input: 
  def predict_stock_movement(data_path: str) -> float:
      """ Predict stock market movements using a deep learning model.
      The function takes a CSV file path as input, processes the data,
      builds a deep learning model, trains it, and evaluates it.
      It returns the mean squared error (MSE) on the test data.
  
      >>> predict_stock_movement('data.csv')
      0.045
      """
      #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]
      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 MSE
  
output:     #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]
      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 MSE