input: 
  def predict_stock_movements(data_path: str):
      """ Create an AI-based system for predicting stock market movements using a deep learning model.
      The function takes the path to a CSV file containing stock market data, splits it into training 
      and testing datasets, builds a deep learning model using TensorFlow, trains the model, and 
      evaluates its performance.
      
      >>> predict_stock_movements('data.csv')
      [1234.56]
      """
      
      #import libraries
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      import seaborn as sns
      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
  
output:     # predict stock movements
      predictions = model.predict(X_test)
      
      return predictions[0].tolist()