the data not needed for now; rename a column and drop the first (and strange) negative number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Year', 'Month', 'Day', 'Sun'], dtype='object')\n",
      "Index(['Year', 'Month', 'Day', 'avg_temp'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# drop the Hour and Minute columns\n",
    "dfw.drop(['Hour', 'Minute'], axis=1, inplace=True)\n",
    "dft.drop(['Hour', 'Minute'], axis=1, inplace=True)\n",
    "\n",
    "# change long named column to just 'Sun'\n",
    "dfw.columns = ['Year', 'Month', 'Day', 'Sun']\n",
    "dft.columns = ['Year', 'Month', 'Day', 'avg_temp', 'snow', 'max', 'min']\n",
    "\n",
    "# Convert negative numbers to zero\n",
    "dfw[dfw < 0] = 0\n",
    "dft[dft < 0] = 0\n",
    "\n",
    "# drop 'snow', 'max', 'min' for now\n",
    "dft.drop(['snow', 'max', 'min'], axis=1, inplace=True)\n",
    "\n",
    "print(dfw.columns)\n",
    "print(dft.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, look at the first five lines of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>418.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>743.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>436.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>848.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day     Sun\n",
       "0  2009      6   25    0.00\n",
       "1  2009      6   26  418.70\n",
       "2  2009      6   27  743.23\n",
       "3  2009      6   28  436.08\n",
       "4  2009      6   29  848.28"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the data to plot the number of minutes of sun per day over the entire time horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw_plot = dfw\n",
    "dfw_plot['dt'] = pd.to_datetime((dfw.Year*10000+dfw.Month*100+dfw.Day).apply(str),format='%Y%m%d')\n",
    "dfw_plot.columns = ['Year', 'Month', 'Day', 'Sun', 'dt']\n",
    "dfw_plot.index = pd.to_datetime(dfw_plot.dt)\n",
    "dfw_plot.drop(['Year'], axis=1, inplace=True)\n",
    "dfw_plot.drop(['Month'], axis=1, inplace=True)\n",
    "dfw_plot.drop(['Day'], axis=1, inplace=True)\n",
    "dfw_plot.drop(['dt'], axis=1, inplace=True)\n",
    "\n",
    "dft['dt'] = pd.to_datetime((dft.Year*10000+dft.Month*100+dft.Day).apply(str),format='%Y%m%d')\n",
    "dft.columns = ['Year', 'Month', 'Day', 'avg_temp', 'dt']\n",
    "dft.index = pd.to_datetime(dft.dt)\n",
    "dft.drop(['Year'], axis=1, inplace=True)\n",
    "dft.drop(['Month'], axis=1, inplace=True)\n",
    "dft.drop(['Day'], axis=1, inplace=True)\n",
    "dft.drop(['dt'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index has been changed to a date-time object.  Next, the plot shows the amount of sunshine in minutes for New York City."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfw_plot.plot(figsize=(14, 6))\n",
    "plt.title('Minutes of Sunshine Per Day in New York City')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Minutes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The seasonality is obvious from the plot above.  Many more minutes of sunshine in the summer months than in the winter.  Does this have any affect on trading activity or price change in the equity markets ?  New York City was chosen because it is a financial hub, wall street is there, the largest banks and hedge funds are there, and it's a generally a massively influencial city in the world of Finance.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.  Financial Data  \n",
    "\n",
    "First, read in all of the data from FRED in csv format.  Then convert the price column to a number, and remove NaN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in all of the local csv files into pandas:\n",
    "df = pd.read_csv('SP500.csv')\n",
    "df_10 = pd.read_csv('DGS10.csv')\n",
    "df_10_10 = pd.read_csv('DGS10_10.csv')\n",
    "df_gold = pd.read_csv('gold.csv')\n",
    "\n",
    "# copy the gold dataframe for later use:\n",
    "df_goldc = df_gold.copy()\n",
    "\n",
    "df_oil = pd.read_csv('oil.csv')\n",
    "df_btc = pd.read_csv('btc.csv')\n",
    "\n",
    "\n",
    "# Convert price to a number, with coersion to throw NA's if conversion not possible\n",
    "df['price'] = pd.to_numeric(df.SP500, errors='coerce')\n",
    "df_10['price'] = pd.to_numeric(df_10.DGS10, errors='coerce')\n",
    "df_10_10['price'] = pd.to_numeric(df_10_10.DGS10, errors='coerce')\n",
    "df_gold['price'] = pd.to_numeric(df_gold.GOLDAMGBD229NLBM, errors='coerce')\n",
    "df_oil['price'] = pd.to_numeric(df_oil.DCOILWTICO, errors='coerce')\n",
    "df_btc['price'] = pd.to_numeric(df_btc.CBBTCUSD, errors='coerce')\n",
    "\n",
    "\n",
    "# Remove NA's\n",
    "df.dropna(inplace=True)\n",
    "df_10.dropna(inplace=True)\n",
    "df_10_10.dropna(inplace=True)\n",
    "df_gold.dropna(inplace=True)\n",
    "df_oil.dropna(inplace=True)\n",
    "df_btc.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell looks at the first 5 lines of the S&P 500 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>918.90</td>\n",
       "      <td>918.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>927.23</td>\n",
       "      <td>927.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>919.32</td>\n",
       "      <td>919.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>923.33</td>\n",
       "      <td>923.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>896.42</td>\n",
       "      <td>896.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE   SP500   price\n",
       "0  2009-06-26  918.90  918.90\n",
       "1  2009-06-29  927.23  927.23\n",
       "2  2009-06-30  919.32  919.32\n",
       "3  2009-07-01  923.33  923.33\n",
       "4  2009-07-02  896.42  896.42"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next plot, shows the graph of the S&P 500 data as an example.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df.DATE)\n",
    "df.plot(figsize=(12, 6))\n",
    "plt.title('S&P 500 Time Series')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen in the plot above, in the middle of 2009, the price was about 900 and then rises to the value of about 3000 by the end of June, 2019.  The next plot shows the 10 Yr Bond prices from the mid-1960's to June, 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_10.index = pd.to_datetime(df_10.DATE)\n",
    "df_10.plot(figsize=(12, 6))\n",
    "plt.title('10 Yr Bond Time Series')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step separates the weekday, day of month, month and year from the date object for all 5 securities studied in this notebook:  \n",
    "- S&P 500  \n",
    "- 10 Yr Bond \n",
    "- Gold  \n",
    "- Oil  \n",
    "- Bitcoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For S&P 500\n",
    "df['date_time'] = pd.to_datetime(df['DATE'])\n",
    "\n",
    "df['weekday'] = df.date_time.dt.weekday\n",
    "df['day'] = df.date_time.dt.day\n",
    "df['month'] = df.date_time.dt.month\n",
    "df['year'] = df.date_time.dt.year\n",
    "\n",
    "# For 10 Yr Bond\n",
    "df_10['date_time'] = pd.to_datetime(df_10['DATE'])\n",
    "\n",
    "df_10['weekday'] = df_10.date_time.dt.weekday\n",
    "df_10['day'] = df_10.date_time.dt.day\n",
    "df_10['month'] = df_10.date_time.dt.month\n",
    "df_10['year'] = df_10.date_time.dt.year\n",
    "\n",
    "# For 10 Yr Bond (last 10 years only)\n",
    "df_10_10['date_time'] = pd.to_datetime(df_10_10['DATE'])\n",
    "\n",
    "df_10_10['weekday'] = df_10_10.date_time.dt.weekday\n",
    "df_10_10['day'] = df_10_10.date_time.dt.day\n",
    "df_10_10['month'] = df_10_10.date_time.dt.month\n",
    "df_10_10['year'] = df_10_10.date_time.dt.year\n",
    "\n",
    "# For gold (last 10 years only)\n",
    "df_gold['date_time'] = pd.to_datetime(df_gold['DATE'])\n",
    "\n",
    "df_gold['weekday'] = df_gold.date_time.dt.weekday\n",
    "df_gold['day'] = df_gold.date_time.dt.day\n",
    "df_gold['month'] = df_gold.date_time.dt.month\n",
    "df_gold['year'] = df_gold.date_time.dt.year\n",
    "\n",
    "# For oil (last 10 years only)\n",
    "df_oil['date_time'] = pd.to_datetime(df_oil['DATE'])\n",
    "\n",
    "df_oil['weekday'] = df_oil.date_time.dt.weekday\n",
    "df_oil['day'] = df_oil.date_time.dt.day\n",
    "df_oil['month'] = df_oil.date_time.dt.month\n",
    "df_oil['year'] = df_oil.date_time.dt.year\n",
    "\n",
    "# For bitcoin\n",
    "df_btc['date_time'] = pd.to_datetime(df_btc['DATE'])\n",
    "\n",
    "df_btc['weekday'] = df_btc.date_time.dt.weekday\n",
    "df_btc['day'] = df_btc.date_time.dt.day\n",
    "df_btc['month'] = df_btc.date_time.dt.month\n",
    "df_btc['year'] = df_btc.date_time.dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 = Monday  \n",
    "1 = Tuesday  \n",
    "2 = Wednesday  \n",
    "3 = Thursday  \n",
    "4 = Friday  \n",
    "\n",
    "Prices are the closing price for the day listed.  The next cell shows the last few lines of the S&P 500 dataframe."
   ]
  },
  {
   "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>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>price</th>\n",
       "      <th>date_time</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-19</th>\n",
       "      <td>2019-06-19</td>\n",
       "      <td>2926.46</td>\n",
       "      <td>2926.46</td>\n",
       "      <td>2019-06-19</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>2954.18</td>\n",
       "      <td>2954.18</td>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21</th>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>2950.46</td>\n",
       "      <td>2950.46</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-24</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>2945.35</td>\n",
       "      <td>2945.35</td>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-25</th>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>2917.38</td>\n",
       "      <td>2917.38</td>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE    SP500    price  date_time  weekday  day  month  year\n",
       "DATE                                                                          \n",
       "2019-06-19  2019-06-19  2926.46  2926.46 2019-06-19        2   19      6  2019\n",
       "2019-06-20  2019-06-20  2954.18  2954.18 2019-06-20        3   20      6  2019\n",
       "2019-06-21  2019-06-21  2950.46  2950.46 2019-06-21        4   21      6  2019\n",
       "2019-06-24  2019-06-24  2945.35  2945.35 2019-06-24        0   24      6  2019\n",
       "2019-06-25  2019-06-25  2917.38  2917.38 2019-06-25        1   25      6  2019"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This next step creates a new column in the dataframe that represents the change in price per day - for all 5 securities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a column for the change in price each day\n",
    "df['change'] = df.price - df.price.shift()\n",
    "df_10['change'] = df_10.price - df_10.price.shift()\n",
    "df_10_10['change'] = df_10_10.price - df_10_10.price.shift()\n",
    "df_gold['change'] = df_gold.price - df_gold.price.shift()\n",
    "df_goldc['change_g'] = df_gold.price - df_gold.price.shift()\n",
    "df_oil['change'] = df_oil.price - df_oil.price.shift()\n",
    "df_btc['change'] = df_btc.price - df_btc.price.shift()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the new dataframe for S&P 500 with the 'change' column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>price</th>\n",
       "      <th>date_time</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-06-26</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>918.90</td>\n",
       "      <td>918.90</td>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-29</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>927.23</td>\n",
       "      <td>927.23</td>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>919.32</td>\n",
       "      <td>919.32</td>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>-7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>923.33</td>\n",
       "      <td>923.33</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-02</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>896.42</td>\n",
       "      <td>896.42</td>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>-26.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE   SP500   price  date_time  weekday  day  month  year  \\\n",
       "DATE                                                                           \n",
       "2009-06-26  2009-06-26  918.90  918.90 2009-06-26        4   26      6  2009   \n",
       "2009-06-29  2009-06-29  927.23  927.23 2009-06-29        0   29      6  2009   \n",
       "2009-06-30  2009-06-30  919.32  919.32 2009-06-30        1   30      6  2009   \n",
       "2009-07-01  2009-07-01  923.33  923.33 2009-07-01        2    1      7  2009   \n",
       "2009-07-02  2009-07-02  896.42  896.42 2009-07-02        3    2      7  2009   \n",
       "\n",
       "            change  \n",
       "DATE                \n",
       "2009-06-26     NaN  \n",
       "2009-06-29    8.33  \n",
       "2009-06-30   -7.91  \n",
       "2009-07-01    4.01  \n",
       "2009-07-02  -26.91  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the overall mean of the price change per day over the 10 year period for the S&P 500.  This gives a benchmark for camparing other metrics later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7946242544731611\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DATE         0\n",
       "SP500        0\n",
       "price        0\n",
       "date_time    0\n",
       "weekday      0\n",
       "day          0\n",
       "month        0\n",
       "year         0\n",
       "change       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print mean of 'change' column\n",
    "print(df.change.mean())\n",
    "\n",
    "# print missing values:\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. A.  Question:  Do different days of the week yield significant price change ?  \n",
    "\n",
    "**Answer:**  Yes, for the 10 Yr Bond (last 50 years and last 10 years), Gold and Bitcoin.  No, for S&P 500 and Oil.  The security with the most significant difference over just the past 10 years was **gold**.  The data and steps taken to determine follow:  first, sum the daily price changes for each security.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101.3599999999933\n",
      "641.9000000000053\n",
      "601.8699999999991\n",
      "320.37999999999533\n",
      "332.9700000000071\n"
     ]
    }
   ],
   "source": [
    "# For S&P 500\n",
    "print(df.loc[df['weekday'] == 0, 'change'].sum())\n",
    "print(df.loc[df['weekday'] == 1, 'change'].sum())\n",
    "print(df.loc[df['weekday'] == 2, 'change'].sum())\n",
    "print(df.loc[df['weekday'] == 3, 'change'].sum())\n",
    "print(df.loc[df['weekday'] == 4, 'change'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, this means that on Mondays, the total change in the value of the S&P 500 index was about +100.  The total change on all Tuesdays was about +640."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.210000000000022\n",
      "-6.320000000000043\n",
      "1.0500000000000325\n",
      "-2.120000000000004\n",
      "-7.880000000000006\n"
     ]
    }
   ],
   "source": [
    "# For 10 Yr Bond\n",
    "print(df_10.loc[df_10['weekday'] == 0, 'change'].sum())\n",
    "print(df_10.loc[df_10['weekday'] == 1, 'change'].sum())\n",
    "print(df_10.loc[df_10['weekday'] == 2, 'change'].sum())\n",
    "print(df_10.loc[df_10['weekday'] == 3, 'change'].sum())\n",
    "print(df_10.loc[df_10['weekday'] == 4, 'change'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.2999999999999974\n",
      "-0.09999999999999942\n",
      "1.7399999999999969\n",
      "-0.8699999999999986\n",
      "-1.9900000000000015\n"
     ]
    }
   ],
   "source": [
    "# For 10 Yr Bond (just last 10 years)\n",
    "print(df_10_10.loc[df_10_10['weekday'] == 0, 'change'].sum())\n",
    "print(df_10_10.loc[df_10_10['weekday'] == 1, 'change'].sum())\n",
    "print(df_10_10.loc[df_10_10['weekday'] == 2, 'change'].sum())\n",
    "print(df_10_10.loc[df_10_10['weekday'] == 3, 'change'].sum())\n",
    "print(df_10_10.loc[df_10_10['weekday'] == 4, 'change'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "538.5429999999996\n",
      "415.8369999999994\n",
      "-40.4830000000004\n",
      "-459.0160000000003\n",
      "93.1730000000016\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# For gold (just last 10 years)\n",
    "print(df_gold.loc[df_gold['weekday'] == 0, 'change'].sum())\n",
    "print(df_gold.loc[df_gold['weekday'] == 1, 'change'].sum())\n",
    "print(df_gold.loc[df_gold['weekday'] == 2, 'change'].sum())\n",
    "print(df_gold.loc[df_gold['weekday'] == 3, 'change'].sum())\n",
    "print(df_gold.loc[df_gold['weekday'] == 4, 'change'].sum())\n",
    "\n",
    "# count the number of days where there was no change.\n",
    "print(df_gold.loc[df_gold['change'] == 0.0, 'change'].count())\n",
    "print(df_gold.loc[df_gold['change'] == 0.000, 'change'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-21.540000000000035\n",
      "31.520000000000003\n",
      "23.049999999999805\n",
      "-30.409999999999826\n",
      "-13.029999999999951\n"
     ]
    }
   ],
   "source": [
    "# For oil (just last 10 years)\n",
    "print(df_oil.loc[df_oil['weekday'] == 0, 'change'].sum())\n",
    "print(df_oil.loc[df_oil['weekday'] == 1, 'change'].sum())\n",
    "print(df_oil.loc[df_oil['weekday'] == 2, 'change'].sum())\n",
    "print(df_oil.loc[df_oil['weekday'] == 3, 'change'].sum())\n",
    "print(df_oil.loc[df_oil['weekday'] == 4, 'change'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1421.7199999999955\n",
      "978.4300000000011\n",
      "-338.36000000000377\n",
      "-1088.6800000000028\n",
      "5006.700000000002\n",
      "6821.430000000004\n",
      "-853.5099999999965\n"
     ]
    }
   ],
   "source": [
    "# For bitcoin\n",
    "print(df_btc.loc[df_btc['weekday'] == 0, 'change'].sum())\n",
    "print(df_btc.loc[df_btc['weekday'] == 1, 'change'].sum())\n",
    "print(df_btc.loc[df_btc['weekday'] == 2, 'change'].sum())\n",
    "print(df_btc.loc[df_btc['weekday'] == 3, 'change'].sum())\n",
    "print(df_btc.loc[df_btc['weekday'] == 4, 'change'].sum())\n",
    "print(df_btc.loc[df_btc['weekday'] == 5, 'change'].sum())\n",
    "print(df_btc.loc[df_btc['weekday'] == 6, 'change'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to use a t-test to calculate the p-value of the two days of the week that had the biggest price difference.  In order to do this, create the dataframe for each security with new columns for each trading day of the week.  That way, the entire distribution of the data is captured when running the t-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For S&P 500\n",
    "df['Monday'] = df.change.where(df.weekday == 0)\n",
    "df['Tuesday'] = df.change.where(df.weekday == 1)\n",
    "df['Wednesday'] = df.change.where(df.weekday == 2)\n",
    "df['Thursday'] = df.change.where(df.weekday == 3)\n",
    "df['Friday'] = df.change.where(df.weekday == 4)\n",
    "\n",
    "# For 10 Yr Bond\n",
    "df_10['Monday'] = df_10.change.where(df_10.weekday == 0)\n",
    "df_10['Tuesday'] = df_10.change.where(df_10.weekday == 1)\n",
    "df_10['Wednesday'] = df_10.change.where(df_10.weekday == 2)\n",
    "df_10['Thursday'] = df_10.change.where(df_10.weekday == 3)\n",
    "df_10['Friday'] = df_10.change.where(df_10.weekday == 4)\n",
    "\n",
    "# For 10 Yr Bond (last 10 years)\n",
    "df_10_10['Monday'] = df_10_10.change.where(df_10_10.weekday == 0)\n",
    "df_10_10['Tuesday'] = df_10_10.change.where(df_10_10.weekday == 1)\n",
    "df_10_10['Wednesday'] = df_10_10.change.where(df_10_10.weekday == 2)\n",
    "df_10_10['Thursday'] = df_10_10.change.where(df_10_10.weekday == 3)\n",
    "df_10_10['Friday'] = df_10_10.change.where(df_10_10.weekday == 4)\n",
    "\n",
    "# For gold (last 10 years)\n",
    "df_gold['Monday'] = df_gold.change.where(df_gold.weekday == 0)\n",
    "df_gold['Tuesday'] = df_gold.change.where(df_gold.weekday == 1)\n",
    "df_gold['Wednesday'] = df_gold.change.where(df_gold.weekday == 2)\n",
    "df_gold['Thursday'] = df_gold.change.where(df_gold.weekday == 3)\n",
    "df_gold['Friday'] = df_gold.change.where(df_gold.weekday == 4)  \n",
    "\n",
    "# For oil (last 10 years)\n",
    "df_oil['Monday'] = df_oil.change.where(df_oil.weekday == 0)\n",
    "df_oil['Tuesday'] = df_oil.change.where(df_oil.weekday == 1)\n",
    "df_oil['Wednesday'] = df_oil.change.where(df_oil.weekday == 2)\n",
    "df_oil['Thursday'] = df_oil.change.where(df_oil.weekday == 3)\n",
    "df_oil['Friday'] = df_oil.change.where(df_oil.weekday == 4)\n",
    "\n",
    "# For bitcoin\n",
    "df_btc['Monday'] = df_btc.change.where(df_oil.weekday == 0)\n",
    "df_btc['Tuesday'] = df_btc.change.where(df_oil.weekday == 1)\n",
    "df_btc['Wednesday'] = df_btc.change.where(df_oil.weekday == 2)\n",
    "df_btc['Thursday'] = df_btc.change.where(df_oil.weekday == 3)\n",
    "df_btc['Friday'] = df_btc.change.where(df_oil.weekday == 4)\n",
    "df_btc['Saturday'] = df_btc.change.where(df_oil.weekday == 5)\n",
    "df_btc['Sunday'] = df_btc.change.where(df_oil.weekday == 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, t-test and p-values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For S&P 500:\n",
      "Ttest_indResult(statistic=-0.9729604233090785, pvalue=0.3308115971409148)\n",
      "Ttest_indResult(statistic=-0.9729604233090785, pvalue=0.3308115971409148)\n",
      "\n",
      "For 10 Yr Bond:\n",
      "Ttest_indResult(statistic=4.070425782579718, pvalue=4.757789969930339e-05)\n",
      "\n",
      "For 10 Yr Bond just last 10 years:\n",
      "Ttest_indResult(statistic=2.2201929261497884, pvalue=0.026625706685281653)\n",
      "\n",
      "For gold:\n",
      "Ttest_indResult(statistic=3.3440189329939844, pvalue=0.0008567701430384474)\n",
      "\n",
      "For oil:\n",
      "Ttest_indResult(statistic=1.371972242519745, pvalue=0.17037285733436644)\n",
      "\n",
      "For bitcoin:\n",
      "Ttest_indResult(statistic=2.3029987853710954, pvalue=0.02134134968122411)\n"
     ]
    }
   ],
   "source": [
    "# For S&P 500\n",
    "print('For S&P 500:')\n",
    "print(ttest_ind(df['Monday'].dropna(), df['Tuesday'].dropna()))\n",
    "print(ttest_ind(df.change.where(df.weekday == 0).dropna(), df.change.where(df.weekday == 1).dropna()))\n",
    "\n",
    "# For 10 Yr Bond\n",
    "print('\\nFor 10 Yr Bond:')\n",
    "print(ttest_ind(df_10['Monday'].dropna(), df_10['Friday'].dropna()))\n",
    "\n",
    "print('\\nFor 10 Yr Bond just last 10 years:')\n",
    "# For 10 Yr Bond (last 10 years only)\n",
    "print(ttest_ind(df_10_10['Wednesday'].dropna(), df_10_10['Friday'].dropna()))\n",
    "\n",
    "print('\\nFor gold:')\n",
    "print(ttest_ind(df_gold['Monday'].dropna(), df_gold['Thursday'].dropna()))\n",
    "\n",
    "print('\\nFor oil:')\n",
    "print(ttest_ind(df_oil['Tuesday'].dropna(), df_oil['Thursday'].dropna()))\n",
    "\n",
    "print('\\nFor bitcoin:')\n",
    "# For bitcoin\n",
    "df_btc = df_btc.fillna(0)\n",
    "print(ttest_ind(df_btc['Thursday'].dropna(), df_btc['Saturday'].dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, calculate the F statistics for gold to further prove the significant difference in days of the week return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8675627733544693\n",
      "0.0038892555965370883\n"
     ]
    }
   ],
   "source": [
    "# Calc F statistic for gold (last 10 years only)\n",
    "F, p = stats.f_oneway(\n",
    "    df_gold['Monday'].dropna(),\n",
    "    df_gold['Tuesday'].dropna(),\n",
    "    df_gold['Wednesday'].dropna(),\n",
    "    df_gold['Thursday'].dropna(),\n",
    "    df_gold['Friday'].dropna(),\n",
    ")\n",
    "\n",
    "# The F statistic.\n",
    "print(F)\n",
    "\n",
    "# The probability. A p < .05 would lead us to believe the group means were\n",
    "# not all similar in the population.\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.546179669499771\n",
      "0.15869572325805537\n"
     ]
    }
   ],
   "source": [
    "# Calc F statistic for bitcoin (last 10 years only)\n",
    "F, p = stats.f_oneway(\n",
    "    df_btc['Monday'].dropna(),\n",
    "    df_btc['Tuesday'].dropna(),\n",
    "    df_btc['Wednesday'].dropna(),\n",
    "    df_btc['Thursday'].dropna(),\n",
    "    df_btc['Friday'].dropna(),\n",
    "    df_btc['Saturday'].dropna(),\n",
    "    df_btc['Sunday'].dropna(),\n",
    ")\n",
    "\n",
    "# The F statistic.\n",
    "print(F)\n",
    "\n",
    "# The probability. A p < .05 would lead us to believe the group means were\n",
