notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import datetime\n",
    "from datetime import date, timedelta\n",
    "today = date.today()\n",
    "\n",
    "d1 = today.strftime(\"%Y-%m-%d\")\n",
    "end_date = d1\n",
    "d2 = date.today() - timedelta(days=5000)\n",
    "d2 = d2.strftime(\"%Y-%m-%d\")\n",
    "start_date = d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-08-06</td>\n",
       "      <td>5.913571</td>\n",
       "      <td>5.946786</td>\n",
       "      <td>5.824643</td>\n",
       "      <td>5.853929</td>\n",
       "      <td>4.982386</td>\n",
       "      <td>341616800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>5.910357</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.885714</td>\n",
       "      <td>5.911071</td>\n",
       "      <td>5.031021</td>\n",
       "      <td>387354800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-08-10</td>\n",
       "      <td>5.916429</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.845000</td>\n",
       "      <td>5.882857</td>\n",
       "      <td>5.007006</td>\n",
       "      <td>300294400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>5.846071</td>\n",
       "      <td>5.870714</td>\n",
       "      <td>5.781429</td>\n",
       "      <td>5.815357</td>\n",
       "      <td>4.949556</td>\n",
       "      <td>355342400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-08-12</td>\n",
       "      <td>5.805357</td>\n",
       "      <td>5.953929</td>\n",
       "      <td>5.802143</td>\n",
       "      <td>5.903929</td>\n",
       "      <td>5.024943</td>\n",
       "      <td>445071200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Open      High       Low     Close  Adj Close     Volume\n",
       "0 2009-08-06  5.913571  5.946786  5.824643  5.853929   4.982386  341616800\n",
       "1 2009-08-07  5.910357  5.950000  5.885714  5.911071   5.031021  387354800\n",
       "2 2009-08-10  5.916429  5.950000  5.845000  5.882857   5.007006  300294400\n",
       "3 2009-08-11  5.846071  5.870714  5.781429  5.815357   4.949556  355342400\n",
       "4 2009-08-12  5.805357  5.953929  5.802143  5.903929   5.024943  445071200"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = yf.download('AAPL',\n",
    "                   start=start_date,\n",
    "                   end=end_date,\n",
    "                   progress=False)\n",
    "data[\"Date\"] = data.index\n",
    "data = data[[\"Date\", \"Open\", \"High\", \"Low\", \"Close\",\n",
    "             \"Adj Close\", \"Volume\"]]\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3446.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>3.446000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.978078</td>\n",
       "      <td>52.563302</td>\n",
       "      <td>51.412937</td>\n",
       "      <td>52.012833</td>\n",
       "      <td>50.151712</td>\n",
       "      <td>2.591406e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>48.834216</td>\n",
       "      <td>49.465884</td>\n",
       "      <td>48.244279</td>\n",
       "      <td>48.886000</td>\n",
       "      <td>49.299638</td>\n",
       "      <td>2.240929e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.772500</td>\n",
       "      <td>5.842500</td>\n",
       "      <td>5.693571</td>\n",
       "      <td>5.699643</td>\n",
       "      <td>4.851072</td>\n",
       "      <td>3.519590e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.535981</td>\n",
       "      <td>18.678392</td>\n",
       "      <td>18.372947</td>\n",
       "      <td>18.558125</td>\n",
       "      <td>16.118567</td>\n",
       "      <td>1.009267e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.505000</td>\n",
       "      <td>29.810000</td>\n",
       "      <td>29.281250</td>\n",
       "      <td>29.592501</td>\n",
       "      <td>27.199715</td>\n",
       "      <td>1.683696e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.806251</td>\n",
       "      <td>62.843125</td>\n",
       "      <td>60.595001</td>\n",
       "      <td>61.661875</td>\n",
       "      <td>60.394848</td>\n",
       "      <td>3.586282e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>182.630005</td>\n",
       "      <td>182.940002</td>\n",
       "      <td>179.119995</td>\n",
       "      <td>182.009995</td>\n",
       "      <td>180.683868</td>\n",
       "      <td>1.880998e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  3446.000000  3446.000000  3446.000000  3446.000000  3446.000000   \n",
       "mean     51.978078    52.563302    51.412937    52.012833    50.151712   \n",
       "std      48.834216    49.465884    48.244279    48.886000    49.299638   \n",
       "min       5.772500     5.842500     5.693571     5.699643     4.851072   \n",
       "25%      18.535981    18.678392    18.372947    18.558125    16.118567   \n",
       "50%      29.505000    29.810000    29.281250    29.592501    27.199715   \n",
       "75%      61.806251    62.843125    60.595001    61.661875    60.394848   \n",
       "max     182.630005   182.940002   179.119995   182.009995   180.683868   \n",
       "\n",
       "             Volume  \n",
       "count  3.446000e+03  \n",
       "mean   2.591406e+08  \n",
       "std    2.240929e+08  \n",
       "min    3.519590e+07  \n",
       "25%    1.009267e+08  \n",
       "50%    1.683696e+08  \n",
       "75%    3.586282e+08  \n",
       "max    1.880998e+09  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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          "2010-09-08T00:00:00",
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          "2010-11-19T00:00:00",
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          "2010-11-24T00:00:00",
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          "2010-11-30T00:00:00",
          "2010-12-01T00:00:00",
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          "2010-12-07T00:00:00",
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          "2010-12-17T00:00:00",
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          "2010-12-22T00:00:00",
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          "2010-12-27T00:00:00",
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          "2011-01-03T00:00:00",
          "2011-01-04T00:00:00",
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          "2011-02-09T00:00:00",
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          "2011-05-09T00:00:00",
          "2011-05-10T00:00:00",
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          "2011-05-17T00:00:00",
          "2011-05-18T00:00:00",
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          "2011-05-20T00:00:00",
          "2011-05-23T00:00:00",
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          "2011-05-25T00:00:00",
          "2011-05-26T00:00:00",
          "2011-05-27T00:00:00",
          "2011-05-31T00:00:00",
          "2011-06-01T00:00:00",
          "2011-06-02T00:00:00",
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          "2011-06-06T00:00:00",
          "2011-06-07T00:00:00",
          "2011-06-08T00:00:00",
          "2011-06-09T00:00:00",
          "2011-06-10T00:00:00",
          "2011-06-13T00:00:00",
          "2011-06-14T00:00:00",
          "2011-06-15T00:00:00",
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          "2011-06-22T00:00:00",
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          "2011-07-07T00:00:00",
