{"cells":[{"cell_type":"markdown","metadata":{},"source":["# What is LSTM?\n","\n","Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) architecture. Unlike standard feedforward neural networks, LSTM has feedback connections that make it a \"general-purpose computer\" for processing sequences of data. This allows it to store information over long sequences and is exceptionally well-suited for various time-series prediction tasks.\n","\n","Why LSTM for Stock Market Prediction?\n","Memory Cells: LSTMs are designed to remember past information and are ideal for sequence prediction problems. In the stock market, past stock prices are a good indicator of future stock behavior.\n","\n","Handling Long Sequences: Traditional RNNs suffer from vanishing or exploding gradient problems. This makes them ineffective in learning from earlier time steps as the sequence gets longer. LSTMs are designed to combat these issues.\n","\n","Feature Learning: LSTMs can learn to recognize important events (like economic indicators or market sentiments) and forget the non-important ones, making them very effective for financial time series where we have lots of irrelevant data points.\n","\n","Robustness: LSTMs are less susceptible to noise in the data. Financial markets are highly volatile and noisy, but LSTMs can filter out the noise to make more accurate predictions.\n","\n","Multivariate Time Series: LSTMs can handle scenarios where multiple variables influence the time series. For example, stock prices are influenced by opening price, lowest price of the day, highest price of the day, etc.\n","\n","Real-Time Analysis: LSTMs can analyze and make predictions in real-time, a crucial requirement for stock trading where prices can change within seconds.\n","\n","By using LSTM networks, you're leveraging these advantages to make more accurate and informed predictions about stock market movements. This can be a powerful tool in a financial analyst's arsenal, provided it's used responsibly and in conjunction with other methods for risk assessment."]},{"cell_type":"markdown","metadata":{},"source":["# LSTM Stock Market Prediction for S&P 500\n","This notebook aims to predict the stock market moves for the S&P 500 using Long Short-Term Memory (LSTM) networks. The steps involved are as follows:\n","1. **Data Retrieval**: Fetch historical stock data for the S&P 500 from the Yahoo Finance API.\n","2. **Data Preprocessing**: Prepare the data for training, including normalization and reshaping.\n","3. **Model Building**: Create an LSTM model for time-series prediction.\n","4. **Training**: Train the model on the historical data.\n","5. **Prediction**: Use the model to predict stock prices for 2023.\n","6. **Evaluation**: Compare the predictions with actual historical data for 2023.\n","7. **Visualization**: Plot the predictions alongside the actual data.\n","Let's get started!"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["Requirement already satisfied: tensorflow in /home/hexuser/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages (2.12.0)\n","Requirement already satisfied: absl-py>=1.0.0 in 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/home/hexuser/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.4.8)\n","Requirement already satisfied: oauthlib>=3.0.0 in /home/hexuser/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.1.1)\n"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["!pip install tensorflow"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["!pip install -q yfinance"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import yfinance as yf\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.preprocessing import MinMaxScaler\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Dense, LSTM"]},{"cell_type":"markdown","metadata":{},"source":["## Step 1: Data Retrieval\n","Let's start by fetching the historical stock data for the S&P 500 from the Yahoo Finance API. We'll retrieve data from 2010 to 2023."