input: def predict_stock_movements():
      """
      This function creates an AI-based system in Python for predicting stock market movements.
      It imports necessary libraries, loads stock data, splits the data into training and test sets,
      builds a deep learning model using TensorFlow, compiles and trains the model, and then evaluates the model.
      >>> predict_stock_movements()
      # Note: The output will be the evaluation score of the deep learning model, which varies depending on the input data.
      """
output:     import numpy as np
      import pandas as pd
      import tensorflow as tf
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import MinMaxScaler
  
      # Load stock data
      data = pd.read_csv('stock_data.csv')
      data = data.dropna()
  
      # Prepare features and target
      features = data[['Open', 'High', 'Low', 'Volume']].values
      target = data['Close'].values
  
      # Normalize the data
      scaler = MinMaxScaler()
      features = scaler.fit_transform(features)
      target = scaler.fit_transform(target.reshape(-1, 1))
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      # Build the deep learning model
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          tf.keras.layers.Dense(32, activation='relu'),
          tf.keras.layers.Dense(1)
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)
  
      # Evaluate the model
      evaluation_score = model.evaluate(X_test, y_test)
      return evaluation_score