top10days.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <center>inspired by this tweet https://twitter.com/MEKhoko/status/981868845794283520</center>\n",
    "\n",
    "<img src=\"fundstrat.jpg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "%matplotlib inline\n",
    "import quandl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get price data from Quandl\n",
    "data = quandl.get(\"BCHAIN/MKPRU\")\n",
    "data = data.shift(-1) # data set has daily open, we want daily close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-01-03</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-04</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-05</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-06</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-07</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "Date             \n",
       "2009-01-03    0.0\n",
       "2009-01-04    0.0\n",
       "2009-01-05    0.0\n",
       "2009-01-06    0.0\n",
       "2009-01-07    0.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-08-16</th>\n",
       "      <td>0.076900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-17</th>\n",
       "      <td>0.074000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-18</th>\n",
       "      <td>0.068800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-19</th>\n",
       "      <td>0.066700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-20</th>\n",
       "      <td>0.066899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Value\n",
       "Date                \n",
       "2010-08-16  0.076900\n",
       "2010-08-17  0.074000\n",
       "2010-08-18  0.068800\n",
       "2010-08-19  0.066700\n",
       "2010-08-20  0.066899"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See where the 0's end\n",
    "data.loc[data['Value'] > 0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the 0's\n",
    "data = data.loc['2010-08-17':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-08-17</th>\n",
       "      <td>0.074000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-18</th>\n",
       "      <td>0.068800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-19</th>\n",
       "      <td>0.066700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-20</th>\n",
       "      <td>0.066899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-21</th>\n",
       "      <td>0.066400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Value\n",
       "Date                \n",
       "2010-08-17  0.074000\n",
       "2010-08-18  0.068800\n",
       "2010-08-19  0.066700\n",
       "2010-08-20  0.066899\n",
       "2010-08-21  0.066400"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look again\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2801 entries, 2010-08-17 to 2018-04-17\n",
      "Data columns (total 1 columns):\n",
      "Value    2800 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 43.8 KB\n"
     ]
    }
   ],
   "source": [
    "# Summary, no. of rows looks correct\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1202701d0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120285e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual check of price data\n",
    "data['Value'].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123d303d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using a log scale\n",
    "fig , ax = plt.subplots(figsize=(12,10))\n",
    "y = data['Value']\n",
    "y.plot()\n",
    "plt.yscale('log')\n",
    "ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))\n",
    "#ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter('%.1f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11be32710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bdd8890>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11be18e10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11be6b450>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11c046850>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11c084690>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11c0c6d10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11c1180d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11c15bb50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be521d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Freq plot by year showing distribution of returns\n",
    "(data.pct_change() * 100).hist(bins=100, figsize = (12,10), by = lambda x: x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worst--------------------\n",
      "Date\n",
      "2010-09-15   -64.628571\n",
      "2013-04-11   -38.016700\n",
      "2013-04-10   -37.676273\n",
      "2010-11-07   -25.999852\n",
      "2010-10-11   -23.904689\n",
      "2015-01-13   -23.556672\n",
      "2011-10-17   -22.871520\n",
      "2012-08-18   -21.363762\n",
      "2010-10-24   -21.052632\n",
      "2010-11-09   -20.813539\n",
      "Name: Value, dtype: float64\n",
      "Best--------------------\n",
