input: 
  from typing import List
  
  def predict_stock_movements(data_path: str, epochs: int = 20, batch_size: int = 64) -> float:
      """
      Predicts stock market movements using a deep learning model trained on data from a CSV file.
      The model uses a sequential architecture with Dense layers and ReLU activations.
      The model is trained for a specified number of epochs and batch size, and the performance is evaluated using Mean Squared Error (MSE).
      
      >>> predict_stock_movements('data.csv', epochs=20, batch_size=64)
      0.0345
      
      >>> predict_stock_movements('other_data.csv', epochs=10, batch_size=32)
      0.0456
      """
      import pandas as pd
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      # Import data
      data = pd.read_csv(data_path)
  
      # Split data into train and test data
      X_train = data.iloc[:, 0]
      y_train = data.iloc[:, 1]
      X_test = data.iloc[:, 0]
      y_test = data.iloc[:, 1]
  
      # Build a deep learning model
      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=batch_size, epochs=epochs, validation_data=(X_test, y_test), verbose=0)
  
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[0]
  
output:     # Predict stock movements
      predictions = model.predict(X_test, verbose=0)
  
      # Calculate Mean Squared Error (MSE)
      mse = tf.keras.losses.MeanSquaredError()
      mse_value = mse(y_test, predictions).numpy()
  
      return mse_value