Step1_RawY_variable_and_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Raw  Y variable and its processing\n",
    "This notebook has two functions:\n",
    "1. download original Y, crude oil price, from Quandl of all dates available\n",
    "2. process original Y into more possible direct Ys, and Y-based input variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "import quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "quandl.ApiConfig.api_key = 'GFC1VTVV3SXKRmR9w-mr'\n",
    "output = quandl.get(\"EIA/PET_RWTC_D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-01-02</th>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-03</th>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-06</th>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-07</th>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-08</th>\n",
       "      <td>25.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "Date             \n",
       "1986-01-02  25.56\n",
       "1986-01-03  26.00\n",
       "1986-01-06  26.53\n",
       "1986-01-07  25.85\n",
       "1986-01-08  25.87"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_csv('WTICrudeOilPrice.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function 2\n",
    "Before generating Ys and Y-based Xs, daily return,r(t), is calculated at every t:\n",
    "\n",
    "            r(t) = P(t)/P(t-1) - 1\n",
    "\n",
    "12 input variables are generated, shown as the following:\n",
    "\n",
    "            previous-n-month-daily-average-return = mean([r(t-n*30) to r(t-1)])\n",
    "            previous-n-month-daily-return-volatility = std([r(t-n*30) to r(t-1)])\n",
    "            previous-n-month-daily-return-range = min([r(t-n*30) to r(t-1)]),max([r(t-n*30) to r(t-1)])\n",
    "            n = 1,2,3,6\n",
    "Similarly, 8 Y variables are calculated as the following:\n",
    "\n",
    "            next-n-month-daily-average-return = mean([r(t+1) to r(t+30*n)])\n",
    "            next-n-month-daily-return-volatility = std([r(t+1) to r(t+30*n)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986-01-02</td>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986-01-03</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1986-01-06</td>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986-01-07</td>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1986-01-08</td>\n",
       "      <td>25.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Value\n",
       "0  1986-01-02  25.56\n",
       "1  1986-01-03  26.00\n",
       "2  1986-01-06  26.53\n",
       "3  1986-01-07  25.85\n",
       "4  1986-01-08  25.87"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('WTICrudeOilPrice.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date'].is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date'] = pd.to_datetime(data['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1986-01-02\n",
       "1   1986-01-03\n",
       "2   1986-01-06\n",
       "3   1986-01-07\n",
       "4   1986-01-08\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>2010-01-04</th>\n",
       "      <td>81.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>81.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>83.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>82.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>82.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>82.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>80.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>79.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>79.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-15</th>\n",
       "      <td>77.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-19</th>\n",
       "      <td>78.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-20</th>\n",
       "      <td>77.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-21</th>\n",
       "      <td>75.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-22</th>\n",
       "      <td>74.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-25</th>\n",
       "      <td>74.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-26</th>\n",
       "      <td>74.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-27</th>\n",
       "      <td>73.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-28</th>\n",
       "      <td>73.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>72.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>74.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-02</th>\n",
       "      <td>77.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "Date             \n",
       "2010-01-04  81.52\n",
       "2010-01-05  81.74\n",
       "2010-01-06  83.12\n",
       "2010-01-07  82.60\n",
       "2010-01-08  82.74\n",
       "2010-01-11  82.54\n",
       "2010-01-12  80.79\n",
       "2010-01-13  79.66\n",
       "2010-01-14  79.35\n",
       "2010-01-15  77.96\n",
       "2010-01-19  78.98\n",
       "2010-01-20  77.42\n",
       "2010-01-21  75.84\n",
       "2010-01-22  74.25\n",
       "2010-01-25  74.90\n",
       "2010-01-26  74.67\n",
       "2010-01-27  73.64\n",
       "2010-01-28  73.62\n",
       "2010-01-29  72.85\n",
       "2010-02-01  74.41\n",
       "2010-02-02  77.21"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['20100101':'20100202']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={'Value':'Price'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate r(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-03</th>\n",
       "      <td>0.017214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-06</th>\n",
       "      <td>0.020385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-07</th>\n",
       "      <td>-0.025631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-08</th>\n",
       "      <td>0.000774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               Price\n",
       "Date                \n",
       "1986-01-02       NaN\n",
       "1986-01-03  0.017214\n",
       "1986-01-06  0.020385\n",
       "1986-01-07 -0.025631\n",
       "1986-01-08  0.000774"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### try calculate r(t)\n",
    "data.head()/data.head().shift(1) - 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Daily_Return</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>1986-01-02</th>\n",
       "      <td>25.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-03</th>\n",
       "      <td>26.00</td>\n",
       "      <td>0.017214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-06</th>\n",
       "      <td>26.53</td>\n",
       "      <td>0.020385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-07</th>\n",
       "      <td>25.85</td>\n",
       "      <td>-0.025631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-08</th>\n",
       "      <td>25.87</td>\n",
       "      <td>0.000774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            Price  Daily_Return\n",
       "Date                           \n",
       "1986-01-02  25.56           NaN\n",
       "1986-01-03  26.00      0.017214\n",
       "1986-01-06  26.53      0.020385\n",
       "1986-01-07  25.85     -0.025631\n",
       "1986-01-08  25.87      0.000774"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Daily_Return is calculated by P(t)/P(t-1) - 1\n",
    "data['Daily_Return'] = data['Price']/data['Price'].shift(1) - 1\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(data['Price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Daily_Return'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['Daily_Return'][1:],bins=100) # the first is NA\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MovingAvg(DATA, tLength, FUN, title,pre=True):\n",
    "    ### DATA is a series with time index, \n",
    "    ### tLength is the time lenght, \n",
    "    ### FUN is the function working over the series\n",
    "    ### title is the name of the output for this function\n",
    "    ### pre is to decide to look forward or backward of the time series\n",
    "    ### output is a dataframe with time index of DATA, and column, named with title\n",
    "    \n",
    "    ### pandas and numpy should be loaded in advance\n",
    "    tPoints = DATA.index\n",
    "    outputDf = pd.DataFrame(index=tPoints,columns=[title])\n",
    "    for t in tPoints:\n",
    "        if pre:\n",
    "            startT = t - datetime.timedelta(tLength+1)\n",
    "            endT = t - datetime.timedelta(1)\n",
    "        else:\n",
    "            startT = t + datetime.timedelta(1)\n",
    "            endT = t + datetime.timedelta(tLength+1)\n",
    "        #print('time t:',t)\n",
    "        temp_output = FUN(DATA[startT:endT])\n",
    "        outputDf.loc[t,title] = temp_output\n",
    "        if pre:\n",
    "            outputDf.iloc[:tLength,0] = np.nan ### the first tLength-days data is not involved in the data\n",
    "        else:\n",
    "            outputDf.iloc[-tLength:,0] = np.nan\n",
    "    return outputDf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Previous-1-Month-Avg-Daily-Return and regrading Volatility, and look the graph to make sure the algo runs correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fzhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: Mean of empty slice\n",
      "C:\\Users\\fzhan\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1545: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  keepdims=keepdims)\n"
     ]
    }
   ],
   "source": [
    "Pre1MonthAvgDailyRet = MovingAvg(data['Daily_Return'],30,np.nanmean,'Pre1MonthAvgDailyRet')\n",
    "Pre1MonthVolDaily = MovingAvg(data['Daily_Return'],30,np.nanstd,'Pre1MonthVolDaily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8296"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Pre1MonthAvgDailyRet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Pre1MonthAvgDailyRet)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Pre1MonthVolDaily)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Daily_Return</th>\n",
       "      <th>Pre1MonthAvgDailyRet</th>\n",
       "      <th>Pre1MonthVolDaily</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>1986-01-30</th>\n",
       "      <td>19.58</td>\n",
       "      <td>-0.001530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-31</th>\n",
       "      <td>18.95</td>\n",
       "      <td>-0.032176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-03</th>\n",
       "      <td>17.42</td>\n",
       "      <td>-0.080739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-04</th>\n",
       "      <td>15.58</td>\n",
       "      <td>-0.105626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-05</th>\n",
       "      <td>16.28</td>\n",
       "      <td>0.044929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-06</th>\n",
       "      <td>16.60</td>\n",
       "      <td>0.019656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-07</th>\n",
       "      <td>17.70</td>\n",
       "      <td>0.066265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-10</th>\n",
       "      <td>16.78</td>\n",
       "      <td>-0.051977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-11</th>\n",
       "      <td>16.28</td>\n",
       "      <td>-0.029797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-12</th>\n",
       "      <td>15.74</td>\n",
       "      <td>-0.033170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-13</th>\n",
       "      <td>16.43</td>\n",
       "      <td>0.043837</td>\n",
       "      <td>-0.0199941</td>\n",
       "      <td>0.0446474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-14</th>\n",
       "      <td>16.03</td>\n",
       "      <td>-0.024346</td>\n",
       "      <td>-0.0171219</td>\n",
       "      <td>0.0464981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-18</th>\n",
       "      <td>14.70</td>\n",
       "      <td>-0.082969</td>\n",
