Draft-2.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draft 2 - Capstone\n",
    "\n",
    "## Stock Price Predictor\n",
    "\n",
    "### Inputs\n",
    "* stock symbol of NYSE or NASDAQ\n",
    "* daily trading data\n",
    "* past date range for training model\n",
    "  * range of 1 month or more\n",
    "  * end date at least 1 month ago\n",
    "  \n",
    "\n",
    "#### Trading Data Features\n",
    "* Opening Price (Open)\n",
    "* Highest stock trading price (High)\n",
    "* Number of stock shares traded (Volume)\n",
    "* Closing price adjusted for splits and dividends (Adjusted Close)\n",
    "\n",
    "### Processing\n",
    "\n",
    "#### Parameters\n",
    "* test date range, after training date range\n",
    "* prediction date, after test date range\n",
    "\n",
    "#### Model\n",
    "* regression model\n",
    "\n",
    "*TODO: select a model or set of models*\n",
    "\n",
    "### Output\n",
    "Predicted Adjusted Close for the prediction date, a specific trading day.\n",
    "\n",
    "### Metric for Evaluation\n",
    "Predicted stock price within -/+5% of actual Adjusted Close\n",
    "\n",
    "_If the prediction date was a day when the stock was not traded (such as a holiday), then the Predicted Adjusted Close will be reported without the comparison percentage._\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration\n",
    "\n",
    "### Inputs\n",
    "First, the stock symbol and date range are collected and minimally checked.\n",
    "\n",
    "The data range will identify the stock data to be used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock for price prediction:\n",
      "* AAPL\n",
      "\n",
      "Training data: \n",
      "* start date 2015-12-31 \n",
      "* end date 2017-01-01\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Stock Symbol - Apple Inc. (AAPL) NASDAQ\n",
    "stock_symbol = 'aapl'.upper()\n",
    "\n",
    "# TODO: early moved from 2012 to 2015\n",
    "# Date range for training data; backwards so courtesy swap flagged\n",
    "start_date = datetime.strptime('2017-01-01', '%Y-%m-%d')\n",
    "end_date = datetime.strptime('2015-12-31', '%Y-%m-%d')\n",
    "\n",
    "# Validate date range; exchange values if invalid range\n",
    "if (start_date > end_date):\n",
    "    (start_date, end_date) = (end_date, start_date)\n",
    "\n",
    "# Adjusted end date one month out\n",
    "end_date_plus_month = datetime.fromordinal(end_date.toordinal() + 31)\n",
    "\n",
    "# Check end date plus one month is past today\n",
    "today = datetime.now()\n",
    "if (end_date_plus_month > today):\n",
    "    print('{} is not the minimum 31 days before today {} (end date + 31 = {})'.format(end_date.date(), today.date(),\n",
    "                                                                                       end_date_plus_month.date()))\n",
    "elif ((end_date.toordinal()) - (start_date.toordinal()) < 31):\n",
    "    print('{} to {} is not the minimum 30 days'.format(start_date.date(), end_date.date()))\n",
    "else:\n",
    "    print('Stock for price prediction:\\n* {}'.format(stock_symbol))\n",
    "    print('\\nTraining data: \\n* start date {} \\n* end date {}'.format(start_date.date(), end_date.date()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process\n",
    "Select test date range after training date range, ? before ?\n",
    "Select prediction date between today's date and test end date.\n",
    "\n",
    "\n",
    "#### Model\n",
    "* Regression... TODO\n",
    "\n",
    "Try multiples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is 2017-03-27\n",
      "The prediction date is a weekday. No adjustment has been made for holidays or stock-specific non-trading day.\n",
      "The random test date is 2017-01-14\n"
     ]
    }
   ],
   "source": [
    "# A random date in test range\n",
    "import random\n",
    "test_date = random.randrange(end_date.toordinal(), today.toordinal(), 1)\n",
    "\n",
    "# Prediction date\n",
    "prediction_date = datetime.fromordinal(end_date.toordinal() + ((today.toordinal() - end_date.toordinal()) / 2))\n",
    "\n",
    "# adjust if non-weekday (ordinals 0-4 are M-F): Saturday back one day, Sunday forward one day\n",
    "if (prediction_date.weekday() == 5):\n",
    "    prediction_date = datetime.fromordinal(prediction_date.toordinal() - 1)\n",
    "elif (prediction_date.weekday() == 6):\n",