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["[*********************100%***********************]  1 of 1 completed\n"]},"execution_count":null,"metadata":{},"output_type":"execute_result"},{"data":{"application/vnd.hex.export+parquet":"{\"success\":true,\"exportKey\":\"4a8043e5-f038-4821-9c1d-4b8d3d5b0fcd/98a9ce9b-e050-4dc6-b092-144a0fc82520/exports/d053b411-1317-4cb1-92ba-71f8b615c325\"}","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>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","    <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>2010-01-04</th>\n","      <td>1116.560059</td>\n","      <td>1133.869995</td>\n","      <td>1116.560059</td>\n","      <td>1132.989990</td>\n","      <td>1132.989990</td>\n","      <td>3991400000</td>\n","    </tr>\n","    <tr>\n","      <th>2010-01-05</th>\n","      <td>1132.660034</td>\n","      <td>1136.630005</td>\n","      <td>1129.660034</td>\n","      <td>1136.520020</td>\n","      <td>1136.520020</td>\n","      <td>2491020000</td>\n","    </tr>\n","    <tr>\n","      <th>2010-01-06</th>\n","      <td>1135.709961</td>\n","      <td>1139.189941</td>\n","      <td>1133.949951</td>\n","      <td>1137.140015</td>\n","      <td>1137.140015</td>\n","      <td>4972660000</td>\n","    </tr>\n","    <tr>\n","      <th>2010-01-07</th>\n","      <td>1136.270020</td>\n","      <td>1142.459961</td>\n","      <td>1131.319946</td>\n","      <td>1141.689941</td>\n","      <td>1141.689941</td>\n","      <td>5270680000</td>\n","    </tr>\n","    <tr>\n","      <th>2010-01-08</th>\n","      <td>1140.520020</td>\n","      <td>1145.390015</td>\n","      <td>1136.219971</td>\n","      <td>1144.979980</td>\n","      <td>1144.979980</td>\n","      <td>4389590000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# Fetch historical stock data for S&P 500 from 2010 to 2023\n","ticker = '^GSPC'\n","start_date = '2010-01-01'\n","end_date = '2023-09-02'\n","data = yf.download(ticker, start=start_date, end=end_date)\n","data.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Step 2: Data Preprocessing\n","Now that we have the historical data, let's preprocess it for our LSTM model. We'll focus on the 'Close' prices and perform the following steps:\n","1. Normalize the data\n","2. Create training and test datasets\n","3. Reshape the data for LSTM input"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["((2692, 60, 1), (628, 60, 1))"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# Extract 'Close' prices and convert to numpy array\n","close_data = data['Close'].values.reshape(-1, 1)\n","\n","# Normalize the data using MinMaxScaler\n","scaler = MinMaxScaler(feature_range=(0, 1))\n","scaled_data = scaler.fit_transform(close_data)\n","\n","# Create training and test datasets\n","train_size = int(len(scaled_data) * 0.8)\n","test_size = len(scaled_data) - train_size\n","train_data, test_data = scaled_data[0:train_size, :], scaled_data[train_size:len(scaled_data), :]\n","\n","# Reshape the data for LSTM input\n","X_train, y_train, X_test, y_test = [], [], [], []\n","for i in range(60, len(train_data)):\n","    X_train.append(train_data[i-60:i, 0])\n","    y_train.append(train_data[i, 0])\n","for i in range(60, len(test_data)):\n","    X_test.append(test_data[i-60:i, 0])\n","    y_test.append(test_data[i, 0])\n","\n","X_train, y_train = np.array(X_train), np.array(y_train)\n","\n","X_test, y_test = np.array(X_test), np.array(y_test)\n","\n","X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n","\n","X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n","\n","X_train.shape, X_test.shape"]},{"cell_type":"markdown","metadata":{},"source":["## Step 3: Model Building\n","With our data preprocessed, we can now build the LSTM model. The architecture will consist of:\n","1. An LSTM layer with 50 units and a 'relu' activation function\n","2. A Dense layer with 25 units\n","3. A Dense layer with 1 unit (output layer)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," lstm_2 (LSTM)               (None, 60, 50)            10400     \n","                                                                 \n"," lstm_3 (LSTM)               (None, 50)                20200     \n","                                                                 \n"," dense_2 (Dense)             (None, 25)                1275      \n","                                                                 \n"," dense_3 (Dense)             (None, 1)                 26        \n","                                                                 \n","=================================================================\n","Total params: 31,901\n","Trainable params: 31,901\n","Non-trainable params: 0\n","_________________________________________________________________\n"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# Build the LSTM model\n","model = Sequential()\n","model.add(LSTM(units=50, activation='relu', return_sequences=True, input_shape=(X_train.shape[1], 