Project Risk and Returns The Sharpe Ratio.ipynb

{"cells":[{"metadata":{"dc":{"key":"3"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 1. Meet Professor William Sharpe\n<p>An investment may make sense if we expect it to return more money than it costs. But returns are only part of the story because they are risky - there may be a range of possible outcomes. How does one compare different investments that may deliver similar results on average, but exhibit different levels of risks?</p>\n<p><img style=\"float: left ; margin: 5px 20px 5px 1px;\" width=\"200\" src=\"https://assets.datacamp.com/production/project_66/img/sharpe.jpeg\"></p>\n<p>Enter William Sharpe. He introduced the <a href=\"https://web.stanford.edu/~wfsharpe/art/sr/sr.htm\"><em>reward-to-variability ratio</em></a> in 1966 that soon came to be called the Sharpe Ratio. It compares the expected returns for two investment opportunities and calculates the additional return per unit of risk an investor could obtain by choosing one over the other. In particular, it looks at the difference in returns for two investments and compares the average difference to the standard deviation (as a measure of risk) of this difference. A higher Sharpe ratio means that the reward will be higher for a given amount of risk. It is common to compare a specific opportunity against a benchmark that represents an entire category of investments.</p>\n<p>The Sharpe ratio has been one of the most popular risk/return measures in finance, not least because it's so simple to use. It also helped that Professor Sharpe won a Nobel Memorial Prize in Economics in 1990 for his work on the capital asset pricing model (CAPM).</p>\n<p>The Sharpe ratio is usually calculated for a portfolio and uses the risk-free interest rate as benchmark. We will simplify our example and use stocks instead of a portfolio. We will also use a stock index as benchmark rather than the risk-free interest rate because both are readily available at daily frequencies and we do not have to get into converting interest rates from annual to daily frequency. Just keep in mind that you would run the same calculation with portfolio returns and your risk-free rate of choice, e.g, the <a href=\"https://fred.stlouisfed.org/series/TB3MS\">3-month Treasury Bill Rate</a>. </p>\n<p>So let's learn about the Sharpe ratio by calculating it for the stocks of the two tech giants Facebook and Amazon. As benchmark we'll use the S&amp;P 500 that measures the performance of the 500 largest stocks in the US. When we use a stock index instead of the risk-free rate, the result is called the Information Ratio and is used to benchmark the return on active portfolio management because it tells you how much more return for a given unit of risk your portfolio manager earned relative to just putting your money into a low-cost index fund.</p>"},{"metadata":{"dc":{"key":"3"},"tags":["sample_code"],"trusted":true,"collapsed":true},"cell_type":"code","source":"# Importing required modules\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Settings to produce nice plots in a Jupyter notebook\nplt.style.use('fivethirtyeight')\n%matplotlib inline\n\n# Reading in the data\nstock_data = pd.read_csv('datasets/stock_data.csv', \n    parse_dates=['Date'],\n    index_col='Date'\n    ).dropna()\nbenchmark_data = pd.read_csv('datasets/benchmark_data.csv', \n    parse_dates=['Date'],\n    index_col='Date'\n    ).dropna()","execution_count":2,"outputs":[]},{"metadata":{"dc":{"key":"11"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 2. A first glance at the data\n<p>Let's take a look the data to find out how many observations and variables we have at our disposal.</p>"},{"metadata":{"dc":{"key":"11"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# Display summary for stock_data\nprint('Stocks\\n')\nstock_data.info()\nprint(stock_data.head())\n\n# Display summary for benchmark_data\nprint('\\nBenchmarks\\n')\nbenchmark_data.info()\nbenchmark_data.head()","execution_count":4,"outputs":[{"output_type":"stream","text":"Stocks\n\n<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 252 entries, 2016-01-04 to 2016-12-30\nData columns (total 2 columns):\nAmazon      252 non-null float64\nFacebook    252 non-null float64\ndtypes: float64(2)\nmemory usage: 5.9 KB\n                Amazon    Facebook\nDate                              \n2016-01-04  636.989990  102.220001\n2016-01-05  633.789978  102.730003\n2016-01-06  632.650024  102.970001\n2016-01-07  607.940002   97.919998\n2016-01-08  607.049988   97.330002\n\nBenchmarks\n\n<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 252 entries, 2016-01-04 to 2016-12-30\nData columns (total 1 columns):\nS&P 500    252 non-null float64\ndtypes: float64(1)\nmemory usage: 3.9 KB\n","name":"stdout"},{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"            S&P 500\nDate               \n2016-01-04  2012.66\n2016-01-05  2016.71\n2016-01-06  1990.26\n2016-01-07  1943.09\n2016-01-08  1922.03","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>S&amp;P 500</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-04</th>\n      <td>2012.66</td>\n    </tr>\n    <tr>\n      <th>2016-01-05</th>\n      <td>2016.71</td>\n    </tr>\n    <tr>\n      <th>2016-01-06</th>\n      <td>1990.26</td>\n    </tr>\n    <tr>\n      <th>2016-01-07</th>\n      <td>1943.09</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>1922.03</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"dc":{"key":"18"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 3. Plot & summarize daily prices for Amazon and Facebook\n<p>Before we compare an investment in either Facebook or Amazon with the index of the 500 largest companies in the US, let's visualize the data, so we better understand what we're dealing with.</p>"},{"metadata":{"dc":{"key":"18"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# visualize the stock_data\nstock_data.plot(title='Stock Data', subplots=True);\n\n# summarize the stock_data\nstock_data.describe()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"           Amazon    Facebook\ncount  252.000000  252.000000\nmean   699.523135  117.035873\nstd     92.362312    8.899858\nmin    482.070007   94.160004\n25%    606.929993  112.202499\n50%    727.875000  117.765000\n75%    767.882492  123.902503\nmax    844.359985  133.279999","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>Amazon</th>\n      <th>Facebook</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>252.000000</td>\n      <td>252.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>699.523135</td>\n      <td>117.035873</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>92.362312</td>\n      <td>8.899858</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>482.070007</td>\n      <td>94.160004</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>606.929993</td>\n      <td>112.202499</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>727.875000</td>\n      <td>117.765000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>767.882492</td>\n      <td>123.902503</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>844.359985</td>\n      <td>133.279999</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"stream","text":"DEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal bold normal>) = 4.971363636363637\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbi.ttf) italic normal bold normal>) = 7.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal bold condensed>) = 10.535\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Italic.