    "    prediction_date = datetime.fromordinal(prediction_date.toordinal() + 1)\n",
    "\n",
    "print('Prediction is {}'.format(prediction_date.date()))\n",
    "print('The prediction date is a weekday. No adjustment has been made for holidays or stock-specific non-trading day.')\n",
    "print('The random test date is {}'.format(datetime.fromordinal(test_date).date()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stock Data\n",
    "Stock data will be downloaded from [Quandl](https:///www.quandl.com).\n",
    "\n",
    "> \"Quandl (/ˈkwɑːndəl/) is a platform for financial, economic, and alternative data that serves investment professionals. Quandl sources data from over 500 publishers. All Quandl's data are accessible via an API. API access is possible through packages for multiple programming languages including R, Python, Matlab, Maple (software) and Stata.\" [Wikipedia](https://en.wikipedia.org/wiki/Quandl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import quandl\n",
    "\n",
    "#token = '' # TODO:  quandl token, .gitignore item\n",
    "\n",
    "# time series call for pd.DataFrame object\n",
    "data = quandl.get(\"WIKI/\"+stock_symbol, start_date=start_date, end_date=end_date)\n",
    "\n",
    "# to pull as a numpy array\n",
    "# data = quandl.get(\"WIKI/AAPL\", returns=\"numpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>107.01</td>\n",
       "      <td>107.030</td>\n",
       "      <td>104.82</td>\n",
       "      <td>105.26</td>\n",
       "      <td>40912316.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>103.807060</td>\n",
       "      <td>103.826461</td>\n",
       "      <td>101.682609</td>\n",
       "      <td>102.109440</td>\n",
       "      <td>40912316.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>102.61</td>\n",
       "      <td>105.368</td>\n",
       "      <td>102.00</td>\n",
       "      <td>105.35</td>\n",
       "      <td>67649387.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.538757</td>\n",
       "      <td>102.214207</td>\n",
       "      <td>98.947015</td>\n",
       "      <td>102.196746</td>\n",
       "      <td>67649387.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>105.75</td>\n",
       "      <td>105.850</td>\n",
       "      <td>102.41</td>\n",
       "      <td>102.71</td>\n",
       "      <td>55790992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.584773</td>\n",
       "      <td>102.681780</td>\n",
       "      <td>99.344744</td>\n",
       "      <td>99.635764</td>\n",
       "      <td>55790992.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>100.56</td>\n",
       "      <td>102.370</td>\n",
       "      <td>99.87</td>\n",
       "      <td>100.70</td>\n",
       "      <td>68457388.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97.550116</td>\n",
       "      <td>99.305941</td>\n",
       "      <td>96.880769</td>\n",
       "      <td>97.685926</td>\n",
       "      <td>68457388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>98.68</td>\n",
       "      <td>100.130</td>\n",
       "      <td>96.43</td>\n",
       "      <td>96.45</td>\n",
       "      <td>81094428.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.726387</td>\n",
       "      <td>97.132987</td>\n",
       "      <td>93.543732</td>\n",
       "      <td>93.563134</td>\n",
       "      <td>81094428.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              Open     High     Low   Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                   \n",
       "2015-12-31  107.01  107.030  104.82  105.26  40912316.0          0.0   \n",
       "2016-01-04  102.61  105.368  102.00  105.35  67649387.0          0.0   \n",
       "2016-01-05  105.75  105.850  102.41  102.71  55790992.0          0.0   \n",
       "2016-01-06  100.56  102.370   99.87  100.70  68457388.0          0.0   \n",
       "2016-01-07   98.68  100.130   96.43   96.45  81094428.0          0.0   \n",
       "\n",
       "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
       "Date                                                                      \n",
       "2015-12-31          1.0  103.807060  103.826461  101.682609  102.109440   \n",
       "2016-01-04          1.0   99.538757  102.214207   98.947015  102.196746   \n",
       "2016-01-05          1.0  102.584773  102.681780   99.344744   99.635764   \n",
       "2016-01-06          1.0   97.550116   99.305941   96.880769   97.685926   \n",
