Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.api.types import is_list_like\n",
    "import pandas_datareader as pdr\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly\n",
    "from plotly.plotly import plot_mpl\n",
    "plotly.tools.set_credentials_file(username='rkawall', api_key='lE6jNupk5R5RQQuDEUet')\n",
    "from datetime import datetime\n",
    "import requests\n",
    "import matplotlib\n",
    "from matplotlib import pyplot\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from pyramid.arima import auto_arima\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_MONTHLY&symbol=MSFT&apikey=K87BB8H31SVY3OBA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Meta Data', 'Monthly Time Series'])\n"
     ]
    }
   ],
   "source": [
    "print(data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(data['Time Series (5min)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(data['Monthly Time Series'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "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>2019-02-06</th>\n",
       "      <th>2019-01-31</th>\n",
       "      <th>2018-12-31</th>\n",
       "      <th>2018-11-30</th>\n",
       "      <th>2018-10-31</th>\n",
       "      <th>2018-09-28</th>\n",
       "      <th>2018-08-31</th>\n",
       "      <th>2018-07-31</th>\n",
       "      <th>2018-06-29</th>\n",
       "      <th>2018-05-31</th>\n",
       "      <th>...</th>\n",
       "      <th>1998-11-30</th>\n",
       "      <th>1998-10-30</th>\n",
       "      <th>1998-09-30</th>\n",
       "      <th>1998-08-31</th>\n",
       "      <th>1998-07-31</th>\n",
       "      <th>1998-06-30</th>\n",
       "      <th>1998-05-29</th>\n",
       "      <th>1998-04-30</th>\n",
       "      <th>1998-03-31</th>\n",
       "      <th>1998-02-27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1. open</th>\n",
       "      <td>103.7750</td>\n",
       "      <td>99.5500</td>\n",
       "      <td>113.0000</td>\n",
       "      <td>107.0500</td>\n",
       "      <td>114.7500</td>\n",
       "      <td>110.8500</td>\n",
       "      <td>106.0300</td>\n",
       "      <td>98.1000</td>\n",
       "      <td>99.2798</td>\n",
       "      <td>93.2100</td>\n",
       "      <td>...</td>\n",
       "      <td>106.3700</td>\n",
       "      <td>108.0600</td>\n",
       "      <td>95.2500</td>\n",
       "      <td>109.1200</td>\n",
       "      <td>109.1200</td>\n",
       "      <td>83.8700</td>\n",
       "      <td>90.1900</td>\n",
       "      <td>89.8100</td>\n",
       "      <td>85.8700</td>\n",
       "      <td>151.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. high</th>\n",
       "      <td>107.2700</td>\n",
       "      <td>107.9000</td>\n",
       "      <td>113.4200</td>\n",
       "      <td>112.2400</td>\n",
       "      <td>116.1800</td>\n",
       "      <td>115.2900</td>\n",
       "      <td>112.7770</td>\n",
       "      <td>111.1500</td>\n",
       "      <td>102.6900</td>\n",
       "      <td>99.9900</td>\n",
       "      <td>...</td>\n",
       "      <td>130.2700</td>\n",
       "      <td>110.1200</td>\n",
       "      <td>114.6200</td>\n",
       "      <td>113.7500</td>\n",
       "      <td>119.6200</td>\n",
       "      <td>108.5600</td>\n",
       "      <td>91.0000</td>\n",
       "      <td>99.1200</td>\n",
       "      <td>90.9400</td>\n",
       "      <td>160.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3. low</th>\n",
       "      <td>102.3500</td>\n",
       "      <td>97.2000</td>\n",
       "      <td>93.9600</td>\n",
       "      <td>99.3528</td>\n",
       "      <td>100.1100</td>\n",
       "      <td>107.2300</td>\n",
       "      <td>104.8400</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>97.2600</td>\n",
       "      <td>92.4500</td>\n",
       "      <td>...</td>\n",
       "      <td>104.7500</td>\n",
       "      <td>87.7500</td>\n",
       "      <td>94.5000</td>\n",
       "      <td>95.7500</td>\n",
       "      <td>105.3700</td>\n",
       "      <td>83.1200</td>\n",
       "      <td>81.8700</td>\n",
       "      <td>86.6200</td>\n",
       "      <td>79.2500</td>\n",
       "      <td>79.3700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4. close</th>\n",
       "      <td>106.0300</td>\n",
       "      <td>104.4300</td>\n",
       "      <td>101.5700</td>\n",
       "      <td>110.8900</td>\n",
       "      <td>106.8100</td>\n",
       "      <td>114.3700</td>\n",
       "      <td>112.3300</td>\n",
       "      <td>106.0800</td>\n",