      "Date\n",
      "2010-09-14    173.010920\n",
      "2011-01-31     90.000000\n",
      "2010-10-23     74.295936\n",
      "2010-11-06     72.413448\n",
      "2011-06-07     67.418153\n",
      "2011-04-29     53.702222\n",
      "2013-04-12     43.159698\n",
      "2011-05-12     42.017284\n",
      "2010-11-02     40.664962\n",
      "2010-10-07     37.931034\n",
      "Name: Value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Best and worst days from entire data set\n",
    "print \"Worst\" + \"-\" * 20\n",
    "print((data['Value'].pct_change() * 100).dropna().sort_values().head(10).sort_values())\n",
    "print \"Best\" + \"-\" * 20\n",
    "print((data['Value'].pct_change() * 100).dropna().sort_values().tail(10).sort_values(ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by year, calculate yearly returns, drop years without full year of data\n",
    "data_annual = data.asfreq(\"Y\")\n",
    "data_annual['Returns'] = data_annual['Value'].pct_change() * 100\n",
    "data_annual.index = data_annual.index.year\n",
    "data_annual.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2011    1633.344889\n",
       "2012     160.788462\n",
       "2013    5407.705921\n",
       "2014     -57.731959\n",
       "2015      35.996199\n",
       "2016     123.568733\n",
       "2017    1375.780574\n",
       "Name: Returns, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at results\n",
    "data_annual['Returns'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8tM7jq4HrUkrdgKXAaaX204ClKaXPANeV+hERPYDjgApgGPCTiGi2dcOXJEmSGk5WeI6IcuCLwG2lxwH8J3BvqcsEYGTp/ojSY0rHP1/qPwKYmFJamVL6K/AaMKAuJiFJkiQ1hNyV5+uBi4Dq0uP2wLKU0prS44XA7qX7uwMLAErHl5f617Zv4BxJkiRpm7fJ8BwRw4F3Ukoz1m3eQNe0iWOfdM66r3dGREyPiOmLFy/e1PAkSZKkBpOz8jwIOCIi5gMTqdmucT3QLiKal/qUA4tK9xcCewCUju8EvLdu+wbOqZVSujWlVJlSquzQocNmT0iSJEmqL5sMzyml76SUylNKXaj5wN9jKaUTgceBUaVuo4Eppfv3lx5TOv5YSimV2o8rXY2jK9ANeKbOZiJJkiTVs+ab7rJR3wImRsQVwEzgZ6X2nwF3RMRr1Kw4HweQUnoxIiYBc4E1wFkppbVb8fqSJElSg9qs8JxSmgpMLd3/Cxu4WkZK6QPgmI2cfyVw5eYOUpIkSdoW+A2DkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmTYZniOiVUQ8ExHPR8SLEXFZqb1rRPw5IuZFxD0RsUOpvWXp8Wul413Wea7vlNpfiYhD6mtSkiRJUn3IWXleCfxnSqkP0BcYFhH7AVcD16WUugFLgdNK/U8DlqaUPgNcV+pHRPQAjgMqgGHATyKiWV1ORpIkSapPmwzPqca/Sg9blH4S8J/AvaX2CcDI0v0RpceUjn8+IqLUPjGltDKl9FfgNWBAncxCkiRJagBZe54jollEzALeAR4GXgeWpZTWlLosBHYv3d8dWABQOr4caL9u+wbOWfe1zoiI6RExffHixZs/I0mSJKmeZIXnlNLalFJfoJya1eJ9NtStdBsbObax9o++1q0ppcqUUmWHDh1yhidJkiQ1iM262kZKaRkwFdgPaBcRzUuHyoFFpfsLgT0ASsd3At5bt30D50iSJEnbvJyrbXSIiHal+62BLwAvAY8Do0rdRgNTSvfvLz2mdPyxlFIqtR9XuhpHV6Ab8ExdTUSSJEmqb8033YXdgAmlK2OUAZNSSg9ExFxgYkRcAcwEflbq/zPgjoh4jZoV5+MAUkovRsQkYC6wBjgrpbS2bqcjSZIk1Z9NhueU0myg3wba/8IGrpaRUvoAOGYjz3UlcOXmD1OSJEkqnt8wKEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSpk2G54jYIyIej4iXIuLFiPh6qf3TEfFwRMwr3e5cao+IuDEiXouI2RHRf53nGl3qPy8iRtfftCRJkqS6l7PyvAb4ZkppH2A/4KyI6AF8G3g0pdQNeLT0GOBQoFvp5wzgZqgJ28ClwEBgAHDph4FbkiRJagw2GZ5TSm+llJ4r3f8n8BKwOzACmFDqNgEYWbo/AvhFqjENaBcRuwGHAA+nlN5LKS0FHgaG1elsJEmSpHq0WXueI6IL0A/4M7BrSuktqAnYQMdSt92BBeuctrDUtrF2SZIkqVHIDs8R0Rb4NXBeSukfn9R1A23pE9o/+jpnRMT0iJi+ePHi3OFJkiRJ9S4rPEdEC2qC810ppftKzX8vbcegdPtOqX0hsMc6p5cDiz6hfT0ppVtTSpUppcoOHTpszlwkSZKkepVztY0Afga8lFL60TqH7gc+vGLGaGDKOu0nl666sR+wvLSt4yFgaETsXPqg4NBSmyRJktQoNM/oMwg4CXghImaV2r4LfB+YFBGnAX8Djikd+z1wGPAaUAV8BSCl9F5EjAWeLfW7PKX0Xp3MQpIkSWoAmwzPKaU/seH9ygCf30D/BJy1kecaD4zfnAFKkiRJ2wq/YVCSJEnKZHiWJEmSMhmeJUmSpEyGZ0mSJCmT4VmSJEnKZHiWJEmSMhmeP8Gpp55Kx44d6dmz50b7TJo0iR49elBRUcEJJ5wAwBtvvMG+++5L3759qaio4JZbbqntP2zYMPr06UNFRQVnnnkma9euBeBb3/oWvXv35uSTT67te8cdd3DDDTfU0+wkSZK0uQzPn+CUU07hwQcf3OjxefPmcdVVV/Hkk0/y4osvcv311wOw22678dRTTzFr1iz+/Oc/8/3vf59Fi2q+iXzSpEk8//zzzJkzh8WLF/OrX/2K5cuX89RTTzF79mzWrl3LCy+8wIoVK7j99tv52te+1iBzlSRJ0qYZnj/BQQcdxKc//emNHv+f//kfzjrrLHbeeWcAOnbsCMAOO+xAy5YtAVi5ciXV1dW153zqU58CYM2aNaxatYqIoKysjFWrVpFSYsWKFbRo0YJrrrmGc889lxYtWtTX9CRJkrSZDM9b4dVXX+XVV19l0KBB7LfffuutUi9YsIDevXuzxx578K1vfYtOnTrVHjvkkEPo2LEjO+64I6NGjWLHHXfk6KOPpl+/fnTt2pWddtqJZ599lhEjRhQxLUmSJG2E4XkrrFmzhnnz5jF16lTuvvtuvvrVr7Js2TIA9thjD2bPns1rr73GhAkT+Pvf/1573kMPPcRbb73FypUreeyxxwC46KKLmDVrFj/84Q8ZM2YMl19+ObfddhvHHnssV1xxRSHzkyRJ0voMz1uhvLycESNG0KJFC7p27cree+/NvHnz1uvTqVMnKioqeOKJJ9Zrb9WqFUcccQRTpkxZr33mzJkA7LXXXvziF79g0qRJzJkz52PPK0mSpIZneN5MN910EzfddBMAI0eO5PHHHwdgyZIlvPrqq+y5554sXLiQFStWALB06VKefPJJ9t57b/71r3/x1ltvATWr1r///e/p3r37es//4arz6tWra6/EUVZWRlVVVUNNUZIkSRtheP4Exx9/PPvvvz+vvPIK5eXl/OxnP+Pll1+mffv2QM3e5fbt29OjRw8+97nPcc0119C+fXteeuklBg4cSJ8+fRg8eDAXXHABvXr14v333+eII46gd+/e9OnTh44dO3LmmWfWvt7kyZP57Gc/S6dOnWjXrh37778/vXr1IiLo06dPUWWQJElSSaSUih7DRlVWVqbp06cXPYz1DB8+nPvuu48ddtih6KFIWyUui6KHsMXSpdvu+5YkqXGKiBkppcpN9WveEIPZnjzwwANFD0GSJEkFcduGJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlKlJX22jMV+qC7xclyRJUkNz5VmSJEnKZHiWJEmSMhmeJUmSxKmnnkrHjh3p2bPnBo/fcsst9OrVi759+3LAAQcwd+5cAO666y769u1b+1NWVsasWbMAWLVqFWeccQZ77bUX3bt359e//jUAP/7xj+nZsyeHHXYYq1atAuBPf/oT3/jGNxpgplvH8CxJkiROOeUUHnzwwY0eP+GEE3jhhReYNWsWF110UW3QPfHEE5k1axazZs3ijjvuoEuXLvTt2xeAK6+8ko4dO/Lqq68yd+5cBg8eDMBtt93G7Nmz6devHw899BApJcaOHcuYMWPqf6JbqUl/YFCSJEk1DjroIObPn7/R45/61Kdq77///vtEfPzCC3fffTfHH3987ePx48fz8ssvA1BWVsYuu+xSe2z16tVUVVXRokUL7rjjDg477DB23nnnOphJ/TI8S5IkKcu4ceP40Y9+xKpVq3jsscc+dvyee+5hypQpACxbtgyAMWPGMHXqVP7jP/6Dm266iV133ZULLriA/fbbj4qKCgYNGsTIkSM/cdV7W+K2DUmSJGU566yzeP3117n66qu54oor1jv25z//mTZt2tTumV6zZg0LFy5k0KBBPPfcc+y///5ccMEFAJx00knMnDmTO++8kx/96Eece+65/OEPf2DUqFGcf/75VFdXN/jcchmeJUmStFmOO+44Jk+evF7bxIkT19uy0b59e9q0acORRx4JwDHHHMNzzz233jmLFi3i2WefZcSIEVxxxRXcc889tGzZkkcffbT+J7GFDM+SJEnaoJtuuombbroJgHnz5tW2/+53v6Nbt261j6urq/nVr37FcccdV9sWERx++OFMnToVgEcffZQePXqs9/xjxoxh7NixAKxYsYKIoKysjKqqqvqa0lZzz7MkSZI4/vjjmTp1KkuWLKG8vJzLLruMl19+mUGDBgE1QfqRRx6hRYsW7LzzzkyYMKH23P/7v/+jvLycPffcc73nvPrqqznppJM477zz6NChAz//+c9rj82cOROAfv36AXDaaafRq1cv9thjDy699NL6nu4Wi5S23a94rqysTNOnT6+35/frudWUNebff3/3JalhDB8+nPvuu48ddtih6KHUu4iYkVKq3FQ/V54lSZK0QQ888EDRQ9jmuOdZkiRJymR4liRJkjIZniVJkqRMhmdJkiQpk+FZkiRJymR4liRJkjJ5qTpJkqQmoDFf3x+2nWv8u/IsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZk2GZ4jYnxEvBMRc9Zp+3REPBwR80q3O5faIyJujIjXImJ2RPRf55zRpf7zImJ0/UxHkiRJqj85K8+3A8M+0vZt4NGUUjfg0dJjgEOBbqWfM4CboSZsA5cCA4EBwKUfBm5JkiSpsdhkeE4p/R/w3keaRwATSvcnACPXaf9FqjENaBcRuwGHAA+nlN5LKS0FHubjgVySJEnapm3pnuddU0pvAZRuO5badwcWrNNvYaltY+2SJElSo1HXHxiMDbSlT2j/+BNEnBER0yNi+uLFi+t0cJIkSdLW2NLw/PfSdgxKt++U2hcCe6zTrxxY9AntH5NSujWlVJlSquzQocMWDk+SJEmqe1sanu8HPrxixmhgyjrtJ5euurEfsLy0reMhYGhE7Fz6oODQUpskSZLUaDTfVIeIuBs4GNglIhZSc9WM7wOTIuI04G/AMaXuvwcOA14DqoCvAKSU3ouIscCzpX6Xp5Tmz1JhAAASoUlEQVQ++iFESZIkaZu2yfCcUjp+I4c+v4G+CThrI88zHhi/WaOTJEmStiF+w6AkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MkSZKUyfAsSZIkZTI8S5IkSZkMz5IkSVImw7MalVNPPZWOHTvSs2fP9dp//OMfs/fee1NRUcFFF10EwLvvvsvnPvc52rZty9lnn13bd+XKlQwbNoyePXvyk5/8pLb9jDPOYObMmQ0zEUmS1CgZntWonHLKKTz44IPrtT3++ONMmTKF2bNn8+KLL3LBBRcA0KpVK8aOHcu11167Xv+HHnqIfffdl9mzZ3PrrbcC8Pzzz1NdXU2/fv0aZiKSJKlRMjyrUTnooIP49Kc/vV7bzTffzLe//W1atmwJQMeOHQH4t3/7Nw444ABatWq1Xv8WLVqwYsUK1qxZU9s2ZswYLr/88noevSRJauwMz2r0Xn31VZ544gkGDhzI4MGDefbZZz+x/5AhQ3j77bcZOHAgF110Effffz/77rsvnTp1aqARS5I2x4a27F144YV0796d3r17c+SRR7Js2bL1zvnb3/5G27Zta//6uHjxYg444AB69uzJ5MmTa/uNGDGCRYsWNcxEtF0wPKvRW7NmDUuXLmXatGlcc801HHvssaSUNtq/efPm/PKXv2TmzJkcc8wxXH/99Xzzm9/kG9/4BqNGjeL+++9vwNFLkjZlQ1v2hgwZwpw5c5g9ezZ77bUXV1111XrHzz//fA499NDax3fffTejR4/m6aef5pprrgHgt7/9Lf3793fxRJvF8KxGr7y8nKOOOoqIYMCAAZSVlbFkyZKsc3/yk5/UvpnusMMO3HPPPVxxxRX1PGJJ0ubY0Ja9oUOH0rx5cwD2228/Fi5cWHts8uTJ7LnnnlRUVNS2fbhlb+XKlZSVlbFmzRquv/56LrzwwoaZhLYbhmc1eiNHjuSxxx4DarZwrFq1il122WWT5y1dupQHHniAk08+maqqKsrKyogIPvjgg/oesiSpDo0fP752lfn999/n6quv5tJLL12vzwknnMBDDz3EsGHD+K//+i9+8pOfcPLJJ9OmTZsihqxGrHnRA5A2x/HHH8/UqVNZsmQJ5eXlXHbZZZx66qmceuqp9OzZkx122IEJEyYQEQB06dKFf/zjH6xatYrJkyfzxz/+kR49egBw+eWX873vfY+I4JBDDmHcuHH06tWLM888s8gpSpI2w5VXXknz5s058cQTAbj00ks5//zzadu27Xr9dtppJ373u98BNYsnV199Nffddx+nn346S5cu5Zvf/Cb7779/g49fjU980t7QolVWVqbp06fX2/PHZVFvz90Q0qXb7r+dtn2N+fff332p6Zk/fz7Dhw9nzpw5tW0TJkzglltu4dFHH61dQT7wwANZsGABAMuWLaOsrIzLL798vev9n3/++YwcOZJXX32VtWvXcsIJJzBixAgef/zxhp1UA2vM7/tQ/+/9ETEjpVS5qX5u25AkaTN88MEHDBgwgD59+lBRUVG7PSClxMUXX8xee+3FPvvsw4033gjAr3/9ayoqKjjwwAN59913AXj99dc57rjjCpvD9uDBBx/k6quv5v77719v68UTTzzB/PnzmT9/Pueddx7f/e531wvO8+bNY9GiRQwePNgte9oibtuQJGkztGzZkscee4y2bduyevVqDjjgAA499FBeeuklFixYwMsvv0xZWRnvvPMOAD/84Q+ZNm0aEydO5Je//CXnnHMO3/ve9xg7dmzBM2k8NrRl76qrrmLlypUMGTIEqPnQ4C233LLJ57r44ou58sora5935MiR3HDDDV7rX9kMz5IkbYaIqN1Pu3r1alavXk1EcPPNN/PLX/6SsrKaP+p++IVNZWVlrFy5kqqqKlq2bMkTTzzBbrvtRrdu3QqbQ2Nz9913f6zttNNO2+R5//Vf//WxtkmTJtXe79ixI0899dRWjU1Nj9s2JEnaTGvXrqVv37507NiRIUOGMHDgQF5//XXuueceKisrOfTQQ5k3bx5Q8wG2Qw45hEceeYTjjz+eK664gjFjxhQ8A0lbyvAsSdJmatasGbNmzWLhwoU888wzzJkzh5UrV9KqVSumT5/O6aefzqmnngrUfJnHjBkz+O1vf8vkyZM57LDDeOWVVxg1ahSnn346VVVVBc9G0uYwPEuStIXatWvHwQcfzIMPPkh5eTlHH300AEceeSSzZ89er29VVRUTJkzga1/7Gt/5zncYP348++67L3fddVcRQ5e0hQzPkiRthsWLF7Ns2TIAVqxYwSOPPEL37t3X+8Km//3f/2WvvfZa77wf/OAHfP3rX6/9pruIoKyszJVnqZHxA4MqjNeblLbM1KlTGTFiBF27dgXgqKOO4pJLLgHguuuu47bbbiMi6NWrFz//+c9p1aoVJ554Ii+88ALDhw/nv//7vwEYO3YsvXv3ZsSIEYXNpTF66623GD16NGvXrqW6uppjjz2W4cOHc8ABB3DiiSdy3XXX0bZtW2677bbacxYtWsT06dNrP8D2zW9+k/3224927doxefLkgmYiaUsYniWpETrwwAN54IEH1mt78803ufHGG5k7dy6tW7fm2GOPZeLEifTv3x+A2bNnc+CBB7J8+XKqqqp45pln/ODaFujduzczZ878WHu7du1qv8Huozp16rTev9cxxxzDMcccU29jlFR/3LYhSduRNWvWsGLFCtasWUNVVRWdOnWq3SZQXV3NqlWraNasGZdcconXtZWkLeDKsyQ1Qk8//TR9+vShU6dOXHvttVRUVLD77rtzwQUX0LlzZ1q3bs3QoUMZOnQoAJ07d6Z///6cdNJJvPbaa6SU6NevX8GzUFPUmLfsuV1PYHiWpEanf//+vPHGG7Rt25bf//73jBw5knnz5rF06VKmTJnCX//6V9q1a8cxxxzDnXfeyZe//GWuv/762vMPP/xwfvrTn3LllVfy/PPPM2TIEE4//fQCZyRJjYfbNiSpERg3bhx9+/alb9++/Otf/6r9hrvDDjuM1atXs2TJEh555BG6du1Khw4daNGiBUcdddTHvj1typQpVFZW8v777zNnzhwmTZrEHXfc4RUfJCmT4VmSGoGzzjqLWbNmMWvWLMrKykip5s/HzzzzDNXV1bRv357OnTszbdo0qqqqSCnx6KOPss8++9Q+x+rVq7nhhhu48MILqaqqIqLmz+cf7oWWJG2a2zYkqZG59957ufnmm2nevDmtW7dm4sSJRAQDBw5k1KhR9O/fn+bNm9OvXz/OOOOM2vPGjRvH6NGjadOmDb179yalRK9evTjssMNo165dgTOSpMYjPly9aLAXjBgG3AA0A25LKX1/Y30rKyvT9OnT628sjfhDC9D4P7hg/YvVmOvf2GsvNWW+9xSnMdce6r/+ETEjpVS5qX4NuvIcEc2AccAQYCHwbETcn1Ka25DjkCQ1XQYISVujofc8DwBeSyn9JaW0CpgI+NVWkiRJahQadNtGRIwChqWUvlp6fBIwMKV09jp9zgDOAOjcufO+b7zxRoONT5IaRDTulU8aeLufJDWE3G0bDb3yvKH/Yqz3LpxSujWlVJlSquzQoUMDDUuSJEnatIYOzwuBPdZ5XA4sauAxSJIkSVukocPzs0C3iOgaETsAxwH3N/AYJEmSpC3SoFfbSCmtiYizgYeouVTd+JTSiw05BkmSJGlLNfiXpKSUfg/8vqFfV5K2GX7gTpIaLb+eW5IkScpkeJYkSZIyGZ4lSZKkTIZnSZIkKZPhWZIkScpkeJYkSZIyGZ4lSZKkTIZnSZIkKZPhWZIkScpkeJYkSZIyGZ4lSZKkTIZnSZIkKZPhWZIkScpkeJYkSZIyGZ4lSZKkTIZnSZIkKZPhWZIkScpkeJYkSZIyRUqp6DFsVEQsBt4oehxbYRdgSdGDaMKsf3GsfbGsf7Gsf3GsfbEae/3/PaXUYVOdtunw3NhFxPSUUmXR42iqrH9xrH2xrH+xrH9xrH2xmkr93bYhSZIkZTI8S5IkSZkMz/Xr1qIH0MRZ/+JY+2JZ/2JZ/+JY+2I1ifq751mSJEnK5MqzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsNzA4iIrxQ9hqYgIrpHxOcjou1H2ocVNaamIiIGRMRnS/d7RMQ3IuKwosfVVEXEL4oeQ1MVEQeUfv+HFj2W7V1EDIyIT5Xut46IyyLitxFxdUTsVPT4tncRcW5E7FH0OIrg1TYaQET8LaXUuehxbM8i4lzgLOAloC/w9ZTSlNKx51JK/Ysc3/YsIi4FDgWaAw8DA4GpwBeAh1JKVxY3uu1fRNz/0Sbgc8BjACmlIxp8UE1IRDyTUhpQun86Ne9DvwGGAr9NKX2/yPFtzyLiRaBPSmlNRNwKVAH3Ap8vtR9V6AC3cxGxHHgfeB24G/hVSmlxsaNqGIbnOhIRszd2CNgrpdSyIcfT1ETEC8D+KaV/RUQXat5A70gp3RARM1NK/Qod4HasVPu+QEvgbaA8pfSPiGgN/Dml1LvQAW7nIuI5YC5wG5Coec+5GzgOIKX0v8WNbvu37vtLRDwLHJZSWhwR/wZMSyn1KnaE26+IeCmltE/p/nqLJBExK6XUt7jRbf8iYiawLzULJV8CjgBmUPP+c19K6Z8FDq9eNS96ANuRXYFDgKUfaQ/gqYYfTpPTLKX0L4CU0vyIOBi4NyL+nZp/A9WfNSmltUBVRLyeUvoHQEppRURUFzy2pqAS+DpwMXBhSmlWRKwwNDeYsojYmZptkPHhyltK6f2IWFPs0LZ7cyLiKymlnwPPR0RlSml6ROwFrC56cE1ASilVA38E/hgRLaj5K+TxwLVAhyIHV58Mz3XnAaBtSmnWRw9ExNSGH06T83ZE9P2w/qUV6OHAeMCVn/q1KiLapJSqqFmFAKC059DwXM9K//G6LiJ+Vbr9O763N6SdqFltCyBFxP+XUnq79NkL/8e9fn0VuCEivgcsAZ6OiAXAgtIx1a/1fr9TSquB+4H7S3953G65bUPbhYgop2YF9O0NHBuUUnqygGE1CRHRMqW0cgPtuwC7pZReKGBYTVZEfBEYlFL6btFjacoiog2wa0rpr0WPZXsXETsCe1LzP40LU0p/L3hITUJE7JVSerXocRTB8NwAIqLth1sK1PCsf3GsfbGsf7Gsf3GsfbG29/p7qbqGMbfoATRx1r841r5Y1r9Y1r841r5Y23X93RdXRyLiGxs7BLTdyDHVEetfHGtfLOtfLOtfHGtfrKZcf1ee685/AzsDO37kpy3WuSFY/+JY+2JZ/2JZ/+JY+2I12fq78lx3ngMmp5RmfPRARPip3/pn/Ytj7Ytl/Ytl/Ytj7YvVZOvvBwbrSETsDby3oW/XiYhd/fRv/bL+xbH2xbL+xbL+xbH2xWrK9Tc8S5IkSZm26z0pDSkidoqI70fEyxHxbunnpVJbu6LHt72z/sWx9sWy/sWy/sWx9sVqyvU3PNedSdR8NffBKaX2KaX2wOdKbb8qdGRNg/UvjrUvlvUvlvUvjrUvVpOtv9s26khEvJJS2ntzj6luWP/iWPtiWf9iWf/iWPtiNeX6u/Jcd96IiIsiYtcPGyJi14j4FrCgwHE1Fda/ONa+WNa/WNa/ONa+WE22/obnuvMloD3wvxGxNCLeA6YCnwaOLXJgTYT1L461L5b1L5b1L461L1aTrb/bNupQRHQHyoFp636ne0QMSyk9WNzImgbrXxxrXyzrXyzrXxxrX6ymWn9XnutIRJwLTAHOBuZExIh1Dv93MaNqOqx/cax9sax/sax/cax9sZpy/f2GwbpzOrBvSulfEdEFuDciuqSUbqDme95Vv6x/cax9sax/sax/cax9sZps/Q3PdafZh3+ySCnNj4iDqflF+ne281+ibYT1L461L5b1L5b1L461L1aTrb/bNurO2xHR98MHpV+o4cAuQK/CRtV0WP/iWPtiWf9iWf/iWPtiNdn6+4HBOhIR5cCalNLbGzg2KKX0ZAHDajKsf3GsfbGsf7Gsf3GsfbGacv0Nz5IkSVImt21IkiRJmQzPkiRJUibDsyRJkpTJ8CxJkiRlMjxLkiRJmf5/Ai+rtdIHUeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2a3690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Chart the results\n",
    "colors = np.sign(data_annual['Returns']).map({1 : 'g', -1 : 'r'}).values\n",
    "data_annual.index.name = '' # Don't want to show 'Date' as a label on chart\n",
    "ax = data_annual['Returns'].plot(kind='bar', figsize=(12,8),color = colors)\n",
    "ax.legend([\"Fully invested\"]);\n",
    "for p in ax.patches:\n",
    "    b = p.get_bbox()\n",
    "    val = \"{:,.0f}%\".format(b.y1 + b.y0)        \n",
    "    ax.annotate(val,((b.x0 + b.x1) / 2 + len(val) * -0.03, b.y1 + 110))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Back to daily data, calculate returns from prices and then group by year\n",
    "data['Returns'] = data['Value'].pct_change()\n",
    "data.dropna(inplace = True)\n",
    "returns_by_year = data.groupby(lambda x: x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010 ------------------------------\n",
      "Full year return 305%\n",
      "top 10 days return 4,403%\n",
      "Return if miss 10 top days -91%\n",
      "10 best days\n",
      "Date\n",
      "2010-09-14    173.010920\n",
      "2010-10-23     74.295936\n",
      "2010-11-06     72.413448\n",
      "2010-11-02     40.664962\n",
      "2010-10-07     37.931034\n",
      "2010-10-08     36.375000\n",
      "2010-10-25     26.666667\n",
      "2010-11-05     20.883702\n",
      "2010-11-12     19.521912\n",
      "2010-10-14     13.333333\n",
      "Name: Returns, dtype: float64\n",
      "2011 ------------------------------\n",
      "Full year return 1,633%\n",
      "top 10 days return 3,489%\n",
      "Return if miss 10 top days -52%\n",
      "10 best days\n",
      "Date\n",
      "2011-01-31    90.000000\n",
      "2011-06-07    67.418153\n",
      "2011-04-29    53.702222\n",
      "2011-05-12    42.017284\n",
      "2011-08-08    36.107987\n",
      "2011-04-27    35.897436\n",
      "2011-06-02    35.288553\n",
      "2011-05-09    32.080264\n",
      "2011-04-23    25.887669\n",
      "2011-05-25    24.234354\n",
      "Name: Returns, dtype: float64\n",
      "2012 ------------------------------\n",
      "Full year return 161%\n",
      "top 10 days return 203%\n",
      "Return if miss 10 top days -14%\n",
