The original taxonomy was four classical families plus a statistics group, with the F1-F12 expansion slotted in as sub-categories. This regroups the whole 71-indicator catalogue into eight top-level families, each with at least five members: Moving Averages (12), Momentum Oscillators (13), Trend & Directional (9), Price Oscillators (5), Volatility & Bands (12), Trailing Stops (5), Volume (9), Price Statistics (7). - Wiki: docs/wiki/indicators/ reorganised into eight family folders; all 71 indicator pages moved with `git mv`. Every internal cross-link is normalised to `../<family>/Indicator-X.md`, each page's `Family` field is set to its new family, and two pre-existing `../Indicator-Chaining.md` links (should have been `../../`) are corrected. A link check confirms every relative wiki link resolves. - Indicators-Overview.md fully rewritten around the eight families; Home.md indicator reference and the README family table follow suit. - Warmup-Periods.md gains the eight F13 indicators; CHANGELOG records the 46-indicator expansion (25 -> 71) and the eight-family taxonomy. - Tests: Node indicators.test.js and Python test_new_indicators.py cover all eight new indicators (Node 91/91, Python 117/117 green). cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests, 25 data tests and 74 doctests green.
5.3 KiB
StochRSI
Stochastic RSI — the Stochastic Oscillator formula applied to the RSI series, sharpening RSI's overbought/oversold turns.
Quick reference
| Field | Value |
|---|---|
| Family | Momentum Oscillators |
| Input type | f64 (single close) |
| Output type | f64 |
| Output range | [0, 100] |
| Default parameters | (rsi_period = 14, stoch_period = 14) (Python) |
| Warmup period | rsi_period + stoch_period |
| Interpretation | Where RSI sits in its own recent range; near 0/100 = extremes. |
Formula
RSI_t = Rsi(rsi_period) of price
StochRSI = 100 · (RSI_t − min(RSI, stoch_period)) / (max(RSI, …) − min(RSI, …))
RSI rarely visits its 0/100 extremes — it spends most of its life
bunched around the middle. StochRSI re-normalises it: it asks where the
current RSI sits within its own high/low range over the last
stoch_period bars. The result swings the full [0, 100] width far more
often than raw RSI, so reversals are easier to spot.
Parameters
| Name | Type | Default | Valid range | Description |
|---|---|---|---|---|
rsi_period |
usize |
14 (Python) |
>= 1 |
Period of the underlying RSI. 0 errors with Error::PeriodZero. |
stoch_period |
usize |
14 (Python) |
>= 1 |
Lookback for the high/low range of RSI. 0 errors with Error::PeriodZero. |
The Python binding defaults the pair to (14, 14) via
#[pyo3(signature = (rsi_period=14, stoch_period=14))]. Node and WASM
take both explicitly. The periods property returns
(rsi_period, stoch_period).
Inputs / Outputs
From crates/wickra-core/src/indicators/stoch_rsi.rs:
impl Indicator for StochRsi {
type Input = f64;
type Output = f64;
// update(&mut self, input: f64) -> Option<f64>
}
A single f64 close in, an Option<f64> out. Python maps this to
float | None / numpy.ndarray (NaN warmup); Node to number | null /
Array<number> (NaN warmup).
Warmup
StochRsi::new(rsi_period, stoch_period).warmup_period() == rsi_period + stoch_period. The inner RSI emits its first value on
input rsi_period + 1; the stochastic window then needs stoch_period
RSI values, so the first non-None output lands on input
rsi_period + stoch_period.
Edge cases
- Flat RSI window. When every RSI value in the window is equal — for
example a constant price (RSI pinned at
50) or a pure trend (RSI pinned at100) — the range is zero and StochRSI reports the neutral50.0(flat_rsi_window_yields_50andpure_uptrend_yields_50pin this). - Bounds. The output is always within
[0, 100](output_stays_within_0_100pins this). - NaN / infinity inputs. Non-finite inputs are silently dropped; the RSI and the window are not advanced.
- Reset.
stoch_rsi.reset()clears the inner RSI and the window.
Examples
Rust
use wickra::{BatchExt, Indicator, StochRsi};
fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut sr = StochRsi::new(14, 14)?;
let prices: Vec<f64> = (1..=60)
.map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 10.0)
.collect();
let out = sr.batch(&prices);
println!("warmup_period = {}", sr.warmup_period());
println!("ready values: {}", out.iter().flatten().count());
Ok(())
}
Output:
warmup_period = 28
ready values: 33
The first 27 inputs return None; from input 28 onward every output is a
defined [0, 100] value.
Python
import numpy as np
import wickra as ta
sr = ta.StochRSI() # (rsi_period=14, stoch_period=14)
prices = np.full(40, 100.0) # constant series
print(sr.batch(prices)[-1]) # flat RSI window -> neutral 50
Output:
50.0
Node
const ta = require('wickra');
const sr = new ta.StochRSI(14, 14);
const prices = Array.from({ length: 60 }, (_, i) => 100 + Math.sin(i * 0.3) * 10);
console.log('warmupPeriod:', sr.warmupPeriod());
Interpretation
StochRsi is read like any [0, 100] oscillator, but with tighter
thresholds because it saturates so readily: above 80 is overbought,
below 20 oversold, and the 50 line is the midpoint. Because it is two
oscillators deep, it is fast and noisy — excellent for spotting
short-term turns, poor as a standalone trend filter. Many traders smooth
it further (an SMA of StochRSI) and trade the crossover.
Common pitfalls
- Using it as a trend filter.
StochRsiwhipsaws; confirm with a slower indicator before acting on a raw threshold cross. - Forgetting the stacked warmup. Warmup is
rsi_period + stoch_period— for the default(14, 14)that is 28 bars. - Expecting raw-RSI values.
StochRsiis a position within range, not RSI itself; the two are not interchangeable.
References
Tushar Chande and Stanley Kroll, The New Technical Trader (1994). The
implementation is the standard Stochastic-of-RSI; the flat-window
convention (50) matches this library's Stochastic.
See also
- Indicator-Rsi.md — the underlying oscillator.
- Indicator-Stochastic.md — the same formula on price instead of RSI.
- Indicators-Overview.md — the full taxonomy.