input: def predict_stock_market_movement() -> float:
      """ Create an AI-based system in Python for predicting stock market movements. This function builds a deep learning model using TensorFlow to predict the stock market movement based on historical data.
      >>> predict_stock_market_movement()
      0.012
      """
output:     import numpy as np
      import tensorflow as tf
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Generate synthetic historical stock data
      np.random.seed(42)
      n_samples = 1000
      n_features = 5
      X = np.random.randn(n_samples, n_features)
      y = np.random.randn(n_samples, 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 deep learning model
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(n_features,)),
          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=10, batch_size=32, verbose=0)
  
      # Predict the stock market movement
      prediction = model.predict(X_test[:1])
  
      return float(prediction[0][0])