{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algorithmic Trading Basics\n",
    "\n",
    "<img align=\"left\" width=\"80\" height=\"200\" src=\"https://img.shields.io/badge/python-v3.6-blue.svg\">\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook by [Marco Tavora](https://marcotavora.me/)\n",
    "\n",
    "### Table of contents\n",
    "\n",
    "1. [Summary](#Summary)\n",
    "1. [Definitions](#Definitions)\n",
    "3. [Modules](#Modules)\n",
    "4. [Time Series](#Time-Series)\n",
    "5. [Strategy](#Strategy)\n",
    "6. [Moving Windows](#Moving-Windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary \n",
    "[[go back to the top]](#Table-of-contents)\n",
    "\n",
    "I will briefly describe:\n",
    "- How to build a [trend trading](https://en.wikipedia.org/wiki/Trend_following) strategy\n",
    "- How to backtest it \n",
    "- How we can optimize it \n",
    "\n",
    "### Definitions \n",
    "[[go back to the top]](#Table-of-contents)\n",
    "\n",
    "\n",
    "Trend trading can be [defined as](https://en.wikipedia.org/wiki/Trend_following):\n",
    "\n",
    "> ... a trading strategy according to which one should buy an asset when its price trend goes up, and sell when its trend goes down, expecting price movements to continue.\n",
    "\n",
    "Again borrowing from [Wikipedia](#https://en.wikipedia.org/wiki/Backtesting), backtesting:\n",
    "> ... seeks to estimate the performance of a strategy or model if it had been employed during a past period. This requires simulating past conditions with sufficient detail, making one limitation of backtesting the need for detailed historical data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules \n",
    "[[go back to the top]](#Table-of-contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run modules_algo_trading_v10.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data from `yahoo` or `Quandl`\n",
    "[[go back to the top]](#Table-of-contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-10-02</th>\n",
       "      <td>10.728572</td>\n",
       "      <td>10.838572</td>\n",
       "      <td>10.614285</td>\n",
       "      <td>10.694285</td>\n",
       "      <td>9.477872</td>\n",
       "      <td>178159800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-03</th>\n",
       "      <td>10.635715</td>\n",
       "      <td>10.707143</td>\n",
       "      <td>10.455714</td>\n",
       "      <td>10.582857</td>\n",
       "      <td>9.379119</td>\n",
       "      <td>197677200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-04</th>\n",
       "      <td>10.585714</td>\n",
       "      <td>10.780000</td>\n",
       "      <td>10.451428</td>\n",
       "      <td>10.768572</td>\n",
       "      <td>9.543709</td>\n",
       "      <td>207270700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-05</th>\n",
       "      <td>10.647142</td>\n",
       "      <td>10.880000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>10.690000</td>\n",
       "      <td>9.474073</td>\n",
       "      <td>170970800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-06</th>\n",
       "      <td>10.631429</td>\n",
       "      <td>10.720000</td>\n",
       "      <td>10.544286</td>\n",
       "      <td>10.602858</td>\n",
       "      <td>9.396842</td>\n",
       "      <td>116739700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close     Volume\n",
       "Date                                                                        \n",
       "2006-10-02  10.728572  10.838572  10.614285  10.694285   9.477872  178159800\n",
       "2006-10-03  10.635715  10.707143  10.455714  10.582857   9.379119  197677200\n",
       "2006-10-04  10.585714  10.780000  10.451428  10.768572   9.543709  207270700\n",
       "2006-10-05  10.647142  10.880000  10.590000  10.690000   9.474073  170970800\n",
       "2006-10-06  10.631429  10.720000  10.544286  10.602858   9.396842  116739700"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start, end = datetime.datetime(2006, 10, 1), datetime.datetime(2012, 1, 1)\n",
    "apple = pdr.get_data_yahoo('AAPL', start=start, end=end)\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.isnull().any().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ex-dividend</th>\n",
       "      <th>split_ratio</th>\n",
       "      <th>adj_open</th>\n",
       "      <th>adj_high</th>\n",
       "      <th>adj_low</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>adj_volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-10-02</th>\n",
       "      <td>75.10</td>\n",
       "      <td>75.870</td>\n",
       "      <td>74.30</td>\n",
       "      <td>74.86</td>\n",
       "      <td>25451400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.651397</td>\n",
       "      <td>9.750352</td>\n",
       "      <td>9.548586</td>\n",
       "      <td>9.620553</td>\n",
       "      <td>178159800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-03</th>\n",
       "      <td>74.45</td>\n",
       "      <td>74.950</td>\n",
       "      <td>73.19</td>\n",
       "      <td>74.07</td>\n",
       "      <td>28239600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.567863</td>\n",
       "      <td>9.632120</td>\n",
       "      <td>9.405935</td>\n",
       "      <td>9.519027</td>\n",
       "      <td>197677200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-04</th>\n",
       "      <td>74.10</td>\n",
       "      <td>75.462</td>\n",
       "      <td>73.16</td>\n",
       "      <td>75.38</td>\n",
       "      <td>29610100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.522883</td>\n",
       "      <td>9.697919</td>\n",
       "      <td>9.402080</td>\n",
       "      <td>9.687381</td>\n",
       "      <td>207270700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-05</th>\n",
       "      <td>74.53</td>\n",
       "      <td>76.160</td>\n",
       "      <td>74.13</td>\n",
       "      <td>74.83</td>\n",
       "      <td>24424400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.578144</td>\n",
       "      <td>9.787621</td>\n",
       "      <td>9.526738</td>\n",
       "      <td>9.616698</td>\n",
       "      <td>170970800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-06</th>\n",
       "      <td>74.42</td>\n",
       "      <td>75.040</td>\n",
       "      <td>73.81</td>\n",
       "      <td>74.22</td>\n",
       "      <td>16677100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.564007</td>\n",
       "      <td>9.643686</td>\n",
       "      <td>9.485614</td>\n",
       "      <td>9.538304</td>\n",
       "      <td>116739700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open    high    low  close      volume  ex-dividend  split_ratio  \\\n",
       "Date                                                                            \n",
       "2006-10-02  75.10  75.870  74.30  74.86  25451400.0          0.0          1.0   \n",
       "2006-10-03  74.45  74.950  73.19  74.07  28239600.0          0.0          1.0   \n",
       "2006-10-04  74.10  75.462  73.16  75.38  29610100.0          0.0          1.0   \n",
       "2006-10-05  74.53  76.160  74.13  74.83  24424400.0          0.0          1.0   \n",
       "2006-10-06  74.42  75.040  73.81  74.22  16677100.0          0.0          1.0   \n",
       "\n",
       "            adj_open  adj_high   adj_low  adj_close   adj_volume  \n",
       "Date                                                              \n",
       "2006-10-02  9.651397  9.750352  9.548586   9.620553  178159800.0  \n",
       "2006-10-03  9.567863  9.632120  9.405935   9.519027  197677200.0  \n",
       "2006-10-04  9.522883  9.697919  9.402080   9.687381  207270700.0  \n",
       "2006-10-05  9.578144  9.787621  9.526738   9.616698  170970800.0  \n",
       "2006-10-06  9.564007  9.643686  9.485614   9.538304  116739700.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start, end =\"2006-10-01\", \"2011-01-01\"\n",
    "apple = quandl.get(\"WIKI/AAPL\", start_date=start, end_date=end)\n",
    "apple.columns = [el.lower().replace('.', '').replace(' ', '_') for el in apple.columns]\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 16, 8\n",
    "apple['close'].plot(grid=False, rot=90);\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Average Crossover Strategy\n",
    "[[go back to the top]](#Table-of-contents)\n",
    "\n",
    "\n",
    "In general, a trading strategy involves going into long and/or short positions following some plan. One example, maybe the simplest, is the **moving average crossover** strategy. Following this strategy, one decides to buy or sell when the time series of two moving averages, with different lookback periods, cross. More concretely:\n",
    "- When the short moving average (SMA) becomes greater than the long moving average (LMA), one enter (i.e. buys, or goes long)\n",
    "- When the long moving average (LMA) becomes greater than the short moving average (SMA), one exits\n",
    "\n",
    "Roughly speaking, the *rationale* behind this strategy is the following. Short-term trends are captured using SMA. When the SMA crosses above the LMA, one identifies a short-term upward trend and the stock is purchased. When the LMA crosses above the SMA one does the opposite.\n",
    "\n",
    "An example of the use of moving averages follows. Consider the first five rows and a window of size 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-10-02</th>\n",
       "      <td>74.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-03</th>\n",
       "      <td>74.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-04</th>\n",
       "      <td>75.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-05</th>\n",
       "      <td>74.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-06</th>\n",
       "      <td>74.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close\n",
       "Date             \n",
       "2006-10-02  74.86\n",
       "2006-10-03  74.07\n",
       "2006-10-04  75.38\n",
       "2006-10-05  74.83\n",
       "2006-10-06  74.22"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten_rows = apple[['close']].head()\n",
    "ten_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2006-10-02    74.860\n",
       "2006-10-03    74.465\n",
