input: Write a function to create an AI-based system in Python for predicting stock market movements using deep learning. The function should load data from a CSV file, split it into training and testing sets, build a neural network model, compile it, train it, and then evaluate the model's performance.
  assert stock_prediction_system('data.csv') == None
output: def stock_prediction_system(csv_file):
      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 data
      data = pd.read_csv(csv_file)
      X = data.drop('target', axis=1)
      y = data['target']
  
      # Split data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Build 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 model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Train model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluate model
      model.evaluate(X_test, y_test, verbose=0)