1)))\n","model.add(LSTM(units=50, activation='relu', return_sequences=False))\n","model.add(Dense(units=25))\n","model.add(Dense(units=1))\n","\n","# Compile the model\n","model.compile(optimizer='adam', loss='mean_squared_error')\n","\n","# Summary of the model architecture\n","model.summary()"]},{"cell_type":"markdown","metadata":{},"source":["## Step 4: Training\n","Now that our model is built, let's train it on our training data. We'll use the following parameters:\n","1. `epochs=25`: Number of iterations over the entire dataset\n","2. `batch_size=64`: Number of samples per gradient update\n","3. `validation_data`: Data on which to evaluate the loss and any model metrics at the end of each epoch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["Epoch 1/25\n","43/43 [==============================] - 13s 236ms/step - loss: 0.0200 - val_loss: 0.1125\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 2/25\n","43/43 [==============================] - 6s 145ms/step - loss: 4.1500e-04 - val_loss: 0.0092\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 3/25\n","43/43 [==============================] - 6s 146ms/step - loss: 2.6775e-04 - val_loss: 0.0062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 4/25\n","43/43 [==============================] - 6s 150ms/step - loss: 2.5859e-04 - val_loss: 0.0064\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 5/25\n","43/43 [==============================] - 6s 150ms/step - loss: 2.6269e-04 - val_loss: 0.0062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 6/25\n","43/43 [==============================] - 6s 148ms/step - loss: 2.3273e-04 - val_loss: 0.0047\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 7/25\n","43/43 [==============================] - 6s 145ms/step - loss: 2.2621e-04 - val_loss: 0.0017\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 8/25\n","43/43 [==============================] - 6s 143ms/step - loss: 2.3289e-04 - val_loss: 0.0019\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 9/25\n","43/43 [==============================] - 6s 141ms/step - loss: 2.6048e-04 - val_loss: 0.0013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 10/25\n","43/43 [==============================] - 6s 147ms/step - loss: 2.0728e-04 - val_loss: 8.1923e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 11/25\n","43/43 [==============================] - 6s 143ms/step - loss: 1.8845e-04 - val_loss: 7.5523e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 12/25\n","43/43 [==============================] - 6s 143ms/step - loss: 2.3096e-04 - val_loss: 6.5598e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 13/25\n","43/43 [==============================] - 6s 143ms/step - loss: 1.8330e-04 - val_loss: 0.0045\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 14/25\n","43/43 [==============================] - 6s 142ms/step - loss: 4.0736e-04 - val_loss: 9.6526e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 15/25\n","43/43 [==============================] - 6s 149ms/step - loss: 1.8099e-04 - val_loss: 5.9542e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 16/25\n","43/43 [==============================] - 6s 142ms/step - loss: 1.8071e-04 - val_loss: 6.1794e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 17/25\n","43/43 [==============================] - 6s 141ms/step - loss: 1.5913e-04 - val_loss: 6.6051e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 18/25\n","43/43 [==============================] - 6s 150ms/step - loss: 1.6552e-04 - val_loss: 8.3063e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 19/25\n","43/43 [==============================] - 6s 150ms/step - loss: 1.4760e-04 - val_loss: 5.1630e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 20/25\n","43/43 [==============================] - 6s 148ms/step - loss: 1.5399e-04 - val_loss: 5.1267e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 21/25\n","43/43 [==============================] - 6s 144ms/step - loss: 1.5396e-04 - val_loss: 6.5893e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 22/25\n","43/43 [==============================] - 6s 141ms/step - loss: 1.5969e-04 - val_loss: 6.7979e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 23/25\n","43/43 [==============================] - 6s 141ms/step - loss: 1.4065e-04 - val_loss: 6.0979e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 24/25\n","43/43 [==============================] - 6s 147ms/step - loss: 1.3754e-04 - val_loss: 5.3160e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","Epoch 25/25\n","43/43 [==============================] - 6s 148ms/step - loss: 1.4118e-04 - val_loss: 7.8068e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# Train the model\n","history = model.fit(\n","    X_train,\n","    y_train,\n","    epochs=25,\n","    batch_size=64,\n","    validation_data=(X_test, y_test),\n","    verbose=1\n",")"]},{"cell_type":"markdown","metadata":{},"source":["## Step 5: Prediction\n","Let's use the trained model to make predictions on our test data. We'll then transform these predictions back to their original scale using the MinMaxScaler."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["20/20 [==============================] - 1s 42ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"]},"execution_count":null,"metadata":{},"output_type":"execute_result"},{"data":{"application/vnd.hex.export+parquet":"{\"success\":true,\"exportKey\":\"4a8043e5-f038-4821-9c1d-4b8d3d5b0fcd/98a9ce9b-e050-4dc6-b092-144a0fc82520/exports/cea029cf-c42d-4832-8ea4-ffa4a7cc27d7\"}","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>Actual</th>\n","      <th>Predicted</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2021-03-08</th>\n","      <td>3821.350098</td>\n","      <td>3808.218750</td>\n","    </tr>\n","    <tr>\n","      <th>2021-03-09</th>\n","      <td>3875.439941</td>\n","      <td>3796.629150</td>\n","    </tr>\n","    <tr>\n","      <th>2021-03-10</th>\n","      <td>3898.810059</td>\n","      <td>3793.227783</td>\n","    </tr>\n","    <tr>\n","      <th>2021-03-11</th>\n","      <td>3939.340088</td>\n","      <td>3797.271484</td>\n","    </tr>\n","    <tr>\n","      <th>2021-03-12</th>\n","      <td>3943.340088</td>\n","      <td>3809.306152</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# Make predictions using the test data\n","predictions = model.predict(X_test)\n","# Transform predictions back to original scale\n","predictions = scaler.inverse_transform(np.reshape(predictions, (-1, 1)))\n","# Create a DataFrame to store the actual and predicted values\n","test_data_range = data.iloc[train_size + 60:]['Close'].index\n","comparison_df = pd.DataFrame({'Actual': data.iloc[train_size + 60:]['Close'].values, 'Predicted': np.squeeze(predictions)}, index=test_data_range)\n","comparison_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Step 6: Evaluation\n","Now that we have our predictions, let's evaluate the model's performance. We'll calculate the Root Mean Square Error (RMSE) to quantify the model's accuracy."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["105.4471718225548"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.metrics import mean_squared_error\n","from math import sqrt\n","\n","# Calculate RMSE\n","rmse = sqrt(mean_squared_error(comparison_df['Actual'], comparison_df['Predicted']))\n","rmse"]},{"cell_type":"markdown","metadata":{},"source":["Interpretation of results: The Root Mean Square Error (RMSE) is a measure of the differences between the predicted and actual values. In the context of stock prices, an RMSE of 105.45 means that, on average, the model's predictions are approximately $105.45 away from the actual closing prices of the S&P 500 in the test dataset.\n","\n","Now, whether this is a \"good\" or \"bad\" result depends on several factors:\n","\n","Scale of Data: The S&P 500 is a high-value index, often ranging in the thousands of dollars. In that context, an RMSE of 105.45 may not be overly concerning.\n","\n","Investment Strategy: If you're looking at long-term investment, this level of error might be acceptable. However, for short-term trading, even small errors can be significant.\n","\n","Benchmark: It's also useful to compare this RMSE with those of other models or industry standards to get a sense of how well this particular model is performing."]},{"cell_type":"markdown","metadata":{},"source":["## Step 7: Visualization\n","Finally, let's visualize our predictions alongside the actual data. This will give us a better sense of how well our model is performing."