      "10 best days\n",
      "Date\n",
      "2012-07-16    14.476652\n",
      "2012-08-26    14.416059\n",
      "2012-01-04    14.139450\n",
      "2012-08-02    13.457143\n",
      "2012-01-05    12.830130\n",
      "2012-08-16    11.262110\n",
      "2012-02-01     9.968074\n",
      "2012-08-15     9.243804\n",
      "2012-03-12     9.091276\n",
      "2012-02-22     8.475991\n",
      "Name: Returns, dtype: float64\n",
      "2013 ------------------------------\n",
      "Full year return 5,408%\n",
      "top 10 days return 798%\n",
      "Return if miss 10 top days 514%\n",
      "10 best days\n",
      "Date\n",
      "2013-04-12    43.159698\n",
      "2013-04-08    29.342391\n",
      "2013-04-02    27.646464\n",
      "2013-11-17    25.952484\n",
      "2013-04-18    23.958549\n",
      "2013-03-18    21.217209\n",
      "2013-12-08    20.471929\n",
      "2013-05-03    20.225217\n",
      "2013-03-05    18.631369\n",
      "2013-11-20    16.798319\n",
      "Name: Returns, dtype: float64\n",
      "2014 ------------------------------\n",
      "Full year return -58%\n",
      "top 10 days return 205%\n",
      "Return if miss 10 top days -86%\n",
      "10 best days\n",
      "Date\n",
      "2014-03-02    19.043366\n",
      "2014-11-11    17.814486\n",
      "2014-10-05    12.779651\n",
      "2014-04-13    11.886420\n",
      "2014-02-25    10.867424\n",
      "2014-05-24    10.254640\n",
      "2014-10-08     9.437995\n",
      "2014-01-04     8.952066\n",
      "2014-05-31     8.788782\n",
      "2014-05-19     8.644326\n",
      "Name: Returns, dtype: float64\n",
      "2015 ------------------------------\n",
      "Full year return 36%\n",
      "top 10 days return 175%\n",
      "Return if miss 10 top days -51%\n",
      "10 best days\n",
      "Date\n",
      "2015-01-14    23.575071\n",
      "2015-11-03    20.050486\n",
      "2015-01-25     9.857958\n",
      "2015-11-02     9.370787\n",
      "2015-01-21     8.689591\n",
      "2015-02-12     8.163909\n",
      "2015-01-17     7.132711\n",
      "2015-11-26     7.076923\n",
      "2015-11-12     7.002259\n",
      "2015-02-26     6.947368\n",
      "Name: Returns, dtype: float64\n",
      "2016 ------------------------------\n",
      "Full year return 124%\n",
      "top 10 days return 113%\n",
      "Return if miss 10 top days 5%\n",
      "10 best days\n",
      "Date\n",
      "2016-06-12    11.844175\n",
      "2016-05-28    11.086776\n",
      "2016-06-16    10.133183\n",
      "2016-08-03     9.705665\n",
      "2016-01-20     7.835525\n",
      "2016-06-23     6.313729\n",
      "2016-06-24     5.773850\n",
      "2016-06-13     5.598285\n",
      "2016-01-07     5.330238\n",
      "2016-06-30     5.243358\n",
      "Name: Returns, dtype: float64\n",
      "2017 ------------------------------\n",
      "Full year return 1,376%\n",
      "top 10 days return 313%\n",
      "Return if miss 10 top days 258%\n",
      "10 best days\n",
      "Date\n",
      "2017-07-20    27.968576\n",
      "2017-12-07    21.866893\n",
      "2017-11-13    14.588558\n",
      "2017-12-06    13.996346\n",
      "2017-09-15    13.695360\n",
      "2017-12-26    13.315506\n",
      "2017-12-11    12.725867\n",
      "2017-07-17    12.707507\n",
      "2017-08-05    11.979185\n",
      "2017-10-12    10.491678\n",
      "Name: Returns, dtype: float64\n",
      "2018 ------------------------------\n",
      "Full year return -51%\n",
      "top 10 days return 147%\n",
      "Return if miss 10 top days -80%\n",
      "10 best days\n",
      "Date\n",
      "2018-01-20    13.380270\n",
      "2018-01-05    12.992426\n",
      "2018-02-06    12.382503\n",
      "2018-03-11    11.609815\n",
      "2018-01-02     8.642151\n",
      "2018-02-14     8.570432\n",
      "2018-02-17     7.058542\n",
      "2018-02-15     6.883191\n",
      "2018-03-20     6.834436\n",
      "2018-02-26     6.724114\n",
      "Name: Returns, dtype: float64\n",
      "['2012-02-22', '2012-03-12', '2012-08-15', '2012-02-01', '2012-08-16', '2012-01-05', '2012-08-02', '2012-01-04', '2012-08-26', '2012-07-16', '2013-11-20', '2013-03-05', '2013-05-03', '2013-12-08', '2013-03-18', '2013-04-18', '2013-11-17', '2013-04-02', '2013-04-08', '2013-04-12', '2014-05-19', '2014-05-31', '2014-01-04', '2014-10-08', '2014-05-24', '2014-02-25', '2014-04-13', '2014-10-05', '2014-11-11', '2014-03-02', '2015-02-26', '2015-11-12', '2015-11-26', '2015-01-17', '2015-02-12', '2015-01-21', '2015-11-02', '2015-01-25', '2015-11-03', '2015-01-14', '2016-06-30', '2016-01-07', '2016-06-13', '2016-06-24', '2016-06-23', '2016-01-20', '2016-08-03', '2016-06-16', '2016-05-28', '2016-06-12', '2017-10-12', '2017-08-05', '2017-07-17', '2017-12-11', '2017-12-26', '2017-09-15', '2017-12-06', '2017-11-13', '2017-12-07', '2017-07-20', '2018-02-26', '2018-03-20', '2018-02-15', '2018-02-17', '2018-02-14', '2018-01-02', '2018-03-11', '2018-02-06', '2018-01-05', '2018-01-20']\n"
     ]
    }
