input: 
  from typing import Tuple
  
  def build_stock_predictor(data: pd.DataFrame) -> Tuple[Sequential, float]:
      """ This function builds a deep learning model for predicting stock market movements
      using the given data. It returns the model and the evaluation score.
      
      >>> import pandas as pd
      >>> data = pd.read_csv('data.csv')
      >>> model, score = build_stock_predictor(data)
      >>> type(model) == Sequential
      True
      >>> isinstance(score, float)
      True
      """
      
      # 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 tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      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 model, score
  
output:     # Preprocess the data
      X_train = X_train.values.reshape(-1, 1)
      y_train = y_train.values.reshape(-1, 1)
      X_test = X_test.values.reshape(-1, 1)
      y_test = y_test.values.reshape(-1, 1)
  
      # Normalize the data
      from sklearn.preprocessing import StandardScaler
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Return the model and the evaluation score
      return model, score[0]