Algorithmic trading with directional changes

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 minutes 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 directional changes as an effective algorithmic trading technique.