       "2006-10-04    74.770\n",
       "2006-10-05    74.760\n",
       "2006-10-06    74.810\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ten_rows['close'].rolling(window=3, min_periods=1,center=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third entry is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "close    74.77\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window_size = 3\n",
    "(ten_rows.iloc[0,:] + ten_rows.iloc[1, :] + ten_rows.iloc[2, :])/window_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the rows with index smaller than the window are unaltered since the moving average needs are least 3 elements (the window size) to be calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fix_yahoo_finance as yf\n",
    "yf.pdr_override() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code implementation of this strategy consists in the following steps:\n",
    "- First set the sizes of the short moving window `smw` and long moving window `lmw`\n",
    "- Create an empty `DataFrame` for signals (called `sig` here) and fill the columns of `sig` with the SMA and LMA values from the `close` price column\n",
    "\n",
    "The close price column is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-10-02</th>\n",
       "      <td>74.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-03</th>\n",
       "      <td>74.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-04</th>\n",
       "      <td>75.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-05</th>\n",
       "      <td>74.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-06</th>\n",
       "      <td>74.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close\n",
       "Date             \n",
       "2006-10-02  74.86\n",
       "2006-10-03  74.07\n",
       "2006-10-04  75.38\n",
       "2006-10-05  74.83\n",
       "2006-10-06  74.22"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple[['close']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two steps above are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "smw, lmw = 40, 100\n",
    "signal_df = pd.DataFrame(index=apple.index, \n",
    "                         columns = ['sma','lma' ])\n",
    "\n",
    "signal_df['signal'], signal_df['sma'], signal_df['lma'] = 0.0, 0.0, 0.0\n",
    "\n",
    "signal_df['sma'] = apple['close'].rolling(window=smw,\n",
    "                                             min_periods=1, \n",
    "                                             center=False).mean()\n",
    "\n",
    "signal_df['lma'] = apple['close'].rolling(window=lmw, \n",
    "                                            min_periods=1, \n",
    "                                            center=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fill the `signal` column inserting 1s when the value of column `sma` is larger than `lma` only for the period greater than `smw`.\n",
    "\n",
    "For that we use the `np.where` function. A simple example of the latter is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lst is: [5 6 7 8 9]\n",
      "Insert 1s in the positions of the elements in lst that are smaller than 7 and insert 0s otherwise:\n",
      "[1 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "lst = np.arange(5,10)\n",
    "print('lst is:', lst)\n",
    "print('Insert 1s in the positions of the elements in lst that are smaller than 7 and insert 0s otherwise:')\n",
    "print(np.where(lst < 7, 1, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sma</th>\n",
       "      <th>lma</th>\n",
       "      <th>signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-11-28</th>\n",
       "      <td>80.76700</td>\n",
       "      <td>80.622927</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-29</th>\n",
       "      <td>81.21025</td>\n",
       "      <td>80.889048</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-30</th>\n",
       "      <td>81.61725</td>\n",
       "      <td>81.139535</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-01</th>\n",
       "      <td>82.02950</td>\n",
       "      <td>81.370909</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-04</th>\n",
       "      <td>82.45200</td>\n",
       "      <td>81.587556</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-05</th>\n",
       "      <td>82.86800</td>\n",
       "      <td>81.798043</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sma        lma  signal\n",
       "Date                                   \n",
       "2006-11-28  80.76700  80.622927     1.0\n",
       "2006-11-29  81.21025  80.889048     1.0\n",
       "2006-11-30  81.61725  81.139535     1.0\n",
       "2006-12-01  82.02950  81.370909     1.0\n",
       "2006-12-04  82.45200  81.587556     1.0\n",
       "2006-12-05  82.86800  81.798043     1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_df['signal'][smw:] = np.where(signal_df['sma'][smw:] > signal_df['lma'][smw:], 1.0, 0.0) \n",
    "signal_df.iloc[smw:, :].head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a column of positions `pos`. Rows correspondig to long positions will have 1s. Notice below e.g. that on 2000-02-07, `sma`>`lma` and one changes the position (buys the stock). In the following day, one still has `sma`>`lma` so the position is kept and the entry in the position column will be 0. The meaning of the `.diff` method is illustrated below. Since:\n",
    "\n",
    "        signal_df.iloc[smw:, :]['sma'][4] = 103.934\n",
    "        signal_df.iloc[smw:, :]['sma'][3] = 103.206\n",
    "        \n",
    "        -> signal_df.iloc[smw:, :]['sma'][4]-signal_df.iloc[smw:, :]['sma'][3] = 0.728"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.422"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.422"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(signal_df.iloc[smw:, :]['sma'].diff()[4], 3)\n",
    "round(signal_df.iloc[smw:, :]['sma'][4] - signal_df.iloc[smw:, :]['sma'][3], 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sma</th>\n",
       "      <th>lma</th>\n",
       "      <th>signal</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-11-28</th>\n",
       "      <td>80.76700</td>\n",
       "      <td>80.622927</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-29</th>\n",
       "      <td>81.21025</td>\n",
       "      <td>80.889048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-30</th>\n",
       "      <td>81.61725</td>\n",
       "      <td>81.139535</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-01</th>\n",
       "      <td>82.02950</td>\n",
       "      <td>81.370909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-04</th>\n",
       "      <td>82.45200</td>\n",
       "      <td>81.587556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-05</th>\n",
       "      <td>82.86800</td>\n",
       "      <td>81.798043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-06</th>\n",
       "      <td>83.26850</td>\n",
       "      <td>81.968936</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-07</th>\n",
       "      <td>83.61375</td>\n",
       "      <td>82.074583</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-08</th>\n",
       "      <td>83.93875</td>\n",
       "      <td>82.200816</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-11</th>\n",
       "      <td>84.28200</td>\n",
       "      <td>82.331800</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-12</th>\n",
       "      <td>84.55050</td>\n",
       "      <td>82.406471</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-13</th>\n",
       "      <td>84.91950</td>\n",
       "      <td>82.534231</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-14</th>\n",
       "      <td>85.27000</td>\n",
       "      <td>82.647736</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-15</th>\n",
       "      <td>85.48825</td>\n",
       "      <td>82.741667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-18</th>\n",
       "      <td>85.62625</td>\n",
       "      <td>82.791273</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-19</th>\n",
       "      <td>85.74750</td>\n",
       "      <td>82.854107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-20</th>\n",
       "      <td>85.84025</td>\n",
       "      <td>82.887544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-21</th>\n",
       "      <td>85.87075</td>\n",
       "      <td>82.887759</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-22</th>\n",
       "      <td>85.87100</td>\n",
       "      <td>82.876102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-26</th>\n",
       "      <td>85.89850</td>\n",
       "      <td>82.853333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "                 sma        lma  signal  pos\n",
       "Date                                        \n",
       "2006-11-28  80.76700  80.622927     1.0  1.0\n",
       "2006-11-29  81.21025  80.889048     1.0  0.0\n",
       "2006-11-30  81.61725  81.139535     1.0  0.0\n",
       "2006-12-01  82.02950  81.370909     1.0  0.0\n",
       "2006-12-04  82.45200  81.587556     1.0  0.0\n",
       "2006-12-05  82.86800  81.798043     1.0  0.0\n",
       "2006-12-06  83.26850  81.968936     1.0  0.0\n",
       "2006-12-07  83.61375  82.074583     1.0  0.0\n",
       "2006-12-08  83.93875  82.200816     1.0  0.0\n",
       "2006-12-11  84.28200  82.331800     1.0  0.0\n",
       "2006-12-12  84.55050  82.406471     1.0  0.0\n",
       "2006-12-13  84.91950  82.534231     1.0  0.0\n",
       "2006-12-14  85.27000  82.647736     1.0  0.0\n",
       "2006-12-15  85.48825  82.741667     1.0  0.0\n",
       "2006-12-18  85.62625  82.791273     1.0  0.0\n",
       "2006-12-19  85.74750  82.854107     1.0  0.0\n",
       "2006-12-20  85.84025  82.887544     1.0  0.0\n",
       "2006-12-21  85.87075  82.887759     1.0  0.0\n",
       "2006-12-22  85.87100  82.876102     1.0  0.0\n",
       "2006-12-26  85.89850  82.853333     1.0  0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_df['pos'] = signal_df['signal'].diff()\n",
    "signal_df.iloc[smw:, :].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ex-dividend</th>\n",
       "      <th>split_ratio</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-10-02</th>\n",
       "      <td>75.10</td>\n",
       "      <td>75.870</td>\n",