    "# not all similar in the population.\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A high F value means that the data does not well support the null hypothesis. ... So a large F value indicates that a linear model is more compatible with the data than a constant average model. These predictors have high F values leading to small p values so they are sufficient for predicting the response.  \n",
    "\n",
    "A null hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis.  In the case of gold, there is a roughly **0.3%** probability that the different sums for the day of the week could have happened due to chance.  Therefore, it is likely that there really is a significant difference in the distributions.  \n",
    "\n",
    "While t-test is used to compare two related samples, f-test is used to test the equality of two populations.\n",
    "\n",
    "**To summarize the t-test calculations:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For S&P 500:  \n",
    "So, this means that on Mondays, the total change in the value of the S&P 500 index was about +100.  The total change on all Tuesdays was about +640.  \n",
    "\n",
    "For 10 Yr Bond:  \n",
    "The pvalue here is much less than .05.  Therefore, the difference in price from Monday and Friday, which are the two most extreme price changes in the data, is significant.  In other words the difference here is probably NOT just due to chance and the values are taken from the DIFFERENT populations.  \n",
    "\n",
    "For Gold:  \n",
    "A null hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis.  In the case of gold, since the pvalue is less than 0.05, the null hypotheses cannot be rejected and it would be likely that the two variables indeed have a statistically significant difference.  \n",
    "\n",
    "For Bitcoin:  \n",
    "For the case of bitcoin, there is an approximately **2.1%** probability that the difference in sums of Thursday vs. Saturday happened randomly.  Thursday was the lowest value, Saturday was the highest.  \n",
    "\n",
    "### 2. B.  Question:  Do 'winter' months perform better than 'summer' months ?  \n",
    "\n",
    "**Answer:**  No, winter did have a slightly higher average daily return, but was found to be insignificant.  The data analysis follows:  \n",
    "\n",
    "First group by month to see the total sum of the daily price changes by month:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>change</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>378230.08</td>\n",
       "      <td>426</td>\n",
       "      <td>3338</td>\n",
       "      <td>406938</td>\n",
       "      <td>268.09</td>\n",
       "      <td>-135.64</td>\n",
       "      <td>124.86</td>\n",
       "      <td>92.77</td>\n",
       "      <td>51.86</td>\n",
       "      <td>134.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>361761.37</td>\n",
       "      <td>402</td>\n",
       "      <td>2767</td>\n",
       "      <td>386783</td>\n",
       "      <td>375.10</td>\n",
       "      <td>-48.96</td>\n",
       "      <td>205.81</td>\n",
       "      <td>13.81</td>\n",
       "      <td>-23.71</td>\n",
       "      <td>228.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>417535.75</td>\n",
       "      <td>435</td>\n",
       "      <td>3448</td>\n",
       "      <td>439151</td>\n",
       "      <td>240.67</td>\n",
       "      <td>201.06</td>\n",
       "      <td>-44.14</td>\n",
       "      <td>-12.37</td>\n",
       "      <td>16.65</td>\n",
       "      <td>79.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>402318.47</td>\n",
       "      <td>397</td>\n",
       "      <td>3221</td>\n",
       "      <td>417001</td>\n",
       "      <td>247.74</td>\n",
       "      <td>6.40</td>\n",
       "      <td>193.27</td>\n",
       "      <td>139.98</td>\n",
       "      <td>46.25</td>\n",
       "      <td>-138.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>416424.88</td>\n",
       "      <td>446</td>\n",
       "      <td>3294</td>\n",
       "      <td>429099</td>\n",
       "      <td>-185.89</td>\n",
       "      <td>61.17</td>\n",
       "      <td>-116.86</td>\n",
       "      <td>-84.02</td>\n",
       "      <td>-75.88</td>\n",
       "      <td>29.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>408419.00</td>\n",
       "      <td>425</td>\n",
       "      <td>3315</td>\n",
       "      <td>429052</td>\n",
       "      <td>128.84</td>\n",
       "      <td>-130.05</td>\n",
       "      <td>127.44</td>\n",
       "      <td>53.69</td>\n",
       "      <td>142.01</td>\n",
       "      <td>-64.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>375239.75</td>\n",
       "      <td>424</td>\n",
       "      <td>3499</td>\n",
       "      <td>424842</td>\n",
       "      <td>438.02</td>\n",
       "      <td>46.86</td>\n",
       "      <td>94.98</td>\n",
       "      <td>96.46</td>\n",
       "      <td>74.06</td>\n",
       "      <td>125.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>397742.10</td>\n",
       "      <td>442</td>\n",
       "      <td>3557</td>\n",
       "      <td>447006</td>\n",
       "      <td>-93.05</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-18.94</td>\n",
       "      <td>18.88</td>\n",
       "      <td>-158.66</td>\n",
       "      <td>68.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>364306.26</td>\n",
       "      <td>430</td>\n",
       "      <td>3282</td>\n",
       "      <td>410745</td>\n",
       "      <td>97.79</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-20.86</td>\n",
       "      <td>181.24</td>\n",
       "      <td>52.32</td>\n",
       "      <td>-112.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>397624.42</td>\n",
       "      <td>439</td>\n",
       "      <td>3475</td>\n",
       "      <td>440966</td>\n",
       "      <td>206.16</td>\n",
       "      <td>-30.88</td>\n",
       "      <td>88.37</td>\n",
       "      <td>-102.16</td>\n",
       "      <td>132.00</td>\n",
       "      <td>118.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>375225.76</td>\n",
       "      <td>400</td>\n",
       "      <td>3088</td>\n",
       "      <td>412770</td>\n",
       "      <td>347.65</td>\n",
       "      <td>142.00</td>\n",
       "      <td>-32.32</td>\n",
       "      <td>129.16</td>\n",
       "      <td>106.62</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>383889.11</td>\n",
       "      <td>422</td>\n",
       "      <td>3286</td>\n",
       "      <td>422818</td>\n",
       "      <td>-72.64</td>\n",
       "      <td>-5.23</td>\n",
       "      <td>40.29</td>\n",
       "      <td>74.43</td>\n",
       "      <td>-43.14</td>\n",
       "      <td>-138.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price  weekday   day    year  change  Monday  Tuesday  Wednesday  \\\n",
       "month                                                                         \n",
       "1      378230.08      426  3338  406938  268.09 -135.64   124.86      92.77   \n",
       "2      361761.37      402  2767  386783  375.10  -48.96   205.81      13.81   \n",
       "3      417535.75      435  3448  439151  240.67  201.06   -44.14     -12.37   \n",
       "4      402318.47      397  3221  417001  247.74    6.40   193.27     139.98   \n",
       "5      416424.88      446  3294  429099 -185.89   61.17  -116.86     -84.02   \n",
       "6      408419.00      425  3315  429052  128.84 -130.05   127.44      53.69   \n",
       "7      375239.75      424  3499  424842  438.02   46.86    94.98      96.46   \n",
       "8      397742.10      442  3557  447006  -93.05   -2.68   -18.94      18.88   \n",
       "9      364306.26      430  3282  410745   97.79   -2.69   -20.86     181.24   \n",
       "10     397624.42      439  3475  440966  206.16  -30.88    88.37    -102.16   \n",
       "11     375225.76      400  3088  412770  347.65  142.00   -32.32     129.16   \n",
       "12     383889.11      422  3286  422818  -72.64   -5.23    40.29      74.43   \n",
       "\n",
       "       Thursday  Friday  \n",
       "month                    \n",
       "1         51.86  134.24  \n",
       "2        -23.71  228.15  \n",
       "3         16.65   79.47  \n",
       "4         46.25 -138.16  \n",
       "5        -75.88   29.70  \n",
       "6        142.01  -64.25  \n",
       "7         74.06  125.66  \n",
       "8       -158.66   68.35  \n",
       "9         52.32 -112.22  \n",
       "10       132.00  118.83  \n",
       "11       106.62    2.19  \n",
       "12       -43.14 -138.99  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month = df.groupby('month').sum()\n",
    "df_month.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like May has the weakest return over the past 10 years and July has the strongest.  Is this difference significant or is it just due to random chance ?  If the total return is added for months November to the end of April, the price gain is about **1407**.  This compares to a price gain of just **592** for the months of May through October over the entire 10 year horizon.  It looks like buying the index in early November and selling in early May might be a good strategy.  However, there is a gain for the \"summer\" months (May - October), so why not just buy and hold all year round ?  Well, the past 10 years has been the longest running bull market in history and 10 years happens to be the total time frame for this study.  More data is needed to examine this concept more robustly.  \n",
    "\n",
    "Next, add a 'summer' and 'winter' column to see if there is a significant difference in the rate of return:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the column for change in the summer months:\n",
    "df['summer'] = np.where(((df.year > 2008) & ((df.month == 5) | (df.month == 6) | (df.month == 7) | \n",
    "            (df.month == 8) | (df.month == 9) | (df.month == 10))), df.change, 0)\n",
    "\n",
    "# add the column for change winter months:\n",
    "df['winter'] = np.where(((df.year < 2020) & ((df.month == 11) | (df.month == 12) | (df.month == 1) | \n",
    "            (df.month == 2) | (df.month == 3) | (df.month == 4))), df.change, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23533598409542744\n"
     ]
    }
   ],
   "source": [
    "# print the average daily return for summer months:\n",
    "print(df.summer.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5590659777424484\n"
     ]
    }
   ],
   "source": [
    "# print the average daily return for winter months:\n",
    "print(df.winter.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.969513403992707, pvalue=0.33233572022424096)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# execute ttest for the two different columns of summer and winter\n",
    "ttest_ind(df['summer'].dropna(), df['winter'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average return for summer vs winter are **0.23** and **0.56**, respectively.  However, this difference is not significant.  The computed pvalue for the distributions is **0.332**, which is too high to conclude anything.  There is a roughly 33% chance that the data of the two distributions came from the same population.  Much too high to draw any conclusions.  \n",
    "\n",
    "### 2. C.  Question:  Does the 3rd Presidential Year yield better results in the 'winter' months vs. 'summer' ?  \n",
    "\n",
    "**Answer:**  No, again winter did have a slightly higher average daily return, but was found to be insignificant.  The data analysis follows:  \n",
    "\n",
    "Now, winter vs summer just in year 3 of Presidential term.  Create new columns to capture the 3rd year data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the column for change in Presidential 3rd year, summer months:\n",
    "df['yr3s'] = np.where(((df.year == 2011) | (df.year == 2015) | (df.year == 2019))\n",
    "            & ((df.month == 5) | (df.month == 6) | (df.month == 7) | \n",
    "            (df.month == 8) | (df.month == 9) | (df.month == 10)), df.change, 0)\n",
    "\n",
    "# add the column for change in Presidential 3rd year, winter months:\n",
    "df['yr3w'] = np.where(((df.year == 2011) | (df.year == 2015) | (df.year == 2019)\n",
    "                       & (df.month == 1) | (df.month == 2) | \n",
    "                       (df.month == 3) | (df.month == 4)), df.change, 0) \n",
    "df['yr3w_2'] = np.where(((df.year == 2010) | (df.year == 2014) | (df.year == 2018)\n",
    "                       & (df.month == 11) | (df.month == 12)), df.change, 0) \n",
    "df['yr3w_t'] = df.yr3w + df.yr3w_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the new dataframe below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>price</th>\n",
       "      <th>date_time</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>change</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>summer</th>\n",
       "      <th>winter</th>\n",
       "      <th>yr3s</th>\n",
       "      <th>yr3w</th>\n",
       "      <th>yr3w_2</th>\n",
       "      <th>yr3w_t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-06-26</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>918.90</td>\n",
       "      <td>918.90</td>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-29</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>927.23</td>\n",
       "      <td>927.23</td>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>919.32</td>\n",
       "      <td>919.32</td>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>923.33</td>\n",
       "      <td>923.33</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-02</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>896.42</td>\n",
       "      <td>896.42</td>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE   SP500   price  date_time  weekday  day  month  year  \\\n",
       "DATE                                                                           \n",
       "2009-06-26  2009-06-26  918.90  918.90 2009-06-26        4   26      6  2009   \n",
       "2009-06-29  2009-06-29  927.23  927.23 2009-06-29        0   29      6  2009   \n",
       "2009-06-30  2009-06-30  919.32  919.32 2009-06-30        1   30      6  2009   \n",
       "2009-07-01  2009-07-01  923.33  923.33 2009-07-01        2    1      7  2009   \n",
       "2009-07-02  2009-07-02  896.42  896.42 2009-07-02        3    2      7  2009   \n",
       "\n",
       "            change  Monday  Tuesday  Wednesday  Thursday  Friday  summer  \\\n",
       "DATE                                                                       \n",
       "2009-06-26     NaN     NaN      NaN        NaN       NaN     NaN     NaN   \n",
       "2009-06-29    8.33    8.33      NaN        NaN       NaN     NaN    8.33   \n",
       "2009-06-30   -7.91     NaN    -7.91        NaN       NaN     NaN   -7.91   \n",
       "2009-07-01    4.01     NaN      NaN       4.01       NaN     NaN    4.01   \n",
       "2009-07-02  -26.91     NaN      NaN        NaN    -26.91     NaN  -26.91   \n",
       "\n",
       "            winter  yr3s  yr3w  yr3w_2  yr3w_t  \n",
       "DATE                                            \n",
       "2009-06-26     0.0   0.0   0.0     0.0     0.0  \n",
       "2009-06-29     0.0   0.0   0.0     0.0     0.0  \n",
       "2009-06-30     0.0   0.0   0.0     0.0     0.0  \n",
       "2009-07-01     0.0   0.0   0.0     0.0     0.0  \n",
       "2009-07-02     0.0   0.0   0.0     0.0     0.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average daily return for summer months in 3rd Presidential Term\n",
      "-0.057595389507154156\n"
     ]
    }
   ],
   "source": [
    "print(\"Average daily return for summer months in 3rd Presidential Term\")\n",
    "print(df.yr3s.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average daily return for winter months in 3rd Presidential Term\n",
      "0.4523807631160576\n"
     ]
    }
   ],
   "source": [
    "print(\"Average daily return for winter months in 3rd Presidential Term\")\n",
    "print(df.yr3w_t.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.4998938416684096, pvalue=0.13370466363760292)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(df['yr3s'].dropna(), df['yr3w_t'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The daily returns for summer and winter months in the 3rd Presidential Term are not significant.  There is definitely a bigger difference in the 3rd Presidential Term compared to overall summer vs. winter.  However, the pvalue is **0.134** which is not less than 0.05.  So, there is a 13% chance that the sample data of each distribution came from the same population.  This is interesting, but not conclusive enough to take strong action."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. D.  Question:  Does any month of the S&P 500 over the past 10 years perform significantly better than any other ?  \n",
    "\n",
    "**Answer:**  Yes.  The data analysis follows:  \n",
    "\n",
    "First, look at the monthly totals, then create the month column of the dataframe to execute a t-test for significance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>price</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>change</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>summer</th>\n",
       "      <th>winter</th>\n",
       "      <th>yr3s</th>\n",
       "      <th>yr3w</th>\n",
       "      <th>yr3w_2</th>\n",
       "      <th>yr3w_t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>378230.08</td>\n",
       "      <td>426</td>\n",
       "      <td>3338</td>\n",
       "      <td>406938</td>\n",
       "      <td>268.09</td>\n",
       "      <td>-135.64</td>\n",
       "      <td>124.86</td>\n",
       "      <td>92.77</td>\n",
       "      <td>51.86</td>\n",
       "      <td>134.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>268.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.82</td>\n",
       "      <td>-107.00</td>\n",
       "      <td>54.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>361761.37</td>\n",
       "      <td>402</td>\n",
       "      <td>2767</td>\n",
       "      <td>386783</td>\n",
       "      <td>375.10</td>\n",
       "      <td>-48.96</td>\n",
       "      <td>205.81</td>\n",
       "      <td>13.81</td>\n",
       "      <td>-23.71</td>\n",
       "      <td>228.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>375.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>375.10</td>\n",
       "      <td>107.48</td>\n",
       "      <td>482.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>417535.75</td>\n",
       "      <td>435</td>\n",
       "      <td>3448</td>\n",
       "      <td>439151</td>\n",
       "      <td>240.67</td>\n",
       "      <td>201.06</td>\n",
       "      <td>-44.14</td>\n",
       "      <td>-12.37</td>\n",
       "      <td>16.65</td>\n",
       "      <td>79.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>240.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>240.67</td>\n",
       "      <td>77.83</td>\n",
       "      <td>318.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>402318.47</td>\n",
       "      <td>397</td>\n",
       "      <td>3221</td>\n",
       "      <td>417001</td>\n",
       "      <td>247.74</td>\n",
       "      <td>6.40</td>\n",
       "      <td>193.27</td>\n",
       "      <td>139.98</td>\n",
       "      <td>46.25</td>\n",
       "      <td>-138.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>247.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>247.74</td>\n",
       "      <td>28.87</td>\n",
       "      <td>276.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>416424.88</td>\n",
       "      <td>446</td>\n",
       "      <td>3294</td>\n",
       "      <td>429099</td>\n",
       "      <td>-185.89</td>\n",
       "      <td>61.17</td>\n",
       "      <td>-116.86</td>\n",
       "      <td>-84.02</td>\n",
       "      <td>-75.88</td>\n",
       "      <td>29.70</td>\n",
       "      <td>-185.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-190.30</td>\n",
       "      <td>3.47</td>\n",
       "      <td>-57.66</td>\n",
       "      <td>-54.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>408419.00</td>\n",
       "      <td>425</td>\n",
       "      <td>3315</td>\n",
       "      <td>429052</td>\n",
       "      <td>128.84</td>\n",
       "      <td>-130.05</td>\n",
       "      <td>127.44</td>\n",
       "      <td>53.69</td>\n",
       "      <td>142.01</td>\n",
       "      <td>-64.25</td>\n",
       "      <td>128.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>96.48</td>\n",
       "      <td>-68.84</td>\n",
       "      <td>-22.04</td>\n",
       "      <td>-90.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>375239.75</td>\n",
       "      <td>424</td>\n",
       "      <td>3499</td>\n",
       "      <td>424842</td>\n",
       "      <td>438.02</td>\n",
       "      <td>46.86</td>\n",
       "      <td>94.98</td>\n",
       "      <td>96.46</td>\n",
       "      <td>74.06</td>\n",
       "      <td>125.66</td>\n",
       "      <td>438.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.37</td>\n",
       "      <td>12.37</td>\n",
       "      <td>41.33</td>\n",
       "      <td>53.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>397742.10</td>\n",
       "      <td>442</td>\n",
       "      <td>3557</td>\n",
       "      <td>447006</td>\n",
       "      <td>-93.05</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-18.94</td>\n",
       "      <td>18.88</td>\n",
       "      <td>-158.66</td>\n",
       "      <td>68.35</td>\n",
       "      <td>-93.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-205.05</td>\n",
       "      <td>-205.05</td>\n",
       "      <td>20.43</td>\n",
       "      <td>-184.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>364306.26</td>\n",
       "      <td>430</td>\n",
       "      <td>3282</td>\n",
       "      <td>410745</td>\n",
       "      <td>97.79</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-20.86</td>\n",
       "      <td>181.24</td>\n",
       "      <td>52.32</td>\n",
       "      <td>-112.22</td>\n",
       "      <td>97.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-139.62</td>\n",
       "      <td>-139.62</td>\n",
       "      <td>60.79</td>\n",
       "      <td>-78.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>397624.42</td>\n",
       "      <td>439</td>\n",
       "      <td>3475</td>\n",
       "      <td>440966</td>\n",
       "      <td>206.16</td>\n",
       "      <td>-30.88</td>\n",
       "      <td>88.37</td>\n",
       "      <td>-102.16</td>\n",
       "      <td>132.00</td>\n",
       "      <td>118.83</td>\n",
       "      <td>206.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>281.21</td>\n",
       "      <td>281.21</td>\n",
       "      <td>87.82</td>\n",
       "      <td>369.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>375225.76</td>\n",
       "      <td>400</td>\n",
       "      <td>3088</td>\n",
       "      <td>412770</td>\n",
       "      <td>347.65</td>\n",
       "      <td>142.00</td>\n",
       "      <td>-32.32</td>\n",
       "      <td>129.16</td>\n",
       "      <td>106.62</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>347.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>95.23</td>\n",
       "      <td>89.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>383889.11</td>\n",
       "      <td>422</td>\n",
       "      <td>3286</td>\n",
       "      <td>422818</td>\n",
       "      <td>-72.64</td>\n",
       "      <td>-5.23</td>\n",
       "      <td>40.29</td>\n",
       "      <td>74.43</td>\n",
       "      <td>-43.14</td>\n",
       "      <td>-138.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-72.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-25.83</td>\n",
       "      <td>-72.64</td>\n",
       "      <td>-98.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price  weekday   day    year  change  Monday  Tuesday  Wednesday  \\\n",
       "month                                                                         \n",
       "1      378230.08      426  3338  406938  268.09 -135.64   124.86      92.77   \n",
       "2      361761.37      402  2767  386783  375.10  -48.96   205.81      13.81   \n",
       "3      417535.75      435  3448  439151  240.67  201.06   -44.14     -12.37   \n",
       "4      402318.47      397  3221  417001  247.74    6.40   193.27     139.98   \n",
       "5      416424.88      446  3294  429099 -185.89   61.17  -116.86     -84.02   \n",
       "6      408419.00      425  3315  429052  128.84 -130.05   127.44      53.69   \n",
       "7      375239.75      424  3499  424842  438.02   46.86    94.98      96.46   \n",
       "8      397742.10      442  3557  447006  -93.05   -2.68   -18.94      18.88   \n",