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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"},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# Plot the actual and predicted values\n","plt.figure(figsize=(16, 8))\n","plt.title('S&P 500 Price Prediction')\n","plt.xlabel('Date')\n","plt.ylabel('Close Price (USD)')\n","plt.plot(comparison_df['Actual'], label='Actual', color='blue')\n","plt.plot(comparison_df['Predicted'], label='Predicted', color='red')\n","plt.legend(loc='upper left')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["## Step 8: Future Predictions\n","Now that we have a trained model, let's use it to make future predictions for the rest of 2023. To do this, we'll:\n","1. Use the most recent 60 days of data to predict the next day's closing price.\n","2. Append the predicted value to our data and repeat the process for as many days as we want to forecast."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["1/1 [==============================] - 0s 29ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","1/1 [==============================] - 0s 29ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","1/1 [==============================] - 0s 26ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","1/1 [==============================] - 0s 44ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","1/1 [==============================] - 0s 55ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","1/1 [==============================] - 0s 51ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","1/1 [==============================] - 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0s 28ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n","1/1 [==============================] - 0s 26ms/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"]},"execution_count":null,"metadata":{},"output_type":"execute_result"},{"data":{"application/vnd.hex.export+parquet":"{\"success\":true,\"exportKey\":\"4a8043e5-f038-4821-9c1d-4b8d3d5b0fcd/98a9ce9b-e050-4dc6-b092-144a0fc82520/exports/b1ee8c5d-e499-4f7e-9c27-a5ad2e669e68\"}","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>Predicted</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2023-09-02</th>\n","      <td>4364.480469</td>\n","    </tr>\n","    <tr>\n","      <th>2023-09-03</th>\n","      <td>4372.985840</td>\n","    </tr>\n","    <tr>\n","      <th>2023-09-04</th>\n","      <td>4372.103516</td>\n","    </tr>\n","    <tr>\n","      <th>2023-09-05</th>\n","      <td>4364.647949</td>\n","    </tr>\n","    <tr>\n","      <th>2023-09-06</th>\n","      <td>4352.214355</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["from datetime import timedelta\n","\n","# Initialize variables for future prediction\n","future_days = 120  # Number of days to predict into the future\n","future_predictions = []\n","last_60_days_scaled = scaled_data[-60:]  # Most recent 60 days of data\n","\n","# Loop to predict future prices\n","for i in range(future_days):\n","    last_60_days_reshaped = np.reshape(last_60_days_scaled, (1, 60, 1))\n","    next_day_prediction_scaled = model.predict(last_60_days_reshaped)\n","    next_day_prediction = scaler.inverse_transform(next_day_prediction_scaled)[0][0]\n","    future_predictions.append(next_day_prediction)\n","    last_60_days_scaled = np.append(last_60_days_scaled[1:], next_day_prediction_scaled, axis=0)\n","    \n","# Create a DataFrame to store the future predictions\n","future_dates = [data.index[-1] + timedelta(days=i+1) for i in range(future_days)]\n","future_predictions_df = pd.DataFrame(future_predictions, columns=['Predicted'], index=future_dates)\n","future_predictions_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Step 9: Visualizing Future Predictions\n","We've successfully generated future predictions for the S&P 500 for the next 120 days. Now let's visualize these predictions alongside the historical data to get a comprehensive view."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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"},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# Plot the historical and future predicted values\n","plt.figure(figsize=(16, 8))\n","plt.title('S&P 500 Future Price Prediction')\n","plt.xlabel('Date')\n","plt.ylabel('Close Price (USD)')\n","plt.plot(data['Close'], label='Historical', color='blue')\n","plt.plot(future_predictions_df['Predicted'], label='Future Predicted', color='red')\n","plt.legend(loc='upper left')\n","plt.show()"]}],"metadata":{"hex_info":{"author":"Brandon Doey","exported_date":"Wed Apr 17 2024 17:07:02 GMT+0000 (Coordinated Universal Time)","project_id":"98a9ce9b-e050-4dc6-b092-144a0fc82520","version":"draft"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"orig_nbformat":4},"nbformat":4,"nbformat_minor":4}