Adaptive Market Cycle Indicators Using Hilbert Transforms in MQL5
Summary
This article explains how to estimate a market’s dominant cycle and use it to adapt technical indicators. It represents a cycle as a complex signal with in-phase and quadrature components, derives those components with a truncated Hilbert transform, and estimates the period from smoothed changes in phase. A median filter reduces noisy phase measurements; bounds and exponential smoothing further stabilize the period estimate.
The implementation discussion covers a cycle-period indicator and adaptive versions of the Cyber Cycle and Relative Vigor Index, whose calculation windows respond to the estimated cycle. The author refers to tests on a gradually lengthening synthetic sine wave and includes indicator code and chart examples, but the supplied excerpt gives no systematic out-of-sample trading results. The method depends on cycle structure being measurable in price data; smoothing and period constraints can delay or limit responses to changing conditions. The article presents signal-processing concepts and MQL5 implementations, not evidence of a profitable trading strategy.
Key ideas
- A market cycle can be represented by in-phase and quadrature components of an analytic signal.
- A truncated Hilbert transform provides estimates of those components for period measurement.
- Median filtering and exponential smoothing reduce noise in the estimated dominant cycle.
- Adaptive indicators vary their calculation windows according to the estimated cycle period.
- The article demonstrates indicator construction but does not establish trading profitability.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.