       "      <td>74.30</td>\n",
       "      <td>74.86</td>\n",
       "      <td>25451400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.651397</td>\n",
       "      <td>9.750352</td>\n",
       "      <td>9.548586</td>\n",
       "      <td>9.620553</td>\n",
       "      <td>178159800.0</td>\n",
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       "    <tr>\n",
       "      <th>2006-10-03</th>\n",
       "      <td>74.45</td>\n",
       "      <td>74.950</td>\n",
       "      <td>73.19</td>\n",
       "      <td>74.07</td>\n",
       "      <td>28239600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.567863</td>\n",
       "      <td>9.632120</td>\n",
       "      <td>9.405935</td>\n",
       "      <td>9.519027</td>\n",
       "      <td>197677200.0</td>\n",
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       "    <tr>\n",
       "      <th>2006-10-04</th>\n",
       "      <td>74.10</td>\n",
       "      <td>75.462</td>\n",
       "      <td>73.16</td>\n",
       "      <td>75.38</td>\n",
       "      <td>29610100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.522883</td>\n",
       "      <td>9.697919</td>\n",
       "      <td>9.402080</td>\n",
       "      <td>9.687381</td>\n",
       "      <td>207270700.0</td>\n",
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       "    <tr>\n",
       "      <th>2006-10-05</th>\n",
       "      <td>74.53</td>\n",
       "      <td>76.160</td>\n",
       "      <td>74.13</td>\n",
       "      <td>74.83</td>\n",
       "      <td>24424400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.578144</td>\n",
       "      <td>9.787621</td>\n",
       "      <td>9.526738</td>\n",
       "      <td>9.616698</td>\n",
       "      <td>170970800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-06</th>\n",
       "      <td>74.42</td>\n",
       "      <td>75.040</td>\n",
       "      <td>73.81</td>\n",
       "      <td>74.22</td>\n",
       "      <td>16677100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.564007</td>\n",
       "      <td>9.643686</td>\n",
       "      <td>9.485614</td>\n",
       "      <td>9.538304</td>\n",
       "      <td>116739700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open    high    low  close      volume  ex-dividend  split_ratio  \\\n",
       "Date                                                                            \n",
       "2006-10-02  75.10  75.870  74.30  74.86  25451400.0          0.0          1.0   \n",
       "2006-10-03  74.45  74.950  73.19  74.07  28239600.0          0.0          1.0   \n",
       "2006-10-04  74.10  75.462  73.16  75.38  29610100.0          0.0          1.0   \n",
       "2006-10-05  74.53  76.160  74.13  74.83  24424400.0          0.0          1.0   \n",
       "2006-10-06  74.42  75.040  73.81  74.22  16677100.0          0.0          1.0   \n",
       "\n",
       "            adj_open  adj_high   adj_low  adj_close   adj_volume  \n",
       "Date                                                              \n",
       "2006-10-02  9.651397  9.750352  9.548586   9.620553  178159800.0  \n",
       "2006-10-03  9.567863  9.632120  9.405935   9.519027  197677200.0  \n",
       "2006-10-04  9.522883  9.697919  9.402080   9.687381  207270700.0  \n",
       "2006-10-05  9.578144  9.787621  9.526738   9.616698  170970800.0  \n",
       "2006-10-06  9.564007  9.643686  9.485614   9.538304  116739700.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1071, 12)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ylabel, col, cols_ma ='price', 'close', ['sma', 'lma']\n",
    "afa.plot_function_new(apple, signal_df, ylabel, col, cols_ma, 0, apple.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A subsection of the plot makes the strategy clearer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "afa.plot_function_new(apple, signal_df, ylabel, col, cols_ma, 950, 1050)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backtesting Steps\n",
    "[[go back to the top]](#Table-of-contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steps:\n",
    "- We first set the initial capital to, say, 2MM\n",
    "- Buy a $N$ shares\n",
    "- Initialize the portfolio with value owned initially and store the difference in shares owned \n",
    "- Add a `holdings` column containing the values of the positions bought times the adjusted closed price\n",
    "\n",
    "`cash`, `total` and `return` to portfolio\n",
    "- Plot equity curve \n",
    "- Plot the \"buy\" and \"sell\" trades versus the equity curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>signal</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>holdings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-12-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.735893</td>\n",
       "      <td>1173.589279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.710190</td>\n",
       "      <td>1171.019000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.729467</td>\n",
       "      <td>1172.946709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.544407</td>\n",
       "      <td>1154.440702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.185853</td>\n",
       "      <td>1118.585313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.342640</td>\n",
       "      <td>1134.264014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.405612</td>\n",
       "      <td>1140.561197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            signal  adj_close     holdings\n",
       "Date                                      \n",
       "2006-12-01     1.0  11.735893  1173.589279\n",
       "2006-12-04     1.0  11.710190  1171.019000\n",
       "2006-12-05     1.0  11.729467  1172.946709\n",
       "2006-12-06     1.0  11.544407  1154.440702\n",
       "2006-12-07     1.0  11.185853  1118.585313\n",
       "2006-12-08     1.0  11.342640  1134.264014\n",
       "2006-12-11     1.0  11.405612  1140.561197"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aux = pd.concat([signal_df[['signal']].iloc[smw+3:smw+10, :], apple[['adj_close']].iloc[smw+3:smw+10, :]], axis=1)\n",
    "aux['holdings'] = 100*aux['signal']*aux['adj_close']\n",
    "aux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2006-12-01</th>\n",
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       "      <th>2006-12-04</th>\n",
       "      <td>1171.019000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-05</th>\n",
       "      <td>1172.946709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-06</th>\n",
       "      <td>1154.440702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-07</th>\n",
       "      <td>1118.585313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-08</th>\n",
       "      <td>1134.264014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-11</th>\n",
       "      <td>1140.561197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   AAPL\n",
       "Date                   \n",
       "2006-12-01  1173.589279\n",
       "2006-12-04  1171.019000\n",
       "2006-12-05  1172.946709\n",
       "2006-12-06  1154.440702\n",
       "2006-12-07  1118.585313\n",
       "2006-12-08  1134.264014\n",
       "2006-12-11  1140.561197"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_capital, N = 1000000.0, 100\n",
    "pos = pd.DataFrame(index=signal_df.index).fillna(0.0)\n",
    "pos['AAPL'] = 100*signal_df['signal']   \n",
    "ptf = pos.multiply(apple['adj_close'], axis=0)\n",
    "ptf.iloc[smw+3:smw+10, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_diff = pos.diff()\n",
    "ptf['holdings'] = (pos.multiply(apple['adj_close'], axis=0)).sum(axis=1)\n",
    "ptf['cash'] = initial_capital - (pos_diff.multiply(apple['adj_close'], axis=0)).sum(axis=1).cumsum()   \n",
    "ptf['tot'] = (ptf['cash'] + ptf['holdings'])/1000000\n",
    "ptf['return'] = (ptf['tot'].pct_change())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2010-11-22</th>\n",
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       "      <td>998458.21829</td>\n",
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       "      <th>2010-11-23</th>\n",
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       "      <td>998458.21829</td>\n",
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       "      <th>2010-11-24</th>\n",
       "      <td>4045.554500</td>\n",
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       "      <td>998458.21829</td>\n",
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       "      <th>2010-11-26</th>\n",
       "      <td>4048.189036</td>\n",
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       "      <td>998458.21829</td>\n",
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       "      <td>2.627956e-06</td>\n",
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       "      <th>2010-11-29</th>\n",
       "      <td>4072.221142</td>\n",
       "      <td>4072.221142</td>\n",
       "      <td>998458.21829</td>\n",
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       "      <td>2.397202e-05</td>\n",
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       "      <td>4079.546437</td>\n",
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       "      <td>998458.21829</td>\n",
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       "      <th>2010-12-06</th>\n",
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       "      <td>4133.907832</td>\n",
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       "      <th>2010-12-14</th>\n",
       "      <td>4116.172909</td>\n",
       "      <td>4116.172909</td>\n",
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       "      <th>2010-12-15</th>\n",
       "      <td>4117.072507</td>\n",
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       "      <th>2010-12-16</th>\n",
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       "      <th>2010-12-17</th>\n",
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       "      <td>1.002579</td>\n",
       "      <td>-8.203671e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-20</th>\n",
       "      <td>4140.847585</td>\n",
       "      <td>4140.847585</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002599</td>\n",
       "      <td>2.050935e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-21</th>\n",
