Directional Change

Market prices are traditionally sampled in fixed time-intervals to form time series. Directional change (DC) is an alternative approach to record price movements. DC is data-driven: price changes dictate when a price is sampled and recorded. DC allows us to observe features in data that may not be observable in time series. Time series and DC-based summaries should complement each other: they allow us to see data from two different angles (like seeing things with two eyes instead of one). Our research agenda is to develop ways under DC to turn data into information, knowledge and applications.

Lykke

History is recorded by events, not end of year (or decade) snapshots

History is recorded by key events. For example, when we describe the development of currencies in the last century, we report when they untied with gold, when they floated against each other, etc. We don't take snapshots at fixed intervals: that is, we don't report the situations at the end of every year or every 10 years.

Yet, when we describe price movements in financial markets, we tend to use snapshots taken at fixed intervals. First we decide how often we sample the data. Then we take snapshots at the frequency that we have chosen. These snapshots form an interval-based summary. For example, end of day prices are frequently used.

Directional Changes: data-driven summaries of price movements

Snapshots at fixed intervals are problematic. For example, if we record the end of day prices only, we wouldn't have noticed the flash crash on 6 May 2010. This motivated Richard Olsen to invent Directional Changes as a new way of summarising price movements.

A directional change is defined by a threshold that the observer cares about, e.g. 5%. Suppose we want to summarise the series shown here with 5% directional changes. We look for extreme points from which price dropped or rose by 5% or more.

Why are Directional Changes useful?

History is recorded by significant events. So should price movements. The advantage of using Directional Changes for price summaries is that it captures significant points in price movements. It would have captured the significant points during the 2010 flash crash.

This new concept provides traders with new perspectives to price movements (as demonstrated by Olsen Ltd in foreign exchange trading). This new concept has enabled researchers to discover new regularities in markets which cannot be captured by interval-based summaries. Such newly observed regularities give rise to new opportunities. As a new concept, directional changes open a rich research area waiting to be explored. Tsang 2021 argued that directional change is particularly useful for handling tick-to-tick data.

Sample DC summaries

• Shengnan Li, Sample DC summaries: EUR-USD-GBP from 2016.05.02.00:01 to 2016.10.26.19:02 computed with a threshold of 0.05%

Selected References

• Video explaining what directional changes are: YouTube / Weibo
• Video explaining why directional changes are useful: YouTube / Weibo
• Formal definition of directional changes: Appendix A, J. Chen & E.P.K.Tsang, Detecting Regime Change in Computational Finance, Data Science, Machine Learning and Algorithmic Trading, CRC Press, 2021
• Excel Spreadsheet showing how directional changes can be calculated
• Important discovery on directional changes: Glattfelder, J.B., Dupuis, A. & Olsen, R. Patterns in high-frequency FX data: discovery of 12 empirical scaling laws, Quantitative Finance, Volume 11 (4), 2011, 599-614
• Demos and data:
  • AO, Han: A demo for contrasting interval-based and Directional-Changes-based summaries of price movements
  • TAO, Ran: TR1, A program to produce DC indicators
  • MA, Shuai: Foreign exchange (EUR/USD) 2009-2016 DC Data Files
• Notations to be shared between researchers in DC

• DC-based trading:
  A. Bakhach, V.L.R. Chinthalapati, E.P.K. Tsang & A.R. El Sayed, Intelligent Dynamic Backlash Agent: a trading strategy based on the directional change framework, Algorithms, MDPI Open Access Publishing (ISSN 19994893), 11(11), 2018
• DC-based trading:
  Bakhach, A., Tsang, E.P.K. & Raju Chinthalapati, V.L., TSFDC: A trading strategy based on forecasting directional change, Intelligent Systems in Accounting, Finance and Management, Vol.25, Issue 3, May 2018
• DC Profiling (technical report):
  Edward P K Tsang, Ran Tao and Shuai Ma, Profiling Financial Market Dynamics under Directional Changes, Working Paper WP074-15, Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, 2015
• DC Profiling:
  Edward P K Tsang, Ran Tao, Antoaneta Serguieva and Shuai Ma, Profiling High-Frequency Equity Price Movements in Directional Changes, Quantitative Finance, Vol.17, Issue 2, 2017, 217-225 (published on line: 07 June 2016)
  (An earlier version of this paper appeared as Working Paper WP077-15, Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, June 2015)
• Detecting regime changes:
  E.P.K.Tsang & J. Chen, Regime change detection using directional change indicators in the foreign exchange market to chart Brexit, IEEE Transactions in Emerging Technology in Computational Intelligence (TETCI), Vol.2, Issue 3, June 2018, pages 185-193 (DOI: 10.1109/TETCI.2017.2775235 / Electronic ISSN: 2471-285X)
• Characterising normal regimes:
  J. Chen & E.P.K. Tsang, Classification of Normal and Abnormal Regimes in Financial Markets, Algorithms, MDPI Open Access Publishing (ISSN 19994893), published online, December 2018
• Market Tracking and Nowcasting:
  S. Ma, Tracking and nowcasting directional changes in the Forex market, PhD thesis, Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, 2022
• Nowcasting:
  E.P.K.Tsang, S. Ma & V.L. Raju Chinthalapati, Nowcasting directional change in high frequency FX markets, Intelligent Systems in Accounting, Finance and Management, Wiley, March 2024 (Archive)
• Relative volatility and jumps:
  Shengnan Li, Relating Volatility and Jumps between two markets under Directional Change, PhD thesis, Centre for Computational Finance & Economic Agents, University of Essex, September 2022
• Relative volatility:
  S. Li, E.P.K. Tsang & J. O'Hara, Measuring relative volatility in high‐frequency data under the directional change approach, Intelligent Systems in Accounting, Finance and Management, 02 June 2022; http://dx.doi.org/10.1002/isaf.1510 (Our reference: LiTsOH-RelativeVolatility-ISAFM2022.pdf)
• Why directional change is better than time series for handling tick-to-tick data:
  E.P.K.Tsang, Directional change for handling tick-to-tick data, Journal of Chinese Economic and Business Studies, 2021 (DOI: 10.1080/14765284.2021.1989883) (early version)

Projects based on Directional Change (in chronological order):

This is part of the High-Frequency Finance Project. Some of the works decribed below were presented in the HFF Workshop 2016.