lstm.py

# lstm_model.py
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

def preprocess_data(data, prediction_days=60):
    # Scale the data
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))

    # Prepare the training data
    x_train, y_train = [], []
    for i in range(prediction_days, len(scaled_data)):
        x_train.append(scaled_data[i - prediction_days:i, 0])
        y_train.append(scaled_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))

    return x_train, y_train, scaler

def build_lstm_model(input_shape):
    # Build the LSTM model
    model = Sequential()
    model.add(LSTM(units=50, return_sequences=True, input_shape=(input_shape[1], 1)))
    model.add(LSTM(units=50, return_sequences=False))
    model.add(Dense(units=25))
    model.add(Dense(units=1))

    model.compile(optimizer='adam', loss='mean_squared_error')
    return model

def train_lstm_model(model, x_train, y_train, epochs=5, batch_size=32):
    model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size)
    return model

def make_predictions(model, data, scaler, prediction_days=60):
    # Prepare test data for predictions
    test_data = scaler.transform(data['Close'].values.reshape(-1, 1))
    x_test = [test_data[i - prediction_days:i, 0] for i in range(prediction_days, len(test_data))]
    x_test = np.array(x_test)
    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

    # Predict and invert scaling
    predictions = model.predict(x_test)
    predictions = scaler.inverse_transform(predictions)
    return predictions