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 and preprocess the data, build and compile the model, train it on the training data, and finally evaluate the model on the test data.
  assert create_stock_prediction_system(pd.DataFrame({'Open': [10.5, 9.6, 11.2, 10.8], 'Close': [10.7, 9.8, 11.4, 11.0]}), 64, 20) == (0.001, 0.001)
  assert create_stock_prediction_system(pd.DataFrame({'Open': [12.3, 11.9, 12.8, 12.5], 'Close': [12.5, 12.1, 13.0, 12.7]}), 64, 20) == (0.002, 0.002)
  assert create_stock_prediction_system(pd.DataFrame({'Open': [7.6, 8.4, 7.9, 8.1], 'Close': [7.8, 8.6, 8.1, 8.3]}), 64, 20) == (0.003, 0.003)
  assert create_stock_prediction_system(pd.DataFrame({'Open': [15.2, 14.7, 15.6, 15.1], 'Close': [15.4, 14.9, 15.8, 15.3]}), 64, 20) == (0.004, 0.004)
  assert create_stock_prediction_system(pd.DataFrame({'Open': [5.8, 6.2, 5.9, 6.1], 'Close': [6.0, 6.4, 6.1, 6.3]}), 64, 20) == (0.005, 0.005)
output: def create_stock_prediction_system(data, batch_size, epochs):
      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
  
      X = data[['Open']].values
      y = data[['Close']].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([
          Dense(64, input_shape=(1,), activation='relu'),
          Dense(32, activation='relu'),
          Dense(1)
      ])
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0)
  
      test_loss = model.evaluate(X_test, y_test, verbose=0)
  
      return (test_loss, test_loss)