input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should include data import, splitting the data into training and testing sets, building a deep learning model, compiling the model, training the model, and evaluating the model.
  assert predict_stock_movements()==None
  assert predict_stock_movements().find('model = Sequential()')!=-1
  assert predict_stock_movements().find('model.fit(')!=-1
  assert predict_stock_movements().find('model.evaluate(')!=-1
  assert predict_stock_movements() is not None
output: def predict_stock_movements():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.optimizers import Adam
  
      # Data import
      data = pd.read_csv('stock_data.csv')
  
      # Splitting the data into training and testing sets
      X = data.drop('target', axis=1)
      y = data['target']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Building a 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'))
  
      # Compiling the model
      model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
  
      # Training the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluating the model
      model.evaluate(X_test, y_test, verbose=0)
  
      return None