The method
An event-based description of a price path, with explicit thresholds and open questions about measurement.
01 / A clock chosen by movement
A price series has timestamps, but no natural clock for describing its movement. Equal calendar intervals need not contain comparable changes. Choosing an hourly or daily sampling interval is an analytical decision. An event-based representation instead asks how far price must move before the description changes. The threshold makes that decision explicit; it does not eliminate the need to choose a scale.
Leonid S. Drits has independently developed this method since the late 1990s. It belongs within the recognized research direction of directional change and intrinsic time. This page describes the author’s current terminology and reference implementation.
02 / Events and thresholds
An event, written F→S, connects a starting extremum F with a subsequent extremum S. F and S denote prices at those endpoints. Threshold, abbreviated THR and written δ in standard notation, sets the movement required to confirm a change of direction. The algorithm follows an extremum while price continues in that direction. Small reversals can remain inside the event.
An extremum is confirmed retrospectively, only when price has moved away from it by more than the threshold. The event endpoint therefore becomes known after the price has left it. Markup lags price by one threshold, and the latest event is always open.
The module is the author’s unit for measuring price changes. It is set in micropercent, where 1 m% is 10⁻⁵ of the price, and chosen no smaller than the cost of a trade, such as commission and spread. The Threshold is set in modules, and its absolute value follows from the formula
Here Price is the price of the extremum at which the event starts, so δ is a share of the price. This is how the author’s current research script, Vavilon (2026), computes it; his 2016 script TrendMetr took the percentage from the current extremum instead. The literature commonly sets δ directly, without a unit of this kind. No direct counterpart to the author’s module has been identified.
See δ = Threshold · module · Price at work in the threshold explorer →
03 / Event time-frame
The event time-frame is the sequence of confirmed events rather than a sequence of equal calendar intervals. Its standard-literature counterpart is intrinsic time. A new step occurs when the movement condition is met; elapsed clock time between steps can vary.
See price drawn against event number in the event chart →
The threshold is part of the representation. Changing it can change event boundaries and the number of events. Event time is a way of organizing observations, not evidence that the resulting sequence has predictive properties.
04 / Correction within an event
A rising or falling event can develop with a correction: a move in the opposite direction that is smaller than the threshold. The correction of an event is its deepest such move, measured in modules and taken as at least one module. A second, smaller threshold separates mature events, whose correction exceeds it, from immature ones; in the author’s script it is one third of the Threshold.
Directional-change literature uses the term overshoot for the continuation beyond a directional-change confirmation. The author’s correction is a counter-move inside the event, so the two terms are not interchangeable. A reproduction must use the definition stated here and document its measurement procedure.
05 / Strength Φ
Strength, denoted Φ, is the author’s own measure. It relates the length of the event to its correction:
Length is S − F, the signed difference between the endpoint prices; correction is the measure defined above. The formula is shown as supplied, with no absolute value added. No direct analogue has been identified in the literature. The name “strength” labels this ratio; it does not establish an interpretation as a probability or an expected outcome.
Φ was computed by a single measurement tool in 2016. Later trading programs use event markup as geometry only.
06 / Babylon: an open question
Babylon, written Φlim, names the limiting value of strength in the author’s terminology. It remains an open research question. There is no formal definition yet. This site does not assign a limiting procedure, numerical value or established law to the term.
07 / Glossary
| Term on this site | Standard-literature equivalent or qualification |
|---|---|
| Event (F→S) | Directional change event; also a ZigZag leg / Renko brick. These are vocabulary correspondences, not identical implementation specifications. |
| Threshold (THR) | Threshold δ / brick size. |
| Module | Unit for measuring price changes, set in micropercent (1 m% = 10⁻⁵ of the price), no smaller than trading costs. δ = Threshold · module · Price. No direct counterpart found; the literature sets δ directly. |
| Correction | Deepest counter-move inside an event, smaller than the threshold and at least one module. Not the same as overshoot in the literature; see section 04. |
| Event time-frame | Intrinsic time. |
| Strength Φ | Φ = Length : correction. The author’s own measure; no direct analogue found. |
| Babylon (Φlim) | The limiting value of strength. Open research question; no formal definition yet. |
08 / Status of evidence
Φ’s empirical properties have not been established. Tools for reproducing them are planned, but are not available on this site yet. Historical results, if included in future archive documents, must be described as reported by the author in the stated year and not independently verified. They must never be presented as expected performance. Definitions, implementation descriptions and empirical evidence are separate parts of the research record.
09 / Related work
The directional-change algorithm and the formalization of overshoot provide relevant context for the terminology used here. These references support that context; they do not validate Φ or Babylon.
- Guillaume, D. M., Dacorogna, M. M., Davé, R. R., Müller, U. A., Olsen, R. B., and Pictet, O. V. (1997). “From the bird’s eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets.” Finance and Stochastics, 1, 95–129. doi:10.1007/s007800050018.
- Glattfelder, J. B., Dupuis, A., and Olsen, R. B. (2011). “Patterns in high-frequency FX data: discovery of 12 empirical scaling laws.” Quantitative Finance, 11(4), 599–614. doi:10.1080/14697688.2010.481632. Author manuscript (2008).