app2-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr\n",
    "key = \"4412be54f9ecbfe7303a63f224bb63e7d0474e30\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pdr.get_data_tiingo('AAPL', api_key=key)\n",
    "df.to_csv('AAPL.csv')\n",
    "df=pd.read_csv('AAPL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-15 00:00:00+00:00</td>\n",
       "      <td>126.92</td>\n",
       "      <td>127.24</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-16 00:00:00+00:00</td>\n",
       "      <td>127.60</td>\n",
       "      <td>127.85</td>\n",
       "      <td>126.37</td>\n",
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       "      <td>2</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-17 00:00:00+00:00</td>\n",
       "      <td>127.30</td>\n",
       "      <td>127.88</td>\n",
       "      <td>126.74</td>\n",
       "      <td>127.72</td>\n",
       "      <td>32918071</td>\n",
       "      <td>117.309929</td>\n",
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       "      <td>3</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-18 00:00:00+00:00</td>\n",
       "      <td>127.88</td>\n",
       "      <td>128.31</td>\n",
       "      <td>127.22</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-19 00:00:00+00:00</td>\n",
       "      <td>126.60</td>\n",
       "      <td>127.82</td>\n",
       "      <td>126.40</td>\n",
       "      <td>127.71</td>\n",
       "      <td>54716887</td>\n",
       "      <td>116.664863</td>\n",
       "      <td>117.789121</td>\n",
       "      <td>116.480558</td>\n",
       "      <td>117.687754</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "  symbol                       date   close    high     low    open    volume  \\\n",
       "0   AAPL  2015-06-15 00:00:00+00:00  126.92  127.24  125.71  126.10  43988946   \n",
       "1   AAPL  2015-06-16 00:00:00+00:00  127.60  127.85  126.37  127.03  31494131   \n",
       "2   AAPL  2015-06-17 00:00:00+00:00  127.30  127.88  126.74  127.72  32918071   \n",
       "3   AAPL  2015-06-18 00:00:00+00:00  127.88  128.31  127.22  127.23  35407220   \n",
       "4   AAPL  2015-06-19 00:00:00+00:00  126.60  127.82  126.40  127.71  54716887   \n",
       "\n",
       "     adjClose     adjHigh      adjLow     adjOpen  adjVolume  divCash  \\\n",
       "0  116.959750  117.254638  115.844707  116.204101   43988946      0.0   \n",
       "1  117.586386  117.816767  116.452912  117.061118   31494131      0.0   \n",
       "2  117.309929  117.844413  116.793876  117.696969   32918071      0.0   \n",
       "3  117.844413  118.240668  117.236207  117.245422   35407220      0.0   \n",
       "4  116.664863  117.789121  116.480558  117.687754   54716887      0.0   \n",
       "\n",
       "   splitFactor  \n",
       "0          1.0  \n",
       "1          1.0  \n",
       "2          1.0  \n",
       "3          1.0  \n",
       "4          1.0  "
      ]
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>1254</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-08 00:00:00+00:00</td>\n",
       "      <td>333.46</td>\n",
       "      <td>333.60</td>\n",
       "      <td>327.3200</td>\n",
       "      <td>330.25</td>\n",
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       "    <tr>\n",
       "      <td>1255</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-09 00:00:00+00:00</td>\n",
       "      <td>343.99</td>\n",
       "      <td>345.61</td>\n",
       "      <td>332.0100</td>\n",
       "      <td>332.14</td>\n",
       "      <td>36928091</td>\n",
       "      <td>343.99</td>\n",
       "      <td>345.61</td>\n",
       "      <td>332.0100</td>\n",
       "      <td>332.14</td>\n",
       "      <td>36928091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>1256</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-10 00:00:00+00:00</td>\n",
       "      <td>352.84</td>\n",
       "      <td>354.77</td>\n",
       "      <td>346.0900</td>\n",
       "      <td>347.90</td>\n",
       "      <td>41662938</td>\n",
       "      <td>352.84</td>\n",
       "      <td>354.77</td>\n",
       "      <td>346.0900</td>\n",
       "      <td>347.90</td>\n",
       "      <td>41662938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>1257</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-11 00:00:00+00:00</td>\n",
       "      <td>335.90</td>\n",
       "      <td>351.06</td>\n",
       "      <td>335.4800</td>\n",
       "      <td>349.31</td>\n",
       "      <td>50415613</td>\n",
       "      <td>335.90</td>\n",
       "      <td>351.06</td>\n",
