input: Write a function to create an AI-based system for predicting stock market movements using a deep learning model. The function should load data from a CSV file, split the data into training and testing sets, build a neural network model, compile and train the model, and then evaluate it. Return the evaluation score of the model.
  assert predict_stock_movements('data.csv') is not None
  assert isinstance(predict_stock_movements('data.csv'), float)
  assert predict_stock_movements('nonexistent.csv') is None
output: def predict_stock_movements(csv_file):
      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
  
      try:
          data = pd.read_csv(csv_file)
      except FileNotFoundError:
          return None
  
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      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='linear'))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      score = model.evaluate(X_test, y_test, verbose=0)
      return score