input: def predict_stock_movements(data_path: str) -> float:
      """Predicts stock market movements based on historical data using a deep learning model.
      The function loads the data from a CSV file, splits it into training and testing datasets, 
      builds a neural network model, trains it, and evaluates the model's performance.
      
      >>> predict_stock_movements('data.csv')
      26936.891964285714
      """
      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].values.reshape(-1, 1) #Choosing the independent variable 
      y_train = data.iloc[:, 1].values.reshape(-1, 1)  #Choosing the dependent variable
      X_test = data.iloc[:, 0].values.reshape(-1, 1) #Choosing the independent variable 
      y_test = data.iloc[:, 1].values.reshape(-1, 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 score[0]
output:     #Predict the stock movements
      predictions = model.predict(X_test)
      return predictions.mean()