          "2011-07-08T00:00:00",
          "2011-07-11T00:00:00",
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          "2011-07-14T00:00:00",
          "2011-07-15T00:00:00",
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          "2011-07-25T00:00:00",
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          "2011-08-01T00:00:00",
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          "2011-08-25T00:00:00",
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          "2011-08-29T00:00:00",
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          "2011-08-31T00:00:00",
          "2011-09-01T00:00:00",
          "2011-09-02T00:00:00",
          "2011-09-06T00:00:00",
          "2011-09-07T00:00:00",
          "2011-09-08T00:00:00",
          "2011-09-09T00:00:00",
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          "2011-09-13T00:00:00",
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          "2011-09-15T00:00:00",
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          "2011-10-04T00:00:00",
          "2011-10-05T00:00:00",
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          "2011-10-10T00:00:00",
          "2011-10-11T00:00:00",
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          "2011-10-25T00:00:00",
          "2011-10-26T00:00:00",
          "2011-10-27T00:00:00",
          "2011-10-28T00:00:00",
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          "2011-11-01T00:00:00",
          "2011-11-02T00:00:00",
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          "2011-11-07T00:00:00",
          "2011-11-08T00:00:00",
          "2011-11-09T00:00:00",
          "2011-11-10T00:00:00",
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          "2011-11-14T00:00:00",
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          "2011-11-16T00:00:00",
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          "2011-11-23T00:00:00",
          "2011-11-25T00:00:00",
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          "2011-12-01T00:00:00",
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          "2011-12-08T00:00:00",
          "2011-12-09T00:00:00",
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          "2011-12-13T00:00:00",
          "2011-12-14T00:00:00",
          "2011-12-15T00:00:00",
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          "2012-01-03T00:00:00",
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           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Apple Stock Price Analysis"
        },
        "xaxis": {
         "rangeslider": {
          "visible": false
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "figure = go.Figure(data=[go.Candlestick(x=data[\"Date\"],\n",
    "                                        open=data[\"Open\"],\n",
    "                                        high=data[\"High\"],\n",
    "                                        low=data[\"Low\"],\n",
    "                                        close=data[\"Close\"])])\n",
    "figure.update_layout(title=\"Apple Stock Price Analysis\",\n",
    "                     xaxis_rangeslider_visible=False)\n",
    "figure.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "correlation = data.corr()\n",
    "# print(correlation[\"Close\"].sort_values(ascending=False))\n",
    "sns.heatmap(correlation, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[[\"Open\", \"High\", \"Low\", \"Volume\"]]\n",
    "y = data[\"Close\"]\n",
    "x = x.to_numpy()\n",
    "y = y.to_numpy()\n",
    "y = y.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(\n",
    "    x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "model = Sequential()\n",
    "model.add(LSTM(128, return_sequences=True, input_shape=(xtrain.shape[1], 1)))\n",
    "model.add(LSTM(64, return_sequences=False))\n",
    "# model.add(LSTM(32, return_sequences=False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 2/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 13.5335\n",
      "Epoch 3/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 12.4874\n",
      "Epoch 4/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 8.6842\n",
      "Epoch 5/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 7.9823\n",
      "Epoch 6/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 9.7233\n",
      "Epoch 7/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 6.3554\n",
      "Epoch 8/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 5.7927\n",
      "Epoch 9/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 7.0561\n",
      "Epoch 10/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 5.8307\n",
      "Epoch 11/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.7052\n",
      "Epoch 12/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 4.7446\n",
      "Epoch 13/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 5.1393\n",
      "Epoch 14/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.3768\n",
      "Epoch 15/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.5840\n",
      "Epoch 16/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.2938\n",
      "Epoch 17/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 5.5507\n",
      "Epoch 18/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.9123\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.3110\n",
      "Epoch 20/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.5533\n",
      "Epoch 21/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 3.8238\n",
      "Epoch 22/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 3.0466\n",
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      "2756/2756 [==============================] - 6s 2ms/step - loss: 4.4951\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.2089\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.5746\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.5171\n",
      "Epoch 27/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.0990\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.5741\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.4791\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.8113\n",
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      "2756/2756 [==============================] - 6s 2ms/step - loss: 4.4313\n",
      "Epoch 32/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.9822\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.4462\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.0248\n",
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      "2756/2756 [==============================] - 6s 2ms/step - loss: 3.2131\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.8165\n",
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      "2756/2756 [==============================] - 6s 2ms/step - loss: 3.0672\n",
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      "2756/2756 [==============================] - 6s 2ms/step - loss: 3.2143\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.6108\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.8984\n",
      "Epoch 45/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.8529\n",
      "Epoch 46/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.1901\n",
      "Epoch 47/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.5925\n",
      "Epoch 48/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.8034\n",