       "      <td>-0.0179961</td>\n",
       "      <td>0.0489958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-19</th>\n",
       "      <td>15.08</td>\n",
       "      <td>0.025850</td>\n",
       "      <td>-0.0210901</td>\n",
       "      <td>0.0497767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-20</th>\n",
       "      <td>14.13</td>\n",
       "      <td>-0.062997</td>\n",
       "      <td>-0.0189564</td>\n",
       "      <td>0.0496055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-21</th>\n",
       "      <td>13.63</td>\n",
       "      <td>-0.035386</td>\n",
       "      <td>-0.0173956</td>\n",
       "      <td>0.0476151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-24</th>\n",
       "      <td>14.68</td>\n",
       "      <td>0.077036</td>\n",
       "      <td>-0.0175532</td>\n",
       "      <td>0.0499657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-25</th>\n",
       "      <td>14.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0124972</td>\n",
       "      <td>0.0540021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-26</th>\n",
       "      <td>14.62</td>\n",
       "      <td>-0.004087</td>\n",
       "      <td>-0.0119021</td>\n",
       "      <td>0.0527678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-27</th>\n",
       "      <td>14.05</td>\n",
       "      <td>-0.038988</td>\n",
       "      <td>-0.0115469</td>\n",
       "      <td>0.0515803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Daily_Return Pre1MonthAvgDailyRet Pre1MonthVolDaily\n",
       "Date                                                                  \n",
       "1986-01-30  19.58     -0.001530                  NaN               NaN\n",
       "1986-01-31  18.95     -0.032176                  NaN               NaN\n",
       "1986-02-03  17.42     -0.080739                  NaN               NaN\n",
       "1986-02-04  15.58     -0.105626                  NaN               NaN\n",
       "1986-02-05  16.28      0.044929                  NaN               NaN\n",
       "1986-02-06  16.60      0.019656                  NaN               NaN\n",
       "1986-02-07  17.70      0.066265                  NaN               NaN\n",
       "1986-02-10  16.78     -0.051977                  NaN               NaN\n",
       "1986-02-11  16.28     -0.029797                  NaN               NaN\n",
       "1986-02-12  15.74     -0.033170                  NaN               NaN\n",
       "1986-02-13  16.43      0.043837           -0.0199941         0.0446474\n",
       "1986-02-14  16.03     -0.024346           -0.0171219         0.0464981\n",
       "1986-02-18  14.70     -0.082969           -0.0179961         0.0489958\n",
       "1986-02-19  15.08      0.025850           -0.0210901         0.0497767\n",
       "1986-02-20  14.13     -0.062997           -0.0189564         0.0496055\n",
       "1986-02-21  13.63     -0.035386           -0.0173956         0.0476151\n",
       "1986-02-24  14.68      0.077036           -0.0175532         0.0499657\n",
       "1986-02-25  14.68      0.000000           -0.0124972         0.0540021\n",
       "1986-02-26  14.62     -0.004087           -0.0119021         0.0527678\n",
       "1986-02-27  14.05     -0.038988           -0.0115469         0.0515803"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_OUT = data.join([Pre1MonthAvgDailyRet,Pre1MonthVolDaily])\n",
    "data_OUT.iloc[20:40,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_OUT.iloc[:,1:].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the input Y-based X for n = 2,3,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fzhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: Mean of empty slice\n",
      "C:\\Users\\fzhan\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1545: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  keepdims=keepdims)\n"
     ]
    }
   ],
   "source": [
    "Pre2MonthAvgDailyRet = MovingAvg(data['Daily_Return'],60,np.nanmean,'Pre2MonthAvgDailyRet')\n",
    "Pre2MonthVolDaily = MovingAvg(data['Daily_Return'],60,np.nanstd,'Pre2MonthVolDaily')\n",
    "Pre3MonthAvgDailyRet = MovingAvg(data['Daily_Return'],90,np.nanmean,'Pre3MonthAvgDailyRet')\n",
    "Pre3MonthVolDaily = MovingAvg(data['Daily_Return'],90,np.nanstd,'Pre3MonthVolDaily')\n",
    "Pre6MonthAvgDailyRet = MovingAvg(data['Daily_Return'],180,np.nanmean,'Pre6MonthAvgDailyRet')\n",
    "Pre6MonthVolDaily = MovingAvg(data['Daily_Return'],180,np.nanstd,'Pre6MonthVolDaily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Daily_Return</th>\n",
       "      <th>Pre1MonthAvgDailyRet</th>\n",
       "      <th>Pre2MonthAvgDailyRet</th>\n",
       "      <th>Pre3MonthAvgDailyRet</th>\n",
       "      <th>Pre6MonthAvgDailyRet</th>\n",
       "      <th>Pre1MonthVolDaily</th>\n",
       "      <th>Pre2MonthVolDaily</th>\n",
       "      <th>Pre3MonthVolDaily</th>\n",
       "      <th>Pre6MonthVolDaily</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-01-30</th>\n",
       "      <td>19.58</td>\n",
       "      <td>-0.001530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-31</th>\n",
       "      <td>18.95</td>\n",
       "      <td>-0.032176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-03</th>\n",
       "      <td>17.42</td>\n",
       "      <td>-0.080739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-04</th>\n",
       "      <td>15.58</td>\n",
       "      <td>-0.105626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-05</th>\n",
       "      <td>16.28</td>\n",
       "      <td>0.044929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-06</th>\n",
       "      <td>16.60</td>\n",
       "      <td>0.019656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-07</th>\n",
       "      <td>17.70</td>\n",
       "      <td>0.066265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-10</th>\n",
       "      <td>16.78</td>\n",
       "      <td>-0.051977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-11</th>\n",
       "      <td>16.28</td>\n",
       "      <td>-0.029797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-12</th>\n",
       "      <td>15.74</td>\n",
       "      <td>-0.033170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-13</th>\n",
       "      <td>16.43</td>\n",
       "      <td>0.043837</td>\n",
       "      <td>-0.0199941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0446474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-14</th>\n",
       "      <td>16.03</td>\n",
       "      <td>-0.024346</td>\n",
       "      <td>-0.0171219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0464981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-18</th>\n",
       "      <td>14.70</td>\n",
       "      <td>-0.082969</td>\n",
       "      <td>-0.0179961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0489958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-19</th>\n",
       "      <td>15.08</td>\n",
       "      <td>0.025850</td>\n",
       "      <td>-0.0210901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0497767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-20</th>\n",
       "      <td>14.13</td>\n",
       "      <td>-0.062997</td>\n",
       "      <td>-0.0189564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0496055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-21</th>\n",
       "      <td>13.63</td>\n",
       "      <td>-0.035386</td>\n",
       "      <td>-0.0173956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0476151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-24</th>\n",
       "      <td>14.68</td>\n",
       "      <td>0.077036</td>\n",
       "      <td>-0.0175532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0499657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-25</th>\n",
       "      <td>14.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0124972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0540021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-26</th>\n",
       "      <td>14.62</td>\n",
       "      <td>-0.004087</td>\n",
       "      <td>-0.0119021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0527678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-27</th>\n",
       "      <td>14.05</td>\n",
       "      <td>-0.038988</td>\n",
       "      <td>-0.0115469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0515803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Daily_Return Pre1MonthAvgDailyRet Pre2MonthAvgDailyRet  \\\n",
       "Date                                                                        \n",
       "1986-01-30  19.58     -0.001530                  NaN                  NaN   \n",
       "1986-01-31  18.95     -0.032176                  NaN                  NaN   \n",
       "1986-02-03  17.42     -0.080739                  NaN                  NaN   \n",
       "1986-02-04  15.58     -0.105626                  NaN                  NaN   \n",
       "1986-02-05  16.28      0.044929                  NaN                  NaN   \n",
       "1986-02-06  16.60      0.019656                  NaN                  NaN   \n",
       "1986-02-07  17.70      0.066265                  NaN                  NaN   \n",
       "1986-02-10  16.78     -0.051977                  NaN                  NaN   \n",
       "1986-02-11  16.28     -0.029797                  NaN                  NaN   \n",
       "1986-02-12  15.74     -0.033170                  NaN                  NaN   \n",
       "1986-02-13  16.43      0.043837           -0.0199941                  NaN   \n",
       "1986-02-14  16.03     -0.024346           -0.0171219                  NaN   \n",
       "1986-02-18  14.70     -0.082969           -0.0179961                  NaN   \n",
       "1986-02-19  15.08      0.025850           -0.0210901                  NaN   \n",
       "1986-02-20  14.13     -0.062997           -0.0189564                  NaN   \n",
       "1986-02-21  13.63     -0.035386           -0.0173956                  NaN   \n",
       "1986-02-24  14.68      0.077036           -0.0175532                  NaN   \n",
       "1986-02-25  14.68      0.000000           -0.0124972                  NaN   \n",
       "1986-02-26  14.62     -0.004087           -0.0119021                  NaN   \n",
       "1986-02-27  14.05     -0.038988           -0.0115469                  NaN   \n",
       "\n",
       "           Pre3MonthAvgDailyRet Pre6MonthAvgDailyRet Pre1MonthVolDaily  \\\n",
       "Date                                                                     \n",
       "1986-01-30                  NaN                  NaN               NaN   \n",
       "1986-01-31                  NaN                  NaN               NaN   \n",
       "1986-02-03                  NaN                  NaN               NaN   \n",
       "1986-02-04                  NaN                  NaN               NaN   \n",
       "1986-02-05                  NaN                  NaN               NaN   \n",
       "1986-02-06                  NaN                  NaN               NaN   \n",
       "1986-02-07                  NaN                  NaN               NaN   \n",
       "1986-02-10                  NaN                  NaN               NaN   \n",
       "1986-02-11                  NaN                  NaN               NaN   \n",
       "1986-02-12                  NaN                  NaN               NaN   \n",
       "1986-02-13                  NaN                  NaN         0.0446474   \n",
       "1986-02-14                  NaN                  NaN         0.0464981   \n",
       "1986-02-18                  NaN                  NaN         0.0489958   \n",
       "1986-02-19                  NaN                  NaN         0.0497767   \n",
       "1986-02-20                  NaN                  NaN         0.0496055   \n",
