data_wrangling/pandas/never_on_a_friday.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Never on a Friday or Turning Tuesday\n",
    "People in financial world especially traders talk about \"Turning Tuesdays\". The [idea](https://www.advisorperspectives.com/commentaries/2013/06/07/never-on-a-friday) is basically if the markets is down for several days in a row, it will never turn around on Friday because traders don't want to stay in a position over the weekend. Their pessism looms over the weekend. Likewise, on a Monday, traders have been sitting around depressed for the whole weekend (because the market is down) so Monday will stay down. It's on Tuesday that more rational heads prevail, so a down market will more likely \"turn around\" on a Tuesday. Hence **Turning Tuesday**.\n",
    "\n",
    "### Let's see if data says the same.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will analyze sp-500 data to see if these facts line up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Adj Close</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>2016-01-11</th>\n",
       "      <td>1926.119995</td>\n",
       "      <td>1935.650024</td>\n",
       "      <td>1901.099976</td>\n",
       "      <td>1923.670044</td>\n",
       "      <td>4607290000</td>\n",
       "      <td>1923.670044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1985.319946</td>\n",
       "      <td>1985.319946</td>\n",
       "      <td>1918.459961</td>\n",
       "      <td>1922.030029</td>\n",
       "      <td>4664940000</td>\n",
       "      <td>1922.030029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1985.319946</td>\n",
       "      <td>1985.319946</td>\n",
       "      <td>1938.829956</td>\n",
       "      <td>1943.089966</td>\n",
       "      <td>5076590000</td>\n",
       "      <td>1943.089966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>2011.709961</td>\n",
       "      <td>2011.709961</td>\n",
       "      <td>1979.050049</td>\n",
       "      <td>1990.260010</td>\n",
       "      <td>4336660000</td>\n",
       "      <td>1990.260010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>2013.780029</td>\n",
       "      <td>2021.939941</td>\n",
       "      <td>2004.170044</td>\n",
       "      <td>2016.709961</td>\n",
       "      <td>3706620000</td>\n",
       "      <td>2016.709961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close      Volume  \\\n",
       "Date                                                                         \n",
       "2016-01-11  1926.119995  1935.650024  1901.099976  1923.670044  4607290000   \n",
       "2016-01-08  1985.319946  1985.319946  1918.459961  1922.030029  4664940000   \n",
       "2016-01-07  1985.319946  1985.319946  1938.829956  1943.089966  5076590000   \n",
       "2016-01-06  2011.709961  2011.709961  1979.050049  1990.260010  4336660000   \n",
       "2016-01-05  2013.780029  2021.939941  2004.170044  2016.709961  3706620000   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "2016-01-11  1923.670044  \n",
       "2016-01-08  1922.030029  \n",
       "2016-01-07  1943.089966  \n",
       "2016-01-06  1990.260010  \n",
       "2016-01-05  2016.709961  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500 = pd.read_csv('data/table.csv', index_col=0, parse_dates=True)\n",
    "sp500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1923.670044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1922.030029</td>\n",
       "    </tr>\n",
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       "      <th>2016-01-07</th>\n",
       "      <td>1943.089966</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1990.260010</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
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      ],
      "text/plain": [
       "              Adj Close\n",
       "Date                   \n",
       "2016-01-11  1923.670044\n",
       "2016-01-08  1922.030029\n",
       "2016-01-07  1943.089966\n",
       "2016-01-06  1990.260010\n",
       "2016-01-05  2016.709961"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt = sp500[['Adj Close']]\n",
    "sp500tt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-09</th>\n",
       "      <td>17.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-06</th>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-05</th>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-04</th>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-03</th>\n",
       "      <td>16.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close\n",
       "Date                 \n",
       "1950-01-09      17.08\n",
       "1950-01-06      16.98\n",
       "1950-01-05      16.93\n",
       "1950-01-04      16.85\n",
       "1950-01-03      16.66"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-11', '2016-01-08', '2016-01-07', '2016-01-06',\n",
       "               '2016-01-05', '2016-01-04', '2015-12-31', '2015-12-30',\n",
       "               '2015-12-29', '2015-12-28',\n",
       "               ...\n",
       "               '1950-01-16', '1950-01-13', '1950-01-12', '1950-01-11',\n",
       "               '1950-01-10', '1950-01-09', '1950-01-06', '1950-01-05',\n",
       "               '1950-01-04', '1950-01-03'],\n",
