LSTM_strategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41165ffb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6719af08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import math\n",
    "import pandas_datareader as pdr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4fd535c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>14.732143</td>\n",
       "      <td>14.607143</td>\n",
       "      <td>14.621429</td>\n",
       "      <td>14.686786</td>\n",
       "      <td>302220800.0</td>\n",
       "      <td>12.591938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>14.810000</td>\n",
       "      <td>14.617143</td>\n",
       "      <td>14.642857</td>\n",
       "      <td>14.765714</td>\n",
       "      <td>260022000.0</td>\n",
       "      <td>12.659608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>14.948214</td>\n",
       "      <td>14.738214</td>\n",
       "      <td>14.819643</td>\n",
       "      <td>14.929643</td>\n",
       "      <td>271269600.0</td>\n",
       "      <td>12.800156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>15.098214</td>\n",
       "      <td>14.972143</td>\n",
       "      <td>14.991786</td>\n",
       "      <td>15.085714</td>\n",
       "      <td>318292800.0</td>\n",
       "      <td>12.933965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>15.276786</td>\n",
       "      <td>15.048214</td>\n",
       "      <td>15.196429</td>\n",
       "      <td>15.061786</td>\n",
       "      <td>394024400.0</td>\n",
       "      <td>12.913450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>180.419998</td>\n",
       "      <td>177.070007</td>\n",
       "      <td>177.089996</td>\n",
       "      <td>180.330002</td>\n",
       "      <td>74919600.0</td>\n",
       "      <td>180.330002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>181.330002</td>\n",
       "      <td>178.529999</td>\n",
       "      <td>180.160004</td>\n",
       "      <td>179.289993</td>\n",
       "      <td>79144300.0</td>\n",
       "      <td>179.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>180.630005</td>\n",
       "      <td>178.139999</td>\n",
       "      <td>179.330002</td>\n",
       "      <td>179.380005</td>\n",
       "      <td>62348900.0</td>\n",
       "      <td>179.380005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>180.570007</td>\n",
       "      <td>178.089996</td>\n",
       "      <td>179.470001</td>\n",
       "      <td>178.199997</td>\n",
       "      <td>59773000.0</td>\n",
       "      <td>178.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>179.229996</td>\n",
       "      <td>177.259995</td>\n",
       "      <td>178.089996</td>\n",
       "      <td>177.570007</td>\n",
       "      <td>64025500.0</td>\n",
       "      <td>177.570007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2517 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close       Volume  \\\n",
       "Date                                                                      \n",
       "2012-01-03   14.732143   14.607143   14.621429   14.686786  302220800.0   \n",
       "2012-01-04   14.810000   14.617143   14.642857   14.765714  260022000.0   \n",
       "2012-01-05   14.948214   14.738214   14.819643   14.929643  271269600.0   \n",
       "2012-01-06   15.098214   14.972143   14.991786   15.085714  318292800.0   \n",
       "2012-01-09   15.276786   15.048214   15.196429   15.061786  394024400.0   \n",
       "...                ...         ...         ...         ...          ...   \n",
       "2021-12-27  180.419998  177.070007  177.089996  180.330002   74919600.0   \n",
       "2021-12-28  181.330002  178.529999  180.160004  179.289993   79144300.0   \n",
       "2021-12-29  180.630005  178.139999  179.330002  179.380005   62348900.0   \n",
       "2021-12-30  180.570007  178.089996  179.470001  178.199997   59773000.0   \n",
       "2021-12-31  179.229996  177.259995  178.089996  177.570007   64025500.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2012-01-03   12.591938  \n",
       "2012-01-04   12.659608  \n",
       "2012-01-05   12.800156  \n",
       "2012-01-06   12.933965  \n",
       "2012-01-09   12.913450  \n",
       "...                ...  \n",
       "2021-12-27  180.330002  \n",
       "2021-12-28  179.289993  \n",
       "2021-12-29  179.380005  \n",
       "2021-12-30  178.199997  \n",
       "2021-12-31  177.570007  \n",
       "\n",
       "[2517 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get stock quote\n",
    "df = pdr.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2022-01-01')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d7bdf9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2517 entries, 2012-01-03 to 2021-12-31\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   High       2517 non-null   float64\n",
      " 1   Low        2517 non-null   float64\n",
      " 2   Open       2517 non-null   float64\n",
      " 3   Close      2517 non-null   float64\n",
      " 4   Volume     2517 non-null   float64\n",
      " 5   Adj Close  2517 non-null   float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 137.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "771b913f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize closing data\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Closing price history')\n",
    "plt.plot(df['Close'])\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Close Price',fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5d4210fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create new dataframe with only close price\n",
    "data = df.filter(['Close'])\n",
    "#np array\n",
    "dataset = data.values\n",
    "training_len = math.ceil(len(dataset) * .8)\n",
    "training_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d341bce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00444329],\n",
       "       [0.00491767],\n",
       "       [0.00590292],\n",
       "       ...,\n",
       "       [0.99429028],\n",
       "       [0.98719814],\n",
       "       [0.98341175]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scale the data\n",
    "scaler = MinMaxScaler(feature_range = (0,1))\n",
    "scaled_data = scaler.fit_transform(dataset)\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e51ee428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1954, 60, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create training dataset\n",
    "train_data = scaled_data[:training_len,:]\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(60,training_len):\n",
    "    x_train.append(train_data[i-60:i,0])\n",
    "    y_train.append(train_data[i,0])\n",
    "    \n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c5687554",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-08 14:28:58.208656: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-01-08 14:28:58.209035: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50,return_sequences=True,input_shape=(x_train.shape[1],1)))\n",
    "model.add(LSTM(50,return_sequences = False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a16a98c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compile model\n",
    "model.compile(optimizer='adam',loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "79caaa0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-08 14:31:40.829432: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2022-01-08 14:31:40.829879: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-01-08 14:31:48.777482: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-01-08 14:31:48.863886: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-01-08 14:31:49.418317: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-01-08 14:31:50.540303: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-01-08 14:31:51.872031: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1954/1954 [==============================] - 93s 42ms/step - loss: 3.3664e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2805561f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train model\n",
    "model.fit(x_train, y_train, batch_size = 1, epochs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5f19529f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create testing data set\n",
    "#create new array containing scaled values from index \n",
    "test_data = scaled_data[training_len-60:,:]\n",
    "#create x,y test\n",
    "\n",
    "x_test = []\n",
    "y_test = dataset[training_len:,:]\n",
    "for i in range(60,len(test_data)):\n",
    "    x_test.append(test_data[i-60:i,0])\n",
    "\n",
    "x_test = np.array(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "78ce9deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c1c82fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get models predicted price\n",
    "predictions = model.predict(x_test)\n",
    "predictions = scaler.inverse_transform(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a1c45215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.37599846477774"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#evaluate model(RMSE)\n",
    "rmse = np.sqrt( np.mean(predictions - y_test)**2)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4302e587",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/p2/8n198nt1447c8x18n34bntr80000gn/T/ipykernel_63151/415344204.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  valid['Predictions'] = predictions\n"
     ]
    }
   ],
   "source": [
    "train = data[:training_len]\n",
    "valid = data[training_len:]\n",
    "valid['Predictions'] = predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c9242f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize closing data\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Model')\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Close Price',fontsize=18)\n",
    "plt.plot(train['Close'])\n",
    "plt.plot(valid[['Close','Predictions']])\n",
    "plt.legend(['Train','Val','Predictions'],loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "663c336d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}