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold.ttf) normal normal bold normal>) = 3.9713636363636367\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal bold condensed>) = 11.535\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Italic.ttf) italic normal 400 normal>) = 4.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (Arial_Black.ttf) normal normal black normal>) = 10.525\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal bold normal>) = 3.9713636363636367\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New.ttf) normal normal 400 normal>) = 10.05\n","name":"stderr"},{"output_type":"stream","text":"DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial.ttf) normal normal 400 normal>) = 6.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold_Italic.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (andalemo.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana.ttf) normal normal 400 normal>) = 3.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold_Italic.ttf) italic normal bold normal>) = 4.971363636363637\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (Impact.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (Andale_Mono.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbi.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (Webdings.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold_Italic.ttf) italic normal bold normal>) = 7.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold.ttf) normal normal bold normal>) = 6.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbd.ttf) normal normal bold normal>) = 6.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (ariblk.ttf) normal normal black normal>) = 10.525\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Italic.ttf) italic normal 400 normal>) = 7.413636363636363\nDEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\nDEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n","name":"stderr"},{"output_type":"stream","text":"DEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal bold normal>) = 4.971363636363637\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbi.ttf) italic normal bold normal>) = 7.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal bold condensed>) = 10.535\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Italic.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold.ttf) normal normal bold normal>) = 3.9713636363636367\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal bold condensed>) = 11.535\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Italic.ttf) italic normal 400 normal>) = 4.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (Arial_Black.ttf) normal normal black normal>) = 10.525\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal bold normal>) = 3.9713636363636367\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial.ttf) normal normal 400 normal>) = 6.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold_Italic.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (andalemo.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold.ttf) normal normal bold normal>) = 10.335\n","name":"stderr"},{"output_type":"stream","text":"DEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana.ttf) normal normal 400 normal>) = 3.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold_Italic.ttf) italic normal bold normal>) = 4.971363636363637\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (Impact.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (Andale_Mono.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbi.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (Webdings.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold_Italic.ttf) italic normal bold normal>) = 7.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold.ttf) normal normal bold normal>) = 6.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbd.ttf) normal normal bold normal>) = 6.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (ariblk.ttf) normal normal black normal>) = 10.525\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Italic.ttf) italic normal 400 normal>) = 7.413636363636363\nDEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAagAAAEdCAYAAABZtfMGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydd3hUxfrHP3O2poeEEEogtIQmiigIghRBRFBRrNefil7rtd5rwa5c9SpXvRYsiGBDUVBBARtSRLoUQekklBBCSUhv2+f3x2422eymkpA2n+fJs3vmzMyZ2d2c75mZd95XSClRKBQKhaKxoTV0AxQKhUKhCIQSKIVCoVA0SpRAKRQKhaJRogRKoVAoFI0SJVAKhUKhaJQogVIoFApFo0QJlEJRC4QQh4QQoxu6HQpFc0YJlKJZIYQYKoRYJ4TIFUJkCSHWCiEGeM7dIoRY0wBtkkKIQiFEgRAiUwixXAhxXQ3KjxBCHKnPNioUjRElUIpmgxAiHPgeeBuIAjoA/wasDdkuD2dJKUOBHsAnwDtCiOcatkkKReNGNAZPErm5uQ3fCEWTZ+vWrUyYMIHDhw/7ndu7dy/Dhg3DbrcTFBSETqfj8OHD5ObmMnnyZJYtW0ZQUBCTJk3i4YcfRtPcz26ffvop7777LkePHqVDhw7MmDGDfv360bdvX95++21GjBjB3r17ueaaa3j22We5+uqr/a4dGRnJH3/8QdeuXb1pCxcu5M4772T37t1ERUXx+eefM23aNI4ePUp0dDT//Oc/ufXWWyksLKRbt25YrVaCg4MB2Lx5M0ePHuXxxx9n7969BAUFcfnll/Of//wHo9FYT5+uQlH/REREiLLHagSlaDZ069YNnU7H3XffzdKlS8nJyfGe69GjB6+//joDBw4kLS3NK2KTJ08mLy+Pbdu28cMPPzB37lw+//xzAL777jumTp3K+++/T2pqKl9++SVRUVE+19y2bRtXXXUVr7zySkBxqohx48bhcDjYsmULADExMcybN4/U1FTeffddnnzySbZt20ZISAhff/017dq1Iy0tjbS0NNq1a4dOp+Oll17iwIED/PLLL/z222/MmjXrVD9ChaJRoQRK0WwIDw/n559/RgjBgw8+SLdu3bj++utJT08PmN/pdLJgwQKee+45wsLCiI+P57777mPevHkAzJ49mwceeID+/fsjhKBr16506tTJW37dunXccMMNTJ8+nbFjx9aorQaDgejoaLKzswG4+OKL6dKlC0IIhg4dysiRI1m/fn2F5fv168eAAQPQ6/XEx8dzyy23sHbt2hq1QaFo7DR5gUpKSmroJpwWWko/4dT62qNHD6ZPn86uXbtYv349x48f54knngiYNzMzE7vdTseOHb1pHTt25NixYwCkpaXRpUuXCq/18ccfM3DgQC644IIat9Nut3Py5EmKiooAWLp0KaNHj6Zz58506tSJpUuXkpmZWWH55ORkrrvuOhITE+nYsSMvvPACWVlZNW7H6Ub9jpsn9dXXJi9QCkVFJCYmcsMNN7B7924AhPCZ3iY6OhqDwUBqaqo37ciRI7Rr1w6ADh06cPDgwQrrf/311zly5EiFAlgZP/74I3q9nj59+mC1Wrn55pu5//77SUpK4vDhw1x00UXevOXbDfDQQw+RkJDAli1bSE1N5ZlnnqExrCc3ZjZn2Ljo+3TGfJ/BtweLkFJS7JDsyLJzosjZ0M1TBEAJlKLZsG/fPt5++23S0tIAt9jMnz+fc889F4A2bdqQlpaGzWYDQKfTceWVV/LCCy+Qn5/P4cOHeffdd7nuOrcF+M0338w777zDtm3bkFJy4MABHwOMsLAwvvnmG9atW8eUKVOq1cbs7Gy++uorHnnkER588EEiIyOx2WxYrVaio6PR6/UsXbqUX3/91VumTZs2ZGVlkZub600rKCggLCyM0NBQ9u3bx0cffXRKn11zREpJRrGTArsLu0tyy69ZbMqwszHDxq0rszl7/gniPj/K0IXp9P36OItTiqtdd0axk/UnrNic6qGgPtE3dAMqQ0pJQUEBLperwjxms9nnH7e5UlU/NU0jNDQ04NN2SyE0NJQtW7bw3nvvkZubS0REBBdffDHPP/88AMOGDaNXr14kJiaiaRoHDhzglVdeYfLkyfTr1w+z2czNN9/MjTfeCMAVV1xBVlYWt99+O8eOHaNTp068//77PutQkZGRfPfdd1x66aXo9XqefvrpgG0bOnQoQggMBgNnnHEGL730Etdccw1JSUmEhYXx3//+l1tvvRWr1crYsWO55JJLvGUTExO56qqr6NevH06nk99//50XXniBf/7zn0ybNo0zzzyTK6+8ktWrV9fjp9u0+CPDxt+WZ3KiuOJ7x6H80lGTzQU3rchi93VtaResq7LuK5acJM8uiQvR8enIKM6JUdaT9UGjNjPPz8/HZDJVajprsVgwm8311rbGQlX9LHkKDwsLO42tqh+SkpJISEho6GacFlpSX+H09NclJQMXpJOc56hV+dkjo7i8c1DAc3aXZNjCdHbnlNbd2qyx4co2tDb7CltL+m7rqq9Nyszc5XKpfR3VxGg0VjrSVChaAictTrp+cazW4gRw9+psdmXbA56btbvQR5zc13Tx1f7qTw8qqk+jFiiFQqGoCc9syiPHVvmsUJ9WehaNbc3Ci6OZNiTS73yRQ/LA2mxc5WaXMoqdvLwtL2CdS1IttW+0okIa9RqUQqFQVBerU7L4kP9IZs2ENph08OGeQuwueOCMUOLD9N4yM3cXsj3Ld8S0OcPOi3/k8Uz/cOwucEl4fkseeRWI36pjVo4UOIgLVbfUukR9mtXg+++/58Ybb2Tjxo0kJiY2dHMUCkUA1h23UuDwFZCXBkZwRpQBgKnn+Y+WTDrBtxdHs/iQhSc25mApY23++l8FvP5XQbWuLYF/rM5m8SUxtW6/wh81xVcN5s+fz+DBg/nmm28auikKhaICfi43zfZ/CcHc0ye0ynKtzTpu7RnC71fGEqqvnhVsjwg9T5zta5C0+riNPzNt1W+wokqa1Agq8uO0Oq0v59YOVeYpKChgw4YNLF68mOuvv54nn3yS1atX8/LLLxMREcGuXbu48sor6d27N++//z4Wi4U5c+bQpUsXfvrpJ1577TVsNhtRUVHMnDmTNm3acM0113i9FRw+fJipU6cyceJEHnroIbZt24ZOp+M///kPw4YNY86cOfz0008UFhaSkpLCpZde6jWbVigUpaw57uu0/pKONbPujQ/T8/WYaK5flkluFetYLw6MYFg7E9N3Fvisee3PdXBWtDLsqitqNYISQvxLCLFTCLFDCPGlEMIshOgihPhdCJEshJgnhDB68po8x8me853rsgP1zY8//sioUaPo3r07UVFRbNu2DYAdO3bwxhtvsHHjRubNm0dycjIrVqzgpptuYsaMGQAMHjyYZcuWsXr1aq666ireeustAL7++mvWrFnDO++8Q1xcHOPHj2fmzJkIIVi3bh0ffvgh99xzDxaL+4lw+/btzJgxg3Xr1rFgwQKOHFGhgRQtGykls/cVMnJxOt2+OMZNKzLZme1rXTe0ranG9Q6ONbFkfAxxIRXvheoVqWd0BxMmneDKLr7m6OkWZUlbl9RYoIQQHYAHgHOllGcAOuB64L/AG1LK7kA2cJunyG1Atif9DU++JsP8+fO56qqrAJg4caJ3mq9///60bdsWk8lE586dufDCCwHo3bu319tAWloaEydO5Pzzz2fatGns2bPHW29mZiZ33XUXs2bNIiIigg0bNnDttdcCeP2rJScnAzB8+HDCw8Mxm8307NnTxzWPQtESWZRi4YG1OWw9aSfT6mJxiu/0XmKEnkhT7VYwekYa+GV8DIPaBB4J3XdG6Yb4NkG+QpZRrFwm1SW1neLTA0FCCDsQDBwDLgRu8Jz/FJgCTAcmeN4DfIM7UJuQjWGHcBVkZ2ezatUqdu7ciRACl8uFEIIxY8ZgMpU+nWma5j3WNA2n0/0jnTx5Mvfeey/jxo1j9erVTJ06FXB70f773//O5MmT6d27d5XtKHstnU6Hw1H7PR4KRXPg6/1FlZ4/9xQ9O7QP0fHTuNYcL3YRbdJ4a3s+S45YuDjOzA3dg7352gT5imB6JZ4rFDWnxgIlpUwTQrwGHAaKgV+ALUCOlLLkznkEdzRTPK+pnrIOIUQuEA2crOm1A60Z1acniYULF3Ldddfx5ptvetPGjRtXaRiEsuTl5dG+fXsAvvzyS2/6lClT6NOnj3dkBu7pwK+//prhw4eTnJxMamoqCQkJ/Pnnn3XUG4WieeCSkrUnKg+SPLCC0U9NEEJ43R492i+cR/uF++WJKec9Qk3x1S01FighRCvco6IuQA7wNVCzYDiVUNZtu9ls9hk9VETJWk1d89VXX3Hffff51D9u3Dg+/fRT4uPjvekulwur1YrFYsFms+F0OrFYLDz88MPcfPPNREZGMmTIEFwuFxaLhbfffpsePXowZMgQwD3SuvHGG3nssccYNGgQer2eN998EykldrvdO2KyWCw4nU5sNlvAPufl5VUY+6ipoUIVNF9K+pvngK+O6tELuK69g6DKXeCVli8UZFt9137CdJJ8p3vaLUiTJNqPkZRU/5M01lwNKH1A3nS8mCsXpZBjF9zRSYNy362UsLdQEKGHduZGP4lUI2r7O67MRVKNffEJIa4Bxkopb/Mc3wwMBq4B2npGSYOBKVLKi4UQSzzv1wsh9MBxIKbsFF9FvvhKHH5WhvLFV0p1Pq+mgPJh1nwp6W9GsZOLf8jggMdha5hB8M1F0ZwXW/UD6Ts78nl6U6lHh0s6mnnvglbM2l1AUp6DSYkhDKmFgURt2J/r4JwFJwKe0wvJwrEx3rakFTq55ddMNmW4NwXPGNaK67oFByxbFiklnyUVsTnDxsQuQYxo3/jud43JF99hYJAQIli4VwpHAbuAX4GSmNeTgIWe94s8x3jOr2gK608KhaJ+yLO5uHppplecAPLtkot/PMmMXZVvjJVS8kWy7/rTBe1MtDJpPNovnA+GRZ02cQKICar4FuqQgnvXZON0SZwuyQ3LS8UJ4MU/ArtNKs///irggbU5zN5XxBVLMrluWSZfJheRZWn+Bhm1WYP6XQjxDfAH4AC2Ah8APwBzhRAvetI+9BT5EPhMCJEMZOG2+FMoFC0QmwtuXJHFn5mBnbFO217AXb1LN9dKKXl5Wz5fJBXRIURHhFGwq4w5uSbwM/U+nYQZBGYdVKQVh/Kd7M5xkF7s9OtzaoGT1AIHHStxj7TtpI2Xt/oK2ZJUC0tSLcSF6Ph5XOtm7V6pVj2TUj4HPFcu+QAwMEBeC+7pP4VC0YQ4UeRkW6adUIPgvDZG9FrNY405XRIXYNAETpfk2b1GVmVWbOCQVuQkx+pCp0GoXrDkiIVXtuUDcKTQXwXGxJmrjN9UnwghMGgCSyWBC7dn2Vl2JPA6+foTtgoFSkrJ47/nUlHVRwqd/G15Fj+Na02ooWaTYXOTi5i5u4AekQb+OyiCsHLlnS6JJvyjOdtdkp1ZdvSaoGekvla/iZrQqKVX0zRsNpsKuVENbDYbmqY8VymqptghsTplwH1CDpdk3Qkbn+0rZMHBYu/NMcascVXXIG7tEUKPSEOl9a89bmXhoWK2nbTzV5bNO7roFKrjcIHvLadnpJ495cJXXP7zSXZm24mtZPqshLt7h1SZp77pEalnc0bgESG4PVx8fzhwOI4NJ2xcW8E61HeHitmQXrnrpO1Zdu5alc1nF0ahBQhWujHdygtb8rC7YER7EyPbm4gya+6pRwlbTtrJsrr4clQUQghyrC7+vjKLX49aiTZr3NMnlAfPCEWnCRYeKuaxDTkc95jSXxZv5pMRUejqUaQadcDC6kTUzcvLIzzc3/yzuVFVP5tTRN2WZDhwOvsqpeSTvUVM2ZJLrk3yz76h3N07lFe25ZOUayffLjmU76gyXMX4Tmau7x7M+bFGosuZWS88VMykX7Oq1Z7ECD0/j2vNg+ty/DbaVodbEoN5c0irGpera978K58pW9zTcOEGwb1nhPLy1vxqle0bZWD1hDZ+6RaHZOC3Jzhc4Dtq/Fv3YL5M9t8D9s++oUw519dA6lC+gyHfpVNYzoFuXIjObzT6wbBWXN01iBuWZ/n5NLyhezA6AZ8l+V93+gWt+Fv34HozkmjUIyghRJURYtPT0+nYseNpalHD0VL6qagfLA7JIxty+LzMTebN7QW8ub163rrL8sNhCz8cthBmEPw4Lobu4Xre3J7PhhM2fjtW+f6kEjoE65g/Jpoos47EiOrdhnpG6ukYoiOlwMnwdiZeGNA4LFYf6BtKlFnjUL6DmxNDcEmqLVCH8h1IKf0eLKfvKvARJ72A9Ve2ISHCwKuDIhj740l2lAkR8ub2AhIj9NyQ4B5RuqTk3jXZfuIEgadKH16fw2f7Cll93H/EVt4opSwvbc3jmq71twbYqAVKoVCcOpkWJ9cuzWTLyYqnoSrDoIE9wCRGvl1ywcJ0gnSC4krWYHzqEpKru4Xw7Dnh3rWjhIjKpwxLeOLscCZUEIq9IdGE4ObE0qlGl5SEG0WFsaPKkm+X5NgkrUylAnUo38Hrf/kK3G09Q7yfU6hBY+6oKEZ9n8GJMp4rJm/IZVynICJNGjN2FbI2gNhU1o5A4lQVqQVONmfYiK5xyeqhFi0UikbM0UInb/6Vz+x9hThdtZuOv29NTo3EyajBRR1MvDQwgtQb25H6f+15rF/FMxkVidMFbY0sGdeaKzsHMaiNkSfODmPxgGKmX9DKx7ChOiOouBAd4zs1vv0/gdCE4N4Kwnxc0NZIj3L9TckvXYPLtbm4/OeT5NtLP9NIo+Dxs32n9+NC9XwxKpqyM6wFDsnPqRbSCp28UE0T9pqSEKEnoVz7ayKENUWNoBSKRsrX+4t4eEOO90l8wwkb711QszWXX1It/FTNcOQLL25N22CNuBAdIeWsup44O5xekQY+TypkWVr1pvHu6BXKebEmn823SUn+m1r7RBmIDdK8o4FWJkGOVVJW9h4/O6zeLcbqksf6hXM8I4uPj/iODm9ICGHBgSL25paKUkqBk36t3e8/3FPot+70+NnhtApg0HJOjJF7+4TyvzJBFb9PKeaXIxaKykzthRsF66+IJcqkMXlDTsC1pLKcH2tk5vAo5u0v4vktpUJ3U0IwU8+L4Kv9xfxrfY43fe1xK5d3rbTKWqMESqFoZOTbXTy8LoevDvhafn2RXMQlncxcFu87zXUo38Ga41ZC9RpxoTp6Req9AvPh3kKfvGdEGXh3aCRjfzjpM/JZeHE0w9tXvsH1ii5BXNEliF3Zdsb+kEGeveIR3Q3dg7k0vnojHpNO8OWoaF77K58ok8ZT/cOxOiXzDxSTY3MxtK2Ji2sY26kxcHsnO9+mG71GJ+EGweXxZrZk2IBSkT/sGUGVhBApy/hOZu7sVbGl4mXxQb4Cddj/YWTKORF08IQPeXtoK0bHmfkyuYjECD0XtDNxx29Z5NjcZuWP9QvjkTPD0GmCf/UNJdqkseWkjcvjgxgd5/4OhrT1tar+Pd2Go0sNPpgaoARKoWhk3PFbtp8lVQl3/pbNorE6BnicoW7PsnPpTxk+AfbCDIJbe4RwV+9Q1pdzqvraoAjOijYy/YJW/GN1NsVOyVNnhzG8Bu5zercysPTSGH49aiXUIDgr2j1ttSHdxqZ0GyPbm+hfQ2/i/WOMfDHKdyXj4bMqN5Bq7Bg1eGtIK/65LhuXhGlDWhFi0IgP9bV8TPGMmJ7bnMehMt41DBq8NSQyoPl4CWdFGwJa5ZU9PynR14x9Qucgn7W87de2ZeVRK/1bG71CBm4jtUk9QpjUw1cgEyL0DI51f+dD2po4v62JoqMHqvg0aocSKIWikSCl5K3tBRWKE7jXe8b9lMFHI6IYE2fmvjXZftFf8+2SaTsKmLbD10Iv0ii8Xr6v6BLEiPYmHFLS2lzzja49Ig1++6GGtTMxrN3pczPUFJjQOYhLOprRBN4pyk5hvrfdWXsKGd/J7Pd9jetkrvK7EUJwW88Q/r0l8JrTK+dFVLlPKcyg+Y3Kq7rmT+NifNLqy92xEiiFopHwxMZc3t/lO8WTEKFnSKyRT/aVrhvYXXDTiurtNSrLebEmn6fx2gb0U9QMo85XILqE+YvOlb9k+qU9FiC8RyDu6h3CzN0FHC3yNbW8vltQtZzvNmbUL1ShaAQcK3Iyc3ehX/rz54bz6uBILo6r3o1GV8nD8uA6iJGkOHXOiDJwTuvKTetv7xlC71bVM78P1mtMv6CVj0VfmEHw73Mbxz6xU0GNoBQK4ECegztXZZFe7OLMKAPntTFyotiFC4nDBbuz7UQYNV4YEEGX8Lr5tzmY52DBcR05WTlsSLf5+VzrHKbjojgzek3wxaho7l+bU+GmyRKvDEF6jS+SC3l+S57P1J+AJmlo0BzRhGD+mNZc9EMGSbmBo2NfFFez72p4ezM/XhLDv7fkYXFK/n1uOLEN6KOwrlACpWjR2F2S71OKuXVltjftcIEzoDUUwIliJ0sv9XdNEzBvkZMtJ20MbWsi3Og7WfHtwSL+vjIbiQnwHzkBfDoyyrtuodMEbw+JxOaSfFPGuk/gtqqaOTyKKM8j9G09Qzk/1sStK7PYk+MgLkTHE2eH0auaT+SK+ifSpPHaoAgmLPGf2gP89hpVh/4xRhaObX2qTWtUKIFSNEmklGzLtNPKpNE5rHY/4+RcO3esymZrDTaxbsqws+2kDYMm+HhvIZ3DdFwaH0RciM5nn87BPAfDF6WTZ5dEmTQ2T2zjFRCXlDy3OY+KjLSDdIJd17X12/ui0wSzhkfx0kAnFqck3KARahAB9wf1amVg7YQ2FDslIXrRLHw0NjeGtzcztqM5oFFMp9CmP/qpC5RAKZocdpfktpVZLEqxIICZw1txddeqI5MCrD5mZVOGjWNFTuYkFflsaKwuIxZn+Bw/vSmPKJPGL+NbE23WMXN3AS+V8cWWZXUxJ6mI+/uGYXdJrv4l028zZgkRRsGrgyIDbswsoU01Y6PrNEFoE9rc2hJ5YUC4n0CZdTSpTcn1iRIoRZPj35vzWOTxfi2B23/LJsviQgjc60cVWC4tPFTMLb9mVThyKSFYL7iuWxAbTtjYn+fAVrEzfS9ZVhfnLkgnyqSRZfUvMO9AMff3DeO5zbl+DlUHxxq5q1coiZF6uoXrMVVm6aBoViREGLi9Zwiz9pRO817SsfH5G2wolEApmhRphU7eCxAWfPLvud73swKMqArsLh7/PadKcbqqSxAzh7fymmPn2104XTBycToH86sOsR1InMC9VnS00MmsAJZ6j5wVxqgOyoChpfJU/3DWHLeyJ8eBQYM7G0GMq8ZCjQVKCNEDmFcmqSvwLDDbk94ZOARcK6XMFu7J77eAcUARcIuU8o9Ta7aipfLV/iKq8pk6c3chF8WZWXnUSu9WerqG6fnH6myOFfmLR6heMHVQBBd1MJNpddEzUu+zV6gk0uj8Ma157c98Vh2zVrhrvzIO5jl4f1eB32hsRLSDC6twMaRo3rQyaSwZH8OmdBvdI/S1XlNtjtT4k5BS7gX6AQghdEAa8C3wOLBcSjlVCPG45/gx4BIgwfN3HjDd86pQ1Ji5lcSmKeH3dBt95h2noIr1pWHtTLx1fqTXbLwys9yu4Xqvo9bDBQ52ZtmxOKWP9V9lFDgknyX5jp7u6BnCHdEZyoBBQYRR8/q6U5RyqlI9CtgvpUwRQkwARnjSPwVW4haoCcBs6Q7du0EIESmEaCelPHaK11a0MI4VOX28QAOM7mBCE7Ax3eYTCbYyceoRoeebMdF0DK3dz79TqJ5OnrLnxBg5/9t07/XOjDIw/YJW5NtdjP3xpE+5bKtvm57qH07GYV+DC4VCUcophXwXQnwE/CGlfEcIkSOljPSkCyBbShkphPgemCqlXOM5txx4TEq5uaSesiHfk5Lqy6uToqnze47GfTtKnzJ7hTqZ3c9tcPD8PiOL06sWHJMm+bq/hXbm2v/uy7MjX+PndB0JIS7GtXFSEqnihSQji04EblOXYBdf9a95mHOForlRNlR8nYV8F0IYgcuBJ8qfk1JKIUSt7gA1jWuflJRU4zJNkZbST6i4ryt2FQClxhBnxYaSkNAJgLEUsjg9x69MeW7vFcawvnF11lZwz11fGSD9piALi5YG3og5tEMoCQkdW9T3Cup33Fypr76eii++S3CPnkoikJ0QQrQD8Lyme9LTgI5lysV50hQKP6SUrDip4/1dBZy0+Boj7Cs3vVfWm/Zl8UG0DSr9OfeK1PPG4EhmDW9Fd88a04AYQ6WRYeuai+LMzBreigij/xrTAOUXT6GoklNZg/ob8GWZ40XAJGCq53VhmfT7hBBzcRtH5Kr1J0VF/O+vAl7cYwJy+WxfIb9e1sbrDXpPjq/Hh7KhwsONGisua8N3h4pJjNBzYYdSz91XdQki2+qilUk77QYJV3cN5vxYEw+uzWapJxJtbJDmE49HoVAEplYCJYQIAS4C7iqTPBX4SghxG5ACXOtJ/xG3iXkybjPzW2vdWkWzJtPi5H9/lnpg2Jnt4LdjVs6PNTJrT6EnEmkpPcvFI2ofouOePqF+9QohvG6GGoL2ITq+uiiaVces7Mh2cHm8mQijCiSgUFRFrQRKSlkIRJdLy8Rt1Vc+rwTurVXrFC2KmbsLfcKQA9y5KgujJjhR7LuByKSD+ABxdRorQgiGtzczvH1Dt0ShaDqoHWGKRkGRw8UHAbwsuE2z/e1tJnYJVv7KFIpmjhIoRaPg831FFboJKku4QXBPn1AeOvP0GTsoFIqGQQmUosFxuCTv7PT3r1ees6INfDsmukHXkxQKxelDrdQqGpyFh4orDD9RlnGdzEqcFIoWhBIoRYOzKKXY5/jKtnb+vDrWL98FbZVTVYWiJaEEStHgHC/nZXxMayfxYXqGti3dzBoXouOcGLW5VaFoSag1KEWDk1suBkWEwW21994FrXhuUx55dheP9wtXgfwUihaGEihFg5NTznqvJBxOp1A9H4+MaoAWKRSKxoCa4lPUK9Xxlp9TbgQVrq87T+MKhaLp0qwEKqPYyRVLTvJzanG1buzcNkwAACAASURBVIzVIb3YyWU/ZRA/5yhPb8yts3qbO0UOF4+szyF+zjHOW3CCD3YV+PnSA7A4JGV9wuoEBDWrX6VCoagtzeZWUOyQ3LA8k5VHrVy/LItHN+RWXagafLCrkNXHbeTa3Ht1/jjpf5NV+HK8yMmlP51k1p5C8uySvbkOJv+ey6Bv07lrVRaWMsEEy68/RRo1VIBZhUIBzWQNyiUl967JZlNGqXh8tLeQp/qH08p0ahr82l/5PsfTduTz6cjoCnIr/sq08bdlWaQVBd7XNG9/MYkRBqJMGtuz7PSM9P0JRpqUOikUCjfNQqCcEj8LL5eEvTl2BsXW7d6ZQrua4quItEInE5ac9AttXp4X/sir8Jzy8q1QKEpoFncDgyaYfkErWpt9u5OU66DQ7uLjPYW8tDWPr/cX4TrFNSSX0qeASCl5YG12leJUFZFKoBQKhYdmMYIq4ZbEEJ8pubXHrUzfWcCunNJIrNuz7Dw/IKLW11D6FJhP9xWx3BOQr4Q7eobQNVzP/INFbM6o3tqdGkEpFIoSmtXdoHuEr97O3V/sI04A8w/4utWpjEAWe2oE5U+ezcWzm3yNUoa0NfLfQRH8o08oyy5tw5wLq7efSa1BKRSKEprVCCohouruHCt24nDJasUSyg+w3lToqDokREvj51QLeWU+qxC94N2hrbwh1wHGxwcx9bwIfjps4ZwYA3k2yaw9/vGf1BSfQqEooVZ3AyFEpBDiGyHEHiHEbiHEYCFElBBiqRAiyfPaypNXCCGmCSGShRB/CSH6120XSukWXrVAuSRkWKonMmmF/pZomdUs2xxxScnKoxZ+SbVgLRP5dnE5Z6939Aqhc5j/d3F371AWjm3Ns+dEcG+A0OygpvgUCkUptb0bvAX8LKXsCZwF7AYeB5ZLKROA5Z5jgEuABM/fncD0U2pxJUSaNBKrM4oKIDzl2ZVtZ/B36X7pmdUIqtfU+T6lmNf/ymf2vkJm7yvkQJ57mvSFLXlcsSSTa5dl8sDabMDtpmjZEd+1pwmdg6q8RpdwPUPa+jt/jTzFbQEKhaL5UOMpPiFEBDAMuAVASmkDbEKICcAIT7ZPgZXAY8AEYLZ0L+hs8Iy+2kkpj51y6wPw6qAI7lqVzfFit5DckhhMSoGTX4+W3kSPFDrpH+Nbzu6SPLo+hwUHi32mq8qTZ5PYXRJDMw03/p8/8nj1T9+9X5qAcR3NfH/Y4k2bt7+YtMIM1hy3+eSNC9HRL9pQrWv9q28Ya49n+qRFmTRQe6EVCgUgauq6RwjRD/gA2IV79LQFeBBIk1JGevIIIFtKGSmE+B6YKqVc4zm3HHhMSrm5pM7c3FxvI5KSkk6tR4DDBfuLBCYNOgdLXkwysvCErxZ/1q+YnqGlfV92UscTe6q3Z+qngUW0boaRHzbmaNy7w3xKddwTb+PWjo6qM3qYkWJgVqpb0EJ0ku8HFBParFZGFQpFZSQkJHjfR0RE+Dz51+ZWoAf6A/dLKX8XQrxF6XQeAFJKKYSolb1b2cZWh6SkpIBlepV536MgD074jgo+zYjkm7Nbe49nZuYA/ov2gYho35mEVtUbJdQVFfWzLnl6WSZgqTJfRcSYNZ68IJ5QQ/Wn6V5LgJEpxezMtnNN12C6hOtPS18bCy2pr9Cy+qv6eurURqCOAEeklL97jr/BLVAnSqbuhBDtgJIFnDSgY5nycZ6000a7IP8w4cvSrEgpER5Ls93Z1Z9XSs510Ps0C1R98Femjd+OWWkfrOPMaANrjlmrLlQBoXrB20MjayROJYyPD2J8fNXrVgqFomVRY4GSUh4XQqQKIXpIKfcCo3BP9+0CJgFTPa8LPUUWAfcJIeYC5wG59bX+VBHtQgLfNI8Xu2gXrENKya5s32mpTRPbMGCBv5EEwIPrskmM1NMz8vSI1C+pFj7YY8R54CQ3JwZzRecgr7DWlj8zbYxcnFHpvq5ruwZxZ+9QVh618mIA90RD2xr5e48QuoTrSYzQE1ILcVIoFIqKqO1s//3AHCGEETgA3IrbIvArIcRtQApwrSfvj8A4IBko8uQ9rbQNMIICt68+vYAVR60+1nlBOkHXMD3ntTHye7rNr1y2VTJxyUl+Hh9Dp3peMJm9r5AH1ubg/qqs/HrUyrfxxbx+fiStzYH7JaXb8/o7OwqIC9Hx8nkRDGzju772yd7CysWpWxAfDHNvrj2ntYFN6VaWeKz1IoyCX8bH0OM0CbRCoWiZ1OruKqXcBpwb4NSoAHklcG9trlNX9G5lIC5Ex5Fy5uV3rcrmRLG/2XiPSD06TTD1vAgu/iEDWwDL8qNFLm5cnsXKy2N8NqTWNV8mF/mlLUqxsDM7g5/HxRBTRnxzrC5+OFzMZ/uK2OAR1hPFLi758SRzR0dzUZzbAEJKybK0yqfzLmxfaiwhhOCjEVG8s7OAArvkzl4hdFSWDAqFop5pEXMyRp3g8wCudgKJE+BdXzq7tZFNE2P5YlQUB29ox997hPjk+yvLzpZq+pirLcm5gS3i9uc5uXppJnke9Zy9r5Ce845x75ocrziV4JTwRhkfhUm5DlILSsVaLyDcUCqyfaMMXNnFd00oxKDxWL9wXhgQocRJoVCcFlqEQAH0a21k3ujqxXG6vWepEMWH6RnXKYhWJo1XB0UwtNzm0p01MK6oKXk2V6VeL/7MtHPjiiyOFTl5dEOOT2Ta8mzKsFHkcdNUdj8TwIj2Jg79Xzs2TWzD0vExLL80xi98iUKhUJxuWoxAAZzXxkiYoeIbr0GDaUMi6R8TeJOTThMMb+e7lrOrDgTqYJ6D2fsKScr1ravEg0MJnUJ1jOrge/1Vx6z0mnccaxXOMewu2JRuw+mSfLLX15z+ojgzmhAkRBgY0MaIUYmTQqFoBLSouZpIk8aXo6N5d0cBoQbByPYmRseZCdYL1p+wcXZrQ4WGByX0KmdeXhPz9EDsybEzenEGBQ5JsF7wxvmRmDSBRPpMwwH0jNTz0YgoJvx8ki2VhJ6f0NnM5LPCmb6rgM+TStewVh2zkmeXHC5Tr0kH13RVJt4KhaLx0aIECmBoWxND2/p7jCgxIKiKXuUs13bnVN9rQiBe3ppHgcNtTlfkkNy1KrvCvF3C9IQaNL66KJqxP54kqYL1qefOiaBruJ4L2pl8BOp/fxX45b2ycxBRVYiyQqFQNAQtaoqvLugcpqPs/fykxUVGcdXOZwNxMM/BwkPV99xQ4q092qxj8djWjGzvL7Qj25vo6sl3UQcTQZVM12kC7j8jrIatVigUitNDixtBnSo6TdAj0sCfmaVTbLuyHQyvYK9VZby7039EUxk9Iku/rrbBOhaMieb3dBsb023syXEQZdJ4sG9pGIsos447eoUwbUfg69zaI4Q+UWovk0KhaJwogaoFvSL1PgK1O8fO8ACjmcrItDiZk+S/x8moweBYd12bM2wUeqb/eoU6/aYmhRAMijUxKLbia/+zbyjfpxRzIL90lKcTMK6TmX+fG16jNisUCsXpRAlULXDvkyoN0vfiljz+3iOkRtZvs/YUUlwm6F9rs8aScTHEBmtef3Z2l2RHlp18u6R13mF0tQjxEWXWsWR8DL8dsyKAnpEGukfolRm5QqFo9CiBqgXlLfkKHJK/Lc9k/pjWFZQo5eM9hby9I99nRAPwYN9QupULtmjQBGd74nok1Ww20IeYIB1Xdw2ufQUKhULRACiBqgU9I/0/tuVpVo4UOIirxMvC9iw7D63PobwLvHCDYFJiSMAyCoVC0VJRVny1IC5EF3DDb2oVoeS/3l/kJ04Ak3qEEG5UX4VCoVCURd0Va4EQIqCJ99EqBGpZWmCT8kmJavpNoVAoyqMEqpb8Z2CEX1plAvXbUatfzClwu1/qHqFMvRUKhaI8SqBqScdQvZ+ZdlpRYIH6IaWYa5ed9EvXBDx5tjL1VigUikAoI4lToH2w7+bcQCOouclF3LsmG2e5xadxncw80z/czyJQoVAoFG6UQJ0C7UPKCVS5EdSMXQU89nuuX7kXzg3n/r7KxZBCoVBURq2m+IQQh4QQ24UQ24QQmz1pUUKIpUKIJM9rK0+6EEJME0IkCyH+EkL0r8sONCQdygtUmRHUW9vz/cRJ4A7nocRJoVAoquZU1qBGSin7SSlLQr8/DiyXUiYAyz3HAJcACZ6/O4Hpp3DNRkW7clN8x4tdpBc7SS928uIfeT7nDBp8PCKKm9V+J4VCoagWdWkkMQH41PP+U+CKMumzpZsNQKQQol0dXrfBMOkEcWVGUS4Jk37N4udUC/ZygXC/HBXNFV1U3CWFQqGoLkLKQFtHqygkxEEgG5DADCnlB0KIHCllpOe8ALKllJFCiO+BqVLKNZ5zy4HHpJSbS+rLzc31NiIpKemUOnS6+ShVz/SUwBF4S7ihvZ1/da2/0PAKhULRVElISPC+j4iI8PGAUFsjiaFSyjQhRBtgqRBiT9mTUkophKi58uHb2OqQlJRU4zJ1yQvdJLt+yeS3Y9YK89zcrx0JlXgcrw4N3c/Tiepr86Ul9Vf19dSp1RSflDLN85oOfAsMBE6UTN15XtM92dOAjmWKx3nSmgV6TfDRiFZ0DA0cDyrcKDg3pvIRlkKhUCj8qbFACSFChBBhJe+BMcAOYBEwyZNtErDQ834RcLPHmm8QkCulPHbKLW9ERJt1zLkwKmD02qu6BKGvRZgMhUKhaOnUZgQVC6wRQvwJbAR+kFL+DEwFLhJCJAGjPccAPwIHgGRgJnDPKbe6EXJmtJEXBvh7hbinT2iA3AqFQqGoihqvQUkpDwBnBUjPBEYFSJfAvbVqXRPj1h4hLEqxsMqzHnVjQjAJys+eQqFQ1ArlSaIO0WmCeaOj+eZAESadYEJnZVauUCgUtUUJVB0TpBfcpDbjKhQKxSlTq31QdU3ZfVAKhUKhaJmU3welwm0oFAqFolGiBEqhUCgUjZJGMcWnUCgUCkV51AhKoVAoFI0SJVAKhUKhaJQogVIoFApFo0QJlEKhUCgaJUqgFAqFQtEoUQKlUCgUikaJEiiFQqFQNEoahS8+5epIoVAoFMrVkUKhUCiaBEqgFAqFQtEoafIClZSU1NBNOC20lH6C6mtzptn0Nz8H3e6tYCmqMEuz6Ws1qK++Noo1KIVCoWgq6DatxDxzKsJqwRnXleKn34YgFQOuPmjyIyiFQqE4Xeg2rsT83vMIq8V9fOQAhhULG7hVzZdGPYKSUlJQUIDL5aowj9lsJjc39zS2qmE4lX5qmkZoaChCiKozKxSKgOh//xXT+y8gyt2PDL8uxn7J9aCp5/26plELVEFBASaTCaPRWGEek8mE2Ww+ja1qGE6lnzabjYKCAsLCwuq4VQpFy0AcTQkoTgBaxjF0u7fi7HNOA7SsedOoJd/lclUqTorqYTQaKx2FKhQNShOISWf4dXFAcSrB/OZTGL+eiX7DckRO5mlsWfOmUY+gFApF80a3eRWmz6aByYzlzidwde/T0E0KiO7QPp9jR59z0O/c4j0WNgvG7+d4j51xXQgbdgUkJJy2NjZHajyCEkJ8JIRIF0LsKJP2ghDiLyHENiHEL0KI9p50IYSYJoRI9pzvX5eNVygUTRhLEeZZ/0XLOYl24gjmt58Fj/HBacXldP9VeN6FdtjXjNp64wNIY8VT7rojB+m8YAYUF9ZVK1sktZni+wQYWy7tVSnlmVLKfsD3wLOe9EuABM/fncD0WrazwYiKimLo0KHev5SUlDqr+x//+AcLF566BdCcOXN49NFH66BFCsXpQ7d9E6LMDVzLycSw/LtqlRVZ6Ri++xTTzJfRbV1XuwZIiXHBR4TcPobgh65D278rYDbjvPcRluLSYiHhyHadcPYdUGn1ekuRe6+UotbUeIpPSrlKCNG5XFpemcMQoGRSeQIwW0opgQ1CiEghRDsp5bHaNDZ00gj/tNpU5KHg05VV5gkKCmLNmjWncBWFQhEIfQBhMaz6Efu46ystJ44dJnjKXV7RMKxZQtHzM3HF12A6TUqMX76HccnX7jqzT2Ka8zbFz/o+Q+t2bsb481c+ac7OCSAEjv5D0W9ZXelldDs24+w/tPrtUvhQZ2tQQoj/ADcDucBIT3IHILVMtiOetAoFquyOZLPZjMlk8h6fihgFwmKpejpBSumX7/Dhw9x///0UFbl3kb/00ksMGOB+mnr77beZP38+mqZx4YUX8vTTT3Po0CGeeOIJMjMzCQoK4rXXXiMhIQGn08ny5cv53//+R0FBAVOmTGHMmDFYLBYee+wx/vzzT/R6PVOmTGHo0KHk5OQETLfb7TgcDiwWC0uXLuXNN99k9uzZREdHe9ucl5dHenp6HX569Yvahd98SUpKQrNZ6RPg5i6OHyFp7x7QdKVpTgfBRw9hi4jGHt6KuB8/J6TMiAYg95eFHB8xAZwO2mxcjjHnJJn9h1Ec29G/AVLS7tdvabvuJ59k3f7dpK5biSWmgzet4y8LCSpXPDO8NUeTktCFteHMcueskTGYcjK8x66t60gaPL7Sz6O5UNvfcUIl63R1JlBSyqeAp4QQTwD3Ac/Vpp6yjc3Nza1XE/Lq1G2xWBg9ejQA8fHxzJkzh7i4OBYuXIjZbGb//v3cdtttrFy5kqVLl7J06VJWrFhBcHAw2dnZmM1mJk+ezBtvvEG3bt3YvHkzTz75JIsXL0an05GWlsbKlSs5ePAgl112GWPGjOGzzz5Dr9ezYcMG9u3bx8SJE1mzZk3A9M2bN2MwGNDr9SxdupT33nuP+fPnExkZ6dOP8PBwOnYM8M/aCElKSqr0R9ucaEl9hdL+Gn74En0AN0FCukgMC0JLP4ps3RZXVAxBL96HLu0QAM6EM9Al7fArF1OQSVj37phm/AfD+mUAtN63lcKpnyFsVvR/rAGnA/uQMRi//QRjOXEqodeMKRQ98Saunv0ACD7if9MNvfwGEtrHA+Do3R/9rj+851y3PoSc9gzC6QDAnJVOYkwUMjLar57mRH39juvDim8O8CNugUoDyt4V4zxpTYZAU3x2u51HH32UHTt2oGka+/fvB2DlypX83//9H8HBwQC0atWKgoICNm7cyKRJk7zlbTab9/0VV1yBpml069aN+Ph49u3bx4YNG7jzzjsBSExMpGPHjhw4cCBgenJyMgCrVq1i69atLFiwgPDw8Pr7QBSKU8VShPGnuRWeDnmi9H/F1ToW7eQJ73EgcQLQDiejX7fUK04AIj+X0HsvR2qa10Tc9MW7VTbPNG8Gxc9NR6QfRTt53Odc0b8/QHrECcD2t3vQ/ecBhKUI+8CROM8ahKtTd3QH95S2LfUAzmYuUPVFnQiUECJBSlnyqDEBKPl2FgH3CSHmAucBubVdf4LAa0YWi+W0b9R97733aNOmDWvWrMHlchEbG1thXpfLRURERIXrWOW9O9TW20Pnzp1JSUlh//79nH322bWqQ6E4HRiWf4fIr55XlLLiVGm+rAyM334S8Fxl+5cCoTuwG8Oyb/0s8By9zsbVOdEnzdWpO4XvfIfIyUTGtHOndezqK1BHDlRpUKEITG3MzL8E1gM9hBBHhBC3AVOFEDuEEH8BY4