Create app.py

app.py

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+# Step 1: Install Required Libraries
+# Uncomment and run this if you haven't installed these packages yet.
+# !pip install pandas numpy matplotlib yfinance tensorflow scikit-learn
+
+# Step 2: Import Required Libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import yfinance as yf
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+from datetime import datetime, timedelta
+
+# Step 3: Load and Preprocess Data
+def load_data(ticker, start_date, end_date):
+    # Fetch data from Yahoo Finance
+    data = yf.download(ticker, start=start_date, end=end_date)
+    data = data[['Close']]  # We only need the closing prices
+    return data
+
+def preprocess_data(data):
+    # Scale the data
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data)
+
+    # Create training data
+    training_data_len = int(np.ceil(len(scaled_data) * .8))  # 80% for training
+    train_data = scaled_data[0:training_data_len, :]
+
+    # Create the dataset with X_train and y_train
+    x_train, y_train = [], []
+    for i in range(60, len(train_data)):
+        x_train.append(train_data[i-60:i, 0])
+        y_train.append(train_data[i, 0])
+
+    x_train, y_train = np.array(x_train), np.array(y_train)
+    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))  # Reshape for LSTM
+
+    return x_train, y_train, scaler, training_data_len, scaled_data
+
+# Step 4: Build the LSTM Model
+def build_model(input_shape):
+    model = Sequential()
+    model.add(LSTM(50, return_sequences=True, input_shape=input_shape))
+    model.add(Dropout(0.2))
+    model.add(LSTM(50, return_sequences=False))
+    model.add(Dropout(0.2))
+    model.add(Dense(25))
+    model.add(Dense(1))  # Prediction of the next closing price
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    return model
+
+# Step 5: Train the Model
+def train_model(model, x_train, y_train, epochs=10, batch_size=1):
+    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs)
+
+# Step 6: Make Predictions
+def make_predictions(model, scaled_data, training_data_len, scaler):
+    # Create the test dataset
+    test_data = scaled_data[training_data_len - 60:, :]
+    x_test = []
+    for i in range(60, len(test_data)):
+        x_test.append(test_data[i-60:i, 0])
+    x_test = np.array(x_test)
+
+    # Reshape the data
+    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+
+    # Get the predicted price
+    predictions = model.predict(x_test)
+    predictions = scaler.inverse_transform(predictions)  # Inverse scaling
+    return predictions
+
+# Step 7: Visualize the Results
+def visualize_results(data, predictions):
+    train = data[:len(data) - len(predictions)]
+    valid = data[len(data) - len(predictions):]
+    valid['Predictions'] = predictions
+
+    # Plotting
+    plt.figure(figsize=(16, 8))
+    plt.title('Model')
+    plt.xlabel('Date')
+    plt.ylabel('Close Price USD')
+    plt.plot(train['Close'])
+    plt.plot(valid[['Close', 'Predictions']])
+    plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
+    plt.show()
+
+# Example Usage
+if __name__ == "__main__":
+    # Set parameters
+    ticker = "AAPL"  # Example ticker (Apple Inc.)
+    start_date = "2020-01-01"
+    end_date = datetime.now().strftime("%Y-%m-%d")
+
+    # Load data
+    data = load_data(ticker, start_date, end_date)
+
+    # Preprocess data
+    x_train, y_train, scaler, training_data_len, scaled_data = preprocess_data(data)
+
+    # Build and train the model
+    model = build_model((x_train.shape[1], 1))
+    train_model(model, x_train, y_train, epochs=50)
+
+    # Make predictions
+    predictions = make_predictions(model, scaled_data, training_data_len, scaler)
+
+    # Visualize results
+    visualize_results(data, predictions)