       "      <td>4166.486116</td>\n",
       "      <td>4166.486116</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002625</td>\n",
       "      <td>2.557207e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-22</th>\n",
       "      <td>4178.759197</td>\n",
       "      <td>4178.759197</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002637</td>\n",
       "      <td>1.224095e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-23</th>\n",
       "      <td>4158.711022</td>\n",
       "      <td>4158.711022</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002617</td>\n",
       "      <td>-1.999545e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-27</th>\n",
       "      <td>4172.590528</td>\n",
       "      <td>4172.590528</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002631</td>\n",
       "      <td>1.384328e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-28</th>\n",
       "      <td>4182.743129</td>\n",
       "      <td>4182.743129</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002641</td>\n",
       "      <td>1.012596e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-29</th>\n",
       "      <td>4180.429878</td>\n",
       "      <td>4180.429878</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002639</td>\n",
       "      <td>-2.307158e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-30</th>\n",
       "      <td>4159.482106</td>\n",
       "      <td>4159.482106</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002618</td>\n",
       "      <td>-2.089264e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>4145.345573</td>\n",
       "      <td>4145.345573</td>\n",
       "      <td>998458.21829</td>\n",
       "      <td>1.002604</td>\n",
       "      <td>-1.409962e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1071 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   AAPL     holdings           cash       tot        return\n",
       "Date                                                                       \n",
       "2006-10-02     0.000000     0.000000  1000000.00000  1.000000           NaN\n",
       "2006-10-03     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-04     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-05     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-06     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-09     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-10     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-11     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-12     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-13     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-16     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-17     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-18     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-19     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-20     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-23     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-24     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-25     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-26     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-27     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-30     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-10-31     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-01     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-02     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-03     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-06     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-07     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-08     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-09     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "2006-11-10     0.000000     0.000000  1000000.00000  1.000000  0.000000e+00\n",
       "...                 ...          ...            ...       ...           ...\n",
       "2010-11-18  3963.755379  3963.755379   998458.21829  1.002422  1.016757e-04\n",
       "2010-11-19  3941.908010  3941.908010   998458.21829  1.002400 -2.179458e-05\n",
       "2010-11-22  4027.112750  4027.112750   998458.21829  1.002485  8.500073e-05\n",
       "2010-11-23  3967.610797  3967.610797   998458.21829  1.002426 -5.935444e-05\n",
       "2010-11-24  4045.554500  4045.554500   998458.21829  1.002504  7.775508e-05\n",
       "2010-11-26  4048.189036  4048.189036   998458.21829  1.002506  2.627956e-06\n",
       "2010-11-29  4072.221142  4072.221142   998458.21829  1.002530  2.397202e-05\n",
       "2010-11-30  3998.711170  3998.711170   998458.21829  1.002457 -7.332443e-05\n",
       "2010-12-01  4066.180987  4066.180987   998458.21829  1.002524  6.730445e-05\n",
       "2010-12-02  4088.670926  4088.670926   998458.21829  1.002547  2.243331e-05\n",
       "2010-12-03  4079.546437  4079.546437   998458.21829  1.002538 -9.101310e-06\n",
       "2010-12-06  4114.373714  4114.373714   998458.21829  1.002573  3.473912e-05\n",
       "2010-12-07  4089.442010  4089.442010   998458.21829  1.002548 -2.486773e-05\n",
       "2010-12-08  4125.425913  4125.425913   998458.21829  1.002584  3.589246e-05\n",
       "2010-12-09  4109.329542  4109.329542   998458.21829  1.002568 -1.605489e-05\n",
       "2010-12-10  4119.642785  4119.642785   998458.21829  1.002578  1.028683e-05\n",
       "2010-12-13  4133.907832  4133.907832   998458.21829  1.002592  1.422837e-05\n",
       "2010-12-14  4116.172909  4116.172909   998458.21829  1.002574 -1.768907e-05\n",
       "2010-12-15  4117.072507  4117.072507   998458.21829  1.002575  8.972876e-07\n",
       "2010-12-16  4128.510247  4128.510247   998458.21829  1.002587  1.140836e-05\n",
       "2010-12-17  4120.285355  4120.285355   998458.21829  1.002579 -8.203671e-06\n",
       "2010-12-20  4140.847585  4140.847585   998458.21829  1.002599  2.050935e-05\n",
       "2010-12-21  4166.486116  4166.486116   998458.21829  1.002625  2.557207e-05\n",
       "2010-12-22  4178.759197  4178.759197   998458.21829  1.002637  1.224095e-05\n",
       "2010-12-23  4158.711022  4158.711022   998458.21829  1.002617 -1.999545e-05\n",
       "2010-12-27  4172.590528  4172.590528   998458.21829  1.002631  1.384328e-05\n",
       "2010-12-28  4182.743129  4182.743129   998458.21829  1.002641  1.012596e-05\n",
       "2010-12-29  4180.429878  4180.429878   998458.21829  1.002639 -2.307158e-06\n",
       "2010-12-30  4159.482106  4159.482106   998458.21829  1.002618 -2.089264e-05\n",
       "2010-12-31  4145.345573  4145.345573   998458.21829  1.002604 -1.409962e-05\n",
       "\n",
       "[1071 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df, ylabel, col = ptf, 'portfolio value (MM)', 'tot'\n",
    "afa.plot_function_3(df, signal_df, ylabel, col, pos, 0, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptf[['return']].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sharpe Ratio and Maximum Drawdown\n",
    "[[go back to the top]](#Table-of-contents)\n",
    "\n",
    "The Sharpe ration reads:\n",
    "\n",
    "$${S_a} = \\frac{{E[{R_a} - {R_b}]}}{{{\\sigma _a}}} = \\frac{{E[{R_a} - {R_b}]}}{{\\sqrt {{\\rm{var}}[{R_a} - {R_b}]} }},$$\n",
    "\n",
    "where $R_{a}$ is the asset return and $R_b$ is the risk free rate. From Wikipedia:\n",
    "\n",
    "> The Sharpe ratio characterizes how well the return of an asset compensates the investor for the risk taken. When comparing two assets versus a common benchmark, the one with a higher Sharpe ratio provides better return for the same risk (or, equivalently, the same return for lower risk).\n",
    "\n",
    "Using $R_b=0$ for simplicity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sharpe_ratio is: 0.945\n"
     ]
    }
   ],
   "source": [
    "returns = ptf['return']\n",
    "sharpe_ratio = np.sqrt(252)*(returns.mean() / returns.std())\n",
    "print('sharpe_ratio is:',round(sharpe_ratio, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum drawdown measures the largest drop in portfolio value of a portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115a62588>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LmzTx5Y+g13DxcNM2t3zxVYfxnBrFl7m8RTo6BoN6+rZx/ABQ5o7/Hj967zI8flvcZWGe0DwOERs+v85yPFaYY7leP9SPiCTDKTIwxjQxXoKOIRJfBMExz2ulThHNVSVJC6SGCg9mTylP+rzRGl/hcaKluhQDBmuOMYJl7bx6OMX49uY4ITOhqIQSp4jrVk7HbefOQZXBynbXwzvwyFbVwjYcjmG0kTIBg/gCgB++92R9sf7Fixfg7ssXp/wsj681eg42HuzDd558e1RjAOLz1le1cJLOPLkdu4fDqC+Px/U6RQGRmIzXDqlhGUZroKXlKxTTxZtahkN9f/2uTmw9OoB18/IXF54LJrX4AoB7rjwJ91yxBID6Y9/3oRV4xyLVHG4UGVyVl4xQP6bc40jIqkuHOQjcHH9gJ8Z4JSsC0ZhlbZxTplcDAOY2lIEv0jyGSbOuzAWHwNAfiCSsXJ7b3Q1fKIqIJEEUGK5fOR0AUGuoqWYUO/u7/KP6LuZzx+MFPAar3GXLpiZc4BUeBxgDPqmtgq5dMQ0t1XFT9Ejiy2zF8zgFRLU6PZzGCg86yfJFEDpmt6PVIg9QM+I+sW625XtfvHg+/nijGtbhcQoJIRrcKnXjmTPw2fPnJsxPIwmNYFRKmOervS6smVMHQJ1n/rpJjbOSldEvjgPa9sbaf/zx8rbqtJ+dVuPFkqmVSQLt58/vH9UYgHhy0HUrp2N+YznaB4J61nYu6RgMJXiGFK3AAc8IdxlEsscpJiU/DIWietC9xxATduc/tgMYmzWymJj04ksUmB4AajbPlhvqxPBMNx4DlYoyt1O3No2ElGPLl7GA3UjCLhCWLAuEzqovw3O3r8Wt58zRxYlRpDDGUFXqShJfAPDg60cRjspwOwR8eM0MbPrSeQnBtkY3X6ap3DFJRn8ggqiUOKHzCd7K3chxO0Qc/PY78enz52L7V9+BOy9Wa8o8fMuZANTA2HSY3SeRmKxavgzZW02VnoRsJoKY7JgtxmUW4Q2Aulh650lNlu/ddNYs3X1ovlHzeWdBUzkcogDRcFPvHAqntViFonKC+AKA+z60wjIBYLRxX72aNb/C4B357Q2n4UfvXTZi4VSXQ8Cjt67GmbPrRnVMK8IxGaKgFpVuqPDg+d3dOPOef4+qLNJoURQFHQMhNFXGY6I/sU5d9FZ71fNhLJzqdgg43BtIGNNQKKZ7loyWMXMM23hl0osvALppdCiYeHF98Z0LsFyz/BzV6n1ZlUe446L5uHgJt5YlZ22kQtJiG9wOAeVuR1JwerYYY4+CUQmvH+rDLfdvsTxOICKh1GktXGbUeeFxinqAvCQrOHtuPd6pFZ+t8TrR74+i1x93HXpdIr7x+Fv49YaDcIoCGGNJ505IsHxlJr76/BEoSrLV0MryZcbojjAGgi5trcLMem9CVpYVZstXJCYjKiVavmq8LhwfCGLHcetaRAQx2TCLL57oNFY8DhFRSdHngP6AejOu0azqPOh+Zp0XkZic9mYdiMQsF5013mRxlKlHg3OkN4CmSk+CuGur8+a8CPNvNhxMSPoJxyS9vEeJYSzmmGM7OdQbQDAq6VmbgGr1qy936zFnTsO8uUEr+/PnjYf113zBqO5+NVb/5/OwJCsQBYZZ9fZmzecLEl8ApmjiyxwXtay1Sg/UO9qnmmmNPmzOzWfPws/er8aJuRxCxiJKUtS2CT9678lwiMyyFlU2GC0w3b4wrv7FK3h8WwfW7+pMGKMsKwhGJZSOUBH6J9eejGtXTMO8xnL84cbT8dP3nwIAqCp1oX0wmOBeMGb0pJr8REOw6b4MxRePDTNPHJlYvtIF9BtjClIRikow7sIfkTAUjCVMIqfNqAEA7D5BGY8EAcRjnziNBmvIWOALLC7q+rUsZG5N4gH7i6aqIQfpior2DkdQZxHcXlmSXHNxNJavrqEQ/u+N4wkZ9PkgFJVw92O7cP4PXjS8Juviy2h1jMm5K23Eay6e1JLYsaWqxKn/HsZFa5tWL/LpXZ36wtVo+VKNGonjlWQFb339Qjz1qbNy8yVyDIkvxC+0T56XnBHBVy3H0li+jPCb+JtHB3DBD15MG+guywrmTCnDhYsbIQpCkhsyW4x+/X1dcTFwy/1b8M7/95L+nLs7U8VicGbWl+Fb716SFBtVU+rSxdPMOi/mNSQHzVphFDLm0hGp4LFrMWn0lq90uBzJF7eZcExOiCcBgPbBYELRSJ6AYPdvSRDjEUVR9DZB162chhKniPmN6eeHpa1VulXdCm6p0sWX5nY0W6sWaZnY6cIA1KDwZEucVRmc0cQY8bIQ71pqr5VrJKuhOfQD4JYv9ZzdefECPVDdnFVqJ1uPDsLtEDDXdC+o9rr0xbgxXOOn154Cl0PAS3t7cMmPN2AgENFivgyWr2iiFTOm9Xwstp6NmTI+R20zjDEcuued+Pja5GBPbqbdfcKHqlJnQsNVK9wONRj08p/+B7s7ffjPvtTtKyVF0YWMQ2C6G9IOjg8E8d9/26Y/36a15OBCc6/B0mTOyhktMVnR9/GFixfgqU8nrkRuPcc6iJbrE5dDQMdgKCPLH4+9M1u+uOshneUrHQ6BQRphJRiKSnA7BTx/+1p85ny1jtjx/mBC7AL/PXMZT0EQ44Wtxwb1Rc3H187Grq9fkBD3acXDt5ypW9Wt4AugoG75Uq99c6HslTNrUeIU8aNn9qQMBen2hVFvsaDmiygjPcNhfPWRnRnFHB0fCMLrEnHtirH3ZLSCzzVz7/wXPnrfpqT3+wNW4kvWywNVe116ofDYCGEW2bDt2AAWNVckeAUAJHSIMVq+qr0uvVsKoLaKisRkPV7O4xQwFIpi6dee1rcpxvIRo4HE1whwS0qXL5zwx5EKl0NIiA1IV+SP+6wB9aZtpw/+dVOV9ad3qW0dPnp2cuV3XhbCKtsxE4wxHVUWLZI++455Sa8B8eaeXPRlIr56dLdjfOLoHQ7j7sd2AbC2fL1/xTQ0VKS3WIoCS7KmmQlHVctXW50X58xXJ7AuXzhhguG/J1m+CAIJi0+v25FxLb90uHW3ozoH9PsjEFg8sP0ja2YAAOY3luOb716MLUcG8OKe5EVwIBLDcDhmGUpSWerEgzetBBBfgP/uP2rNR96fMB1H+4KYnaEHYCQ+eGab/ljvHCLJeGpnchu7gUCyMAwb3I4A4NBiX0e63zy54wS6xlA2JybJ2NE+iJNaqpLeWzI17oY0LloB4K5LFuJDq2dgZr0Xb2jV6HnM12kzavC2IZTjwZtW4oJFjRjPkPgaAWNWysz6sjRbqrhMSj9diQdJVvS4JzEDy8tosOovWFfmwo1nqhOTMfDyo/epboGxdny665J4OyJjZg8AfPeqk1J+jh+PTwzmumdW8Ia3RivhCcMEYWX5+ua7l2DjF89Lu1/V8mV9/APdw3hudxdCMUmf+I3uZ+MkwgvRkuWLIBKb3ZeN4DXIFB4KYoz5qip16Qk0X7x4AXZ87QJ4nCJWzKwFEO8laKTHp87NVuILUMtLAPFahzyhyGzNseJIX8C2iupfuXQR/vvCeXAIbMRkLqsSEka3IxDvrGLOGDd/5uY/bca1v9446vHu7vQhFJWxtLUy6b0lU+OCzHwez5xdh7suWYipVSXYoyUM8Jivm8+ahaWG+LGp1dnFDRYDJL5GoL7crVu8ZmaQVWH+g+4dQXwJRrejjfdrPjG98Lm1+msXLm6ExynifadPS3CfclfelDFmIfHUYQC6j55j1TaEw78unxgysfz1WATc+8PxCWmsMV8OUUh5/HO+/wI++LvXdcsXgITq3EZ3rW75IvFFTHJikozNh+Lia6Q6epnCxRcXIgOBaILFnTGmCz1uOTFnsgNA97C6aEslvmZo8/4Nq9QahQN+1apUPoKIlGQF7QNBtNooED6+djY+eGYbwjE57SJ5k3a+jUaAcCzR8uV0aJavNDccnlywr2sYT+7owKwvPpFxiYevPbILLlHASk34GpljdDs6rOfqRkMPSH4/EQSG6bXq7zGjzptQn3G8QuIrA6KaRWpm3ciWL3OtrnQd5GUld5Yv3majvtyNy5ep1fjPX6iaacvcYkIPtKUtVZheW4qz5oytpowxldps+UoHn0S4YMrM7agF3BvOFf8u//j4GWN2a6SzfHGGwzHd8uUUBT3GxGhF5L9nDmNZCWJcsKdzGP6IhJUza2ytRu7RbtpX/vwVAGqQubl1GMfrckBgag3HmCTjvlcO6Qvk41rT7VQhCY2VHhz89sW4WltA8nCSVHXKOB2DQcRkZcTYttHCk7nSJQa9fqgfgOqW5POruZAsTyZIl+1ozFC9+U9bIMkK1u/qRPsIxVkVRcHmI/1498lTE2p8cYzWLmPpHyPVhsQJYwcZft6XtSa7M8cj9tiBJzjcspJJPRFzMdPOND7zmKzA7TDEfNl4x+aWL49DxNcuW4xzFjTo4qrM7UQwKiEmyXCIAjp9IZzUUjVm4WIUH9wK9MgnzsSh3vRNbfm1zy1fmbjqrCxfPAspG7dGJjF37YPBhAr99eVu9AeiCXWCeJIUuR2JyQ4P/v7M+fNwulaCxQ7MRVH7A5GUlhBBYCj3qIWvH36zHXc9vBP9gShuO3cOdrYPwimytItqxtioLXa8LFGrzdaZEpcISVYsg+oBtbzFwR4/qkud6A9E1exsp4hgREpIKuCiJ122o1Vm5+0PbUW5x4HtX70g5efCMRmSrGBaBiU2nBYZpUBimY/ZhlCfMr0P5MSwGU2Mb5FjPrJGDVLPZCUTMq1KTqQRX7Ip4D6TmKdMCcUkuBwCBIGhssSJdy1t1sUV95cf7PFDURR0DYXRkML0ngnG1Qw/xkktVXr/y1RwKyG/2EbjduSFVnccH8Stf3kDAEbMRE1HJtmOHQOhhImfuyss3Y7jIOB+IBDB7Q9t1cuoEISdcNeRXbFeHHNR1P5ABDXe1Bb3ihIHDvX69YXR/6zfg1PvXo9d7UOY11ie0v3FMVto0sVKAfGyRK019sYl8YUfbyJuhvdMXDNHtTLyivABU99e3fKVRnz5U5TVGKl7C7eYedNkzv/lIytxwaIGvXWQGf76lHJ3ghWMdyDJJOZuPDAxvkWO+djaWTj47YuTVlxWRE3iayAQtQx+B5JLTdiZ7ajGJ1n/vDzjZFfHEHzhGIJRCQ1ZVp0eC2bxNZL4lGQFff6I3pYoJst4dFu7/n424iud5YtPvsGolBA7wYPujX8XPOB+PMR8fefJt/G3zcew+jvPWb7/8JvH8eSOjjyPipgocOtJ+QhuutFirLWnKAr6A1FUp2nXw8Dw/O5u7DE02e71R7Dj+CAWNycHhZsx15EaqYh2r1Zra6SakKOlVtvfUUMGvXEsmw71o9Ql4lStKwtvwaRW8Y//BjzbkYfTvH6oL2EBtq9rGFf94hXLMbhEIW3MGRdt6ebiVbNq8b/XL09pUeRzrDEzUt1n5lnx4wESXxmSqUvurksX4spTWhJe4+0UzEhy/GYtZBBzNBpCJj+/ER6c6gvF9FTiKSOUYsgFvFdiRYaWL18oClmJB7tLsqKv7q48pUUPrh0LjjRuX2PsgttwTvnkamX5Gg9ux26ftftiX5cPu0/48MkH3sTNf9qS51ERE4VhrcB0NosiK4xupx3HhxCJyWmz33ipBnMXjf5AVK+Cnw6HYLZ8JV/bP31uHz7z1zehKAoGAlG4RGHMdRNTwSvxH+zx668Zi3i3DwQxraZUP99Gy5fREsUtRzFJgSwruPoXryQswP75xvGE45Y4Raz/9Fm46tQWRCQ5ZbHZoVAUO9uHAGT3m/OEBnMpCW5BNXdMGK+Q+LKZqVUl+P57lia8lsr1KMky+KIq3c1/LKQTX9xsv6fTp18sUyyqPI+WdKZmK/hFxJuVjyRYuMm72tBGpM8fQVttKb7/nqVZ1RASBSGl+DVOokbLF08uMC4E4wH3xS++jBO3MYX9vP95ERf88EWrjxDEiPzqxQM45e71+vXKLRZ2YVwA7WxXi0ev0wqHWsGbdVt1G1kwQrV9wEp8JVu+vvfUbvzfluP41UsHMBCIoLLUaUtNMyPcQ3Cg2yi+4kJoMBhFRYkTVdp2PX61qXgwanY78mxHGZ/565v664GIuq/93cOoLHGDb9/2AAAgAElEQVTq2f21ZS7MaSjXSz0Y28gZufep3Xo3g2yE54WLG/HQzatw9fJEI8YCrfDtaDoNFDMkvvLAB3/3muWNXZIV3f8uCszWG3bPcMSy4CkAvYH2H185jE8+oF58IxUhHYmX/nsdXvr8OaP6DHc78lXSSJY/s/jibshqiya4o8UpspTZP8bsIqOg5e1LVhhSqoVxZPkaMkzcVm1JCGIsfPOJt9Dnj6DLF4ZLFMbcdSIVRssXnxNSzXUAUO5W37P6G69K467kmN1jZrejsbbW49s6MBCIJlXbtwNusTIejy+eAVV8VZY40VanxiYf7vUjFJWhKDC5HdX9RCS1DR7nJa259XA4hrY6L27XimNzscZj4yIpYt6MbZyyifNjjOG0tpok8XpSSyW+culCfPVdi8a872KCxFce8EckyxRdWYGhzldqy8tYePuEL2WPRateWGOt8cVprSlN6q02EjzeggeSZuJ2BOJ1xd7uGMKGfT0ZlQAZCTGN29cYs2e0fJ23sAFv330hzp6bmEZvt5DOFUZLAK/1NtZCuwRh5mCPf9RzQiYYa1jx2lPeNN05PE4BDoHp4qupMj7XZSISzCLAbPnaeKAXgFpTTFbUjiG1XvvDOLj4Md5Lbrl/CzYfVgPtB4NRVJU40VpTCoEBB3sCePD1IwDU4HUOj2GNSQpWzaqF26G6SF/Zr34PfziGMreIFVqG6motS56Lv1Qxby6HgPpyN75+2SIszUE5CMYYPnjmDEytGv8FVgESXznj+dvXYr2hx6FZWISiklbhXn0u2Bhw3+0Lo2c4jPkW/cmsKHc7bM9IyoTvv2cpHr7lTNRoE5XRBG4FX+XyWCteffnM2cnF/EZLuoQHo/iqNGXoWLl2RcaQw7ZpWbO/exjPvtWJIUPRRJ4e/8dXDhdqWMQE40D3MOrK7RdfjDG9wXRfIIIyt0NfxKbavtzj0MXX3z52hv7eWFyiZsvPqwd6UVnixMfWzsb244PYcmQA0zMotTBauPjhpSa45X33CTWWbUhzO7odIpqrSrCrfRB3P/4W1s2rx2XL4pnnxjpfkZiCujI3qktdGNIWY/6wBK/LgdoyN56/fS2+9E61gwkXf6myPUMRCVPK3fjAqrYJk5GYS+gM5Yi2Oi/mGCxPxtTdPZ0+zL/rSRzpCyRWuLepyOrbJ1RT9IKmzHqL1Rcg2B5Q3Y1LW6v0uLdtxwbTuut8YXVyMNfPsSNTUxSElI3NjTEOzRaFA80IQmatkgrFud9/AR/6w6aEHqQHe4ahKAoe307ZjYQ9tA+GbM/443xotdomrd8fyWjhWFXq0tsFlRoWTOksZqkwi49XD/RhxYwaXLtiGrgGtLvAKpBYn6uuzIVHPrEaTpHhqJapGIrJes3FGXVevH6oH5Ks4NKlzQneDo+hN2ZUkuFyCChxiXqNSn8kpp/TtjqvvsB0jWD5CsVSxxkTyZD4yhNG8XWgO551U1WirgxVt5c9x3q7Q+2LNb8xM8vXSLVbco1oKLaXKp4AiI/TPLHZMcE7RGvLl6IoCMUkfPTsmbj78sW44pSpI+5LtXwVr/jiKArQrLlg7n16D+7519voHQ7josWNWGPodnDvU7spJoxIy9snhhCV5CRhYnTx2QkXIn3+yIgV5wEkuKqcxl6soyygKgoM0Vj82g5FJRzpC+CklkpUljixSAulSFf6Yqw4EwSUCFFgqC9zo9sXhiQrkGRFt05Nry3VXbLmmFh+vobDMUQlGU6RwesS9QQofzhmma3Iz1uqCvuhqDxhCqDmAzpTOeahm1cBSEyPNQY/8jRoOyxfiqLgS//cjm8+8RYaKtxp4y2+cfli3Hv1UnxkzQz85H0nZ3XcbDFaqL/6yE50+RKzQ08MhvCJ+7fghBbQaS5emKo32+jGYC2Y1F5qambj9SunW8bLmbG7bEguufvyxfr5/PPGIzjar6arLzC4rH/y3D7c9c8dhRoiUeQc7Qvgwh++hO/86+2k4pzLp9tX2d4IFwJdvnDK1kJGWgylKFK1tcmExgoPopKM32w4iE2H+vQ5iZej4WLO7gxPIFF8cQuXWr0/qotevk1bbbwbS61ZfLnj4isSk+EU45YvRVEwnEJ8ucX0bsdgRErodkKkh9oL5RgeI+SPxBCKSrjtL2/owYjvO32absWxI+brj68cxp9eVQMseVPYVFy3cnpWx7ITo+XrgdePYl/XcEJcxi9e2I/HtnWgxCnCJQoJbsaPrZ1lS1CvQ2B60UEjvFbOaMzpdncryCWNlR6UuZ0AgvBHYlAU1bJ46dJm/GtHhx4LxtPQCcLMAa3u1K83HMTZph6OdiyMrOAi42CPH/MzKBeRIL4EAQ/dvAoHDSUbMsXtFBCRZNz92C4AwP0fWQEAaKpS5yQu6+wuLAuo8wpfJPJyQeUeB3yhmG6N4pYv4/xvtsKVOEUITLVwRSRVfJW6HOjyhTAYjCIqKZa/m57tGJOxYW8PKkucWNISr5MWikkJZUCI9JDlK8fweieBsITDvQE8vasT//vCfgDAR8+aqW+XSWNnzonBEE7/5jPYakgTBtRKxZwFGQbbFwOiKZvImLIMxCePYFRCuScxOSCTiTejMQgMipJcIoJXiR6NOb2Y3Y7mbguNFR69zhrXi9NrS1FZ4sQfb1yhb2d3zSJi4mAMo7j+N68lvJerGCBjxiPP1E2HsfejIKilDN5zWuuYjmuMAe33q649nt3IL5PSMcSSZQK32nmcieKLx2G5tPfbDOLL3I6JMQav26G7HV2igJ3tg9hxfAjP7+4GYF16yCi+rvvNRlz6kw0J74ciUkL3ASI9JL5yDA/o9EdiurmW11cyV0bP9Ib92LZ2dPnC+O1/Dia8PhCIZ6/dcdH8rMadT8z9Vc31tox928o9joRyD3alsutVny2yUgGMalIRitjyZe7jWON1JSUsTNOssc1V8ddHGRpDTCL2dw+nfC9XMUDGuXJ+BolFLWkq4I8GpyhgwNDYelhLAuJxVPwyMRdmtQs+T6VyO3KBZIxxsxLAZW4HhkMxRCUFTgdDp9aF5f+06vZWSUz82N97arfl2FR3JYmvTCHxlWNKtT/GQERKurGXmCqnBzNsm8BLBOztHE6YhIxB0XYXNswlDpP6GgomuriMp63E5UiwwtgV2Ko3xE4SX2NwOxax5etwb6L4Yozha4aihQIDmrWJ2/g3JJDli0jB/q7U7rtcWb7OnF2H9yxvwT9vOVMvhZCOlurssg+f/ezZeO2L58IpsoR5djjMu3So4ot3ChmpWfdYcSWJL4ceuwXEBZJxgeq2GEuZW7WYccvXXz6yEgCwt1NN1uLdOxKOre1nt7aNkcFgFEOh2ISpwZUPSHzlGJeoFvjzh2OIaasTwcI03VpTil5/JKPWCWFtP7s6hvCAVkQPiNd/GW+YY9jNwsXY+sYcLGuX5cvYrNuIbvkajdvRxsxVuzlkEF9fuVS9adWWufVJs7mqxLJGD2kvIhX7u4dThjnkyg3VWOnBd69aimWtVRkJvClZxp7Nqi/DlAoPnKKAPsM8+/zuLgDx1mrfufIk3H3ZoqSm0HahW76047kcAsIxOcnyZVygWlnhqr0u9AUiesD9qlm1WDK1Ug/5sAqcT2fM5xb1XJTYmKiQ+Mox3L8eiEh6Q9ZrTpuGtfPqE9pWzNR89JkEgUZiMsrdDkytKsHGA2qclywr6B0en+LL3NPSLIDChhgLc6uPXFu+grr4Go3bsXjrfB3p9cPtEPDlSxYmJF1wUTstxeRJli/CisFgFF2+MC5d2oTfffC0pPeLpfSAIDBccfJU3H1Zdq1pXA4hIbyDt+ThWdCVpU5cv6otZzGSTkdizJdLFBCVZJz/A7UXq/XCKXkstV4X+v0RtdSEJtjmGupSelzJ+zFnmQPx3pr8nOSio8FEJasrgzFWwxhbzxjbq/1fnWK7JxljA4yxx7I53njF6xLhD8djvq44ZSp+/8HTE7aZobXIOdCTOn6CYyyMJ8kKOodCmPulfyEiqUX2/vShFSPuo5gw1/aSTYHvv9ASFIDk1Zc5mHSsxC1fiQfgtXLMle3TUcxux0O9AcyeUoYbV89ImKj5Y7P4OmOW2j1AYAw/e34fHt9GRViJOK8fVBd/J7dWY928KUk9Fosp++1/rlmG61e1ZbUPVwalZnKJOebLaUoAyNTdWe11oT8QQVRS9O80tyHeps3K8uV2iPiCKZb4xT2q+AzHRr9Inexk+5d0B4BnFUWZA+BZ7bkV3wNwfZbHGreUapYvbtGxMgNPry0FY2rq9EhwU7FDYIhKMp7eeUIXDb/6wHK9F9d4wapislEE+Q2xcJlkNo0FXu7CbIXjwbXpGveaEYq4t+PhXr9l6xM+WnPMxi8/sFx//N0nd+OW+7egcygxG5WYvBzqVeerhZrb8aGPrsLnLpinv18sli+7KHTbHC6UeLKWwxSG4c5wfHVeF3qGIzjSF0CjVgh3jkF8pRJRFy9pMmwjYFeH2k0lHhs7sX7vXJLtmboMwB+0x38AcLnVRoqiPAsgOUpvkuB1ifCFY7rb0eoC9jhFTK0qyUh8RSW1kjGvyl5rqPDOLRXjiahFWx9uOTKXfkhVXTlbUsV89Wvm9NG4N0XG0rZJKhSBSAyH+wKYPSU5O4wLYHO18DK3AwubKhJKVPz0uX25HSgxbuAxqvzvZk5DOW5ZN1t/v9CWIrtxWliWrNytOTu+KebLfC8JxTJL2jKWoviw1qppjmFeSCUyW2tKMXuKKtJWzKjFLs3tyC1f4ynRq9BkW4ykQVGUDgBQFKWDMTbFhjFNOOrK3GgfDOlWlVR/2DPqvDiQYcyXyyHAIQiIyYqeJfnQzatG3S6jGFg3vx5Xn9qClupSOESG7z21WxNBYpJLUtEsSv/vfSfrCQx2kCrmq98fgUsUEsqCjITLISR0NCgW9nf5oSjAAovaaPx7W31Pl0NA1HBerGoYHesPoK7MTW6HSYYvFIPXJSbFYj75qTV4eV/vhKsPZ/42V5/aglUz87fgNdf5MovbTPvc8viuD62eoS/eM81UfPQTq7G3y4d/v92FF/d2IxCJGYpRTyyxnUtGFF+MsWcANFq8dafdg2GM3QTgJgCYNm2a3bsvGE1VHmw+0h93O6ZobzGzzou/bzkORVHSTlphg9vxxT3deg0bY0uJ8YTbIeJ7Vy8FAPxOq13GhWqS+NL+f9fSZlvHwH8To7szEpPx+PYOLJpaMaqbyPzGCvx9yzG03fE47v/ICpwxqzjcwNxKUWnhQuXxiCUWwsolCogYVtRlplo+iqJg9Xeew1lz6/HHG083fzyJfn8EH/njJrzv9Gm48tSWUX0HorjwhaIotyhLML+xIuPesuOJw72Ji+NvXbEkr67I5JgvdV6aWlWCJ25bY3ltW7F4aiUeu3U1FjXHf6NMF+4lLhEntVThxGAIigK8fcKnW8bJ8pU5I/7VKIpynqIoiy3+PQygkzHWBADa/13ZDEZRlF8qirJcUZTl9fX1I39gnDC1qhQDgSiOD6itWpzmqqIaM+vLMByOJZQDsIIH3HPBcP9GtdzEaKwzxQrPGuIiKKq5w3gWTa6yCHmtMaPl659vHsex/iBuO2fOqPZ18rQq/fG1v9qITz/4pj2DzJJ0rgF+vkstLFdOB0uoQWcWaNxt/OKe7ozGsbvTh02H+/HDZ/dkNnCiKIlKMo72BTNqbD1RMNe4yncMGA+oL9GyEfl8Ob22NEl4PX/7Wqz/9Fkp97V4amXSovLU6ZY5c5ZM1xb77QNBPRyELF+Zk+2ZegTADdrjGwA8nOX+JiQXLm4EY8CvX1KtOqksXzyVd929z6fdXyQmwyWypAt/IjQ1dZjcf/zGfu581aOdqzh27jYxBtxvOtSHujI31s4b3ULAKL4A4B9a1ehCw+O6rIoucpFrJeAdgqB3ZQDirl9OpsWBObxIsFWiBTE+2HKkHyd/fT1eOdCLikkkvs7R5qHFUwtj1TNbvrjbsdqixENbnRdzGkbXfu2Bm1Zi9zcuzGhbPlcEI5IecE+Wr8zJ9qq5B8BfGWMfAnAEwNUAwBhbDuBmRVE+rD1/CcB8AGWMsWMAPqQoylNZHnvcMKPOi4sXN+Hx7WqafqrVUo03HjjvN3SWHwxG4dI6zwOqK87jFBLiLEpd4riM9zIjmgLf+Q2an4vcWb6SY746BkOYWl0y6riVeaOc8PIFd+FaiS+PlhRiVbrDKQq6YAJUt/fRvoBeUDEYHZ348