AHPdl/BA4AycBM4J66aXbDkpeXR2xsLJqmMXfuXJxOt4nqyJEjmTNnjndtKjs7m/DwcOLj4/nuO7d1kpSS7du3e+tauHAhLpeLgwcPkpKSQkJCAoMHD+brr92Lt8nJyaSmptKtW7eA6SXD6k6dOjF79mzuvvtudu/efdo+C4WiJmhWC8YfKx49nVLdGUdrlN/VOpai597HdvE1fudMn72F6ZtZPmkViozB6BUnAFeHLr7tOnKwRu1SlFIbK76/BUj+sIK8Eri3ptdo7Nx+++3cdNNNzJ07l9GjRxMS4vZkPHr0aLZv387IkSMxGAyMGTOGZ599lg8++ICHH36YV199FYfDwcSJE+nbty8AcXFxXHjhheTn5/P6669jNpu5/fbbeeihhzj//PPR6XS89957mEymCtNLSExM5IMPPuCWW25h7ty5dOnSJWD7FYqGImbzCkRBqdGvDArBdtn/YfrqgyrLWq+5AyTokneCJtDt2IywWWvVDtuoK7BdeyeYg7F17QlGE8bFn1eYXwoNx+CLqlW3q2M5gUpTAlVbhGwEbkYqCvmem5tLREREpWUbYoqvITjVflbns2wstCTDgWbf14JcRHERMjoWrBbM/7oGfZmpM9uESTgT+xL06iOVVmMfPBrr3U/7pGkH9xD00oMVipQ0mnGeORD76CtxRbXB9NUMcDmxXXwtrp5n+eQVOZkEP3ajz36nsjj6nY/lXy9Vp8eInExCHryqTDtMFM740ccysblRV7/j8iHflasjhUJRL+i2rcc840VEUSGO3v1Bb/ARJxkcgu3iqxFFBZXW44qOxXrTg/7pXXpS9O8PMH09022lV1Kv0Ch+fiaujl2hzJqu5f7nK7yGjIym+NHXMKxYhCguREs7hHbiiPucwYDtyluq221kRBQyOARR5O6rsFkRBXnI8FbVrkPhRgmUQqGoc8TRFMzTn/eOSMqaYpdgH3M1hIQhQ8JwnDUI/Z8bALBdfhMiNwvDbz/giohyC0tIYE/8sn08lgdfREveifGHLxHpadhHT8TVqVuN2+zq3gdrGV+AIv0oWtohXJ26I6PbVL8iIZAh4V6BAtwGF0qgaowSKIVC4fYoXpiHyMtx79kJPoVt8dZizO88V+F0GbjdBdnGXO09tjz4Iro/f0eGReBKOMNdzbV3QVAw6Kq+Tbm698Hy4Iu1b3OgNrZpj7NN+9qVLRdhVxQV0vCLKU2PRi1QmqZhs9lUyI1TxGazoalgai0TKdFtW4925CCOgcORsXE+p7UDezB9+jpa6gHv5lIpBM5zh+E4ZxgyNAxn114VjmACXc/0yevejbUBswSFYLn/37516vQ4+w/xzRjahPfzlReoAJuSFVXTqAUqNDSUgoICiosrfhLLy8trERtTT6WfJRF1FS0P/dpfMM98GQDDsm8penEWhHm8jNismKc9jZZ90qeMkBL9pt/Qb/oNAKk3YJtwM/bLbnSv6bhc7tfye/acDvQbV2JYtzRgW2RwCBlnDCbk2tt8zLKbIzIo2DehinU2RWAatUAJIaqMApuent5kXPicCi2ln4q6xbCi1Du4lnOSkPsnujeb2iwQHOonToEQDjum+R+iO5yMNAWh37QSZ7feWP/+KDI6Fv2m3zD88AVa6n6/TbHO9p2x3XgfFBbgPHMgaalpJDRzcYIAU3zF7hGUOHYY4y/zkWER2Mb/DUzlPf0pytKoBUqhUJwCliJ0+303bQvp8vFyUB5XeCu0vOyA50pGVOA2etA/8jdkaLjPvqaySCGw3DcF2aFzzdve1PETqEJwOAh66ym0Y27/2SLjGNa7nmqI1jUZlEApFM0UXXLg+EaBkEJQOO1bCItA99dG9BuWoaUfQ5cc2PddCRWJE4Czz7ktU5zwH0FRXIiWkuQVJwDDuqXYrrkDGVUDC8EWhhIohaKZotv7Z7Xzurr1gXD32pTzrPNwnnUe4H7KN7/1NLrU/TW+vmPYJTUu01wINMWnpezzy6df/TP2CTefrmY1OZRpl0LRTAkUIdZx1qCAeZ3dewdMlzHtKH7mHZzdz6jwOlL430Ycfc7BMWBE9RraDJHB5QWqAN0h/9Ad+s2rTleTmiRKoBSKpo6UiPSjbuu6Mmm6FN8bYuHLn2J5aCrO+ETK4+raq+L6TUFY7n4KV5mNps6uvXB26YF9+HiKXv6Egvd/wNHrbKTBgH3oWPeepJa8tcFczoqvghGUdjQFPOb9Cn/UFJ9C0ZSxWQl66UF0B/fgimlH0bPTISwC/dolvk5ZjWZkW/ceKNt1dxH0ysM+1VQ0gvKWj2lH8ZQZ6Latx9WxK67Evn55LI+/4RbJlixMHmS5jc665B2I7Ey/fMJhR5xI84kx5YPLhfHbj9Ht2oqj3yDsl1wHekN9NLlRogRKoWjCGFYs8lrlaRnHCHr5n2Ay+1nquTp18zordfY5B9sVkzB+9yngdsQqoyuOaVaCjG6DY9SEyjMpcQL890FVFtdKSzuIswKB0q9ZgnHRZ4Bb5PR/bqD40dfA1PwdZIMSKIWiSWNc/JnPse7ooYD5nPG+nqZtV96K46zBiOJCnL1UgMs6p7wVXyVoRw7hrCDUlH7jCp9jXdIOTF+8i/WWh9wbpS1Fbh+EuVnYLr6m2VlN1lighBAfAZcC6VLKMzxprwKXATZgP3CrlDLHc+4J4DbACTwgpVxSR21XtEC0Q/swfvcp0mTGccEluNrGIVu3behmNQgiN6tSM++yuOL9QyG4uvas6yYpPPiZmZc9p9MhPEFOAXRpB7EHyuiwo9u73S/ZsHIxIj0NGdsBw6+LS+v583eKXvsCDNVwDWezotu+0eNg9yiurj2x3PVU9V1anSZqMx7/BBhbLm0pcIaU8kxgH/AEgBCiN3A90MdT5j0hRPMNiqKoXxx2zNOeQb91LYYNywl69RGCJ9+IYemChm5Zg6D7Y2218smwCBznDqvn1ijKUt6KrwTHWYMofvwNnzQtgHUfuP0kCpsl4Dn9rj98xAncnkK0g3urbpzDQdC//0HQtGfQ7d+Flp+D/s8NmD55veqyp5naRNRdJYToXC7tlzKHG4ASN8UTgLlSSitwUAiRDAzEHTJeoagaKdHt+sM9UigqQMv0ncsXTgemz6chg0NxDBlTZXUi8wRa6n5cMe3dC9Pl/ck1IfRbApsoO7v0wDrpX6Dp0A7tw3nWoEb3ZNzsKW/FBzjjumK551kQms8oSss4isjNQkZE+eTX7d5a48tqR1MCGrDotm/EuHA2MiIKx1mD0R054JfHsPFX0Bs8m4djanzt+qA+1qD+DszzvO+AW7BKOOJJq5CkpMBPE3VdpinSUvoJpX1tt2IBbdf9VGV+0wcvk56aQuY5IyrME3QshcRPX0Fz2ACwRsaQfcZAss44D2trj384n5HcSAAAIABJREFUKU+7aNXme9UsRfTd6RtjSQpB+nkXcWz45UiHZ6KifSJkZLn/Ggkt5Xd8RnAohjJOYg+MnEhBahoAibEdCSmzXnhi1VJye/b3Kd99y1pMZY7TB4wiKP0IYSkVj5Lydm4lrUMPnzRDTia9338GzeGeSKxs75Vh3S/oNv5K5llDKOjck9zufZHVmTKk9t9rZZF461SghBBPAQ5gTm3rqGnY4GYfMttDS+knQPKuHSS0b4cMDSdk86/VKiOQdPppDrFhIdjH3xAwj2nFV15xAjDlZNB2zQ+0XfMDrtgO7lGa3YZj8EU4Oyeg27fd/UR52Y3IktAPdTwSqe33ql/+HZqrdB3D1bYjRVNnEyIE3euygXVMS/odZwwYRfvfFgJgG3UF7UZf6j1nOONcKCNQcYVZtCn7udishBz1HeWUeIEvStqO7sAe0DR0W9f5BIOMKswluNzna/rwW684VQfNYSdmy0pitqzEFdWGohc/rPJ3X1/fa50JlBDiFtzGE6OklCWxudKAsi644zxpCoU/UqJfsZC+c99HZ7Pg7NKjwjn4ijB99QEyKhbH4FF+det2VTxlop0o/VkafvseQ6lfVAyr3SM4aTRhueMJnANH1KhNdY7DgfGHL32TBgxv0tOVzZETF1xK5PCxIF24uvnuM3MmnAG/fOM9Lv/b1O3fhbCXioqrdaw7RIkQuBLPxJV4JgCOPueif/IWbz6tnBWnyDyBfs3PlbbTdvE16P9Yg5ZxzO+clpWOYdWP7v1XDUCdbFoQQowFJgOXSynLRuZaBFwvhDAJIboACcDGurimopnhsGP6+H+YZ7+JziNKunILvrLMDdjZrReWfzxD8b9e8puCMPw0j/JoaYfQcqoOLVEZwmbFNG+6exrwNCEyT2BY9BmmGS9hXPAxWIrQr1vqsxYnDQbso644bW1SVB9Xt164uvfxe3hw9TjT5/esS9mHyEp3H1iLMX79gU9+Z8+zAz6AyNgOSF2p3ZmWfdIn9pRhxSK/ECjlsV98DUVTP8Nyy0O4WrX2O6/bvdX9gPfX75jfepqgKXej27iy0jrritqYmX8JjABaCyGOAM/httozAUuF+0PcIKW8W0q5UwjxFbAL99TfvVJKZ+CaFS2W4kKCXn/cPaVWCdabHsTZ73ykyQShEaXFH3mFoKn/QniEQ5eyj+B/XYv174+4o8E6nQQ/datPXc5uvbCPvBz9+uVuIwxZ+T9xCdrJE4jsjHr3QC1yMjF8PwfDr4sRZaZntLSDaAd8N+Hah43n/9s78/ioqvP/v587SxJiICyCsouAC26Au4JY9auouLW472vVKrXt17UurWL1q7Za/Vmt1bpVrbsobrjhXnEHxQoqIiICsoghyWzP7497M8yeSTJJJjPP+/XKK7nnnHvn+cyd3GfOOc95jmZ4sBjFi/boRWzYZvgS8iX6PniTyI570O2iE9L26YpuPib1Ei7+ANq3P5KQJd1ZupjYoGH4X5lO8KnmZ1u01/ogQmT3A4iMm0hg+v1UPHrHupf46G2qLjwhaY1d5a1TWTtkBNovZ0hBm2lNFN8RGYpvz9F+KjC1pa9jlA8V99/crHMC71tn73THENt0G2Ibb45v/ifxMmfFUqquPZdYTS2xoelj45ExuxIZN5HIuInw4yqcxV/j++ozAi88htStgVBD0lqVRJwvPyPajg7K//aLVNx+TcbhzdQJbvX5CO97eLvZYrQfkTG7JDmoyruvh7uvz9g2utk2Wa8T67NB0jYesnwJlU/chT9lGYJWVaPVNTjLl6y77tCRyT0zf4DwpKMJPv9w0hq71AXgEgkTeP1ZQj8/KafGtmJ5SYxOJZ8xcoDo8C2I9R+atT4yZpeM5c6aVfhnz0pvv2vCUr7utcQ23ZrwxMNYe90D1N0ynbrbX8iYVBVwJ6jbAVmxlIp/XE3l3y7Pe+4tssveZbtQuasTGbNrXu20sipnjz31/gefui/NOQGEJh5G/W+vRoPr0iRFtt0t/YKOQ3TT7A6xCf8bz7d7oltzUEYSsnyJuwC0vg4aG5A8tgRvC4GXn0zqqcT69mfpdskBDqEDjqFhyuU587xFdtwTzTM/2doLbkBre+duJELDlMsJ735AWuJP58u5WU5qGxW3XxMPyMgHFYdQlohFo/jRDQcT6zew2XaRHffMWR/rneygMu2YHO0/lPC+h6P9h1B/4Q2EJ0yi8bBfEt5ncsZrhsel5mJIx/nhewLT7m22XVuwXHxGHOfzj6m6ckp8LkcDQSQcIrzbfjSe8Lt2iRJzUjbCC+1zGN8O3JSaHcYjP60msv3ueYV2a+++1P/uGqqu/V+kMXvvI1bbh9gmW+Vlm/buR+PxvyE08VCqzz06Xu777CNk2XduVFWhaFiLf06Gnt624wlNOppul56aXrfTnvEM5UYXRITImF0IZgjqaUIdh/CeB+e8jPbJneg3OnwLGs7+YzwFUmyjTWjcaJPc52yzM/XnXEngpWk4S78lMmpbwntPJjjtnqQvUcEn7yXyswNyXqstWA/KiBN89qG4cwKQsLtmKDBzOs685ueIWoPUr0061g0HuUMM2+1GZPcDWrTuKDZyS+pufYbQgccSy/JPGx25ZYsdrfYdQHTgRuts1hiB5x7OcUbLcb5bmLE8vMvexIaOpPEXp8SjFdXv7rnUePw5BbXB6HiyDU2Du6xh7dR/Ehs0LOc1Yjky0YcmHU39xTelZanIh+g2O9Pwm6tYe9U9hI6ZgvbtT+jQU9GadQFKEo3g++DNFl87X6wH1VLq1lD5z2uR5UsI7X8U0RLKceZ/77WsdYEZj9E4Mr+eR4toSHFQld3ctMKtRYTQIScSOuREiESovP6CpDmo6JZZ0kY3c83wPofi+8fV8aLgjEeg23pEh21KdKsd0ocfVQk+ege+Oe8S2W43dx1JDsfoLE53UBoIEB3lRm+FJx1FeL/D41tmGKVBbPgo1HHSQsFj/Yew9qK/JkWrZiNbDyrWux+hSUdnrGst2r0noT0OpuLxO+Nl/vdfhwG5e2StxXpQLaTyjmvwz5qJ76v/UnXjJR22HqDdCTXmrPbN/aBdJkQlzUFVFe7ifj8NZ/6B8I57oNXdCe+8F5Gdm8/Xl4nITnsSq00O5Q4+cRdVf7mAyhsvTp6rUyXw+F0Ep92D78u5VPz7lmbzqjnfLkgrCx18IlQkvB/mnEoPx0csQzDO2ivvzMs5AWhtb9SX3tdoPPLMdtk3KvFLuYq4z448l2m0FOtBtQBZuhhfSi+j8p/XUrfV9hmTQ3Ylsg0xxevXrML36Qet64HkIsVBUVUNdaHMbVtDVTcaT7+Y3O43D/wBwv9zCBUP/j296v038M39kPpzryPWfzBVf/o1vgXJ23v7337JXcsSDhF47iH8b72ArFnNiJqeVHWrRlLe/9CBxxHeL9OKDqPUCB10HFV/uSB+HP7ZgS0bhnZ8xAYNS/rMhbebQHTsuEKaGSc2cCPC4yYSHT6K6DY7uQFH7ZRf0RxUCwi8+HjSHA2ArP0J37w5RLfcvpOsKgzO4q+bbeOfOR1n4Xzkp9Xuh3P0Lm3eQVXq65OOtbIbsLJN12wvwhMmEZx2D9JQn1Yn9XUEp91DbPDGac4J1vWQAk/dlzQ8st7qzElcI6N3KojNRvET3XpHwuP3xf/6s8Q2HNyqYbnQ4adTcfMfkVAD4b1+Tuig49sv9ZUIjSef1z7XTsEcVL40NhB49emMVc6Xn3VZB+V8PQ//zOkE3prRbNvArFdg1ivx4/CESTSe8NvmX0SV4IO34n/nFaKjxtJ47K/BH4BoJH29TzFvZV1dQ3j8fgSfzxwg4fvk3ewh6JGQO/T3RvP7dao/QGzDwW2x1OhKiNB40rk0Hn0WBCtb5Viim41m7V8fhWgU/KXzWLc5qDzxv/UCkpDjKhFfO62LaVfq66i8/iK6XXIKwRcfR9bWtfgS/teehhwh3fF2b84g+PQDOMuXEJg5Hf/r3kO6IUPvqY09svYmvM/kpIWOiUioESdLj8hZ9h3y/bcZE3KmvcZ+R3b5IWOjFVRUta3XI1JSzgmsB5UfqgRefCxrtfPlZ52yj1CriYSpvPVK/B9k3pE11qMnjSeei6xZhW/uh1m/9Us0iixfgg4Ymv21YlEq/35lUpH/nZeJTNg/bahMu8BDWXv3o2HKFQSe+bebwyzUSODtF5s9T+rW4P/PS8nXqq5h/iGnMWDgIHAEELRnn8KurzKMLow5qDxw5s3Gt/CL7PU/rkR++L7oUs74Z04n+PQDxPoNoOGU86GmFn5c5SZmzbDaPFZTS2TcPoT3nhzPtBDrNzDnsJSzdDHRbA4qEqYqwwJTp+m9TAuQKGAEXzsS3WJboltsC4BvzrsZHVSsdz9k7U9I/bqeaWqWiND+R/HTkE2Ilcn+SIbRUsxB5UHg9eeTjiNjdkHWrMY3b068zFk4n2gROSj5fhEVd/0ZiUZxlnxDt9+fTGTsrgReezZt3ifWoxeNx5ztBj34A8l1w0e5ySgTEkwm4ixbnHXZkn/Wq/gWfZVuG26gSXqIefH3oFKJjhpLaM+DCbzyFNq9J9GtdiCy9Q5Et9iOyv93Gf4P34q3TR3eiw7fPPVyhmEkYA4qD5xvkx+y4XH74pszK8VBfUE0z+SPHYF/1sykHHfOquUEX3w8rV2szwbUX3gDmm01uuPQeMwUKm+4KOO+MrI0+5yK7/OPM5bLmtVQtyY9i0RVddZrFS0ihI6ZQuiYKWlVzeVZ0159YeWa9rLMMLo8xT0jXSRIyjff2IChxAYnb6zt+yb7EGBn4Pvso2bbREaNZe0fb8vunDyi2+xEw7nXEd1k67Q6Z6m7E62sWIZv1itU3HIFwYf/AaFGnIStBDKelzrEV8hFukVAdPTOWetUpPmEtYZR5rRmw8I7cLd2X6qqW3hlk4HLgM2A7VX13YT2FwAn4SawOVtVm4+zLSYaG5Iis1QctHc/YoM2TmrmLJzf0ZZlJxLG99/MvRdwh9LC4/clNPkUCFbkdcnoZqOp32w0zlf/pdtlp8XLnaWLCd5/M8FnH0xqL6tXpCWCTSQwc3paDrGuOMSXi+im2xDrNyBpO/kmtHtt2nCqYRjJtGaI707gJuDuhLI5wCHArYkNRWRz4HBgFNAfeEFERnalXXUlZe5Fe68Pfj+xgRuh4sR3YnWWLob6tVBV+IesLF1MvzeeJvh+JeHd9kU3GJSzfeCZBzPuJxTrN4DGw08nusV2eTumtGv07Z907Hy3kGCGLBTZ1ozF619+Mr2wxBwUIoTH70vFQ7elVWnP9TvBIMPoWrRmR91XRWRoStlcAEkPsz4QeEBVG4GvRGQ+sD3wVmrDYiV1Yju2vveArqhEe/dNcmCyegVaYAflmz2LyhsupDrsbvsdePExGqZMJTpqbHJDVXzvv07g5Sfxz34n7TqhfQ8ntP9RLcoOnpHqGqJDRuL7Oj1bQi60qjopoi1zmxJzULgbIwYfuT1t/k57mYMyjOZo7yCJAcDbCceLvLKszGtFTqfWnJMvfT79iMT+yupgNxZ6r7eJP0jiI3XRfz9l7Zr0NDhtYePH7qLKc04A0thA4MZLmXfmVKIJPY5BT91Nnw/Ts5HHfH4+PWMq4R69YPESIHM0XkuonnAwI+6+Jt57bI5I1XrMO/q39P7oDfq8+zJOLHMHenldPd9772173tOOZqPhW1H7+YdJZSudIItKUGs+lJNe09o8I3Issyi6KL5cxmZi3rx5LT6nJQRnJYeYr7fxJvHXC/bqA0vWDW8N7tWTaIFt6bZqWVqZv/4nRq5cTGT8RADkx5V0++j1jOdH9jmUodvuUFCbGDGC0NoVGYeu0l5/9C40Hv8bBtX2hl13Z+2aXxF4aRoVj96R1rb3gEF0HzGi3e9pR+Pb71BIcVA9NhxAVQlqbY5y0mta2057R/F9C0kdkIFeWZdBVv2QdJy0GDdlK/BsqZBaRCwGdWvczBShRmTF0ozN/O+sy0rgLPg8LYltdMhIGk46l9AvTmq7TRkI73sEkVHbJpVpIEhor0NQn49Yr740nHYRDVOuSI5Wq6klPOkoYjW1adcstSCJJjLlabRce4bRPO3dg5oG3Ccif8YNkhgBpE+QFDFNu8o2oQnJTDXFQdFWB/XjKqquPgffoq+Ird/fXRCc4nia8H3yHrJyOdqzD87XyRGE4V33ofGU89tmS3M4Do2nXYhzyak4q9y9kCI77E7o6LO96MAcSS8dH9HRO+OkBFLEhrXPpmedjs9P4xFnUHH/zYDryAu+bYlhlCCtCTO/H5gA9BGRRcClwArgRmB9YLqIfKiqe6vqJyLyIPApEAHO7EoRfABEwsnHCaHBqQ6qrT2o4JP3xDMvOMsWE3zuoaxtJRYjMOMRwuMmUvFw8lBbdNhmbbIjX7RHL+ovvZnAsw+hVdWE9/6FW1HR/Hqm0AHH4Pv4PzirfkCrqgkdfDyxgbm3tu7KhPeeDCI4C+cTHr8f2r1nZ5tkGEVPa6L4su2iljGbqqpOBaa29HWKhlQHFSiwg4rFCN53E/53X8VJ3JU1DwIvTcP/zsz0Sw7tuHFv7dWX0JFntvy89Tdk7f/9C1m5zF0oHAi2g3VFhIjrpAzDyBvLJNEMEkoZ4vMnPEgL4KACLzxKcMajeTmnxoOOR2vWbQMt9XU4yxYn2+c4aYuIi5aKSndNV6k7J8MwWoU5qOaIpGw/nmOIr8VzUJEwFf+6Ke/msUHDCO9xUM420TG7tnoRrmEYRjFhDqo5cg3xpSx6bWkPyv/mC1nrGk4+LylKLlpRRXTzMYT2OBjN0uPQYCUNJ/5vi2wwDMMoVopuHVSxIeFkB6W5giTq8shMHYngLPqSWK++BJ++L3uzbXcjsvNe+N+YgfPdQr4YtCmDvNeLjJtI4KUn0s6pv+D6tmeKMAzDKBLMQTVHWg+qDXNQsShVV/06aZuOTGiwIp7Tr2kxbkPCKu3QPpPxvzwtKQQ91qMnsSHJGdYNwzC6MjbE1xwFnIPyzf2gWecEEJqcvgttItpvYNJclFZ3p/Gk88Fn3zcMwygd7InWDGlDfElzUC3rQfk++k/Wulhtb2T1SqJbbU/4Zwc2a1foyDOJjhqLhMNEttqhXbKoG4ZhdCbmoJoj1xBfsBL1+eI710o4BKHG7FF0TuYOa2SbnWg450+5z03F5y+qHXwNwzAKjQ3xNUc4eyYJRND1uidVO0u+yXqp1Lx+8ZdoGq6z8HDDMIw4peOgVPH/5yWqpp5F1cUn4Zs9q+3XjEaStpRQcdLmeWLDNk86zvW6zg+ZE7+m7e1kGIZhlI6DCj50G5U3/xHf57PxLfyCijv+D6KRtl00xxqoeJOUTNW+DJsFNpEpM3nooOMsuMEwDCMDJfFkdBbOJzg9eU2Rs2KZm+07cXuMlpJreM8jNSu17/PZ7tbvfj/+d17B+eJTCFYQHbEFkpLOKDx+X0KTjmm9fYZhGCVMSTio2ODhNJxxKZU3/yGpXNasapODkkj2CL54Wd/+xDYYFJ97kmgE39z3CbzwGP5P3lvX8Jl/J5/XbT0aTzq31bYZhmGUOiUzxBfZYXdiPXollcma1W27aDh1DVTmFEOpw3wVD/wt2TllINarb5tMMwzDKHVKxkEBRDcbnXRceAeV3oOC9B1Tne+b3zRYNxjYarMMwzDKgRY7KBG5Q0SWisichLJeIjJDROZ5v3t65SIifxWR+SLysYiMKaTxqSRuRQHuEF9byGeIDyC66dZZ6wCiG21CdPBw1NthVisqCe318zbZZhiGUeq0Zg7qTuAm4O6EsvOBF1X1KhE53zs+D5iIu837CGAH4G/e73ZBa2qTjtveg2o+SAKAikqiI7fG/8m7aVXRISOov+xW96BuDc53C9He/dCefdpmm2EYRonT4h6Uqr6Ku8V7IgcCd3l/3wUclFB+t7q8DdSKyIatNbZZ29bL0oNSxfnsI5yFX7TsgmnbvWffWC+61faZy7fZad1BdQ2x4aPMORmGYeRBoaL4+qnqd97fS4B+3t8DgMTUCou8su/IwryErN350nRObV09GyWU1y35lq/mzWPIY7fR6xN3fdI3E49i+dgJeV23ZsFXJOYHXxsOMz+LfZXd+7FZhvIFtRtS1wpNmWjNe9NVMa2lSznpNa3NM2LEiKx1BQ8zV1UVEW2+ZWZyGZuJefPmxc/xRZKTtdbEIozsXkX1J+sWzw744FV6Hn5KXtf21SWvW6rq3iO7fcOHE50+DN+iL+NF2m09+o/fC/xtf5sTdZY6prV0KSe9prXtFCqK7/umoTvvd1PKhG+BQQntBnpl7UJakMRPq/HN/TCpzFnyDVVX/ApZsqj5C+ZKFJuKCA1TriCy6TauLT4fjYf9siDOyTAMoxwp1NNzGnAccJX3+4mE8l+JyAO4wRGrE4YCC05akMSPq3ESejRN+ObNoeLh22j4VcLC3miE4IN/x//uq0Q33ZrGw0/PudVGxtfv25+G8//iZowQsbkmwzCMNtBiByUi9wMTgD4isgi4FNcxPSgiJwFfA4d6zZ8G9gXmA2uBEwpgc1a0OjmzuNT9SHDGoxnb+mfNTDr2ffwOwWcfBMB5fQm+T99Ha3qmnJSjBxV/UUF7rZ+/0YZhGEZGWuygVPWILFV7ZGirwJktfY1W4/cT6zcgr4WyAEQi8SE435dzk6qcFctgxbKU6+fuQRmGYRiFo6QySQCE/+cXebeVVeuCIFITuWaiuSE+wzAMo3CUnoP62YFEtt4xr7ay/Pt1f6f2ljJhPSjDMIwOo/RCzByHhrOvwP/mDOSn1cT69sc/exaBV55Mb/rD9zRtR5hPDypnFJ9hGIZRUErPQQH4/UTGT1x3rLGMDso3z00nKKt+wLd4QVJd/Tl/ouovFySVqfWgDMMwOozSdFCpVFRlLA68PI3Ay9PSytUfILrJ1uknOCU3ImoYhlG0lMUTN7rx5mgLhue0tjdUdUsrlx9XFtIswzAMIwdl4aCorqHxqLPybp5tHZPzw9KM5YZhGEbhKQ8HBUR2n8RPd71CaM+D42Wx3v0yto15GSCig4cnlUdG79x+BhqGYRhJlMccVAKhY6YQOuRE96C6BuebL6m65GQkFou3iXmOqfGE31F15VlIOEysV18iY8d1hsmGYRhlSdk5KACqa+J/xgYNo+F31xB85B84X/2X2IgtCU+Y5NYN25S1V96Fs+hLoiO3TDrPMAzDaF/K00GlEB01lvpRYyEaAccH3tbs4CaAjfbt34nWGYZhlCfmoBLx2dthGIZRLIibz7VzWb16decbYRiGYXQqPXr0kMTjsoniMwzDMLoW5qAMwzCMoqQohvgMwzAMIxXrQRmGYRhFiTmoIkJEpPlWhlHc2OfYKBTmoIqLeJx7qf+Ti8gmIlIWnz8ROVJEtvb+Lun76lEW99Vof4r6gyQiB4nI5Z1tR3sjIvuIyHPAtSJyMICW6OSgiOwlIv8BTqbIP39tRUT2FJHXgOuB0VC69xVARPYTkaeAy0Vkl862pz3xnk03ikivzralvenM53DRrUz1vmE6wAnA+cAQEXleVV/rXMsKi6czAFwJ7ARcDQwEJovIHFWd15n2FRJPqx+4GDgCOE9VH02sL5UHt6e1ErgL6AtcARwIdPPqfaoa7TwL2wcRGQtcClwGdAeOE5ERqnqniDiqGst5gS6Cd38PBqYCNcArIvJYqehroliew0X3DVZdosB83G+dZwAl14vydIaAZ4HdVHUa8CYQBr7qVOMKjKc1DMSAh5uck4iME5GS2qbY01oP/EtVJ6jqc7j39RivvuSck8eewGuq+jTwBLAEOFtEeqhqrFSGNr0vUl8CuwJTgKNxv1iWFMXyHC4aByUiZ4vIbSJyslc0U1XXqOptQLWInOS1KxqbW0OqTlV9QVUjIrIv8CiwCXCliBzmte+y/9gJWk/1im4BNhSRf4rIbOBc4HbgRK99KWg9BUBVn/DKfbhfOD4RkUGdaWMhSdULvAxMEpGenoMOA6uB86BrD22KyHEisldC0RxV/UFVH8HVeYiI5L8jahFTbM/honjYi8jxwJHAI8AxInIBMCyhySXAb7wPf5ftSmfQeaGING06tRzYR1V3xP1nP1FEhnbVf+wUrUeJyO+BRuBxIAhMBg7w6g8RkcElovVo774Og3iP6Udga2BVpxlZQDLovQhYADwH3OPNuw0DrgJqRaS6k0xtEyLSU0QextVxnfdlAyCxR3gDMAnYIuXcLvdlqxifw0XhoIA9gKtV9Vngt7hj+Ec1VarqM8Bc4FQRqRGRyZ1jZptJ1RnE06mq76jq5167ucAyINIpVhaGVK0VwGmq+jhwqqp+5jmkj3Ef3OHOM7XNZLqvRzdVqupsoAE4vHPMKziZ/l+PVdWzcIeC/qiqJ+BqrlLVus4ztfWo6krgeWAz4D3cB3RTnXq/3wA+BCaKyKZNowVd9MtW0T2HO9VBJXQTPwD2B1DVd4G3gAEpkUDnAX8C5gEbdKSdbSWHzreB/hkino7HnVT/oaNsLBQ5tL4BbCQiu6Q8sI4DqoCVHWpoAWjmvg4QkV29doLbu6jsit+sm2jm3o4UkXGqulBVZ3jt9gO+6HhL207CfbpbVVcBN+P29Id4c2q+hPfjeuACYCZuYEyX6kEV83O4Qx2UiIwSkcqm44Ru4huAIyLjveM5wHdAf++84bgfkMeBMap6Y8dZ3XJaqHMx63QeKyJzgI2A072x/KKmDff05yLyEe4Qwumq2tCBZreKVtzXDb12ivvgqutK36xboXcD77zxIjITGIE771j0ZNDa1ENq8H7PAp7Bjd5DVaOeo+oH3AS8BGyjqlcknl+MiMguIrJx03ExP4c7xEGJyFYi8jpuyG3vhPKm158HfAIcJm4Y7iKgHzDUq18N/EpVD1HVxR1hc2topc4NcB0SuMNdp6rqcar6fQea3mLacE+btH4O/FJVjy1RrRuw7vMkGQ/0AAAGyUlEQVQL8DtVvaODTG4TBbi3C4AzVPVgVV3ecZa3nBxaJUMgwE3AcM+ZrS8iG+HOHZ+lqgeo6ncdZ3nLEZExIvI8rjPtkVBetM/hjupB/R43vPhgVf0W4utBmjz3GuA13HmKa8UNPe6JN8Slqsu0a6wLaq3O5QCq+qGqvtkJdreGtmqdrapvdYLdraFNn18AdZcUdBXaem8XquonnWB3a8imVb0eUpWIrAeuLuAxYDau/p5eT2php1mfByISEJFbgb8Df8Udbp7g1RX1c7hdHZSIOF4000+qer1XtpeI1ALiHV8B3IfrnS/GfUNe847vak/7CkW56ATTWqpaobz05qn1cuBfeJFsInIEbhDItcCWqvp+pxjfciqAV4FxqvoU7nKWzUTE70WZIiJ/oAjva8EzSYjIjsAKVf3c+wayHBgnIvvjprepAr4H5orI/bg3/wJVne+dfyJQraprCm1bISkXnWBaKVGtUF56W6F1Y+DcJq2469kmqGrRL6RP1Io79/mvhGofEFV3/aUAW+LOF56vql945xfHfVXVgvwAtcB03G7i73HFNdVdCLwPHOAdj8ddbb5TQhunULa050+56DStpau13PQWQKuvszW0VStur9Dx/h6O64h7NtUV630t5BBfNe7Y5lne3+MT6p7CnWhrSqz4Lm4qlAZwu9vadRbglotOMK1NlJpWKC+9bdXaldJTZdSqLjEvIGKB12a3pjoozvvaJgclblj0biLSXd0Jxr8DD+Le3B1EZACAqn4M/C9wpoj0wV3EuCXrJt+K6k1JpVx0gmmlRLVCeek1rUlam8LExdNT4Z3a5IQFilNri7d898RsgDuhFsNdiFcNTFEvpFTchV2HAu+q6j0J5/4Gdwx7BHCOqn5aCBHtQbnoBNNKiWqF8tJrWrNqnaWq93plPlWNisi9wHxVvawz7M+bFo5v+rzfI4F7m8qAG4FHU9qeg7u2oAdQk1Ae6OxxTdNpWktda7npNa15ae2WUB7sbB35/OQ1xCduWo8rcbNs74abcTsK8WSYU4CdvbombgPWA2YA85u6mepuu1CUlItOMK2UqFYoL72mtUVav0rQ2iXW5TXroDyx7+HGxc/H3RMkDOwuIttDfOzyMu+nif1w1wx8hLtmoGgzQED56ATTSolqhfLSa1pLU2si+ayDigHXqTdeKyKjcVOaXAL8DRjrRYY8DvxM3C0iFuBOwO2pqq+2i+WFp1x0gmktVa1QXnpNa2lqjZPPEN97wIOybi+UN4DBqnon4BORszzPPRB38dcCcDds62JvSrnoBNNaqlqhvPSa1tLUGqdZB6Wqa1W1UdetBdgLd68icPer30xEngLux13wFg9b7EqUi04wrZSoVigvvaa1NLUmkneqI89zK25222le8RrcldhbAF+pl2xRVYs21XxzlItOMK2UqFYoL72mtTS1QssW6saAAG7G4q08b30xEFPV15velBKgXHSCaS1VrVBeek1raWpt2UJdcRMQvun9/FNVb28vwzqTctEJprWTTWpXykmvaS1NWuqgBgLHAH9W1cZ2s6qTKRedYFpLmXLSa1pLkxanOjIMwzCMjqCjdtQ1DMMwjBZhDsowDMMoSsxBGYZhGEWJOSjDMAyjKDEHZRiGYRQl5qAMwzCMosQclGEUEBFZICL1IrJGRFaJyJsi8ksv03Rz5w4VERWRvFOQGUYpYw7KMArPJFWtAYYAVwHnASW72t8w2gtzUIbRTqjqalWdBhwGHCciW4jIfiLygYj8KCLfiMhlCac0bYuwSkR+EpGdAETkRBGZKyIrReQ5ERnSwVIMo1MwB2UY7YyqvgMsAsYBdcCxQC3ubqeni8hBXtPx3u9aVV1PVd8SkQNxM1UfAqwPvIa7pYJhlDzmoAyjY1gM9FLVV1R1tqrGVPVjXGezW47zfgn8SVXnqmoEuBLYxnpRRjlgDsowOoYBwAoR2UFEXhaRZSKyGtcB9clx3hDgBi/gYhWwAhDveoZR0piDMox2RkS2w3UorwP34W40N0hVewC34DoccDeiS+Ub4DRVrU34qVLVNzvCdsPoTMxBGUY7ISLdRWR/4AHgXlWdDdQAK1S1QUS2B45MOGUZ7oZ0wxLKbgEuEJFR3jV7iMjkjlFgGJ2LrbcwjMLzpIhEcJ3Np8CfcR0NwBnAdSJyEzATeBA3YAJVXSsiU4E3RCQA7KOqj4nIesAD3rzTamAG8FCHKjKMTsD2gzIMwzCKEhviMwzDMIoSc1CGYRhGUWIOyjAMwyhKzEEZhmEYRYk5KMMwDKMoMQdlGIZhFCXmoAzDMIyixByUYRiGUZT8f/E2zMwhOaAOAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"25"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 4. Visualize & summarize daily values for the S&P 500\n<p>Let's also take a closer look at the value of the S&amp;P 500, our benchmark.</p>"},{"metadata":{"dc":{"key":"25"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# plot the benchmark_data\nbenchmark_data.plot();\n\n# summarize the benchmark_data\nbenchmark_data.describe()\n","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"           S&P 500\ncount   252.000000\nmean   2094.651310\nstd     101.427615\nmin    1829.080000\n25%    2047.060000\n50%    2104.105000\n75%    2169.075000\nmax    2271.720000","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>S&amp;P 500</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>252.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2094.651310</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>101.427615</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1829.080000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2047.060000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2104.105000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2169.075000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2271.720000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"32"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 5. The inputs for the Sharpe Ratio: Starting with Daily Stock Returns\n<p>The Sharpe Ratio uses the difference in returns between the two investment opportunities under consideration.</p>\n<p>However, our data show the historical value of each investment, not the return. To calculate the return, we need to calculate the percentage change in value from one day to the next. We'll also take a look at the summary statistics because these will become our inputs as we calculate the Sharpe Ratio. Can you already guess the result?</p>"},{"metadata":{"dc":{"key":"32"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# calculate daily stock_data returns\nstock_returns = stock_data.pct_change()\n\n# plot the daily returns\nstock_returns.plot();\n\n# summarize the daily returns\nstock_returns.describe()","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"           Amazon    Facebook\ncount  251.000000  251.000000\nmean     0.000818    0.000626\nstd      0.018383    0.017840\nmin     -0.076100   -0.058105\n25%     -0.007211   -0.007220\n50%      0.000857    0.000879\n75%      0.009224    0.008108\nmax      0.095664    0.155214","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>Amazon</th>\n      <th>Facebook</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>251.000000</td>\n      <td>251.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.000818</td>\n      <td>0.000626</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.018383</td>\n      <td>0.017840</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-0.076100</td>\n      <td>-0.058105</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-0.007211</td>\n      <td>-0.007220</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000857</td>\n      <td>0.000879</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.009224</td>\n      <td>0.008108</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.095664</td>\n      <td>0.155214</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"39"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 6. Daily S&P 500 returns\n<p>For the S&amp;P 500, calculating daily returns works just the same way, we just need to make sure we select it as a <code>Series</code> using single brackets <code>[]</code> and not as a <code>DataFrame</code> to facilitate the calculations in the next step.</p>"},{"metadata":{"dc":{"key":"39"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# calculate daily benchmark_data returns\nsp_returns = benchmark_data['S&P 500'].pct_change()\n\n# plot the daily returns\nsp_returns.plot();\n\n# summarize the daily returns\nsp_returns.describe()","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"count    251.000000\nmean       0.000458\nstd        0.008205\nmin       -0.035920\n25%       -0.002949\n50%        0.000205\n75%        0.004497\nmax        0.024760\nName: S&P 500, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"46"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 7. Calculating Excess Returns for Amazon and Facebook vs. S&P 500\n<p>Next, we need to calculate the relative performance of stocks vs. the S&amp;P 500 benchmark. This is calculated as the difference in returns between <code>stock_returns</code> and <code>sp_returns</code> for each day.</p>"},{"metadata":{"dc":{"key":"46"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# calculate the difference in daily returns\nexcess_returns = stock_returns.sub(sp_returns, axis=0)\n\n# plot the excess_returns\nexcess_returns.plot();\n\n# summarize the excess_returns\nexcess_returns.describe()","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"           Amazon    Facebook\ncount  251.000000  251.000000\nmean     0.000360    0.000168\nstd      0.016126    0.015439\nmin     -0.100860   -0.051958\n25%     -0.006229   -0.005663\n50%      0.000698   -0.000454\n75%      0.007351    0.005814\nmax      0.100728    0.149686","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>Amazon</th>\n      <th>Facebook</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>251.000000</td>\n      <td>251.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.000360</td>\n      <td>0.000168</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.016126</td>\n      <td>0.015439</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-0.100860</td>\n      <td>-0.051958</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-0.006229</td>\n      <td>-0.005663</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000698</td>\n      <td>-0.000454</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.007351</td>\n      <td>0.005814</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.100728</td>\n      <td>0.149686</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"53"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 8. The Sharpe Ratio, Step 1: The Average Difference in Daily Returns Stocks vs S&P 500\n<p>Now we can finally start computing the Sharpe Ratio. First we need to calculate the average of the <code>excess_returns</code>. This tells us how much more or less the investment yields per day compared to the benchmark.