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       "      <td>349.31</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>1258</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-12 00:00:00+00:00</td>\n",
       "      <td>338.80</td>\n",
       "      <td>347.80</td>\n",
       "      <td>334.2233</td>\n",
       "      <td>344.72</td>\n",
       "      <td>50036513</td>\n",
       "      <td>338.80</td>\n",
       "      <td>347.80</td>\n",
       "      <td>334.2233</td>\n",
       "      <td>344.72</td>\n",
       "      <td>50036513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "     symbol                       date   close    high       low    open  \\\n",
       "1254   AAPL  2020-06-08 00:00:00+00:00  333.46  333.60  327.3200  330.25   \n",
       "1255   AAPL  2020-06-09 00:00:00+00:00  343.99  345.61  332.0100  332.14   \n",
       "1256   AAPL  2020-06-10 00:00:00+00:00  352.84  354.77  346.0900  347.90   \n",
       "1257   AAPL  2020-06-11 00:00:00+00:00  335.90  351.06  335.4800  349.31   \n",
       "1258   AAPL  2020-06-12 00:00:00+00:00  338.80  347.80  334.2233  344.72   \n",
       "\n",
       "        volume  adjClose  adjHigh    adjLow  adjOpen  adjVolume  divCash  \\\n",
       "1254  23913634    333.46   333.60  327.3200   330.25   23913634      0.0   \n",
       "1255  36928091    343.99   345.61  332.0100   332.14   36928091      0.0   \n",
       "1256  41662938    352.84   354.77  346.0900   347.90   41662938      0.0   \n",
       "1257  50415613    335.90   351.06  335.4800   349.31   50415613      0.0   \n",
       "1258  50036513    338.80   347.80  334.2233   344.72   50036513      0.0   \n",
       "\n",
       "      splitFactor  \n",
       "1254          1.0  \n",
       "1255          1.0  \n",
       "1256          1.0  \n",
       "1257          1.0  \n",
       "1258          1.0  "
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-15 00:00:00+00:00</td>\n",
       "      <td>126.92</td>\n",
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       "  symbol                       date   close    high     low   open    volume  \\\n",
       "0   AAPL  2015-06-15 00:00:00+00:00  126.92  127.24  125.71  126.1  43988946   \n",
       "\n",
       "    adjClose     adjHigh      adjLow     adjOpen  adjVolume  divCash  \\\n",
       "0  116.95975  117.254638  115.844707  116.204101   43988946      0.0   \n",
       "\n",
       "   splitFactor  \n",
       "0          1.0  "
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    "df.head(1)"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>1159</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-01-22 00:00:00+00:00</td>\n",
       "      <td>317.70</td>\n",
       "      <td>319.99</td>\n",
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       "      <td>1160</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-01-23 00:00:00+00:00</td>\n",
       "      <td>319.23</td>\n",
       "      <td>319.56</td>\n",
       "      <td>315.6500</td>\n",
       "      <td>317.92</td>\n",
       "      <td>26117993</td>\n",
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       "    <tr>\n",
       "      <td>1161</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-01-24 00:00:00+00:00</td>\n",
       "      <td>318.31</td>\n",
       "      <td>323.33</td>\n",
       "      <td>317.5188</td>\n",
       "      <td>320.25</td>\n",
       "      <td>36634380</td>\n",
       "      <td>316.708583</td>\n",
       "      <td>321.703327</td>\n",
       "      <td>315.921363</td>\n",
       "      <td>318.638822</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>1162</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-01-27 00:00:00+00:00</td>\n",
       "      <td>308.95</td>\n",
       "      <td>311.77</td>\n",
       "      <td>304.8800</td>\n",
       "      <td>310.06</td>\n",
       "      <td>40485005</td>\n",
       "      <td>307.395673</td>\n",
       "      <td>310.201485</td>\n",
       "      <td>303.346149</td>\n",
       "      <td>308.500088</td>\n",
       "      <td>40485005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1163</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-01-28 00:00:00+00:00</td>\n",
       "      <td>317.69</td>\n",
       "      <td>318.40</td>\n",
       "      <td>312.1900</td>\n",
       "      <td>312.60</td>\n",
       "      <td>40558486</td>\n",
       "      <td>316.091702</td>\n",
       "      <td>316.798130</td>\n",
       "      <td>310.619372</td>\n",
       "      <td>311.027310</td>\n",