       "9      364306.26      430  3282  410745   97.79   -2.69   -20.86     181.24   \n",
       "10     397624.42      439  3475  440966  206.16  -30.88    88.37    -102.16   \n",
       "11     375225.76      400  3088  412770  347.65  142.00   -32.32     129.16   \n",
       "12     383889.11      422  3286  422818  -72.64   -5.23    40.29      74.43   \n",
       "\n",
       "       Thursday  Friday  summer  winter    yr3s    yr3w  yr3w_2  yr3w_t  \n",
       "month                                                                    \n",
       "1         51.86  134.24    0.00  268.09    0.00  161.82 -107.00   54.82  \n",
       "2        -23.71  228.15    0.00  375.10    0.00  375.10  107.48  482.58  \n",
       "3         16.65   79.47    0.00  240.67    0.00  240.67   77.83  318.50  \n",
       "4         46.25 -138.16    0.00  247.74    0.00  247.74   28.87  276.61  \n",
       "5        -75.88   29.70 -185.89    0.00 -190.30    3.47  -57.66  -54.19  \n",
       "6        142.01  -64.25  128.84    0.00   96.48  -68.84  -22.04  -90.88  \n",
       "7         74.06  125.66  438.02    0.00   12.37   12.37   41.33   53.70  \n",
       "8       -158.66   68.35  -93.05    0.00 -205.05 -205.05   20.43 -184.62  \n",
       "9         52.32 -112.22   97.79    0.00 -139.62 -139.62   60.79  -78.83  \n",
       "10       132.00  118.83  206.16    0.00  281.21  281.21   87.82  369.03  \n",
       "11       106.62    2.19    0.00  347.65    0.00   -5.29   95.23   89.94  \n",
       "12       -43.14 -138.99    0.00  -72.64    0.00  -25.83  -72.64  -98.47  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('month').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the last 10 years, May performed the worst while July performed the best (see 5th column, 'change')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For S&P 500 grouped by month\n",
    "df['Jan'] = df.change.where(df.month == 1)\n",
    "df['Feb'] = df.change.where(df.month == 2)\n",
    "df['Mar'] = df.change.where(df.month == 3)\n",
    "df['Apr'] = df.change.where(df.month == 4)\n",
    "df['May'] = df.change.where(df.month == 5)\n",
    "df['June'] = df.change.where(df.month == 6)\n",
    "df['July'] = df.change.where(df.month == 7)\n",
    "df['Aug'] = df.change.where(df.month == 8)\n",
    "df['Sep'] = df.change.where(df.month == 9)\n",
    "df['Oct'] = df.change.where(df.month == 10)\n",
    "df['Nov'] = df.change.where(df.month == 11)\n",
    "df['Dec'] = df.change.where(df.month == 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the p-value by comparing May vs. July which were the worst and best months, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.1657996427395316, pvalue=0.030885931123197312)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For S&P 500 groupby month\n",
    "ttest_ind(df['May'].dropna(), df['July'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above ttest does show that May is significantly weaker than July for the S&P 500 over the last 10 years.  The calculated pvalue is **0.03** which is less than 0.05, so the differences are likely to be real, rather than just due to chance.  This same process could be performed for all 5 securities to determine if any month or months behaved significantly different from one or more of the others.  \n",
    "\n",
    "The following histogram shows the distribution of the 'change' values for the S&P 500 to make sure they are close to normally distributed.  If they are, then using standard measures of statistics, such as the mean and using a t-test is an acceptable method to measure significance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df.change.hist()\n",
    "ax = df.change.hist()\n",
    "ax.set(xlabel='daily change', ylabel='frequency', title='Histogram of daily price change for S&P 500')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot is definitely close to normally distributed.  Next, check one more security - gold, for example to see if it has normally distributed data also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df_gold.change.hist()\n",
    "ax = df_gold.change.hist()\n",
    "ax.set(xlabel='daily change', ylabel='frequency', title='Histogram of daily price change for Gold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks close to normal as well.  \n",
    "\n",
    "Finally, compute the F statistice for the S&P 500 grouped by month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6825850026550205\n",
      "0.7563506335281399\n"
     ]
    }
   ],
   "source": [
    "# Calc F statistic for S&P 500 grouped by month\n",
    "F, p = stats.f_oneway(\n",
    "    df['Jan'].dropna(),\n",
    "    df['Feb'].dropna(),\n",
    "    df['Mar'].dropna(),\n",
    "    df['Apr'].dropna(),\n",
    "    df['May'].dropna(),\n",
    "    df['June'].dropna(),\n",
    "    df['July'].dropna(),\n",
    "    df['Aug'].dropna(),\n",
    "    df['Sep'].dropna(),\n",
    "    df['Oct'].dropna(),\n",
    "    df['Nov'].dropna(),\n",
    "    df['Dec'].dropna(),\n",
    ")\n",
    "\n",
    "# The F statistic.\n",
    "print(F)\n",
    "\n",
    "# The probability. A p < .05 would lead us to believe the group means were\n",
    "# not all similar in the population.\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, the F value is not very high (compared to gold, for example) and the p-value is **0.75** which means there really is no significant difference between the months.  This conflicts with the t-test.  Which one is correct ?  It depends.  If the study comprises the entire population of the data - in other words, all of the returns since its inception - the F statistic is the most relevant metric.  However, it can be argued that the last 10 years of S&P 500 data is just a sample of the overall population - in which case the t-test is the most relevant metric.  \n",
    "\n",
    "### 2. E.  Question:  Is it possible to build a supervised learning model to predict the daily price change of gold just by using day of the week as the only feature ?  \n",
    "\n",
    "**Answer:**  Possibly.  The data analysis follows:  \n",
    "\n",
    "First, set up the dataframe so the change per day is a 1 for positive change and 0 for negative change.  In other words the threshold is 0.  There is only 1 value of 0 in the data, so no need to worry about that.  Next, create columns for each day of the week and signify the day for each row with a 1 (while all other values will be 0).  Note:  It might be reasonable to keep only the 2 most signigicant summations which are Mondays and Thursdays in this case - this was performed and yielded the same results as looking at all 5 days. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare Gold data for building a Supervised Learning model to predict the price change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>GOLDAMGBD229NLBM</th>\n",
       "      <th>price</th>\n",
       "      <th>date_time</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>change</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>572.566</td>\n",
       "      <td>572.566</td>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>570.133</td>\n",
       "      <td>570.133</td>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>-2.433</td>\n",
       "      <td>-2.433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>566.697</td>\n",
       "      <td>566.697</td>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>-3.436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>567.296</td>\n",
       "      <td>567.296</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>571.708</td>\n",
       "      <td>571.708</td>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.412</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE GOLDAMGBD229NLBM    price  date_time  weekday  day  month  year  \\\n",
       "0  2009-06-26          572.566  572.566 2009-06-26        4   26      6  2009   \n",
       "1  2009-06-29          570.133  570.133 2009-06-29        0   29      6  2009   \n",
       "2  2009-06-30          566.697  566.697 2009-06-30        1   30      6  2009   \n",
       "3  2009-07-01          567.296  567.296 2009-07-01        2    1      7  2009   \n",
       "4  2009-07-02          571.708  571.708 2009-07-02        3    2      7  2009   \n",
       "\n",
       "   change  Monday  Tuesday  Wednesday  Thursday  Friday  \n",
       "0     NaN     NaN      NaN        NaN       NaN     NaN  \n",
       "1  -2.433  -2.433      NaN        NaN       NaN     NaN  \n",
       "2  -3.436     NaN   -3.436        NaN       NaN     NaN  \n",
       "3   0.599     NaN      NaN      0.599       NaN     NaN  \n",
       "4   4.412     NaN      NaN        NaN     4.412     NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of this dataframe for later:\n",
    "df_gold_barplot = df_gold.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gold['change'].fillna(0, inplace=True)\n",
    "\n",
    "df_gold.drop(['DATE'], axis=1, inplace=True)\n",
    "df_gold.drop(['GOLDAMGBD229NLBM'], axis=1, inplace=True)\n",
    "df_gold.drop(['price'], axis=1, inplace=True)\n",
    "df_gold.drop(['date_time'], axis=1, inplace=True)\n",
    "df_gold.drop(['year'], axis=1, inplace=True)\n",
    "df_gold.drop(['Monday'], axis=1, inplace=True)\n",
    "df_gold.drop(['Tuesday'], axis=1, inplace=True)\n",
    "df_gold.drop(['Wednesday'], axis=1, inplace=True)\n",
    "df_gold.drop(['Thursday'], axis=1, inplace=True)\n",
    "df_gold.drop(['Friday'], axis=1, inplace=True)\n",
    "df_gold.drop(['day'], axis=1, inplace=True)\n",
    "df_gold.drop(['month'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of this dataframe for later:\n",
    "df_bin = df_gold.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>weekday</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-2.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4.412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weekday  change\n",
       "0        4   0.000\n",
       "1        0  -2.433\n",
       "2        1  -3.436\n",
       "3        2   0.599\n",
       "4        3   4.412"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gold.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the new format for the gold dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the 'binary column for positive or negative change\n",
    "df_gold['binary'] = np.where(df_gold.change >= 0, 1, 0)\n",
    "\n",
    "# Create the 5 columns for each day of the week\n",
    "df_gold['zero'] = np.where(df_gold.weekday == 0, 1, 0)\n",
    "df_gold['one'] = np.where(df_gold.weekday == 1, 1, 0)\n",
    "df_gold['two'] = np.where(df_gold.weekday == 2, 1, 0)\n",
    "df_gold['three'] = np.where(df_gold.weekday == 3, 1, 0)\n",
    "df_gold['four'] = np.where(df_gold.weekday == 4, 1, 0)\n",
    "\n",
    "# drop 'weekday and change columns'\n",
    "df_gold.drop(['weekday'], axis=1, inplace=True)\n",
    "df_gold.drop(['change'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>binary</th>\n",
       "      <th>zero</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   binary  zero  one  two  three  four\n",
       "0       1     0    0    0      0     1\n",
       "1       0     1    0    0      0     0\n",
       "2       0     0    1    0      0     0\n",
       "3       1     0    0    1      0     0\n",
       "4       1     0    0    0      1     0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gold.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the training and test data sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2020, 6)\n",
      "(505, 6)\n"
     ]
    }
   ],
   "source": [
    "# Create training and test sets.\n",
    "offset_gold = int(df_gold.shape[0] * 0.8)\n",
    "\n",
    "df_train_gold = df_gold[:offset_gold]\n",
    "df_test_gold = df_gold[offset_gold:]\n",
    "\n",
    "print(df_train_gold.shape)\n",
    "print(df_test_gold.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try Logistic Regression first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Duration: 0:00:00.122007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fred\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n",
      "C:\\Users\\Fred\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 1.  Logistic Regression\n",
    "\n",
    "from datetime import datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "# Instantiate our model.\n",
    "regr_gold = linear_model.LogisticRegression()\n",
    "\n",
    "# set features and dependent variable for training data\n",
    "y_train_gold = df_train_gold['binary'].values\n",
    "\n",
    "# drop the 'binary' column to obtain the feature inputs\n",
    "df_train_gold.drop(['binary'], axis=1, inplace=True)\n",
    "\n",
    "# normalize the training data\n",
    "# x_train_bin = sklearn.preprocessing.normalize(df_train_bin)\n",
    "x_train_gold = df_train_gold\n",
    "\n",
    "# now for test...\n",
    "y_test_gold = df_test_gold['binary'].values\n",
    "\n",
    "# drop the 'binary' column to obtain the feature inputs\n",
    "df_test_gold.drop(['binary'], axis=1, inplace=True)\n",
    "\n",
    "# normalize the test data\n",
    "# x_test_bin = sklearn.preprocessing.normalize(df_test_bin)\n",
    "x_test_gold = df_test_gold\n",
    "\n",
    "# fit model to training data\n",
    "regr_gold.fit(x_train_gold, y_train_gold)\n",
    "\n",
    "end_time = datetime.now()\n",
    "print('\\nDuration: {}'.format(end_time - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [[ 0.34285275  0.06100424 -0.02081022 -0.23235677 -0.14899687]]\n",
      "Intercept: \n",
      " [0.00169314]\n",
      "0.4866967871485944\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[149, 100],\n",
       "       [160,  96]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the attributes calculated.\n",
    "print('Coefficients: \\n', regr_gold.coef_)\n",
    "print('Intercept: \\n', regr_gold.intercept_)\n",
    "\n",
    "# print the area under the curve score for the accuracy metric\n",
    "y_test_pred = regr_gold.predict(x_test_gold)\n",
    "print(sklearn.metrics.roc_auc_score(y_test_gold, y_test_pred))\n",
    "\n",
    "# print the consfusion matrix\n",
    "sklearn.metrics.confusion_matrix(y_test_gold, y_test_pred, labels=None, sample_weight=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ROC accuracy score is **0.49** which is just slightly less than the ground truth of 0.50.  Looks like there are a large number of False Positives from the Confusion Matrix (160) relative to the other quadrants.  The Random Forest Classifier algorithm is next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=50, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.  Random Forest Classifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Instantiate our model.\n",
    "rfc_gold = RandomForestClassifier(n_estimators=500, max_depth=50)\n",
    "\n",
    "# fit model to data\n",
    "rfc_gold.fit(x_train_gold, y_train_gold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4866967871485944\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[149, 100],\n",
       "       [160,  96]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the area under the curve score for the accuracy metric\n",
    "y_test_pred = rfc_gold.predict(x_test_gold)\n",
    "print(sklearn.metrics.roc_auc_score(y_test_gold, y_test_pred))\n",
    "\n",
    "# print the consfusion matrix\n",
    "sklearn.metrics.confusion_matrix(y_test_gold, y_test_pred, labels=None, sample_weight=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same result as Logistic Regression.  Maybe Gradient Boosting Classifier will work better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=500,\n",
       "              n_iter_no_change=None, presort='auto', random_state=None,\n",
       "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.  Gradient Boosting Classifier\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Instantiate our model.\n",
    "gbc_gold = GradientBoostingClassifier(n_estimators=500)\n",
    "\n",
    "# fit model to data\n",
    "gbc_gold.fit(x_train_gold, y_train_gold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4866967871485944\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[149, 100],\n",
       "       [160,  96]], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the area under the curve score for the accuracy metric\n",
    "y_test_pred = gbc_gold.predict(x_test_gold)\n",
    "print(sklearn.metrics.roc_auc_score(y_test_gold, y_test_pred))\n",
    "\n",
    "# print the consfusion matrix\n",
    "sklearn.metrics.confusion_matrix(y_test_gold, y_test_pred, labels=None, sample_weight=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmmmm.  Same results.  These machine learning methods are not predicting the price change very well.  Is the data balanced ?  In other words, are the number of 1's and 0's about the same ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1264\n",
      "1261\n"
     ]
    }
   ],
   "source": [
    "print(df_gold.loc[df_gold['binary'] == 1, 'binary'].count())\n",
    "print(df_gold.loc[df_gold['binary'] == 0, 'binary'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, the data is balanced.  Next, check in another way to see if the days of the week really can be reasonable features for a predictive machine learning model.  See the barplot below (the dataframe used here is called df_gold_barplot which was copied from the original gold dataframe to perserve the actual 'change' values):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fred\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", rc={'figure.figsize':(10, 6)})\n",
    "sns.barplot(x='weekday', y='change', ci=95, data=df_gold_barplot)\n",
    "plt.xlabel('Day of Week: 0 = Monday')\n",
    "plt.ylabel('Daily Price Change')\n",
    "plt.title('Average daily price change - Gold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This clearly shows a difference in the day of the week for Mondays and Thursdays at the very least.  With a 95% level of confidence, this shows that the mean range (which is the average daily change over the entire 10 years) of Monday or Tuesday does not even overlap with the mean range on Thursday.  This is illustrated by the vertical black line for each day of the week above.  \n",
    "\n",
    "There are probably better modeling techniques using machine learning for predicting this price discrepancy.  Many methods were experimented with besides the one shown above.  Using the actual change numbers for a continuous model yielded the same results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.  Weather and Financial Data Combined  \n",
    "\n",
    "This notebook captures the amount of sunshine (in minutes) per day and the daily temperature (in degrees F) in New York City.  The weather data is combined with the 'change' from the financial data above for the S&P 500.  The hypothesis is that there will be better returns on sunny days on average.  There are numerous theories that suggest this correlation.  For example, the following study:  \n",
    "https://news.fordham.edu/inside-fordham-category/the-sunshine-effect-financial-markets-respond-to-the-weather/  \n",
    "\n",
    "To validate or invalidate these claims, see the following analysis.  First, the amount of sunhine and its affects is covered; then, the daily temperature data is compared to the daily returns.  \n",
    "\n",
    "### 3.A. Question:  Does the daily temperature or amount of sunshine in NYC affect the price change of the S&P 500 ?  \n",
    "\n",
    "**Answer:**  No.  See analysis below.\n",
    "\n",
    "Set up the data for the two new dataframes - 'dfw' for Sunshine and 'dft' for Temperature.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Sun</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-06-25</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-26</th>\n",
       "      <td>418.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-27</th>\n",
       "      <td>743.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-28</th>\n",
       "      <td>436.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-29</th>\n",
       "      <td>848.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sun\n",
       "dt                \n",
       "2009-06-25    0.00\n",
       "2009-06-26  418.70\n",
       "2009-06-27  743.23\n",
       "2009-06-28  436.08\n",
       "2009-06-29  848.28"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>avg_temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-06-25</th>\n",
       "      <td>71.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-26</th>\n",
       "      <td>76.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-27</th>\n",
       "      <td>76.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-28</th>\n",
       "      <td>72.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-29</th>\n",
       "      <td>75.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_temp\n",
       "dt                  \n",
       "2009-06-25     71.72\n",
       "2009-06-26     76.91\n",
       "2009-06-27     76.34\n",
       "2009-06-28     72.35\n",
       "2009-06-29     75.43"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset the index of these dataframe in order to merge this data with another dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the index of this dataframe in order to merge this data with another dataframe\n",
    "dfw.reset_index(level=0, inplace=True)\n",
    "dft.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the columns of the data frame for S&P 500, drop some unnecessary columns and reset the index for merging data with weather data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['DATE', 'SP500', 'price', 'date_time', 'weekday', 'day', 'month',\n",
      "       'year', 'change', 'Monday', 'Tuesday', 'Wednesday', 'Thursday',\n",
      "       'Friday', 'summer', 'winter', 'yr3s', 'yr3w', 'yr3w_2', 'yr3w_t', 'Jan',\n",
      "       'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov',\n",
      "       'Dec'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# print columns and reset index\n",
    "print(df.columns)\n",
    "\n",
    "# Fill Nan values with 0\n",
    "df['change'].fillna(0, inplace=True)\n",
    "\n",
    "# drop unnecessary columns \n",
    "df.drop(['DATE'], axis=1, inplace=True)\n",
    "df.drop(['SP500'], axis=1, inplace=True)\n",
    "df.drop(['price'], axis=1, inplace=True)\n",
    "df.drop(['date_time'], axis=1, inplace=True)\n",
    "df.drop(['year'], axis=1, inplace=True)\n",
    "df.drop(['Monday'], axis=1, inplace=True)\n",
    "df.drop(['Tuesday'], axis=1, inplace=True)\n",
    "df.drop(['Wednesday'], axis=1, inplace=True)\n",
    "df.drop(['Thursday'], axis=1, inplace=True)\n",
    "df.drop(['Friday'], axis=1, inplace=True)\n",
    "df.drop(['Jan'], axis=1, inplace=True)\n",
    "df.drop(['Feb'], axis=1, inplace=True)\n",
    "df.drop(['Mar'], axis=1, inplace=True)\n",
    "df.drop(['Apr'], axis=1, inplace=True)\n",
    "df.drop(['May'], axis=1, inplace=True)\n",
    "df.drop(['June'], axis=1, inplace=True)\n",
    "df.drop(['July'], axis=1, inplace=True)\n",
    "df.drop(['Aug'], axis=1, inplace=True)\n",
    "df.drop(['Sep'], axis=1, inplace=True)\n",
    "df.drop(['Oct'], axis=1, inplace=True)\n",
    "df.drop(['Nov'], axis=1, inplace=True)\n",
    "df.drop(['Dec'], axis=1, inplace=True)\n",
    "df.drop(['summer'], axis=1, inplace=True)\n",
    "df.drop(['winter'], axis=1, inplace=True)\n",
    "df.drop(['yr3s'], axis=1, inplace=True)\n",
    "df.drop(['yr3w'], axis=1, inplace=True)\n",
    "df.drop(['yr3w_2'], axis=1, inplace=True)\n",
    "df.drop(['yr3w_t'], axis=1, inplace=True)\n",
    "\n",
    "# reset index for merging data with weather data\n",
    "df.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2511</th>\n",
       "      <td>2019-06-19</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>27.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>-3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>-5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>-27.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE  weekday  day  month  change\n",
       "2511 2019-06-19        2   19      6    8.71\n",
       "2512 2019-06-20        3   20      6   27.72\n",
       "2513 2019-06-21        4   21      6   -3.72\n",
       "2514 2019-06-24        0   24      6   -5.11\n",
       "2515 2019-06-25        1   25      6  -27.97"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe shown above has the financial data needed.  The next step is to merge the weather data with the financial data for the S&P 500:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe for S&P 500 and Sunshine in NYC in minutes\n",
    "df_sun = (dfw.merge(df, left_on='dt', right_on='DATE')\n",
