input: Write a function to build and train an AI-based system for predicting stock market movements using Python. The function should load a CSV file, split the data into training and testing sets, build a deep learning model, compile it, train it, and evaluate it. If successful, the function should return the evaluation score.
  assert predict_stock_movements()==score
output: def predict_stock_movements():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load the CSV file
      data = pd.read_csv('stock_data.csv')
  
      # Split the data into features and target
      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)
  
      # Build the deep learning 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