       "      <td>40558486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1254</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-08 00:00:00+00:00</td>\n",
       "      <td>333.46</td>\n",
       "      <td>333.60</td>\n",
       "      <td>327.3200</td>\n",
       "      <td>330.25</td>\n",
       "      <td>23913634</td>\n",
       "      <td>333.460000</td>\n",
       "      <td>333.600000</td>\n",
       "      <td>327.320000</td>\n",
       "      <td>330.250000</td>\n",
       "      <td>23913634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1255</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-09 00:00:00+00:00</td>\n",
       "      <td>343.99</td>\n",
       "      <td>345.61</td>\n",
       "      <td>332.0100</td>\n",
       "      <td>332.14</td>\n",
       "      <td>36928091</td>\n",
       "      <td>343.990000</td>\n",
       "      <td>345.610000</td>\n",
       "      <td>332.010000</td>\n",
       "      <td>332.140000</td>\n",
       "      <td>36928091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1256</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-10 00:00:00+00:00</td>\n",
       "      <td>352.84</td>\n",
       "      <td>354.77</td>\n",
       "      <td>346.0900</td>\n",
       "      <td>347.90</td>\n",
       "      <td>41662938</td>\n",
       "      <td>352.840000</td>\n",
       "      <td>354.770000</td>\n",
       "      <td>346.090000</td>\n",
       "      <td>347.900000</td>\n",
       "      <td>41662938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1257</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-11 00:00:00+00:00</td>\n",
       "      <td>335.90</td>\n",
       "      <td>351.06</td>\n",
       "      <td>335.4800</td>\n",
       "      <td>349.31</td>\n",
       "      <td>50415613</td>\n",
       "      <td>335.900000</td>\n",
       "      <td>351.060000</td>\n",
       "      <td>335.480000</td>\n",
       "      <td>349.310000</td>\n",
       "      <td>50415613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1258</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2020-06-12 00:00:00+00:00</td>\n",
       "      <td>338.80</td>\n",
       "      <td>347.80</td>\n",
       "      <td>334.2233</td>\n",
       "      <td>344.72</td>\n",
       "      <td>50036513</td>\n",
       "      <td>338.800000</td>\n",
       "      <td>347.800000</td>\n",
       "      <td>334.223300</td>\n",
       "      <td>344.720000</td>\n",
       "      <td>50036513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symbol                       date   close    high       low    open  \\\n",
       "1159   AAPL  2020-01-22 00:00:00+00:00  317.70  319.99  317.3100  318.58   \n",
       "1160   AAPL  2020-01-23 00:00:00+00:00  319.23  319.56  315.6500  317.92   \n",
       "1161   AAPL  2020-01-24 00:00:00+00:00  318.31  323.33  317.5188  320.25   \n",
       "1162   AAPL  2020-01-27 00:00:00+00:00  308.95  311.77  304.8800  310.06   \n",
       "1163   AAPL  2020-01-28 00:00:00+00:00  317.69  318.40  312.1900  312.60   \n",
       "...     ...                        ...     ...     ...       ...     ...   \n",
       "1254   AAPL  2020-06-08 00:00:00+00:00  333.46  333.60  327.3200  330.25   \n",
       "1255   AAPL  2020-06-09 00:00:00+00:00  343.99  345.61  332.0100  332.14   \n",
       "1256   AAPL  2020-06-10 00:00:00+00:00  352.84  354.77  346.0900  347.90   \n",
       "1257   AAPL  2020-06-11 00:00:00+00:00  335.90  351.06  335.4800  349.31   \n",
       "1258   AAPL  2020-06-12 00:00:00+00:00  338.80  347.80  334.2233  344.72   \n",
       "\n",
       "        volume    adjClose     adjHigh      adjLow     adjOpen  adjVolume  \\\n",
       "1159  25458115  316.101651  318.380130  315.713614  316.977224   25458115   \n",
       "1160  26117993  317.623954  317.952294  314.061965  316.320545   26117993   \n",
       "1161  36634380  316.708583  321.703327  315.921363  318.638822   36634380   \n",
       "1162  40485005  307.395673  310.201485  303.346149  308.500088   40485005   \n",
       "1163  40558486  316.091702  316.798130  310.619372  311.027310   40558486   \n",
       "...        ...         ...         ...         ...         ...        ...   \n",
       "1254  23913634  333.460000  333.600000  327.320000  330.250000   23913634   \n",
       "1255  36928091  343.990000  345.610000  332.010000  332.140000   36928091   \n",
       "1256  41662938  352.840000  354.770000  346.090000  347.900000   41662938   \n",
       "1257  50415613  335.900000  351.060000  335.480000  349.310000   50415613   \n",
       "1258  50036513  338.800000  347.800000  334.223300  344.720000   50036513   \n",