       "1986-02-21                  NaN                  NaN         0.0476151   \n",
       "1986-02-24                  NaN                  NaN         0.0499657   \n",
       "1986-02-25                  NaN                  NaN         0.0540021   \n",
       "1986-02-26                  NaN                  NaN         0.0527678   \n",
       "1986-02-27                  NaN                  NaN         0.0515803   \n",
       "\n",
       "           Pre2MonthVolDaily Pre3MonthVolDaily Pre6MonthVolDaily  \n",
       "Date                                                              \n",
       "1986-01-30               NaN               NaN               NaN  \n",
       "1986-01-31               NaN               NaN               NaN  \n",
       "1986-02-03               NaN               NaN               NaN  \n",
       "1986-02-04               NaN               NaN               NaN  \n",
       "1986-02-05               NaN               NaN               NaN  \n",
       "1986-02-06               NaN               NaN               NaN  \n",
       "1986-02-07               NaN               NaN               NaN  \n",
       "1986-02-10               NaN               NaN               NaN  \n",
       "1986-02-11               NaN               NaN               NaN  \n",
       "1986-02-12               NaN               NaN               NaN  \n",
       "1986-02-13               NaN               NaN               NaN  \n",
       "1986-02-14               NaN               NaN               NaN  \n",
       "1986-02-18               NaN               NaN               NaN  \n",
       "1986-02-19               NaN               NaN               NaN  \n",
       "1986-02-20               NaN               NaN               NaN  \n",
       "1986-02-21               NaN               NaN               NaN  \n",
       "1986-02-24               NaN               NaN               NaN  \n",
       "1986-02-25               NaN               NaN               NaN  \n",
       "1986-02-26               NaN               NaN               NaN  \n",
       "1986-02-27               NaN               NaN               NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_OUT2 = data.join([Pre1MonthAvgDailyRet,Pre2MonthAvgDailyRet,Pre3MonthAvgDailyRet,Pre6MonthAvgDailyRet,\n",
    "                      Pre1MonthVolDaily,Pre2MonthVolDaily,Pre3MonthVolDaily,Pre6MonthVolDaily])\n",
    "data_OUT2.iloc[20:40,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_OUT2.iloc[:,2:6].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_OUT2.iloc[:,6:].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function getRange that works with MovingAvg to the range of price movings given a time frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.021201841531378718"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmin(data['Daily_Return']['20100101':'20100202'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getRange(series):\n",
    "    if series.size != 0: # np.nanmin/max cannot work with zero-size array\n",
    "        return (np.nanmin(series),np.nanmax(series))\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.021201841531378718, 0.03762935089369712)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = getRange(data['Daily_Return']['20100101':'20100202']) # test \n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fzhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: All-NaN axis encountered\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "Pre1MonthDailyPriceRange = MovingAvg(data['Daily_Return'],30,getRange,'Pre1MonthDailyPriceRange')\n",
    "Pre2MonthDailyPriceRange = MovingAvg(data['Daily_Return'],60,getRange,'Pre2MonthDailyPriceRange')\n",
    "Pre3MonthDailyPriceRange = MovingAvg(data['Daily_Return'],90,getRange,'Pre3MonthDailyPriceRange')\n",
    "Pre6MonthDailyPriceRange = MovingAvg(data['Daily_Return'],180,getRange,'Pre6MonthDailyPriceRange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pre1MonthDailyPriceRange</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-02-13</th>\n",
       "      <td>(-0.10562571756601613, 0.07300771208226231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-14</th>\n",
       "      <td>(-0.10562571756601613, 0.07300771208226231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-18</th>\n",
       "      <td>(-0.10562571756601613, 0.07300771208226231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-19</th>\n",
       "      <td>(-0.10562571756601613, 0.07300771208226231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-20</th>\n",
       "      <td>(-0.10562571756601613, 0.07300771208226231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-21</th>\n",
       "      <td>(-0.10562571756601613, 0.07300771208226231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-24</th>\n",
       "      <td>(-0.10562571756601613, 0.07300771208226231)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-25</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-26</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-27</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-02-28</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-03</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-04</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-05</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-06</th>\n",
       "      <td>(-0.10562571756601613, 0.07703595011005127)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-07</th>\n",
       "      <td>(-0.10562571756601613, 0.09143807148794703)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-10</th>\n",
       "      <td>(-0.09448223733938022, 0.09143807148794703)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-11</th>\n",
       "      <td>(-0.09448223733938022, 0.09143807148794703)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-12</th>\n",
       "      <td>(-0.09448223733938022, 0.09143807148794703)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-13</th>\n",
       "      <td>(-0.09448223733938022, 0.09143807148794703)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Pre1MonthDailyPriceRange\n",
       "Date                                                   \n",
       "1986-02-13  (-0.10562571756601613, 0.07300771208226231)\n",
       "1986-02-14  (-0.10562571756601613, 0.07300771208226231)\n",
       "1986-02-18  (-0.10562571756601613, 0.07300771208226231)\n",
       "1986-02-19  (-0.10562571756601613, 0.07300771208226231)\n",
       "1986-02-20  (-0.10562571756601613, 0.07300771208226231)\n",
       "1986-02-21  (-0.10562571756601613, 0.07300771208226231)\n",
       "1986-02-24  (-0.10562571756601613, 0.07300771208226231)\n",
       "1986-02-25  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-02-26  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-02-27  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-02-28  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-03-03  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-03-04  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-03-05  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-03-06  (-0.10562571756601613, 0.07703595011005127)\n",
       "1986-03-07  (-0.10562571756601613, 0.09143807148794703)\n",
       "1986-03-10  (-0.09448223733938022, 0.09143807148794703)\n",
       "1986-03-11  (-0.09448223733938022, 0.09143807148794703)\n",
       "1986-03-12  (-0.09448223733938022, 0.09143807148794703)\n",
       "1986-03-13  (-0.09448223733938022, 0.09143807148794703)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pre1MonthDailyPriceRange.iloc[30:50,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Daily_Return</th>\n",
       "      <th>Pre1MonthAvgDailyRet</th>\n",
       "      <th>Pre2MonthAvgDailyRet</th>\n",
       "      <th>Pre3MonthAvgDailyRet</th>\n",
       "      <th>Pre6MonthAvgDailyRet</th>\n",
       "      <th>Pre1MonthVolDaily</th>\n",
       "      <th>Pre2MonthVolDaily</th>\n",
       "      <th>Pre3MonthVolDaily</th>\n",
       "      <th>Pre6MonthVolDaily</th>\n",
       "      <th>Pre1MonthDailyPriceRange</th>\n",
       "      <th>Pre2MonthDailyPriceRange</th>\n",
       "      <th>Pre3MonthDailyPriceRange</th>\n",
       "      <th>Pre6MonthDailyPriceRange</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1989-12-04</th>\n",
       "      <td>20.24</td>\n",
       "      <td>-0.000987</td>\n",
       "      <td>0.000685825</td>\n",
       "      <td>0.000199755</td>\n",
       "      <td>0.00115369</td>\n",
       "      <td>3.47191e-05</td>\n",
       "      <td>0.0137083</td>\n",
       "      <td>0.01208</td>\n",
       "      <td>0.0116323</td>\n",
       "      <td>0.0158419</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-05</th>\n",
       "      <td>20.22</td>\n",
       "      <td>-0.000988</td>\n",
       "      <td>0.000115269</td>\n",
       "      <td>0.000153715</td>\n",
       "      <td>0.0011385</td>\n",
       "      <td>7.24438e-05</td>\n",
       "      <td>0.0135153</td>\n",
       "      <td>0.0120807</td>\n",
       "      <td>0.0116344</td>\n",
       "      <td>0.0158337</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-06</th>\n",
       "      <td>20.46</td>\n",
       "      <td>0.011869</td>\n",
       "      <td>6.51141e-05</td>\n",
       "      <td>0.000384393</td>\n",
       "      <td>0.000960325</td>\n",
       "      <td>0.000323435</td>\n",
       "      <td>0.0132066</td>\n",
       "      <td>0.0119614</td>\n",
       "      <td>0.0115768</td>\n",
       "      <td>0.0155558</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-07</th>\n",
       "      <td>20.45</td>\n",
       "      <td>-0.000489</td>\n",
       "      <td>0.000578345</td>\n",
       "      <td>0.000730369</td>\n",
       "      <td>0.000876845</td>\n",
       "      <td>0.000321007</td>\n",
       "      <td>0.0131387</td>\n",
       "      <td>0.0120729</td>\n",
       "      <td>0.0114778</td>\n",
       "      <td>0.015554</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-08</th>\n",
       "      <td>20.41</td>\n",
       "      <td>-0.001956</td>\n",
       "      <td>0.000922459</td>\n",
       "      <td>0.000702661</td>\n",
       "      <td>0.000877252</td>\n",
       "      <td>0.000344431</td>\n",
       "      <td>0.0130019</td>\n",
       "      <td>0.0119363</td>\n",
       "      <td>0.0114778</td>\n",
       "      <td>0.0155505</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-11</th>\n",
       "      <td>20.69</td>\n",
       "      <td>0.013719</td>\n",
       "      <td>0.0019654</td>\n",
       "      <td>0.000300851</td>\n",
       "      <td>0.000553742</td>\n",
       "      <td>0.000551644</td>\n",
       "      <td>0.0129157</td>\n",
       "      <td>0.0119707</td>\n",
       "      <td>0.0112546</td>\n",
       "      <td>0.0153389</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-12</th>\n",
       "      <td>20.80</td>\n",
       "      <td>0.005317</td>\n",
       "      <td>0.00208472</td>\n",
       "      <td>0.000654413</td>\n",
       "      <td>0.0007648</td>\n",
       "      <td>0.00057766</td>\n",
       "      <td>0.0130118</td>\n",
       "      <td>0.0121361</td>\n",
       "      <td>0.0113702</td>\n",
       "      <td>0.0153583</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-13</th>\n",
       "      <td>20.75</td>\n",
       "      <td>-0.002404</td>\n",
       "      <td>0.00223162</td>\n",
       "      <td>0.000420805</td>\n",
       "      <td>0.00091663</td>\n",
       "      <td>0.000273018</td>\n",
       "      <td>0.0127305</td>\n",
       "      <td>0.0119459</td>\n",
       "      <td>0.0113641</td>\n",
       "      <td>0.0148636</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-14</th>\n",
       "      <td>20.67</td>\n",
       "      <td>-0.003855</td>\n",
       "      <td>0.00203008</td>\n",
       "      <td>-0.000111792</td>\n",
