Directional changes (DC) is a recent technique that summarises physical time data (e.g. daily closing prices, hourly data) into events, offering traders a unique perspective of the market to create novel trading strategies. This paper proposes the use of a genetic algorithm (GA) to optimize the recommendations of multiple DC-based trading strategies. Each trading strategy uses a novel framework that combines classification and regression techniques to predict when a trend will reverse. We evaluate the performance of the proposed multiple DC-strategy GA algorithm against nine benchmarks: five single DC-based trading strategies, three technical analysis indicators, as well as buy-and-hold, which is a popular financial benchmark. We perform experiments using 200 monthly physical time datasets from 20 foreign exchange markets—these datasets were created from snapshots of 10 min intervals. Experimental results show that our proposed algorithm is able to statistically significantly outperform all DC and non-DC benchmarks in terms of both return and risk, and establish multi-threshold DCs as an effective algorithmic trading technique.

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets.

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[ ] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[ ] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[ ] producing ordered rules are also presented.