       "              dtype='datetime64[ns]', name='Date', length=16613, freq=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which days did the market go up or down?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_change(x):\n",
    "    return (x[0] - x[1])/x[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Sidenote*: You can get Pandas documentation open by using `?`\n",
    "\n",
    "`sp500_adj_close.assign?`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>up_down</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>2016-01-11</th>\n",
       "      <td>1923.670044</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1922.030029</td>\n",
       "      <td>0.000853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1943.089966</td>\n",
       "      <td>-0.010838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1990.260010</td>\n",
       "      <td>-0.023700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>2016.709961</td>\n",
       "      <td>-0.013115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Adj Close   up_down\n",
       "Date                             \n",
       "2016-01-11  1923.670044       NaN\n",
       "2016-01-08  1922.030029  0.000853\n",
       "2016-01-07  1943.089966 -0.010838\n",
       "2016-01-06  1990.260010 -0.023700\n",
       "2016-01-05  2016.709961 -0.013115"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt = sp500tt.assign(up_down=sp500tt.rolling(2)\n",
    "                         .apply(relative_change, raw=True))\n",
    "sp500tt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we need to shift the results \"up\" one. January 11 was an \"up\" day, but its entry is one row too low in the \"up_down\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500tt.up_down = sp500tt.up_down.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1923.670044</td>\n",
       "      <td>0.000853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1922.030029</td>\n",
       "      <td>-0.010838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1943.089966</td>\n",
       "      <td>-0.023700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1990.260010</td>\n",
       "      <td>-0.013115</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
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       "      <td>0.002012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>2012.660034</td>\n",
       "      <td>-0.015304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>2043.939941</td>\n",
       "      <td>-0.009412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>2063.360107</td>\n",
       "      <td>-0.007217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>2078.360107</td>\n",
       "      <td>0.010630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>2056.500000</td>\n",
       "      <td>-0.002179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Adj Close   up_down\n",
       "Date                             \n",
       "2016-01-11  1923.670044  0.000853\n",
       "2016-01-08  1922.030029 -0.010838\n",
       "2016-01-07  1943.089966 -0.023700\n",
       "2016-01-06  1990.260010 -0.013115\n",
       "2016-01-05  2016.709961  0.002012\n",
       "2016-01-04  2012.660034 -0.015304\n",
       "2015-12-31  2043.939941 -0.009412\n",
       "2015-12-30  2063.360107 -0.007217\n",
       "2015-12-29  2078.360107  0.010630\n",
       "2015-12-28  2056.500000 -0.002179"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt.iloc[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000853</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
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       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1990.260010</td>\n",
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      ],
      "text/plain": [
       "              Adj Close   up_down\n",
       "Date                             \n",
       "2016-01-11  1923.670044  0.000853\n",
       "2016-01-08  1922.030029 -0.010838\n",
       "2016-01-07  1943.089966 -0.023700\n",
       "2016-01-06  1990.260010 -0.013115\n",
       "2016-01-05  2016.709961  0.002012"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "    .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>Adj Close</th>\n",
       "      <th>up_down</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>1950-01-09</th>\n",
       "      <td>17.08</td>\n",
       "      <td>0.005889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-06</th>\n",
       "      <td>16.98</td>\n",
       "      <td>0.002953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-05</th>\n",
       "      <td>16.93</td>\n",
       "      <td>0.004748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-04</th>\n",
       "      <td>16.85</td>\n",
       "      <td>0.011405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-03</th>\n",
       "      <td>16.66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close   up_down\n",
       "Date                           \n",
       "1950-01-09      17.08  0.005889\n",
       "1950-01-06      16.98  0.002953\n",
       "1950-01-05      16.93  0.004748\n",
       "1950-01-04      16.85  0.011405\n",
