stock-price-prediction-with-rnn.ipynb

{"metadata":{"colab":{"name":"RNN - Stock price prediction.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":445568,"sourceType":"datasetVersion","datasetId":202531},{"sourceId":445572,"sourceType":"datasetVersion","datasetId":202534},{"sourceId":7733065,"sourceType":"datasetVersion","datasetId":4518853}],"dockerImageVersionId":29661,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\n\n\n","metadata":{"id":"nOsB84br0_S_","colab_type":"code","colab":{}}},{"cell_type":"code","source":"\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport pandas_datareader.data as web\nimport datetime\nimport math\nfrom sklearn.model_selection import train_test_split\n\n\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\n\n# Importing the Keras libraries and packages\nfrom keras.models import Sequential  # linear stack of layers\nfrom keras.layers import Dense\nfrom keras.layers import LSTM        # Long Short-Term Memory layer\nfrom keras.layers import Dropout     # simple way to prevent overfitting","metadata":{"id":"TM_VlCeM1AT-","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:10:12.983944Z","iopub.execute_input":"2024-03-01T12:10:12.984353Z","iopub.status.idle":"2024-03-01T12:10:12.992541Z","shell.execute_reply.started":"2024-03-01T12:10:12.984282Z","shell.execute_reply":"2024-03-01T12:10:12.991126Z"},"trusted":true},"execution_count":130,"outputs":[]},{"cell_type":"code","source":"# Configuration\n\nSHARE = 'AAPL'\nSERVICE = 'fred' #'quandl' #'yahoo'\n\nPREDICTORS = ['High', 'Low', 'Open','Close']  #['High', 'Low', 'Open']   # column names with prices\nTARGET = 'Close'\n\nTIMESTEP = 90  # the number of previous days used for prediction\n\nSTART_DATE = datetime.datetime(2010, 1, 1)  # doesn't work for Kaggle Notebook, train set is used instead \nEND_DATE = datetime.datetime(2019, 9, 30)\n\nSTART_DATE_TO_PREDICT = datetime.datetime(2019, 10, 1)  # doesn't work for Kaggle,test set is used instead\nEND_DATE_TO_PREDICT = datetime.datetime(2019, 10, 31)\n\nN_EPOCHS = 20\n","metadata":{"id":"lspUruAu7a-Z","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:10:12.999704Z","iopub.execute_input":"2024-03-01T12:10:13.000362Z","iopub.status.idle":"2024-03-01T12:10:13.008718Z","shell.execute_reply.started":"2024-03-01T12:10:13.000300Z","shell.execute_reply":"2024-03-01T12:10:13.007505Z"},"trusted":true},"execution_count":131,"outputs":[]},{"cell_type":"code","source":"# Download stock price data\n\n# For some reason web.DataReader doesn't work on Kaggle\n\n#df_train = web.DataReader(SHARE, SERVICE, START_DATE, END_DATE)\n#df_test = web.DataReader(SHARE, SERVICE, START_DATE_TO_PREDICT, END_DATE_TO_PREDICT)\n\n#df_train = pd.read_csv('/kaggle/input/adaniports/ADANIPORTS.csv')\n\n#df_test = pd.read_csv('/kaggle/input/adaniports/ADANIPORTS.csv')\ndf_total =  pd.read_csv('/kaggle/input/adaniports/ADANIPORTS.csv')\n\ndf_train, df_test = train_test_split(df_total, test_size=0.3, shuffle=False).copy()","metadata":{"id":"IwfQ9AP77sjc","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:10:13.015684Z","iopub.execute_input":"2024-03-01T12:10:13.016371Z","iopub.status.idle":"2024-03-01T12:10:13.043141Z","shell.execute_reply.started":"2024-03-01T12:10:13.016098Z","shell.execute_reply":"2024-03-01T12:10:13.042281Z"},"trusted":true},"execution_count":132,"outputs":[]},{"cell_type":"code","source":"df_test.tail()\n","metadata":{"id":"OgdzIuuc9Xwj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":235},"outputId":"83c605d1-6f21-47a3-8004-aedf34300c80","execution":{"iopub.status.busy":"2024-03-01T12:10:13.045659Z","iopub.execute_input":"2024-03-01T12:10:13.045982Z","iopub.status.idle":"2024-03-01T12:10:13.072907Z","shell.execute_reply.started":"2024-03-01T12:10:13.045932Z","shell.execute_reply":"2024-03-01T12:10:13.071755Z"},"trusted":true},"execution_count":133,"outputs":[{"execution_count":133,"output_type":"execute_result","data":{"text/plain":"            Date      Symbol Series  Prev Close    Open    High     Low  \\\n2988  