       "      <td>98.6100</td>\n",
       "      <td>98.8400</td>\n",
       "      <td>...</td>\n",
       "      <td>122.0000</td>\n",
       "      <td>105.8700</td>\n",
       "      <td>110.0600</td>\n",
       "      <td>95.9400</td>\n",
       "      <td>109.9400</td>\n",
       "      <td>108.3700</td>\n",
       "      <td>84.8100</td>\n",
       "      <td>90.1200</td>\n",
       "      <td>89.5000</td>\n",
       "      <td>84.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5. volume</th>\n",
       "      <td>114720446</td>\n",
       "      <td>714204787</td>\n",
       "      <td>944287635</td>\n",
       "      <td>720228643</td>\n",
       "      <td>927547942</td>\n",
       "      <td>480255674</td>\n",
       "      <td>456630721</td>\n",
       "      <td>569501573</td>\n",
       "      <td>602585341</td>\n",
       "      <td>509418119</td>\n",
       "      <td>...</td>\n",
       "      <td>290676700</td>\n",
       "      <td>410762300</td>\n",
       "      <td>298081100</td>\n",
       "      <td>337509900</td>\n",
       "      <td>307324500</td>\n",
       "      <td>308288100</td>\n",
       "      <td>273845600</td>\n",
       "      <td>254744300</td>\n",
       "      <td>274981900</td>\n",
       "      <td>215523150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 253 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          2019-02-06 2019-01-31 2018-12-31 2018-11-30 2018-10-31 2018-09-28  \\\n",
       "1. open     103.7750    99.5500   113.0000   107.0500   114.7500   110.8500   \n",
       "2. high     107.2700   107.9000   113.4200   112.2400   116.1800   115.2900   \n",
       "3. low      102.3500    97.2000    93.9600    99.3528   100.1100   107.2300   \n",
       "4. close    106.0300   104.4300   101.5700   110.8900   106.8100   114.3700   \n",
       "5. volume  114720446  714204787  944287635  720228643  927547942  480255674   \n",
       "\n",
       "          2018-08-31 2018-07-31 2018-06-29 2018-05-31  ... 1998-11-30  \\\n",
       "1. open     106.0300    98.1000    99.2798    93.2100  ...   106.3700   \n",
       "2. high     112.7770   111.1500   102.6900    99.9900  ...   130.2700   \n",
       "3. low      104.8400    98.0000    97.2600    92.4500  ...   104.7500   \n",
       "4. close    112.3300   106.0800    98.6100    98.8400  ...   122.0000   \n",
       "5. volume  456630721  569501573  602585341  509418119  ...  290676700   \n",
       "\n",
       "          1998-10-30 1998-09-30 1998-08-31 1998-07-31 1998-06-30 1998-05-29  \\\n",
       "1. open     108.0600    95.2500   109.1200   109.1200    83.8700    90.1900   \n",
       "2. high     110.1200   114.6200   113.7500   119.6200   108.5600    91.0000   \n",
       "3. low       87.7500    94.5000    95.7500   105.3700    83.1200    81.8700   \n",
       "4. close    105.8700   110.0600    95.9400   109.9400   108.3700    84.8100   \n",
       "5. volume  410762300  298081100  337509900  307324500  308288100  273845600   \n",
       "\n",
       "          1998-04-30 1998-03-31 1998-02-27  \n",
       "1. open      89.8100    85.8700   151.7500  \n",
       "2. high      99.1200    90.9400   160.0600  \n",
       "3. low       86.6200    79.2500    79.3700  \n",
       "4. close     90.1200    89.5000    84.7500  \n",
       "5. volume  254744300  274981900  215523150  \n",
       "\n",
       "[5 rows x 253 columns]"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = df.iloc[1].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "open_price = []\n",
    "for price in range(len(df.iloc[1])):\n",
    "    open_price.append(df.iloc[1][price])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_price = []\n",
    "for price in range(len(df.iloc[2])):\n",
    "    high_price.append(df.iloc[2][price])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_price = []\n",
    "for price in range(len(df.iloc[3])):\n",
    "    low_price.append(df.iloc[3][price])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_price = []\n",
    "for price in range(len(df.iloc[4])):\n",
    "    close_price.append(df.iloc[4][price])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trace_1 = go.Scatter(x=time, y=open_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trace_2 = go.Scatter(x=time, y=close_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "#graph_data = [trace_1, trace_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "#py.iplot(graph_data, filename='contour-scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate change\n",