       "      <td>0.000731117</td>\n",
       "      <td>0.000182078</td>\n",
       "      <td>0.0124865</td>\n",
       "      <td>0.0115427</td>\n",
       "      <td>0.0113183</td>\n",
       "      <td>0.0148438</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-15</th>\n",
       "      <td>21.13</td>\n",
       "      <td>0.022254</td>\n",
       "      <td>0.00234457</td>\n",
       "      <td>-0.000196874</td>\n",
       "      <td>0.0007802</td>\n",
       "      <td>0.000370874</td>\n",
       "      <td>0.0122408</td>\n",
       "      <td>0.0114244</td>\n",
       "      <td>0.0112913</td>\n",
       "      <td>0.0146349</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.04504504504504503)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-18</th>\n",
       "      <td>22.20</td>\n",
       "      <td>0.050639</td>\n",
       "      <td>0.00299766</td>\n",
       "      <td>0.000571314</td>\n",
       "      <td>0.000943232</td>\n",
       "      <td>0.000194203</td>\n",
       "      <td>0.0133638</td>\n",
       "      <td>0.0118667</td>\n",
       "      <td>0.0115214</td>\n",
       "      <td>0.0142264</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.03508771929824561, 0.024226804123711476)</td>\n",
       "      <td>(-0.05538771399798581, 0.0340068999507146)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-19</th>\n",
       "      <td>22.17</td>\n",
       "      <td>-0.001351</td>\n",
       "      <td>0.00528927</td>\n",
       "      <td>0.00186142</td>\n",
       "      <td>0.00179166</td>\n",
       "      <td>0.000958014</td>\n",
       "      <td>0.0167752</td>\n",
       "      <td>0.0140275</td>\n",
       "      <td>0.0130215</td>\n",
       "      <td>0.0142691</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-20</th>\n",
       "      <td>21.58</td>\n",
       "      <td>-0.026613</td>\n",
       "      <td>0.00498742</td>\n",
       "      <td>0.0020673</td>\n",
       "      <td>0.00191798</td>\n",
       "      <td>0.00100213</td>\n",
       "      <td>0.0164478</td>\n",
       "      <td>0.0139134</td>\n",
       "      <td>0.0129504</td>\n",
       "      <td>0.0142531</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-21</th>\n",
       "      <td>21.53</td>\n",
       "      <td>-0.002317</td>\n",
       "      <td>0.00361351</td>\n",
       "      <td>0.00182515</td>\n",
       "      <td>0.00150856</td>\n",
       "      <td>0.000878298</td>\n",
       "      <td>0.017329</td>\n",
       "      <td>0.0143141</td>\n",
       "      <td>0.0134168</td>\n",
       "      <td>0.0144221</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-22</th>\n",
       "      <td>21.31</td>\n",
       "      <td>-0.010218</td>\n",
       "      <td>0.00307646</td>\n",
       "      <td>0.00173101</td>\n",
       "      <td>0.0014651</td>\n",
       "      <td>0.000788891</td>\n",
       "      <td>0.0173131</td>\n",
       "      <td>0.0141639</td>\n",
       "      <td>0.0134246</td>\n",
       "      <td>0.0144058</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-26</th>\n",
       "      <td>21.91</td>\n",
       "      <td>0.028156</td>\n",
       "      <td>0.00371169</td>\n",
       "      <td>0.00209429</td>\n",
       "      <td>0.00148148</td>\n",
       "      <td>0.000450768</td>\n",
       "      <td>0.016325</td>\n",
       "      <td>0.0142346</td>\n",
       "      <td>0.0131166</td>\n",
       "      <td>0.0142711</td>\n",
       "      <td>(-0.02661253946774933, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-27</th>\n",
       "      <td>21.78</td>\n",
       "      <td>-0.005933</td>\n",
       "      <td>0.00487569</td>\n",
       "      <td>0.00291044</td>\n",
       "      <td>0.0018172</td>\n",
       "      <td>0.000801071</td>\n",
       "      <td>0.0167604</td>\n",
       "      <td>0.0147147</td>\n",
       "      <td>0.0135141</td>\n",
       "      <td>0.014396</td>\n",
       "      <td>(-0.02661253946774933, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-28</th>\n",
       "      <td>21.60</td>\n",
       "      <td>-0.008264</td>\n",
       "      <td>0.00438437</td>\n",
       "      <td>0.00239027</td>\n",
       "      <td>0.00173245</td>\n",
       "      <td>0.000665053</td>\n",
       "      <td>0.0165291</td>\n",
       "      <td>0.0146317</td>\n",
       "      <td>0.0135454</td>\n",
       "      <td>0.0143768</td>\n",
       "      <td>(-0.02661253946774933, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-12-29</th>\n",
       "      <td>21.84</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.00444402</td>\n",
       "      <td>0.00214249</td>\n",
       "      <td>0.00130054</td>\n",
       "      <td>0.000592778</td>\n",
       "      <td>0.0164809</td>\n",
       "      <td>0.0145495</td>\n",
       "      <td>0.0134159</td>\n",
       "      <td>0.0143982</td>\n",
       "      <td>(-0.02661253946774933, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>22.88</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.00408888</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.00149654</td>\n",
       "      <td>0.000683658</td>\n",
       "      <td>0.016166</td>\n",
       "      <td>0.01487</td>\n",
       "      <td>0.0135006</td>\n",
       "      <td>0.014396</td>\n",
       "      <td>(-0.02661253946774933, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.03508771929824561, 0.050638902035021305)</td>\n",
       "      <td>(-0.05538771399798581, 0.050638902035021305)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Daily_Return Pre1MonthAvgDailyRet Pre2MonthAvgDailyRet  \\\n",
       "Date                                                                        \n",
       "1989-12-04  20.24     -0.000987          0.000685825          0.000199755   \n",
       "1989-12-05  20.22     -0.000988          0.000115269          0.000153715   \n",
       "1989-12-06  20.46      0.011869          6.51141e-05          0.000384393   \n",
       "1989-12-07  20.45     -0.000489          0.000578345          0.000730369   \n",
       "1989-12-08  20.41     -0.001956          0.000922459          0.000702661   \n",
       "1989-12-11  20.69      0.013719            0.0019654          0.000300851   \n",
       "1989-12-12  20.80      0.005317           0.00208472          0.000654413   \n",
       "1989-12-13  20.75     -0.002404           0.00223162          0.000420805   \n",
       "1989-12-14  20.67     -0.003855           0.00203008         -0.000111792   \n",
       "1989-12-15  21.13      0.022254           0.00234457         -0.000196874   \n",
       "1989-12-18  22.20      0.050639           0.00299766          0.000571314   \n",
       "1989-12-19  22.17     -0.001351           0.00528927           0.00186142   \n",
       "1989-12-20  21.58     -0.026613           0.00498742            0.0020673   \n",
       "1989-12-21  21.53     -0.002317           0.00361351           0.00182515   \n",
       "1989-12-22  21.31     -0.010218           0.00307646           0.00173101   \n",
       "1989-12-26  21.91      0.028156           0.00371169           0.00209429   \n",
       "1989-12-27  21.78     -0.005933           0.00487569           0.00291044   \n",
       "1989-12-28  21.60     -0.008264           0.00438437           0.00239027   \n",
       "1989-12-29  21.84      0.011111           0.00444402           0.00214249   \n",
       "1990-01-02  22.88      0.047619           0.00408888             0.002149   \n",
       "\n",
       "           Pre3MonthAvgDailyRet Pre6MonthAvgDailyRet Pre1MonthVolDaily  \\\n",
       "Date                                                                     \n",
       "1989-12-04           0.00115369          3.47191e-05         0.0137083   \n",
       "1989-12-05            0.0011385          7.24438e-05         0.0135153   \n",
       "1989-12-06          0.000960325          0.000323435         0.0132066   \n",
       "1989-12-07          0.000876845          0.000321007         0.0131387   \n",
       "1989-12-08          0.000877252          0.000344431         0.0130019   \n",
       "1989-12-11          0.000553742          0.000551644         0.0129157   \n",
       "1989-12-12            0.0007648           0.00057766         0.0130118   \n",
       "1989-12-13           0.00091663          0.000273018         0.0127305   \n",
       "1989-12-14          0.000731117          0.000182078         0.0124865   \n",
       "1989-12-15            0.0007802          0.000370874         0.0122408   \n",
       "1989-12-18          0.000943232          0.000194203         0.0133638   \n",
       "1989-12-19           0.00179166          0.000958014         0.0167752   \n",
       "1989-12-20           0.00191798           0.00100213         0.0164478   \n",
       "1989-12-21           0.00150856          0.000878298          0.017329   \n",
       "1989-12-22            0.0014651          0.000788891         0.0173131   \n",
       "1989-12-26           0.00148148          0.000450768          0.016325   \n",
       "1989-12-27            0.0018172          0.000801071         0.0167604   \n",
       "1989-12-28           0.00173245          0.000665053         0.0165291   \n",
       "1989-12-29           0.00130054          0.000592778         0.0164809   \n",
       "1990-01-02           0.00149654          0.000683658          0.016166   \n",
       "\n",
       "           Pre2MonthVolDaily Pre3MonthVolDaily Pre6MonthVolDaily  \\\n",
       "Date                                                               \n",
       "1989-12-04           0.01208         0.0116323         0.0158419   \n",
       "1989-12-05         0.0120807         0.0116344         0.0158337   \n",
       "1989-12-06         0.0119614         0.0115768         0.0155558   \n",
       "1989-12-07         0.0120729         0.0114778          0.015554   \n",
       "1989-12-08         0.0119363         0.0114778         0.0155505   \n",
       "1989-12-11         0.0119707         0.0112546         0.0153389   \n",
       "1989-12-12         0.0121361         0.0113702         0.0153583   \n",
       "1989-12-13         0.0119459         0.0113641         0.0148636   \n",
       "1989-12-14         0.0115427         0.0113183         0.0148438   \n",
       "1989-12-15         0.0114244         0.0112913         0.0146349   \n",
       "1989-12-18         0.0118667         0.0115214         0.0142264   \n",
       "1989-12-19         0.0140275         0.0130215         0.0142691   \n",
       "1989-12-20         0.0139134         0.0129504         0.0142531   \n",
       "1989-12-21         0.0143141         0.0134168         0.0144221   \n",
       "1989-12-22         0.0141639         0.0134246         0.0144058   \n",
       "1989-12-26         0.0142346         0.0131166         0.0142711   \n",
       "1989-12-27         0.0147147         0.0135141          0.014396   \n",
       "1989-12-28         0.0146317         0.0135454         0.0143768   \n",
       "1989-12-29         0.0145495         0.0134159         0.0143982   \n",
       "1990-01-02           0.01487         0.0135006          0.014396   \n",
       "\n",
       "                                Pre1MonthDailyPriceRange  \\\n",
       "Date                                                       \n",
       "1989-12-04  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-05  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-06  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-07  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-08  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-11  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-12  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-13  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-14  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-15  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-18  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-19  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-20  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-21  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-22  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-26  (-0.02661253946774933, 0.050638902035021305)   \n",