       "2016-01-07          1.0   95.726387   97.132987   93.543732   93.563134   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
       "2015-12-31   40912316.0  \n",
       "2016-01-04   67649387.0  \n",
       "2016-01-05   55790992.0  \n",
       "2016-01-06   68457388.0  \n",
       "2016-01-07   81094428.0  "
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   "source": [
    "data.head()"
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       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
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       "      <th>2016-12-23</th>\n",
       "      <td>60.45</td>\n",
       "      <td>61.350</td>\n",
       "      <td>60.2</td>\n",
       "      <td>61.20</td>\n",
       "      <td>150911.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>150911.0</td>\n",
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       "      <th>2016-12-27</th>\n",
       "      <td>61.40</td>\n",
       "      <td>61.650</td>\n",
       "      <td>60.8</td>\n",
       "      <td>60.85</td>\n",
       "      <td>168563.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>60.831665</td>\n",
       "      <td>61.079351</td>\n",
       "      <td>60.237219</td>\n",
       "      <td>60.286756</td>\n",
       "      <td>168563.0</td>\n",
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       "      <th>2016-12-28</th>\n",
       "      <td>61.05</td>\n",
       "      <td>61.300</td>\n",
       "      <td>59.6</td>\n",
       "      <td>60.05</td>\n",
       "      <td>181645.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.484905</td>\n",
       "      <td>60.732591</td>\n",
       "      <td>59.048326</td>\n",
       "      <td>59.494161</td>\n",
       "      <td>181645.0</td>\n",
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       "      <th>2016-12-29</th>\n",
       "      <td>60.20</td>\n",
       "      <td>60.525</td>\n",
       "      <td>59.6</td>\n",
       "      <td>60.00</td>\n",
       "      <td>116698.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>59.444624</td>\n",
       "      <td>116698.0</td>\n",
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       "      <th>2016-12-30</th>\n",
       "      <td>59.95</td>\n",
       "      <td>59.950</td>\n",
       "      <td>59.1</td>\n",
       "      <td>59.40</td>\n",
       "      <td>154468.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.395087</td>\n",
       "      <td>59.395087</td>\n",
       "      <td>58.552955</td>\n",
       "      <td>58.850178</td>\n",
       "      <td>154468.0</td>\n",
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       "             Open    High   Low  Close    Volume  Ex-Dividend  Split Ratio  \\\n",
       "Date                                                                         \n",
       "2016-12-23  60.45  61.350  60.2  61.20  150911.0          0.0          1.0   \n",
       "2016-12-27  61.40  61.650  60.8  60.85  168563.0          0.0          1.0   \n",
       "2016-12-28  61.05  61.300  59.6  60.05  181645.0          0.0          1.0   \n",
       "2016-12-29  60.20  60.525  59.6  60.00  116698.0          0.0          1.0   \n",
       "2016-12-30  59.95  59.950  59.1  59.40  154468.0          0.0          1.0   \n",
       "\n",
       "            Adj. Open  Adj. High   Adj. Low  Adj. Close  Adj. Volume  \n",
       "Date                                                                  \n",
       "2016-12-23  59.890459  60.782128  59.642773   60.633516     150911.0  \n",
       "2016-12-27  60.831665  61.079351  60.237219   60.286756     168563.0  \n",
       "2016-12-28  60.484905  60.732591  59.048326   59.494161     181645.0  \n",
       "2016-12-29  59.642773  59.964764  59.048326   59.444624     116698.0  \n",
       "2016-12-30  59.395087  59.395087  58.552955   58.850178     154468.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore if both NASDAQ and NYSE stock symbol data are available (Quandl), via 'Applied Industrial Technologies' check\n",
    "nyse_stock_symbol = 'AIT'\n",
    "nyse_data = quandl.get(\"WIKI/\"+nyse_stock_symbol, start_date=start_date, end_date=end_date)\n",
    "\n",
    "nyse_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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       "      <th>2017-01-03</th>\n",
       "      <td>115.80</td>\n",
