input: Write a function to create an AI-based system in Python for predicting stock market movements using a deep learning model. The function should import necessary libraries, load the data, split it into training and testing sets, build and compile the model, train it, and finally evaluate the model. You do not need to provide a specific dataset, but the function should work with a CSV file named 'data.csv'.
  assert predict_stock_movements()  # No direct assert case, but the function should execute the steps mentioned
  assert predict_stock_movements()  # No direct assert case, but the function should execute the steps mentioned
  assert predict_stock_movements()  # No direct assert case, but the function should execute the steps mentioned
  assert predict_stock_movements()  # No direct assert case, but the function should execute the steps mentioned
  assert predict_stock_movements()  # No direct assert case, but the function should execute the steps mentioned
output: 
  def predict_stock_movements():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load the data
      data = pd.read_csv('data.csv')
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Build the model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluate the model
      model.evaluate(X_test, y_test, verbose=0)
  