       "1989-12-27  (-0.02661253946774933, 0.050638902035021305)   \n",
       "1989-12-28  (-0.02661253946774933, 0.050638902035021305)   \n",
       "1989-12-29  (-0.02661253946774933, 0.050638902035021305)   \n",
       "1990-01-02  (-0.02661253946774933, 0.050638902035021305)   \n",
       "\n",
       "                                Pre2MonthDailyPriceRange  \\\n",
       "Date                                                       \n",
       "1989-12-04  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-05  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-06  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-07  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-08  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-11  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-12  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-13  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-14  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-15  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-18  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-19  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-20  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-21  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-22  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-26  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-27  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-28  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-29  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1990-01-02  (-0.03508771929824561, 0.050638902035021305)   \n",
       "\n",
       "                                Pre3MonthDailyPriceRange  \\\n",
       "Date                                                       \n",
       "1989-12-04  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-05  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-06  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-07  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-08  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-11  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-12  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-13  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-14  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-15  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-18  (-0.03508771929824561, 0.024226804123711476)   \n",
       "1989-12-19  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-20  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-21  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-22  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-26  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-27  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-28  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1989-12-29  (-0.03508771929824561, 0.050638902035021305)   \n",
       "1990-01-02  (-0.03508771929824561, 0.050638902035021305)   \n",
       "\n",
       "                                Pre6MonthDailyPriceRange  \n",
       "Date                                                      \n",
       "1989-12-04   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-05   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-06   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-07   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-08   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-11   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-12   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-13   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-14   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-15   (-0.05538771399798581, 0.04504504504504503)  \n",
       "1989-12-18    (-0.05538771399798581, 0.0340068999507146)  \n",
       "1989-12-19  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1989-12-20  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1989-12-21  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1989-12-22  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1989-12-26  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1989-12-27  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1989-12-28  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1989-12-29  (-0.05538771399798581, 0.050638902035021305)  \n",
       "1990-01-02  (-0.05538771399798581, 0.050638902035021305)  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_OUT3 = data_OUT2.join([Pre1MonthDailyPriceRange,Pre2MonthDailyPriceRange,Pre3MonthDailyPriceRange,Pre6MonthDailyPriceRange])\n",
    "data_OUT3.iloc[1000:1020,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Y calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fzhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: Mean of empty slice\n",
      "C:\\Users\\fzhan\\Anaconda3\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1545: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  keepdims=keepdims)\n"
     ]
    }
   ],
   "source": [
    "next1MonthAvgDailyRet = MovingAvg(data['Daily_Return'],30,np.nanmean,'next1MonthAvgDailyRet',pre=False)\n",
    "next1MonthVolDaily = MovingAvg(data['Daily_Return'],30,np.nanstd,'next1MonthVolDaily',pre=False)\n",
    "next2MonthAvgDailyRet = MovingAvg(data['Daily_Return'],60,np.nanmean,'next2MonthAvgDailyRet',pre=False)\n",
    "next2MonthVolDaily = MovingAvg(data['Daily_Return'],60,np.nanstd,'next2MonthVolDaily',pre=False)\n",
    "next3MonthAvgDailyRet = MovingAvg(data['Daily_Return'],90,np.nanmean,'next3MonthAvgDailyRet',pre=False)\n",
    "next3MonthVolDaily = MovingAvg(data['Daily_Return'],90,np.nanstd,'next3MonthVolDaily',pre=False)\n",
    "next6MonthAvgDailyRet = MovingAvg(data['Daily_Return'],180,np.nanmean,'next6MonthAvgDailyRet',pre=False)\n",
    "next6MonthVolDaily = MovingAvg(data['Daily_Return'],180,np.nanstd,'next6MonthVolDaily',pre=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pre6MonthVolDaily.join(next6MonthVolDaily).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Daily_Return</th>\n",
       "      <th>Pre1MonthAvgDailyRet</th>\n",
       "      <th>Pre2MonthAvgDailyRet</th>\n",
       "      <th>Pre3MonthAvgDailyRet</th>\n",
       "      <th>Pre6MonthAvgDailyRet</th>\n",
       "      <th>Pre1MonthVolDaily</th>\n",
       "      <th>Pre2MonthVolDaily</th>\n",
       "      <th>Pre3MonthVolDaily</th>\n",
       "      <th>Pre6MonthVolDaily</th>\n",
       "      <th>...</th>\n",
       "      <th>Pre3MonthDailyPriceRange</th>\n",
       "      <th>Pre6MonthDailyPriceRange</th>\n",
       "      <th>next1MonthAvgDailyRet</th>\n",
       "      <th>next2MonthAvgDailyRet</th>\n",
       "      <th>next3MonthAvgDailyRet</th>\n",
       "      <th>next6MonthAvgDailyRet</th>\n",
       "      <th>next1MonthVolDaily</th>\n",
       "      <th>next2MonthVolDaily</th>\n",
       "      <th>next3MonthVolDaily</th>\n",
       "      <th>next6MonthVolDaily</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-04-14</th>\n",
       "      <td>12.94</td>\n",
       "      <td>-0.050624</td>\n",
       "      <td>0.00634561</td>\n",
       "      <td>-0.00231575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0690974</td>\n",
       "      <td>0.06315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00907724</td>\n",
       "      <td>0.00240556</td>\n",
       "      <td>-0.00155169</td>\n",
       "      <td>0.00196909</td>\n",
       "      <td>0.0368783</td>\n",
       "      <td>0.0411855</td>\n",
       "      <td>0.0371498</td>\n",
       "      <td>0.0402677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-15</th>\n",
       "      <td>12.72</td>\n",
       "      <td>-0.017002</td>\n",
       "      <td>0.00401284</td>\n",
       "      <td>-0.00274146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0701853</td>\n",
       "      <td>0.0634148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0109256</td>\n",
       "      <td>0.00286763</td>\n",
       "      <td>-0.000405486</td>\n",
       "      <td>0.00212085</td>\n",
       "      <td>0.0365891</td>\n",
       "      <td>0.0415626</td>\n",
       "      <td>0.0377644</td>\n",
       "      <td>0.0403925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-16</th>\n",
       "      <td>11.50</td>\n",
       "      <td>-0.095912</td>\n",
       "      <td>0.00301215</td>\n",
       "      <td>-0.00422534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0686398</td>\n",
       "      <td>0.0630181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0157818</td>\n",
       "      <td>0.00484136</td>\n",
       "      <td>0.00222871</td>\n",
       "      <td>0.00265851</td>\n",
       "      <td>0.0292781</td>\n",
       "      <td>0.0386943</td>\n",
       "      <td>0.0367858</td>\n",
       "      <td>0.0395219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-17</th>\n",
       "      <td>11.75</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>-0.0014844</td>\n",
       "      <td>-0.00597086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070156</td>\n",
       "      <td>0.0645244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0154982</td>\n",
       "      <td>0.00432376</td>\n",
       "      <td>0.00140795</td>\n",
       "      <td>0.00263855</td>\n",
       "      <td>0.0299375</td>\n",
       "      <td>0.0386101</td>\n",
       "      <td>0.036918</td>\n",
       "      <td>0.0395129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-18</th>\n",
       "      <td>11.88</td>\n",
       "      <td>0.011064</td>\n",
       "      <td>-0.00314022</td>\n",
       "      <td>-0.00531109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0691513</td>\n",
       "      <td>0.0638914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0180808</td>\n",
       "      <td>0.00400801</td>\n",
       "      <td>0.00187758</td>\n",
       "      <td>0.0023882</td>\n",
       "      <td>0.0317293</td>\n",
       "      <td>0.0386079</td>\n",
       "      <td>0.0372247</td>\n",
       "      <td>0.0395579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-21</th>\n",
       "      <td>12.48</td>\n",
       "      <td>0.050505</td>\n",
       "      <td>-0.0010583</td>\n",
       "      <td>-0.00307221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0699337</td>\n",
       "      <td>0.0627686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0115826</td>\n",
       "      <td>0.00413068</td>\n",
       "      <td>0.00141073</td>\n",
       "      <td>0.00216035</td>\n",
       "      <td>0.0349773</td>\n",
       "      <td>0.0381958</td>\n",
       "      <td>0.0367939</td>\n",
       "      <td>0.0393616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-22</th>\n",
       "      <td>13.13</td>\n",
       "      <td>0.052083</td>\n",
       "      <td>-0.00323893</td>\n",
       "      <td>-0.00248519</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0675721</td>\n",
       "      <td>0.0631505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0117847</td>\n",
       "      <td>0.00298895</td>\n",
       "      <td>-0.00207566</td>\n",
       "      <td>0.00191607</td>\n",
       "      <td>0.0352234</td>\n",
       "      <td>0.0379157</td>\n",
       "      <td>0.0418816</td>\n",
       "      <td>0.0391452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-23</th>\n",
       "      <td>13.70</td>\n",
       "      <td>0.043412</td>\n",
       "      <td>-0.000604535</td>\n",
       "      <td>0.000254826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0669877</td>\n",
