Real-time chart analysis needs deterministic updates per bar and explicit handling of warm-up periods. SSF-DSP addresses this by implementing Calculates SSF-based Detrended Synthetic Price using dual Super Smooth Filters with parameterized inputs and direct state progression.
This implementation favors streaming execution over batch recomputation. The trade-off is more attention to state initialization, but latency stays predictable when charts scale.
Calculates SSF-based Detrended Synthetic Price using dual Super Smooth Filters
| Parameter | Purpose |
|---|---|
source |
Series to detrend |
period |
Dominant cycle period for quarter/half-cycle SSF calculation |
- Detrended synthetic price (difference between quarter-cycle and half-cycle SSFs)
| Input variable | Type | Configuration |
|---|---|---|
i_source |
input.source |
default: hlc3, label: "Source" |
- Declared optimization: not explicitly annotated in source comments.
- Streaming model: single-pass update on each new bar.
- Warm-up behavior: outputs can be unstable until enough samples satisfy
period. - Memory model: state is kept in Pine series context rather than external buffers.
Streaming logic keeps incremental cost stable, but initialization and edge-case handling become first-class concerns. That is a deliberate choice: predictable execution beats opaque recalculation spikes in live charts.
- Open the script in TradingView and confirm it compiles under Pine Script v6.
- Validate warm-up behavior on sparse data and short histories.
- Compare output against a trusted reference implementation for the same parameters.
- Confirm parameter bounds reject invalid values without silent fallback.
- Source code:
indicators/cycles/ssfdsp.pine - Documentation file:
indicators/cycles/ssfdsp.md - GitHub source view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/cycles/ssfdsp.pine
- GitHub documentation view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/cycles/ssfdsp.md