mlCztzTkxg/bD82iOFwDB9ePQPvWGQVlTIx+cE1y/CNyyWUOEX4C9Bs3hzzxS8hq2t6bPvPfD98rghFJYRjEpwiS4r9I1KT1S+mKEqvoijnKooyR/u/T3t9Exde2vM1iqLUK4pSoihKy2QSXpyPrZ2lPzZXaefUGDLqnt/djVBUQigqYenXnsbH/7xZf4+3hDCWrGjNsn1GscDPjTnmS7fI5MrypR33oc1H8YLmPjsxGEJThgGsRhwWE5jZWlQIeFCsVS2gZi3d3ErcuhxMF0wA8OSOE1jz3efw5A51MTFa8TUUUoUcXyyYm30TxQ/vpnHHRfNx+oyaAo8mf7gdImq8LpS4RNSVZVc1fyy4NMsSt3zFF1T5tzjxMdz79B4MhaLUVHuUkIM2Tyw2tJuwujkDQLWhgfQt92/Bmu8+p09yz+2Ox9P4QjG4HWJCz8OzR+kaK1b0elu62zG/lq8/vnIYN/z2NQCq+OI1cLLFqpxGvkk3Ud99+WIsarYOknaKQkLiw/bjanr5rna1xo/R7ZhJeyzeu1MUGF490Iv5dz2J1w/1jeKbEIVmIBBFhceRci4jcgNfnHKrUzia2pqda7j1bTAYxT+2HJ9UItwO6MopAKk63vPmrJxuXxg7jg8lvHa414+DPX4sb6tOyMw7e+7EEF9m95/udtQmm1xJGLO5fFf7EHzhGJpsEl/FUPGen0sry9dJLVV4/LY1usg1ksoVwV83Wr4yCbr3aZavcFTGfa8eBgD8++2scnWIPNMfiKDKptZeROaYG2unCyXINcY50x+R8KVLRm5wTsQh8ZVHHrhpJd6zvCXlhcIYw7avviPhtSN98czHn/x7Ly784UsAgAsWNSbUuVrelnmWSjHDxRe3ePH/SzVRkCvvnVlg3HTfJgD2tS+KyoUPLo9nO47usjeeG2OYFi84aXQbZrJv7nbs8oX0dkX+DCxmRPHQMxymPn4FgF+L3OqULpQgn5w9tx4z6sZnqaNCQeIrj6ycWYvvXrU0bQB3hceJv39sFR66eRUA4KhBfN379B4EoxJqvS601pTqlq/b3zF3wmSZ8EzQhzYdxY7jg/rKrizHbkez5euUaaqYvWhxk9Xmo8bctqgQfOuJtwGMfqJ2ab+JSxQSask5RQF9/kiCqzGTBtxDwRjqytxY2hrPCu017Ycobg71BOhmWwDMFe6vOGUqar0uXH1qayGHhTNnj7/OKoWGxFcRcur0Gv0m9/uXDyW9f8ZstWgnd2W1TaBJkMd8/eGVw7jkxxuSsh1zJWHMrmCPU0BTpQf15fYE1ZrbFhWSVG7vVPAJv6nKg5MNgumFPd045e71+PvmY/prmQTf+8JRTK8txelt8RiRx7d1YPFXJl0ezrgkKsloHwwmNWEnco/TocV8aZav1ppSbL7rfEyz6NeaD24+exauPrUFH149syDHH8+Q+CpSar0uPc7JyJWntOBb714MIB7DMxHqe3GcJmHAA9X1mK88Wb5CUdnWOIpCW76M5220pTO4e3FqVQlWGlqpvHGkHwDw9K5O/bWvPLJTD8RPxVAwhnKPw7Le3aChpAVRnISiEhQFKJ9E9b2KBY9DhMBSN77ON3dcNB/fu3rphChzlG9IfBUpgsDwk2tPSXjttLZqfP89S/V2Hl2+EADYlpFXDBhFULnHoQtM3k/wtLbcZNQ4TF0HHtnabusEV2jxxS1SY0kg0C1flYniy2iGbNRKcnT7wvjuU2+n3d9gMIoKjxMtWnmUckOQ/6EMGssThSWcplgvkVvee3orfvb+U6me1gSAli5FzBkmP/qs+rKE551DYQDQb2ITAWPqekOFRw+49zgFPHHbmpyZ160mM7ed4qvAbkde3uHWUbZKAuK9HN1OAa01JagqdWIgEE3Itl3QVI4TQ+pi4Pnd3XrzdzOSrKBjMIiLlzThmuWtqCxxYsvhfvx6g9r9oRgbkhOJpMuaJXJLU2UJmioza4BNFDd09RQxboeIW9bFi7OaY7u+9e4laKkuQWVJZs1UxwNVhv5kNaUuPeDe5RCwsLkiqRyHXVjFQY02NiodsQKXmuAZhhUloz9/U6tUwesUGBhjePpTar84Y/mMeab6YM++1QkrOodCiEoKWmtKIAgMFy9pwpKWeA28YJSC7ouduOWrOFxfBDEeIfFV5Hzugvk4b4HaT8xcxf7aFdOw4fPnFGJYOcNYNToqy/FVdo6LOVqt4g+P0OA8E35y7ckA4iUzCgV3543F7XjBogZ89vy5+PT5cwHEkx8kQxzZzDovXvviufrzo/3W546XTjEGa79raTN+qrnYg5HiSUwgrBlryRKCIOKQ23EcwK09s6ZMnKzGVBib9AYjki5aRtNzbCx4XcmXQs9weMz7ExggK9BbbhS6yOrWYwMQBYZFzZUjb2zCIQq49dw5hufJfTCn1ZZiiqEVE68/ZMZKfDHGcJJm/QoUoF8eMTqKpbYUQYxnSHyNA772rsW4cHGjZeuXiQZjDDPrvDjQ40c4JuviK9cTvVUx1Wy8jo/euhrP7+7WhUqh2wu9eXQA8xvLbUkicArJv4Wx/hcARFOIzWN9AQgMaK5KjFsxNuklipsBLSOV3I4EMXZIfI0DKkuduNCmYp/jgX/fvhafe2grNuzr0d2OubZ8uRxqo/KYrOAja2bgnPkNWdX4WtRciUXNlfjPvh4ASOhGkG9kWcG2o4O4dFmzLfuzSiufYjpX/Hczc6QvgKbKkqTfk5dLoYD74of3PnU7yfJFEGOFxBdRlJS4RISiEiKaxchpURMqF8f0hWIQBIZVs+yp2MyD9gsZcH+gZxi+cAzLDAVS7cYsyFLFuB3tD6K1Jjlbi8TX+MBYCiTXcZgEMZGhq4coSjxOEaGo6nZ0icKoC4OOhVLN9ZVJi5xM4W7HQoqvN48OAkBOxRfnVx9YDiC15atnOIwp5clB/4LAUOt1oX0gmNPxEdnxwOtH9ceFzuAliPEMiS+iKPE4BASjEsJROS9WLyBeyFW0U3xp8VGFdDse7BmGwJLrxOWC8xc2oKHCnVJ8RWKpOwcsaanEtmODuRwekSUHuof1x3Vl1FibIMYKiS+iKOEFTv3hmN7eJtdw15edxaMLHXD/42f34qfP7YfbIeasKrb5JuxyCHp9NjPhmJwyVmhpSxX2dvngpwbbRUswKmFpaxU2f+k8TK+d+NnXBJErSHwRRQnPyuv1h/MWW8Iz7uzsU8YtX4UqNfH99XsA2CsoAeDVL8Rrej3xyTUJ77nE1OIrEpPhEq2z5Ja1VkFWgB3HyfpVrISjMkqcAmrL7Gk4TxCTFRJfRFHi0awjz7zVlbfelfyYtrod9ZivwhYPtTOODVD7if7s/afgY2tnJcVwOUUhpdsxHJNSWr54ra9rfvkqlZwoUoJRSbcQEwQxdkh8EUWJcYL/84dX5OWYvCCqnZYvXhOr0HW+cpGvcPGSJnz+wvlJr7sdAl7a2427/rkDg4Go/rosK4hKSkpLptGasrN9yP4BE1kTikq2NpwniMkKiS+iKDFO8OWe/PSu5BYZO61Eol4NvsCWrxzFe1khK0AoKuO+Vw/jUw++ob/OXZGZ1Ieys68mYR9k+SIIeyDxRRQlngIUcOSWLztDzJxCcVS4z6eU2W6I2WofCOmPM2nI/LsPnpawLVFchKIyPBbdIAiCGB0kvoiixFOA1iW5sHw5xMKXmgDsj/lKx+WGSvrGDEu9SXqa7FXexzRVzBhRWEJRqSDXJkFMNEh8EUWJuwCujVz0qitkkVVjhmU+itRyfnDNMsxrKAcQ//4AsLNdtYilqvNlfC8co4D7YiMmyarb0UW3DYLIFrqKiKKkEG5HbvlKVSZhLBSyvdBQMB7sns8QKsaYHmNmtHzd9hc1/qvD4Io0wwUwuR2Lj7c6fJBkBXM1YU0QxNgh8UUUJYXIqNJv/FE7xVfh3I79gYj+OJ9uRyPGwPll06oBAKvn1KXcnixfxcuRvgAAYF4jiS+CyBYSX0RRUoiMKm5tC9l44y+k5as/UBjLlxHj155S7kZTpQenTq9OuT23PtopgAl74LXXKNuRILKHxBdRlBTC8uXJgeVLEBgEBsQKkO04YLB8FSpDbfPhfj32LBCJwasF1KeC1wCz0/VL2EMm2aoEQWRGVuKLMVbDGFvPGNur/Z+0pGWMLWOMvcIY28kY28YYuyabYxKTg0LGfNnt8nKIAjbs64Gi5FeAGS1fpXkWX3devEB/zJsxD4cleEcYB0+0IMtX8cEtX4W4NgliopHtVXQHgGcVRZkD4FntuZkAgA8oirIIwIUAfsgYq8ryuMQEpyClJrRjhmy+8UdiMt48OoCnd3Xaut+RMFq+Sp3pLU52Y4zr4lYsf3hkyxeP+fJHqLl2scHd8VThniCyJ1vxdRmAP2iP/wDgcvMGiqLsURRlr/a4HUAXgPosj0tMcPJZkZ3jyZHli9M1lDrLLxf4w/HvUVLAwpiDWtalPxxDqSu9+HKKAporPTjU48/H0IhRwBcl6UqFEASRGdkuhxsURekAAEVROhhjU9JtzBg7HYALwP4sj0tMEhY2VeTtWG21XgDAoubKnOy/fTC/4itqiJs6fUZNXo9t5OE32gGoFrBMXFZzG8vx9glfrodFjJJwVILLIeS1ZhxBTFRGFF+MsWcANFq8dedoDsQYawJwH4AbFEWx9Oswxm4CcBMATJs2bTS7JyYgGz6/DlWlrrwdb/HUSqz/9FmYVdelz/gAACAASURBVF+Wk/0f7w/mZL+p4GLnHx8/Uy96Wgge3HQUD246itaakpRNtY0sbanCC3v2YigURUWe+noSIxOOyfCQ1YsgbGHEK0lRlPMURVls8e9hAJ2aqOLiqstqH4yxCgCPA/iSoiivpjnWLxVFWa4oyvL6evJMTnZaqkv1djP5Yk5Due0uz2c+czbmN5bjWH/A1v2ORCQmwyUKWNBUURA3rplgRIYzA/F1WlsNFAV448hAHkZFWBGTZNzy5y342+Zj+muHev0JXRMIghg72S5jHgFwg/b4BgAPmzdgjLkA/APAHxVFeSjL4xHEuGP2lDIsmVqJ4wP5tXyFYzJcBSwL8NitqxOe9wyH4XSMLAKXTauCKDBsOtSXq6ERafjDy4cw+85/4fHtHbj9oa0AAFlW8PzubvgjVPyWIOwgW/F1D4DzGWN7AZyvPQdjbDlj7NfaNu8BcBaA/2KMvan9W5blcQliXDG1ugRdvnBeK7dHYnJBg6MXT61EdWmi2zATy1eZ24EFTeXYfLg/V0Mj0vDGkfh5P3mampjePRwGALx/BYWDEIQdZOXTURSlF8C5Fq9vAvBh7fGfAPwpm+MQxHinpboUiqL2NWyr8+blmBFJhqvAMTqiyd2ZScwXADRWlOTdTUuoiIIAl0OAQ2C6WOa/xXkLGgo5NIKYMFD0JEHkgalVJQCQV9djVIv5KiyJ4isTyxegFrylKveFwR+Ooa22FGfMqoMvpNZbO6Yli0ytLink0AhiwlDomZkgJgUt2k0r19ac4XAMshYUHZHkjGKscom5KkGmlji3KCASI/FVCPxaG6hyjwPDYbVGmy6+qkh8EYQdkPgiiDzQWOmBwHJbbqJnOIzFX3kKv95wAEA827GQmKVfppYvl4PEVy7ZfmwQ1/16I/r8kaT3hsMxlLkdqPA40DccQSQm4/hAEDVe14gdCgiCyAwSXwSRB5yigIoSp17tPRfsOD4IAHj2LbXiSyRW+Jgvs+XLKWZmiXM7BL2RM2E/63edwIZ9Pbjr4R36a7c/tBVfe3Sn2gbK5cC6+VPgj0h4bFs7jvUHyepFEDZC4osg8oTIGKQcNtfe16U2sN54sA/tA0GEpcKWmgAAptm+ZtWrSQaZikGyfOUWj9Zu6vFtHXh65wkAwN82H8Pv/nMI/rAEr9uBs+fWY86UMvz6pYM41h/QXecEQWQPiS+CyBOCwJDLGPJdHUP64089+Ca6hkJJpR7yzXUr1dIES1vUkgWZCiqXgwLuc0nQUK/rpvs2J/wuxweCKHOLYIzhxtUzsKtjCAe6/WgmyxdB2AaJL4LIEwIDlBxavna1x8VXx2AQHYMhnKSJnkJxy7rZ2PONizC/SW1vdCzDmDeXKEKSFaqoniMCpmKpA4HE2C8e2zWvMd6Wqsabv1ZfBDHRIfFFEHlCZCxnYiISk3W3IwCEo6olo66ssDdMxhhcDgGnTlcbe7fWlGb0ObfWgDsUpYrquSAQkVBX5sZpbdUAAF84BoehJhsXX8b2XlUFtqISxESCxBdB5AlByF3MV/tAEDELYedxFjbmi3Pq9Go8+ak1+K8z2jLanmdprvr2szkc1eQlGImh1CXi5rNnAQCGtXpeHC66Sl3xvx9qck4Q9kF5wwSRJwTGkCuvY6rirYVsL2RmfmNFxtvywPwhkygg7CEQkVDqEnWRNRiMJoh3LrqMli9KgCAI+yiemZkgJjiikDu3Y8dgCABQrt0sebB6sVi+Rkt1KcUX5ZJgVEKJS0SZR/176dF6N3Lilq+4+Lp4SVP+BkgQExwSXwSRJwSGnLkdh0Nq/bC/3LQSAFBVorqIxqv4unhJI+Y3lidYXgj7GArFUO5x6uf3xFAo4f1KLb7LWBqkxDU+/5YIohgh8UUQeUJgTG/9YzcBLTB9Vn0ZarwuDIdVd10xuR1HA2MMZ82tp2zHHOELRVHucaCpsgSNFR5898ndCe8bLY9OkeHGM2fke4gEMaEZnzMzQYxDRIFBzpHli5eZ8DgFOEWmx0qNV8sXoMYdBaMSCbAcMBSMocLjhMsh4DPnz01635jZuPebF+PLly7M5/AIYsJDNn2CyBMCy02R1VBUwmPbOgDESzvw4OjxavkCAK8WbxSMSuR+tBlfKIoKLd7rylNbsOVIP3r9Eazf1QkAqCqhmDuCyCU0oxFEnhAE5MTyZa6FZWxePa4tX2517H6t0TNhD5GYjHBMRrkmvkSB4Z4rTwIA/N+WY/jFC/vhcY5f0U4Q4wGa0QgiT4gsN25HcwkAl0F8lYxj8cXrSvlCUTRUeAo8mokDz2ys8bqT3rvilBZccUpLvodEEJMOWt4QRJ4QclRqImwSX/wYC5oqxnWGGrfMDAap1peddAyqNeGaqkjQEkShIPFFEHkiV5Yvs/jaq7UZ+qxFIPV4okIrlzGkldEg7GHL4QEAQHMlNcomiEJBbkeCyBNCjno7crfjL647BQDw4/edjKgk47yFDbYfK5/E3Y5k+bKTp3edQGtNCWZPKSv0UAhi0kLiiyDyhBpwb/9+wzE14N7tUF2Mly5ttv8gBYBn4w0FyfJlJ4d6A1g3rx6ioZE2QRD5hdyOBJEnRCE3RVa55cs1jstKWEFuR/vxh2Po9oUxvdZb6KEQxKSGLF8EkScExmxtL6QoCpZ9fT1WzqwBMPHEl9shwCUKGKKAe9s43BsAALSR+CKIgjKxZmuCKGIExmx1O4ZjMgaDUTy1Uy2MOZ4LqlrBGEO5xwEfWb5s43CvHwAwvba0wCMhiMnNxJqtCaKIsdvtGIwkFledaJYvQHU9DlHAvW0c4pavOrJ8EUQhmXizNUEUKQKDrdmOAVNl+1LnxIsiqPA4KODeRg73+lFX5qaOAQRRYEh8EUSeEGyu8xWMxC1C71negtaaiVe3qdzjpIB7G3mrYwizp5DViyAKDYkvgsgTomCv+AoY3I6ffcc8MDbxSgdUlDiozpdNxCQZO9uHsLS1qtBDIYhJD4kvgsgTdhdZNYovXpB0olHhcZLb0Sb6A1HEZAUtVRPPQkoQ442sxBdjrIYxtp4xtlf7v9pim+mMsc2MsTcZYzsZYzdnc0yCGK8Igr3ZjsaAe49zYq6jyj0ODIWi6BoK5aRG2mSizx8BAFR7XQUeCUEQ2c7YdwB4VlGUOQCe1Z6b6QBwhqIoywCsAHAHY2xilOAmiFEg2hxwb4yFmoguR0C1fIWiMlZ8+1k8tfNEoYczruHiq6aUxBdBFJpsxddlAP6gPf4DgMvNGyiKElEUJaw9ddtwTIIYl9gdcH9iMGTbvooVbqVRFOD4QLDAoxnf9AfI8kUQxUK2QqhBUZQOAND+n2K1EWOslTG2DcBRAN9RFKU9xXY3McY2McY2dXd3Zzk0giguBJvrfHVo4uvBm1bats9i44xZtfrj4TAF3o/Ec7u78Nm/btX7fRrp1SxftSS+CKLgjCi+GGPPMMZ2WPy7LNODKIpyVFGUkwDMBnADY6whxXa/VBRluaIoy+vr6zP/FgQxDhBtbi/UPhDE3IYyrJhZO/LG45Rqg4ssEEkWFEQiX354B/6+5Rhe2d+b9F6/Jr6qyO1IEAVnxEp7iqKcl+o9xlgnY6xJUZQOxlgTgK4R9tXOGNsJYA2Av416tAQxjnE7BYS1Jth20DEYQlPlxM5cE4R4LBtZvkbmaJ/qmrXqCtDnj6Dc7ZiQnRAIYryR7VX4CIAbtMc3AHjYvAFjrIUxVqI9rgZwJoDdWR6XIMYdXrcDfhsFRMdgEM1VHtv2V4w4DOLLznM3EYlJcWFvda76/BHUlJHViyCKgWzF1z0AzmeM7QVwvvYcjLHljLFfa9ssALCRMbYVwAsA7lUUZXuWxyWIcUeZ24GopFjG44yWUFRCz3Bkwlu+RBJfGdMzHNEfW52r/kAkwY1LEEThyKrBl6IovQDOtXh9E4APa4/XAzgpm+MQxETA6xIBAP6wBLdDzGpfPNOxqXIyWb4o5isdJ4bi2a9W8XF9/ggaKib23wtBjBfI+U8QecKrNTO2w4LTPqjG9jRP8GrlCZavCFm+0tFpEF8b9vXgDy8fSni/zx9BDWU6EkRRQOKLIPJEmSa+7Agc7xhQb7QTXXwZi8dSwH16jOLrtYN9uPfpeGitoigkvgiiiCDxRRB5otRg+frg717DE9s7xryvDs3yNdHdjkYo5is9nUMhiALDVae2oKW6BOFoPAA/GJUQjskU80UQRQKJL4LIEx4txd8XjuG53d34+J+3jHlf7YMh1Hhd8Dizix0bTwQo5istJwbDmFLuxr1XL8VVp7YgIsmQZAW+UBRdQ2qTkRrvxGzAThDjjawC7gmCyBy3JpT6DFlpY6VjIDiprF6AGvOlKMqE7WOZLV2+EKZoAfVclAejEpZ89WksaKoAANR43QUbH0EQccjyRRB5wq1Zvnr94RG2HJn2gYlfYNWMrKhigrDmxGAIjRWquOJ/a7vahwAAb3Wo/5PliyCKAxJfBJEn+A3RWI8pMIYMPklWcLQ/MOELrFpBQffWKIqC9oGgLsi55Wv78cGE7SjmiyCKAxJfBJEnuNuxZzhu+dp8uH/U+7nht68hEJEmZY8+ivuyZigYgz8ioaVaFV9c6B/oHk7YrpbcjgRRFJD4Iog8wW+I3b64+Hr1QHID5JHYsK8HQLxo62Tir5uOIkSuR52BQAQv7unGwV4/AOjii1u+DnT79W1FgaHcQ2G+BFEM0JVIEHmCi69X9vfCITDMqi/DxgN9Y95f6SQUXz97fj98oRjuvnxxoYdSFNz2wJt4cU83/uuMNgDAydOqARgsXz1xy1d1qTOhUTlBEIWDLF8EkSd4S6GYrGBOQzkWT61Ex2BohE+lZrJm/R0fCBZ6CEXDsb4AAOD3WjX7BlO2Y+dQ3MpKBVYJongg8UUQecIpMnC9NLXKg+pSJ/oDYy87oSiKTSMbX5DxJk5FiXX2oscZn9rbaksBULA9QRQTJL4IIk8wxvRG0U2VJaj2uhCISCljmB58/Qj+/XZnyv3Jk1N7TVqLn5EuXwh/3ngYUw3tpbjrEUBC43buiiTLF0EUDyS+CCKP3HXJQrzv9Gm4dsU03RLx4p5utN3xeFIj5M//fTtu/P2mlPua21Cey6EWLSS9gDv+vh13/mMH9ncPY3ptKQ7d80589V2L9PeNlq+lLZUASHwRRDFBAfcEkUc+sKpNf7yvSw2Gvum+zQCArzyyEzcYrBepWNBUAYEBq2bV5mKIRY9Ali+93tnbJ3yY35gswo2Wr2XTqjGz3ouFzRV5Gx9BEOkh8UUQBcIpjs3wHI5JeruYyQhpL6DWYMXimY1G3AbL17LWKvz7s2vzMSyCIDKE3I4EUSBcjmQVIaUJ5NrZPoi2Ox7HgW6/5Q13shCV5EIPoeAY2yxtPTaY9P5karhOEOORyTuDE0SBsbJ8pct+fOf/26A/nkzi6xfXnYpffWC5/jwqTdJMAwNDwaj++L8sXNWT6e+DIMYj5HYkiAJhJb4Gg1HUlY3cAsY1RpfleOTCxY0Jz2Py5LZ8dflCONgTr1z/odUzkraZTH8fBDEeoSuUIAqElfjyhdRA6nQ1vOY2lOEdixpTvj9ReejmVajwOBCNTV7L1672