   ],
   "source": [
    "# Go through each year, find the top 10 days, then calculate returns with and without\n",
    "\n",
    "dates = []\n",
    "\n",
    "data_annual['MissTop10'] = 0.0\n",
    "data_annual['MissTop5'] = 0.0\n",
    "\n",
    "for year, frame in returns_by_year:\n",
    "    \n",
    "    full_return = (frame['Returns'] + 1).cumprod()[-1] - 1\n",
    "    top10 = frame['Returns'].sort_values()[-10:]\n",
    "    top5 = top10[-5:]\n",
    "    top10_return = (top10 + 1).cumprod()[-1] - 1\n",
    "    top5_return = (top5 + 1).cumprod()[-1] - 1\n",
    "    full_less_top10_return = ((1 + full_return) / (1 + top10_return)) - 1\n",
    "    full_less_top5_return = ((1 + full_return) / (1 + top5_return)) - 1\n",
    "    \n",
    "    data_annual.loc[year, 'MissTop10'] = full_less_top10_return * 100\n",
    "    data_annual.loc[year, 'MissTop5'] = full_less_top5_return * 100\n",
    "    \n",
    "    print year, '-' * 30 \n",
    "    # print top10.index\n",
    "    if year >= 2012:\n",
    "        dates += list(top10.index.astype(str))\n",
    "    print \"Full year return {:,.0f}%\".format(full_return * 100 )\n",
    "    print \"top 10 days return {:,.0f}%\".format(top10_return * 100)\n",
    "    print \"Return if miss 10 top days {:,.0f}%\".format(full_less_top10_return * 100)\n",
    "    #if year in [2017]:\n",
    "    print \"10 best days\"\n",
    "    print top10[::-1] * 100\n",
    "\n",
    "print(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Value</th>\n",
       "      <th>Returns</th>\n",
       "      <th>MissTop10</th>\n",
       "      <th>MissTop5</th>\n",
       "    </tr>\n",
       "    <tr>\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>2011</th>\n",
       "      <td>5.200</td>\n",
       "      <td>1633.344889</td>\n",
       "      <td>-51.706494</td>\n",
       "      <td>83.410366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>13.561</td>\n",
       "      <td>160.788462</td>\n",
       "      <td>-13.849582</td>\n",
       "      <td>36.267335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>746.900</td>\n",
       "      <td>5407.705921</td>\n",
       "      <td>513.529183</td>\n",
       "      <td>1392.509398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>315.700</td>\n",
       "      <td>-57.731959</td>\n",
       "      <td>-86.135490</td>\n",
       "      <td>-78.457498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>429.340</td>\n",
       "      <td>35.996199</td>\n",
       "      <td>-50.563494</td>\n",
       "      <td>-29.804194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>959.870</td>\n",
       "      <td>123.568733</td>\n",
       "      <td>4.918951</td>\n",
       "      <td>38.110350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>14165.575</td>\n",
       "      <td>1375.780574</td>\n",
       "      <td>257.705112</td>\n",
       "      <td>537.173617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Value      Returns   MissTop10     MissTop5\n",
       "                                                     \n",
       "2011      5.200  1633.344889  -51.706494    83.410366\n",
       "2012     13.561   160.788462  -13.849582    36.267335\n",
       "2013    746.900  5407.705921  513.529183  1392.509398\n",
       "2014    315.700   -57.731959  -86.135490   -78.457498\n",
       "2015    429.340    35.996199  -50.563494   -29.804194\n",
       "2016    959.870   123.568733    4.918951    38.110350\n",
       "2017  14165.575  1375.780574  257.705112   537.173617"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the results\n",
    "data_annual.dropna(inplace = True)\n",
    "data_annual.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be79850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Chart the results\n",
    "ax = data_annual[['Returns','MissTop10']].plot(kind='bar', figsize=(12,8), width = 0.8)\n",
    "ax.legend([\"Fully invested\", \"Miss Top 10\"]);\n",
    "for p in ax.patches:\n",
    "    b = p.get_bbox()\n",
    "    val = \"{:,.0f}%\".format(b.y1 + b.y0)        \n",
    "    ax.annotate(val,((b.x0 + b.x1) / 2 + len(val) * -0.03, b.y1 + 110))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total returns\n",
      "-------------------------\n",
      "Fully invested since start 2011 4,721,790%\n",
      "Missed top 10 each year -34%\n",
      "Missed top 5 each year 4,864%\n",
      "\n",
      "Compound annual returns\n",
      "-------------------------\n",
      "Fully invested since start 2011 365.3%\n",
      "Missed top 10 each year -5.8%\n",
      "Missed top 5 each year 74.7%\n"
     ]
    }
   ],
   "source": [
    "# Calculate total returns for period and compound annual returns\n",
    "print \"Total returns\"\n",
    "print \"-\" * 25\n",
    "print \"Fully invested since start 2011 {:,.0f}%\".format((((data_annual['Returns'] / 100 + 1).cumprod().iloc[-1])- 1) * 100)\n",