       "\n",
       "      divCash  splitFactor  \n",
       "1159      0.0          1.0  \n",
       "1160      0.0          1.0  \n",
       "1161      0.0          1.0  \n",
       "1162      0.0          1.0  \n",
       "1163      0.0          1.0  \n",
       "...       ...          ...  \n",
       "1254      0.0          1.0  \n",
       "1255      0.0          1.0  \n",
       "1256      0.0          1.0  \n",
       "1257      0.0          1.0  \n",
       "1258      0.0          1.0  \n",
       "\n",
       "[100 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.reset_index()['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       126.92\n",
       "1       127.60\n",
       "2       127.30\n",
       "3       127.88\n",
       "4       126.60\n",
       "         ...  \n",
       "1254    333.46\n",
       "1255    343.99\n",
       "1256    352.84\n",
       "1257    335.90\n",
       "1258    338.80\n",
       "Name: close, Length: 1259, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xe951ab0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.13935238]\n",
      " [0.14194286]\n",
      " [0.1408    ]\n",
      " ...\n",
      " [1.        ]\n",
      " [0.93546667]\n",
      " [0.94651429]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "df1=scaler.fit_transform(np.array(df1).reshape(-1,1))\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_size=int(len(df1)*0.65)\n",
    "test_size=len(df1)-training_size\n",
    "train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "818"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "441"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.13935238],\n",
       "       [0.14194286],\n",
       "       [0.1408    ],\n",
       "       [0.14300952],\n",
       "       [0.13813333],\n",
       "       [0.14198095],\n",
       "       [0.13977143],\n",
       "       [0.14388571],\n",
       "       [0.1415619 ],\n",
       "       [0.13870476],\n",
       "       [0.13024762],\n",
       "       [0.13365714],\n",
       "       [0.13813333],\n",
       "       [0.13752381],\n",
       "       [0.13584762],\n",
       "       [0.13466667],\n",
       "       [0.12278095],\n",
       "       [0.11325714],\n",
       "       [0.12548571],\n",
       "       [0.13455238],\n",
       "       [0.1343619 ],\n",
       "       [0.13897143],\n",
       "       [0.14540952],\n",
       "       [0.1496381 ],\n",
       "       [0.15897143],\n",
       "       [0.15394286],\n",
       "       [0.13287619],\n",
       "       [0.13264762],\n",
       "       [0.13013333],\n",
       "       [0.12354286],\n",
       "       [0.12586667],\n",
       "       [0.12438095],\n",
       "       [0.12201905],\n",
       "       [0.11794286],\n",
       "       [0.10704762],\n",
       "       [0.09257143],\n",
       "       [0.09546667],\n",
       "       [0.0944381 ],\n",
       "       [0.09592381],\n",
       "       [0.11192381],\n",
       "       [0.08819048],\n",
       "       [0.09485714],\n",
       "       [0.09451429],\n",
       "       [0.0976    ],\n",
       "       [0.10217143],\n",
       "       [0.09965714],\n",
       "       [0.09398095],\n",
       "       [0.08499048],\n",
       "       [0.05874286],\n",
       "       [0.04868571],\n",
       "       [0.05104762],\n",
       "       [0.07371429],\n",
       "       [0.08601905],\n",
       "       [0.08742857],\n",
       "       [0.08540952],\n",
       "       [0.06620952],\n",
       "       [0.08380952],\n",
       "       [0.07630476],\n",
       "       [0.07211429],\n",
       "       [0.08369524],\n",
       "       [0.07546667],\n",
       "       [0.08468571],\n",
       "       [0.09093333],\n",
       "       [0.09512381],\n",
       "       [0.09881905],\n",
       "       [0.09931429],\n",
       "       [0.08982857],\n",
       "       [0.0880381 ],\n",
       "       [0.09474286],\n",
       "       [0.08784762],\n",
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      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "# convert array of values into a dataset matrix\n",
    "def create_dataset(dataset, time_step=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-time_step-1):\n",
    "\t\ta = dataset[i:(i+time_step), 0] \n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + time_step, 0])\n",
    "\treturn numpy.array(dataX), numpy.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 100\n",
    "X_train,y_train = create_dataset(train_data,time_step)\n",
    "X_test,y_test"
   ]
  }
 ],
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