This study presents a hybrid AI artificial intelligence approach to the implementation of trading strategies in the S & P 500 stock index futures market. The hybrid AI approach integrates the rule-based systems technique and the neural networks technique to accurately predict the direction of daily price changes in S & P 500 stock index futures. By highlighting the advantages and overcoming the limitations of both the neural networks technique and rule-based systems technique, the hybrid approach can facilitate the development of more reliable intelligent systems to model expert thinking and to support the decision-making processes. Our methodology differs from other studies in two respects. First, the rule-based systems approach is applied to provide neural networks with training examples. Second, we employ Reasoning Neural Networks Ε½ . RN instead of Back Propagation Networks. Empirical results demonstrate that RN outperforms the other two ANN models Ε½ . Back Propagation Networks and Perceptron . Based upon this hybrid AI approach, the integrated futures trading system Ε½ . IFTS is established and employed to trade the S & P 500 stock index futures contracts. Empirical results also confirm that IFTS outperformed the passive buy-and-hold investment strategy during the 6-year testing period from 1988 to 1993.