       "      <td>116.3300</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
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       "      <th>2017-01-04</th>\n",
       "      <td>115.85</td>\n",
       "      <td>116.5100</td>\n",
       "      <td>115.75</td>\n",
       "      <td>116.02</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.883333</td>\n",
       "      <td>115.537826</td>\n",
       "      <td>114.784167</td>\n",
       "      <td>115.051914</td>\n",
       "      <td>21118116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>115.92</td>\n",
       "      <td>116.8642</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
       "      <td>22193587.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.952749</td>\n",
       "      <td>115.889070</td>\n",
       "      <td>114.843667</td>\n",
       "      <td>115.636991</td>\n",
       "      <td>22193587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>116.78</td>\n",
       "      <td>118.1600</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.805573</td>\n",
       "      <td>117.174058</td>\n",
       "      <td>115.498159</td>\n",
       "      <td>116.926144</td>\n",
       "      <td>31751900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>117.95</td>\n",
       "      <td>119.4300</td>\n",
       "      <td>117.94</td>\n",
       "      <td>118.99</td>\n",
       "      <td>33561948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.965810</td>\n",
       "      <td>118.433461</td>\n",
       "      <td>116.955894</td>\n",
       "      <td>117.997132</td>\n",
       "      <td>33561948.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open      High     Low   Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                    \n",
       "2017-01-03  115.80  116.3300  114.76  116.15  28781865.0          0.0   \n",
       "2017-01-04  115.85  116.5100  115.75  116.02  21118116.0          0.0   \n",
       "2017-01-05  115.92  116.8642  115.81  116.61  22193587.0          0.0   \n",
       "2017-01-06  116.78  118.1600  116.47  117.91  31751900.0          0.0   \n",
       "2017-01-09  117.95  119.4300  117.94  118.99  33561948.0          0.0   \n",
       "\n",
       "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
       "Date                                                                      \n",
       "2017-01-03          1.0  114.833750  115.359328  113.802428  115.180830   \n",
       "2017-01-04          1.0  114.883333  115.537826  114.784167  115.051914   \n",
       "2017-01-05          1.0  114.952749  115.889070  114.843667  115.636991   \n",
       "2017-01-06          1.0  115.805573  117.174058  115.498159  116.926144   \n",
       "2017-01-09          1.0  116.965810  118.433461  116.955894  117.997132   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
       "2017-01-03   28781865.0  \n",
       "2017-01-04   21118116.0  \n",
       "2017-01-05   22193587.0  \n",
       "2017-01-06   31751900.0  \n",
       "2017-01-09   33561948.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = quandl.get(\"WIKI/\"+stock_symbol, start_date=end_date, end_date=prediction_date)\n",
    "test_data.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-27</th>\n",
       "      <td>139.39</td>\n",
       "      <td>141.22</td>\n",
       "      <td>138.62</td>\n",
       "      <td>140.88</td>\n",
       "      <td>23575094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>138.821908</td>\n",
       "      <td>140.644449</td>\n",
       "      <td>138.055046</td>\n",
       "      <td>140.305835</td>\n",
       "      <td>23575094.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                  \n",
       "2017-03-27  139.39  141.22  138.62  140.88  23575094.0          0.0   \n",
       "\n",
       "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
       "Date                                                                      \n",
       "2017-03-27          1.0  138.821908  140.644449  138.055046  140.305835   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
       "2017-03-27   23575094.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual stock data for prediction date\n",
    "actual_data = quandl.get(\"WIKI/\"+stock_symbol, start_date=prediction_date, end_date=prediction_date)\n",
    "actual_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj. Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>253.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>2.530000e+02</td>\n",
