input: 
  def predict_stock_movements(data_path: str) -> float:
      """ Creates an AI-based system for predicting stock market movements using a deep learning model.
      The function first splits the data into training and testing sets, then builds a neural network model,
      trains it on the training data, and finally evaluates the model on the test data. It returns the mean 
      squared error (MSE) of the model's predictions on the test data.
      >>> predict_stock_movements('data.csv')
      1000.0
      """
      # Import necessary 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 score[0]
  
output:     # Predict on the test data
      y_pred = model.predict(X_test)
      
      # Calculate the mean squared error
      mse = np.mean((y_pred - y_test) ** 2)
      
      return mse