input: from typing import List
  
  def predict_stock_movements(data: pd.DataFrame) -> float:
      """Build and train a deep learning model to predict stock market movements based on historical data.
      The function will return the evaluation score of the model on the test data. 
      
      >>> data = pd.read_csv('data.csv')
      >>> predict_stock_movements(data)
      0.023456789
      """
      #split data into train and test data
      X_train = data.iloc[:, 0] #Choosing the independent variable 
      y_train = data.iloc[:, 1]  #Choosing the dependent variable
      X_test = data.iloc[:, 0] #Choosing the independent variable 
      y_test = data.iloc[:, 1]  #Choosing the dependent variable
      
      #Build a deep learning model
      #import necessary libraries
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
      
      #create a model and add layers
      model = Sequential()
      model.add(Dense(64, input_shape=(1,), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
      
      #compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
      
      #train the model
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
      
      #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score
output:     #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score