       "      <td>0.0629618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0103471</td>\n",
       "      <td>0.00131228</td>\n",
       "      <td>-0.00283768</td>\n",
       "      <td>0.00159514</td>\n",
       "      <td>0.0353489</td>\n",
       "      <td>0.0376472</td>\n",
       "      <td>0.0414818</td>\n",
       "      <td>0.0389677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-24</th>\n",
       "      <td>13.65</td>\n",
       "      <td>-0.003650</td>\n",
       "      <td>0.00139622</td>\n",
       "      <td>0.00213096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0660867</td>\n",
       "      <td>0.0630458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0110136</td>\n",
       "      <td>0.00128055</td>\n",
       "      <td>-0.00260387</td>\n",
       "      <td>0.00142986</td>\n",
       "      <td>0.0360455</td>\n",
       "      <td>0.0376519</td>\n",
       "      <td>0.0415181</td>\n",
       "      <td>0.0390334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-25</th>\n",
       "      <td>14.23</td>\n",
       "      <td>0.042491</td>\n",
       "      <td>0.00693251</td>\n",
       "      <td>0.00199653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0600551</td>\n",
       "      <td>0.0623145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00943979</td>\n",
       "      <td>-0.00100847</td>\n",
       "      <td>-0.00345228</td>\n",
       "      <td>0.00110609</td>\n",
       "      <td>0.0362246</td>\n",
       "      <td>0.0379977</td>\n",
       "      <td>0.0411323</td>\n",
       "      <td>0.0388589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-28</th>\n",
       "      <td>13.34</td>\n",
       "      <td>-0.062544</td>\n",
       "      <td>0.013243</td>\n",
       "      <td>0.00122156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0608748</td>\n",
       "      <td>0.0622882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0046434</td>\n",
       "      <td>0.000736053</td>\n",
       "      <td>-0.00207845</td>\n",
       "      <td>0.00163333</td>\n",
       "      <td>0.0407576</td>\n",
       "      <td>0.0364495</td>\n",
       "      <td>0.0405773</td>\n",
       "      <td>0.0384365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-29</th>\n",
       "      <td>13.63</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.00963406</td>\n",
       "      <td>-0.000170263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061561</td>\n",
       "      <td>0.0630399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0030283</td>\n",
       "      <td>0.000235979</td>\n",
       "      <td>-0.00165062</td>\n",
       "      <td>0.00117542</td>\n",
       "      <td>0.0407522</td>\n",
       "      <td>0.0367348</td>\n",
       "      <td>0.0409664</td>\n",
       "      <td>0.0385351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-04-30</th>\n",
       "      <td>13.38</td>\n",
       "      <td>-0.018342</td>\n",
       "      <td>0.0101843</td>\n",
       "      <td>0.00127561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0601984</td>\n",
       "      <td>0.0628291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00404593</td>\n",
       "      <td>-0.000359411</td>\n",
       "      <td>-0.001223</td>\n",
       "      <td>0.00119994</td>\n",
       "      <td>0.0414372</td>\n",
       "      <td>0.0372304</td>\n",
       "      <td>0.0409305</td>\n",
       "      <td>0.0385236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-01</th>\n",
       "      <td>13.80</td>\n",
       "      <td>0.031390</td>\n",
       "      <td>0.00894402</td>\n",
       "      <td>0.0022285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0591619</td>\n",
       "      <td>0.0622178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00267872</td>\n",
       "      <td>-0.00186944</td>\n",
       "      <td>-0.00239785</td>\n",
       "      <td>0.000694936</td>\n",
       "      <td>0.0419957</td>\n",
       "      <td>0.0371982</td>\n",
       "      <td>0.0410397</td>\n",
       "      <td>0.0385383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-02</th>\n",
       "      <td>14.65</td>\n",
       "      <td>0.061594</td>\n",
       "      <td>0.0145226</td>\n",
       "      <td>0.00290667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0548059</td>\n",
       "      <td>0.061647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00214924</td>\n",
       "      <td>-0.00400856</td>\n",
       "      <td>-0.0029091</td>\n",
       "      <td>0.000988781</td>\n",
       "      <td>0.0404676</td>\n",
       "      <td>0.0360527</td>\n",
       "      <td>0.0404355</td>\n",
       "      <td>0.0391369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-05</th>\n",
       "      <td>14.32</td>\n",
       "      <td>-0.022526</td>\n",
       "      <td>0.0123408</td>\n",
       "      <td>0.00669199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0559842</td>\n",
       "      <td>0.0610941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00288575</td>\n",
       "      <td>-0.0041446</td>\n",
       "      <td>0.000798809</td>\n",
       "      <td>0.00125917</td>\n",
       "      <td>0.0390006</td>\n",
       "      <td>0.036133</td>\n",
       "      <td>0.0483939</td>\n",
       "      <td>0.0390902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-06</th>\n",
       "      <td>14.43</td>\n",
       "      <td>0.007682</td>\n",
       "      <td>0.00699461</td>\n",
       "      <td>0.00605629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0536492</td>\n",
       "      <td>0.0612557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00488655</td>\n",
       "      <td>-0.00443304</td>\n",
       "      <td>0.00107371</td>\n",
       "      <td>0.000909194</td>\n",
       "      <td>0.0395326</td>\n",
       "      <td>0.0365232</td>\n",
       "      <td>0.0484813</td>\n",
       "      <td>0.0392268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-07</th>\n",
       "      <td>15.13</td>\n",
       "      <td>0.048510</td>\n",
       "      <td>0.00702584</td>\n",
       "      <td>0.00406209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0524159</td>\n",
       "      <td>0.0597894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00742925</td>\n",
       "      <td>-0.00670022</td>\n",
       "      <td>0.000801464</td>\n",
       "      <td>0.00071159</td>\n",
       "      <td>0.0386653</td>\n",
       "      <td>0.0360484</td>\n",
       "      <td>0.0482618</td>\n",
       "      <td>0.0390483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-08</th>\n",
       "      <td>15.70</td>\n",
       "      <td>0.037673</td>\n",
       "      <td>0.0088295</td>\n",
       "      <td>0.00683099</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0519572</td>\n",
       "      <td>0.0590868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00968438</td>\n",
       "      <td>-0.00759727</td>\n",
       "      <td>0.000611023</td>\n",
       "      <td>0.000346415</td>\n",
       "      <td>0.0382487</td>\n",
       "      <td>0.0353845</td>\n",
       "      <td>0.0481395</td>\n",
       "      <td>0.0389141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-09</th>\n",
       "      <td>15.83</td>\n",
       "      <td>0.008280</td>\n",
       "      <td>0.00487498</td>\n",
       "      <td>0.00754825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0457832</td>\n",
       "      <td>0.0585804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0105697</td>\n",
       "      <td>-0.00821336</td>\n",
       "      <td>0.000113612</td>\n",
       "      <td>0.000360548</td>\n",
       "      <td>0.0380269</td>\n",
       "      <td>0.0353225</td>\n",
       "      <td>0.0482195</td>\n",
       "      <td>0.0389173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-12</th>\n",
       "      <td>15.75</td>\n",
       "      <td>-0.005054</td>\n",
       "      <td>0.00861656</td>\n",
       "      <td>0.00602987</td>\n",
       "      <td>-0.00014533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0403202</td>\n",
       "      <td>0.0587124</td>\n",
       "      <td>0.0567413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00545101</td>\n",
       "      <td>-0.00761609</td>\n",
       "      <td>0.000290159</td>\n",
       "      <td>0.000438113</td>\n",
       "      <td>0.042344</td>\n",
       "      <td>0.0350505</td>\n",
       "      <td>0.0482206</td>\n",
       "      <td>0.0389161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-13</th>\n",
       "      <td>15.65</td>\n",
       "      <td>-0.006349</td>\n",
       "      <td>0.00773863</td>\n",
       "      <td>0.00443382</td>\n",
       "      <td>0.000599491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0404075</td>\n",
       "      <td>0.0580774</td>\n",
       "      <td>0.0563627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00469758</td>\n",
       "      <td>-0.00764699</td>\n",
       "      <td>0.00100799</td>\n",
       "      <td>0.000568114</td>\n",
       "      <td>0.0424686</td>\n",
       "      <td>0.0354749</td>\n",
       "      <td>0.0484525</td>\n",
       "      <td>0.0389204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-14</th>\n",
       "      <td>15.53</td>\n",
       "      <td>-0.007668</td>\n",
       "      <td>0.00709827</td>\n",
       "      <td>0.00673986</td>\n",
       "      <td>0.000971684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0395874</td>\n",
       "      <td>0.055629</td>\n",
       "      <td>0.0562381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00455614</td>\n",
       "      <td>-0.00724084</td>\n",
       "      <td>0.000904403</td>\n",
       "      <td>0.000676515</td>\n",
       "      <td>0.0434629</td>\n",
       "      <td>0.0355675</td>\n",
       "      <td>0.0484779</td>\n",
       "      <td>0.0389162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-15</th>\n",
       "      <td>15.68</td>\n",
       "      <td>0.009659</td>\n",
       "      <td>0.00645627</td>\n",
       "      <td>0.00665178</td>\n",
       "      <td>0.00137647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0388341</td>\n",
       "      <td>0.0556488</td>\n",
       "      <td>0.0560824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00526688</td>\n",
       "      <td>-0.00612985</td>\n",
       "      <td>0.000906911</td>\n",
       "      <td>0.000568056</td>\n",
       "      <td>0.044417</td>\n",
       "      <td>0.0367691</td>\n",
       "      <td>0.0484784</td>\n",
       "      <td>0.0389099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-16</th>\n",
       "      <td>16.08</td>\n",
       "      <td>0.025510</td>\n",
       "      <td>0.00907724</td>\n",
       "      <td>0.00672171</td>\n",
       "      <td>0.000833957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0368783</td>\n",
       "      <td>0.0549998</td>\n",
       "      <td>0.0558338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00719315</td>\n",
       "      <td>-0.0050437</td>\n",
       "      <td>0.000913472</td>\n",
       "      <td>0.000478782</td>\n",
       "      <td>0.0438725</td>\n",
       "      <td>0.038313</td>\n",
       "      <td>0.0484817</td>\n",
       "      <td>0.0388653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-19</th>\n",
       "      <td>17.13</td>\n",
       "      <td>0.065299</td>\n",
       "      <td>0.0154982</td>\n",
       "      <td>0.00475953</td>\n",
       "      <td>0.00162532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0299375</td>\n",
       "      <td>0.0545857</td>\n",
       "      <td>0.0558246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00924014</td>\n",
       "      <td>-0.00622401</td>\n",
       "      <td>-0.000373692</td>\n",
       "      <td>2.32749e-05</td>\n",
       "      <td>0.038998</td>\n",
