input: 
  def predict_stock_market(data_path: str) -> float:
      """ Predicts stock market movements using a deep learning model trained on historical data.
      
      >>> predict_stock_market('data.csv')
      0.345
      >>> predict_stock_market('another_data.csv')
      0.789
      """
      # Import libraries
      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_path)
  
      # 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
      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 round(score[0], 3)
  
output:     # Predict the stock market movement
      prediction = model.predict(np.array([X_test.iloc[0]]))
      return round(prediction[0][0], 3)