input: 
  from typing import Tuple
  
  def predict_stock_movements(data_path: str) -> float:
      """ Predicts stock market movements using a deep learning model trained on provided data.
      The function reads a CSV file, splits the data into training and testing sets, 
      builds a neural network, trains it, and returns the model's evaluation score.
      >>> predict_stock_movements('data.csv')
      0.0827
      """
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      data = pd.read_csv(data_path)
      X_train = data.iloc[:, 0]
      y_train = data.iloc[:, 1]
      X_test = data.iloc[:, 0]
      y_test = data.iloc[:, 1]
  
      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))
  
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      model.fit(np.array(X_train), np.array(y_train), batch_size=64, epochs=20, validation_data=(np.array(X_test), np.array(y_test)))
  
      score = model.evaluate(np.array(X_test), np.array(y_test), verbose=0)
      
      return score
  
output:     return score[0]