Evolving Directional Changes Trading Strategies with a New Event-based Indicator

The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets, making the flow of physical time discontinuous. An alternative to this is event-based summaries. Directional changes (DC), which is a new event-based summary method, allows for new regularities in data to be discovered and exploited, as part of trading strategies. Under this paradigm, the timeline is divided in directional change events (upwards or downwards), and overshoot events, which follow exactly after a directional change has been identified. Previous work has shown that the duration of overshoot events is on average twice the duration of a DC event. However, this was empirically observed on the specific currency pairs DC was tested with, and only under the specific time periods the tests took place. Thus, this observation is not easily generalised. In this paper, we build on this regularity, by creating a new event-based indicator. We do this by calculating the average duration time of overshoot events on each training set of each individual dataset we experiment with. This allows us to have tailored duration values for each dataset. Such knowledge is important, because it allows us to more accurately anticipate trend reversal. In order to take advantage of this new indicator, we use a genetic algorithm to combine different DC trading strategies, which use our proposed indicator as part of their decision-making process. We experiment on 5 different foreign exchange currency pairs, for a total of 50 datasets. Our results show that the proposed algorithm is able to outperform its predecessor, as well as other well-known financial benchmarks, such as a technical analysis

Using Directional Change for Information Extraction in Financial Market Data

Directional change (DC) is a new concept for summarizing market dynamics. Instead of sampling the financial market at fixed intervals as in the traditional time series analysis, by contrast, DC is data-driven: the price change itself dictates when a price is recorded. DC provides us with a complementary way to extract information from data. The data sampled at irregular time intervals in DC allows us to observe features that may not be recognized under time series. In this thesis we propose our new method for the summarizing of financial markets through the use of the DC framework. Firstly, we define what is the vocabulary needed for a DC market summary. The vocabulary includes DC indicators and metrics. DC indicators are used to build a DC market summary for a single market. DC metrics help us quantitatively measure the differences between two markets under the directional change method. We demonstrate how such metrics could quantitatively measure the differences between different DC market summaries. Then, with real financial market data studied using DC, we aim to demonstrate the practicability of DC market analysis, as a complementary method to that of time series, in the analysis of the financial market