       "      <td>253.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>104.515099</td>\n",
       "      <td>105.433398</td>\n",
       "      <td>3.844579e+07</td>\n",
       "      <td>102.727382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.571679</td>\n",
       "      <td>7.540072</td>\n",
       "      <td>1.709515e+07</td>\n",
       "      <td>7.949825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>91.670000</td>\n",
       "      <td>1.147592e+07</td>\n",
       "      <td>88.646385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>97.320000</td>\n",
       "      <td>98.210000</td>\n",
       "      <td>2.719398e+07</td>\n",
       "      <td>95.053967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>105.520000</td>\n",
       "      <td>106.310000</td>\n",
       "      <td>3.379445e+07</td>\n",
       "      <td>103.302789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>110.365000</td>\n",
       "      <td>111.510000</td>\n",
       "      <td>4.439276e+07</td>\n",
       "      <td>109.330085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118.180000</td>\n",
       "      <td>118.690000</td>\n",
       "      <td>1.333697e+08</td>\n",
       "      <td>116.657871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open        High        Volume  Adj. Close\n",
       "count  253.000000  253.000000  2.530000e+02  253.000000\n",
       "mean   104.515099  105.433398  3.844579e+07  102.727382\n",
       "std      7.571679    7.540072  1.709515e+07    7.949825\n",
       "min     90.000000   91.670000  1.147592e+07   88.646385\n",
       "25%     97.320000   98.210000  2.719398e+07   95.053967\n",
       "50%    105.520000  106.310000  3.379445e+07  103.302789\n",
       "75%    110.365000  111.510000  4.439276e+07  109.330085\n",
       "max    118.180000  118.690000  1.333697e+08  116.657871"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Input features dataframe\n",
    "# test_data[['Open','High', 'Volume','Adj. Close']]\n",
    "\n",
    "# Review of P4F ch 6 items\n",
    "# test_data.index\n",
    "# test_data.columns\n",
    "# test_data.ix[['2016-12-23']] -- why NaNs\n",
    "# test_data.ix[test_data.index[1:3]] -- works ['2013-01-02','2013-01-03']\n",
    "# test_data[['Open','High', 'Volume','Adj. Close']].std()\n",
    "# test_data[['Open','High', 'Volume','Adj. Close']].mean()\n",
    "\n",
    "data[['Open','High', 'Volume','Adj. Close']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbac66aa090>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JSeHatWscPHiQ4OBgLCwsOHfuHGXLlgXI3NHtv2rYsCE9e/bk77//pnr16o88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\nN7EzOMr7oFj960W6Vo6sEgef6cUNTfZsN3A3atiZXgZnxt3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbac8e9a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tnzL1u6meImH1ffxaYz8lBfhBrZR+YeULVDVV+e/imlbZXbmbwc8P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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbac8e9a690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p3y2J5FBbGzoLJFErNbXkUE6bg10PoRLJwXVN/fLL5ORQTLVSId5KUFxy8JJD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\nOUTtZFG1kksI8+dntp+4UtX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2AavOs/XOta80BlH14/ffJ1v3JQpPDs0Ma66p8ZhywYaa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\n0KhGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbac66f9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "data[['Open','High', 'Close', 'Adj. Close']].cumsum().plot(lw=2.0)\n",
    "\n",
    "data[['Open','High']].plot(lw=2.0)\n",
    "\n",
    "data[['Volume']].plot(lw=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Why is the 'Adj. Close' lower than 'Close'\n",
    ">An adjusted closing price is a stock's closing price on any given day of trading that has been amended to include any distributions and corporate actions that occurred at any time prior to the next day's open. The adjusted closing price is often used when examining historical returns or performing a detailed analysis on historical returns. \n",
    "[Investopedia 'What is Adjusted Closing Price](http://www.investopedia.com/terms/a/adjusted_closing_price.asp) \n",
    "\n",
    "#### What happened to Apple's stock during June 2014?\n",
