Comparing Technical Indicators Through Digital Filter Responses
Summary
The article treats many technical indicators as digital filters acting on sampled price data. It introduces frequency and period, explains low-pass, high-pass, band-pass, and band-stop filters, and describes practical response characteristics such as transition width, attenuation, and passband distortion. It then proposes examining an indicator’s impulse response, or kernel, and its frequency response to compare how it smooths or suppresses different signal components. Simple and exponential moving averages are presented as low-pass examples, alongside universal filter types.
The approach is conceptual and demonstrated through indicator plots and a spectrum analyzer, rather than trading outcomes. The author notes that filter properties involve trade-offs: stronger rejection can mean a gentler transition, while longer kernels can increase lag or distort accepted frequencies. These comparisons can characterize signal processing behavior, but do not establish predictive value or profitability.
Key ideas
- Price bars can be analyzed as samples of a discrete-time signal, with cycles described by frequency or period.
- Low-pass, high-pass, band-pass, and rejection filters retain or suppress different frequency ranges.
- An indicator’s impulse response and frequency response provide a basis for comparing its filtering behavior.
- The article characterizes SMA and EMA as low-pass filters with different transition behavior.
- Filter design involves trade-offs among attenuation, transition steepness, lag, and passband distortion.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.