2019-12-24  ADANIPORTS     EQ      366.90  368.00  372.00  366.00   \n2989  2019-12-26  ADANIPORTS     EQ      368.60  368.80  370.60  362.10   \n2990  2019-12-27  ADANIPORTS     EQ      363.65  366.75  367.90  362.80   \n2991  2019-12-30  ADANIPORTS     EQ      363.70  371.00  371.00  363.75   \n2992  2019-12-31  ADANIPORTS     EQ      365.95  366.00  368.65  364.90   \n\n        Last   Close    VWAP   Volume      Turnover   Trades  \\\n2988  368.50  368.60  368.88  2948878  1.087779e+14  45244.0   \n2989  362.10  363.65  366.52  2503435  9.175530e+13  33520.0   \n2990  363.85  363.70  365.19  2146972  7.840569e+13  30299.0   \n2991  366.00  365.95  366.81  4732203  1.735840e+14  45583.0   \n2992  366.20  366.00  366.52  2043206  7.488704e+13  16901.0   \n\n      Deliverable Volume  %Deliverble  \n2988             1063340       0.3606  \n2989              774317       0.3093  \n2990              445974       0.2077  \n2991              529147       0.1118  \n2992              816396       0.3996  ","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>Date</th>\n      <th>Symbol</th>\n      <th>Series</th>\n      <th>Prev Close</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Last</th>\n      <th>Close</th>\n      <th>VWAP</th>\n      <th>Volume</th>\n      <th>Turnover</th>\n      <th>Trades</th>\n      <th>Deliverable Volume</th>\n      <th>%Deliverble</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>2988</td>\n      <td>2019-12-24</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>366.90</td>\n      <td>368.00</td>\n      <td>372.00</td>\n      <td>366.00</td>\n      <td>368.50</td>\n      <td>368.60</td>\n      <td>368.88</td>\n      <td>2948878</td>\n      <td>1.087779e+14</td>\n      <td>45244.0</td>\n      <td>1063340</td>\n      <td>0.3606</td>\n    </tr>\n    <tr>\n      <td>2989</td>\n      <td>2019-12-26</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>368.60</td>\n      <td>368.80</td>\n      <td>370.60</td>\n      <td>362.10</td>\n      <td>362.10</td>\n      <td>363.65</td>\n      <td>366.52</td>\n      <td>2503435</td>\n      <td>9.175530e+13</td>\n      <td>33520.0</td>\n      <td>774317</td>\n      <td>0.3093</td>\n    </tr>\n    <tr>\n      <td>2990</td>\n      <td>2019-12-27</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>363.65</td>\n      <td>366.75</td>\n      <td>367.90</td>\n      <td>362.80</td>\n      <td>363.85</td>\n      <td>363.70</td>\n      <td>365.19</td>\n      <td>2146972</td>\n      <td>7.840569e+13</td>\n      <td>30299.0</td>\n      <td>445974</td>\n      <td>0.2077</td>\n    </tr>\n    <tr>\n      <td>2991</td>\n      <td>2019-12-30</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>363.70</td>\n      <td>371.00</td>\n      <td>371.00</td>\n      <td>363.75</td>\n      <td>366.00</td>\n      <td>365.95</td>\n      <td>366.81</td>\n      <td>4732203</td>\n      <td>1.735840e+14</td>\n      <td>45583.0</td>\n      <td>529147</td>\n      <td>0.1118</td>\n    </tr>\n    <tr>\n      <td>2992</td>\n      <td>2019-12-31</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>365.95</td>\n      <td>366.00</td>\n      <td>368.65</td>\n      <td>364.90</td>\n      <td>366.20</td>\n      <td>366.00</td>\n      <td>366.52</td>\n      <td>2043206</td>\n      <td>7.488704e+13</td>\n      <td>16901.0</td>\n      <td>816396</td>\n      <td>0.3996</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_test.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-01T12:10:13.074945Z","iopub.execute_input":"2024-03-01T12:10:13.075347Z","iopub.status.idle":"2024-03-01T12:10:13.101888Z","shell.execute_reply.started":"2024-03-01T12:10:13.075285Z","shell.execute_reply":"2024-03-01T12:10:13.100920Z"},"trusted":true},"execution_count":134,"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":"            Date      Symbol Series  Prev Close   Open    High     Low  \\\n2095  2016-05-13  ADANIPORTS     EQ      195.15  194.6  200.25  179.40   \n2096  2016-05-16  ADANIPORTS     EQ      188.55  189.0  192.20  183.70   \n2097  