       "      <td>0.0371247</td>\n",
       "      <td>0.0478273</td>\n",
       "      <td>0.0384342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-20</th>\n",
       "      <td>16.18</td>\n",
       "      <td>-0.055458</td>\n",
       "      <td>0.0180808</td>\n",
       "      <td>0.00763795</td>\n",
       "      <td>0.00262021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0317293</td>\n",
       "      <td>0.0545146</td>\n",
       "      <td>0.0559469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00436878</td>\n",
       "      <td>-0.00502317</td>\n",
       "      <td>-0.000104686</td>\n",
       "      <td>0.000436329</td>\n",
       "      <td>0.0396092</td>\n",
       "      <td>0.0367599</td>\n",
       "      <td>0.0475636</td>\n",
       "      <td>0.0381118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-21</th>\n",
       "      <td>15.53</td>\n",
       "      <td>-0.040173</td>\n",
       "      <td>0.0147381</td>\n",
       "      <td>0.00721599</td>\n",
       "      <td>0.00305008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0345779</td>\n",
       "      <td>0.0549312</td>\n",
       "      <td>0.0553905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00266381</td>\n",
       "      <td>-0.00352886</td>\n",
       "      <td>0.000797885</td>\n",
       "      <td>0.000773078</td>\n",
       "      <td>0.0397447</td>\n",
       "      <td>0.0365453</td>\n",
       "      <td>0.0473337</td>\n",
       "      <td>0.0379371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-22</th>\n",
       "      <td>16.04</td>\n",
       "      <td>0.032840</td>\n",
       "      <td>0.0123506</td>\n",
       "      <td>0.00401859</td>\n",
       "      <td>0.00201846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0356236</td>\n",
       "      <td>0.0536738</td>\n",
       "      <td>0.0555708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00443899</td>\n",
       "      <td>-0.00841664</td>\n",
       "      <td>0.00027662</td>\n",
       "      <td>0.000443907</td>\n",
       "      <td>0.0399055</td>\n",
       "      <td>0.0439961</td>\n",
       "      <td>0.0471585</td>\n",
       "      <td>0.0378358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-23</th>\n",
       "      <td>16.95</td>\n",
       "      <td>0.056733</td>\n",
       "      <td>0.0115826</td>\n",
       "      <td>0.00468885</td>\n",
       "      <td>0.00351591</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0349773</td>\n",
       "      <td>0.0532236</td>\n",
       "      <td>0.0550878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>(-0.1254480286738351, 0.1286274509803922)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00862606</td>\n",
       "      <td>-0.00991246</td>\n",
       "      <td>-0.000363351</td>\n",
       "      <td>-0.000226453</td>\n",
       "      <td>0.0375937</td>\n",
       "      <td>0.0427814</td>\n",
       "      <td>0.0466589</td>\n",
       "      <td>0.037574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price  Daily_Return Pre1MonthAvgDailyRet Pre2MonthAvgDailyRet  \\\n",
       "Date                                                                        \n",
       "1986-04-14  12.94     -0.050624           0.00634561          -0.00231575   \n",
       "1986-04-15  12.72     -0.017002           0.00401284          -0.00274146   \n",
       "1986-04-16  11.50     -0.095912           0.00301215          -0.00422534   \n",
       "1986-04-17  11.75      0.021739           -0.0014844          -0.00597086   \n",
       "1986-04-18  11.88      0.011064          -0.00314022          -0.00531109   \n",
       "1986-04-21  12.48      0.050505           -0.0010583          -0.00307221   \n",
       "1986-04-22  13.13      0.052083          -0.00323893          -0.00248519   \n",
       "1986-04-23  13.70      0.043412         -0.000604535          0.000254826   \n",
       "1986-04-24  13.65     -0.003650           0.00139622           0.00213096   \n",
       "1986-04-25  14.23      0.042491           0.00693251           0.00199653   \n",
       "1986-04-28  13.34     -0.062544             0.013243           0.00122156   \n",
       "1986-04-29  13.63      0.021739           0.00963406         -0.000170263   \n",
       "1986-04-30  13.38     -0.018342            0.0101843           0.00127561   \n",
       "1986-05-01  13.80      0.031390           0.00894402            0.0022285   \n",
       "1986-05-02  14.65      0.061594            0.0145226           0.00290667   \n",
       "1986-05-05  14.32     -0.022526            0.0123408           0.00669199   \n",
       "1986-05-06  14.43      0.007682           0.00699461           0.00605629   \n",
       "1986-05-07  15.13      0.048510           0.00702584           0.00406209   \n",
       "1986-05-08  15.70      0.037673            0.0088295           0.00683099   \n",
       "1986-05-09  15.83      0.008280           0.00487498           0.00754825   \n",
       "1986-05-12  15.75     -0.005054           0.00861656           0.00602987   \n",
       "1986-05-13  15.65     -0.006349           0.00773863           0.00443382   \n",
       "1986-05-14  15.53     -0.007668           0.00709827           0.00673986   \n",
       "1986-05-15  15.68      0.009659           0.00645627           0.00665178   \n",
       "1986-05-16  16.08      0.025510           0.00907724           0.00672171   \n",
       "1986-05-19  17.13      0.065299            0.0154982           0.00475953   \n",
       "1986-05-20  16.18     -0.055458            0.0180808           0.00763795   \n",
       "1986-05-21  15.53     -0.040173            0.0147381           0.00721599   \n",
       "1986-05-22  16.04      0.032840            0.0123506           0.00401859   \n",
       "1986-05-23  16.95      0.056733            0.0115826           0.00468885   \n",
       "\n",
       "           Pre3MonthAvgDailyRet Pre6MonthAvgDailyRet Pre1MonthVolDaily  \\\n",
       "Date                                                                     \n",
       "1986-04-14                  NaN                  NaN         0.0690974   \n",
       "1986-04-15                  NaN                  NaN         0.0701853   \n",
       "1986-04-16                  NaN                  NaN         0.0686398   \n",
       "1986-04-17                  NaN                  NaN          0.070156   \n",
       "1986-04-18                  NaN                  NaN         0.0691513   \n",
       "1986-04-21                  NaN                  NaN         0.0699337   \n",
       "1986-04-22                  NaN                  NaN         0.0675721   \n",
       "1986-04-23                  NaN                  NaN         0.0669877   \n",
       "1986-04-24                  NaN                  NaN         0.0660867   \n",
       "1986-04-25                  NaN                  NaN         0.0600551   \n",
       "1986-04-28                  NaN                  NaN         0.0608748   \n",
       "1986-04-29                  NaN                  NaN          0.061561   \n",
       "1986-04-30                  NaN                  NaN         0.0601984   \n",
       "1986-05-01                  NaN                  NaN         0.0591619   \n",
       "1986-05-02                  NaN                  NaN         0.0548059   \n",
       "1986-05-05                  NaN                  NaN         0.0559842   \n",
       "1986-05-06                  NaN                  NaN         0.0536492   \n",
       "1986-05-07                  NaN                  NaN         0.0524159   \n",
       "1986-05-08                  NaN                  NaN         0.0519572   \n",
       "1986-05-09                  NaN                  NaN         0.0457832   \n",
       "1986-05-12          -0.00014533                  NaN         0.0403202   \n",
       "1986-05-13          0.000599491                  NaN         0.0404075   \n",
       "1986-05-14          0.000971684                  NaN         0.0395874   \n",
       "1986-05-15           0.00137647                  NaN         0.0388341   \n",
       "1986-05-16          0.000833957                  NaN         0.0368783   \n",
       "1986-05-19           0.00162532                  NaN         0.0299375   \n",
       "1986-05-20           0.00262021                  NaN         0.0317293   \n",
       "1986-05-21           0.00305008                  NaN         0.0345779   \n",
       "1986-05-22           0.00201846                  NaN         0.0356236   \n",
       "1986-05-23           0.00351591                  NaN         0.0349773   \n",
       "\n",
       "           Pre2MonthVolDaily Pre3MonthVolDaily Pre6MonthVolDaily  \\\n",
       "Date                                                               \n",
       "1986-04-14           0.06315               NaN               NaN   \n",
       "1986-04-15         0.0634148               NaN               NaN   \n",
       "1986-04-16         0.0630181               NaN               NaN   \n",
       "1986-04-17         0.0645244               NaN               NaN   \n",
       "1986-04-18         0.0638914               NaN               NaN   \n",
       "1986-04-21         0.0627686               NaN               NaN   \n",
       "1986-04-22         0.0631505               NaN               NaN   \n",
       "1986-04-23         0.0629618               NaN               NaN   \n",
       "1986-04-24         0.0630458               NaN               NaN   \n",
       "1986-04-25         0.0623145               NaN               NaN   \n",
       "1986-04-28         0.0622882               NaN               NaN   \n",
       "1986-04-29         0.0630399               NaN               NaN   \n",
       "1986-04-30         0.0628291               NaN               NaN   \n",
       "1986-05-01         0.0622178               NaN               NaN   \n",
       "1986-05-02          0.061647               NaN               NaN   \n",
       "1986-05-05         0.0610941               NaN               NaN   \n",
       "1986-05-06         0.0612557               NaN               NaN   \n",
       "1986-05-07         0.0597894               NaN               NaN   \n",
       "1986-05-08         0.0590868               NaN               NaN   \n",
       "1986-05-09         0.0585804               NaN               NaN   \n",
       "1986-05-12         0.0587124         0.0567413               NaN   \n",
       "1986-05-13         0.0580774         0.0563627               NaN   \n",
       "1986-05-14          0.055629         0.0562381               NaN   \n",
       "1986-05-15         0.0556488         0.0560824               NaN   \n",
       "1986-05-16         0.0549998         0.0558338               NaN   \n",
       "1986-05-19         0.0545857         0.0558246               NaN   \n",
       "1986-05-20         0.0545146         0.0559469               NaN   \n",
       "1986-05-21         0.0549312         0.0553905               NaN   \n",
       "1986-05-22         0.0536738         0.0555708               NaN   \n",
       "1986-05-23         0.0532236         0.0550878               NaN   \n",
       "\n",
       "                  ...                           Pre3MonthDailyPriceRange  \\\n",
       "Date              ...                                                      \n",
       "1986-04-14        ...                                                NaN   \n",
       "1986-04-15        ...                                                NaN   \n",
       "1986-04-16        ...                                                NaN   \n",
       "1986-04-17        ...                                                NaN   \n",
       "1986-04-18        ...                                                NaN   \n",
       "1986-04-21        ...                                                NaN   \n",