    "diff_in_price = float(close_price[-1]) - float(open_price[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143838.4649"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_in_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame(index=range(0,len(df.iloc[1].index)),columns=['Time', 'Open', 'High', 'Low', 'Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df.iloc[1].index)):\n",
    "    new_data['Time'] = time[i]\n",
    "    new_data['Open'] = open_price[i]\n",
    "    new_data['High'] = high_price[i]\n",
    "    new_data['Low'] = low_price[i]\n",
    "    new_data['Close'] = close_price[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace_0 = go.Scatter(x=time, y=open_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_data = [trace_0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryan/codebase/dash-demo/lib/python3.6/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~rkawall/4.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.iplot(graph_data, filename='Time-series-analysis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_df = pd.DataFrame(index=time, columns=['Open'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = list(map(float, open_price))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_df['Open'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "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": [
    "autocorrelation_plot(auto_df)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_df.index = pd.to_datetime(auto_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryan/codebase/dash-demo/lib/python3.6/site-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning:\n",
      "\n",
      "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "\n",
      "/home/ryan/codebase/dash-demo/lib/python3.6/site-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning:\n",
      "\n",
      "A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = ARIMA(auto_df, order=(5,1,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_fit = model.fit(disp=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                 D.Open   No. Observations:                  252\n",
      "Model:                 ARIMA(5, 1, 0)   Log Likelihood                -886.099\n",
      "Method:                       css-mle   S.D. of innovations              8.138\n",
      "Date:                Wed, 06 Feb 2019   AIC                           1786.198\n",
      "Time:                        21:35:53   BIC                           1810.904\n",
      "Sample:                             1   HQIC                          1796.139\n",
      "                                                                              \n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const            0.1249      0.346      0.361      0.719      -0.554       0.804\n",
      "ar.L1.D.Open     0.0219      0.072      0.304      0.762      -0.119       0.163\n",
      "ar.L2.D.Open    -0.0020      0.072     -0.028      0.978      -0.144       0.140\n",
      "ar.L3.D.Open    -0.2861      0.071     -4.034      0.000      -0.425      -0.147\n",
      "ar.L4.D.Open    -0.1313      0.073     -1.798      0.073      -0.274       0.012\n",
      "ar.L5.D.Open    -0.0926      0.073     -1.267      0.206      -0.236       0.051\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            0.8572           -0.9458j            1.2764           -0.1328\n",
      "AR.2            0.8572           +0.9458j            1.2764            0.1328\n",
      "AR.3           -1.5133           -0.0000j            1.5133           -0.5000\n",
      "AR.4           -0.8094           -1.9301j            2.0929           -0.3132\n",
      "AR.5           -0.8094           +1.9301j            2.0929            0.3132\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(model_fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = pd.DataFrame(model_fit.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "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": [
    "pyplot.plot(residuals);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = auto_df[:\"2010-12-31\"]\n",
    "test = auto_df[\"2011-01-31\":]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train['Open'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = [train['Open'].iloc[x] for x in range(len(train['Open']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted=[27.84549784] Expected=29.46\n",
      "Predicted=[28.78578268] Expected=28.4\n",
      "Predicted=[27.56807909] Expected=27.49\n",
      "Predicted=[26.80356411] Expected=27.2\n",
