input: def predict_stock_movement() -> None:
      """
      Create an AI-based system in Python for predicting stock market movements.
      The function includes importing necessary libraries, loading data from a CSV file,
      splitting the data into training and testing sets, building a deep learning model, 
      training the model, and evaluating its performance.
      >>> predict_stock_movement()
      """
output:     
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import MinMaxScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, LSTM
  
      # Load data from CSV file
      data = pd.read_csv('stock_data.csv')
  
      # Preprocess data
      scaler = MinMaxScaler(feature_range=(0, 1))
      scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
  
      # Split data into training and testing sets
      train_size = int(len(scaled_data) * 0.8)
      train_data = scaled_data[:train_size]
      test_data = scaled_data[train_size:]
  
      # Prepare training data
      def create_dataset(data, time_step=1):
          X, Y = [], []
          for i in range(len(data) - time_step - 1):
              X.append(data[i:(i + time_step), 0])
              Y.append(data[i + time_step, 0])
          return np.array(X), np.array(Y)
  
      time_step = 60
      X_train, y_train = create_dataset(train_data, time_step)
      X_test, y_test = create_dataset(test_data, time_step)
  
      # Reshape input to be [samples, time steps, features]
      X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
      X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
  
      # Build the LSTM model
      model = Sequential()
      model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))
      model.add(LSTM(50, return_sequences=False))
      model.add(Dense(25))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, batch_size=1, epochs=1)
  
      # Evaluate the model
      predictions = model.predict(X_test)
      predictions = scaler.inverse_transform(predictions)
      rmse = np.sqrt(np.mean((predictions - y_test) ** 2))
      print(f'Root Mean Squared Error: {rmse}')
  