2016-05-17  ADANIPORTS     EQ      185.80  186.6  187.35  182.70   \n2098  2016-05-18  ADANIPORTS     EQ      183.25  182.4  185.50  180.55   \n2099  2016-05-19  ADANIPORTS     EQ      183.20  184.5  184.50  169.70   \n\n        Last   Close    VWAP    Volume      Turnover    Trades  \\\n2095  188.80  188.55  193.31  15272242  2.952351e+14  125916.0   \n2096  185.55  185.80  186.66   9997240  1.866135e+14   90069.0   \n2097  182.80  183.25  184.45   7208437  1.329615e+14   63213.0   \n2098  183.20  183.20  183.05   5909252  1.081665e+14   48273.0   \n2099  170.90  171.55  177.20  13478073  2.388273e+14   74235.0   \n\n      Deliverable Volume  %Deliverble  \n2095             7334006       0.4802  \n2096             6444435       0.6446  \n2097             4982441       0.6912  \n2098             3278248       0.5548  \n2099             7891714       0.5855  ","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>Date</th>\n      <th>Symbol</th>\n      <th>Series</th>\n      <th>Prev Close</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Last</th>\n      <th>Close</th>\n      <th>VWAP</th>\n      <th>Volume</th>\n      <th>Turnover</th>\n      <th>Trades</th>\n      <th>Deliverable Volume</th>\n      <th>%Deliverble</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>2095</td>\n      <td>2016-05-13</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>195.15</td>\n      <td>194.6</td>\n      <td>200.25</td>\n      <td>179.40</td>\n      <td>188.80</td>\n      <td>188.55</td>\n      <td>193.31</td>\n      <td>15272242</td>\n      <td>2.952351e+14</td>\n      <td>125916.0</td>\n      <td>7334006</td>\n      <td>0.4802</td>\n    </tr>\n    <tr>\n      <td>2096</td>\n      <td>2016-05-16</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>188.55</td>\n      <td>189.0</td>\n      <td>192.20</td>\n      <td>183.70</td>\n      <td>185.55</td>\n      <td>185.80</td>\n      <td>186.66</td>\n      <td>9997240</td>\n      <td>1.866135e+14</td>\n      <td>90069.0</td>\n      <td>6444435</td>\n      <td>0.6446</td>\n    </tr>\n    <tr>\n      <td>2097</td>\n      <td>2016-05-17</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>185.80</td>\n      <td>186.6</td>\n      <td>187.35</td>\n      <td>182.70</td>\n      <td>182.80</td>\n      <td>183.25</td>\n      <td>184.45</td>\n      <td>7208437</td>\n      <td>1.329615e+14</td>\n      <td>63213.0</td>\n      <td>4982441</td>\n      <td>0.6912</td>\n    </tr>\n    <tr>\n      <td>2098</td>\n      <td>2016-05-18</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>183.25</td>\n      <td>182.4</td>\n      <td>185.50</td>\n      <td>180.55</td>\n      <td>183.20</td>\n      <td>183.20</td>\n      <td>183.05</td>\n      <td>5909252</td>\n      <td>1.081665e+14</td>\n      <td>48273.0</td>\n      <td>3278248</td>\n      <td>0.5548</td>\n    </tr>\n    <tr>\n      <td>2099</td>\n      <td>2016-05-19</td>\n      <td>ADANIPORTS</td>\n      <td>EQ</td>\n      <td>183.20</td>\n      <td>184.5</td>\n      <td>184.50</td>\n      <td>169.70</td>\n      <td>170.90</td>\n      <td>171.55</td>\n      <td>177.20</td>\n      <td>13478073</td>\n      <td>2.388273e+14</td>\n      <td>74235.0</td>\n      <td>7891714</td>\n      <td>0.5855</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_train = df_train.copy()\ndf_test = df_test.copy()\n\nif 'Date' in df_train.columns:\n    df_train.loc[:, 'Date'] = pd.to_datetime(df_train['Date'])\nelse:\n    print(\"Error: 'Date' column not found in df_train.