    "print \"Missed top 10 each year {:,.0f}%\".format((((data_annual['MissTop10'] / 100 + 1).cumprod().iloc[-1]) - 1) * 100)\n",
    "print \"Missed top 5 each year {:,.0f}%\".format((((data_annual['MissTop5'] / 100 + 1).cumprod().iloc[-1])- 1) * 100)\n",
    "print\n",
    "print \"Compound annual returns\"\n",
    "print \"-\" * 25\n",
    "print \"Fully invested since start 2011 {:,.1f}%\".format((((data_annual['Returns'] / 100 + 1).cumprod().iloc[-1]) ** (1.0/len(data_annual)) - 1) * 100)\n",
    "print \"Missed top 10 each year {:,.1f}%\".format((((data_annual['MissTop10'] / 100 + 1).cumprod().iloc[-1]) ** (1.0/len(data_annual)) - 1) * 100)\n",
    "print \"Missed top 5 each year {:,.1f}%\".format((((data_annual['MissTop5'] / 100 + 1).cumprod().iloc[-1]) ** (1.0/len(data_annual)) - 1) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.561 746.9\n",
      "5407.7059213922275\n",
      "5407.705921392224\n",
      "Date\n",
      "2012-12-25    13.39890\n",
      "2012-12-26    13.47000\n",
      "2012-12-27    13.64990\n",
      "2012-12-28    13.67000\n",
      "2012-12-29    13.56998\n",
      "2012-12-30    13.59000\n",
      "2012-12-31    13.56100\n",
      "2013-01-01    13.40000\n",
      "2013-01-02    13.46400\n",
      "2013-01-03    13.48986\n",
      "2013-01-04    13.54800\n",
      "2013-01-05    13.52999\n",
      "Name: Value, dtype: float64\n",
      "Date\n",
      "2013-12-25    734.42\n",
      "2013-12-26    715.51\n",
      "2013-12-27    701.61\n",
      "2013-12-28    714.79\n",
      "2013-12-29    739.10\n",
      "2013-12-30    731.00\n",
      "2013-12-31    746.90\n",
      "2014-01-01    758.01\n",
      "2014-01-02    806.21\n",
      "2014-01-03    822.38\n",
      "2014-01-04    896.00\n",
      "2014-01-05    934.21\n",
      "Name: Value, dtype: float64\n",
      "0.299998 14165.575\n",
      "4721789.812598752\n",
      "4721789.81259875\n"
     ]
    }
   ],
   "source": [
    "# sanity check on 2013 returns\n",
    "start, end = data.loc['2012-12-31','Value'], data.loc['2013-12-31','Value']\n",
    "print start, end\n",
    "print (end / start - 1) * 100\n",
    "print ((data.loc['2013-1-1':'2013-12-31','Returns'] + 1).cumprod()[-1] - 1) * 100\n",
    "\n",
    "# check prices a few days before and after\n",
    "print data.loc['2012-12-25':'2013-01-5','Value']\n",
    "print data.loc['2013-12-25':'2014-01-5','Value']\n",
    "\n",
    "# sanity check on total returns\n",
    "start, end = data.loc['2010-12-31','Value'], data.loc['2017-12-31','Value']\n",
    "print start, end\n",
    "print (end / start - 1) * 100\n",
    "print ((data.loc['2011-1-1':'2017-12-31','Returns'] + 1).cumprod()[-1] - 1) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = returns_by_year.describe().drop(columns=['Value'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.columns = [u'count', u'mean', u'std', u'min', u'25%', u'50%', u'75%', u'max']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>skew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>136.0</td>\n",
       "      <td>0.024930</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>-0.646286</td>\n",
       "      <td>-0.016380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019319</td>\n",
       "      <td>1.730109</td>\n",
       "      <td>0.371343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>365.0</td>\n",
       "      <td>0.012189</td>\n",
       "      <td>0.102492</td>\n",
       "      <td>-0.228715</td>\n",
       "      <td>-0.031291</td>\n",
       "      <td>-0.007201</td>\n",
       "      <td>0.027983</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.567564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>366.0</td>\n",
       "      <td>0.003167</td>\n",
       "      <td>0.033017</td>\n",
       "      <td>-0.213638</td>\n",
       "      <td>-0.009172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012097</td>\n",
       "      <td>0.144767</td>\n",
       "      <td>0.287733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>365.0</td>\n",
       "      <td>0.013870</td>\n",
       "      <td>0.074678</td>\n",
       "      <td>-0.380167</td>\n",
       "      <td>-0.011880</td>\n",
       "      <td>0.009049</td>\n",
       "      <td>0.038661</td>\n",
       "      <td>0.431597</td>\n",
       "      <td>0.193670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>365.0</td>\n",
       "      <td>-0.001677</td>\n",
       "      <td>0.037173</td>\n",
       "      <td>-0.130463</td>\n",
       "      <td>-0.019533</td>\n",
       "      <td>-0.001610</td>\n",
       "      <td>0.013322</td>\n",
       "      <td>0.190434</td>\n",
       "      <td>-0.005387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>365.0</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.035803</td>\n",
       "      <td>-0.235567</td>\n",
       "      <td>-0.009617</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.013704</td>\n",
       "      <td>0.235751</td>\n",