    "Apple stock underwent a 7-to-1 stock split on June 9, 2014 [CNN Money](http://money.cnn.com/2014/06/09/investing/apple-stock-split-reactions/index.html) which is reflected in the adjusted close."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbac66ae250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kyHwLEuC665Kth9dfz7XY/BFPSVhx2GwzfV+0KG6oFy9OjjnY423eli/XOMqJ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\nwf8BMGyJBe2EnGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbac664f890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data['Return'] = np.log(data['Close'] / data['Close'].shift(1))\n",
    "\n",
    "data[['Return']].plot(lw=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When examining returns using the close, that data naively shows a large drop on the day of the stock split. \n",
    "\n",
    "The adjusted close data shows the clearer indications of returns by accounting for the 7-to-1 stock split and the log return values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fbac62c9cd0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fbac6220890>], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a+X3/fp2HNGWKb/dr00bbeLRrV/C2+vW1Osg5TaGXkaF97l59tWCzbbLJ6ktX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a7xHKcaSIMmVYHMOQRBUKUnjLWsKEtWJGX2D5GywNFrpIwgCA1BELsIgrhIEMTPBEFwrgQEQcwk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\nKGVBEARBEAR1QChlQRAEQRAEdcD/A2qMzyHAw/E6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbac627bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove return column based on close\n",
    "del data['Return']\n",
    "\n",
    "data['Return'] = np.log(data['Adj. Close'] / data['Adj. Close'].shift(1))\n",
    "\n",
    "data[['Adj. Close', 'Return']].plot(subplots=True, figsize=(10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbac610a4d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WLoR27fRi3ZUrQ/fu5mXKQkLMD8oiIvSTfs6gXDkYMkTPpfvtN/3AwnvvQXJq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\nJEnSAFIpS5IkSZIkaQD/A+ocSpRwPyyjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbac61123d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data['42d Mean'] = data['Adj. Close'].rolling(window=42, center=False).mean()\n",
    "data['252d Mean'] = data['Adj. Close'].rolling(window=252, center=False).mean()\n",
    "\n",
    "data[['Adj. Close', '42d Mean', '252d Mean']].plot(figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Golden Cross\n",
    ">The golden cross is a bullish breakout pattern formed from a crossover involving a security's short-term moving average (such as the 15-day moving average) breaking above its long-term moving average (such as 50-day moving average) or resistance level. [Investopedia](http://www.investopedia.com/terms/g/goldencross.asp)\n",
    "\n",
    "For the previous graph, the 'moving average' (or rolling mean) uses 42 and 252 as short and long term values.\n",
    "* 42 is the typical number of trading days for 2 month period\n",
    "* 252 is the typical number of trading days in a 1 year\n",
    "\n",
    "It may be noted there are varied opinions on the value of the Golden Cross as a technical trading tool:\n",
    "* http://www.stockopedia.com/content/trading-the-golden-cross-does-it-really-work-69694/\n",
    "* http://www.cnbc.com/2017/05/22/golds-golden-cross-the-metal-just-formed-a-chart-pattern-that-can-signal-a-breakout.html\n",
    "* http://www.cnbc.com/2016/04/26/looks-like-the-golden-cross-isnt-so-golden-after-all.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = data[['Open','High', 'Volume']]\n",
    "y = data[['Adj. Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore like BigData MadeSimple linear regression example\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated intercept coefficient [-4.93757668]\n",
      "Number of coefficients 3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Open</td>\n",
       "      <td>-2.340081e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High</td>\n",
       "      <td>1.263563e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Volume</td>\n",
       "      <td>-2.859719e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Features         Coeff\n",
       "0     Open -2.340081e-01\n",
       "1     High  1.263563e+00\n",
       "2   Volume -2.859719e-08"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Estimated intercept coefficient {}'.format(linear_model.intercept_))\n",
