input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should import necessary libraries, load historical stock data, split the data into training and testing sets, build a deep learning model using TensorFlow and Keras, compile and train the model, and evaluate its performance. If the function is executed successfully, it should return the evaluation score of the model.
  assert predict_stock_movements() == score
  assert predict_stock_movements() == score
output: import numpy as np
  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
  
  def predict_stock_movements():
      # Load historical stock data
      data = pd.read_csv('stock_data.csv')
      
      # Preprocess data
      X = data.drop('target', axis=1)
      y = data['target']
      
      # Split 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 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
      _, score = model.evaluate(X_test, y_test, verbose=0)
      
      return score