      "Predicted=[26.35387897] Expected=25.53\n",
      "Predicted=[24.69612607] Expected=26.38\n",
      "Predicted=[25.61506989] Expected=26.41\n",
      "Predicted=[25.64566151] Expected=26.93\n",
      "Predicted=[26.40104013] Expected=31.0606\n",
      "Predicted=[30.57077468] Expected=31.58\n",
      "Predicted=[31.33808069] Expected=30.57\n",
      "Predicted=[30.42478379] Expected=29.03\n",
      "Predicted=[28.20210745] Expected=31.24\n",
      "Predicted=[30.39314147] Expected=31.5\n",
      "Predicted=[30.91812574] Expected=30.14\n",
      "Predicted=[29.82432614] Expected=29.35\n",
      "Predicted=[28.7302086] Expected=26.25\n",
      "Predicted=[25.37263519] Expected=25.49\n",
      "Predicted=[24.64340886] Expected=25.72\n",
      "Predicted=[24.81420907] Expected=24.34\n",
      "Predicted=[23.72023563] Expected=20.94\n",
      "Predicted=[20.24910776] Expected=21.2\n",
      "Predicted=[20.30274228] Expected=18.88\n",
      "Predicted=[17.89678373] Expected=19.93\n",
      "Predicted=[19.28458092] Expected=21.0\n",
      "Predicted=[20.28178968] Expected=21.25\n",
      "Predicted=[20.86396912] Expected=23.66\n",
      "Predicted=[23.28949527] Expected=27.47\n",
      "Predicted=[27.28257095] Expected=27.76\n",
      "Predicted=[27.80901314] Expected=28.5\n",
      "Predicted=[28.4920946] Expected=27.91\n",
      "Predicted=[27.38076913] Expected=29.57\n",
      "Predicted=[29.1860319] Expected=30.53\n",
      "Predicted=[30.20145473] Expected=32.1\n",
      "Predicted=[32.04275012] Expected=29.59\n",
      "Predicted=[29.28776986] Expected=33.25\n",
      "Predicted=[32.92633344] Expected=35.96\n",
      "Predicted=[35.73998658] Expected=36.72\n",
      "Predicted=[37.04131382] Expected=37.5\n",
      "Predicted=[37.68088469] Expected=37.0\n",
      "Predicted=[36.87995093] Expected=29.85\n",
      "Predicted=[28.97581839] Expected=30.1\n",
      "Predicted=[29.01768949] Expected=31.84\n",
      "Predicted=[30.66346714] Expected=30.9\n",
      "Predicted=[30.57585621] Expected=31.16\n",
      "Predicted=[31.02358889] Expected=30.74\n",
      "Predicted=[30.19350132] Expected=28.55\n",
      "Predicted=[27.99982835] Expected=30.94\n",
      "Predicted=[30.46569917] Expected=31.48\n",
      "Predicted=[30.9708829] Expected=30.26\n",
      "Predicted=[30.14402871] Expected=30.0001\n",
      "Predicted=[29.57157244] Expected=28.8501\n",
      "Predicted=[28.20037534] Expected=27.52\n",
      "Predicted=[26.96165251] Expected=26.2501\n",
      "Predicted=[25.57297192] Expected=24.6\n",
      "Predicted=[23.88257239] Expected=23.65\n",
      "Predicted=[22.96261145] Expected=25.0\n",
      "Predicted=[24.43224441] Expected=27.941\n",
      "Predicted=[27.71248615] Expected=28.22\n",
      "Predicted=[28.1822291] Expected=28.07\n",
      "Predicted=[27.99632578] Expected=28.38\n",
      "Predicted=[27.94921276] Expected=28.101\n",
      "Predicted=[27.64498175] Expected=28.25\n",
      "Predicted=[27.89287089] Expected=25.8\n",
      "Predicted=[25.22525109] Expected=27.39\n",
      "Predicted=[26.9739744] Expected=27.94\n",
      "Predicted=[27.38047724] Expected=26.48\n",
      "Predicted=[26.26833896] Expected=26.0\n",
      "Predicted=[25.57748069] Expected=26.09\n",
      "Predicted=[25.48328728] Expected=25.45\n",
      "Predicted=[24.99070028] Expected=25.79\n",
      "Predicted=[25.42943193] Expected=26.5\n",
      "Predicted=[26.10511309] Expected=27.1\n",
      "Predicted=[26.84669485] Expected=27.44\n",
      "Predicted=[27.20903578] Expected=30.2\n",
      "Predicted=[30.09666045] Expected=28.89\n",
      "Predicted=[28.62814644] Expected=27.79\n",
      "Predicted=[27.62595533] Expected=28.55\n",
      "Predicted=[27.97492038] Expected=29.89\n",
      "Predicted=[29.50343975] Expected=28.8\n",
      "Predicted=[28.57443069] Expected=26.6\n",
      "Predicted=[26.24464199] Expected=27.72\n",
      "Predicted=[27.19678626] Expected=26.72\n",
      "Predicted=[26.07222738] Expected=27.8\n",
      "Predicted=[27.62935136] Expected=28.83\n",
      "Predicted=[28.48848879] Expected=27.55\n",
      "Predicted=[27.31608464] Expected=26.75\n",
      "Predicted=[26.43768271] Expected=29.46\n",
      "Predicted=[29.06325504] Expected=30.0\n",
      "Predicted=[29.69766817] Expected=26.95\n",
      "Predicted=[26.83149211] Expected=27.81\n",
      "Predicted=[27.51936154] Expected=26.51\n",
      "Predicted=[25.69192644] Expected=26.5\n",
      "Predicted=[26.23312272] Expected=26.43\n",
      "Predicted=[25.95710694] Expected=26.8\n",
      "Predicted=[26.47228828] Expected=49.1\n",