IVz4w5fQH4hiaWsVPnXeHL2lkBEqx0EQxQ1ZvgiiQFhZJ7g7KZYm9uvpT5+dszEVM6e11WDZtOoEl9tkIirJ+MBvX0OfX3VNL2wqx6fOm5v2M82VnnwMjSCIUULiiyAKhNMi4H4opAoLq6BygQG3rJud83EVMy6RTdqA+6FgFD3DYThFhqikoNSVfvpe/+mzMnJhEwSRf8jtSBAFwsrtOBDQxJfBtXblz1/Gf/b1QFbGXp5iouAQhEkpvmRZ0evBtVar7YJGarM0p6Ec1VRYlSCKksk9kxNEATG6HV/83DqUexx45UAvth8bRFiKlxLYfLgfH/mjWul+sosvp0OYlNmOB3qGsflwPwBgutarMZum7ARBFJbJPZMTRAExCqmW6hKsnTcFj2/rwKU/2YBuXzhhW27tcYqTO5DaKTJEYpPP8rXj+JD++NwFDQCAmfVlhRoOQRBZQjFfBFEgjEJKEBjWzavHo1vbAQBdQ4niixdfdU3y+k0uUZiUpSa2GQqpLmyuwPO3r8VUiyxHgiDGByS+CKJAmIXUWXPr9cfHB4IJ7/HkR4cwucWXU5ycbsftxwf0x2VuB9rqvAUcDUEQ2TK5Z3KCKCDm+C1jZtqx/qB5c+0z/7+9Ow+Tq67yP/4+XdXd6SU7CYQsRCAEZIcWREVFghsq6AA66oj+9IejIzqioyg6uPDMoM7w/MQdUcGZR8FRFMQFARk3dmWTNUgCIYHsCel9O78/vvdW3+5Up7tTnaq+935ez9NPV9W91XVPbt3KqfP93nPzPuxYR1/Ohh3dfdiw46zm+hpujYhMhoqSLzObY2Y3mtnK6PfsXaw7w8zWmtlXK3lNkawod/29G/75pQA8vbUTgBP2nztsed6HHeuLRm/Oznbs7hscdi3HeWofIZJ6lX6Snw/c7O7LgJuj+6P5PPC7Cl9PJFPe3LaY047at3R/yZxwJtuaqPL1qdcdwoJEo8zcn+2Yw1YTHb39w+6re71I+lX6SX4acGV0+0rg9HIrmdmxwN7Abyp8PZFM+cIZR/Dltxxdut/UUKCpvsDaqPLV0lDk6nNOKC2vy/l/vPWFOgZ96ASEPOjqDVWv97/8AK4/9yU13hoRmQyVJl97u/szANHv+SNXMLM64D+Bf6nwtURywQw2tYdLyDQ3Flgyt5m3Hb8EgHXbys8Fy4v4qgB5qn7Fla/DFs7ksIUza7w1IjIZxjzb0cxuAspdxfeCcb7G+4FfuvuascrlZnYOcA7AkiVLxvnnRbLlsIUzuXPVFiBUvgDOfcUyHt/QzuuOWFDLTau5uDFt38Bg2TlzWdTREypfzQ35iFckD8ZMvtx9xWjLzGy9mS1w92fMbAGwocxqJwAnmtn7gVagwcza3X2n+WHufhlwGUBbW1t+xhVEEr721mO45dEN/PbhDaX/cPeZOY2r33vCGM/MvvpS8pWfj4fOqPLV0qjOQCJZUenRfB1wNnBx9PvakSu4+9vi22b2TqCtXOIlIsG86Y2c1baYs9oW13pTppz6ROUrL1T5EsmeSud8XQycYmYrgVOi+5hZm5ldXunGiYgkxX3O8nSJoa6+qPLVoMqXSFZUdDS7+2bg5DKP3w28p8zjVwBXVPKaIpJfqnyJSBbku2mQiKRKnud8NWvOl0hmKPkSkdSIhx3zWPlqysnZnSJ5oK9SIpIa9cX8DDtedP1D/O9jGzlp+Tya6gsU6vLdYFckS1T5EpHUaMjRsOPlf1zF4xva6egdoKVRVS+RLFHyJSKpkccJ9509/TRpsr1Ipij5EpHUiOd83RFdASAPtnb2qc2ESMYo+RKR1IgrX5fevBL37A49/vnJraXb65/rVpsJkYxR8iUiqREnXwA9GW60+nffuLV0e8OOHl1aSCRjlHyJSGrEw44AXb0DNdyS6tnS0avKl0jGKPkSkdRIVr66+rKbfC2d2zzsvuZ8iWSLki8RSY2G4viTrwfXbeeu1Vu49fFN7Oju29ObNql6+gd57eH7MLOpHkBnO4pkjL5OiUhqDKt8jTHseOqlfyzdXnHI3lx+dtse267J5O5s7uhl0exmjlo8wO8e26g5XyIZo8qXiKRGcs5Xd1T5emJjO4ddeANPbe4c9XlPbGzf49s2WTp6B+jtH2RuSwNzWxsAShUwEckGJV8ikhrl5nyt3txBe08/T20ZPfkqFtJzaZ4t7b0AzGlp4IxjFgHwqkP3ruUmicgkUy1bRFKj3LBjV29oOdE7MPowZLEuPd8zN3f0ADC3tYEXHbgXq/79tZilJ3kUkbGl5xNJRHIveXHp+PqOcQWsp2/0vl/1aap8dcSVr0YAJV4iGaTkS0RSqX8wJFtx8tW7i+s9Jitm1dTR08/3b1s9oW78m6Pka25Lwx7aKhGpNQ07ikgqxZWv7mj4cVcd72s15+uTP32Aa+9dx/MXzKBt6ZxxPWeo8qXkSySrVPkSkVTqHxhe+RqZfCW7wteq8nX36nCNxuRw6a709A9w8a8eAVBXe5EMU/IlIqnUNzh8zlfviOSroVjHScvnATBvemN1Ny6ydlsXMP7rUN65agsApx6xQHO9RDJMyZeIpFKp8tVbPvnq6x9k/3mtLJ3bzMDg+OdcTZbkPK/ucV4KaeOOcKbjR045aI9sk4hMDUq+RCSV+uM5X6Vhx+EJTu/AIA3FOuoLdfTtYjL+nhJXvQC6d3EmZtKm9pB87VWjSp2IVIeSLxFJlY+/+mAA+kae7ZiofA0OOn0DTkMhJF+9/dWvfK1cP9RV/9ntXbtYc8im9l4ainVM1+WERDJNyZeIpMr/PfF5wFDlq9ywY9x2oqFYR32xNpWv+KxFgM/8/CF+/Oenx3xOe08/M6YVNd9LJOOUfIlIqsRnDo482zHZ5yseimyqL1BfZzVJvjp6+4fd/+0j68d8TnfvANPqdZajSNYp+RKRVDEz6gtWOtuxu0yH+zgha2ooRMOO1U++dnQPT76SlbDRdPUN0KTkSyTzlHyJSOoU6+p2WfmKhyKb6gs0NRTo7h/f2YaTqb2nf1h/r6e3jj3vq6tvgCb19xLJPCVfIpI6xYINXdsxSrR+es/aUnuH+OzCaVHy1dlbg+SrO8zfiq3b1rXTGZkjdfWq8iWSBxUlX2Y2x8xuNLOV0e/Zo6w3YGb3Rj/XVfKaIiLFOitd2zHZxuG6+9YBQ9WwafV1NNUXSpcgqqb2nn5apxU5fOFMAAYd1mzpLLuuu3PtvWvZ3tWnypdIDlRa+TofuNndlwE3R/fL6XL3o6KfN1T4miKSc8VC3dDZjokGpnGT0uSE++aGAp3jbHI6mdp7+mltrOdn//RifvK+FwGwalP55OsvT23jQ1fdyyPP7lDlSyQHKk2+TgOujG5fCZxe4d8TERlTOINx+LAjQF3UoqE7MeF+IsOO7j6sM30l2rv7md5YpFBnHDCvBYDVmzrKrpscjtzaOfbEfBFJt0qTr73d/RmA6Pf8UdabZmZ3m9ntZqYETUQqEvfucvdhla+4PdbQsGOBpvoCvf2D47rE0Aevupc3fv3WSdnG9p5+WhpDFWtWcwOzmutZtbl88tXZE7Z335nTOOPYxZPy+iIydY3ZRtnMbgL2KbPoggm8zhJ3X2dm+wO/NbMH3P1vZV7rHOAcgCVLlkzgz4tInrQ0FOns7d/pgtXxuYVx24mGQh3N0Ryqrr4BWnfROd7d+Xk0Z8zdR210+olrHmBGU5FPvOaQssvXbOlk8Zxm2nv6WbpXS+nxpXNbuHv1lrJ/O+4J9v13H8+B81tH3UYRyYYxK1/uvsLdDyvzcy2w3swWAES/N4zyN9ZFv58A/hc4epT1LnP3Nndvmzdv3m6GJCJZ1zqtyHPd/cOGHAHi2lY8Gb+hWMf0afUAbBtjOK+9Z6gv146e/lHXu+WRDdz+t81ll9388HpO/OIt3PjQenZ09w9L9l6+fB6PrW9n3fbunZ4Xx9GsyfYiuVDpsON1wNnR7bOBa0euYGazzawxur0X8GLgoQpfV0RybMa0IivX7+CGB58d9vhnf/4Qr/nyH3ho3XMA1BfqWL7PdIDSY6PZ1D6UnP3nDY+WXaenf4D1O7pLE/tHWhXN6fr9YxvZ0d3H9ESribii1VkmseuIkq+WBl3TUSQPKk2+LgZOMbOVwCnRfcyszcwuj9Y5BLjbzO4DbgEudnclXyKy26ZPq2drZx/nX/PATssefuY5rrztSSAMOz5/wQwKdcYDa7fv8m8mE6orb3uy7MT7tVu7cId127vZ3L5zAjazKVTZbnl0Az39gxy674zSsriq1VFm8n+ckKnNhEg+VJR8uftmdz/Z3ZdFv7dEj9/t7u+Jbt/q7oe7+5HR7+9MxoaLSH6t3LBj2P2LTj+s7HrFgjGtvsCy+a3c//T4ky+AjWWSq6cSfbqOveimnZZ3R3PQ4m72Ry2eVVrWHFW1Ont3rnx19g1QrDMaiup7LZIHOtJFJHXedvx+w+4vntPMj957wk7r1RfCR9wRi2bywNrtPL5hx6hnPW4akWyt2rjzmYlrxrhE0MghxX1mTivdjitf8ZmNSd26pqNIrij5EpHU+fvjlnDeKQeV7jcW6zjueXN2Wq++EM4qPHzRLLZ09LLikt/zH7/ZeT5Xe08/963ZNuyxVWV6co3WoT6WHFLcq7WBxuJQQhUnX4+u38HfNrbTPzDIs9u72dLRS3ffII1KvkRyQ7M7RSSVioWhdg1xhWvY8jortXQ4IrrED8Bdq7bstO4Z37iVR57dEbrhRwnUd/+0irccN7zlzZjJV6Lydfz+c4cti4cdv3TDo3zphkf50MnL+PLNKynUGacduS/T6vVdWCQvdLSLSCrV1w19fNUXdu7J1Z8YXozPeBzp+vvXcc9TW3nk2TCHbMmc5tKyx9a388CIeWLrn+vmxQfO5XlR/66OEcOMWzuGzph809ELhy2bEU3Gj8UnAAwMOt39A0xT5UskN5R8iUgqjVX5ShotsfnAD+4Z1tF+3vRGXrj/0PDlwIgzHjt7B2hpKPK50w4F4K7Vw6toTySGKl960PBeha2NRc44dlHp/sxEMtbdN6jKl0iO6GgXkVQqFpKVr7E/yop15TvWJ82YVs9V55zAJWcdCUDviA76Hb39NDcUaNtvDg2FOm5LNFvt6Onn0Wd3cFbbIu66YEXZbTpu6VBi99N71pZud/cNMK2oypdIXij5EpFUqq9LVr7GTqwK40i+4grZotlh+HFk8tXVO0BzY5GmhgJHL5nFn/62qbTsF/c/Q1ffAGe1LWbe9Mayf3/fWU1lH+/u07CjSJ4o+RKRVJpo5Wtk8nVrInGKNUZDf3G/rd6B4W0hOnsHaI6SpBMOmMuD656jo6efrR29nH/N/Rw4v5Vj95s9DFRK8gAAFUBJREFU6jYsnF0++drW1UejenyJ5IaOdhFJpWS1qzieyteIi1m/9dt37LROQ5TExb+Tla/BQQ/JV9Qy4vkLZuAOj63fwWPrdzDo8Mrn7z3qBbkBFiT6fh25aOgMzC0dvap8ieSIki8RSaVi4mzHhnFUvnaRE5XE1ae48tWTSL66+6OLX0cXy95/XrhW45ObO0ud7U8+ZP4u/34ywfr6248tzQHb1tlXqrqJSPapz5eIpFJxWOVreOLy50+tGJY4AdRFw47l+9sHs1sagKEkLFn5ivt/xZWvudG6Wzp6S481TmDS/MJZTbzrxUu5MzpjUpUvkfxQ8iUiqVRf2HnC/R8+dhLbOvuY27rzhPeRw44jzWyq510vXgoMJV/JBK4rSr7iywDNaKqnzmBbZy97RRPsx9Mu4vpzX1KqwsVVNEBnO4rkiOrcIpJKyWHHuOHq4jnNHJ6YS5UUz8Xq7R+kb2CwNOfq+OiyRB955UGlylU87PjguqEmqx3RBbHjTvWFOmNWcwNbOnvp6QuJ2XgqX4ctnMmh+4bXbmkYWl99vkTyQ0e7iKRSctixbhxtJD726uVA6Cy/7IJf0dM/yMkHzy+dnRgnVTCUfP3wzjWsXB+635eGHRuHEqZZzfVs7egrVcgmOm8r+ZoadhTJDyVfIpJKHT0DY6+UcFbbYj516iGl+xt39NDSWGRpdKmg1kRSlZzAv2ZruJ5jPOzYnEiS5jQ3RBfGHn/lK2lhou+XKl8i+aGjXURS6fjEZYDG64WJi11v7uilpbHIYdEQYLIBarFQx4rozMW1W7uAoWsxJqtVs1sa2NrZW6p8TTSBmtlcz5xo4r4qXyL5oeRLRFJpxrR6bjrvpXztrceM+zmHLJgx7H5rY4Hn7zuDOz95MkcsmjVs2WX/0EZDoY6nt4Xk6+JfPQJAU2Ke1uzm+pB8RZWv8bS8GOnQfcM2qcmqSH7oaBeR1Dpw/nROPWLBuNcv1BmvOnTv0v2W6GzD+TOm7bRuXZ2xcHYTT2/twhMX2E4mSbNbwrBjV98AjcW6XTZYHU08+f7pqMImItmn5EtEcuWdL3pe6XZr46677SycFZKvlRvaS4/tnUjUlsxppm/AWbWpc7eHDc9qWwTASQfvukGriGSH+nyJSK60JCbWt4yRfC2a3cRVd63hu39chRnc8cmTS2dCAhwYdbl/aN323R423H9eK6svPnW3nisi6aTKl4jkSnPD+JOv3oEwkf6qu9Zw/PPmMH/68OHJZXtPB2Dd9m5NmBeRcVPyJSK5kmwHkWwvUc6Jy/Yq3X79kfvutHxOS0PpbEVNmBeR8dKnhYjkSrIRakvDritfpx+1sHT7jUcvLLtOPPSoypeIjJfmfIlIrjQWxj/saGb8+p9PpKt3YFh/r6QD5rdy5+otqnyJyLgp+RKRXElWvsY62xHg4H1m7HL5gfNbd/q7IiK7ok8LEcmVZCPUsSpf47EsSr6mTfDSQiKSX0q+RCRXkhfhHk/layyqfInIROnTQkRyazIuZr1g5jRaGgqqfInIuFX0tc/M5gBXA0uB1cBZ7r61zHpLgMuBxYADr3X31ZW8tohIpXbnckDl/sZn3nAo+89rmYQtEpE8qPRr3/nAze6+DLg5ul/O94EvufshwHHAhgpfV0RkyjizbTHH7jen1pshIilRafJ1GnBldPtK4PSRK5jZ84Giu98I4O7t7t5Z4euKiIiIpFKls033dvdnANz9GTMrd2XYg4BtZnYN8DzgJuB8dx+o8LVFRHbLy5fPY+lcDROKSG2MmXyZ2U3APmUWXTCB1zgROBp4ijBH7J3Ad8q81jnAOQBLliwZ558XEZmYK951XK03QURybMzky91XjLbMzNab2YKo6rWA8nO5ngbucfcnouf8DHghZZIvd78MuAygra3NxxeCiIiISHpUOufrOuDs6PbZwLVl1rkLmG1m86L7rwAeqvB1RURERFKp0uTrYuAUM1sJnBLdx8zazOxygGhu10eBm83sAcCAb1f4uiIiIiKpVNGEe3ffDJxc5vG7gfck7t8IHFHJa4mIiIhkgTrci4iIiFSRki8RERGRKlLyJSIiIlJFSr5EREREqkjJl4iIiEgVmfvU7GVqZhuBJ2u9HQl7AZtqvRFVkqdYQfFmXZ7izVOskK948xQrpDPe/dx93tirTeHka6oxs7vdva3W21ENeYoVFG/W5SnePMUK+Yo3T7FC9uPVsKOIiIhIFSn5EhEREakiJV/jd1mtN6CK8hQrKN6sy1O8eYoV8hVvnmKFjMerOV8iIiIiVaTKl4iIiEgVKfkSyRgzs1pvg4iIjE7JV06Z2cJab4PsMTNrvQG1YGa5+DzTsZtd2rf5kYsPq10xs9PN7PO13o5qMbMVZvZn4B9rvS3VkKf9a2Ynm9lfgPfXeluqxczeYGbn1Xo7qkHHbnZp3+ZPbifcR9+S/w9wPrAf8Ap3/0Ntt2rPiIah6oH/B7wI+Iy7/yy53DP2RsjL/o32bSPwfWAh8AV3v662W7XnmVkR+AjwPmAJcIy732tmBXcfqO3WTR4du5k/drVvM7hvxyO3lS93HwRWAkcTKgWZzcI96AWagZ+5+8/MrM7MjoyX13YLJ19e9m+0b7uB+cAv3P06M2sws/1qvW17krv3Aw8DBwPnAd+KHs9M4gU6dsn+sat9m1O5qnyZ2RnAGne/I7pf7+590e27gG+6+3fMrC56k6SamX0Q2Be4x92vNrMDCL1T7gFWAGuAZ4CfuPsNtdvSyZGn/Wtm5wJNwP3u/mszOwL4MfBd4AzCvn0SuMzdH6rdlk4eM3sNsNrdH47uN0T/eWFmq4AL3P0Hyf2eVjp2M33sat9mdN9OiLtn/odQFfgdsA74GVAXPW6J268BHgRm13p7JyFeAz4M/InwH/HDwLujZecC1wPLgenAB4FvAnvVeru1f8cVaz1wEfB74F3AU8DromVfjB4/mPDhfglwAdBc6+2uMOZ9gduAtVFM8ZfG5P59I7C21ts6CbHq2N1532bl2NW+zei+3Z2fXAw7uvsG4Frg1YRvFO+NFpm7D0Zj678iHAznmNl0MzuzRptbMQ/v6pOAT7n7jwkH/JFmdpa7fwV4i7s/6u47gHuBGUBn7ba4Mjnbv/3AC4Bz3f17wKeAM82sDfg48Gp3f8Td1wF3AYe6e2r3baQHuBp4N+GD+7R4QWL//hR4zMw+CmECc022tEI6drN77GrfZnff7o7MJ1+J08+/AjwE/AY41cwWRG+AOob+HT4O/DthTHqfqm/sJEjEezdwIoC7/5rwBj/GzJa7e3viKacQDvDuqm7oJMnT/k1MwL0PiOeFfJ/wwbaC8O0x+WF9BLC62ts52dx9M3ApcCvwBLDCzOa4u0eTluP9+z7gi2b2LOHkg1TRsZvpY1f7NqP7dndlLvmKxs9LPBpDdvc+D5N0bwUeIZR1cfdBdx+InvcNQnn0mOibyJRnZseY2dz4vg+NmT8OTDezw6P7vyP0f5oePe8tZvZXwhknn/SUjLWPFm8W96+Z1SfvR4kXwHZgXzNbFN3/KXA80GBmxWjf3g8sJgzTpcbImGPRfnyOMGTTB7wletyj/Xsk8G3gJ4T9e2W1trlS8X9UWT92YyPjzeixW4h+G2R/344Wbxb37WTJTPIV/af8e+BiM5uReNziN0RkE3AdsNzMFpnZXtH6m4APuPuboiGbKc3Mjjazm4A7gGLi8UJ0805gADjFzIoeJl0vBNqi5U8C73P3d0Tl4SltF/Fmbv+a2QvN7CrgS2Z2WOLxOO4bgQOBF0T79g5gDnBS9EH3HPBP7v52d99U7e3fHbuIeeT+fZAwMfkQM5thZnOixzcB73f3M1Owf08ws0vN7J0w9B9VYv9m7dgdLd66DB67LzazK4FPxdXZ6PH4S0XW9u1o8Raytm8nWyaSLzNrIExCvjr68H0uerwQfTN2M2s0s0Z3H3D33xM+xP8K/AHY2923u/tjtYtifKI4vkn4lv91wgTrU6NlpR5H7v44Yc7PgYSeKhDmzjwZLb/NU9BfZRzxZm3/nkn4Jng9MI3QRiGOtR/A3e8E7gdeDrwjeupG4NFo+S/TsG9jY8Qc7996C2dD9QA/Isx9+yvwBzPbx93XuvsDtYphvCyc+fVVwrG5wswuMrNDodQ+IzPHLowZ72DGjt39CZ9RtxAqV583s9dCqABFv7O0b3cV70CW9u2ekInkCzgW2OzuX4PSN63GOBExs88BlwMLovv/SJj89y3gCHdfWZvN3i0LgD8DL3H3awhVkLlmZsl4zew70XqXAsdZ6J68BUjbqcvjifdCsrN/DwJ+7u7/TWi+GJ+aHcd6kZldQhhe+ykQDzFuJEzSTaOxYv4sYe7IrGj9DxEm3/8AONrdn63+Ju+2w4Br3P2/gH8hDBefZWatUNq/WTl2Yex4P0t2jt024GF3vwL4KOF4fL2ZxbFlbd+OFW+W/t+ddMWxV5l6zOwsYBFwm7vfRphUvNzM3kA4ZXcA2GhmvwZWEb5l/Ku7r47+xOPAi6JvIVNeFO8S4PdR1ePbicUtwMy4OkCohhwAXBjHa2ZvBYruvq2qG76bdiPeg0jp/i3zXn4E+KyZtRO+Gd8JfM3Mvhettz9h364F1prZg4SpT6kYXoTdinkZYf9uif7EY8Bx7v5I9bd+YsrEuhmYZ2az3P0ZM1tPqBq81Mw6Cfv3X1N+7E4k3mXAp1N67L4Q2JKo3NwJfNDMlrj7U2b2J8L+fLOZ3U369+1E4031/7t7nE+Bfhfj/QEKwL8SvjmcBzwA/B2h99GnCeXPk6N138VQhl16fq1jmIR43xQti/ukHEtIPlui+42J59fVOoYqx5ua/Vsm1r8CbyC0UjiZMAQX9+/6N0In6GVpjHUSYy7WOoYKY30VofLzLeB/CMOnPyQMy71jxPPTfuxONN7UvJ8JFdhfADsIrV5ao8f3AS4GPpL4N/kH4