    "print('Number of coefficients {}'.format(len(linear_model.coef_[0])))\n",
    "pd.DataFrame(zip(X.columns, linear_model.coef_[0]), columns=['Features', 'Coeff'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Adj. Close 140.210271196 vs actual Adj. Close 140.305835166; within -0.07% of actual\n"
     ]
    }
   ],
   "source": [
    "actual_adj_close = actual_data[['Adj. Close']].iloc[0]['Adj. Close']\n",
    "predicted_y = linear_model.predict(actual_data[['Open','High','Volume']])\n",
    "print('Predicted Adj. Close {} vs actual Adj. Close {}; within {:.2f}% of actual'.format(predicted_y[0][0], actual_adj_close,\n",
    "                                                                             ((predicted_y[0][0] - actual_adj_close) / actual_adj_close)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_test = 0.2\n",
    "X_train = None\n",
    "X_test = None\n",
    "y_train = None\n",
    "y_test = None\n",
    "random_state = 42\n",
    "\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=num_test, random_state=random_state)\n",
    "\n",
    "linear_model.fit(X_train,y_train)\n",
    "pred_train = linear_model.predict(X_train)\n",
    "pred_test = linear_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Adj. Close 140.233539419 vs actual Adj. Close 140.305835166; within -0.05% of actual\n"
     ]
    }
   ],
   "source": [
    "predicted_y = linear_model.predict(actual_data[['Open','High','Volume']])\n",
    "print('Predicted Adj. Close {} vs actual Adj. Close {}; within {:.2f}% of actual'.format(predicted_y[0][0],\n",
    "                                                                                         actual_adj_close,\n",
    "                                                                             (( predicted_y[0][0] - actual_adj_close) / actual_adj_close)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [[ -1.89045296e-01   1.22102140e+00  -2.78322110e-08]]\n",
      "Mean squared error: {0.77}\n",
      "Variance score: 0.99\n"
     ]
    }
   ],
   "source": [
    "# The coefficients\n",
    "print('Coefficients: {}'.format(linear_model.coef_))\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: {%.2f}\" % np.mean((linear_model.predict(X_test) - y_test) ** 2))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % linear_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import LogisticRegression \n",
    "#logreg_model = LogisticRegression(random_state=random_state)\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=num_test, random_state=random_state)\n",
    "\n",
    "#logreg_model.fit(X_train, y_train.values)\n",
    "#pred_train = logreg_model.predict(X_train)\n",
    "#pred_test = logreg_model.predict(X_test)\n",
    "\n",
    "#predicted_y = logreg_model.predict(actual_data[['Open','High','Volume']])\n",
    "#print('Predicted Adj. Close {} vs actual Adj. Close {}; within {:.2f}% of actual'.format(predicted_y[0][0],\n",
    "#                                                                                         actual_adj_close,\n",
    "#                                                                             (( predicted_y[0][0] - actual_adj_close) / actual_adj_close)*100))\n",
    "#y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# http://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\n",
    "#svr_lin = SVR(kernel='linear', C=1e3)\n",
    "#svr_poly = SVR(kernel='poly', C=1e3, degree=2)\n",
    "\n",
    "# y is data[['Adj. Close']]\n",
    "y_val_ravel = y.values\n",
    "y_val_ravel = y_val_ravel.ravel()\n",
    "\n",
    "y_rbf = svr_rbf.fit(X, y_val_ravel).predict(X)\n",
    "#y_lin = svr_lin.fit(X, y).predict(X)\n",
    "#y_poly = svr_poly.fit(X, y_val_ravel).predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#svr_lin = SVR(kernel='linear', C=1e2)\n",
    "\n",
    "#y_lin = svr_lin.fit(X, y_val_ravel).predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lw = 2\n",
    "plt.scatter(X, y, color='darkorange', label='data')\n",
    "plt.hold('on')\n",
    "plt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')\n",
    "#plt.plot(X, y_lin, color='c', lw=lw, label='Linear model')\n",
    "#plt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')\n",
    "plt.xlabel('data')\n",
    "plt.ylabel('target')\n",
    "plt.title('Support Vector Regression')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
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