      "Predicted=[50.42500144] Expected=57.32\n",
      "Predicted=[59.48537369] Expected=58.96\n",
      "Predicted=[62.35696523] Expected=58.64\n",
      "Predicted=[58.22104012] Expected=54.07\n",
      "Predicted=[52.70541415] Expected=51.1\n",
      "Predicted=[49.99127024] Expected=53.45\n",
      "Predicted=[53.40638983] Expected=54.93\n",
      "Predicted=[55.30049116] Expected=56.44\n",
      "Predicted=[56.90900641] Expected=56.44\n",
      "Predicted=[56.00114846] Expected=60.4\n",
      "Predicted=[60.92838678] Expected=65.0\n",
      "Predicted=[65.66093074] Expected=64.5\n",
      "Predicted=[64.5390612] Expected=70.62\n",
      "Predicted=[71.62238441] Expected=69.89\n",
      "Predicted=[69.3098948] Expected=68.34\n",
      "Predicted=[68.51418619] Expected=63.63\n",
      "Predicted=[61.84976027] Expected=59.08\n",
      "Predicted=[57.90996697] Expected=67.54\n",
      "Predicted=[68.81489617] Expected=73.15\n",
      "Predicted=[74.36607227] Expected=76.15\n",
      "Predicted=[77.34066586] Expected=72.15\n",
      "Predicted=[69.97663827] Expected=71.1\n",
      "Predicted=[70.73773328] Expected=61.13\n",
      "Predicted=[58.62963065] Expected=65.06\n",
      "Predicted=[65.92369937] Expected=64.75\n",
      "Predicted=[64.80864138] Expected=60.63\n",
      "Predicted=[60.91005322] Expected=72.37\n",
      "Predicted=[72.70120465] Expected=70.12\n",
      "Predicted=[69.96623707] Expected=72.06\n",
      "Predicted=[73.30603786] Expected=74.87\n",
      "Predicted=[72.48529648] Expected=82.87\n",
      "Predicted=[84.62231956] Expected=82.19\n",
      "Predicted=[82.11169895] Expected=74.0\n",
      "Predicted=[72.60822735] Expected=96.5\n",
      "Predicted=[96.58451464] Expected=115.0\n",
      "Predicted=[116.69177767] Expected=110.0\n",
      "Predicted=[110.21589654] Expected=118.62\n",
      "Predicted=[121.7726941] Expected=119.94\n",
      "Predicted=[117.84538567] Expected=94.5\n",
      "Predicted=[91.18961346] Expected=95.25\n",
      "Predicted=[97.49723854] Expected=97.87\n",
      "Predicted=[90.9041405] Expected=96.37\n",
      "Predicted=[101.41560683] Expected=100.75\n",
      "Predicted=[103.91219332] Expected=90.25\n",
      "Predicted=[87.12077491] Expected=81.81\n",
      "Predicted=[83.30237623] Expected=95.62\n",
      "Predicted=[95.6246512] Expected=180.38\n",
      "Predicted=[211.71741614] Expected=175.94\n",
      "Predicted=[146.37324133] Expected=175.13\n",
      "Predicted=[178.23142985] Expected=144.0\n",
      "Predicted=[132.72196806] Expected=130.27\n",
      "Predicted=[125.99895935] Expected=110.12\n",
      "Predicted=[118.4323729] Expected=114.62\n",
      "Predicted=[123.45383795] Expected=113.75\n",
      "Predicted=[125.14395733] Expected=119.62\n",
      "Predicted=[123.35045215] Expected=108.56\n",
      "Predicted=[110.60085926] Expected=91.0\n",
      "Predicted=[88.07751982] Expected=99.12\n",
      "Predicted=[103.30216488] Expected=90.94\n",
      "Predicted=[93.26822473] Expected=160.06\n",
      "Test MSE: 127.05197824495306\n"
     ]
    }
   ],
   "source": [
    "for t in test.index:\n",
    "    my_model = ARIMA(history, order=(5,1,0))\n",
    "    my_model_fit = my_model.fit(disp=0)\n",
    "    output = my_model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = test['Open'][t]\n",
    "    history.append(obs)\n",
    "    print(\"Predicted={} Expected={}\".format(yhat, obs))\n",
    "try:\n",
    "    error = mean_squared_error(test, predictions)\n",
    "except ValueError:\n",
    "    print(\"error\")\n",
    "print(\"Test MSE: {}\".format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show predicted vs expected on graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_axis = np.arange(10, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in predictions:\n",
    "    count = 0\n",
    "    my_predictions.append(i[count])\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace_predict = go.Scatter(x=predictions, y=y_axis, mode = 'lines+markers', name = 'Predictions')\n",
    "trace_actual = go.Scatter(x=test['Open'], y=y_axis, mode = 'lines+markers', name = 'Actual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_graph_data = [trace_predict, trace_actual]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryan/codebase/dash-demo/lib/python3.6/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~rkawall/6.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.iplot(predict_graph_data, filename='Prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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