       "1950-01-03      16.66       NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets get which day each of these dates falls on. We will use `datetime`'s `dayOfWeek` to get that information. `0` represents Monday.. `6` represents a Sunday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>up_down</th>\n",
       "      <th>day_num</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>2016-01-11</th>\n",
       "      <td>1923.670044</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1922.030029</td>\n",
       "      <td>-0.010838</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1943.089966</td>\n",
       "      <td>-0.023700</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1990.260010</td>\n",
       "      <td>-0.013115</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>2016.709961</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Adj Close   up_down  day_num\n",
       "Date                                      \n",
       "2016-01-11  1923.670044  0.000853        0\n",
       "2016-01-08  1922.030029 -0.010838        4\n",
       "2016-01-07  1943.089966 -0.023700        3\n",
       "2016-01-06  1990.260010 -0.013115        2\n",
       "2016-01-05  2016.709961  0.002012        1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt['day_num'] = sp500tt.index.dayofweek\n",
    "sp500tt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will consider market is down if there are three or more consecutive days if the market closed lower than it opened. So for example, for a Friday, we will check 3 previous trading Tuesday, Wednesady and Thursday. Therefore, question we eventually want to answer, with above definition of down market, do we bounce back on Tuesday more often than other days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def down_last_three_days(x):\n",
    "    if x[1] <= 0 and x[2] <= 0 and x[3] <= 0:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500tt['down_last_three_days'] = \\\n",
    "    sp500tt['up_down'].rolling(4).apply(down_last_three_days, raw=True).shift(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>up_down</th>\n",
       "      <th>day_num</th>\n",
       "      <th>down_last_three_days</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>2016-01-11</th>\n",
       "      <td>1923.670044</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1922.030029</td>\n",
       "      <td>-0.010838</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1943.089966</td>\n",
       "      <td>-0.023700</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1990.260010</td>\n",
       "      <td>-0.013115</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>2016.709961</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Adj Close   up_down  day_num  down_last_three_days\n",
       "Date                                                            \n",
       "2016-01-11  1923.670044  0.000853        0                   1.0\n",
       "2016-01-08  1922.030029 -0.010838        4                   0.0\n",
       "2016-01-07  1943.089966 -0.023700        3                   0.0\n",
       "2016-01-06  1990.260010 -0.013115        2                   0.0\n",
       "2016-01-05  2016.709961  0.002012        1                   1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16613"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sp500tt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500tt['turned'] = (sp500tt['down_last_three_days'] == 1.0) & (sp500tt['up_down'] > 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>up_down</th>\n",
       "      <th>day_num</th>\n",
       "      <th>down_last_three_days</th>\n",
       "      <th>turned</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1923.670044</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1922.030029</td>\n",
       "      <td>-0.010838</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1943.089966</td>\n",
       "      <td>-0.023700</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1990.260010</td>\n",
       "      <td>-0.013115</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>2016.709961</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Adj Close   up_down  day_num  down_last_three_days  turned\n",
       "Date                                                                    \n",
       "2016-01-11  1923.670044  0.000853        0                   1.0    True\n",
       "2016-01-08  1922.030029 -0.010838        4                   0.0   False\n",
       "2016-01-07  1943.089966 -0.023700        3                   0.0   False\n",
       "2016-01-06  1990.260010 -0.013115        2                   0.0   False\n",
       "2016-01-05  2016.709961  0.002012        1                   1.0    True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500tt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will group this by each day of the week and see if Tuesdays are really \"Turning Tuesday\"s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17b36f62f28>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp_turning_days_totals = sp500tt.turned.groupby(sp500tt.day_num).sum()\n",
    "%matplotlib inline\n",
    "sp_turning_days_totals.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly! Tuesdays are winner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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