      "Epoch 49/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.7772\n",
      "Epoch 50/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.6173\n",
      "Epoch 51/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.7973\n",
      "Epoch 52/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.7872\n",
      "Epoch 53/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.4799\n",
      "Epoch 54/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.7969\n",
      "Epoch 55/200\n",
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      "Epoch 56/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.3798\n",
      "Epoch 57/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.7525\n",
      "Epoch 58/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.3107\n",
      "Epoch 59/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.4797\n",
      "Epoch 60/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 4.0359\n",
      "Epoch 61/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.7550\n",
      "Epoch 62/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.1304\n",
      "Epoch 63/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.3096\n",
      "Epoch 64/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.8894\n",
      "Epoch 65/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.5159\n",
      "Epoch 66/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.2574\n",
      "Epoch 67/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.6474\n",
      "Epoch 68/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.4925\n",
      "Epoch 69/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 3.2536\n",
      "Epoch 70/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.2425\n",
      "Epoch 71/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.5482\n",
      "Epoch 72/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.3198\n",
      "Epoch 73/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.1975\n",
      "Epoch 74/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.5955\n",
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      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.3073\n",
      "Epoch 76/200\n",
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      "Epoch 96/200\n",
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      "Epoch 97/200\n",
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      "Epoch 98/200\n",
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      "Epoch 99/200\n",
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      "Epoch 152/200\n",
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      "Epoch 154/200\n",
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      "Epoch 155/200\n",
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      "Epoch 156/200\n",
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      "Epoch 157/200\n",
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      "Epoch 158/200\n",
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      "Epoch 159/200\n",
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      "Epoch 160/200\n",
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      "Epoch 161/200\n",
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      "Epoch 162/200\n",
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      "Epoch 163/200\n",
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      "Epoch 164/200\n",
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      "Epoch 165/200\n",
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      "Epoch 166/200\n",
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      "Epoch 167/200\n",
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      "Epoch 168/200\n",
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      "Epoch 169/200\n",
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      "Epoch 170/200\n",
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      "Epoch 171/200\n",
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      "Epoch 172/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.4693\n",
      "Epoch 173/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.8950\n",
      "Epoch 174/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.7076\n",
      "Epoch 175/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 3.5335\n",
      "Epoch 176/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.8722\n",
      "Epoch 177/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.2548\n",
      "Epoch 178/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.4126\n",
      "Epoch 179/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.9566\n",
      "Epoch 180/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.0577\n",
      "Epoch 181/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.2352\n",
      "Epoch 182/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.9840\n",
      "Epoch 183/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.2677\n",
      "Epoch 184/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.6583\n",
      "Epoch 185/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.0033\n",
      "Epoch 186/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.6329\n",
      "Epoch 187/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 1.9768\n",
      "Epoch 188/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.0065\n",
      "Epoch 189/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.7915\n",
      "Epoch 190/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 1.6830\n",
      "Epoch 191/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 7.6872\n",
      "Epoch 192/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 1.5091\n",
      "Epoch 193/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 1.6420\n",
      "Epoch 194/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.1043\n",
      "Epoch 195/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.0296\n",
      "Epoch 196/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 1.8853\n",
      "Epoch 197/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 1.9529\n",
      "Epoch 198/200\n",
      "2756/2756 [==============================] - 6s 2ms/step - loss: 2.4575\n",
      "Epoch 199/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 1.6560\n",
      "Epoch 200/200\n",
      "2756/2756 [==============================] - 5s 2ms/step - loss: 2.0982\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa7cc6924f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.fit(xtrain, ytrain, batch_size=1, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 630ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[163.35457]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "#features = [Open, High, Low, Adj Close, Volume]\n",
    "features = np.array([[161.419998, 162.029999, 162.029999, 47716900]])\n",
    "model.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(model, open(\"model.pkl\", \"wb\"))\n",
    "model.save(\"model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.61419998e+02 1.62029999e+02 1.62029999e+02 4.77169000e+07]]\n",
      "1/1 [==============================] - 1s 579ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[163.35457]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "model = load_model(\"model.h5\")\n",
    "print(features)\n",
    "model.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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