input: 
  def predict_stock_movement():
      """This function creates an AI-based system to predict stock market movements using a 
      deep learning model. The function imports necessary libraries, loads stock data from 
      a CSV file, splits the data into training and testing sets, builds a neural network 
      model, trains the model, and evaluates its performance.
      Example:
      predict_stock_movement()
      >>> score
      0.012
      """
  
output:     
      import pandas as pd
      import numpy as np
      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 stock data from a CSV file
      data = pd.read_csv('stock_data.csv')
  
      # Preprocess the data
      X = data.drop('target', axis=1)
      y = data['target']
  
      # 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 neural network 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, validation_split=0.2)
  
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print(f'Test loss: {score[0]}, Test accuracy: {score[1]}')
  