input: def predict_stock_movements(data: pd.DataFrame) -> float:
      """Create an AI-based system for predicting stock market movements by training a deep learning model on the given dataset.
      The model evaluates its performance using Mean Squared Error (MSE).
      >>> data = pd.DataFrame({'X': [1, 2, 3, 4, 5], 'y': [2, 4, 6, 8, 10]})
      >>> predict_stock_movements(data)
      Evaluation MSE: 0.0
      """
output:     import tensorflow as tf
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import mean_squared_error
  
      X = data['X'].values.reshape(-1, 1)
      y = data['y'].values
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(10, input_shape=(1,), activation='relu'),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      model.fit(X_train, y_train, epochs=100, verbose=0)
  
      y_pred = model.predict(X_test)
      mse = mean_squared_error(y_test, y_pred)
  
      print(f"Evaluation MSE: {mse}")
      return mse