       "1986-04-22        ...                                                NaN   \n",
       "1986-04-23        ...                                                NaN   \n",
       "1986-04-24        ...                                                NaN   \n",
       "1986-04-25        ...                                                NaN   \n",
       "1986-04-28        ...                                                NaN   \n",
       "1986-04-29        ...                                                NaN   \n",
       "1986-04-30        ...                                                NaN   \n",
       "1986-05-01        ...                                                NaN   \n",
       "1986-05-02        ...                                                NaN   \n",
       "1986-05-05        ...                                                NaN   \n",
       "1986-05-06        ...                                                NaN   \n",
       "1986-05-07        ...                                                NaN   \n",
       "1986-05-08        ...                                                NaN   \n",
       "1986-05-09        ...                                                NaN   \n",
       "1986-05-12        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-13        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-14        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-15        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-16        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-19        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-20        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-21        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-22        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "1986-05-23        ...          (-0.1254480286738351, 0.1286274509803922)   \n",
       "\n",
       "           Pre6MonthDailyPriceRange next1MonthAvgDailyRet  \\\n",
       "Date                                                        \n",
       "1986-04-14                      NaN            0.00907724   \n",
       "1986-04-15                      NaN             0.0109256   \n",
       "1986-04-16                      NaN             0.0157818   \n",
       "1986-04-17                      NaN             0.0154982   \n",
       "1986-04-18                      NaN             0.0180808   \n",
       "1986-04-21                      NaN             0.0115826   \n",
       "1986-04-22                      NaN             0.0117847   \n",
       "1986-04-23                      NaN             0.0103471   \n",
       "1986-04-24                      NaN             0.0110136   \n",
       "1986-04-25                      NaN            0.00943979   \n",
       "1986-04-28                      NaN             0.0046434   \n",
       "1986-04-29                      NaN             0.0030283   \n",
       "1986-04-30                      NaN            0.00404593   \n",
       "1986-05-01                      NaN            0.00267872   \n",
       "1986-05-02                      NaN           -0.00214924   \n",
       "1986-05-05                      NaN           -0.00288575   \n",
       "1986-05-06                      NaN           -0.00488655   \n",
       "1986-05-07                      NaN           -0.00742925   \n",
       "1986-05-08                      NaN           -0.00968438   \n",
       "1986-05-09                      NaN            -0.0105697   \n",
       "1986-05-12                      NaN           -0.00545101   \n",
       "1986-05-13                      NaN           -0.00469758   \n",
       "1986-05-14                      NaN           -0.00455614   \n",
       "1986-05-15                      NaN           -0.00526688   \n",
       "1986-05-16                      NaN           -0.00719315   \n",
       "1986-05-19                      NaN           -0.00924014   \n",
       "1986-05-20                      NaN           -0.00436878   \n",
       "1986-05-21                      NaN           -0.00266381   \n",
       "1986-05-22                      NaN           -0.00443899   \n",
       "1986-05-23                      NaN           -0.00862606   \n",
       "\n",
       "           next2MonthAvgDailyRet next3MonthAvgDailyRet next6MonthAvgDailyRet  \\\n",
       "Date                                                                           \n",
       "1986-04-14            0.00240556           -0.00155169            0.00196909   \n",
       "1986-04-15            0.00286763          -0.000405486            0.00212085   \n",
       "1986-04-16            0.00484136            0.00222871            0.00265851   \n",
       "1986-04-17            0.00432376            0.00140795            0.00263855   \n",
       "1986-04-18            0.00400801            0.00187758             0.0023882   \n",
       "1986-04-21            0.00413068            0.00141073            0.00216035   \n",
       "1986-04-22            0.00298895           -0.00207566            0.00191607   \n",
       "1986-04-23            0.00131228           -0.00283768            0.00159514   \n",
       "1986-04-24            0.00128055           -0.00260387            0.00142986   \n",
       "1986-04-25           -0.00100847           -0.00345228            0.00110609   \n",
       "1986-04-28           0.000736053           -0.00207845            0.00163333   \n",
       "1986-04-29           0.000235979           -0.00165062            0.00117542   \n",
       "1986-04-30          -0.000359411             -0.001223            0.00119994   \n",
       "1986-05-01           -0.00186944           -0.00239785           0.000694936   \n",
       "1986-05-02           -0.00400856            -0.0029091           0.000988781   \n",
       "1986-05-05            -0.0041446           0.000798809            0.00125917   \n",
       "1986-05-06           -0.00443304            0.00107371           0.000909194   \n",
       "1986-05-07           -0.00670022           0.000801464            0.00071159   \n",
       "1986-05-08           -0.00759727           0.000611023           0.000346415   \n",
       "1986-05-09           -0.00821336           0.000113612           0.000360548   \n",
       "1986-05-12           -0.00761609           0.000290159           0.000438113   \n",
       "1986-05-13           -0.00764699            0.00100799           0.000568114   \n",
       "1986-05-14           -0.00724084           0.000904403           0.000676515   \n",
       "1986-05-15           -0.00612985           0.000906911           0.000568056   \n",
       "1986-05-16            -0.0050437           0.000913472           0.000478782   \n",
       "1986-05-19           -0.00622401          -0.000373692           2.32749e-05   \n",
       "1986-05-20           -0.00502317          -0.000104686           0.000436329   \n",
       "1986-05-21           -0.00352886           0.000797885           0.000773078   \n",
       "1986-05-22           -0.00841664            0.00027662           0.000443907   \n",
       "1986-05-23           -0.00991246          -0.000363351          -0.000226453   \n",
       "\n",
       "           next1MonthVolDaily next2MonthVolDaily next3MonthVolDaily  \\\n",
       "Date                                                                  \n",
       "1986-04-14          0.0368783          0.0411855          0.0371498   \n",
       "1986-04-15          0.0365891          0.0415626          0.0377644   \n",
       "1986-04-16          0.0292781          0.0386943          0.0367858   \n",
       "1986-04-17          0.0299375          0.0386101           0.036918   \n",
       "1986-04-18          0.0317293          0.0386079          0.0372247   \n",
       "1986-04-21          0.0349773          0.0381958          0.0367939   \n",
       "1986-04-22          0.0352234          0.0379157          0.0418816   \n",
       "1986-04-23          0.0353489          0.0376472          0.0414818   \n",
       "1986-04-24          0.0360455          0.0376519          0.0415181   \n",
       "1986-04-25          0.0362246          0.0379977          0.0411323   \n",
       "1986-04-28          0.0407576          0.0364495          0.0405773   \n",
       "1986-04-29          0.0407522          0.0367348          0.0409664   \n",
       "1986-04-30          0.0414372          0.0372304          0.0409305   \n",
       "1986-05-01          0.0419957          0.0371982          0.0410397   \n",
       "1986-05-02          0.0404676          0.0360527          0.0404355   \n",
       "1986-05-05          0.0390006           0.036133          0.0483939   \n",
       "1986-05-06          0.0395326          0.0365232          0.0484813   \n",
       "1986-05-07          0.0386653          0.0360484          0.0482618   \n",
       "1986-05-08          0.0382487          0.0353845          0.0481395   \n",
       "1986-05-09          0.0380269          0.0353225          0.0482195   \n",
       "1986-05-12           0.042344          0.0350505          0.0482206   \n",
       "1986-05-13          0.0424686          0.0354749          0.0484525   \n",
       "1986-05-14          0.0434629          0.0355675          0.0484779   \n",
       "1986-05-15           0.044417          0.0367691          0.0484784   \n",
       "1986-05-16          0.0438725           0.038313          0.0484817   \n",
       "1986-05-19           0.038998          0.0371247          0.0478273   \n",
       "1986-05-20          0.0396092          0.0367599          0.0475636   \n",
       "1986-05-21          0.0397447          0.0365453          0.0473337   \n",
       "1986-05-22          0.0399055          0.0439961          0.0471585   \n",
       "1986-05-23          0.0375937          0.0427814          0.0466589   \n",
       "\n",
       "           next6MonthVolDaily  \n",
       "Date                           \n",
       "1986-04-14          0.0402677  \n",
       "1986-04-15          0.0403925  \n",
       "1986-04-16          0.0395219  \n",
       "1986-04-17          0.0395129  \n",
       "1986-04-18          0.0395579  \n",
       "1986-04-21          0.0393616  \n",
       "1986-04-22          0.0391452  \n",
       "1986-04-23          0.0389677  \n",
       "1986-04-24          0.0390334  \n",
       "1986-04-25          0.0388589  \n",
       "1986-04-28          0.0384365  \n",
       "1986-04-29          0.0385351  \n",
       "1986-04-30          0.0385236  \n",
       "1986-05-01          0.0385383  \n",
       "1986-05-02          0.0391369  \n",
       "1986-05-05          0.0390902  \n",
       "1986-05-06          0.0392268  \n",
       "1986-05-07          0.0390483  \n",
       "1986-05-08          0.0389141  \n",
       "1986-05-09          0.0389173  \n",
       "1986-05-12          0.0389161  \n",
       "1986-05-13          0.0389204  \n",
       "1986-05-14          0.0389162  \n",
       "1986-05-15          0.0389099  \n",
       "1986-05-16          0.0388653  \n",
       "1986-05-19          0.0384342  \n",
       "1986-05-20          0.0381118  \n",
       "1986-05-21          0.0379371  \n",
       "1986-05-22          0.0378358  \n",
       "1986-05-23           0.037574  \n",
       "\n",
       "[30 rows x 22 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_OUT4 = data_OUT3.join([next1MonthAvgDailyRet,next2MonthAvgDailyRet,next3MonthAvgDailyRet,next6MonthAvgDailyRet,\n",
    "                next1MonthVolDaily,next2MonthVolDaily,next3MonthVolDaily,next6MonthVolDaily])\n",
    "data_OUT4.iloc[70:100,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_OUT4.to_csv('Step1_RawY_variable_and_processing.csv')\n",
    "data_OUT4.to_pickle(\"Step1_RawY_variable_and_processing.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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