    "          .reindex(columns=['DATE', 'weekday', 'day', 'month', 'change', 'Sun']))\n",
    "\n",
    "# create dataframe for S&P 500 and Temperature in NYC\n",
    "df_temp = (dft.merge(df, left_on='dt', right_on='DATE')\n",
    "          .reindex(columns=['DATE', 'weekday', 'day', 'month', 'change', 'avg_temp']))\n",
    "\n",
    "# create dataframe for S&P 500, Temperature and Sunshine in NYC\n",
    "df_both = (dft.merge(df_sun, left_on='dt', right_on='DATE')\n",
    "          .reindex(columns=['DATE', 'weekday', 'month', 'day', 'change', 'avg_temp', 'Sun']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>change</th>\n",
       "      <th>avg_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>8.33</td>\n",
       "      <td>75.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>77.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.01</td>\n",
       "      <td>73.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>71.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change  avg_temp\n",
       "0 2009-06-26        4   26      6    0.00     76.91\n",
       "1 2009-06-29        0   29      6    8.33     75.43\n",
       "2 2009-06-30        1   30      6   -7.91     77.41\n",
       "3 2009-07-01        2    1      7    4.01     73.40\n",
       "4 2009-07-02        3    2      7  -26.91     71.23"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above dataframe shows the merged S&P data with the temperature data.  Next, print the average of the average daily temps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.04181240063589\n"
     ]
    }
   ],
   "source": [
    "print(df_temp.avg_temp.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>change</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>418.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>8.33</td>\n",
       "      <td>848.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>608.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.01</td>\n",
       "      <td>168.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>225.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change     Sun\n",
       "0 2009-06-26        4   26      6    0.00  418.70\n",
       "1 2009-06-29        0   29      6    8.33  848.28\n",
       "2 2009-06-30        1   30      6   -7.91  608.00\n",
       "3 2009-07-01        2    1      7    4.01  168.86\n",
       "4 2009-07-02        3    2      7  -26.91  225.66"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sun.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above dataframe shows the \"change\" for the S&P 500 and the \"Sun\" for the number of hours of sunshine by date.\n",
    "\n",
    "This next step counts the number of hours when the hours of **sunshine is greater than 300** and less than 300.  This cutoff of 300 is a good number because it has roughly the same amount of days above and below the threshold, keeping our data balanced.  \n",
    "\n",
    "Also, 4 new columns in the temperature dataframe are created to separate the data above and below the average temperature of **55 degrees Farenheit**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creae 4 new columns in the df_out dataframe\n",
    "df_sun['sun_300'] = np.where(df_sun.Sun >= 300.0, 1, 0)\n",
    "df_sun['less_300'] = np.where(df_sun.Sun < 300.0, 1, 0)\n",
    "df_sun['value_3'] = np.where(df_sun.Sun >= 300.0, df_sun.change, 0)\n",
    "df_sun['value_3_less'] = np.where(df_sun.Sun < 300.0, df_sun.change, 0)\n",
    "\n",
    "# Creae 4 new columns in the df_out_temp dataframe\n",
    "df_temp['tmp_55'] = np.where(df_temp.avg_temp >= 55.0, 1, 0)\n",
    "df_temp['less_55'] = np.where(df_temp.avg_temp < 55.0, 1, 0)\n",
    "df_temp['value_55'] = np.where(df_temp.avg_temp >= 55.0, df_temp.avg_temp, 0)\n",
    "df_temp['value_55_less'] = np.where(df_temp.avg_temp < 55.0, df_temp.avg_temp, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>8.33</td>\n",
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       "      <td>8.33</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-7.91</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>4.01</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>225.66</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-26.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>787.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-07-07</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>-17.69</td>\n",
       "      <td>291.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-17.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-07-08</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>636.53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-07-09</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3.12</td>\n",
       "      <td>639.93</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-07-10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>895.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change     Sun  sun_300  less_300  value_3  \\\n",
       "0 2009-06-26        4   26      6    0.00  418.70        1         0     0.00   \n",
       "1 2009-06-29        0   29      6    8.33  848.28        1         0     8.33   \n",
       "2 2009-06-30        1   30      6   -7.91  608.00        1         0    -7.91   \n",
       "3 2009-07-01        2    1      7    4.01  168.86        0         1     0.00   \n",
       "4 2009-07-02        3    2      7  -26.91  225.66        0         1     0.00   \n",
       "5 2009-07-06        0    6      7    2.30  787.34        1         0     2.30   \n",
       "6 2009-07-07        1    7      7  -17.69  291.83        0         1     0.00   \n",
       "7 2009-07-08        2    8      7   -1.47  636.53        1         0    -1.47   \n",
       "8 2009-07-09        3    9      7    3.12  639.93        1         0     3.12   \n",
       "9 2009-07-10        4   10      7   -3.55  895.12        1         0    -3.55   \n",
       "\n",
       "   value_3_less  \n",
       "0          0.00  \n",
       "1          0.00  \n",
       "2          0.00  \n",
       "3          4.01  \n",
       "4        -26.91  \n",
       "5          0.00  \n",
       "6        -17.69  \n",
       "7          0.00  \n",
       "8          0.00  \n",
       "9          0.00  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sun.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>change</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>tmp_55</th>\n",
       "      <th>less_55</th>\n",
       "      <th>value_55</th>\n",
       "      <th>value_55_less</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76.91</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>8.33</td>\n",
       "      <td>75.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>77.41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77.41</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.01</td>\n",
       "      <td>73.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>73.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>71.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change  avg_temp  tmp_55  less_55  \\\n",
       "0 2009-06-26        4   26      6    0.00     76.91       1        0   \n",
       "1 2009-06-29        0   29      6    8.33     75.43       1        0   \n",
       "2 2009-06-30        1   30      6   -7.91     77.41       1        0   \n",
       "3 2009-07-01        2    1      7    4.01     73.40       1        0   \n",
       "4 2009-07-02        3    2      7  -26.91     71.23       1        0   \n",
       "\n",
       "   value_55  value_55_less  \n",
       "0     76.91            0.0  \n",
       "1     75.43            0.0  \n",
       "2     77.41            0.0  \n",
       "3     73.40            0.0  \n",
       "4     71.23            0.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This next step defines some variables to count the number of days when the sunshine hours is above or below a specific threshold; and defines variables that counts the actual points gained or loss over the horizon for each of the two groups.  \n",
    "\n",
    "Temperature data is counted, summed and analyzed as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FOR SUNSHINE\n",
    "# count number of days where sunshine is at least 300 hours\n",
    "a = df_sun.sun_300.sum()\n",
    "\n",
    "# count number of days where sunshine is less than 300 hours\n",
    "a_less = df_sun.less_300.sum()\n",
    "\n",
    "# calculate the points gained when sunshine is at least 300 hours\n",
    "b = np.where(df_sun.Sun >= 300.0, df_sun.change, 0).sum()\n",
    "\n",
    "# calculate the points gained when sunshine is less than 300 hours\n",
    "c = np.where(df_sun.Sun < 300.0, df_sun.change, 0).sum()\n",
    "\n",
    "# FOR TEMPERATURE \n",
    "# count number of days where temp is at least 55 degrees F\n",
    "at = df_temp.tmp_55.sum()\n",
    "\n",
    "# count number of days where temp is less than 55\n",
    "at_less = df_temp.less_55.sum()\n",
    "\n",
    "# calculate the points gained when temp is at least 55\n",
    "bt = np.where(df_temp.avg_temp >= 55.0, df_temp.change, 0).sum()\n",
    "\n",
    "# calculate the points gained when temp is less than 55\n",
    "ct = np.where(df_temp.avg_temp < 55.0, df_temp.change, 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of days when sun hours is greater than 300:\n",
      "1295\n",
      "\n",
      "Total number of days when sun hours is less than 300:\n",
      "1221\n",
      "\n",
      "Total points gained in S&P 500 when number of sun hours is greater than 300:\n",
      "1159.2999999999988\n",
      "\n",
      "Total points gained in S&P 500 when number of sun hours is less than 300:\n",
      "839.1800000000013\n",
      "\n",
      "Total number of days when temperature is greater than or equal to 55:\n",
      "1314\n",
      "\n",
      "Total number of days when temp is less than 55:\n",
      "1202\n",
      "\n",
      "Total points gained in S&P 500 when temp is greater than 55:\n",
      "1038.2800000000007\n",
      "\n",
      "Total points gained in S&P 500 when temp is less than 55:\n",
      "960.1999999999994\n"
     ]
    }
   ],
   "source": [
    "# FOR SUNSHINE\n",
    "print('\\nTotal number of days when sun hours is greater than 300:')\n",
    "print(a)\n",
    "print('\\nTotal number of days when sun hours is less than 300:')\n",
    "print(a_less)\n",
    "print('\\nTotal points gained in S&P 500 when number of sun hours is greater than 300:')\n",
    "print(b)\n",
    "print('\\nTotal points gained in S&P 500 when number of sun hours is less than 300:')\n",
    "print(c)\n",
    "\n",
    "# FOR TEMPERATURE\n",
    "print('\\nTotal number of days when temperature is greater than or equal to 55:')\n",
    "print(at)\n",
    "print('\\nTotal number of days when temp is less than 55:')\n",
    "print(at_less)\n",
    "print('\\nTotal points gained in S&P 500 when temp is greater than 55:')\n",
    "print(bt)\n",
    "print('\\nTotal points gained in S&P 500 when temp is less than 55:')\n",
    "print(ct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, compare the average daily gain / loss of S&P 500 when sunshine hours is greater or less than 300 minutes per day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average daily gain of S&P 500 when sun hours is greater than 300:\n",
      "0.8952123552123543\n",
      "\n",
      "Average daily gain of S&P 500 when sun hours is less than 300:\n",
      "0.6872891072891084\n",
      "\n",
      "Average daily gain of S&P 500 when avg temperature is greater than 55:\n",
      "0.7901674277016748\n",
      "\n",
      "Average daily gain of S&P 500 when avg temp is less than 55:\n",
      "0.7988352745424288\n"
     ]
    }
   ],
   "source": [
    "# FOR SUNSHINE\n",
    "print('\\nAverage daily gain of S&P 500 when sun hours is greater than 300:')\n",
    "print(b/a)\n",
    "\n",
    "print('\\nAverage daily gain of S&P 500 when sun hours is less than 300:')\n",
    "print(c/a_less)\n",
    "\n",
    "# FOR TEMPERATURE\n",
    "print('\\nAverage daily gain of S&P 500 when avg temperature is greater than 55:')\n",
    "print(bt/at)\n",
    "\n",
    "print('\\nAverage daily gain of S&P 500 when avg temp is less than 55:')\n",
    "print(ct/at_less)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is this difference signficant for the **sunshine** data ?  Remember that the overall average for all days over the 10 year period is **0.794** calculated earlier in this notebook.  \n",
    "\n",
    "Obviously, the difference in average price gain / loss for the two different **temperature** distributions is not significant.  The avg daily gain is **0.790** and **0.800** for days above 55 degrees F and below 55, respectively. \n",
    "\n",
    "Let's quickly try the differences for over 75 degree F vs under 35 degree F cutoff:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creae 6 new columns in the df_out_temp dataframe\n",
    "df_temp['tmp_75'] = np.where(df_temp.avg_temp > 75.0, 1, 0)\n",
    "df_temp['tmp_35'] = np.where(df_temp.avg_temp < 35.0, 1, 0)\n",
    "df_temp['value_75'] = np.where(df_temp.avg_temp > 75.0, df_temp.avg_temp, 0)\n",
    "df_temp['value_35'] = np.where(df_temp.avg_temp < 35.0, df_temp.avg_temp, 0)\n",
    "df_temp['change_75'] = np.where(df_temp.avg_temp > 75.0, df_temp.change, 0)\n",
    "df_temp['change_35'] = np.where(df_temp.avg_temp < 35.0, df_temp.change, 0)\n",
    "\n",
    "# drop unnecessary columns\n",
    "df_temp.drop(['tmp_55'], axis=1, inplace=True)\n",
    "df_temp.drop(['less_55'], axis=1, inplace=True)\n",
    "df_temp.drop(['value_55'], axis=1, inplace=True)\n",
    "df_temp.drop(['value_55_less'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe below was created to count the days when the temp is above 75 or below 35 and to compare the two distributions with statistical tests later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>change</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>tmp_75</th>\n",
       "      <th>tmp_35</th>\n",
       "      <th>value_75</th>\n",
       "      <th>value_35</th>\n",
       "      <th>change_75</th>\n",
       "      <th>change_35</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>8.33</td>\n",
       "      <td>75.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>77.41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.01</td>\n",
       "      <td>73.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>71.23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change  avg_temp  tmp_75  tmp_35  value_75  \\\n",
       "0 2009-06-26        4   26      6    0.00     76.91       1       0     76.91   \n",
       "1 2009-06-29        0   29      6    8.33     75.43       1       0     75.43   \n",
       "2 2009-06-30        1   30      6   -7.91     77.41       1       0     77.41   \n",
       "3 2009-07-01        2    1      7    4.01     73.40       0       0      0.00   \n",
       "4 2009-07-02        3    2      7  -26.91     71.23       0       0      0.00   \n",
       "\n",
       "   value_35  change_75  change_35  \n",
       "0       0.0       0.00        0.0  \n",
       "1       0.0       8.33        0.0  \n",
       "2       0.0      -7.91        0.0  \n",
       "3       0.0       0.00        0.0  \n",
       "4       0.0       0.00        0.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FOR TEMPERATURE \n",
    "# count number of days where temp is at least 55 degrees F\n",
    "a_75 = df_temp.tmp_75.sum()\n",
    "\n",
    "# count number of days where temp is less than 55\n",
    "a_35 = df_temp.tmp_35.sum()\n",
    "\n",
    "# calculate the points gained when temp is at least 55\n",
    "b_75 = np.where(df_temp.avg_temp > 75.0, df_temp.change, 0).sum()\n",
    "\n",
    "# calculate the points gained when temp is less than 55\n",
    "c_35 = np.where(df_temp.avg_temp < 35.0, df_temp.change, 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, print the results for comparing the two distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of days when temperature is greater than 75:\n",
      "364\n",
      "\n",
      "Total number of days when temp is less than 35:\n",
      "405\n",
      "\n",
      "Total points gained in S&P 500 when temp is greater than 75:\n",
      "-83.90999999999872\n",
      "\n",
      "Total points gained in S&P 500 when temp is less than 35:\n",
      "411.7500000000002\n"
     ]
    }
   ],
   "source": [
    "# FOR TEMPERATURE\n",
    "print('\\nTotal number of days when temperature is greater than 75:')\n",
    "print(a_75)\n",
    "print('\\nTotal number of days when temp is less than 35:')\n",
    "print(a_35)\n",
    "print('\\nTotal points gained in S&P 500 when temp is greater than 75:')\n",
    "print(b_75)\n",
    "print('\\nTotal points gained in S&P 500 when temp is less than 35:')\n",
    "print(c_35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average daily gain of S&P 500 when avg temperature is greater than 75:\n",
      "-0.2305219780219745\n",
      "\n",
      "Average daily gain of S&P 500 when avg temp is less than 35:\n",
      "1.0166666666666673\n"
     ]
    }
   ],
   "source": [
    "# FOR TEMPERATURE\n",
    "print('\\nAverage daily gain of S&P 500 when avg temperature is greater than 75:')\n",
    "print(b_75/a_75)\n",
    "\n",
    "print('\\nAverage daily gain of S&P 500 when avg temp is less than 35:')\n",
    "print(c_35/a_35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is this significant ?  The next step is to compute the pvalue with a ttest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.0460206812846735, pvalue=0.29560174677866946)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For temp greater than 75 vs less than 35\n",
    "ttest_ind(df_temp['change_75'].dropna(), df_temp['change_35'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the pvalue is **0.295**, the difference in daily price change values is not significant.  This calculation shows that there is about a 30% chance that the difference in the results were due to randam chance.\n",
    "\n",
    "The next graph shows the visual relationship between the temperature and all of the daily change values.  From a quick scan, it looks like the returns above and below the 35 and 75 thresholds are fairly evenly distributed:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0xe614c88>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp.plot(kind='scatter', x='avg_temp', y='change', title='Temperature vs daily price change', \n",
    "                 figsize=(10, 6), grid=True)\n",
    "plt.axvline(x=35.0, color='green')\n",
    "plt.axvline(x=75.0, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated above, this scatter plot shows a fairly even distibution of daily price change above and below the thresholds.\n",
    "\n",
    "Now, going back to analyzing Sunshine, compare the results using a **500 daily minutes** of sun cutoff.  This is the same process and code as above only using a cutoff value of 500 instead of 300."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sun['sun_500'] = np.where(df_sun.Sun >= 500.0, 1, 0)\n",
    "df_sun['less_500'] = np.where(df_sun.Sun < 500.0, 1, 0)\n",
    "df_sun['value_5'] = np.where(df_sun.Sun >= 500.0, df_sun.change, 0)\n",
    "df_sun['value_5_less'] = np.where(df_sun.Sun < 500.0, df_sun.change, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_sun.sun_500.sum()\n",
    "x_less = df_sun.less_500.sum()\n",
    "y = np.where(df_sun.Sun >= 500.0, df_sun.change, 0).sum()\n",
    "z = np.where(df_sun.Sun < 500.0, df_sun.change, 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of days when sun hours is greater than 500:\n",
      "902\n",
      "\n",
      "Total number of days when sun hours is less than 500:\n",
      "1614\n",
      "\n",
      "Total points gained in S&P 500 when number of sun hours is greater than 500:\n",
      "589.170000000003\n",
      "\n",
      "Total points gained in S&P 500 when number of sun hours is less than 500:\n",
      "1409.3099999999972\n"
     ]
    }
   ],
   "source": [
    "print('\\nTotal number of days when sun hours is greater than 500:')\n",
    "print(x)\n",
    "\n",
    "print('\\nTotal number of days when sun hours is less than 500:')\n",
    "print(x_less)\n",
    "\n",
    "print('\\nTotal points gained in S&P 500 when number of sun hours is greater than 500:')\n",
    "print(y)\n",
    "\n",
    "print('\\nTotal points gained in S&P 500 when number of sun hours is less than 500:')\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the average daily gains of the two different groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average daily gain of S&P 500 when sun hours is greater than 500:\n",
      "0.6531818181818215\n",
      "\n",
      "Average daily gain of S&P 500 when sun hours is less than 500:\n",
      "0.8731784386617083\n"
     ]
    }
   ],
   "source": [
    "print('\\nAverage daily gain of S&P 500 when sun hours is greater than 500:')\n",
    "print(y/x)\n",
    "\n",
    "print('\\nAverage daily gain of S&P 500 when sun hours is less than 500:')\n",
    "print(z/x_less)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are these daily differences significant ?  This is strange: now the average is greater in the 'less than' group.\n",
    "\n",
    "What about the days when the number of sunsine hours is greater or less than **700** ?  This is the same process and code as above only using a cutoff value of 700/300 instead of 300 or 500.  Create the new columns in the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sun['sun_700'] = np.where(df_sun.Sun >= 700.0, 1, 0)\n",
    "df_sun['less_700'] = np.where(df_sun.Sun < 700.0, 1, 0)\n",
    "df_sun['value_7'] = np.where(df_sun.Sun >= 700.0, df_sun.change, 0)\n",
    "df_sun['value_7_less'] = np.where(df_sun.Sun < 700.0, df_sun.change, 0)\n",
    "\n",
    "d = df_sun.sun_700.sum()\n",
    "d_less = df_sun.less_700.sum()\n",
    "e = np.where(df_sun.Sun >= 700.0, df_sun.change, 0).sum()\n",
    "f = np.where(df_sun.Sun < 700.0, df_sun.change, 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of days when sun hours is greater than 700:\n",
      "373\n",
      "\n",
      "Total number of days when sun hours is less than 700:\n",
      "2143\n",
      "\n",
      "Total points gained in S&P 500 when number of sun hours is greater than 700:\n",
      "211.11999999999966\n",
      "\n",
      "Total points gained in S&P 500 when number of sun hours is less than 700:\n",
      "1787.3600000000006\n"
     ]
    }
   ],
   "source": [
    "print('\\nTotal number of days when sun hours is greater than 700:')\n",
    "print(d)\n",
    "\n",
    "print('\\nTotal number of days when sun hours is less than 700:')\n",
    "print(d_less)\n",
    "\n",
    "print('\\nTotal points gained in S&P 500 when number of sun hours is greater than 700:')\n",
    "print(e)\n",
    "\n",
    "print('\\nTotal points gained in S&P 500 when number of sun hours is less than 700:')\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the average daily gains of the two different groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average daily gain of S&P 500 when sun hours is greater than 700:\n",
      "0.566005361930294\n",
      "\n",
      "Average daily gain of S&P 500 when sun hours is less than 700:\n",
      "0.834045730284648\n"
     ]
    }
   ],
   "source": [
    "print('\\nAverage daily gain of S&P 500 when sun hours is greater than 700:')\n",
    "print(e/d)\n",
    "\n",
    "print('\\nAverage daily gain of S&P 500 when sun hours is less than 700:')\n",
    "print(f/d_less)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are these daily averge differences significant ?  It seems odd that the daily average gain for days with more than 300 minutes is larger than those days with less than 300.  However, the reverse is true for days where the cutoff of sunshine hours is 500 and 700.  In other words, the average daily price where sunshine hours is more than 500 and 700, is less than the gain on days where there is less than 500 and 700 hours of sun, respectively.  What is going on here ?  The next step uses a t-test to determine the significance of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.3810877859627107, pvalue=0.7031542048478165)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For sunshine more than 300 vs less than 300\n",
    "ttest_ind(df_sun['value_3'].dropna(), df_sun['value_3_less'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.9764164785096374, pvalue=0.328905074534642)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For sunshine more than 500 vs less than 500\n",
    "ttest_ind(df_sun['value_5'].dropna(), df_sun['value_5_less'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.877069483932292, pvalue=0.06056640489040069)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For sunshine more than 700 vs less than 700\n",
    "ttest_ind(df_sun['value_7'].dropna(), df_sun['value_7_less'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these Ttests, none of the differences are significant; all could have happened due to random chance.  However, for days more or less than 700 minutes of sun, the pvalue is just slightly above 0.05 (actual value = 0.061).  Remember, the average gain was higher where number of sunshine minutes was less than 700.  This could be due to the fact that the winter months in general perform better than the summer months, but it could also de due to randome chance.  The p-value is not low enough to draw a concrete conclusion.   \n",
    "\n",
    "The following plot shows the relationship between amount of daily sunhine vs. daily price change.  This should help to further clarify whether a true difference exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0xe6a6e10>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sun.plot(kind='scatter', x='Sun', y='change', title='Amount of sunshine per day vs daily price change', \n",
    "                 figsize=(10, 6), grid=True)\n",
    "plt.axvline(x=300, color='yellow')\n",
    "plt.axvline(x=500, color='orange')\n",
    "plt.axvline(x=700, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the calculations and significance tests above show, it looks like there really is not a significant correlation between the amount of daily sunshine in New York City and the price change of the S&P 500 over the last 10 years.  The winter (and thus less sunny) months might perform slightly better, but very difficult to see any evidence of that in the above scatter plot.  **The safest conclusion and the conclusion best supported by the data is that there really is no signficant difference in daily returns regardless of the season or the amount of sunshine or the average daily temperature**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.B.  Question: Is it possible to build a supervised learning model that uses the day of the week, month, day of month and weather data to predict daily price change of the S&P 500 ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, show the dataframe created earlier that has the day of week, month and day of month - along with the daily price change, average daily temperature and daily sunshine minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>change</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.91</td>\n",
       "      <td>418.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>8.33</td>\n",
       "      <td>75.43</td>\n",
       "      <td>848.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>77.41</td>\n",
       "      <td>608.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4.01</td>\n",
       "      <td>73.40</td>\n",
       "      <td>168.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>-26.91</td>\n",
       "      <td>71.23</td>\n",
       "      <td>225.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  month  day  change  avg_temp     Sun\n",
       "0 2009-06-26        4      6   26    0.00     76.91  418.70\n",
       "1 2009-06-29        0      6   29    8.33     75.43  848.28\n",
       "2 2009-06-30        1      6   30   -7.91     77.41  608.00\n",
       "3 2009-07-01        2      7    1    4.01     73.40  168.86\n",
       "4 2009-07-02        3      7    2  -26.91     71.23  225.66"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_both.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These will be regression models because the dependent variable that will be predicted is daily price change, which is continuous.  First, set up the training and test data sets like we did before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2012, 6)\n",
      "(504, 6)\n"
     ]
    }
   ],
   "source": [
    "# drop the 'DATE' column because it is not needed; the date elements are already included\n",
    "df_both.drop(['DATE'], axis=1, inplace=True)\n",
    "\n",
    "# Create training and test sets.\n",
    "offset_both = int(df_both.shape[0] * 0.8)\n",
    "\n",
    "# set the training and test set dataframes according to the split in the previous line of code\n",
    "df_train_both = df_both[:offset_both]\n",
    "df_test_both = df_both[offset_both:]\n",
    "\n",
    "# print the number of rows and columns for the new dataframes\n",
    "print(df_train_both.shape)\n",
    "print(df_test_both.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown above, the new dataframs have 7 columns and the training consists of 80% of the data while 20% is reserved for the test set.  \n",
    "\n",
    "Try **Linear Regression** first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Duration: 0:00:00.123007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fred\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n",
      "C:\\Users\\Fred\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "# 1.  Linear Regression\n",
    "\n",
    "from datetime import datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "# Instantiate our model.\n",
    "regr_both = linear_model.LinearRegression()\n",
    "\n",
    "# set features and dependent variable for training data\n",
    "y_train_both = df_train_both['change'].values\n",
    "\n",
    "# drop the 'change' column to obtain the feature inputs\n",
    "df_train_both.drop(['change'], axis=1, inplace=True)\n",
    "\n",
    "# normalize the training data\n",
    "x_train_both = sklearn.preprocessing.normalize(df_train_both)\n",
    "# x_train_gold = df_train_gold\n",
    "\n",
    "# now for test...\n",
    "y_test_both = df_test_both['change'].values\n",
    "\n",
    "# drop the 'change' column to obtain the feature inputs\n",
    "df_test_both.drop(['change'], axis=1, inplace=True)\n",
    "\n",
    "# normalize the test data\n",
    "x_test_both = sklearn.preprocessing.normalize(df_test_both)\n",
    "# x_test_gold = df_test_gold\n",
    "\n",
    "# fit model to training data\n",
    "regr_both.fit(x_train_both, y_train_both)\n",
    "\n",
    "end_time = datetime.now()\n",
    "print('\\nDuration: {}'.format(end_time - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting the model above, print the results below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 3.57756166  3.10150904 -1.47460292  2.47753905  2.06545587]\n",
      "Intercept: \n",
      " -1.7000859217184647\n",
      "\n",
      "R-squared train:\n",
      "0.0006136080436329738\n",
      "\n",
      "R-squared test:\n",
      "-0.0020524175019946966\n"
     ]
    }
   ],
   "source": [
    "# Display the attributes we calculated.\n",
    "print('Coefficients: \\n', regr_both.coef_)\n",
    "print('Intercept: \\n', regr_both.intercept_)\n",
    "print('\\nR-squared train:')\n",
    "print(regr_both.score(x_train_both, y_train_both))\n",
    "\n",
    "print('\\nR-squared test:')\n",
    "print(regr_both.score(x_test_both, y_test_both))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too surprising.  The prediction scores are close to zero.  Since there was no correlation with the weather data and the supervised model created and executed earlier in this notebook did not yield useful results, this model as well, is not very helpful.  The Random Forest Regressor algorithm and Gradient Boosting Regression are shown next.  The expectation is that these models also will not yield helpful results, however, a check is needed in the following steps just to make sure.  **Random Forest Regressor** is next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.  Random Forest Regressor\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Instantiate our model.\n",
    "rfr_both = RandomForestRegressor(n_estimators=500, max_depth=10)\n",
    "\n",
    "# fit model to data\n",
    "rfr_both.fit(x_train_both, y_train_both)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit is performed above, the results are next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "R-squared train:\n",
      "0.30660433969652545\n",
      "\n",
      "R-squared test:\n",
      "-0.013001913282428612\n"
     ]
    }
   ],
   "source": [
    "# Display the score for training and test:\n",
    "\n",
    "print('\\nR-squared train:')\n",
    "print(rfr_both.score(x_train_both, y_train_both))\n",
    "\n",
    "print('\\nR-squared test:')\n",
    "print(rfr_both.score(x_test_both, y_test_both))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is interesting - the score for training is reasonably high, however, score for test is very low.  Looks like the model is overfit.  Note:  A hyperparameter of max_depth=50 was used and the R-square for train came out to be 0.84 which is even more overfit.  **Gradient Boosting** is next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=500, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.  Gradient Boosting Regressor\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Instantiate our model\n",
    "gbr_both = GradientBoostingRegressor(n_estimators=500)\n",
    "\n",
    "# fit model to data\n",
    "gbr_both.fit(x_train_both, y_train_both)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "R-squared train:\n",
      "0.6025383942041649\n",
      "\n",
      "R-squared test:\n",
      "-0.08161605412282902\n"
     ]
    }
   ],
   "source": [
    "# Display the score for training and test:\n",
    "\n",
    "print('\\nR-squared train:')\n",
    "print(gbr_both.score(x_train_both, y_train_both))\n",
    "\n",
    "print('\\nR-squared test:')\n",
    "print(gbr_both.score(x_test_both, y_test_both))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here again, a solid accuracy score for the training data, but not useful for test data.  The two dataframes - training and test - have a high degree of variance with these particular parameters.  In conclusion, it is very hard to build a supervised model to predict the daily change in price for the S&P 500 given data data and weather data.  \n",
    "\n",
    "A final section of this notebook is dedicated to supervised learning methods to determine if some kind of relationship or relationships can be derived by looking at the price change for each of the 5 securities (S&P 500, 10 Yr Bond, Gold, Oil, Bitcoin) over the past 10 years.  In other words, maybe one or more of these securities move together or maybe some move in opposite directions and are thus negatively correlated.  The goal is to look for some kind of cluster or grouping of securities in order to take strategic action in the future.  For example, if gold and oil move together - since they are both commodities - maybe holding both of these commodities in a portfolio is not necessary.  Or, another strategy could be to buy one of the commodities if the other has moved up considerably (comparatively) in the recent past.  \n",
    "\n",
    "### 3.C. Question:  Is it possible to build an unsupervised learning model to identify relationships among the 4 securites previously analyzed in this study ?  \n",
    "\n",
    "**Answer:**  Possibly.  See analysis below.\n",
    "\n",
    "Set up the new dataframe which will constist of all of the daily 'change' values for each of the 4 securities over the past 10 years.  Bitcoin 'change' data is not included because the data only covers about 4.5 years.  \n",
    "\n",
    "First, look at the dataframes and rename some columns to merge the dataframes with the relevent information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>weekday</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
       "      <td>4</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>8.33</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
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       "      <td>7</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>-26.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change\n",
       "0 2009-06-26        4   26      6    0.00\n",
       "1 2009-06-29        0   29      6    8.33\n",
       "2 2009-06-30        1   30      6   -7.91\n",
       "3 2009-07-01        2    1      7    4.01\n",
       "4 2009-07-02        3    2      7  -26.91"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2009-06-26</td>\n",
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       "      <th>1</th>\n",
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       "      <td>3.51</td>\n",
       "      <td>3.51</td>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
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       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2009-06-30</td>\n",
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       "      <td>3.53</td>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.51</td>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE DGS10  price  date_time  weekday  day  month  year  change  \\\n",
       "0  2009-06-26  3.52   3.52 2009-06-26        4   26      6  2009     NaN   \n",
       "1  2009-06-29  3.51   3.51 2009-06-29        0   29      6  2009   -0.01   \n",
       "2  2009-06-30  3.53   3.53 2009-06-30        1   30      6  2009    0.02   \n",
       "3  2009-07-01  3.55   3.55 2009-07-01        2    1      7  2009    0.02   \n",
       "4  2009-07-02  3.51   3.51 2009-07-02        3    2      7  2009   -0.04   \n",
       "\n",
       "   Monday  Tuesday  Wednesday  Thursday  Friday  \n",
       "0     NaN      NaN        NaN       NaN     NaN  \n",
       "1   -0.01      NaN        NaN       NaN     NaN  \n",
       "2     NaN     0.02        NaN       NaN     NaN  \n",
       "3     NaN      NaN       0.02       NaN     NaN  \n",
       "4     NaN      NaN        NaN     -0.04     NaN  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_10_10.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'change' column of the above 2 dataframes needs to be combined into 1 dataframe and labeled correctly.  The following code renames the 'change' column for the appropriate security; then the 10 Yr Bond data is merged with the S&P data; then the gold and oil 'change' values are added to the final dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3.53</td>\n",
       "      <td>3.53</td>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.51</td>\n",
       "      <td>2009-07-02</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE DGS10  price  date_time  weekday  day  month  year  change  \\\n",
       "0  2009-06-26  3.52   3.52 2009-06-26        4   26      6  2009     NaN   \n",
       "1  2009-06-29  3.51   3.51 2009-06-29        0   29      6  2009   -0.01   \n",
       "2  2009-06-30  3.53   3.53 2009-06-30        1   30      6  2009    0.02   \n",
       "3  2009-07-01  3.55   3.55 2009-07-01        2    1      7  2009    0.02   \n",
       "4  2009-07-02  3.51   3.51 2009-07-02        3    2      7  2009   -0.04   \n",
       "\n",
       "   Monday  Tuesday  Wednesday  Thursday  Friday  \n",
       "0     NaN      NaN        NaN       NaN     NaN  \n",
       "1   -0.01      NaN        NaN       NaN     NaN  \n",
       "2     NaN     0.02        NaN       NaN     NaN  \n",
       "3     NaN      NaN       0.02       NaN     NaN  \n",
       "4     NaN      NaN        NaN     -0.04     NaN  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_10_10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the 'change' column of the following dataframes:\n",
    "df_10_10.rename(columns={'change': 'change_10'}, inplace=True)\n",
    "df_goldc.rename(columns={'change': 'change_g'}, inplace=True)\n",
    "df_oil.rename(columns={'change': 'change_o'}, inplace=True)\n",
    "df_btc.rename(columns={'change': 'change_b'}, inplace=True)\n",
    "\n",
    "# add the 'dt' column to merge:\n",
    "df['dt'] = df['DATE']\n",
    "\n",
    "# drop the ambiquous 'DATE' columns for merging:\n",
    "# df_10_10.drop(['DATE'], axis=1, inplace=True)\n",
    "\n",
    "# create dataframe for all 5 securites:\n",
    "df_all = (df_10_10.merge(df, left_on='date_time', right_on='dt')\n",
    "          .reindex(columns=['dt', 'change', 'change_10']))\n",
    "\n",
    "# create the columns for gold and oil:\n",
    "df_all['change_g'] = df_goldc['change_g']\n",
    "df_all['change_o'] = df_oil['change_o']\n",
    "\n",
    "# Fill Nan values with 0\n",
    "df_all['change_g'].fillna(0, inplace=True)\n",
    "df_all['change_o'].fillna(0, inplace=True)\n",
    "df_all['change_10'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe with 10 years of daily historical price change for the S&P 500, 10 Yr Bond, Gold, and Oil is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>dt</th>\n",
       "      <th>change</th>\n",
       "      <th>change_10</th>\n",
       "      <th>change_g</th>\n",
       "      <th>change_o</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>2019-06-19</td>\n",
       "      <td>8.71</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-4.03</td>\n",
       "      <td>-0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2493</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>27.72</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2494</th>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>0.06</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2495</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>-5.11</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-11.58</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>-27.97</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.74</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             dt  change  change_10  change_g  change_o\n",
       "2492 2019-06-19    8.71      -0.03     -4.03     -0.98\n",
       "2493 2019-06-20   27.72      -0.02     -0.53     -1.13\n",
       "2494 2019-06-21   -3.72       0.06      2.46      1.49\n",
       "2495 2019-06-24   -5.11      -0.05    -11.58      0.28\n",
       "2496 2019-06-25  -27.97      -0.02      1.74     -0.25"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, look at a pairplot to check out the relationships:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the pairplot parameters\n",
    "sns.set(font_scale=1.3)\n",
    "sns.pairplot(df_all)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows that there might be a significant relationship between 'change' and 'change_10' - that is, as the daily change of the S&P 500 moves higher, the daily change in interest rate of the 10 Yr bond moves higher as well.  It doesn't look like an extremely strong correlation, but it does appear to exist.  The other relationships look fairly random.  \n",
    "\n",
    "The next 3 plots show the relationships between the 10 Yr bond, gold and oil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the 'binary column for positive or negative change\n",
    "df_all['binary'] = np.where(df_all.change > 0, 1, 0)\n",
    "\n",
    "# if 1, yellow; otherwise black:\n",
    "colors = np.where(df_all[\"binary\"] == 1,'y','k')\n",
    "df_all.plot.scatter(x=\"change_10\", y=\"change_g\", c=colors, title='10 Yr Bond vs. Gold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above, it looks like there is random relationship between the 10 Yr Bond and gold.  However, there are more points with a positive change in the 10 Yr Bond that correspond to a positive change in the S&P 500. (there are more yellow points to the right of the plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if 1, yellow; otherwise black:\n",
    "colors = np.where(df_all[\"binary\"] == 1,'y','k')\n",
    "df_all.plot.scatter(x=\"change_10\", y=\"change_o\", c=colors, title='10 Yr Bond vs. Oil')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above, it looks like there is random relationship between the 10 Yr Bond and oil. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zGWsrVqygQ4cOREdHB4/17t2b5cuXH8riCSGEEEIcUZpM0NM0jZycHH766SfOOeccTjvtNMaNG0dlZWWda/Pz80lKSgo5lpCQQEFBAaZpHq4iCyGEEEI0aU2m6zYvLw+/34+qqjz33HMUFhby1FNPceeddzJlypSQa91uN3a7PeTYns8+nw+Hw3HA742Pj/jnhW8iEhMjG7sITZbUTcOkbhomddMwqZuGSd00TOqmYYeqbppM0GvXrh0//fQTMTExKIoCQFxcHEOGDCE3N5f09PTgtU6nk+Li4pD7fT4fqqr+pZAHUFxchWEc+a2AiYmRFBbWbf0UUjf7I3XTMKmbhkndNEzqpmFSNw07kLpRVeVvNU41ma5bgNjY2GDIA8jIyAACXbV7S0lJobCwMORYYWFhne5cIYQQQoijWZMJegsWLKBPnz5UV1cHj61duxZVVetM0Dj22GPZtGlTyPi93377jZ49ex628gohhBBCNHVNJuj16tULh8PB2LFj2bx5M8uWLePBBx9k8ODBJCUlUVJSEgyBvXv3pn379owZM4aNGzfy4Ycf8tlnn3HNNdc08rcQQgghhGg6mkzQi46OZsqUKVRVVXHZZZcxevRo+vXrx7hx4wAYMmQIU6dOBUBVVV555RW8Xi9DhgzhjTfeYPz48dKiJ4QQQgixlyYzGQMgMzMzGOb2tWDBgpDPLVu25M033zwcxRJCCCGEOCI1mRY9IYQQQghxcEnQE0IIIYRopiToCSGEEEI0UxL0hBBCCCGaqSY1GUMIcWhp/koq8pdiaNWExXaGxN6NXaQmSdM0ysrKME01ZBF3IYQ40kjQE+Ioofur2bXufxi6GzBxl63HZfehuLo1dtGaBNMMbIU4e/ZMnnrqccAkJSWVSZOm0qJFy8Yt3F40TWPbtjzsdgepqWkSRIUQ+yVBT4ijRHXpKgzDCwQCjWn62bl5Hmldj+6gZxo6xXlzqCldiwnkrl2D3+8DYPv2bdx++y3Mnv1Z4xZyt5KSEv71r6vYtWsnhmFw0kmnMHHiC1it8qdcCFE/GaMnxFHCNDQwjdBjpt5IpWkaTEOnbMd8asrWAwYKBhec2YFTT0wHwDAMcnI2o+t/Xk+7du1k3bo1uN01h6y8jz02jq1b83C73Xi9XpYuXcKMGR8csvcJIY588jNQiKNEWHQnKvKXYBp+ABTFRlzqcY1cqsbhqcimMPcjTN1D4PdubQB2Oqz06dGChUtzAYiMjMRisez3eS+88Azvv/82Npsdi8XC5Mlv0anTMQe93Bs2rEPTtNrv4fGwZs0fB/09QojmQ1r0hDhK2FyJJLUfjj0sDas9jojEPrTKvLCxi3XYab4KCnM+3B3yYO+QB6BpOsVlgVY5RVEYO3Zc8FxlZSUrV64gL29L8Ngvv/zEhx++h8/no7q6ioqKcu6667ZDUvb09LYhodPhcJCR0fGQvKuxrV79B08++QhPP/0EOTnZjV0cIY5Y0qInxFHEEdGalMwbgp8Vdf8tVc2Rz72Ten/jKlb8Ph8VVV4++nwtENhX+8cfF3PuueezZs0f3HLL9Zimid/v59JLL+O++/7N5s2bMYzQsLhnDJ2qHtzf0v/5z+NcffUwqqoqMQyDzp27Mnz41Qf1HU3BL78s4/bbb8bj8aAoCp98Mou33/6QjIwOjV00IY44EvSEEM2CaZoYuhvV4trvTFSLNYJ9W/FAxRXdkZnvzmT2F2updge6t3VdZ8uWQOvd3XffTmVlZfCOTz6ZxYABp9O2bbs6gS4pKfmghzyA5OQU5sz5ig0b1uFwOOjYsdMheU9je/XVF/F4Ai2upmnidrt56603ePzxCY1cMiGOPBL0hGiGTNM8qpbd8FZtpTD7AwzDj6JYSGw3FNM08FRkY7GFE5HQC9XiAMAeloYr+hjc5ev2TEAmpsUZeKq24PFq+LXaEGi3WejZ8zgMw6CgID/knbpukJubzbBhV3HppZczc+Z07HYbiqLy/POvHLLv6nQ66dGj5yF7flPg8bhDPpumSU1NdSOVRogjmwQ9IZoRT2UORbmzMLQarI54Ettfgc0R19jFOih8NbvwVGajWl2ExXZFVW0AGLqPgs3vYxpeIDCTuGDz+2AqgAaKhaqi30jpdDOqxY6iKMS3uQhPZTd0Xzn2sBTsYWlovnKGXdSDnLxSfl+7C4BuXdowcuRoVFUlNTWNHTu2B8ujqirt2we6EseMGcuVV46gpKSEdu3aER4ecXgrp5m5+OIhvPDCM8HA53Q6ufjiwY1cKiGOTBL0hGgmNF8FhdnTg7NqNW8xBZveJq3L6CO+da+mfAPFObMxTR1FtVCRv5SUTjeiqjY0XynBprk99l42xtTRtSpqytYSEX8sEJhk4YpqH3JLdHI/asrWce7px1Be5QMThl1xIw5HoCXw+edf5aabrkXT/Pj9fq644ip27tzB6aefjNfrZeDAs3jwwYex2+2HsiqOCkOHXomm+Zk+/T0sFgs33zyK/v1Pa+xiCXFEkqAnRDNR3yQDQ6tB16qw2iIbp1AHSenWLzDNQIA1DQPNV0Z1yUoiE3pjsUb86XqApmliGr79XqNaXWwu6sLT/30Drzdw7UP/eQSrPYrTTjuDzMxOfPXVd+TlbSEmJpbc3GxGj741OJbsq68+x2638+CDD//zL3yUUxSFq666lquuuraxiyLEEa/5jeIV4ihlsYaz7yQDExPV4mycAh1ERnAplN1MHUMLdOtZbOHEpJ6OolhRVDuKYsPqiAOldkaxoig4I9v96Xve/+D9YMiDwDp17703LfjZ5XKRmdmJ5ORkFi5cEAx5AF6vl++++/bvfkUhhDgkpEVPiGbCHtYCV1RH3BWbME0DRVGITj0tOJbtSOaMbIe7IivYJasoFpyRbYPno5JPxBnVDr+nCKstmsri5Wi+PwAF1RpBQvol2JwJ9T7b7y6kumwtiqKiqmad86ZWgc+dj92VHHI8JiYGq9WGpvmDxyIjj+yWUyFE8yNBT4hmQlEU4tMvxVOxCc1Xjj0sFUd4y8Yu1kER3+ZiinJnByZjqHZiW55T57vZXcnYXckUb/mUmtLVwVBo6m5US/3j5rzV2yjIeiewPRwwangLxlw/DLdHY9I7v/DTim1cNDCN/A1TSWx/Bc7I9OC9l19+JR99NJ3y8gp0XcNms3HvvQ8emgoQQoi/SYKeEM2Ioii4opvXTgmmaaCodpLaX3FA19eUrwezdpsw09SpKd+EPSytzrVl2+cHJ68ARIQFWj8jwu2M/tcJXLStjA5t4zFNP6Xb55Ha6abgtbGxsXz00Vw+++xT3O4aTjnlVDIzO/3dr3lUKC0tpaAgnxYtWhIRITOThTgcJOgJIQ4b0zTx1WzD0L04wlqgWl0NXqv7qynM/hBfzXYU1Upsy0FExPf403eoqhV977kZihpcQ29fdcb+7cVqVenQNr72Wn9NnWuio2Oa5c4Uh8JHH33I008/ic1mxTRNXnjhNY4//oTGLpYQzZ5MxhBCHBamaVCY9S4FWe9SlDOLHWtfxucuwOfzYZp1x8YV5czEV7MDMDENP6VbP8dbvb3ug/cRk3YmirL7N6xiQbW4CI+rPyCGxXZBUeofw7jvkjRWZ/NYj7Ax5OVtYeLE8fh8Xqqrq6mpqeHOO2/D59v/TGghxD8nLXpCiCDdX01x3hx8NbuwOmKIb30RNmf8n994AKqLf8dbsy3YVVpS7uau4UPYnFuE1Wrl3nsfYMiQYcHrvTXb2HsWsYmJtzoPR3iL/b4nPK4bFlsk7vJNqFYnEQm9sOzTcuit3kbJ1s/R/dVYHDHovkrAxO9zYxgGNptln6CnEhbb5Z9WwVErNzcHm82K11t7zDAMiooKSUvb/7+nEOKfkaAnhAAC3ar5m6aheUsAA59WTf7GN0nrcnuDXZ9/hd9bEjIebsKrS8jOLcIwTHw+H8888xQZGR059tjjAFBVJ4Ze212qKOrufWr/nDMyHWdkOpq/Er+nENMei9UeBYDmLd09ASNQFkN344xsT1L7YVx1xZmkt3DRukU0p57YFrvNgomCwxVDeGy3kHd4PB42bFiH0+kiI6MDCn4U1XHEL059KLRu3Rq/Xws5pigQH1//TGghxMEjQU8IAYDuL0f3lVHbimZimjq+mu0HtAbdn3GEpVGl2oIBa2NOMbpR22WraRq//74iGPTi21xIUc6swElFweZMJCy28wG/r6pkFaV5nwXW0zN1YlqeQ2TCcXgqs2HvrmJTx1OxCdM06XvSubz33tt4PB6+/2kLvbu35OJLriQl86yQmbs7d+7gmmuuoLq6CkPXyWwfx7g7T8Vmt5PQdgiuqIx/UFPNT3p6O2677Q5effVFbDYbuq4zYcLzwV1HhBCHjgQ9IQQAimLD3HcrMQyUg7QOnyvmGMKr86gq+hUFlYgwB2UVtZvXq6pKYmJi7fXRHUnJvAFPdR4WiwtXTCbKXosg74+u1VCS91lg9u3uGbhl277CFd0h8H0UJXTXtN3Pve22O0lJcKD6NuBw2GnX+Tzadq679dbDDz9IUVEhhhEIxWs35fPFgvVceFYninJmktZ5FJa9diP58cclfPrpx4SFhXH11deRnv7Pg/OR5uqrr+ess85l586dtGmTTlycjHkU4nCQoCeEAAI7TITHdqOmbA2m4UdRrNhdqdjDDs4YKkVRiGt5DtEpA6gsL8btnR5y3u/306dP35BjNlciNlcg/Om6jqIYqOr+55CZpoHuKwf26UJVLOjeMlzRnbDs/B7NXwGmjqLaiE7pj6Io+Gq207dTObC7S1H/gcLcUnRvCVZ7LDEtzsRqjyInJzsY8gB8Pp0t28p2f1LxufNx7Q568+Z9xbhx9+PxeFAUha+++pwPPphNmzbpf6X6moWUlFRSUlIbuxhCHFVk1q0QIiiu9QWBZUwSehGTdgZJGSNQlIP7Z8JidbGrsBKrNbSlMCwsjB076s6q9fl83HffXfTp053jj+/OCy9MrHeWrrsii62rnmbr749TmD0DTH/IedPQsDpiUS12EtoNRVUDW8OZponFHmhdKtv5PaAFj2uaRnXxanw1O6gpW8uuDW9g6F46dszEYqn9neywW2ifHrfnRVhsUcFz//3vS8Gt0kzTxO12M2PGB3+x1oQQ4u+RFj0hRJCiKIG16g5gvbq/wzRNvF4vKSkpaFro4HxN0+qdgfnii8+ycOECDEPHMGD69Pdp3TqdSy+9rPZebxlFOTOD4/90f3md5yxbWcT6z54mLCycft2rSI6zoqoKmBolWz7GU7kZb1Ve8HpFUbBYAr28u0uPafjwVObw8MNPcP31V1FYmI+m+UiMDyd7Syk/Ld/OmWdfit2VFHyO379P4DRNfD4vQghxOEiLnhDisPjyy8858cSenHTScVx77ZXcdttoHA4nERGROBwO7rlnLElJyXXu+/HHxXj3WpfD43Hzww+LQq4JrLfX8GzXmZ+t4ZnXvmHWrBm8886bjHv6C7y+vXfP0KguXlGnFbD+GbQKCQmJzJo1lyfuO4uocAc7CyqZt2gzz/5vCdPefpfC7OnoWmDG8ODBQ3E6a5d3cTqdnH/+Rfurqjo0bylFuR9TkPUulUW/1tuiKYQQ9ZEWPSHEIZeVtZFHHnkw2IWZl7eFTz6ZxaeffkVeXi4tW7YmNTWwRZmmaaxfvw7TNOjU6RiSkpLJzc0Jhhur1UpycgoQaB2rrKxEVZwYhh4S9bbuKGf8K4vZkV/BXsPpACir8PDT8m2cdlLbvY7WDU+GYWAYgV0yDANsNifOyMA9iukmb1sx1W4fuh641+vTef+T5Vx6biYFWe+RknkD1177L6xWCx9//BFOp4tRo+6kR4+eB1RvnopsSnd8g99dECyft3orur+SmNS6k0SEEGJfEvSEEIfc6tV/hLSOGYZBTk4OsbFxwdAGUF1dxWvPjWbgyUlYrSpvTyrk1ltGsWbNanRdAxQiIyO54YZb2bZtK7fc8i927dqBoqiMuOwEzj4lOTCpwq9zz2Nf4/Zo9ZQG/H6D6hofHm/gvNMR+qfQ69UwTJP8wmqWLd9K58xkWrftSufuI4LLrGRt3orb66sTInXNwDR1/J4CDN2NxRrGiBHXMWLEdX+pzrzV2yjMno5phn6iNCawAAAgAElEQVQH0/BTWbBMgp4Q4oA0qaC3a9cunnzySZYtW4bVaqV///7cf//9REdH17n28ccf55133gk5Nnz4cMaNG3e4iiuEOEAJCYns27XqdDqw2UInZHw840UuPrMFjt3B65Q+Kaxd9zGzZ3/Gjz8uwWaz0b//aURGRnL99cPZvn1rsKXv7Rk/8tvKJFISw1izoQCfX6chhmHy+bcb2Z5fRZcux9LvWCt7JmGAimFvw6tvfMkvK7Lw+3WujOrNoGE3B8Pq1KmvM3nyqxzXLYW9e3dtNpXe3dMC15lm7VZsf0N1yco6IU8IIf6qJhP0DMNg5MiRxMbGMm3aNHw+Hw8//DD33XcfkyZNqnN9VlYWt99+O0OHDg0ec7ka3iBdCNF4TjqpH8cffwK//PITphlYAuXRR5+qMwYuxlWGw1G7/pzTYaVVsoekpGQuvnhw8Ljf72PLltyQsWqaplFUZmFTzg4G9G1FfmEV+l7dsaqqEBXhwOmwBiZE+A3ik3sw9OpxeCs3UZQ7C0wdwzBwqdu5/7aTMMPuJDK+O1FRgVm0JSXFzJv3Ja+99iKapvHjL7lERdhRVYUwl4PjuqVy0/DjABWrKxFvdV6dxZNN02T79m1UV1fTtm077HY79WogJOqGwjffb+SdUX248IILuO3W63E4Y7DYDmzXECHE0aXJBL1169axZs0alixZElw09cEHH+TKK6+koqIi+Id2j6ysLK655pqQBVaFEE2Tqqo8//wrLF36A0VFhXTr1oN27drXuS48Ig5d92KxqHvdW3f3BKvVhtVqrTOjVVVVnn76BRJtS7HZYN73m/FrOjarhQEnd2P0DSej+coBk+iU04hOORkAT2UO23dVsGVrMb26p+GwW9F8VeBfiDU2noqCNWzJ28nIu8bj9/tDZgxXVPkAyOzUjfvvvZklC2exdUcxMVFOMtKz6dL7MiITegOBH7T//vd9fPvtPKxWK+HhEbz55nu0aNGyznf00hrD/AVFMXa3hSr4DTsz5vzGjLmr6JqZxIWn+Nm5fio2m4WYtIFEJfWt8xwhxNGtyQS9tLQ0Xn/99ZDgtufX/t4z7gDKysooLCykbdu2CCECTNPAXb4JQ6vCHt4Su6vuDNbGpKoqJ598yn6vOe6Ei9m58X3sNhNVVdF0k7Zdh9a5TlEUevbsxc8//xRyXNM0+vXrz671G7jhil707JpK3rZy0lIiSUhqzWU3vklstJMrh13K5V2ODd7nrSnk3xPm8dKjg3DYa/8smoZOUc5MACY++y1VVZU0NOF13bp1vPDadE7sbuXMU9pjt1nwawabVn3McacHgt4XX8zlu+/m4/V68Xq9uN1uHnjgHqZNq1082jRNFi78hrFj76VVWgyDTmtLRvu29O53BU9OnMLcuauwWBQevGMALufurm9Tp3zHt7ii2mFzyo9fIUStJhP0YmNj6d+/f8ixt956i/T09DqtdllZWQBMnTqV77//nvDwcAYPHsx11133p6vmC9EcmaZB4eb38VZv3T050ySu9YWEx3Vt7KIdEL+nkNLt8/FUZOFyWqmq9gEqCaldiEk8pt57hg69kt9/X47PF2hRs9sdDBgQmKAQnXYaRdkz6NUtjV7d0jAMyMot5N93nEiH9Hj8WilbVz3H6rw0vpj3I6ZWTnmlF7dHIzKitgXRMEy+/G4jH366GrdHazDkAXi9HooLc2nfuk8wgFksKjarylefT+eYrn1ZtmwpdptBRnoSqUmRLFu+jezs7OAzqkvXUpjzMfff9z5en86m7F28mL0Lp/N3Jk++kJiYaCwWC9GRdiyWujt/+D3FEvSEECGaTNDb1+TJk5k3bx7/+9//6pzLyspCVVVatmzJ5MmTWb16NU8++SS6rnPTTTc1QmmFaFzuik14q7cFFwwGKNn6GWGxXRpYC67p0Lyl7NowBdMIBDbdCKSpyAgb3qrNDd53xhlncdtto/nvf1/B43Hj9/v47rv5nHP2ObRNdWMPb4VheDE0N77qYjq2i8c0TRRFwW63MG/RJl5/bzpeX+2kjcnv/cqYW07GalPRNYPySi/vf/wHVTW+Ou8P7BhiBscJOp1OoiPD6+wXbAJPTXiKmCgXedvLQIG1GwvZnFvKo/ecxqx5RQD4PUWUbPmE6uqaYB3sYbGo7NixnREjrmPu3DnUeKox9H3eY3hRLTJOWQgRSjGb4Mqbr776Ki+99BLjxo1j+PDhdc6bpkl5eTkxMTHBY1OmTOGtt95i8eLFh7OoQjQJRduWkbd+TkjQA4XjBo5HUS2NVq4DsWPzfHZunsfe69iVVXgoLq2hQ9tkeg58ssF7dV3nhBNOYNeuXcG9Z8NcdiY/fTFREVYUxRqcuVrj9lNQVE1CXBgR4XZuvu9TdhZU1XlmRts4enVNw+3x8+2SbKrdtXWqqkowLA4bdgWLFi2irKwMXddJTk5G91XyzEOnB1v0NM1g285y7hj3JbExTkrLAusItkyN4v9uOokWqVG4wqPpetKdVJZsJm/dbHTNyzV3fkxZhSf4XqfTyVdffUWHDh0oLCxk1qxZxLiK6ZBatlfJFSJi08nsM/Iv/xsIIZqvJtei98QTT/DOO+/w8MMPc8UVV9R7jaIoISEPICMjg6KiInRdx2I58P9jKy6uwjCaXNb9yxITIyksrGzsYjRJR0Pd+IwEQvsVFWzORIqKa/Z7X1Oom+pqD4Zpou7T8Pi/d39j0kuP7rd8O3Zsp6SkNBjyAkw25RTQq1taMOT99scOnnp1MaqioOsmo647vt5u2OioSLJySsjKKan3fRaLwiuPnU9CfAQmCqf3vRhnfH8WLJjPe++9jdfr4eHnFnL3jScSE+Uka0sJT7+2JFCq3UV0Oa08NXYgEeF2VFXF1KpZ+9NrxLUaFAyRj/zfaTz0zAK8Xg0TC/fd929iYlJ214WTwYOHU1n4M6Xb50NwCRaTqtJcCgoqDnkrblP476apkrppmNRNww6kblRVIT7+r8+ub1JB78UXX+Tdd99l/PjxXHLJJQ1eN3HiRFatWhWyjt7atWtJT0//SyFPiObC7koirs1FlOTNxTR82FxJJLar/4fS4fDHHyt58MF7KSwspHPnLkyY8NzutfTqCo/tStHWhVgtgT9kHq/GZ/M3UFmjEp166n7fExkZtXsh5Vq6YdIhPS742TRNtu2owOut7aJ9acoyLh3UiY8+W8ue33lOp5NevXqzaPH3aFptcFRVBcMwcdgtnHBcS1KSIgGTJT/nsnrDMhTLQnJ3mHi9gRa4dZsKufHeT+uUtU3LaCo3eGnfJg6LRQ0ZT2xoVVjsMTijMvBUZNEuPZm3X7gcI/xkUlr1xhUWjtvt5uGHH2TJku8JCwvn8XG3kBalhARWxWJv8l31QojDq8kEvfXr1zNp0iSuv/56+vXrR2FhYfBcbGwsFRUVOBwOwsPDOf3003nzzTeZNGkSgwYNYsWKFbz++us8/PDDjfcFhGhk4bFdCIvpDBgoSuP94CkoyOfmm6+jpibQmvj77yu45ZbrmTnz03pDiM0ZT17ZMVTkLyTMaeX7n3KYvySPSy+9DEVR0DSNgoJ8oqKiiYgI/TVbUVHOGWecxXffzccwDKxWK2cPaEdkhDN4jaIonNirJdM+WoGmBVKRphvM/GxtMCQpisJ//vMEx/fqxMb1v1FcGih7TJSTk3q1orDUTa/eJzGgTyzoJbw143e+WLARr0/HZlOJjgyjdVoUUVFONmwqxL/P+Lm05EhKS2uwqAomhCwfA2AaGrqvjIT0IbjL1qH5K7E5EinbMY+ijYtRVCsvvLWJxT/8is/no7q6mrvve5rxY88kOcGJxaJitdpIbHneP/q3E0I0P00m6H399dcYhsEbb7zBG2+8EXJu7ty53HLLLVxyySXcfvvt9OrVi5deeomXX36Z1157jaSkJMaMGcOFF17YSKUXomkIBKnDE/K8VVupLlkFqpXIxD7YHIFWtJUrV4QEOl3X2LIll4qKchYuXMBzzz2N1+uhf//TePTR8TidTk478zJef72IKa/8D13XGTjwbO666162bMnhxhuvpaKiHF3XGTnyDq666lo2blzPnDkfM2vWh8GWse7dj2X48Ks5/tg0SrbMCilrdKST8wdm8slX64PH9m4Jc7nCME0Ds/p3XnzkXDblFGMCHdvGsz6rkCdeWsy6rHn073UxmmYwZ9764JAPv9+gutrDgL4d+OzbjXVCHkDrFtHcfdNJ2G0WFq5wsC5rK8dkxAcXb1YUKNw8HdUeh+ELTM5AsQS7ZU3Dz49Lf8Hnqx0vWF3j4e6HP6ff8a2JinSwKaecx8YPJSOuzuuFEEexJhP0Ro8ezejRoxs8v2DBgpDPAwcOZODAgYe6WEKIergrsijKnhEcA1ddvIKUzBvxag6ys7NDFhSGwELBq1f/wfjxj+LxBLo4Fy6cz2OPwhNPPo+iKNx000huvPFWoHYNzdGjR1JYWBCc2fraay/x8svPh4zJ0/VAl+wff6wkNSUZmyN0/C6Aw2GlfZuGE5BpGqSmtkDT1mCzWejcMan2XruVGo8fj6+EufNWY7dqdcf1KrB89c6QGbx7czmtOB1Wtu4o57+Tv8LvNzi9X1tapkZRXePH7fETHengorOPqd13d5/tz1xOK25P6ALRmm6wcGlu4LzLxR9/rCQjo0OD31MIcfRpMkFPCNF0bNmSS37+Ltq3zyA+PqHO+bIdC0L2YTUNH9nrvua2Mf+lpqYav98fDGsOh5Prr7+Jn3/+KRjyAHw+P4sXLaC6dC3hsZ0xTZNNmzZSWlpCWloLUlPTyM3NCXnvvjth7M3QdVb/9BoR/nRACbaUAXi8GtlbSuvc43A4AIVBgy6gQxsrJVtC32eaJtlbA/cZhsHUD36qu34dgfX24uPCqKz2Ee6ysWV7ee077BbsditP/3cJHdvGc85pGcz5egPzF2eHPOPfowfUhrx63DyiL89N/hG/X8Nms+Hz+UK2gFMUpd5/KyHE0U2CnhAixMsvP8+7776FzWZD13WeeealOjtamIZW575XX59NaWlJsIXNYrGQmXkMt902mpNPPoU333wDm80WEtbCw+2UbPkEZ1RHxo4dw8KF3wYXQHY6nXXesT+6odO6RWRwPT4w2dPwtzG7iLnfbAheG+a0cvOIPlgsKp/N38QZp55Iad5ndZ6pKArRkaHlSEmM4Nar+zDn6/WszyrC49UYcl4XTjspnegoJ37NYNW6XUz5YDltWsawM7+KBT9k4/cb/PL7drpkJtV5j8WisG1nOcd2ScFuC3S975mBqyg2UBROG3AynY+/haVLfyQyMpLw8AiefPKR3YE6sFNIv3796zz7n9B1na1bt6CqFuIjq/FVb8PqiCE+fsBBfY8Q4tCRoCeECFq7djXvvTctuEUXwJgxo1m8+JeQGe0R8cdSvuv74Lp9imIlv8gTDHkQCAlRUVHBkDh48OV8OH0apaUlaLqB1aJyy4jemJjMm/c5ixZ9Fwx5AD27JJKaFEHO1jJWrN4ZUk5Foc7yKJcO6kpqUmTws4mKaepYLCoxUU4GndEBv18nKsLB+WdmBgPcKce3xjR/xjTr7qpjmibJibUTQJwOK+PHnklUhJ3ux6Sg6wbFZW68PoWEOBeqquCww0m9WnNCz1ZsyinioYnf4fcHEqfXp7NqXT42q4p/98zeuBgXFZUePvx0Nb26pZEYHw6Y2O1WElsPQlEtrNuQw9OPT8PtfpsLL7yEoUOHo6oqnTt3YdWqlSQkJNCv34CDujNQRUUFN9wwgq1b8xh24TGcd0ZH7DYVFCveijXEt7tm96LRQoimTIKeECJo69atdZYo8vs1KioqiI2NDR6LTDoR0zSpLl4OioWY1FM5vq+F3C1bgwHR6XTSu/fxwXuioqL44P13mTltHG6Pj+O6pdKmZQwWq4tt23aEdOuOueVkevdIw2a1oGkGc+dv4J1ZKwHo2TWV+0aezGffbmTews2YpslVg3tw2kmhe19rfg1FDUxNad0ihhuu6IXfr2OaYLfXfsdAq1lgnF5glq+BooKqKGi6QXFJdfDa9FYx2Ky1S6NYLCqR4XbiEtPxe/Jw7PVc3TAABXWfmcaGbnDR2Z1Zv7mArNwSIsLslFV4cHs07nr4KzIzEji5d2uuunoUkUl9WLFiOaPufiy4fMvUqa9jmiY333wbGRkdycjoGPL88vIytm7dSkpKSoNL2uyPz+fDZrPx9NNPkJOTg4LOBQM7YrXuDnWmhqc6H09lDq6o9n/5+UKIw0uCnhAiqH37jJBWOYCwMBfR0dEhxxRFITrlZKJTTg4eGzWqPbm52Sxd+gOmaXLqqadz7bU3hNwXG9+G4dfeQ3HeXMBEtbhIbH8VW6Y/F7ymXetY+hzbIjhezWpVufjsTny5MIc777yHzKQ1OOwWLj+/K5efv/devoEuTL9u8vV361n+x04S48O46KxOJMSFoxsGdpulztIme3+n8koP19z5MQpw1oAMVq3bxa7C2t0zvF4Nhz30z6bFopK/bS1pqVEhx60WlRYpkVisSrAFUlGgZVo0Vw/pjmpR+WJBFm+8/2twcoemG6zPKuSE41oTkdATgC+/nBsMeQAej5tPPpnFzTffVuc7LFr0HffeezdWqwW/38+YMQ8wZMhQDMPg99+XU1lZQbduPYiLi69zb17eFkaNuomtW7cSFuYiMjIav99HeJitzrZuoOzVRS6EaMok6AkhgjIyOnDXXWN49tkJWK1WLBYrr7wy+YC6BB0OBy+//D+qqqpQFAgPr38F9/C4boTFdsbQPKjWMGpqqtm4sXbZk4hwO7puhN6kqEycMIHOnTIo2JyDaXjrebJCZNIJ/N+Dr3Bqn1jG3HoyhmFisajk5JXQLj0OdZ/tN/aMg9ujsLiaXt3SGHXd8WTllLDgh2z0vZZLKa/0sncDnWGYbMopplP7+idBTP90NaeemM7cbzYC0KdHC+74V99g2Dzv9Azmfb+RnLzarcx03cSvtkW1OABwOl0oihIy8aKoqJDi4qKQyRdudw333ns3Ho87eGzixPEcf/wJPPnkI6xc+Xvw33Hy5Dfp0qVbSD3cfPN17Nq1M9BSW12N2+3GYrFQXeNny9Yy2rSKwWa17KlqHOGt6/3OQoimRQZYCCFCDB06nPnzF/Peex/xzTeL6Natx1+6PyIiosGQt4eiWNi2o5Dzzz+T/v37kpW1MXguOy90dqxpgtUWxrG9+mOxR2Ga9S9hAia6vxLVrKTvcS1xOW2Eh9lxOqy0bR2HzWoJCUyGYWKa4PVpeLx+5n6zgZemLsNmU6mu8bGzoLLOMiql5e6QsKhpOknxYaiqUu9i0Kef1Javv9+MokBkhIN7R/YjKiJ0cscZ/dphs9V2+TqdTvqfdgEAPnc+l110AmkpoS2quq7z73/fF3KsoCC/TpC12Wx89NF0Vq5cgdtdQ3V1FdXVVTzwwJiQ68rLyygqKgwJk06nk5iYWMLCwhn/2k9s3lKNYnFhcyWT2ftWLLbw+v8ZhBBNirToCSHqiIqKJioq+s8v/AdGjryBHTu2h4SLuJgwTurdmnmLt3Bm/w44bDrbd1Xx7P++5vIrkhk+/GpiUk+lfOdCUCy7J4Psaf0zqSn9g+SEsDqhy2ar/U2rKArVNT6yckt4Z/ZKWqVGYxoGS37Zis+vs2VbYPLHzVf1RrUosNcE46T48JBn+/w6Toet3pCnKArxcWHB1skad21Xp2mazP1mA18tzMJiUWjfJo7svDJcrjDuvvs+evXqww/fTiIlKh9Q6Nklhe07a1v9DMNg3bq1Ie9LSkoOCaZ9e7agRWoscZH+kPGPAAUFBSGfA8F839ZOGD9+ImFhEVitFjp0yAyO3wyLiqRa9iwV4oggQU8IcdhVV1cFuwn3sFqtjBhyLKeemI6iqFRW1nDjA19SUhboinzuuQmccsoAWrY8gV9//YlYVzFRkTasVjUkotisFhb9lEtyYgTdj0nGNAPBas/6d5pmMPeb9bz/yWoAioqrKSmrDUKmCT6fTkmZmysu6sb2nZXkF1WRnVfK2FGnYBiB9fmmfLCczxdsREGhXZtYHh9zBk5n7Z9UwzDZvKUUzMB4Pb9m8Nq0n7llRB8+/3YD0+esDi6wbLdbGHfX6XQ/JhVXpI/XJz3F6b08wa7SlqkR2O0WfLuvVxSFFi1aAuDxeHjssXEsWrQQp9OJruvcdeMJ9OqWjMNuAwWKLunBu7N/BwLL3nTqdEzIv4fNZuOBB8YxYcLjweVaBgw4lT59TvjTvXNN06C6+Hd8nkLsrmTC43oEJrX4KijOnY3PnY/VHk18+iXYXckH9N+HEOLgkaAnhDjoTFPHW7UVTAN7eEtUiz3kfGDLsdBuUVUxSYh1ERi+ZhAebmfoBV347zu/AnDicS3I+vU5jMJY2qUoqGpgDNueWbIWVWXJz1v44rtNYIKqKhzXNZV7bj0ZXTfw+XXsNgsfzl3Nh5+uDr5375AX+h1MLj23S7CVTNcN5i/ZzITXluD2alRWeXeP3zPJyilm0js/M/qGE0OCkWEY6IaJvvsZC37IYdnybdgdjpBdNHw+nW8XZ9G9UyLuik20Ty7C748Mrql3Vv8MFv+8ley8ciyWwNjJxx+fAMBjj41j/vyvg7OdHQ47ifHhuJy2PaVgyHmdmTt/A26PTlpaCyZMqJ38sscllwyhc+eurFu3hqSkZE488eQDCHkmRdkz8FTmYJp+FNWGp2Iz8emXULBpGpqvDDDxewrI3zSNFp1vR7W69vtMIcTBJUFPCHFQGbqP/I1vovlKAQXVYiO54w1Y7bWzUlVVJTw8gsrKiuCxrp2S6H5M7WLCVotKbEwYAOeclsG/hh2Hw26lqtpLRLij9jqryoasIjq0i+eFN37C568NUL+u2sHvq3fSu0cLiktr+PHXrcz+MrTLc18KYLNZ6Nenze6yBsLOwqVbmPLBipDnB7+zCRERDvyaEQxnqqrQ/ZiUOtdWu/34NIgIs3HFxd05tksKToeVH3/dCgRayD6dt55bRvQJ+Y6P3jOQCsuZeLw6Xbp0IyoqUJ/ff/9dMORBYMeRFat3ktmudjkci9XGZ59+hlezEx+f0GCAy8zsRGZmp/3Wz978nsJgyIPAnrw15RuIqNqG7q+CvWfrmia+mp04o9od8POFEP+cBD0hxEFVnr8Yv7cIdk+a0A0fpVu/ILH9sJDr2rVrz8qVK4Kfj8lIwOvTg8uq6LrBitU7sVlVhl3YDYfdSnFpDR6vFhL0TNNk265yfJqGXwsNYZpuUFHl5dN565n20e8oKMHFi+tjs6m0aRHDOadlkJQQOtng6++z6g15e7jd/jqTN+rsibunXJqGRbXw7uyVvDVzBeefkck1lx0LQHFpDUt/20ZBcQ0PjR6A3W7BrxnEtR5M+5bH1nlWWFgYVVW14+VM0wxZzw9AtTiJiE4l8iAvcGwaflDUkDynKCooJib71rOBYnEghDi8ZNatEKJBpqlTUfATRbkfU5G/dD8zXmtpnuJgyNv9FPze4jrX/ec/jxMVFYXLFUZkuJ1jOiRit1kwTRO/X6eo1Me1Q49j8oQLCXMFuiELS2rYsq2sTrevrps8OGFBnd0yDMPkh1/yeHvWSvx+Y79BDSDcZeeJ+87gxF6t8HhrZ2Fk55WiaQ0HRIBFy7ZQVu7GNE1M00TXDb74biOt0+pOajFNE49Xw+3R8PsD163ZWMDm3BJefesXfH6ddZsKGX77R1x39yc88PRvJNcT8gCuuuqaOsd+/DUPxRKYYGF1JpCUcXVwFwuPx8Pnn89lxowP2LIld7/f6c/YXEkoqo3aiRwKqsWFI6wlEQm9dp8DRbHhiGiNPSztH71PCPHXSYueEEcAQ/NQtuNb/J4C7OEtiU49FVW1/fmN/4BpmhRmf4i3MhfT1HCXrcNTkUVixlX7HbvlCG+Fp2JzsDsPxYLVmcqzz05g9epVtG+fwR13/B/t2rVnzpyvWbr0B5SKr+jYPj7YTWoYJjurMijwhPPCi89x5imtufjsY0hJjMDr8Ye8X1EU0lvF1lcUAH77YwfHdU3FYrGwdlMBlVUNL/QbGeHA5bQFg6TPr7P01zxefvNn7h15MhP/+0NwbN2+27B5fTrzF2dzxcXdUFWFJ15axB/r80PG4jVY1wZ8s3gzS3/dFtwaDQLPr3FrZHToAIDmLcVbswOLNRxHRBsURSEjoyMulwu3u3b9vLwdVShxl9GqZauQ97jdNVx55RB27dqFYQR2Ann55Un06dP3T8tYH1W1kdLxOoq2fILmKcbmSiS+zcUoqoXYFmfjjGiDr2YnVkcc4XHd/3TMnxDi4JOgJ0QTZxo6uzZODYx5M3W8NTvx1ezY3Upz6P6PU/OWBEMegGlqeGu24fcUYnclNXhfZFJfvDXbcZdvABTsrmT+8/Qcflv+Oz6fl9WrV/Hbb78yY8bHxMbGcu6555G3YnnIQsQo8NtPnzLv+ywsFpWZc1djURVWbyjglOPboGlGcEsuwzBo3yaWj98YxpQPlvPVwiy03UuaqIrCuDsHkBgfzgNPzUfTDFRV+X/2zjs8qjr92/cp0zLpvZCEhAChhd4CKEXBAoodCxZQrMhPsXexAAuLKwqKIrjqWsGCoIKIoii9Q6ghhUB6T6ae8v4xyYQhVBVB37mva6+Vk1O+c6Z95imfx5tSDQ02Y7O7PbN3ZZGn7j/Pc/mGxRhkke6d4hlzXVfapUUx9cmhLF25H03VWPZzdrPHvnNviWdsWpmdbbuLvV2yjYQGm3jm/waSkhSGy6XyzsebWPazx2fvl7V5PubMACajgZDQcB5++Ans1fsoy1kADV6ADjUch9yLtLTWHG2NYrEEEBMT2/C86VRVVWGxWPj88wUcPnzIp6bvhReeYdGipcd9Pk+GbAojts1tzbYLgkBAaDsCQtsd4yg/fvz8VfiFnh8/5zgu22FUd01TOt3TC98AACAASURBVFRXcNYfQnVXIxtDz9h1dV1pVn8FAujK8Q7x7CGIRCRfSU72LlRNAUsomzbPxOXyiAu3201xcRFZWTvp3LmrR3QIMkca1mmaTkWlHbtDQRQFUhLD2LW/jJ17SsnJr6JbxzgsFgOCACaj7I0EXnFxO3749QCqpqHr0K9nIu1aR/GfuauprXNydMnczEmXsGp9Hg6nQveMeOJigo56LAKBgSYuHtwaRdVIjAvm7tE92byjEEkSCQk28+nXO7wCbU92OTPe+o3rL+/UbMYtwFWXtqdVy3AEQcBiEbnjhu4Ul9eR0iKMRd/v8dlXkgQ6t4/hueenER0dw8Ft73qipA2PQXcV8MbszymvgYxOGazfsA5N04iJiWXWrLcxGAyUlZVy551jyM/PRdd1OnbM8BF5AFVVVfjx4+efi1/o+fFzztO8oF849uaT4rIVYa/Zz5ffrGLD5gO0bJnMLbeMIzIyqtm+BnMkkmxFcSl4TIkFRDkAg/n40Tzw1IDdddcYdu/OQhAE4uISmtXUgSfSlJNzgLFjR9OjUwR3XN8FSZZQVY2CwhpWb/R0oWqaTlCgmfFj+hESKON0Kcz9aCOKonPX6B7e86mqhiSJvPjoBeTkVVJYUoPRKCNLIsWl9c1EXlREANt2F3Hhea0wGeVjrrERQRAwNKzN4VRITQ6jfesoVq7NQ5ZE73xgRdVYu/kQD47LJDzUQlFpLdoRpX3n92npE4U1GiUeu2cA1gAjG7cdpqCoxpsOVlWdLTsLGXvneD76+Ct01VegCYJAYIDE6o0HEIV8NF3HaDQSGxtLq1ZpADz66IPk5h7wrm/79q0YDAbcbk9a3WAw/O60rR8/fv4e+IWeHz/nOMaAeCRDkMeTTFcRBNmzzXh6kyvs1Xspy1nA3I/W892P+3C6FFavlvn22+/4/PMlXruORgRBIqbNbVTkL8blKMZgjiIiaQSC6Pux4bIVUZa7ENVVjWyO5OMlJWRl7cRsgrgoK5U1JQQFBVNXV4vT6cRgMBAdHU2HDh0ZNeoqKisrWLaynAP5ZXRsE401wMgni3Z406+SJHDfrb0ICfRE7ixmA1de1J63PtzQYF4sNOwnYg0wEGCRSWsZgdutoCgaOtApPYaCwhpvM4bJKNG7awtWrMphzaYCkhNCuOqS9qiqjrGhY/V4afGFS3Zw/cjOiKKAQRab7SeKAkaDxOTHL+C1+WvZm11OcJAJRVFxOn1TuYIgYA3weAw+OeE8Hnnpe2pqnZhMEsPOS+OKi9sRaDWyb+Ns4mPDUBt86RrZubcUAK2xptDlYvfu3Rw+fIiEhBbs2rXTK/LAMzpNFEWMRiOKotKzZ2+ef/7lE79w/Pjx87fGL/T8+DnHEUSZ2DZjqDy8HLe9FJM1gZD4waddn1dx8FtU1c2S5Xu8IkpRFOrr61i5cgUjRoxsdoxkCGxmi3IkmuKgeP976KrHdNhtL2bLpp/o1jGKB8dlojbUvi1afgjNmMaO7Vvp3Kk1Y8bejcFg5ODBPG8kbX9OBftzKppdQ1V1IsMtPnNc5360kXqbu/lsV1kCdAR0jAYJuWEaxuirO3O4pIZN2wrRgS4dYqmpdfD4fQO89Xm/bsinV5cW2Oxu7E6FyLCAZmspq7CxbVcJIy9yYw0w0rtrCz74fCtuRUVVPbYmlw9LRxAEwkIsPD3hfATBUxPodCq88/FG7r65F5IkoqqeZgi7w40siciyxNv/ugxZEtHRWbA4i1feXk1CXDA3XJ6BKJnBGIrLXoGqabz1vw0cLj7WGDIddBW3o5yoqCjy8vJ8/up2u5Ekib59M3n11TeQZf/XgB8//2RO6x2+bds25syZw+7duzEajbRu3ZoxY8bQpcux2/79nLu4HaVUHPwO1V2DOSiVsIQLm0Vq/Jw7iLKFiKQRf+gcmuZJ/WlHpSh1HZ+oz0nPozoRBAlBlHHZi3xbT9FpkxLGtSPaYzI2vZ6uGJpEiw5jKM//CsVRRk3+fFyVaQQEBOByHb8LtpF6m4ugwCYPtqLSOg4V1bJzTwkd2kZjMsk4nQoOh0JYaNPkBVH0NGxIaFx/eQbDh7QhIS6YOe9vYPxtfTCZZBrP2qdbonfNsiyi63ozMR0WakGWRc+MW1UjwGLg308PY+G3u6isttO9UzwD+7b07t94vCgKmEwSFw9ugyrFIFHK5h2FvPPRJsJCLbRvE8U1wzt4r//iqyvZklWEy6WSta+UrTuLmPnCpVhC2lNrq+aZad+Re7Cy2X2SZZmOHdqhlX9CUbnK/be0Z+KkvGb7qarKxo3rWbjwE6677saT3v8j2bVrJ+PGTaW0tIxBg4Zwzz0TzjmxuHjxV7z33jwEQWDMmHEMG3bJ2V6SHz9nDem555577lR23LBhA6NHj8ZkMtG/f38SExPZvXs3c+fOpVevXsTH/z39kex2VzPvrb8jVqsJm+3kX5gAqruOwj1voTjL0FQ7LnsJbnsx1rAOZ3iVZ4fTuTf/ZNy2IhRXJYeLqjlcUouqeoSM2Wzm4Ycfx2q1nvB4TXVQsv8DKgu+o6Z4FZrqwBgQT33FZo8/SAOtUyMQdLxdsQCiZMLtKMFlL2xoKtGx15dxqLCSnPzmguVoDhXV0KtLCxRFBQSy8yo5VFjDytV5lJbXs2N3Md/9uJ/yynrSW0cheeaooes6iqrx+OTlOBwKFw5Iw2I2kN4qApNJ9s6SBXA6laapFoKAono6dI8Uew6nwuadhXy2OIvWqeEYDBK5BdV0bhfNhQNSaZkY1izK2IjQ0C1rMTgRRYHY6CCiIgL4/ucD9OuVROuUCARBoLrGwZsfbPAaO2ua5zGkp0USZq3HahG4oH8q3/64z6erVxRFBg0awsQxrRF0B19/v4uvlu1C03Rcbr1ZDWJ6q3BSWgTRvkNnpFMcS1ZQcJDRo68lNzeXqqpKdu3Kori4iPPPH3RKx/8VLF36DZMmPU1xcTHl5WWsWrWS1NQ0UlL+mokc/s+b4+O/N8fnVO6NIAgEBBhPuM+xOOWfYa+88gpXXnklkyZN8tn+9NNPM3PmTN57773Tvrifs4O9JtvnixldwV69B11XEQTp+Af6+VsTnjQCPU9jwlidiPCtbNlVRVJyKyZMeJioqBM3WABU5C/BWV8ADRMPakvXYbDEYAlujb1mH6riwuVWWbEqmwsGtPI5VkDz1hg2YpAFtu0qPKW1Z+dV8r/Pt+Jya6zdfJBBmSkkxodwIL+Sn1bneq4hQFmljV5dEkhJCkdRNX5YdYDNOwoxm2RGXd7JKz6jIgObdcWaTE0fhw6ngtkkoek6gt40Bk2SRPblVFBWYeOxl5cjSQI9Osfz5PjzfYRUYUktiqKREBfsvY6u6wRajUiSR/CJokCfbol07RiHJDaJ4mP/7hRQjhivZjBIPHB7JlPf+A2DwYiqqtx03VCuvawb9uo9fLV0Nx98vs3r4dezSwtGX5mB0SCy/JcDpLYMp0eneGSDkaJdbxKRchUBIW1P+jz89NMK3O6m7miP+fIinn560gmO+mv55JMPcTia5hc7HA4+/fRDBg++AID6+jrcbjchIaF+Xz8//19wykJvx44dzUQewK233srVV1/9py7Kz5nl2GJO4GgvLj//LETJSFTqNUSlwtM9PMJj9+4tbN26BUVRSEtrc8LjHbU54DPWSqO2dD2xbceSs2cFn33xFvtzyli/9RCbdxTx8N390DUdURL5cV01wy+MB5qsPJwulcpqx9GXaUZifAjTnhqKJAmgw41XZPDgpKXU2Xy7UHUdikvrePjF733EkiBAt45xyFKTmJIlEUVRcbl1bx1hba2TXzfkU1XjoKSsnrAQCwP7tiQ1ORxd16m3uZn25q+UVdi857j5ms6MHNaOrVlFrNtcQFCgCUkU+OTrnciyyMWDWnPt8A5YLAY0TWftpoMEBZnp2iHOu5bGjt9G0RESZKJdWiQ795aiaXpDk4mR9FaRPo+3fZsoHnlgHFHxHYkKsROob27wLoSh56fx3U/ZHCqqoV3rKB65K9MrZEeN7OSd5wsauq5RnvsF5k6PsGbNr5SWltKpU2dSU33FOnhSw0dHLCXp3ErbGo3NIx4mkwlN03jxxWdZtOgLBEGgffuOzJr1NoGBgWdhlX78/HWc8js0ODiYurq6ZttramowGM6sQ7+fPxdLSGvEwxZUTQU0BMHgGVf0J8/B9HPuous6jz02kV9++Qld19E0naeeeu6YDRknQnFWIAgC9e5IFn2fTX295zNi/dZD3PbgF8REBVJabsMaYOK8HoEEmJt+ZNhdcrNZsCajhCgK2B1NUaMH7uiD2SR56+1kWWTUZR148/0NzdbjdKm0S4ukRVwwP67O9XTd6pCTX4WmaRw59dGtaNz+8FckxoXQoW0Ulw5pS4/OCTz84rKGJg6Bb37cx7239CQxPoQnp/7gM+VCEOCSwW1Y/ks2cz7YgNOl+pgxu9wqC7/J4uvv9zBvxkiCA020ax3NpFd+YsyornTt6Cl3URtSxE3nFXh8/ADeX7CN/Xm1hIcYuPXazmzbVUS3TvGYTTIul0p5pZ027TPplNGNw1mzUY4Y2WYyylwyuDVvf7iRgX1b+kQrG6OCR6IqLh544B7WrVsHgK5rTJo0haFDL/LZb+jQi5kz53UURUFVVcxmC2PHjmt2vrPJHXfczZYtm7xRPbPZzJgx4/jiiwV8883XKIrnPmVl7eCll55j8uTpZ3O5fvyccU5Z6GVmZjJ58mRee+01oqI8nlvFxcVMnTqVzMzMM7ZAP38+omQiNn0c1UW/oLqqMQe3IjCi29lelp+/kE2bNvDzzz/6jM164YVnuPji4cctrDcFpWKv2uGzTTJ4oiGeMVwmYiON6OjkHqyi3ubmQJ6n/m7cjd0xGXxHX8S16IAkGZAlj0BSVY1BmSmktQzn9Xc9giPIaiQ1Kcwr8sCTPg0LsaAfI8lpMkoMPT+Nfj0SuWhQax55aRmqqtMiPhhRFHArKoqioSgaVTV2rBYjE+/MxOFUCAkysWzlfiaOy6RTuxgAdu4pYda7a5k7fSSD+6Ww4rcc0D2CUtN1Hnv5e4rL6r0C8GjhCh7RVF5hIzjQhCyLTH96GIqq4XQpfLZ4J+UVNu68qQfGBuNnXdexWozcfXNPXC6F7XtKGP/UN1w+rG2DIBQ4XFxLSOwAbru04X2r+87hFUUBS8N8YJdLRdP049YOAmzbW8v69euw223ebc888zgXXjjMJ70ZHh7OJ598yUcfvcvBg4cZPPhCLrlk+HHPezbo3r0nc+bM59NPP0QQREaNuomOHTs1S+m63W62bt18Flfqx89fwykLvQceeIBRo0YxePBgEhM98xMPHjxIVFQUr7zyyhlboJ8zgyQHEN5i2Nlehp+ToKkOyvO+xlmfjyRbCU++DNPvGAy/fPlSFi78FLPZwu2330lpaUmz+iRN07HZ6gkOPrY/X2j8QE9qsHGGLRKh8UMAj8Ca98qNOG3FaJrOdz9l8+GXO7xTGKIjrN4GCQ86qqsaRXFz+bC2DM5MJSjQSESDpcn+3Ap+XJ3DZUPbNnQFa97jHU6F3IJKWsQGc7CwGkXRvWvo1imOQZkpiKJAi7hgendtwYZth7n+8k689NovOBxu9uVU4HarSJJIl46xREVYcTgVJEkkPS2SdmlR3qhX+zZRjLgwHYdT4e6be9EjI4Gps1cBHtuXA/mVzZocjozqAbgVHUEU2JdTTnKLUIwGydvpe83wDox/eglP/usHrh3RkYSYIGKiA71NIkajTJcOsejoLF6+lw5toklNTeaCK+4iPDzce43AyB5UF/2IrnmeG0GQiUzIpFs3hUp7JIJoBJr+Zo3oSl35JkBHMoTgNrRE15f7PA6Xy4nL5cJkMvlsj46O4cUXX6S09FjWLucGnTt3pXPnrj7bkpKSMRqN3i5vURRJSGhxNpbnx89fyikLvdjYWBYvXsyiRYvYu3cvAKNHj2bEiBE+NQ4VFRWEhYX5i1z9+PkTKM3+BKetAHQVTamnZN97xLW7B9kYfPKDG1i8+CtefPFZbzRj9epfefTRJ7HZbD77RUREEBR0/PMaTOHEtbuT2pI16Joba3hnzEEtAaguWglqFaYGs+FLhrTmh1/zKS6txeVysX13KQlxoazZlI+iaPTsmkRaXBqtWqVRWFxHTJQVi9kTgdI0nauHZzCkfyqpyWHezldN86SYS8rqGTmsHcOHtGX+p5tZunI/AjD1yaGkJDYV2AsChASZmTRxEMFBZrbvKsLlbop8KarG9l3FlFfasNndJMQG0To1wifNaTLKnN8nmeW/HGDYwFYIAl57lca1iqKALAm4FY3rLuvAl9/t9knxmowS+3PKWfrTPp5/aIhP6lRRNCLDrGTnVXiucX4rEuKangNd11m5OhdV1VFVlZ17S+k/YBArVnzPxx9/gCwbGDfuHgYNGgKCQH35JgTRQEjcILpGGIhLzCAlpRUhQRK1JWvRNRfW8AzMQSmEtRiGrroQJBOdTfvR9Rne64qiSHJyy2Yi7+/MLbeM4YcflnHoUAGCICDLBp566vmzvSw/fs44p1VFGxgYyA033HDCfS644AK++uorb9TPjx8/vw9dU3DW53N0H6azLhc5POOUzzNv3ltHdSHaeeut2Yii2FC35qF3774n/YFmMIUTntjck6yuqoC8g2UEB5kIC7FgMoi8+uIo1u4Opaqqknbt0rn7iYex2xyAwH8/28r8d2/gtdfmMGLEUH5ek8vAzBTcbhVZlogKNxMVbvJZjygKiKJAYrxHCJlMMuu2HPLOmXU43Ciq1hQNM8js2FvMTVdmUFJejyAISKKAekS0TZJEcg9WERZqpqbWSVCgqZl/XkiwmSH9U7A7FAKtxmbpWUHwjDYrq7Qx8qJ2fPHtbp+/uxWNmfPWNkzc8D1WlkXq6p1YA4w8MX5Aw/marv3xou189nWW99/f/bSf/v0OM336+zgcnrT7E088xIwZr5OZ2Z/gaM84s7fffpO5c9/AYDCgKApTpsxg4MCL0XUdZ10u9RXbMQbEYzBHAJCW1prnn3+ZZ599ApfLRVJSMq+//tbxXganjL16HxUF36CrTszBrQlPGo4onp2aboslgA8/XMCGDetxuZx07dr9uNFrP37+Sfzp7VInmhfpx4+f00AQ8XRCHyUsxNPzUTrWe7Kurs5H5AFUVZ3cz05RFCoqygkLC8Ng8Kxj9+5djLvrddxuJ4qicc3wDowa2YXgsERGjBhMVtZO7rtvnDeNCzqK6mLq1Jd5Z+58brqyE8FBZhYu2UlxWT13j+6JR+sc349O03TcboXySk9UUhQECotrSU+L8nrnlZTVMyQzFZvDTUSohcBAI5VVvl2+qqoRHmohuUUouQcrsVgMmI7qIhUEAZNJRpY1Fv+wt2HsmkfgiaLIZUPbcvNVXbw1cPeP6c1r89ciSSKKqtEiNpicg5UYZJEX/rOSpyac701Dz3jrN+4f04c6m6uZyNZ1nQVLsrxTTDz3X+M/c5Z4RR547EM+//wzMjP746w7yKH8zezdvgRFcXnv+WOPPcjKlWupPbwYe81ez73VNcKTR2INaw/AsGGXMHToxTidTsxm80lfCyfDZSuiLOczdN3T/GCr2gW6RmTKVX/43L8Xg8FI3779ztr1/fg5G5xbffF+/PjxIggiwbHnUVv8K7ruBkFGNoZiCU47rfPcdNOtTJv2sk8XYu/effnll5+8QsBsttCjx4mH22/btoX77rsTp9OBIAhMnvxvBg0awv3330VNbb13v4XfZNGtazvWfbOBhQsfP+7UjeLiQor3zWfEhW0xyCId2kTx5dLdzP90C107xtK7awuv+Dk6yiaKArIsYQ0wUlfv4upL29OvV7JXbImCQFSElRFD26KqOkt+2IOm6iQlhHC4qNbT9IDO4MxUUpLCAEhJCvOmZI9GFAQ27yrmtw0HURSP8DLIEqMu78gVF7XzXlfXdc7r05K2aZHkH6omOsLK599m0a1jHL27JSAgsD+3gsXL97BxeyEGWeTKi9vR5Qi7lUZ0XUdTfUW6W9FQjrFEk8lIbdlGqgqWImoKd9zQlSH9knli6g9omo6uQ9HBLYj1e711fAAVeV8RENruiHS38KeIPAB7zX70I3wT0ZUGkenHj5+/Er/Q8+PnHCY07jyMlmgcdblIhmCConqe9qi6q666FqPRyMKFn2IymbjrrvtIT2/PAw/cy4YN69B1ncGDL+DGG28+7jlcLhf33juO2toa77bHH3+IBQu+prS0pNn+y3/O46dft51wtFpGuxgUpyfSpSgaz/77R+psLuptbn5Ylc3oqzpz2dB0gIYpHro3EqaqGu8t3IokCggC9OySgPmI2jpJEpEaSuEMMlw2NJ3NO4qQZZHzeiUTFmomJiqQjm1jvMfoOkc1jDThdCl8vWw37VtHIcsiu/aVouvw/sKtLP1pP88+OJAWcSEN59GJiQwkOsKKw6GwP6eCFpnBtGwR5q3/a9sqsqFjt46khNBjXlPTwGKRqat3H7FNY+TIK5k7922cTo9wt1gs3Dx6DJUFn4OuIgpgNsmkJIXRIyOedVsOYTDIhATJ1NYfHTVU0TU3gnT6bvsnQ5RMIEigN9m+CGcpbevHz//P+IWeHz+/g3Xr1pCbm0OrVml0797zjF4rIDSdgND0P3SOESNGNvPIe/PNeRiNGpWVNqzWE5vGFhUVev3HGtE0lZycbMLCwqmoKG/6gwBlZQUIuDkeggAjL0yg4HAZS37YS2W1nXtv7cWS5XtZuSYPHZ13Pt5Mv57JRIRZcLoVAswGb2Tvv59t4dsf93mbHsorbT6duaqqoaoas99bz76cchJig7nn5l5MmfUL3TPiSW4R2sxuZOE3O7n0grY+xsoAJWV1zJy3hkuGtKVzg+2Ky63yyIvLKC5TKCmr56l/reCdf1+Oy6Wy50AZsZFBlFXaeOO99VRU27nionbIR4xbMxokhl/QhpSkMCxm2Zteb4ysKYrGgfwK7xi0I/nmmyVce+315OfnEhYWxg3X30xCtEZ5bnNRHRZqwWq1MnPmm1iDk6gt9D2fZAxBPAMiD8AankFN8W+oSh3oKoIgE5ow9Ixcy48fP8fHL/T8+DlNpk2bzOeff+rtuLzxxpu5774HzvayfhchISG4XCc3yo6IiEBVFYICTQQHGikurcflcjFr1qtMn/4q995zG4LgESjDzk/z1KxJAvX1Lu5/5hufCRiiCI/fdx6apvPg80txOBUeubsf2XmVrFqf761JEwR4/d21PPvAQKwWXzGyck2uT2fr/E830yk9BotZRpJEBFFAdescLq7l4OEaDhfVciCvkpuv6UxIsLmZyCuvtLF4+V4yeyQRENsUdSoorOaB575D03T2ZJeT0S6GJ+4/D7NJ5q7RPXn+lZ/Qgbp6FyVl9RzIr+Rfs1d552cbZJHe3VogHiUeBQGG9E89Zl3eewu28MV3u4/pyQeQk3OAwsLDAEyb9iqh8kYq8g8CvjY0giCwN7uM//3vI1q29Ew9CU8aQXn+16BriLIVUZQp2DYdY0AcEckjkQwnnnd8OoiSibh2d1JXthlNtWMOboU5MPlPO/+5iq6p2Guy0TUXJmui12vSzz+TiopynnrqUbKydhIfH8+kSVNIS2t9tpflwzk1CsHtdjNp0iR69+5N7969mT59erOC8UZqa2uZOHEi3bt3Z8CAAcyfP/8vXq2f/x85eDCfBQs+xm6343Q6sNvtvPfefEpKis/20k6K3W7nhRee4bLLhnHHHbeSm3vglI+1WgOZNuk23p0xkhnPXsTc6ZeTEBtMXl4uBoOR998YzzP/dz4zJ13M7dd3x2iUkCWRkGAzs18eTkhwIJcMSadli1CiI6ys2VTAvI834XR5ooStUyPYn1PhI950HXIPVnmaL45qKDl6ukNxaT2z/7sOTfcIHFEQsJgNPHK3p/Be1XRq653U1bmoqrb7HOtyqyz9aT81dU7e/nAjDqeCqmrYHW4effl7nC4Vt6LhdKls21XM9z8fQJJEYqM8X+AWs8xj9/YnNiqQvt0SGT7EI6rO75PMjGcvYsx13eAYDTFrNxcwZdYv1NQ2ieCf1+SxePler8gLshqJCLMgSQJhIWYCAzwi1OFw4HA4eO65x3DU5qFrLjRNZ3d2GW63SkWVnZdf+5mDhbVs3rwNTXNTnr+Y6qKVmKwJRLW6AV1343aUoqk2HLU5lOx//09vphMlM8ExfQmNH+wVeaq7DkdtDm5H+UmO/v3ous7nn3/Go48+yH/+M52amuozdi2f62oKe9bPpiznM8rzvuJw1ixctlOb5+zn74emadxxx62sW7eGqqpKdu3KYsyYm6iurjr5wX8hf3pE74/4582YMYNff/2Vt956i7q6Oh599FGCg4MZN675iJ0nn3ySkpIS/ve//5Gbm8vjjz9OdHQ0l1566R9Zvh8/J6SiohyDwXBEBykYDAYqKyuJjo45wZFnn4ceup8NG9bhdDo5eDCfm28exapVq4CTp+4ctTmkxNgBCYNBwmSUePL+ATz80kqcTgfpHa/AJFZ6Oix13xSixWLi/flzGD3mHmpraxg+pA39eibx3wVbvPqnsLiO+JhAjAbJ2xAhCBDTIKZsNjcBFoM3EnfjFRnMnL8WRfGMD5NEgbBQC/pRUbDwsAAEwaOzdB227ylmxa85vPDIYO8+5ZU2vly6G1XV2ZpVxBNTluNyq9TbXNTWuXzO53Sp5B+qQlFUdmeXYZBFJoztQ9eOcQiCp17wtlHdCAgwctUl7b11g0cLKEEQqK13sXZzAfmHqnnthUuQJBG7001oiJni0npkSWTa00OJirBikCXcbpXisnrGP73EaylTU1PjrYHLzqvg8cm+pseNlOUs8Mwq1hVqqor5ceVqdB06t4sk0GoENBRnBapSh2wIOunr4fdiq9pDee7nno5yXSUoph+hcef/6deZG2f9GwAAIABJREFUPn0KCxd+isNhx2Aw8P33S1mw4CssloA//VpHUle+GVttoU/DS3neV8S1u+uMXtfP2aGsrJSCgnxvWYtnnKTKjh3b6ddvwFleXRN/utCTpOZzFE8Fp9PJRx99xCuvvELnzp0BmDhxItOnT+f222/3GYF06NAhli1bxqJFi2jTpg3p6ens37+f+fPn+4WenzNKamoaR9t+SJJMUtK5nZJyOBysXv2rN0Ku6zqKorBq1SoyMwef5Ghw2UtA0L1OL6IoEh8TTFhoIOltUxFlK/Ht76WuYhvVhSt8RnKJgo6t7EdUVeGGkZ24bGg6ZpPMNZe2Z0ruKpwulS++y+Lx+wawelNBQxTP4zF37y09cToV5nywnnE39kAUBRwOhZBgE907xVFYUkfPzgkMv6ANK3494GNEo2k6peX16LonhRpgMbB+6yE0Fe59cgkZ7WJwOhXKKmzcfXNPZFnkux/3sye7jP69krj60g68Onc1e3PKvYLUZJQaRrIJZOeUc+XF7ejdtYVPE4ckCgzpn+rTHNJoCdMoVB1OhZ/X5BISZMZmd1NT68TlVhnQK5lBmSm8Nm8tiqp6RR6AwSAREWYhMS6E3IIq5IZJHo2UVdiQJQHlqE7djIwMyoreR1FUdF1n4qSlHjuXhnPOeOYioiOt6Ghn1ONO11TK8z73dJA3LLG2+FcCQtMxWo7/I0nTNMrKSgkKCsZisZz0Om63m48//sDbCOR2u6msLGfVqp+58MKLTnL0H0NxVvmIPADVfe5OEPHzxzCbLaiqb9ZR0zQCAs7sD4rT5bSEntPp5NtvvyU7O5uxY8eyb98+2rRpQ0hIk+nk+vXrf9dCdu3ahd1up0ePHt5tPXr0oKysjPz8fFq2bOndvmXLFoKDg2nTpo3PvrNmzcLpdP6j3Nz9nFsEBQXxxhvv8OCD91FWVkpMTCz/+c/sU/oCOptIknjMaLvBcGpf7AZTGIIg+thlCALMfnEwFfvfAEHAEpRGZMo1aO46akvX+BxvkupISw5m+AVtvQKoe0YCE8b24dOvdzLxzn6YjDKTH7+ANRsP8tq8tXTvFM+KX3PYvruEA3nlSJLAyjV5iIJAQmwwT4wfQFiIhez8CqwBRvp2T+KbH/Yy/IK2KKpnnu3UWb8AntpBq8XAQ3dmMnX2KsoqbKz4NYfUpDCmPHEhRoOEKApUVNkYfXVn2qZGIAgCLz16AeOfXkJFlR1dh15dEhjcLxVRFLj9ph7Y7W6EY8yQjY5oXuvmiSx6rE5kSeCp+89HlARkSWyYvetgyqxfmDC2LxPvPPb8cJNR9nj+FVQRExXII3c1pqY1khNCPWs5QuiFhIQyb95cvvvuawRBIDDAQG29yxsRdLpU5n60kScnDMEa0RVR+nOsVY6Fqtqap7AFCcVZeVyhl5eXwx133Ep1dRWapvHgg49w/fWjT3gdTdOOmYJ2u4/fHPRnYQpMpL58I5rWGAkWMQYknPHr+jk7BAcHc/XV1/HVVwux2+2YzWbS09uTkdHlbC/Nh1MWeuXl5YwaNYrCwkI0TePaa69l7ty57Nq1i/fee89HiP0eiouLCQgIICioKW0QFRUFQFFRkc/5i4uLiY6O9jk+KioKTdMoKSnxT+Xwc0bp1CmD77//GVVVf3cE+6/GYDByzTXX8+WXC73prIiISAYNGkRdnXLS483BrQkIbY+tMqtB7GlHCEdPXtRRe4Dq4l8Iiu5Dbdl6nxSuKApERQQ0TIdool/PJPr1TPKeSxIE+vVMxuXWeH3+WnQ8omjkRe1ZvHyPZxQYOgcLq3n7o008PeF82qZGoigqMZFWWiaG8p93VnO4uJaCwzXeNLAOFJXW8cOvB2jVMoxDRbWUV9q5fFi6V+QBpKdFkZYc7l2P0Sgxe/JwCg7XYDEbiI5sEnCiIGA2yTgcCiaj5B3V1pjCPdL7T1FUr9j2GC1LyEd8+jZG68bd2IOps1cx7amhzbwDG+/jhNv7EBps4sYrMjCZZOZ/splFy3aj65CaHEZegac+yBpgZOTll/Pxp582CDvdR+SBJ+pZUm4jLPFSrGGdTvga0HUde81e3LYiRHccupyGIJx6mbckW0GUQT3i9aarGMxRxz1m/Pi7KC0t8Qq3V1/9N506daFjx+Ov1WQykZnZn3Xr1uJyOT0TUSSJPn2OLZ7/TAJC05HpT1HOTyCAwRxNRMuRJz3Oz9+XRx99ki5durJ9+1aSklpy5ZVXn3PfC6cs9KZNm0ZSUhJffPEFAwZ4cs+TJ09mwoQJTJ8+nddff/0PLcRut2M0+tYKNf67cQj179nXj58zxbn2Zj4Zjz76JG3atGXNmt+Ij09gzJhxWCwW6upOnloSBIGI5MsJiu5Dyb730VRbs310XcFZl09I7PnIpnAUR2nDds+X9NhR3TwZXQnvdqdTRVFVrAFGH1EzKDOFAb2Sqbe5MJkk5n+y2adRQ1V19h3wFPOLooDJ5LFe6dg2mm6d4rnvqSXNzI/diobDqfDUhIGAJ8pXXmnz6cBNTQxr5qUniSLJLY7tdecRbypTZ6/iobv6+TSJOJwKh4trMZtk4mOCTlq/LIoiCbHB5B70TCg53v6yJHLTVZ0xGiRWrcvjmxV7vWPd8g9VMahfCjdf1ZmgQAsz3tnkM0UjIjyA6mqH914aDRKd28dhCog/6fqqDi+nrmwDuuamtsSIMbAlUanXnXJdtiCIRKeOoiT7I0BH11VCW1zkHcN2NIqicPBgfrPo3O7dO08o9MDTjTx9+mTWrVtDTEwsTzzxLOHhx77On01C64sxBPdp8Cc0++e+/8MRBIGLLrqUiy46d8vGTlno/fbbb8yaNYvAwKZW8fDwcB577DHGjBnzhxdiNpubibTGfx+dFjudfU9GRMQ/p/U9KurMFVH/3fHfGw933jmGO+/0fb823hunrYzyws2ATnhcN8wBkcc4QxB1RTHUVeVy9Gg2TYc167ax4b1HeOiBe6jK+xibrZ7C4lr+884aemQkMOb6TDTVhq7rZO0rpXXLcALNpmOm2mRZJDjIM+82PibIt1EDvNG1I6c6GAwSuq5z3629eHzycq6+tAMXD26Npul88+NeRl3WyeuTJzd0zjZGzmx2N7X1TqLCrT7i7+jI2tH/djoVtu8uPvp2IEsiqUlhx53ucSxqap24XCrZeRW0Sg4/5j66rmMyerz3MtrH+mRDXW6NbbuKCQ4yAzqx4bqnkaNhnIbVbKRj62h+WpMLQLdOcYy+sjNR0REYTMd/jyhuGwe3rPOm7jXNhas+F6upGmvIaWRQojoQn/QsLkclBmMQkuHEn9chISFUVTV1MIqiSHp62im8n4OYOfOVU1/Xn0x0TNhZu/a5jv+z+PicqXtzykKvurqa4ODgZtuNRuOfEkWLjY3FZrNRX1+P1er5AC8t9UQEYmJimu3b+LdGSkpKkGWZiIjT+9VWXl53XL+qvxNRUUGUlvqLfo+F/940oTgrqSn5DU11Yg3rSFJad0pLa3HZSyjeO89bSF6Us5KYNrcds3YqKO4SbLXz0DQFdDcg4FZ0yivrmD57GfV2hRU/LKNT2yg2bj+Mu2FkWHFZPZ17Xk73tGJ27ilCEgWMRs9HkCAIPmJPEATcioogCMiSwCWD2/DLunzyD1UjigKiKHD/mD7N1tYopNLTonj2gYGkp0VhNnuucd2IjsiSr9ASGtKtFVV2Jk76DkkUeOmxCwgONCHLoicrjc7+nAqWrsxGFOGSQa1JS4nwXstqNdKlfSxvfbiBcTf0QJI866utdxIWYvE+Ls//+RojN/7N7dZQNY1pb65CB958fwPTnhqKW1H5bUM+mT2SMMiSj1gUGixk2qSGs2NP0+dhRGiTeLryknas2VxAabkDQZSpqhP4v/uu574x+WiaG5PJjDW8K1U1AnD894jirMTjxnWk/Y1AeVk5Ntexo50nxgI25YTXBJgyZQYPPHAvkiShqioDBw6hQ4fu5/T72f95c3z89+b4nMq9EUXhdwWnTlnoZWRksGjRIsaPH++zff78+XTs2PG0L3w06enpWCwWNm7cyHnnnQfAhg0biIyMJCkpyWffLl26UFVVRXZ2Nq1atQJg48aNtG/f3t+I4edvh65rp1Xr9HtRXNUU7nkLXfVYw9ir9hBg0cCYTnXhT+ha0w82XXNRffhHolqNanYegzmC+Pb34awvwCOD4P/+bzxbdhR4I271Nifrtx3yqQdzOhW27tjP8MueIKd8MVrNbz7nFQSBikob4WEBuFwqW7IK6dwuFlkSEUWBKY9fwK79ZZRV2Cg4XM2eA2UkxAYdc2yZ260SFGTyijzwNDLU1TsJtB7xGaEDArz72Waqqh2oms69Ty6hfesoBEFg594SzuudxM9r83E1pDt/WZvP1Ccv9EbcDLLEmFHdeOSlZbiuUbFIMoIgEBZi8YngHV231/iYlyzfzeadxezPraCiypNmraq243C4mfH2atZvPcRni3cy7oYedGrnK7wlUSQo0ILZZEBVPc9F27RonC4Fk1HGYjYw49mLyS9PwBDYhs6duxIYGIitaieKswKDOQZLSBvqyrdQdXgFuq5gDetIWIthCEJTGloyhiAarKiuaryhSwGMAfHN7v2fSZ8+mXz55Xfs2rWTiIgIOnbM8KdC/fg5TaTnnnvuuVPZMSUlhWeeeYb169dz8OBBysvLmT17NmvWrGHy5MnEx/+xN7wsy5SVlfH+++/ToUMHcnNzef7557nlllvo3r07VVVVuN1uTCYTwcHB7Ny5k8WLF9OhQwc2bdrEtGnTmDhxIq1bn54jtd3uOpaX6d8Oq9WEzeavTzwW5+q9cTvKKd47j6pDS6ktXYcxIA7ZdOZSPjUla3DW5hyxRcNWe4igqN7UlW1o+BJvYseeMlau3svhw4dITW3lY3EkiAYM5gjv/16cPB2Xy7er8ej3ldFoZODAIfTo0ZfI6GQ+/uh90ltFeCJneISgLAlU1Tiot7l4evqPuBWVdz/dzJsfbGDRsj20Sg7nvwu2sG1XMWs2FbB9VzH9eiZ5LUgaqa51UlllJzqy6devqmnU1DoJsBhQVY1vVuxj594S0tMi6dMtkZ6d4/nxt1xcbpWi0jqKSupQVZ0D+VU+glXVdOwON5k9mn6AWgM8Ub3QYDOGhjq9YwmSxsjlkX8rLKnj6+/3UFff9Bq12d18tjiLgsIadB3qbS56dU0kMb7J4UDXdRRVo3e3ZHIPlnO4qAZFVcnOq2TdlkMM6Z+CJIrIspH2PW4kJbUdJpMnFW60RGMOTMZgjsRRm01F3lfomgN0Bbe9BE1zYQlu5bPugNB0nPUH0VQH5oBIIluOwnAGX6+NBAYG0rJlCjExsWdU5LndbubNe5v//ncue/bsJiOjMwbD6Y+HO1c/b84F/Pfm+JzKvREEgYCA039NnnJEr2vXrnzyySe88847tGzZkqysLNq0acOkSZPIyMg47Qsfi4cffhin08ntt9+OyWTi6quv5o477gBg/PjxJCQkMGXKFACmTJnCM888w/XXX09ISAgTJkzgkksu+VPW4cfPmUbXNUr2v+f12NJUO6UHPiGu3b3IxuYlEn/KNTWFowvJdM0TpQoI64jLdtibuv1q6V7+98U23IqK0Wjkiy8WMGfO/GM2oLjdLmTZ10T6SIwGEVEUiY60MmJYJ3Rdx2oNZORlg1n+yyq6Z8QjigJ2h5un/rUCh1NB13QuG9qOb1bsxeF0o+tgdyi8+cEGBPA2H2TtK+XtDzcy7sYeXtsWRdEICTYxc14WaSkRGBvq9pwuFadb8czKXbCFrVnFTHtyqKcTFmiZGEaLuGAO5Ff6rL/RcPlIHA6lmWA7XsOGz71SVJwulcAjPqzP653Mzj3FfPdTdtPzcrQLiSCQmBCJy6VgNMqomobdoYCus+znbNZsKvDu63IpFJbUs31vHb27pxPe4iIM5khqitdQXbQSXXMhSGas4Z0Jix+MvWq3x9vOe20FW2UWYQkX+qxBNoYQ23Ys8M9Lwem6zsSJ41m3bg0Oh4M1a37jt99W8b//fXbKFkR+/JyrnJaPXnp6OtOmTTtTa8FkMvHCCy/wwgsvNPvb+++/7/Pv0NBQZs6cecbW4sfPmUR116Ep9qO2irjshWdM6FnDOlBXus4zvQIQBAORCb0ACIzohqY4qC1dg6Jo/PezzSgNhrN2u52srB2sXbuazMz+zc6bnZ193FGFZpPMRQPT6NA2mq4d43CW/UilXk140iXExyUwKDOFyho7RaU2pr/5q88kioXf7Gx2PqNBxOH07aZdt/kQLpfKsIFpuFwqVdV2hgxoxc1XdWbSKz/RrVMcugYtE0MxmyQiQ90s+WEfF/RP9TmPLIkM7NvSR+gJAiS3CCGvoNpHfOUVVFFYXEt8bPAR+woNzvgeY+TG+rsj6w8//XoH27KKef6hwZhNsnd7SPDx/euMRonz+yQTH23G7VbJO1TF7n2l7DlQyeHiGvbnNB8lJooylsgLiEu/GID6yp1UF67wPve6aqeudC2quxqDMRxP/d0RJtfS/18lMKWlJaxZ85u33tzlclFQkM/Ondvp0qXbWV6dHz9/jFMWesezTxEEAaPRSFxcHOedd94xGzb8+PHjiyhbGqrbjkRDks9cF7gxII6oVjdQdXg5murCGt6J+LShlJXVIwgCIbH9CIntR21tLTrv+hzrdruPOy/UbDYdU+hJkkig1cj1IzthMTdGRTTqyjcRmjCEpb8UM+M/n6OqGmNGdfNEqE5CUkIouQervLWAJqNEv56JOF0qZpNMoNVI69RwdF0nNTmclx4dQl29C4tFxu5QePiFpfTtnoiqegyKj56hO+LCtpSU17NsZTY6OumtInnmgYHMnLeGVevyvWKvuKyeCc9+y7+fGUZSQlMkr9Enr/G/weNV17jtqks68P3P2TwxdTkXDUxDEgVkWaRn5wS+/G631/bEIItERVoRBYHeXVtw05UZCIJH9MVGBTHz3Sxqa+0Ul5Qcs/TE4XCQmNiUWrZV7fKKvCZ07FW7CWs/nrryzWiaE3QVQZAJazHspM/FPwlVVZulhQVB8E7X8OPn78wpC73169ezfv16DAYDKSkpAOTl5eFwOIiLi6OyspKAgADee+89b4OEHz9+jo0oGgiNv4Dqwys8GwSwhKSfseJ2Xdd55505fPzxB4DArbeO5aabBjRrAqmoqODppx9t9gXndrtxOI6dmk1OTqFXr96sX78Wh8OBJEkEBgaS2Sud64cnHiHymsjN+pwI43buGt2T/362hR4Z8XywcCsnl3rw4Li+vPvZFhwOhQG9k7j5qi7o4DNy7MhoWlFpHV98t4stO4uoq3ex8JtdgCcSuO9AOa1TI7ymyZIkMu7GHoy7sQe1dU4qquy43CoTx/Vj4p392Ly9kKmzV+FwKiiqxoZth0mMDzlh7ZggNIk+z4zg85k4aSmv56xjxAWtuerSDoQEW3jxkSF8tngnbkWlXw+PkbQ1wNgsRWx3QGFRKXV19ce9pq7rTJhwN4sWfYfFEuAxKz4OkjGIuHZ3U1+xFU1zExDSFmNA7Ck8E/8cYmPjaN26LXv37sblciFJMsHBIXTocGK/Pj9+/g6cstDr0qULbrebmTNnEhnp8deqrKzkoYceomvXrowbN46nn36aadOm8eabb56xBfvx808hOLo3JmsLXPYiZGMI5qBWZ6zY/JNPPmTu3Dle89zXX/8PomsvgzPjEeUwwpOGIxmCufvusWRn72t2vCAIlJYWe/+dlbWDjz76AE3TuOaaUbzyyiw+++xjdu3KIj09nWuvvQERN4V73kZ1NfmgHS6qQ5JlIkL30q51JGkpYWS0i6GyysYDd/RhyqxfT/g49h4oJzE+hDlTRpzQn65xtmxltZ0npiz3MVsGzzza+Nggpr25ioz2sYQFW7jxygzMJk/H7G8b8pnx9mok0XOeh+/qR6+uLejaMY6Jd2by0syfkUTRKy5P5JF3dJdtbEODiCBAn+6JBAeZEQVo2yqSpyacj6bpFBTW8OTUH5j+9DBvs0ojm3ccRDnJOC9N07DZ6tm2bSu9e/clOKYftsodaOoR5QKCRGB4ZwRBQjJYCY75fZMjFHctFfmLcTtKMVpiCE8cjmQ4vrA8FxEEgTffnMe0aZPZsWMbKSmpPPbYU5jNZ24knB8/fxWnLPQWLFjAnDlzvCIPICwsjIkTJ3LHHXdw3333MXbsWG666aYzslA/fv6JmKwJmKxnfhbmkiWLfCYkOBwOvl+xmv5dBwGVFO+dhyX+Jg4c2I+iNI+rmc1mDhzIZurUF4mJiePNN1/D4XAA8MMPy3jttTlcf/3R732Z+Hb3UF+xnZqS1ezcncfU139gztThyLJH/BhkieBAEwsW7+S267piDTBQb2suYuJjAnG5NSqq7Dz28jLmzbjCO4VCUTXkY8zy1XWdfTnlDfYrTUJPlkSmPHEhifHBSJKIrnkaGsDzhV9V42DG26u9dioA0978lfkzriDQaqR7p3hkSSDQamRA72RsdjdfLt3F1Zd0wNQg/JwuhXqbm/BQX0Pgw0U1zPrvOgA6tI2iQ5ton25m8HhlRUda6dg2mnVbCnC6VNq3iSKmQSD275XI0pX7yNrr6yV6NKIABoNnPbIxmLh2d1N+aDWbNqxg555iOmb0YmjnP+bmr2sKxXvne21X7K4aih3vEtfu7lO2DKqpqWH16lUA9O3bn5ycbHJzc0hLa/2XRtQCAwN5/vmX/rLr+fHzV3HKQq8xJXM0uq5js3nGIVkslr9kcLQfP35OjyNnSINnsoTV0phS1dFUF6JedcwJFQaDZzzZ8uXLcLtdiKLoU5PncDh4663Z9OzZu9mxgigTGNmVwMiuPPD8FThdSrMJEgAl5fWMf2oJLWJD2HOgzOdvD9+VyYDeLSktr+dfb6yie6d4dL0pUrb8lwMMykyhpLSWxIRQ73ZRFGiZGIr7qFFofbsnkhgf7JNSvvC8Vl4z5aKSWo6OzYmiSHFpHYHWcFxulfP7tuS2a7tSb3Mz4ZlvsDsUHE6VS4e0IchqxGiQCLQ2ddbquk5VjYOHXlhGvd1T8N8yIQxV0zlK56FpntFsP63J5duf9iHgqft79sFBdGwbjSyJvPjIECY88y0HD1djNlvo3Lkr69evQdM0gqxGnpowkLatIoCllB50EpXYH5ciMnb8NIqKinC7XZi/3kRJlYnRo29r/oScIi57MZpio+lJ1VDdtSiOcgyW48+wbaS4uIjrr7/K+yOkqZlFRNc17rjjbsaOvfN3r8+PHz+eVqtTomfPnrz88stUVFR4t5WXl/Ovf/2Lnj17ArBo0SJ/fZ4fP+cg48c/gMViQRA8Vicms8yoy4+MlmhYzAHccssYb7rKaJBIahHFPXeNQ9d13G6PQDlW48WpTMeprq6its7J1qwinE5P1FBRNOrqXYy7sTsznruY5BYhzY5b9P0eACLCApj21EWMuryTTz3ezr0l3P3414SGBjRLk8ZEBhIYaMRgEDHIIrIsMmxgq2Z1gwZD00dhVITVO82jEVXViAgLwOVWOVRYw4SxfQkOMmO1GLA7FIae34obr8ggKiKAAIsBg0HymXsLsH7LIdyK6m2eKCiqQTnqOh6hLbA7uwyHQ8Ht1nC5PZYsr85d7X1ckihw3229GJDZhdtvv4vNmzd6n5eH7+5PWkqYZ4KIoFF1eBl5+9fyww/fU1ZW6n0eHQ4Hs2a9euIn7SQIokwz5a5rIJ7aHOhXXplGdXUVNpsNm82G3W7H6XRgt9twOBzMmTOLiormXcV+/Pg5dU45ovfMM89w8803M3DgQJKSktA0jYKCAuLi4pg7dy4rV65k9uzZvPrqH/vg8OPHz59Pu3Yd+PDDhXz77WIEQSCzs4HwgEqPf5ogY7DEYAyI4777HiAupIidu/YTExnAhee1RtPrmCcf/zeh2WzmuutuOOka+vUbwOLFX/Hy679ww8hOdGgbgy4GMuON5bw5eQSyLGKzN88I2B0KiqIhGwygN++CvHZ4BxJu7+sznxY83a66rvPGyyMY/9RiyqscaJrOy6/9wn9fucKbZv1/7J13fBR1+sff07Yku+m9N5JQpShdRCwgdhRFsYL11DvbD/VOPb07Pb0TURHsvaDYK0qzIUiVXgOEJIT0vtk6M78/NmyyJMGAcBTn/Xr5MrvznZnvzCbDs8/3eT4fVdXYU9lEU5ObvOwYoiND6J0fz5qNZYFj9cyL5efluxh9ag6VNc0sW7ObxDgbg/ulAHD95QMwm37jcbrP0vLqDWX88Eshpw3LwuNVMZn8wZEiS1RWOwL+tHupb3S3OZRAbZ2T6NhU8vPzMZkUPB7/9u45MUEC0pIksuDbt7HHDWuXsfV6vV3y4O0MxRKHKSQJt2M36D4EQcZsz0A2dU1Iec+e0v12tiqKQnV1FVFRB2ZtaWBg0EqXA72kpCS++uor5syZw/r165Flme7duzN27FgURUGWZb7++mvS09MP53wNDAwOkszMLP70pz8DfsHmxsrlCGo5mhBJWNwQBEHE665hUJ9IBvbu27qjoJOVFsGaDf4uT0VRSE/PQJIkNE3jyisnMXbsub95/ilT/obD4WDBgnl8NKeAuIyzyMrKoaFpDktWFnNS32RGDs1g+ZrdgeYJs0ni1KGZLb6z7QMCXddJSQpH3KcxQ9f1lsBPwKTApef15tnX/bVx0ZFWdFo7c0VRIDbSiupT0XQdSRD4y+RB3PbA14F6wdUbyrCYZYpK61m4uBC324fZLDHvx+1YLVI7Z4590TSdAb0SedMs4/WqaLr/2opLG1A1jd1lDTQ7vfTtmQhA95xYTIoUuA+CALlZrcGO2+3ju8U7WbNxGdExSXi9rXWVjQ5PIIgFvx1c8e5KJp03NCigM5lMDB487Hc1AAmCQFz2FTRU/oLXWY7Jmog9blCXjzl48DC2bNkUqPfcF0mSSElJ63DKltblAAAgAElEQVSbgYFB1+iyBRr4bcry8/MZMWIEw4cPJz8/P1C3Z7fbiYg4GHPrI4thgXb8Y9yb9giCgDk0hZSsE1HFhEDhvK66aapaTlvxXEGUOWn4Faxd7+/GHTZsONOmzWTixKu45JLLyMvL79I5ZVnm9NNHc8MN/rqrPn36kpqahqIozJm7kBO6xwcyajuLazGbZM49PZeLz+7Zok8nB+a1t5ZLEIRAkLf3uvbNUImigChCQpyN+JhQRo/sRnpyOKIothxXQJYlLGaFX1YWk54SQYjVxGnDswi3W0hOsDNpQn8+mbOJjVsr8fk0eubGcsaIbCLDrSTE2ogMtxBmNyO1FNy1lXcBvyOG16ty2vAs1m4qJzHezjmn5zHhvN4oikRoiAmfqhFut/g7c2NtSJLAus2tOnkjBqWTmhROQ6Ob1z/4lZ+WFeFTVcwmiQkTrmDFimVYrVZKy+sY0j8l4OhRUeVg9TYT48dfxoknDmTdurWIosiIESP55z//fVDOD23/pgRBxGJLIySiO2Zb6gEFjv369aewcCfbtxcgCAJDhgyjvr4Or9dHZGQUM2a8RFLS4W9WOpQYz5vOMe5N5xxOCzRB76j6ugNcLhevvvoqq1atCqT72/Lmm28e8MmPBqqrm9C0Yz/SO94siQ4lxr3pGF3XiYwQqalxBZwQdF2ncvs7uJqKQPeBIKFY4kjIm9zlLsoDpXb3ghbHjs4buUyhGcjmSBzVv+Lx+qiudfLAf+Yz7aGzsNvMQcGFpuvomt7SbeuvA9R1HUWRcLl9eH0q9tBg5wdd12l2etlT0URORlTQtuLSem69/6tAwHXK4HRuuWYQJkXCp/oDT1kS0dFRVR2n08tXC7dy7ul5mBQJWRYpKWvii7mb2FRQSXFpAx++cGlgqXbv+aE1MNR1nS3bq3jwiYXtnEDaIkkC54w9nYf/OZ3y8nLKykqJMf2K21EcaC5xe1Sis64lKjZjfx/DAXGo/6b21g0qil830OVyYrFYD6u37eHCeN50jnFvOqcr90YUBaKjD1xUv8tLtw8//DBffPEFQ4YMITraqJcwMDiW0XwuKra/Q4mzDF3XsUX3IzJ1LIIgEJt1GQ3li3A370axxBGeMOKwBXleVzVNlUs7cG1oQbQQFjeoJdO4AqfLgyyJyJLAs4+cg6ODb8CiIKCio6qavztXBLmlOcBilpFEISDJAv5l0K8WbGXkkAzqG50s+7WEj+dsQgcuHNMdi1kiLjqUay7px0vvrmTShP6BZhCTKLXJIArsKq7ho683EhcTyn+f+5naBif+5WOJfj0TGHZiGm6PSmFJLdkZUYEMYEc6gN2yovnn/53Gw9O+w+vV/FlYkz+43Ov1G2o1cdONNwAQHx9PXFwsxau/DAR5/mu2YBJqgIyD+5D+ByhKa5ZCEASs1pAjOBsDg+OLLgd6P/30E//617+44IILDud8jht0XcVRsw7V24g5NAWLPfNIT8nAIEB18Rd4nGWBuremmtWYQpOxRfdFECXCE0/5n8xD9TaAIPmzhx2hebHYM6jc/i667iPE6g8IYqNDEQQhqPt2Xy6/5UPC7Gam/+tsJHNroOrxqmzeXkW/nokIgv/1B19uYNHyXdx36wjC7RYSE+w8/fISZr65jJjIEKY+OIbQEIXcrOh2Hbt7g7TGJjfvfLqOlWtLO1wl2FlU26IfKLCrpI7/u3koIVZTp1krURBYtKwIp9OHpuvIkkh6cjR33TSctZsrkQSdQYMGEG6Tg4LNdvdTEBD+YN61BgYGrXQ50HM6nfTvb5g7dwVd1yjf9gZeZzm65u9EC086lbC4wUd6agYGAHgcJcHNDbpKY+VybNF9O9/pEKPrOi+//hHvvPM2oDNmZA5Xj+8X1D0riCLe5jKgfcar7f/3payyCbNZorK6meraZuJjbMiyX/9P1XSefGExVovCc/8+B0kS6JUfx723nIwo+mv2UhLC+O/9o/GpGnMWbkNRRCRJJDY6NDD3tufWdZ0Hn1jIrpL6TktBfKrG3kbajdsq2bK9mn69EoPGeH0q6KAoEt98X8A3328LZO+8Po31Wyv5ca2Nud9+T1l5LYmfr+XeWwvJ6zGU6PTzEASBiKRR1Jd+F+iolk3hhITndflzOdz4PHXUFn+Dz1OL2ZZORPIZiGJr8KzrOg3li2goXwzohEadQGTK6MOWVTYwON7pcjPGli1baG5upm/f/90/BP8LDkczhqthG01VK9C1vTVHGq6mnYTFDz9sNSdGkWvnGPemPU1Vq9HU5qD3NF9Ty+/o/v9BVb1NuBq3o3obkUz793ndHx988B4vvPg8Lpcbn09jZ1EtiizSvZtfaFfXdZas2s1PKyrZXVxAekpYUONFZ9TWO7np3i9pdvnQgSUri8nOiCLUqrC7rJH1W8oZPTKHpAQ7ZrOMLcSMz6eRkxkVFED69epEMlIiEEQhsNTrnxuB58befapqm1m3uaJL1y7LAj3z4slK99cDqqrG068s4b/PL+aDLzdQXFpPcWkDRbvrg/aTJIlt27ZTWVWHpuk0NLr5eXkhZw6LJCymD6JsxRyagikkCUmxExKRT1Ta2UGB1KHgYP+mNNXFns0v4HWWo/kceJ0VeBwlhEb1CYxx1Kylfs8CdM0DuorXWYmOjsWecQiv4PBhPG86x7g3nXM4mzG6nNHLz89n2rRpLF68mKysLEym4JPdeeedB3zy4xVN7UAqQNfRddX4VmpwVGCL6Ufd7rn7vCuga16E/Yjduh27qSh4K/DaZE0krtsVCIJ/H13XaapaQVPVChBEwhNHdppNmj//2yBbNrdH5fN5BZx7Rj6iJPPa+6uY++MOXC4XFouZRcsKuffWkxFFvyavonRse/be5+sDTRIANXVOHpq6EEGAf91zBiMGZWA2yeRnx6KjU17pYNX63YwalomitL92k8nfxOHzaciyiMvtY+v2KvJzYgMNFYIgMP6cnsz+YkOn964tmh4sl/LR1xtZvKI4kA1cuno3aUnhSJKAqrY2auTm5rFr187W6wUcTg+PTv+e3n3huhvuwG63Yw3Lxhp29InXu5p2tXwB9l+TrvtwNRaiqS5EyS/U3Vy3qc2XZNB1L866TUT8j8oJDAyON7oc6L333ntER0dTUFBAQUFB0DZBEIxArw3m0DSC1eJFFGv8If9WbWBwsIRE9KCudGFQLZdiiUWU92/iXr3rE3+mpQWPsxRHzVps0f1wO0qo3PE+ms/ROn7nR4jZl3VYoxoVFd3OTq2mromJt31ITLSdPeUNATFdl8vNqvV7+NN9X1LX4GToiWnccf2QIA1iXdfRdRjUN5mFi3YENOgAdAROGZxBVlpkQNjYZJLw+TRmvrmMjVsrWbV+Dyf0SMBiltstze4qqadodx2x0aGsWrcHt8dHz7y4oOtRZAlZEvCpHS8RKLKIT9UwKRJ/mTyY1KRWF5A1G8uC5uvxqOjoRIRZAlp+smLllltu5847bw06rtersWpdKes2f8DC739m9uxPsVqDPXaPFoROzZha35fkUPxL9a33UZRDD+u8DAyOZ7oc6C1cuPBwzuO4QjZHEJt9OdWFn6L5mjGFJhGTcfGRnpaBQQDZFEZ8zhXUlnyO19WIKSSBmMzf/h1VvU1Br3XNi89Tj8/TQEXB20FBIPgzNk3Vv3YY6F0zYSQ//TgXj4cW71p/HZrL7aOktLbDbF1ljT+I/OGXQm684sQgP1nwyw/07ZlI926xrN9cEcjsDTsxjduuHdSuTMPrUwN2bP+e/hOjR2aTkxHFoH4p2ELNCMJebT6Ndz9dR129C03XiYsOxeNVse7t3PX4cLl8TJrQn207a/hp6a4g6ZUTesRzx/VDsFgUFFlkb82hKIUgmaNITsli47YaVNUXuI7EODuPTDmd5at34/GqnHf5f4mNjWP06LHMmzcHt9sdVA/o9Xqprq5kyZJFjBp1xv4/yCOE2Z6BpNjweepBVxEEBWtEPqLU+jmGJ47AWb8ZTfMAOoIgE5l8dF6PgcGxwAEJJgOoqorP50NVVVRVxel0snz5ctLSjk318sMlmCybIgiLG0x4wsnYovsGPcgOB0btQ+cY96ZjZFM4md1PQw4biC26X0BLb3+4GrejehrZm20RRIWw2MH43NU4G7Z16F5hCkkiJCJ4+Vb1OXGVf8ypQzOIjLDSt0cCky/rz9pN5TS0sfraH6VlDQw9MTUQjLWtrxsxOJ3aOifbd9UQGqLwj/8b5c/UoSO0jPF6VcqrHLz3+Tp/zR1QUFjDstW7+XL+FmrrXCxfs5uwMDPdMqMZO6obo0/NYd2mcnaXNbJ6QxmZaZGoqk7R7noSYu3kZkXTv1ciA/oksXDxTgRBoHf3eO695WSaHB4sFhlBFFi9oYwNxQmk5l1AgzuOwUPPYP78bwABQYAQq8yUm4f7BZnjbJSUORh2qt9mbuTI08jKyiEzM5tVq1YEaZoqionhw0eQk5PbpXt4sBzs35QgSIRG9kHTvIhyCLbovkQknxYscC1ZCI06AVmxY7FnEpUyBsUaeyinf1gxnjedY9ybzjkqavTWrl3Lfffdx44dOzrcvmnTpgM+uYGBwbFFTMZFVBS8hdddA7qOPW4o1vBuOOu3dryDIGPvoNu8uW4zoBMdGcL5Z/qdNTRNJzrCSnFpfbvxJpPEndeP4KmXF+Ny+wPBJatKuPm+Lznr1G6cd2YePlULLMt6vRpOl5chA1K57rIB2Foejqqqs6WwivzsGAQR3F4f4XYLtfXOoC98ug6qpjN5Qn9KyupZu6mc/zy3iGanl5hIv8ZbQWENd//zWwBmP39JoF5PkkTSUyIYflIa6zZXcNu1gwKSLA/+9zs2FVS2nOU7npnxEqGhoeg6/PvfT+B01FJXsRqrVIWj2cPWHdVsKqgiIum01lsqCIwadQajRp3BmjW/snLlctxut795RJIYOPDo7u4XZQtRKaP3O0ZSbNjjBv2PZmRgcHzT5c6Axx9/HKvVysMPP4wsyzzwwANcddVVSJLEtGnTDuccDQy6jKZ6qC/7karCT2mqWt3OwcXg9yEpNhLybyK51x2knnBvoEDeEpaNbI5usSkDEFCsiSTkXYfJGtfuOF5nWbv3BEEgr+dgkpNT2m2LjrAy7KQ0brv5yoBwsCgKlFU28fbHa9myo5ZfVpbg9am43D5mf7GeH37ZRUZKBLZQEy63381HADJTI3np3ZV8OX8rKQlhPHLPaURHBgv0en0aK9eWYrHIpCWFU1xaj6PZi65DVU1zUG2gJPkFkYOvBVKTwnn+sXMDkizNTi9bd1a1uzaHw0Fzs4N7772T/MRtDD1BISEulJdnreLpV5awp7yRoT3qUb2OoP00TaNXrxMwmy2YTGby8rrz5pvvERVlCNobGBi00uWM3saNG3nttdfo27cvs2fPJj8/n8svv5zY2Fg++OADxowZczjnaWDwm+iaSvnWV/G6q0FXcdZvwuMsJS7u0iM9teMKQRCQ5JB93pNIyL2WpupfUb0NmEPTsIZ3a7fv+vXr2L27hEjTNpYu3czIIRmEhppAB1mWuO9vjzF37jc88MAU3G7/MoZJkRjULwUEkVGjTiYhrJistEh0Hd76eA2fzNlERVMy3y/9mWkv/xLU3PHup+tYt6WCh+4YiSAIKIqEokhcdXFfrrv7U976cA2DB6TSo1sMi5YXB9W8hYb4s3Amk0xmWmTgfR1/hZ1JEZFlCVkSKS1vID7WhiLvDfgEFv68k+z0KAb0SQJgT3njfu0WXS4nlRXVWEMUbv/7HFxuFV33dwVXVDXx8H1RJHe/OjB++vQnmTXrnUDn8q5dO40vNgYGBu3ocqCnqirx8fEApKens3XrVvr378+ZZ57Jq6++etgmaGDQVVxNO/F56gJ1YrrmpalqFZp64RGe2R8DQZSxx57U6fYnn/wPs2fPQpLEluBE55X3VhFiVVBVjQfuGEtaPzjjjDPZuOINZn/hl0kZMSidqy7263fqzRvISI0I+Nhedn5vCovrQI7hlFHns2rNf4LOOWZkDpMm9EeWgxcvRFEgKSEcdJ3brh2E2+1j7aYKHM0efKqGokhcP/FEwC90vLOoNmh/XYdxZ/WgX69EMlIj8HpVNFVHFTQaGt08/covlJY38t7n6+jfO9Fv62VRkEQxSPpl32M2OtxsK6wO+POC37lj1fo91NXsJLnN+I8//jBYnsbtZv78b7nhhj91/iEZGBj84ehyoJeamsqGDRtITEwkIyODjRs3AuByuXA6nb+xt4HB4UfXOrDREkDTOjeFN+gcj8fDrl07sdvDSEhI/O0d9kNBwVZmz34Xl6u9xmSz0y8fsmyDi3MAr7OCcWd1Z9xZ3YPGKSEJeBwlbbJm/mxfj25x5OTkcsst1weNP6lPEiOHZrBqXSmD+6cGLbcqssT9fzmZUKsJWZawmGWefeRsvl+8kz0VjQwdkEqP3Fg0TUcSBXbtrsOkSHi8rb9Li5YXcfmFfqFfi1lGEATcbh+rN5Sxav0eAGRzNKJkRtc8ZKZFcv7ofL6cvwUgSE5lL7M+W8dpw7M6zMy5vf7H9aZNG/j226/xeIKbVkRRRJa7/Eg/IpSXlzNt2uPs3l3CSScN5uabbw3yuTUwMDj0dPmpMG7cOO655x4ef/xxRo0axWWXXUZcXBxLliyhR48eh3OOBgZdwmxLQxDEVvUtQcIUkoSsWIHGIzizY4+iol1MmnQFzc3N+Hxehg8fgclkRlVVLrnkMk466cAK5ffs2YMkBgcvoiCgtQQ0kigSEZUKgKNmXbv9dR08Tbvave/xqlTXOnjyiYcCwVGIVeGePw1j3k87+PsT3yEIAmeNquLKi/oiiv5uW1GAcHuw1lyYzcx5Z+ZTUFjDf59bxLS/n4WsCKDDpEv78ev6MqpqWt1E2gZje7tGzWaZ4YPSmPbyEiRJQsfEvEVllOwuZuIFvbh6fF9OOiGJkj0NvPHhahqbgrvsVqwpJWqfekGA9ORwwpPGsnz5L9x22024XK528jOqqjJ37jecfPIpdOt29Fie7aWxsZGJEy+itrYWVVXZtm0ru3YVMnXqM0d6agYGxzVdDvQmT56MyWRCURR69uzJn//8Z5577jmSkpL473//ezjnaGDQJSQ5hPjcSdQUfYnPU4/ZlkpU6tjDek6Px4OiKIfN2u5IMWXK7dTUVAfq3RYunB/Y9tNP3zN16nSGDTu5y8dLjZfx+YIzWCEhCn26x9O3ZyINTT4um+CvpdT19pnZYGFkcLn9WcBdu+uZ/9OOoOXQv0weDIIQ5DTxyZzNrFxTyt03D+OxZ39i5qPndjhPl9vHJ3M2saeiiZv/+gXDTkpDEASWrCwOCvLMJomzTu3WTlgZCJxTVVU2blzPju2bycuOZmdxLekpEfTIjSM3KxqHS+Sdj1fh8bQGezow78cCRFGkrWBwSZmDEHsCDzz050BWdG+gaTab8Xq9aJrGli2buOaaiXz22RxiYjqWJNE1HzXFX9FctwlBkIlIOh1bzOG3tly+/BecTmcbEWwX33+/AKfTedQKPBsYHA8cUJ7/yiuvDPx83XXXcd111x3yCRkY/B4USwzxudcc9vNUV1dx6603smXLJmRZZsqUv3HxxcdP00dhYWFQU0NbXC4XL70084ACvehIM1P+NIL/zPwBTdOxWhWefvgswsNC8K/Eiuh1X6Il3EBoVG9qy5ahyP4Aat9gqqnZzcvvrqLJ4aGotA6rVcHhcLO3z6F3fjzLV+9u1/hQWdPM0y//Qml5E16fGpBi2UvJngbe/ngNi1cUA1Bb7+LL+a2yMbIk0i3T703bIy+Ogf2SaWzyixaHhppQZP/S7kdftbdBO+f0PHIyohEEUDUNQRDoltubCy/M5oMP3g2aqygKfhcNX+v9V1WVCRPG4fN52x3b7W5dwvW7g2gsXfoLZ5/dcTBbU/ItjtoNoPvQ8VBbMgfZFIYlLKvD8ZrqRvM1Iylh+7XH2x+6rrNz5w58vo6C+OPrS5KBwdHGAQV6y5YtY926dbhcrnbLFrfccsshn5yBwdHKXXf9ma1bt6BpGh6Phyee+DfduuVywgn9jvTUDgkpKSls317QaRen19tBPeR+MIcmM7BvCu89Nx6Hw4PdZiH433cN1duIq34bIZE9+PL7Bgb3EYmPsSGKwYFAqNXE5oJKHrh9JDFRIUiSwHNvLmP+T34P2H8/+yO3XDNwHxMtf7Zu+65aBvVLxutVA5IoewON75fsZOXa0pb3/IGdt02w1a9XAn16JHL++ReBcwO6rvOnv35Bo8PDRWN7EBVhZfnq3fy4NHiJ2etT+ejrjThdXk4ZnIEk+htDclMaGDLyRr764gMczR50/FItiXF2qmuD6541TaO+vq4l0/fbWCydi18767cEWd/pupfm+q0dBnqNFcuoLZ2HIIgIgkJczhWYQhK6NIe2c7/zztv45ZfFQdlLi8XCyJGjsFj2b7tnYGDw++hyoPfSSy8xdepUzGYzdru93XYj0DP4I7Fhw7qAXRX4My6rV686bgK9xx+fxuTJV+DxeGhubg7aJkkSEyZMPKDjmUISiUw5i5qSrwmzS0imKFRPDcGhmI7WYmY/eMTFlG17i8S49s8aURR44oHRWK0KsiSyeXsVPy4tCmxfv6WCV2at4q4bhzLtpSWoml8/T9ehV14cU24ejtIS5LUNZCde2IfIMCuLVxVjDzWzbLU/szegdxLdsqKoqXMSn5SH5lgXCFJLyxvRdXjt/V9br1UR8XjbZuN0NhdUsbOolpI9DVwx7gTA730regt57P6zmfbiD1RWOcjJjOL264fRqOby14dm0tjYEJRZ7SzLKkkSqqpiMpmIjY1j2LARnX4WomRB87WxshNERLn90qmnuYy60gWgq+i6io6Xyh3vktzrwHzNv/tuPkuXLg7qEFYUhUmTbmDSpBsO6FgGBgYHTpcDvbfffpsbb7yR22+/3Ui1G/zhCQsLp7q6VfxWlmViYtoLAx+rZGfn8PXXC5gx4xlmz34Xr7d1yVAQBM4994IO93M2bMfrqkA2R2ENyw16Vthi+hEa3Rdd8yJKJioK3sbVtKuNbZoQ8MQdNGgIm4Rf0bTKdhk9ALutNWO1ZkMZ3jbdsLoOazaWc9XFfXnwjlPw+lQGDcjnjc+q6d+tPhDk7b2Wtj+PPa0bZ47MprLaga7rDOidyMmD0rFaFFxuH47mZkSxtVmiI7mUbpnRaDps2V4VtCTr9vgze5df0AdR9Fu2NVYspXf/sTz1cBh6S5CLaCY1Jpl3Z06ksqqB5974kaWrWrOEfu/d1uPKssLpp5+ByWQhKSmJK664Zr9ZsqjUMVRufw9dV0GQECULIeF5VBS8g89dgxKSRHTqWDzOcn9qs00srnodaOqBWViVlu5ut2SraZohA2Ng8D+iy4FebW0t48ePN4I8AwPgn/98jDvvvLVlSQvy8rozevRZR3pah5SQkFDS0tLbLReaTMHLguvWrWXmzKdpqCtl5OB4zhyRgyhKhET0JDr9vKCxgiAgtPg+x2ReQk3xV7gadyIpdqLTzkE2hQXGZve+iPKtr7YGQAggiO38dO02c6A+bi+2UBM7imr5euFWf8eu3JuJl11M8cZ30DS9w+BxL5IoEhsVyh3XD0FRxMBSq8UsYzYF16hdfmFv3vxwTeC1KMCfJw8mMc7Oyp3dePzxRwONI+APQr1eFbPZ/+jVNQ9h8cORFDtNNWvQfE4EQaKxYingIyoM/u/GwTw8zcO6zXuwWKxccMFFfPrph4GmDFX1MWLEqYwd66/J27lzB5MnX0lJSRGZmVk89tiTpKSkBuZgsWcRnzcZZ/02RFHBGt6d8m0vtzhv6Pi8DZS7q4lMHk1wxhUE0YQgKp3eu47o2bM3kiQFviyIokh2ds4BHcPAwODgkR566KGHujJw0aJFpKamkpXVccHusYrT6eF4EJM3zKI753Dcm9TUNM488yxycrpxzjnnccsttx/1GmYdERpqprZyJ8216/C6qpAtMQhCazCTnJzCxx/Pxuv1W4hZLFauvfY6BgzwCyNv27aFa6+9gsLCHVRW1bF24x7MJom87Ch8ripCwvORlNAOzy2IEiER3QmLH4I9ZgCSErxMKyk2QiK6I4gK5pAUIlPPQhRN1FfvpKy8AbNZRhAgPSWCRSt24/FogI6siJw2PItZn66jvMpBda2Txb+s4IuvvuKSc7qjmCQEWjNj+2b1/J6xIrIsBsa1m3vLez1y40hLDsfR7CE7PZIHbh9JQstyc2x8Fp999RNeb+vvniiKrN1chsvlIy87Bos9E1tUHwTJRMOeH9G8jWi+RqA1SyhJIt1yu2OP7s61117PhAkTeeedNwJNGIosomhF5GbaAZHLJl7J7t0leDweqqqqmDfvWy69dCKS1Pq5SooNiy0Nc2gKHmepX9ImULeno6suwhNHoGsevM5KBFFBEERiMsejWKIO6G8qMTEJRVFYsWIZsqwQH5/AjBkvERYW9ts7H4MYz+LOMe5N53Tl3giCQEjIgetO7vdfpkWLFgV+HjFiBA8++CClpaWkp6cHPTQAhg8ffsAnNzA4lklLSyctLf1IT+N3UbNnNRXb3kfXNQRRorFyKfF51yG2ZG0iIyN5//1Peeml56mpqWLkyNM455zzA/t/8cWnwe4MHpXP523h/NH5IEioajNdzf/ouo7L5QqS2lAsMUQmn47HWU7Vzo/wumooq2zksWd/oqHRTX63WK66ZCjvfzCXWS//BYejmR65cWSmRXLasEzu/+9CGhrd6LqOw+Hi2js/JTMtkrNPyyU2OoSeefHt5tA2sNN1/zKjJIlomobT5cNqUdib6RIEgWEnpTHspLR21xMVYeK1197h7rv/THFxEbquo6oam7ZVUbCzhnNOzyM0sjcADeU/o2vudsfYS3K0i7x0jbVrV5GQkIDL1RrkTX1wNAlxdrSmldQ5fuXEPrHM+xMIdDkAACAASURBVKEG8M+9qamJoqJCcnJyOzy23584+Nuujo4gSESljsUWMwDV24jJGt8uGO8qV189mUsuuRyHo4moqOguN5UYGBj8fvYb6F133XXt6kEeffTRwM9tvxFv2rTp8M3SwMDgsFC0+ZOAbp2uafjcdTTXbsAW3aqrFhcXz9/+9vcO9/cvXQv7dOHv/QFMlvgO99uX775bwF//ejdut5ukpGRmzHiJ9PQMwC/vUbHtTTTV6c/gJYfz6L2nc/2Uz1m5thRTSClDT3cRFa7wyqz1uD0q0ZEhPPCXEdw+eTD/eOqHwHm8Po2tO6rZumMJJpOJyy+7lItGyeia1x/s7pO9c3t87NpdR0piOLqm8/KsVfy6vpRumVFcMa5vkAduMDKyOZKUUI0rJ17K1GnPBJZaQ60Kt03yC07X7p6L6nOgeps7OY4fSYJeWRqLFs3nhg/eQm9pyhg+MJ34WBsW895Huca143sz74ctgX19Ph82W+cBmik0GcUSg8dZAboPQVCwhndDUmz+7dZ4sHbtc9wfVqvV0MszMDgC7DfQW7BgQdBrp9NJU1MTsbGx6LrOnDlzGDXq0LTH67rOSy+9xOzZs6muriY3N5cpU6YwYMCADsdv2LCBcePGBb0XEhLCr7/+2uF4AwOD9mi+4CySrmtovq5bGl5wwUXMnv1uwAbRbJK5aGxPJFMEsZkXI8q//WwoLi7ivvvuCgRCu3eXcNNNk/j66wUIgoDXWY6uty5liqKI1aKQGGejuLQBn6pSVLiex2cuCtiKVVU7eOjJ73ny72M6Pa/H46FwVymJPR6nYudn+Bw72JvZ0nUdR7OHfzz1A5sLqrh+Yn+G9Etl+ZrduN0+Vq4rY+2muTx016n0zG3bhCMgiCbMtnTqSr4FQSIzqp62GbMH7ziFbpkxCAJoPgd1u+eCYAIkoKXOUJAAIUgGxWKWGdQ/lR+XFTH0xAyWrS4lKtIWqCHci9WqYLVaA0LEY8acvV8LO0EQiet2NY3lS/C6KjGFpuzXs9jAwODYYr+BXnJyq4X24sWLuemmm7j22mu54447AJg3bx4zZ87kpZdeChp7MLz55pu88sorPPLII+Tk5DB79myuu+46vvzyyw6PXVBQQHZ2Nm+88UbgPWM5wMDgwLBHdaOhZlugwUEQWjtfu0JmZhZvvPEeL744g6YmB+effyFnnXXOAc1h48YNiG2EeHVdp6KinMcff4R+/fpz6ogTQQ/ubJVlEUezF4vFQq9efZh8073B7hlAk8NDYUltp+e1WCykp2dwzrnn0jffxqQJ/QKZscLiOhqa3FhaRJVfemcVb3+0FpfbF6jpdXtUXp61kml/b2nCESQiEk9DUx00lP/cMhEfcTEW/nH3qbzw7laaGqvIy45t3wyie5DNMag+B7rmwxqWjepz4nG0dtuqqkZDowtN1UhNCuP2ux6jpGQ7imkjbQPEkLA07r//HxQW7iAnJ5czz+w82N2LKCqEJ3YuyWJgYHDs0uXq8SeffJKrr746EOQBzJ49m//85z9MnTqVWbNm/a6JfPjhh0yePJnTTz8dgClTprBgwQLmzZvHNddc0258QUEBOTk5xMZ2bPNjYGDw22T1uZzNK9/C3bgTQTITlXr2AQvi5ubm8cQTB+9XuneFoC2qqvLee2/z2WcfsfLcC7nh8l4467egaz40TWDFuioSk3MYOXIUr7zyQiAb2BZN15m/uBaTyUx8fDzXXXcjb7/9Brt2FaLrOieffAqfffYRdXV1LKyROPeMXOJjbSiyGFiS/WrBVr/CiA5OV3uRaIejtSNYEBUQdBoqlrYb171bAm+/cTu65qFqx/vsWxMHoHrq/T/oXlyN2zHbMhBEBZ/Xg6qpuN0q73+xAatV4YyTs7CJGzkhPxnFMo6akjnoqguzLYOYjHGc3c0QITYwMPDT5UCvoKCAadOmtXt/woQJvzvIA3jggQdIS2tf0NxWSb0t27ZtIy/v6DPuNjA4lpAUK3HZlx3ROfTrN4BTTjmVH374Dk1Tgyy9nE4nn3zyATfddCvR4fl43dUo1jgu7Z/LhMkCzzzzZLsgTxQFFEXh7rvvYfz4YGHn884bR3V1FZIk8/33C5k371sAPB6VOx7+hreeHhdwzABYtW7PfrvyI8ItyJZ4zCEJhCeeQvWuz4OWW/ei6zpVO2YBIrA3m9f2wEJLrWTL0rHmD/aiUi+kuW4ja9cs54W3FmELNfHMP8ZiC7XgatiKq3EH5tBkknvdYUhfGRgYdEiXA73w8HB27NhBampq0PtFRUWEhnYsn3AgDBw4MOj1999/T2FhISeeeGKH4wsKChAEgfPPP5+amhpOOukk7r33XuLijh/RWgODPwKCIPDvfz/B0qVLWLLk56CaP/C7PjidzUQl92i3r91uR1GUIEFnuz2MF198g7y8/A7PFRMTy88//8Sjjz4UtC0rLbKdTp7JFKzPJ0kCiizh9viXcHeVNFHpGcgJ3f2OKKLUUbOBgKPZxYzXl7BpWyWxUaH8359OJiE+HtXbAIgolhi87jrQ23yx1TVqij5CEBXysqN4+pErCIns6V8W3htM6j48jlJ87ioUi7G6YWBg0B5B78zMch8ee+wx5s6dywMPPMAJJ/gtfNauXcsjjzzCiBEjeOCBB/a7/9KlS7nqqqs63HbTTTcFLQkXFBQwceJEhg8fztSpU9uNd7lc9OvXj6FDh/KXv/wFl8vFk08+icPh4OOPP0ZRDkzQ08DA4Oigvr6eoUOHUl9fT4/cWK4Y14dQq5nEzGEMOfWqdlmrmpoaTjvtNGpra/F6/TV706dPZ+zYsZ2eY/Xq1Vx00UVBmcCoCCvPP3Zum+5VP99+X8ALb6/Ap2ooskhUhJWnHhrD0tW7eerlX7DZbLz++usMGTIEgObGUjYvnY6utWb1BEFiyiNzKNhZg9enIQAhISa++Ph5crqP9DtUILJh0WP4vI5O5y2ICgkZp1C+68cgdwpRMpE38BZC7ElducUGBgZ/MLqc0bv99tspKiri5ptvDjxsdV1nzJgx3HXXXb+5f58+ffj666873BYZ2SpRsH79eq6//nq6devGI4880uF4i8XCsmXLsFgsgaDu2Wef5eSTT2bZsmUMGzasq5dFdXVTkE3RsUpsrJ3KysYjPY2jEuPedM7Rd29EXnnlbaZP+xt/uiIvEHi5HKv5+uM6Bo6YtM94hfdmzeLbOZ9RUd3MyJGnccIJ/agor8XnqUdSQhGl4Hq1Dz74uN1yr9vjaxfk6brO6SdnkRhvY9W6PYSHWRh9Sg4hVoUQq/+5I8syycnZVFY2oqkeKgre96++ClLLKqxKk8PJ1h3VqGrLsiygaTpLVxQSlbA3YFOJyb6ass0zO70zuualyeFGlG1oah1+UWURUbbT5AzB4frffY5H3+/NgaFrKj5PHZIc0qHP7+/hWL83hxPj3nROV+6NKApER9sO+NhdDvQsFgszZ85k165dbN68GUVRyMnJ6bCuriOsVivZ2dn7HbN8+XJuvPFGTjjhBGbOnLlf2Ra7PVgXKiYmhoiICMrLy7s0HwMDg6OT7Owcxp3dH4u5tVbPYpaRHNuCxum6Tk3xVzhq1jC8t4gk24jLzcHtKKFi+7uga+i6SmTKGJaurmLhwvlER0cjSRKSJKGqrUuysty+Y9/p8vHiOytIirdz3pn5fDF/CzPfXEaf/HjWbCwD4NJLJwa04erLfsTTXEZbVwvA7627z3dJQZCwhbeqCajeJjRfE7I5Gp+7hrY7eL0qn83dTGFxAycMsDLxyutpLJuH11mBYo0jKmVskJuJwf7xOCuoKHgzoJ0YnjiS8PiuJwcMDI41DtizKT09nfT0Q+8GsHnzZm644QYGDhzI9OnTMZk6t/nYtGkTl19+OZ988gkZGRkAlJWVUVtb+5vBpIHBscjPP//E0qVLiI2N4+KLL8FqDTnSUzqsOJsb0DRTkAyJqgYHUM2163HUrAVdRdf9GZrqwk/8untqa8butZef5N1PN+ByuZAkmZCQkKAgD/xLty53a1ZP13WefX0pS1eV4PVpfDxnE26PD1XVWbSsqMUvV2TgwMGBY7gatrNvkAdgUiTOOT2POd9txePVMJlMJKekM3iwf7nXWb+NqsIPARFdVxElK5rqBEQE2cbfH/uIrTuq8XhVlq15h1/XbmP69BeM5ouDpHLHLDRfq0B1w54fsdjSMYemHMFZGRgcPo4a4bm//vWvxMXF8eCDD1JfX09lZSWVlZU0NTUB4HA4qKnx2/p069aNlJQU7r//fjZv3szatWu5/fbbGTJkSKB+0MDgeOGtt17jrrv+zJtvvsr06dO4/PLxHcqJHC/ouo5FceH2+NBaHCDcbh+FZa21t5rPSV3pgn06XHU8zjI0zRt0vPc/Xxu4X6rqo7m5fR3czqI6Vq8vC5wP4JarBxJmNwPQ7PQGll73lnqMGzc+4Pnrf79jhYD6Bhdbtlfh9WlIksDQgd158833UBQTuqZSVfihP7ukuUH3oWse4nMnkdr3rzjNY9he1BhoCHG5XCxfvpSSkuIu3UuDYHRdbZWxaYPHaawEGRy/HBWBXnFxMRs2bKCwsJBRo0YxfPjwwH8zZswA4NVXX+Xiiy8G/HUxzz//PJGRkVx11VVMmjSJ9PR0nnrqqSN5GQYGhxxd15k+fVrAT9bjcVNWtofvv1/wG3sem+i6SmPlMjJTw6mpbWb5mlJ+Xr6Lj7/ZxIkDRwXGVRV+guptX88imyLbZboevH0kT/59NGecnAXQLpu3lxP7pgZE1wXB31077MSOS1NEUeSee+4Pes8Umtrh2Eef/YlthdXoOqiqzpJl6yks3NEyl2ba6bcIEqq3EUEQcLvd7YTgRVHqVHaqI1RvI46atTTXbmwXBP/REAQJUd4nGy6AYurMys7A4NjngJduDwepqals2bJlv2Nuu+02brvttsDr5ORkpk+ffrinZmBwRNE0DZ8vWJdN1zUcjs67M49VdF2ncvss3E3+bFVyYjiVNU6mvrAYURT46OvbueOO/+Oyy67E7djFvoVvgiATk3EhPncNVTs/ahFh9pGfE4MgCNx6bRTxsTY++GoLHo8nSKR5b90etO2Wpb2DBWA2mxk8eGhQd7/mc6G6a9qNFU2RbNleFdTwpek6q1atpHv3nkhyKIgSqG0+Y11FscQAkJeXT3h4BC6XC1VVkWWZ+Pj4gA/wb+FxllO+9fXAvZLkUBLyr2/XoPJHIiZzPJXbZ+G3mNMIieyN+QDcYAwMjjWOioyegYFBx0iSxMCBQ4JqVgVBYNCgIUdwVocHr7MMt6MYXfdnnbw+lUen/4jL7aPZ6cXj8fDUU1MpLNzRXq9OkAhPGoViicEanktiz1uRWnx292b4BEHgorE9eeG5GTz6j/sDzV5ms5nHHnuSsLhBCEJr8CbLIqlJYQw9MZUzR2ST3y2elJRUzjnnfB5/PFg8vnrXp3ice4Les0b0JCF3UqBDN3CdXo1Zs97iu+8WIAgisVmXIYhmBNHkt1FLOTMQ6JlMJt54YxbDho0gOTmFESNG8uqr7yDLXfuOXlP8FbrmRtc86JoHn7eBhopfurSvq2EHZVtfZc/mF2msWtHOveRYxWJLJ6nHbcRmjic+bxLRaecY9Y4GxzVHRUbPwMCgc5544mkeeuhvLF++lMjIKP7+93+SktLxMuGxjK55afvds67e1S64UBSZwsJCBvU/l6ods9HREQQB2RSFPaZVXF1W7Ogd/OMtSQLRwo9Ep0l88MJlxGZPxGLzF+Hruo5ksuGs3cSePSWEWjycMSKHM0bk4HL7WLOlmfFXP9Hh3F2OooBfcGCu5ghkJZSMzFzWr18ftK2kpJj77ruLJ554huHDR5Dc+05UTx2SbGsn9xEbG8czzzz32zewA9otb3dSo7Yv7qZiKne8Hwi660rmga5jjz3pN/Y8NpCUUCQl60hPw8Dgf4IR6BkYHOXYbDaeeOLpIz2Nw45iTUAQZXTNA+hEhFsRBBFoDaC8Xh8ZGRlYw7JIyL8eV2MhomQhJCIfQQx+nIVGnkBjxaJ259F1X6CJo7rwA5J7+cXaBUEgLHYQ9ugTeevjKYTK5Zw6NBNJFPhpaSFf/VDF+Kv9x9BUDw3lP+N1V2OxpSNKVtQ2nb4IMpLil4Davn1Hh9frcrmYPftdhg8fgSgqiIfB2cJiy8BRuyFwvYKgYLH/doDTVP1rIMgD0HUvjZXLjptAz8Dgj4SxdGtgYHBUIEomEnInYQpNQZRDsUfm8MQTT2K1WrHZbJhMZm677Q4yMvyBimKJwR57IqFRvdoFeQARSacSGj2Avf6ykhIBBOvNqd5GdL2101bzudiz5UXGnR7BqGGZ1NQ1c+1dn/LCO6vp1cvf0a9rPsq3vkJDxWKcdRupK52PYolGEJWW/0wo5mhCo/22aDZb5wKnsnx4XXwiU87CYkvH768rYosdSEhkr9/cryNdPn/Q3TE+n4/HHvsXp5wyiDPOGMHnn3/6O2ZtYGBwKDEyegYGBkcNsjmShNxrA6/jcuCbb76jqKiI+Ph44uLiu3wsQRCITjub6LSzAXA1FlK5Yxa61sa7VgkPCmDqShfgc9cgSzqypBAfY+PaS/vz5XeVTJ58o/84TbvweeoDS7W65sXVsIOEvOvxOPcgiias4bmB4PNvf3uIe+65E6/XEyTfYjbJnDVUoHTTTOKyLkM2H/rOT1EyEZcz0X/NgrDfYK0tttiTcNSubVlO9ze6hCWM6HT89OlP8umnHwZkbB599CFiYqIZOvTk338RBgYGvwsj0DMwMDiqCQ+PoHfviN99HIs9A3vsIBoqliAIEoIgEZt1adAYr6syqNZOUSTSk8OprFzPhAkX8swzz9MzNwJN0/H5VBS5JfMlCEgmO7aQ9oHoyJGjeOONd/nllyXU1FRTXLwD1bmL887oRm5WDD5XNeUFb5HU47bD1hQgiAfmnGGyxhGfO4mG8iXouhdbdD+sYTmdjp8799sgbUeXy8X8+XONQM/A4CjACPQMDAz+MEQkjcIWexKa14FsiUYUg5dOZWsijoZdyJI/4HJ7fGze3ircPmXK7Vx15dXMmPEWmqbTIzeWv/75VKJikqku/ASPsxzFHEVU+nko5qjAcfPze5Cf3wOA5tqNVBd93lKLCKCjehvRfM1ISujhvwldxGSNJybjgi6N3Xd5WpIkwsLCD8e0DAwMDhCjRs/AwOCYwuepx9VUhOo9OC1BWbFjCkloF+QBfDZ3J1u2V+H2+HB7fBQU1vDmh2sC22tra3nhxefxqRqarrN5exUz3lqH5nPiatyJ5mvC7SimfOtraGrHosaiEko781tdP6a17e6++14sFou/A1pWsNvDmDjxqiM9LQMDA4yMnoGBwTFEQ/kS6vd8B4IEaESnjyMkIu+QHf/nxT+zcuVy4qJDEQQor2oNJgVBIDQ0NJDdA/D5NCoqa1tkTPbW3+nomg+Pc09LI0Qw5tA0LPZsXI3b8fl8eL1eXv9gNQX/HsfMmS8TE9PafVtXV0ttbS1JScmYzeZDdp2HmkGDhvD667NYuHAeVquV8867kOjomCM9LQMDA4xAz8DA4BjB66qmfs93wfIouz7GEnZ3h9m5gyEmKgRRFKioDg7wLBYLISGhnH/+ON555w3cbndge0hIGDraPkfSEDqZkyAIxGSOZ/2qr5n9/sts2lZOwc4aJEni7rv/wuuvvwvA66+/wowZT6MoMoqi8MILrwWWf49G8vO7k5/f/UhPw8DAYB+MQM/AwOCYwOeu8Wfy9GBLOM3bhHiIOlYvPzeNpUvNuNz+c5hMEtOn3k9M8hDi4xPw+Xz89NMP7N5dHLCovfPuhwgJL8RZtxld9yIIMqaQZEzWxE7PIwgCK9bu4esF21Bb7M9UVWXjRr+w8oYN63j++el4vR68Xv8S8G233cS8eT8ekus0MDD442AEegYGBscEiiWmnfuEXx/PfsjOER1hYuaj57BybSk6OgN6J5GcEk1EUgoej4f6+jpmzHiJe+65gx07CkhKSsFmsxOdfgEO+2o8jlLkFn2/3+qgjYuLw2RScDpbA9eICH/Aum3b1nb7V1dX4XQ6sVr3sX8zMDAw2A9GoGdgYHBMIJsjiUgZQ23JNwE9uNisSzoUS+4In6eeqp0f4nVWIJnsRGeMwxySFDTGGp6Hpq3nlCEZgF8/zhqeyw8/LOTee+9C03S8Xi+iKODz+WhoaOCaay7js8++JSq6H7SIJHeF0aPH8sknH7Zk8QR0XeORR/4DQFpa+9o+m80e8Oc1MDAw6CpGoGdgYHDMYI/pT0hEPqq3EUkJZ/6ChezY8RUOh4PQ0FCysrI588yzAtmw5rpN1O2ej6Z50TUfuuYGdHzuGiq2vUVSj1uDJE2iUscC4KzfgqSYCU88gyaXhXvuuTNIJ26v7rGu62iaxsqVyzjjjDEHdC2yLPPCC6+x9JcfcTRV06PXIJKT/R7G/fufyIUXjuejj2ajKDKapjF16jOHTWfPwMDg+MUI9AwMDI4pJDkEUbJy//33sHDhPJxOJ5IooGo6FouJH39cyCOPPIGrsZDqwk/8zRud4GkuxRreLfBaEGWi08+juW4zmms7bsduthdXtliVuTo8hq6D2XxwmTZH9XJSQhcj2ET0mkI8UVdisvpFl6dM+Svjx0+gqqqC7OxcoqKifuNoLdfkrMDdtAtRDvF7AHdgZ2ZgYPDHwQj0DAwMjjmKigqZP38ubrcLSRJQVX9nhMvlYd68b7j++psJEzfvN8gDDVFuH6A1lC+hvuz7FvsvEdHhCzRE7IvJZCIxMZHBg4ce8DV4mkupL/0OdBVdV9E1L5XbZ5Hc6/bAmMzMLDIzs7p8zOa6zVQXfoyOv+GjseIX4nOvMYI9A4M/MEagZ2BgcMzR0NCILMtoqogsizjV1oBOEgVqKrYSnmwGBNqJEyMgiDJmWzqmkBQAGhsbEQQBm81GfdmPAY9X0IiPNnH1xPN4890vkGUZr9fHmDFj0XVISUnhyiuvwWQyHfA1eJzl7aanehvRNO9By8XUFH0RCG51HbyuCpprNxIa1fugjmdgYHDsYwR6BgYGxxw5OTn+4Ep3o8gSLrcPXQdB8EuiZKQlY49Kp6l6Fbrqr8sTBBl7/FAEQUQ2RRAS2Quv18tdd93GkiWL0HWdQf1TufuGIchyq2mQjs4VE85m9NlXU1xcRGZmFunpGQc034aGBkpLS4iLSwgswcqm9pIwgmRGEA7+sayp7qDXuq6h+poP+ngGBgbHPoYFmoGBwTGH1RrCa6+9TXJKNtdPHEBGaiQWs0xGSiSP/+1swmO6IZvCScy/kbD44dhiBxHX7UoiEkcSnjCC0Kg+CILIjBlPsWzZL/h8KqqqsXJNCR98uT7oXIIg4hUSsNvtnHLKqQcc5P3880+MHn0KkydfxZgxp/LJJx8CYLFnEBLZB0FQEEQzgqgQmzn+dzVcmEOTaftYFxCw2NIO+ngGBgbHPkZGz8DA4JgkIyOL2bM/RVPdjB79NW5HEbIpgqjUc5Bkv9acbAonIunUTo+xYsWyIJcLt0dl/ZZKABRLJDoWnn9nA19/cy6iKJCdncPzz79KeHhEl+bodDq5++4/43Q6A+899ti/GDhwMMnJKUSnnY2q5LCrsIDU9B5Y7JkHcysCxGReQuXO2XgcJQiiQlTq2ZhCOhduNjAwOP4xAj0DA4NjGlEyE5Nx4UHtm5qaxubNG1FVv16KLIkkxdtBEOl98r28+OLrzJ3/PT6fv2Zv27Zt/OMfDzJ16jNdOn5FRTn+QrxWFEWhqGgXyckpfPfdAu67725kWcLr9XLnnfdw6aWXH9S1AEhKKAm516LruiHFYmBgABhLtwYGBn9g7rrrXqKjY7BaTFj/v717j4uqzP8A/jnDDMOgXBMUQVFEMFdRBO+ikda65WpeEltXMcxLtqmILuItU/l5SS0tTU0lvKRdtNaUVdvctS1TUhFFBMECBQMhLgkOMzBzfn+wTk2AYMLMcObzfr16vZrnnDPnO4+P42fOec45dgq4uqgwaVwQnD2GQhBkuHQpyej+eVVVlUhJudzg93d3d4coGj8Ht7JSi/btvaFW30NMzHxUVKhRVlYGjUaDjRvXITc355E/18OEvMpKLf75z6M4eHA/MjOvP/K+iciy8IgeEVm0iooKbN26CcnJl+Dj0wlz5kQZHhX2qNzc3PHppwk4/91ZVJRloUfXdnBq5QuVYycAQIcOHaBUKg2nd2UyGTw9vRr8/iqVPVav3lDjqJ2npxeys3+ATPbbo31yw9E+U9BqtQgPfwFZWT9Ar9dBEASsXbsRQ4Y8aZL9E1HTY9AjIosliiJmz56J5OQkaDQapKam4MKF7/Dxx0egVCobZR8tWrTEkCeG1bps4sRwfPnlF/jhhxsQBBlsbRVYvjz2od4/NHQoEhK+xK1b2WjTpi1at66+IbK7e2uIovGtXyorqx76Yo9H8c9/HkVW1vdGcwhff30pTp1i0COSCgY9IrJYd+7cwaVLF6HVVt+wuLKyEj/9VIgrV5IRHNynyfevVCoRH38Aly5dhEajQUBATzg4ODz0+7i6utZ4soVKZY833tiEBQvmQCarPtoXHb0Ybdt6Nlb59SouLkZlZaVR2927d022fyJqegx6RGSxaptqVn2/vEe70CA7+wdkZf2A9u071PvkCblc3mShctCgwThx4jRyc2+hdWuPBj/mrLEEBfWGXC5HVVX1TZYVCgUCA3uZtAYialq8GIOILJabmzt69uxlOE2rUCjQqlUrdO/e43e/54cffoCwsNFYvPjveOGFMdi7N66xyv1dHB0d8fjjfzB5yAOA7t0DsGTJ62jZsiVkMhkCAnpi3bo3TV4HETUdQfztJBEr89NPZdDrm38XuLk5oKCAp1xqw76pW3PoG41GY7gYo2NHH8ydO7/B97H7raKinzB8eKjhVDAA2Crkck56KQAAHjJJREFU2LdrFTp3/7PRM2GbQ980poe5JYu19c3DYN/UjX1Tt4b0jUwm4LHHWj70e/PULRFZNKVSicjIv//u7YuKfkJs7OvIyEhHmzZtIZcrjIKeXC4g+/ppuDhUwq3j841RcrPE++4RSRODHhFJVmVlJaZM+Qtu385FVVUVcnNzoNcb39dOrxfh0doe6pJ06HUVkNnYmalaIqLGx6BHRJJ140YmCgsLDBcb6HQ6KJVKCAIg6qsgCAKiXxkEx5b/u1VL85/FQURkhEGPiCRLoVDUmIMrCALi4vZAc+cfcLDXQyEXAEEOO4eOkMl5NI+IpIVBj4gky8enE7p3D8Dly8nQaCqgVCoRENATXbp0h963I4pzv0CVpgjKlt5w9gg1d7lERI3OooJeUFAQysrKjNo++eQTdO/evdb133nnHRw4cABqtRrPPPMMFi9eDJVKZYpSiagZEAQBW7e+h/j43UhLu4YuXR5HeHgEBEGAjcIBrTqMMXeJRERNymKCXl5eHsrKypCQkABHR0dDu4tL7c+03LdvHw4cOIA33ngDLVq0wMKFC7F69WqsWLHCVCUTUTOgUNjipZdmNtr76XQ6fP75Z7h1KxtdunTFsGF/5BWrRGSxLCboZWRkwNnZGZ06dWrQ+nFxcXj55ZcxYMAAAMDrr7+OiIgIzJ8/3ygoEhE1FlEUMWfOy7hw4Tuo1WqoVCqcP/8dYmKWmrs0IqJaWcyTMTIzM9GxY8cGrVtQUICcnBwEBwcb2gIDA6HX63HlypWmKpGIrFxqaooh5AGAWq3G4cMfobi42MyVERHVzmKCXkZGBrRaLcLDwzFw4EBMmjQJycnJta6bn58PAHB3dze0KRQKODs7Iy8vzyT1EpH1KS8vh42NjVGbjY0c5eVldWxhXnq9HpcvX8KZM1+jtLTE3OUQkRmY7NTtuXPnMHny5FqXzZw5Ezdu3EBJSQkiIyPh4uKCgwcPIjw8HEeOHEH79u2N1q+oqAAA2NraGrXb2toa3fG+IX7P40QslZubg7lLsFjsm7qxb+r2274ZNKgP5HI5BEGAKIqwsbFB69buCAjwrxEAzU2n02Hy5MlITEyEjY0NBEHAoUOH0LVr10Z5f46burFv6sa+qVtT9Y3Jgl5AQAASEhJqXebi4oJXXnkFlZWVaNGiBQBg5cqVSE5OxqFDhxAZGWm0/v0HnP821Gm12oe+6pbPupU+9k3d2Dd1q71vZNi5cy8WLZqP27dz4evrhzVrNqCo6F6T1KDX67F162YcOfIplEol5syJwrBhf2zQtv/4x2GcO3fOcJoZAGbOfBmHDh195Lo4burGvqkb+6ZuknjWrUqlqvdCi18foRMEAT4+PobTtL/Wpk0bAEBhYSFcXV0BVD/qqKSkBK1bt27EqomIjPn6dsZHH/3DJPvatu0d7NsXj4qK6rC2ZEk0nJyc0Lt3v3q3zc3NMQp5AJCX92OT1ElElssi5ujpdDqEhobi448/NrTp9Xqkp6fDx8enxvpubm7w9PTEhQsXDG1JSUmQyWTo1q2bSWomouZDrVZj/fo1iIj4K/7v/1bUuF+npTp69Igh5AHV01aOH6/9zMhvdenS1egMh0wmQ+fO/o1eIxFZNou4vYqNjQ1CQkKwefNmeHp6onXr1nj//fdRWlqKsLAwANWToDUajeEI3qRJkwzrOzk54bXXXsPYsWPh4MDz/0T0C71ej5dfnopr165Co9EgJeUykpLO48CBw5DLLeIrsE4qlfEj2WQymWF6S31CQ4di3LgJOHBgH+RyG7i5uWPt2o1NUSYRWTCL+ZZbtGgR7O3tsXDhQpSUlKBnz56Ij4+Hk5MTAGD37t349NNPcerUKQDAlClTUFxcjAULFkCv1+OPf/wjFi1aZM6PQEQWKCfnFtLSUqHRaABUz+XNzc1BWto1dOtW+1N3LMXcuQswf/4caDQV/wt5LTFhwl8btK0gCIiKisa0aTNRXl4Od/fWFnfBCBE1PUEUxeZ/JcIjsOSLMUR9FYpyTqDi50zI5Cq4tvsTlC3a1bouJ7nWjX1TN2vom+zsLISFPWe4Wh8A7O3tsWNH/AODnqX0TXJyEo4fT4BKpcL48S+gTRsPAIBep4Wm/BYEwQbKFu0gyEwX4iylbywR+6Zu7Ju6SeJiDHp4P908gnslaYBYBV1lKe5k7kMb/+lQ2D1m7tKImo127drDz88faWlp0Go1UCgU8PBoC3//LuYurUF69AhEjx6BRm1V2lLkpe+CqK++84Dc1gWt/V6EzMa2trcgIitmERdjUO3ulVwDxCrDa1HUQ/1zphkrImp+ZDIZtm3bjbFjx6N79x4YOXI04uI+gEKhqHdbvU4LdWkG1KUZ0OsrTVBtwxTdSoC+qhyiXgtRr0WlphA/539t7rKIyALxiJ4FEwQbiKLu1y2QyfhHRvSw7O1bIDp68UNtU6n5GT9e2wK9rnpun0xujzb+L8FGbt8UJT6UKk0RgF9NORF1qKwoNFs9RGS5eETPgjl5PAFBuH/UQQYbuQr2zn8wa01E1uJW+ufQVf5y1Eyn/Rmlt/9t7rIAAMoWXoDwy5w8QaaAsmX7B2xBRNaKh4csmKN7P8htXaAuvQ4bRQs4uPeDTG5X/4ZE9Mg09woB6H/Vokel5idzlWPExWs4KisKoVXnAxChcuwMB7c+5i6LiCwQg56Fs3f2h70zb3JKZGotXXxw726eYZ6sIMihbOlt5qqqyWyUaO0XAV1VGQTBxiJOJxORZeKpWyKiWnj6DoedgzcgyABBBjtHXzi1GWTusgwEQYBc4cCQR0QPxCN6RES1kNko4N5pIvRVagACp00QUbPEoEdE9AAyuar+lYiILBSDHhFRI6vSFEP9cyYEmQL2zo9DZqM0d0lEZKUY9IiIGpGmPBd3MvdAFEUIgoDSH0/Do8t0HhkkIrPgxRhERI2o6NYxiPpKQKyCqK+ErqoMPxckmrssIrJSDHpERI1IX3XPuEHUQV/JB7kTkXkw6BERNSI7x06A8MusGEGmgJ2jrxkrIiJrxqBHRNSIXLyGQ+XYGYAMgiCHU5vBsHfuYu6yiMhK8WIMIqJGJJMp4ObzvOFiDCIic+IRPSKiJsCQR0SWgEGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkSm7uAu7z9/evtX3MmDFYvXp1jfarV69izJgxRm329vZISkpqkvqIiIiImhuLCXpff/11jddLly7FxIkTa10/MzMTnTp1Qnx8vKFNJuMBSiIiIqL7LCboubm5Gf7/3r17ePPNN/G3v/0N3bp1q3X9zMxM+Pr6Gm1HRERERL+wyENgcXFxUCgUiIiIqHOdjIwMdOzY0YRVERERETUvFnNE776KigrEx8dj/vz5sLW1rXO9zMxMCIKAUaNGoaioCL1798bChQvh7u5uwmqJiIiILJcgiqJoih2dO3cOkydPrnXZzJkzERkZCQD45JNPsH79epw+fRpKpbLW9SsqKhAYGIgBAwZgzpw5qKiowMaNG1FeXo7Dhw9DoVA02ecgIiIiai5MFvTUajVu375d6zIXFxe4uroCACIiIuDt7Y3XXnvtge939+5d2NnZGUJdYWEhQkJCsHPnTgwcOLDBdf30Uxn0epN0QZNyc3NAQcFdc5dhkdg3dWPf1I19Uzf2Td3YN3Vj39StIX0jkwl47LGWD/3eJjt1q1Kp0KlTpweuc+/ePSQmJmLatGn1vp+Dg4PR61atWsHZ2Rn5+fmPVCcRkbmcP5+IJUuiUVxchD/8oTvWrXsTrVrxgjMi+v0s6mKM1NRUiKKIXr16PXC9a9euITAwEFlZWYa2vLw8FBcX1xsmiYgs0e3buXj11RnIy/sRGo0GycmX8Mor9f/oJSJ6EIsKeunp6fDy8qp1bl55eTmKiooAAJ07d4aXlxeWLFmCtLQ0XL58GXPnzkX//v3Ro0cPU5dNRPTIkpIuQhAEw2udrgqZmRm4d6/cjFURUXNnUUGvsLAQzs7OtS7bvXs3xo0bBwCQy+XYtm0bXFxcMHnyZMO8vrfeesuU5RIRNRonJ6cabYIgg1JpZ4ZqiEgqTHYxhqXixRjSx76pG/umbqbuG51Oh2nTwnHt2lVotVooFAq8+mokJk4MN1kNDcVxUzf2Td3YN3WTxMUYRERUNxsbG2zfHofjx4/hzp189OgRiODgPuYui4iaOQY9IiILoVAo8Oc/P2fuMohIQixqjh4RERERNR4GPSIiIiKJYtAjIiIikigGPSIiIiKJYtAjIiIikigGPSIiIiKJYtAjIiIikigGPSIiIiKJYtAjIiIikigGPSIiIiKJYtAjIiIikigGPSIiIiKJYtAjIiIikii5uQsgIqKaLl48j8TEs3B1fQx//vNzUKlU5i6JiJohBj0iIgvz6aefYM2aVdBqtVAqbfHhh/vxwQeHoFQqzV0aETUzPHVLRGRh1q9fDY2mAqKoR0VFBW7fzsUXXxw3d1lE1Awx6BERWRBRFKFWVxi16XR6lJXdNVNFRNScMegREVkQQRDQt28/KBQKQ5tMJqB3735mrIqImisGPSIiC7Nu3VsYMGAQWrRogTZtPLBx49vo1MnX3GURUTPEizGIiCyMg4MDNm1619xlEJEE8IgeERERkUQx6BERERFJFIMeERERkUQx6BERERFJFIMeERERkUQx6BERERFJFIMeERERkUQx6BERERFJFIMeERERkUQx6BERERFJlNU/Ak0mE8xdQqOR0mdpbOyburFv6sa+qRv7pm7sm7qxb+pWX9/83r4TRFEUf9eWRERERGTReOqWiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9IiIiIgkikGPiIiISKIY9Joxf3//Wv+LiYkBAJSWlta6vKioyMyVm0ZQUFCNz37lyhXD8oMHDyI0NBQ9e/bE7NmzraZfRFHEjh07MGzYMAQGBiIsLAwXLlwwLLf2cVNZWYkVK1agb9++6Nu3L9avXw+9Xm/uskwuLy8Ps2fPRt++fTFw4EDExMSgtLQUABAXF1djfMyYMcPMFZvWyZMna/TBiBEjAAB3795FVFQUgoKCEBISgri4ODNXazqHDx+u89+m7777zirHjlarxbPPPot///vfhrb6xkhjjiGrf9Ztc/b111/XeL106VJMnDgRAJCRkQF7e3ucPHnSaD0XFxeT1WgueXl5KCsrQ0JCAhwdHQ3t9z/7qVOnsHbtWqxevRre3t5YuXIlFixYgF27dpmrZJPZs2cPdu3ahdjYWPj6+uKjjz7CSy+9hKNHj8LT09Oqxw0AbNy4Ed988w127NiBsrIyREdHw9HREdOnTzd3aSaj1+sxa9YsuLi4ID4+HlqtFsuXL0d0dDS2bduGzMxMjBs3DnPnzjVso1QqzVix6WVmZmLQoEFYs2aNoU0ur/4ndfHixbhz5w7279+PrKwsxMTEwN3dHc8++6y5yjWZZ555BiEhIUZtMTExuHv3LgIDA/HZZ59Z1dipqKhAZGQkMjMzjdrrGyONOoZEkoTy8nIxJCREfPfddw1tBw4cEEePHm3Gqsznq6++Evv06VPn8kmTJolr1qwxvM7JyRH9/PzEjIwMU5RnViNGjBC3b99u1Pb000+LcXFxoiha97ipqKgQe/ToIZ46dcrQdvjwYXHAgAGiTqczY2WmlZKSIvr5+Yl37twxtJ0/f1708/MTS0tLxfHjx4t79+41Y4XmFxkZafQdcl9OTo7o7+8vpqenG9refvttcezYsaYsz2J88cUXYrdu3cSbN2+Koiha1dhJSUkRn332WXHkyJGin5+f4XulvjHS2GOIp24lIi4uDgqFAhEREYa2zMxMdOzY0YxVmc+DPrter8fly5cRHBxsaPP09ETbtm1x8eJFU5VoNkuXLsXIkSNrtGu1WgDWPW6uXbsGtVptNDaCg4NRWFiImzdvmrEy02rbti3ee+89uLm5GdoEQQAAaDQaqx4j92VkZNTaB5cuXYKjoyP8/PwMbcHBwbh69So0Go0pSzS7qqoqrF+/Hi+++CLatWsHwLq+X86ePYuhQ4fiww8/NGqvb4w09hhi0JOAiooKxMfHY8aMGbC1tTW0Z2RkoLCwEGFhYRg0aBBmzJiB77//3oyVmk5GRga0Wi3Cw8MxcOBATJo0CcnJyQCq56Cp1Wq4u7sbbdOqVSvk5eWZo1yT6tOnD9q0aWN4/Z///AdZWVmGcGPN4yY/Px/29vZwcHAwtN0PO9YwNu5zcXHB4MGDjdref/99dOjQAVVVVSgrK8PRo0cxdOhQPPXUU9iwYYPhh4I1qKqqwg8//ICzZ89i+PDhCA0NxbJly3D37l3k5+fX+G5xc3ODXq/HnTt3zFSxeZw4cQL5+fmYOnUqAODHH3+0qrEzdepUREZGws7Ozqi9vjHS2GOIc/Qs2Llz5zB58uRal82cORORkZEAgKNHj0Imk2HUqFFG69y4cQPu7u6IiYmBQqHA9u3bMXnyZBw7dgxOTk5NXn9Tqq9vbty4gZKSEkRGRsLFxQUHDx5EeHg4jhw5AoVCAQBGofj+ayl84TR03ADVv66jo6MxYsQI9OrVC4C0x0191Gp1reMCgCTGxu+1Y8cOnDx5Etu3bzfMNXJycsKWLVuQlZWF2NhYlJaWYsWKFWau1DRu3ryJyspKyGQybNy4EQUFBVizZg3mzp2LXr16cQz9zwcffIAxY8YYvjc4dqrV9z3T2N9DDHoWLCAgAAkJCbUu+/XE+ISEBPzpT3+qMaH15MmTkMlkhl8Tb731FoYMGYITJ05g/PjxTVe4CdTXN6+88goqKyvRokULAMDKlSuRnJyMQ4cOITw8HEDNvzBarbbGL6/mqKHjJiUlBdOmTUPnzp0RGxtraJfyuKmPnZ1dreMCAFQqlTlKMrstW7Zg8+bNWLZsGYYMGQIA+Pbbb+Hq6goA6NKlCwBg3rx5WLx4saQn1t/n4+ODs2fPwtnZ2XBK29XVFePGjUP//v05hlB91Or8+fOIjo42tIWEhFj92AHq/55p7O8hBj0LplKp0KlTpweuc+/ePSQmJmLatGk1ltnb2xu9ViqV8PLyQn5+fqPWaQ4N6Ztf/yISBAE+Pj7Iz8+Hi4sL7OzsUFhYaLR+QUEBWrdu3ST1mlJD+ua7777DjBkz0KNHD2zdutUo4Ep53NSnTZs2uHfvHsrLyw0/EgoKCgBAEmPjYcXGxmLv3r1Yvnw5XnjhBUP7/X+o7/P19YVOp0NBQQG8vLxMXaZZ/PYqdF9fXwCAh4eHYczcd+fOHcjlcjz22GMmq8/cvvrqK3h4eCAgIMConWOn+nvmQWOkvuUPi3P0mrnU1FSIomg47XZfUVERevfujW+++cbQVl5ejuzs7HpDQHOn0+kQGhqKjz/+2NCm1+uRnp4OHx8fCIKAgIAAo3vH5ebm4scff6zRj1KUlpaG6dOno3fv3ti+fbvRL0RrHjdA9REGlUplNDbOnz+PVq1aoX379maszPQ2bdqEffv2YfXq1UYhb//+/XjqqacgiqKhLTU1Ffb29kZzP6Xs1KlT6N27N8rLyw1tqampkMlk6NmzJ0pKSnDjxg3DsgsXLqBr165Wc8QKAJKSkowuagI4du6rb4w09hhi0Gvm0tPT4eXlVeMP39XVFQEBAVi9ejWSkpKQnp6OqKgouLm54emnnzZTtaZhY2ODkJAQbN68GWfOnMGNGzfw2muvobS0FGFhYQCASZMmYd++fTh27BjS0tKwYMEChISEWEWYWbRoEdzd3bFs2TKUlpaioKAABQUFKCsrs+pxA1SfUhk3bhxWrVqFixcv4ttvv8WGDRsMp/utRVpaGrZt24aIiAgMGjTIMEYKCgrQv39/FBYWYtWqVcjOzsapU6ewbt06TJ8+3XAfOakLCgqCUqlETEwMbty4gXPnzmHx4sUYO3YsPD09ERoaipiYGKSmpuLEiRPYtWsXXnzxRXOXbVLp6eno3LmzUVtISIjVjx0A9Y6Rxh5DgvjraE3NzqZNm3DmzJkal28DQHFxMdatW4fTp09DrVajf//+WLp0KTw8PMxQqWlVVFTgrbfeQkJCAkpKStCzZ08sWbLE6HL1+Ph4vPfeeygvL0dISAhef/11yd8U+NatWxg2bFityyIiIhAdHW3V4waovn3IqlWrcOzYMSiVSowbNw7z5s0zzMWyBps2bcLWrVtrXfb555/j559/xvr165GWlgZHR0eEhYVh1qxZVtVH6enpWLt2LS5dugRbW1uMGDECf//732Fra4uSkhIsW7YMp0+fhpOTEyIiIjBlyhRzl2xSTzzxBGbOnIkJEyYYtZ8/f94qx46/vz+2bduG0NBQAKh3jDTmGGLQIyIiIpIonrolIiIikigGPSIiIiKJYtAjIiIikigGPSIiIiKJYtAjIiIikigGPSIiIiKJYtAjIquQk5MDf39/fPXVV+YuhYjIZBj0iIiIiCSKQY+IiIhIoqzn4XJEZBXUajU2bdqEhIQElJaWwtfXF/PmzYO3tzeA6oeob9u2DVeuXIGLiwv++te/Yvr06QAAURSxe/duHDp0CLdu3YKNjQ26du2KhQsXIiAgAADw5JNPYsKECbh27RpOnz4Ne3t7DBo0CMuWLYO9vT0AIDc3FytXrkRiYiKUSiXGjx+PpKQk9O7dG6+++iqA6oe+b9y4EcnJyXBycsKAAQOwYMECtGrVqsGftSH7ISLrxiN6RCQpUVFROH78OJYvX47PP/8c/fv3x4wZM6BWqwEA77//PqZMmYKEhASMHj0aGzZswNmzZwEAe/fuxTvvvIM5c+bg+PHj2L17NzQaDaKjo4328fbbb6Nbt244fPgwoqKicPToUezevRtA9XOWw8PDUVZWhj179mD79u1ITExEYmKiYfu0tDSEh4cjKCgIn332GbZs2YKCggJMmDDBUGd9GrIfIiIe0SMiycjKysKXX36JLVu24MknnwQAzJ8/H4IgoKysDAAwc+ZMPP300wCAOXPmIC4uDleuXEG/fv3Qvn17rF271rDc09MTYWFhWLp0KSorK6FQKAAAffv2xdSpUwEAHTp0wLFjx5CUlAQAOH78OPLy8nDgwAG4ubkBADZv3oyhQ4ca6ty1axd69eqFuXPnGto2b96Mfv364fjx4xg9enS9n7Uh+yEiYtAjIslIS0sDAPTs2dOoPSoqCjk5OQCqg9l9MpkMDg4O0Gg0AIAnnngCKSkp2Lx5M7Kzs5GVlYX09HQAgF6vN2zn4+Nj9P4ODg4oLCwEAKSkpKB9+/aG8AUAbm5u6Nixo+F1amoqsrKyEBgYaPQ+VVVVyMzMbNBnbch+iIgY9IhIMu4fcXsQmazmjBVRFAEAO3fuxKZNm/Dcc88hKCgIL7zwAtLT07FixQqj9W1tbet8fxsbG6NQWBu9Xo/hw4dj9uzZNZY5ODjU+xkauh8iIs7RIyLJ8PX1BQBcvnzZqP35559HQkJCvdtv3boV06dPx8qVK/GXv/wFwcHBuHnzJoBfwmB9Hn/8ceTk5KCgoMDQVlxcjOzsbMNrPz8/ZGRkoF27dvD29oa3tzccHBwQGxuLjIyMRtsPERGDHhFJhre3N4YPH47Y2Fj897//RXZ2NtatW4fr168jNDS03u3btm2LM2fOID09HdnZ2di5cyf2798PANBqtQ2q4ZlnnoGHhwfmzZuHlJQUpKamIioqCmq1GoIgAACmTp2K77//HosXL8b169dx9epVzJkzB6mpqfDz82u0/RARMegRkaTExsZi8ODBiI6OxqhRo3Dx4kW89957UKlU9W77xhtvAADCwsIQFhaGb775BuvWrQMAJCcnN2j/tra22LlzJ5RKJSZOnIipU6ciMDAQbdu2NZzyDQgIwO7du3Hr1i08//zzePHFF9GyZUvs2bMHLi4ujbYfIiJBbOj5CCIiqtft27dx/fp1DBkyxHBkTavVok+fPlixYgVGjhzZrPZDRM0bL8YgImpEWq0Ws2bNwqxZszBq1Cio1Wrs2rULKpUKgwcPbnb7IaLmjUf0iIga2RdffIF3330X33//PeRyOYKCgjB//nx07ty5QdsHBwdDp9M9cJ1//etfuHjx4iPth4ikj0GPiMjC3Lx5s96rfL28vGBjY2OiioiouWLQIyIiIpIoXnVLREREJFEMekREREQSxaBHREREJFEMekREREQSxaBHREREJFH/Dx4ANUyILGzoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if 1, yellow; otherwise black:\n",
    "colors = np.where(df_all[\"binary\"] == 1,'y','k')\n",
    "df_all.plot.scatter(x=\"change_g\", y=\"change_o\", c=colors, title='Gold vs. Oil')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows a random relationship between gold and oil and also with the S&P 500."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unsupervised:  K-Means  \n",
    "\n",
    "The features for these unsupervised models will be the change values for the 10 Yr Bond, Gold, and Oil.  Then, the change value of the S&P 500 is converted to binary (1 for up, 0 for down) and is captured for the dependent variable.  The idea is to explore if there are clusters of data that correspond to a positive or negative daily move of the S&P 500.  \n",
    "\n",
    "First, define the features and the dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the features and the outcome.\n",
    "x = df_all.iloc[:, 2:5]\n",
    "y = df_all['change'].values\n",
    "\n",
    "# Binarize y so that 1 means the S&P 500 went up; 0 means it went down or stayed at 0.\n",
    "y = np.where(y > 0, 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, normalize the data, fit the KMeans clustering algorithm to the data and analyze the results using the actual value of the y value with the predicted y value for each cluster.  Three clusters was chosen because there are 3 columns of features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing k-means clusters against the data:\n",
      "col_0    0    1   2   3   4    5   6    7   8   9\n",
      "row_0                                            \n",
      "0      288  277  44  38  42  130  64  131  74  42\n",
      "1      353  343  69  49  52  163  71  145  79  43\n"
     ]
    }
   ],
   "source": [
    "# Normalize the data.\n",
    "x_norm = normalize(x)\n",
    "\n",
    "# Calculate predicted values.\n",
    "y_pred = KMeans(n_clusters=10, random_state=42).fit_predict(x_norm)\n",
    "\n",
    "# Check the solution against the data.\n",
    "print('Comparing k-means clusters against the data:')\n",
    "print(pd.crosstab(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, it looks like there really is not a significant clustering of the data into the 0 or 1 target.  For cluster 0, there were 288 data points for a 0 label and 353 data points for the 1 label.  Similarly, the other clusters were fairly evenly distributed.\n",
    "\n",
    "#### Unsupervised:  Mean Shift  \n",
    "\n",
    "Mean Shift is another clustering method which will determine the number of clusters that best fit the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated clusters: 8\n"
     ]
    }
   ],
   "source": [
    "# Here we set the bandwidth. This function automatically derives a bandwidth\n",
    "# number based on an inspection of the distances among points in the data.\n",
    "bandwidth = estimate_bandwidth(x_norm, quantile=0.2, n_samples=500)\n",
    "\n",
    "# Declare and fit the model.\n",
    "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
    "ms.fit(x_norm)\n",
    "\n",
    "# Extract cluster assignments for each data point.\n",
    "labels = ms.labels_\n",
    "\n",
    "# Coordinates of the cluster centers.\n",
    "cluster_centers = ms.cluster_centers_\n",
    "\n",
    "# Count our clusters.\n",
    "n_clusters_ = len(np.unique(labels))\n",
    "\n",
    "print(\"Number of estimated clusters: {}\".format(n_clusters_))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the number of clusters came to be 8 and the crosstab results are given below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing the assigned categories to the ones in the data:\n",
      "col_0    0    1   2   3  4   5  6  7\n",
      "row_0                               \n",
      "0      480  481  54  83  1  28  1  2\n",
      "1      576  583  88  93  2  25  0  0\n"
     ]
    }
   ],
   "source": [
    "print('Comparing the assigned categories to the ones in the data:')\n",
    "print(pd.crosstab(y, labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the 8 clusters above, most of the data points were placed in either cluster 0 or cluster 1.  \n",
    "\n",
    "# Conclusion  \n",
    "\n",
    "In conclusion, the questions asked at the beginning of this notebook and the answers are summarized below:  \n",
    "\n",
    "2.A.  Do different days of the week yield significant price change ?  \n",
    "\n",
    "Definitely yes, for some securities.  For the 10 Yr Bond - Wednesdays were better than Friday with a p-value of **0.0266**; for gold - Mondays were better than Thursdays with p-value of **0.0009**; for bitcoin - Saturdays were better than Thursdays with p-value of **0.0213**.  \n",
    "\n",
    "2.B.  Do 'winter' months perform better than 'summer' months ?  \n",
    "\n",
    "No, although the winter months did have a slightly higher average rate of return per day, the p-value to compare the two was **0.3323**.  This is definitely not low enough to conclude there really is a statistical difference.  \n",
    "\n",
    "2.C.  Does the 3rd Presidential Year yield better results in the 'winter' months vs. 'summer' ?  \n",
    "\n",
    "No, again, the winter months performed better, but not enough to concluded there is a significant difference.  The p-value was **0.1337**. \n",
    "\n",
    "2.D.  Does any month of the S&P 500 over the past 10 years perform significantly better than any other ?  \n",
    "\n",
    "Yes, July - which was the best performing month - performed statistically signigicantly better than May - which was the worst performing month.  The calculated p-value is **0.0309**.  This isn't a slam dunk metric of significance, but it is less than 0.05 which is the common cutoff for defining significance.\n",
    "\n",
    "2.E.  Is it possible to build a supervised learning model to predict the daily price change of gold just by using day of the week as the only feature ?  \n",
    "\n",
    "Possibly, however, the methods used in this study were not conclusive.\n",
    "\n",
    "3.A.  Does the daily temperature or amount of sunshine in NYC affect the price change of the S&P 500 ?  \n",
    "\n",
    "**No**, this notebook found no correlation between the amount of sunshine or temperature in NYC over the last 10 years with the daily price change of the S&P 500.  All of the p-values were found to be above 0.05.  (It might be interesting to check if the amount of snowfall has an impact on daily price change for the S&P 500.)\n",
    "\n",
    "3.B.  Is it possible to build a supervised learning model that uses the day of the week, month, day of month and weather data to predict daily price change of the S&P 500 ?  \n",
    "\n",
    "It might be possible, but certainly difficult.  The day of the week mattered as found above, but the month, day of month and weather data may introduce too much noise to build a reasonably affective model.\n",
    "\n",
    "3.C.  Is it possible to build an unsupervised learning model to identify relationships among the 5 securites previously analyzed in this study ?  \n",
    "\n",
    "Possibly, more study is needed.  There are cetainly many different ways to structure the model.  Only one method was explored in this notebook.  For instance, a separate feature could be introduced that binarizes daily gold prices, uses that as the dependent variable and uses the actual daily price change of all 5 securities introduced in this notebook as the features.  It could be possible, however, that there is not a predictable pattern.  Furthermore, the pairplot does not show any kind of positive or negative correlation between the 10 Yr Bond, Gold or Oil.   \n",
    "\n",
    "As a final summary, it was interesting to see that the day of the week appears to play a signigicant role among certain securities.  This could be converted to profitable trading or investing opportunities.  Also, it was interesting and surprising to see that the amount of sunshine or temperature in NYC had virtually no affect on daily price change.  The barplots and scatterplots presented in the corresponding sections above provides a quick and easy visual of these statistical findings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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