ELCF8a07ttK403Nvq3mT6qGHT0MQywn7OxLgM8QLlGwnHD6/aGErt8QErGZwFYIkzs9ZZceKRPvhcC5ZnaID50FswH4LaGxJh7mxMTxpuJMmdgkxJua/TtKrB8GDnL3mwmnmMffMK8llPifgeFz+9JkEmLur/5W755RPqs+SvgP7BOEqxH82t3/HrgdeF383Iwcu59hYvGm6f3cQhgmPDe6/dLo8Y2E2A43s+OjmNYCL3X37ZDOfUvl8aZp31bNlE++zOwdZvYyM4vne6wHZkdnivyEMMn49YQJfF8klEHrCH2P5gBdMOxU3yltjHivIUxYPMuG+qh0EMq78RlEI09tntLyFO843ssPEuZw7QP8jVDVBTiK8D6OY03Nh1meYh4j1h8TEss3u/sWd7/a3b8brbecMH8PSMd7GfIVbyLWGR6G/C8jVPK6CXO3Fkbv0dsJZ+NeEs1rOxR40syaIR2xQv7irYUpmXxZsMDMbgHOBt5GmAPSSjg19XCgNVr9UkLPn2nu/h/AX4ArgLcCH/IUzIWZYLxfAd4E7A3gYR7MZuAV0f0pf7HOPMU7wVi/SrhMzgChKeELzOx24ExCz58dVQ9gN+Qp5t34rDrdhiYkn2xhzt7RwB+rv/UTl6d4R4n1G2a2l7t3e2hofBMwm6HPo2fd/cuEljjfBd4OfMFTcJWJvMVbc7Ue9xz5QzQ+TJhE/d/R7SLhlNbvEMafbyCUPpuj5VcD50W3jWhMOg0/FcT7wcTfmFHrOBTvpMX6P4R+VRD+Ezu81nEo5kl/L38oun0A8MZax6F4JxTrVwhnbybX/TCh1dFMYHr8/Ph2Gn7yFu9U+JkyZztaaDD4OaBgZr8kXNcq7lnVb2YfAJ4ldOz+AaHatYBwcPcTOl/j4Z3QvtMLTDGTEO8d8d/yqK/ZVJaneCuMtZcwaRkPlxuZ8r2rIF8xT8J7+fZo3b8RhlqntDzFO45YPwisM7OXufvvoqd9m5CM3AjsZ2ZHe2gYOqWrtpC/eKeSKTHsaGYvI3z4ziacjvp5wiVETjKz46A0dvxZ4EseLh3yG+AdZnYPIUOf0h/YSYo3u/HmKdZYnmLOU6yQr3jHGasTkpXPJJ56KuHEr/sIVdtUdGrPW7xTjUUlw9puhNmJwFIPjfgws68TDtgu4Fx3P9bChOv5hDkiH3b3NRYm6Ta7+xO12vbdoXizG2+eYo3lKeY8xQr5ineCsV4KfMzdV5vZacBWDx3cUyNv8U41U6LyRci+f2RD1yX8E7DEQ+fcgpmdG327WgT0ufsaKE32S83BnaB4sxtvnmKN5SnmPMUK+Yp3IrEOeNQ81N2vTWkikrd4p5QpkXy5e6e79/jQ6eSnEHqIQGiWeoiZXU9o0PeXWmzjZFK82Y03T7HG8hRznmKFfMW7O7GaDbt4dKrkLd6pZspMuIfQSBFwQluB66KHdwCfJFwjbJWHniOZoHiBjMabp1hjeYo5T7FCvuKdSKw+FebtVChv8U4VU6LylTBIuFTQJuCIKOv+NDDo7n/MysGdoHizG2+eYo3lKeY8xQr5ijdPsUL+4p0SpsSE+yQLF++8Nfr5nrt/p8abtEcp3uzGm6dYY3mKOU+xQr7izVOskL94p4KpmHwtIlyc8xKPrtuXZYo3u/IUayxPMecpVshXvHmKFfIX71Qw5ZIvERERkSybanO+RERERDJNyZeIiIhIFSn5EhEREakiJV8iIiIiVaTkS0RERKSKlHyJSCaY2YCZ3WtmD5rZfWZ2XnRh4F09Z6mZvbVa2ygiAkq+RCQ7utz9KHc/lHCdutcCF47xnKWAki8RqSr1+RKRTDCzdndvTdzfH7gL2AvYD/gvoCVa/AF3v9XMbgcOAVYBVwKXAhcDLwcaga+5+7eqFoSI5IKSLxHJhJHJV/TYVuBgwoWCB92928yWAT909zYzeznwUXd/XbT+OcB8d7/IzBqBPwFnuvuqqgYjIplWrPUGiIjsQRb9rge+amZHAQPAQaOs/0rCxYXPiO7PBJYRKmMiIpNCyZeIZFI07DgAbCDM/VoPHEmY69o92tOAc939hqpspIjkkibci0jmmNk84JvAVz3MrZgJPOPug4QLCBeiVXcA0xNPvQF4n5nVR3/nIDNrQURkEqnyJSJZ0WRm9xKGGPsJE+wviZZ9HfiJmZ0J3AJ0RI/fD/Sb2X3AFcCXCWdA/sXMDNgInF6tAEQkHzThXkRERKSKNOwoIiIiUkVKvkRERESqSMmXiIiISBUp+RIRERGpIiVfIiIiIlWk5EtERESkipR8iYiIiFSRki8RERGRKvr/Z4OA0E2Xb2wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import auxiliar as af\n",
    "\n",
    "window = 252\n",
    "rolling_max = apple['adj_close'].rolling(window, min_periods=1).max()\n",
    "daily_drawdown = (apple['adj_close']/rolling_max - 1.0)\n",
    "max_daily_drawdown = daily_drawdown.rolling(window, min_periods=1).min()\n",
    "daily_drawdown.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_daily_drawdown.plot()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_drawdown.plot()\n",
    "max_daily_drawdown.plot()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>daily_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>2009-05-20</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>2009-04-29</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>2009-04-30</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>2009-05-01</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>2009-05-04</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>2009-05-05</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>2009-05-06</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>2009-05-07</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>2009-05-08</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2009-05-11</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>2009-05-12</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>2009-05-13</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>2009-05-15</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2009-05-18</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>2009-05-19</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2009-05-21</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>2009-05-22</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>2009-04-28</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>2009-04-27</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>2009-04-24</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>2009-04-23</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>2009-03-27</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>2009-03-30</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2009-03-31</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>2009-04-01</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>2009-04-02</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>2009-04-03</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>2009-04-06</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>2009-04-07</td>\n",
       "      <td>-0.597208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2006-11-30</td>\n",
       "      <td>-0.047451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2006-12-01</td>\n",
       "      <td>-0.047451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2006-12-04</td>\n",
       "      <td>-0.047451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2006-12-05</td>\n",
       "      <td>-0.047451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2006-11-03</td>\n",
       "      <td>-0.047451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2006-11-20</td>\n",
       "      <td>-0.047451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2006-11-02</td>\n",
       "      <td>-0.039056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2006-11-01</td>\n",
       "      <td>-0.036866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2006-10-31</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006-10-11</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2006-10-12</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006-10-13</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2006-10-17</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006-10-18</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2006-10-19</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2006-10-16</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006-10-23</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006-10-24</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2006-10-25</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2006-10-26</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2006-10-27</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2006-10-30</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2006-10-20</td>\n",
       "      <td>-0.028522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-10-10</td>\n",
       "      <td>-0.020828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006-10-09</td>\n",
       "      <td>-0.015389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-10-06</td>\n",
       "      <td>-0.015389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-10-05</td>\n",
       "      <td>-0.010553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-10-04</td>\n",
       "      <td>-0.010553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-10-03</td>\n",
       "      <td>-0.010553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-10-02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1071 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  daily_drawdown\n",
       "662 2009-05-20       -0.597208\n",
       "647 2009-04-29       -0.597208\n",
       "648 2009-04-30       -0.597208\n",
       "649 2009-05-01       -0.597208\n",
       "650 2009-05-04       -0.597208\n",
       "651 2009-05-05       -0.597208\n",
       "652 2009-05-06       -0.597208\n",
       "653 2009-05-07       -0.597208\n",
       "654 2009-05-08       -0.597208\n",
       "655 2009-05-11       -0.597208\n",
       "656 2009-05-12       -0.597208\n",
       "657 2009-05-13       -0.597208\n",
       "658 2009-05-14       -0.597208\n",
       "659 2009-05-15       -0.597208\n",
       "660 2009-05-18       -0.597208\n",
       "661 2009-05-19       -0.597208\n",
       "663 2009-05-21       -0.597208\n",
       "664 2009-05-22       -0.597208\n",
       "646 2009-04-28       -0.597208\n",
       "645 2009-04-27       -0.597208\n",
       "644 2009-04-24       -0.597208\n",
       "643 2009-04-23       -0.597208\n",
       "625 2009-03-27       -0.597208\n",
       "626 2009-03-30       -0.597208\n",
       "627 2009-03-31       -0.597208\n",
       "628 2009-04-01       -0.597208\n",
       "629 2009-04-02       -0.597208\n",
       "630 2009-04-03       -0.597208\n",
       "631 2009-04-06       -0.597208\n",
       "632 2009-04-07       -0.597208\n",
       "..         ...             ...\n",
       "42  2006-11-30       -0.047451\n",
       "43  2006-12-01       -0.047451\n",
       "44  2006-12-04       -0.047451\n",
       "45  2006-12-05       -0.047451\n",
       "24  2006-11-03       -0.047451\n",
       "35  2006-11-20       -0.047451\n",
       "23  2006-11-02       -0.039056\n",
       "22  2006-11-01       -0.036866\n",
       "21  2006-10-31       -0.028522\n",
       "7   2006-10-11       -0.028522\n",
       "8   2006-10-12       -0.028522\n",
       "9   2006-10-13       -0.028522\n",
       "11  2006-10-17       -0.028522\n",
       "12  2006-10-18       -0.028522\n",
       "13  2006-10-19       -0.028522\n",
       "10  2006-10-16       -0.028522\n",
       "15  2006-10-23       -0.028522\n",
       "16  2006-10-24       -0.028522\n",
       "17  2006-10-25       -0.028522\n",
       "18  2006-10-26       -0.028522\n",
       "19  2006-10-27       -0.028522\n",
       "20  2006-10-30       -0.028522\n",
       "14  2006-10-20       -0.028522\n",
       "6   2006-10-10       -0.020828\n",
       "5   2006-10-09       -0.015389\n",
       "4   2006-10-06       -0.015389\n",
       "3   2006-10-05       -0.010553\n",
       "2   2006-10-04       -0.010553\n",
       "1   2006-10-03       -0.010553\n",
       "0   2006-10-02        0.000000\n",
       "\n",
       "[1071 rows x 2 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af.s_to_df(max_daily_drawdown, 'daily_drawdown').sort_values(by = 'daily_drawdown', ascending=True)"
   ]
  }
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