\")","metadata":{"execution":{"iopub.status.busy":"2024-03-01T12:10:13.103634Z","iopub.execute_input":"2024-03-01T12:10:13.104000Z","iopub.status.idle":"2024-03-01T12:10:13.115408Z","shell.execute_reply.started":"2024-03-01T12:10:13.103932Z","shell.execute_reply":"2024-03-01T12:10:13.114603Z"},"trusted":true},"execution_count":135,"outputs":[]},{"cell_type":"code","source":"# Data preprocessing\n\n# Fix for used input files on Kaggle\ndf_train.loc[:, PREDICTORS + [TARGET]]\ndf_train['Date'] = pd.to_datetime(df_train['Date'])\ndf_test['Date'] = pd.to_datetime(df_test['Date'])\n\ndf_train.set_index('Date', inplace=True)\ndf_test.set_index('Date', inplace=True)\n\n\n# Removing irrelevant columns\ndf_train = df_train[PREDICTORS]\ndf_test = df_test[PREDICTORS]","metadata":{"id":"Qj5un37iNWd9","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:10:13.119047Z","iopub.execute_input":"2024-03-01T12:10:13.119366Z","iopub.status.idle":"2024-03-01T12:10:13.139093Z","shell.execute_reply.started":"2024-03-01T12:10:13.119306Z","shell.execute_reply":"2024-03-01T12:10:13.137834Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"# Creating numpy 2-dim array for the training set\n# size N x K, where N - number of observations\n# K - number of predictors\n\ntraining_set = df_train.values\n","metadata":{"id":"2YO34sRo1U73","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:10:13.141678Z","iopub.execute_input":"2024-03-01T12:10:13.142111Z","iopub.status.idle":"2024-03-01T12:10:13.146994Z","shell.execute_reply.started":"2024-03-01T12:10:13.142037Z","shell.execute_reply":"2024-03-01T12:10:13.145934Z"},"trusted":true},"execution_count":137,"outputs":[]},{"cell_type":"code","source":"# Feature Scaling\nsc = MinMaxScaler(feature_range = (0, 1))\ntraining_set_scaled = sc.fit_transform(training_set)","metadata":{"id":"saiVJ7ot_S1I","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:10:13.149128Z","iopub.execute_input":"2024-03-01T12:10:13.149702Z","iopub.status.idle":"2024-03-01T12:10:13.159634Z","shell.execute_reply.started":"2024-03-01T12:10:13.149462Z","shell.execute_reply":"2024-03-01T12:10:13.158550Z"},"trusted":true},"execution_count":138,"outputs":[]},{"cell_type":"code","source":"print(df_train.columns)\nprint(df_test.columns)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T12:10:13.160967Z","iopub.execute_input":"2024-03-01T12:10:13.161400Z","iopub.status.idle":"2024-03-01T12:10:13.171203Z","shell.execute_reply.started":"2024-03-01T12:10:13.161354Z","shell.execute_reply":"2024-03-01T12:10:13.170489Z"},"trusted":true},"execution_count":139,"outputs":[{"name":"stdout","text":"Index(['High', 'Low', 'Open', 'Close'], dtype='object')\nIndex(['High', 'Low', 'Open', 'Close'], dtype='object')\n","output_type":"stream"}]},{"cell_type":"code","source":"target_col_index = df_train.columns.get_loc(TARGET)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T12:10:13.172341Z","iopub.execute_input":"2024-03-01T12:10:13.172730Z","iopub.status.idle":"2024-03-01T12:10:13.184808Z","shell.execute_reply.started":"2024-03-01T12:10:13.172688Z","shell.execute_reply":"2024-03-01T12:10:13.183930Z"},"trusted":true},"execution_count":140,"outputs":[]},{"cell_type":"code","source":"# Creating a data structure with timesteps and 1 output\n# X_train - 3-dim array size of N x M x K, where \n# N - number of observations\n# M - number of previous days (TIMESTEP)\n# K - number of predictors/indicators\n \nX_train = []\ny_train = []\ntarget_col_index = df_train.columns.get_loc(TARGET)\nfor i in range(TIMESTEP, len(training_set)):\n    X_train.append(training_set_scaled[i-TIMESTEP:i, :])       # X_train - list of Numpy arrays\n    y_train.append(training_set_scaled[i, target_col_index])\nX_train, y_train = np.array(X_train), np.array(y_train)        # convert list to Numpy array\n","metadata":{"id":"nThfDpWU_Xsz","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:10:13.186107Z","iopub.execute_input":"2024-03-01T12:10:13.186544Z","iopub.status.idle":"2024-03-01T12:10:13.204318Z","shell.execute_reply.started":"2024-03-01T12:10:13.186497Z","shell.execute_reply":"2024-03-01T12:10:13.202817Z"},"trusted":true},"execution_count":141,"outputs":[]},{"cell_type":"code","source":"# Building the RNN\n\n\n# Initialising the RNN\nregressor = Sequential()\n\n# Adding the first LSTM layer and some Dropout regularisation\nregressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], X_train.shape[2])))\nregressor.add(Dropout(0.2))\n\n# Adding a second LSTM layer and some Dropout regularisation\nregressor.add(LSTM(units = 50, return_sequences = True))\nregressor.add(Dropout(0.2))\n\n# Adding a third LSTM layer and some Dropout regularisation\nregressor.add(LSTM(units = 50, return_sequences = True))\nregressor.add(Dropout(0.2))\n\n# Adding a fourth LSTM layer and some Dropout regularisation\nregressor.add(LSTM(units = 50))\nregressor.add(Dropout(0.2))\n\n# Adding the output layer - dense layer \n# classic fully connected neural network layer : each input node is connected to each output node\nregressor.add(Dense(units = 1))\n\n# Compiling the RNN\n# use MSE instead RMSE (Root Mean Squared Error) because we are more interested in the directions taken by our predictions, rather than the closeness of their values to the real stock price\nregressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n\n# Fitting the RNN to the Training set\nregressor.fit(X_train, y_train, epochs = N_EPOCHS, batch_size = 32)\n\n","metadata":{"id":"s7omlI4D1HAj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"3b0a4a18-21b7-474e-ce3f-3c6fa61e02d2","execution":{"iopub.status.busy":"2024-03-01T12:10:13.207413Z","iopub.execute_input":"2024-03-01T12:10:13.208125Z","iopub.status.idle":"2024-03-01T12:14:48.419361Z","shell.execute_reply.started":"2024-03-01T12:10:13.208044Z","shell.execute_reply":"2024-03-01T12:14:48.418536Z"},"trusted":true},"execution_count":142,"outputs":[{"name":"stdout","text":"Epoch 1/20\n2005/2005 [==============================] - 16s 8ms/step - loss: 0.0062\nEpoch 2/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0032\nEpoch 3/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0024\nEpoch 4/20\n2005/2005 [==============================] - 14s 7ms/step - loss: 0.0025\nEpoch 5/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0021\nEpoch 6/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0019\nEpoch 7/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0018\nEpoch 8/20\n2005/2005 [==============================] - 13s 6ms/step - loss: 0.0018\nEpoch 9/20\n2005/2005 [==============================] - 14s 7ms/step - loss: 0.0022\nEpoch 10/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0016\nEpoch 11/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0018\nEpoch 12/20\n2005/2005 [==============================] - 14s 7ms/step - loss: 0.0014\nEpoch 13/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0014\nEpoch 14/20\n2005/2005 [==============================] - 14s 7ms/step - loss: 0.0014\nEpoch 15/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0014\nEpoch 16/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0014\nEpoch 17/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0012\nEpoch 18/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0012\nEpoch 19/20\n2005/2005 [==============================] - 14s 7ms/step - loss: 0.0013\nEpoch 20/20\n2005/2005 [==============================] - 13s 7ms/step - loss: 0.0012\n","output_type":"stream"},{"execution_count":142,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.callbacks.History at 0x79e2936b1908>"},"metadata":{}}]},{"cell_type":"code","source":"# Extracting real prices\nreal_stock_price = df_test[TARGET].values\n\n# Preparing input data for making predictions\ndf_total = df_train.append(df_test)\ninputs = df_total[len(df_total) - len(df_test) - TIMESTEP:][PREDICTORS]\n\ninputs = sc.transform(inputs)\n\n\nX_test = []\nfor i in range(TIMESTEP, TIMESTEP+len(df_test)):\n    X_test.append(inputs[i-TIMESTEP:i, :])\nX_test = np.array(X_test)","metadata":{"id":"1KR6C4wGEnwp","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:14:48.421039Z","iopub.execute_input":"2024-03-01T12:14:48.421339Z","iopub.status.idle":"2024-03-01T12:14:48.437103Z","shell.execute_reply.started":"2024-03-01T12:14:48.421297Z","shell.execute_reply":"2024-03-01T12:14:48.436139Z"},"trusted":true},"execution_count":143,"outputs":[]},{"cell_type":"code","source":"\n\n\n\n","metadata":{"id":"s9moCaFHF-m_","colab_type":"code","colab":{},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Making the predictions\n\npredicted_stock_price = regressor.predict(X_test)\n\n# to perform inverse scaler transformation, at first we need to transform \n# 1-dimensional array with predicted prices to 2-dim array of size N x K, \n# where N = number of observations/predictions. \n# K = number of predictors\n\ntemp_matrix = np.zeros((len(predicted_stock_price), len(PREDICTORS)))\ntemp_matrix[:,target_col_index:target_col_index+1] = predicted_stock_price  # temp_matrix[:,[target_col_index]] = predicted_stock_price    \n\npredicted_stock_price = sc.inverse_transform(temp_matrix)[:,target_col_index]","metadata":{"id":"LVr2sizEHSEm","colab_type":"code","colab":{},"execution":{"iopub.status.busy":"2024-03-01T12:14:48.439195Z","iopub.execute_input":"2024-03-01T12:14:48.439967Z","iopub.status.idle":"2024-03-01T12:14:50.924216Z","shell.execute_reply.started":"2024-03-01T12:14:48.439901Z","shell.execute_reply":"2024-03-01T12:14:50.923328Z"},"trusted":true},"execution_count":144,"outputs":[]},{"cell_type":"code","source":"","metadata":{"id":"hqBC3DK_StlR","colab_type":"code","colab":{},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_predicted = pd.DataFrame(index=df_test.index)\n#df_predicted[TARGET] = predicted_stock_price\n\ndf_test['Predicted price'] = predicted_stock_price\ndf_test[TARGET].plot(figsize=(16,4),legend=True)\ndf_test['Predicted price'].plot(figsize=(16,4),legend=True)\nplt.legend(['Real price', 'Predicted price'])\nplt.title('RNN - ' + SHARE + ' Stock Price Prediction')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.show()","metadata":{"id":"GsIt0rILPwB2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":319},"outputId":"a2ed2c27-31d7-4a3f-acfb-9070d68593d0","execution":{"iopub.status.busy":"2024-03-01T12:14:50.926671Z","iopub.execute_input":"2024-03-01T12:14:50.927123Z","iopub.status.idle":"2024-03-01T12:14:51.284009Z","shell.execute_reply.started":"2024-03-01T12:14:50.927074Z","shell.execute_reply":"2024-03-01T12:14:51.282973Z"},"trusted":true},"execution_count":145,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Evaluating model\nrmse = math.sqrt(mean_squared_error(real_stock_price, predicted_stock_price))\nprint(\"The RMSE is {:.3f}.\".format(rmse))","metadata":{"id":"v23DT_FkYX6z","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"683c1979-00d0-4b8f-bfa6-d5b887a6e38c","execution":{"iopub.status.busy":"2024-03-01T12:14:51.286328Z","iopub.execute_input":"2024-03-01T12:14:51.286678Z","iopub.status.idle":"2024-03-01T12:14:51.293562Z","shell.execute_reply.started":"2024-03-01T12:14:51.286613Z","shell.execute_reply":"2024-03-01T12:14:51.292400Z"},"trusted":true},"execution_count":146,"outputs":[{"name":"stdout","text":"The RMSE is 12.792.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Assuming df_train and df_test are your training and testing DataFrames\nprint(\"Training Set:\")\nprint(df_train.head())\n\nprint(\"\\nTesting Set:\")\nprint(df_test.head())\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T12:14:51.295305Z","iopub.execute_input":"2024-03-01T12:14:51.295642Z","iopub.status.idle":"2024-03-01T12:14:51.312393Z","shell.execute_reply.started":"2024-03-01T12:14:51.295568Z","shell.execute_reply":"2024-03-01T12:14:51.311506Z"},"trusted":true},"execution_count":147,"outputs":[{"name":"stdout","text":"Training Set:\n               High    Low    Open   Close\nDate                                      \n2007-11-27  1050.00  770.0  770.00  962.90\n2007-11-28   990.00  874.0  984.00  893.90\n2007-11-29   914.75  841.0  909.00  884.20\n2007-11-30   958.00  890.0  890.00  921.55\n2007-12-03   995.00  922.0  939.75  969.30\n\nTesting Set:\n              High     Low   Open   Close  Predicted price\nDate                                                      \n2016-05-13  200.25  179.40  194.6  188.55       212.210484\n2016-05-16  192.20  183.70  189.0  185.80       208.645719\n2016-05-17  187.35  182.70  186.6  183.25       205.996080\n2016-05-18  185.50  180.55  182.4  183.20       203.909814\n2016-05-19  184.50  169.70  184.5  171.55       202.108410\n","output_type":"stream"}]}]}