</p>"},{"metadata":{"dc":{"key":"53"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# calculate the mean of excess_returns \navg_excess_return = excess_returns.mean()\n\n# plot avg_excess_returns\navg_excess_return.plot.bar(title='Mean of the Return Difference');","execution_count":16,"outputs":[{"output_type":"stream","text":"DEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=14.399999999999999.\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal bold normal>) = 4.971363636363637\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbi.ttf) italic normal bold normal>) = 7.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal bold condensed>) = 10.535\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Italic.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold.ttf) normal normal bold normal>) = 3.9713636363636367\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal bold condensed>) = 11.535\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Italic.ttf) italic normal 400 normal>) = 4.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (Arial_Black.ttf) normal normal black normal>) = 10.525\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal bold normal>) = 3.9713636363636367\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New.ttf) normal normal 400 normal>) = 10.05\n","name":"stderr"},{"output_type":"stream","text":"DEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 6.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 3.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial.ttf) normal normal 400 normal>) = 6.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold_Italic.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 4.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (andalemo.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold_Italic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (Trebuchet_MS_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana.ttf) normal normal 400 normal>) = 3.6863636363636365\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Verdana' (Verdana_Bold_Italic.ttf) italic normal bold normal>) = 4.971363636363637\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (Impact.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Andale Mono' (Andale_Mono.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (courbi.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Webdings' (Webdings.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold_Italic.ttf) italic normal bold normal>) = 7.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (Georgia.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Courier New' (Courier_New_Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Comic Sans MS' (Comic_Sans_MS.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (times.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Bold.ttf) normal normal bold normal>) = 6.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal bold normal>) = 11.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (Times_New_Roman_Bold.ttf) normal normal roman normal>) = 10.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (arialbd.ttf) normal normal bold normal>) = 6.698636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal roman normal>) = 11.145\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 7.413636363636363\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial Black' (ariblk.ttf) normal normal black normal>) = 10.525\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal bold normal>) = 10.335\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05\nDEBUG:matplotlib.font_manager:findfont: score(<Font 'Arial' (Arial_Italic.ttf) italic normal 400 normal>) = 7.413636363636363\nDEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=14.399999999999999 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"60"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 9. The Sharpe Ratio, Step 2: Standard Deviation of the Return Difference\n<p>It looks like there was quite a bit of a difference between average daily returns for Amazon and Facebook.</p>\n<p>Next, we calculate the standard deviation of the <code>excess_returns</code>. This shows us the amount of risk an investment in the stocks implies as compared to an investment in the S&amp;P 500.</p>"},{"metadata":{"dc":{"key":"60"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# calculate the standard deviations\nsd_excess_return = excess_returns.std()\n\n# plot the standard deviations\nsd_excess_return.plot.bar(title='Standard Deviation of the Return Difference');","execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"67"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 10. Putting it all together\n<p>Now we just need to compute the ratio of <code>avg_excess_returns</code> and <code>sd_excess_returns</code>. The result is now finally the <em>Sharpe ratio</em> and indicates how much more (or less) return the investment opportunity under consideration yields per unit of risk.</p>\n<p>The Sharpe Ratio is often <em>annualized</em> by multiplying it by the square root of the number of periods. We have used daily data as input, so we'll use the square root of the number of trading days (5 days, 52 weeks, minus a few holidays): √252</p>"},{"metadata":{"dc":{"key":"67"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"# calculate the daily sharpe ratio\ndaily_sharpe_ratio = avg_excess_return.div(sd_excess_return)\n\n# annualize the sharpe ratio\nannual_factor = np.sqrt(252)\nannual_sharpe_ratio = daily_sharpe_ratio.mul(annual_factor)\n\n# plot the annualized sharpe ratio \nannual_sharpe_ratio.plot.bar(title='Annualized Sharpe Ratio: Stocks vs S&P 500');","execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"dc":{"key":"74"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 11. Conclusion\n<p>Given the two Sharpe ratios, which investment should we go for? In 2016, Amazon had a Sharpe ratio twice as high as Facebook. This means that an investment in Amazon returned twice as much compared to the S&amp;P 500 for each unit of risk an investor would have assumed. In other words, in risk-adjusted terms, the investment in Amazon would have been more attractive.</p>\n<p>This difference was mostly driven by differences in return rather than risk between Amazon and Facebook. The risk of choosing Amazon over FB (as measured by the standard deviation) was only slightly higher so that the higher Sharpe ratio for Amazon ends up higher mainly due to the higher average daily returns for Amazon. </p>\n<p>When faced with investment alternatives that offer both different returns and risks, the Sharpe Ratio helps to make a decision by adjusting the returns by the differences in risk and allows an investor to compare investment opportunities on equal terms, that is, on an 'apples-to-apples' basis.</p>"},{"metadata":{"dc":{"key":"74"},"tags":["sample_code"],"trusted":true,"collapsed":true},"cell_type":"code","source":"# Uncomment your choice.\nbuy_amazon = True\n# buy_facebook = True","execution_count":22,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.7","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}