Evolutionary Arbitrage For FTSE-100 Index Options and Futures

The objective in this paper is to develop and implement FGP-2 (Financial Genetic Programming) on intra daily tick data for stock index options and futures arbitrage in a manner that is suitable for online trading when windows of profitable arbitrage opportunities exist for short periods from one to ten minutes. Our benchmark for FGP-2 is the textbook rule for detecting arbitrage profits. This rule has the drawback that it awaits a contemporaneous profitable signal to implement an arbitrage in the same direction. A novel methodology of randomised sampling is used to train FGP-2 to pick up the fundamental arbitrage patterns. Care is taken to fine tune weights in the fitness function to enhance performance. As arbitrage opportunities are few, missed opportunities can be as costly as wrong recommendations to trade. Unlike conventional genetic programs, FGP-2 has a constraint satisfaction feature supplementing the fitness function that enables the user to train the FGP to specify a minimum and a maximum number of profitable arbitrage opportunities that are being sought. Historical sample data on arbitrage opportunities enables the user to set these minimum and maximum bounds. Good FGP rules for arbitrage are found to make a 3-fold improvement in profitability over the textbook rule. This application demonstrates the success of FGP-2 in its interactive capacity that allows experts to channel their knowledge into machine discovery.