input: 
  def predict_stock_movements(data_file: str) -> float:
      """ Predicts stock market movements based on the provided data file.
      The function imports a dataset, splits it into training and test data, 
      builds and trains a deep learning model, and evaluates the model's performance.
      
      >>> predict_stock_movements('data.csv')
      763.89
      """ 
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      # Import data
      data = pd.read_csv(data_file)
  
      # 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=64, epochs=20, validation_data=(X_test, y_test))
  
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[1]  # Return the mean squared error
  
output:     # Predict stock movements
      predictions = model.predict(X_test)
      return predictions[0][0]