Learning Trading Rules with Inductive Logic Programming

We apply Inductive Logic Programming (ILP) for inducing trading rules formed out of combinations of technical indicators from historical market data. To do this, we first identify ideal trading opportunities in the historical data, and then feed these as examples to an ILP learner, which will try to induce a description of them in terms of a given set of indicators. The main contributions of this paper are twofold. Conceptually, we are learning strategies in a chaotic domain in which learning a predictive model is impossible. Technically, we show a way of dealing with disjunctive positive examples, which create significant problems for most inductive learners.