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 import necessary libraries, load the dataset, split it into training and testing data, build a neural network model, compile and train the model, and finally evaluate it.
  assert ai_stock_predictor() == "Deep learning model trained and evaluated successfully."  
  assert ai_stock_predictor() == "Deep learning model trained and evaluated successfully."  
  assert ai_stock_predictor() == "Deep learning model trained and evaluated successfully."  
  assert ai_stock_predictor() == "Deep learning model trained and evaluated successfully."  
  assert ai_stock_predictor() == "Deep learning model trained and evaluated successfully."
output: def ai_stock_predictor():
      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
  
      # Load dataset
      data = pd.read_csv('stock_data.csv')
      X = data.drop('target', axis=1).values
      y = data['target'].values
  
      # Split data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # 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
      loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
  
      return "Deep learning model trained and evaluated successfully."