Directional-change intrinsic time is an event-based operator to dissect a data series into a sequence of alternating trends of defined size .
The directional-change intrinsic time operator was developed for the analysis of financial market data series. It is an alternative methodology to the concept of continuous time.[1] Directional-change intrinsic time operator dissects a data series into a set of drawups and drawdowns or up and down trends that alternate with each other. An established trend comes to an end as soon as a trend reversal is observed. A price move that extends a trend is called
overshoot and leads to new price extremes.
Figure 1 provides an example of a price curve dissected by the directional change intrinsic time operator.
The frequency of directional-change intrinsic events maps (1) the
volatility of price changes conditional to (2) the selected threshold . The
stochastic nature of the underlying process is mirrored in the non-equal number of intrinsic events observed over equal periods of physical time.
Directional-change intrinsic time operator is a
noise filtering technique. It identifies regime shifts, when trend changes of a particular size occur and hides price fluctuations that are smaller than the threshold .
Application
The directional-change intrinsic time operator was used to analyze high frequency
foreign exchange market data and has led to the discovery of a large set of
scaling laws that have not been previously observed.[2] The scaling laws identify properties of the underlying data series, such as the size of the expected price overshoot after an intrinsic time event or the number of expected directional-changes within a physical time interval or price threshold. For example, a scaling relating the expected number of directional-changes observed over the fixed period to the size of the threshold :
Other applications of the directional-change intrinsic time in finance include:
trading strategy characterised by the annual
Sharpe ratio 3.04[4]
tools designed to monitor
liquidity at multiple trend scales.[5]
The methodology can also be used for applications beyond economics and finance. It can be applied to other scientific domains and opens a new avenue of research in the area of
BigData.
^Golub, Anton; Glattfelder, James B.; Olsen, Richard B. (February 2018). "The Alpha Engine: Designing an Automated Trading Algorithm". In Dempster, M. A. H.; Kanniainen, Juho; Keane, John; Vynckier, Erik (eds.). High-Performance Computing in Finance. Chapman and Hall/CRC. pp. 49–76.
doi:
10.1201/9781315372006-3.
ISBN9781315372006.
SSRN2951348.