       "      <td>0.037055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>366.0</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.024943</td>\n",
       "      <td>-0.150515</td>\n",
       "      <td>-0.004369</td>\n",
       "      <td>0.001934</td>\n",
       "      <td>0.009971</td>\n",
       "      <td>0.118442</td>\n",
       "      <td>0.069694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>365.0</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.048811</td>\n",
       "      <td>-0.161979</td>\n",
       "      <td>-0.010423</td>\n",
       "      <td>0.008752</td>\n",
       "      <td>0.032550</td>\n",
       "      <td>0.279686</td>\n",
       "      <td>-0.011018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>101.0</td>\n",
       "      <td>-0.005212</td>\n",
       "      <td>0.060226</td>\n",
       "      <td>-0.202052</td>\n",
       "      <td>-0.039201</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>0.025344</td>\n",
       "      <td>0.133803</td>\n",
       "      <td>-0.416999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count      mean       std       min       25%       50%       75%  \\\n",
       "2010  136.0  0.024930  0.201400 -0.646286 -0.016380  0.000000  0.019319   \n",
       "2011  365.0  0.012189  0.102492 -0.228715 -0.031291 -0.007201  0.027983   \n",
       "2012  366.0  0.003167  0.033017 -0.213638 -0.009172  0.000000  0.012097   \n",
       "2013  365.0  0.013870  0.074678 -0.380167 -0.011880  0.009049  0.038661   \n",
       "2014  365.0 -0.001677  0.037173 -0.130463 -0.019533 -0.001610  0.013322   \n",
       "2015  365.0  0.001485  0.035803 -0.235567 -0.009617  0.001043  0.013704   \n",
       "2016  366.0  0.002513  0.024943 -0.150515 -0.004369  0.001934  0.009971   \n",
       "2017  365.0  0.008573  0.048811 -0.161979 -0.010423  0.008752  0.032550   \n",
       "2018  101.0 -0.005212  0.060226 -0.202052 -0.039201  0.003159  0.025344   \n",
       "\n",
       "           max      skew  \n",
       "2010  1.730109  0.371343  \n",
       "2011  0.900000  0.567564  \n",
       "2012  0.144767  0.287733  \n",
       "2013  0.431597  0.193670  \n",
       "2014  0.190434 -0.005387  \n",
       "2015  0.235751  0.037055  \n",
       "2016  0.118442  0.069694  \n",
       "2017  0.279686 -0.011018  \n",
       "2018  0.133803 -0.416999  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['skew'] = 3 * (stats['mean'] - stats['50%']) / stats['std'] # Pearson Median Skewness\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2010, 5.004115483320167),\n",
       " (2011, 3.5927591004080996),\n",
       " (2012, 0.10892790283057956),\n",
       " (2013, -0.013816177281385686),\n",
       " (2014, 0.8033188638876836),\n",
       " (2015, 0.13548413153021976),\n",
       " (2016, -0.3676355846793451),\n",
       " (2017, 0.4004076979139101),\n",
       " (2018, -0.41592180494151687)]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# skew by year (3rd standardised moment)\n",
    "[(year, frame['Returns'].skew()) for year, frame in returns_by_year]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-06</th>\n",
       "      <td>13540.980000</td>\n",
       "      <td>0.139963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-07</th>\n",
       "      <td>16501.971667</td>\n",
       "      <td>0.218669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08</th>\n",
       "      <td>16007.436667</td>\n",
       "      <td>-0.029968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-09</th>\n",
       "      <td>15142.834152</td>\n",
       "      <td>-0.054013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>14869.805000</td>\n",
       "      <td>-0.018030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>16762.116667</td>\n",
       "      <td>0.127259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>17276.393333</td>\n",
       "      <td>0.030681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>16808.366667</td>\n",
       "      <td>-0.027091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14</th>\n",
       "      <td>16678.892000</td>\n",
       "      <td>-0.007703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <td>17771.900000</td>\n",
       "      <td>0.065532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Value   Returns\n",
       "Date                              \n",
       "2017-12-06  13540.980000  0.139963\n",
       "2017-12-07  16501.971667  0.218669\n",
       "2017-12-08  16007.436667 -0.029968\n",
       "2017-12-09  15142.834152 -0.054013\n",
       "2017-12-10  14869.805000 -0.018030\n",
       "2017-12-11  16762.116667  0.127259\n",
       "2017-12-12  17276.393333  0.030681\n",
       "2017-12-13  16808.366667 -0.027091\n",
       "2017-12-14  16678.892000 -0.007703\n",
       "2017-12-15  17771.900000  0.065532"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['2017-12-06':].head(10)"
   ]
  }
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