input: Write a function to create an AI-based system in Python for predicting stock market movements using a deep learning model. The function should include data import, splitting data into training and testing sets, building the model, compiling the model, training the model, and evaluating the model.
  
  assert predict_stock_movement() == (None, None)
  assert predict_stock_movement() == (None, None)
  assert predict_stock_movement() == (None, None)
  assert predict_stock_movement() == (None, None)
  assert predict_stock_movement() == (None, None)
output: def predict_stock_movement():
      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
  
      # Data import
      data = pd.read_csv('stock_data.csv')
      X = data.drop('target', axis=1).values
      y = data['target'].values
  
      # Splitting 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)
  
      # Standardizing the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Building 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'))
  
      # Compiling the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Training the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluating the model
      loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
  
      return loss, accuracy