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F13c: restructure the indicator catalogue into eight families

Browse Source 
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.
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2026-05-22 21:21:56 +02:00
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# Indicators Overview
Wickra ships 63 indicators, organised under the four classical families —
trend, momentum, volatility, volume — plus a fifth **statistics** group for
price transforms and rolling regressions. The same family labels are used
here, with a second-level grouping that reflects how the indicators actually
behave (which output range they live in, what data they need, what question
they answer).
Wickra ships **71 indicators** organised into **eight families**. Each family
collects indicators that answer the same kind of question and groups at least
five of them, so the taxonomy here maps one-to-one onto the
`docs/wiki/indicators/<family>/` directory layout.
Every indicator is an O(1) state machine that consumes one input at a time
and produces either `Option<f64>` (Rust), `float | None` (Python), or
@@ -14,255 +12,200 @@ and produces either `Option<f64>` (Rust), `float | None` (Python), or
trait surface and warmup-period semantics are covered in
[Quickstart: Rust](Quickstart-Rust.md) and [Warmup Periods](Warmup-Periods.md).
The "Output range" column below is the value bounds an indicator emits once
warm. "unbounded" means it tracks the price scale of the input. The
"Warmup" column quotes `warmup_period()` as the indicator reports it; this
is the **exact** first-emission index for every indicator — the first
non-`None` output lands on input `warmup_period()` (index
`warmup_period() - 1`).
The "Output range" column is the value bounds an indicator emits once warm;
"unbounded" means it tracks the price scale of the input. The "Warmup" column
quotes `warmup_period()` as the indicator reports it — the **exact**
first-emission index: the first non-`None` output lands on input
`warmup_period()` (0-indexed `warmup_period() - 1`).
## Trend
The eight families:
Trend indicators smooth the price series to surface direction. They are
all single-input, single-output (`f64 → f64`).
| # | Family | Count | What it answers |
|---|--------|-------|-----------------|
| 1 | [Moving Averages](#moving-averages) | 12 | Where is the smoothed trend line? |
| 2 | [Momentum Oscillators](#momentum-oscillators) | 13 | How fast is price changing; is it overbought? |
| 3 | [Trend & Directional](#trend--directional) | 9 | Is there a trend, and which way? |
| 4 | [Price Oscillators](#price-oscillators) | 5 | Difference-of-averages momentum around zero. |
| 5 | [Volatility & Bands](#volatility--bands) | 12 | How wide is the range; where are the envelopes? |
| 6 | [Trailing Stops](#trailing-stops) | 5 | Where is the stop-loss for this trend? |
| 7 | [Volume](#volume) | 9 | Is volume confirming the move? |
| 8 | [Price Statistics](#price-statistics) | 7 | Per-bar price transforms and rolling regressions. |
### Simple averages
## Moving Averages
Pure linear weighting. Mostly used as fast baselines or as comparison
benchmarks against fancier averages.
Smooth the price series to surface direction. All are single-input,
single-output (`f64 → f64`) except `Vwma`, which weights by volume.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `Sma` | Equal-weighted rolling mean over `period` closes. | `f64` | `f64` | unbounded (price scale) | `period` (no default in core; Python defaults vary by binding) | `period` | [Indicator-Sma.md](indicators/trend/Indicator-Sma.md) |
| `Wma` | Linear weights `1, 2, …, period` so the newest bar matters most. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Wma.md](indicators/trend/Indicator-Wma.md) |
| `Trima` | A `period`-window SMA applied twice; triangular weights centred on the middle bar. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Trima.md](indicators/trend/Indicator-Trima.md) |
| `Vwma` | Rolling mean of closes weighted by each bar's volume. | `Candle` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Vwma.md](indicators/trend/Indicator-Vwma.md) |
| `Sma` | Equal-weighted rolling mean over `period` closes. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Sma.md](indicators/moving-averages/Indicator-Sma.md) |
| `Ema` | EMA with `α = 2 / (period + 1)`, SMA-seeded. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Ema.md](indicators/moving-averages/Indicator-Ema.md) |
| `Wma` | Linear weights `1, 2, …, period`; newest bar matters most. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Wma.md](indicators/moving-averages/Indicator-Wma.md) |
| `Dema` | Mulloy's `2·EMA − EMA(EMA)`; removes first-order EMA lag. | `f64` | `f64` | unbounded (price scale) | `period` | `2·period − 1` | [Indicator-Dema.md](indicators/moving-averages/Indicator-Dema.md) |
| `Tema` | Mulloy's `3·EMA − 3·EMA(EMA) + EMA(EMA(EMA))`. | `f64` | `f64` | unbounded (price scale) | `period` | `3·period − 2` | [Indicator-Tema.md](indicators/moving-averages/Indicator-Tema.md) |
| `Hma` | Hull's near-zero-lag `WMA(2·WMA(n/2) − WMA(n), √n)`. | `f64` | `f64` | unbounded (price scale) | `period` | `period + round(√period) − 1` | [Indicator-Hma.md](indicators/moving-averages/Indicator-Hma.md) |
| `Kama` | Kaufman's adaptive average; efficiency ratio picks α per bar. | `f64` | `f64` | unbounded (price scale) | `(er_period=10, fast=2, slow=30)` | `er_period + 1` | [Indicator-Kama.md](indicators/moving-averages/Indicator-Kama.md) |
| `Smma` | Wilder's RMA: SMA-seeded exponential average, `1/period` factor. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Smma.md](indicators/moving-averages/Indicator-Smma.md) |
| `Trima` | A `period`-window SMA applied twice; triangular weights. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Trima.md](indicators/moving-averages/Indicator-Trima.md) |
| `Zlema` | EMA of the de-lagged series `2·price − price[lag]`. | `f64` | `f64` | unbounded (price scale) | `period` | `lag + period` | [Indicator-Zlema.md](indicators/moving-averages/Indicator-Zlema.md) |
| `T3` | Tillson's six-EMA cascade recombined with a volume factor `v`. | `f64` | `f64` | unbounded (price scale) | `(period, v=0.7)` (Python) | `6·period − 5` | [Indicator-T3.md](indicators/moving-averages/Indicator-T3.md) |
| `Vwma` | Rolling mean of closes weighted by each bar's volume. | `Candle` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Vwma.md](indicators/moving-averages/Indicator-Vwma.md) |
### Exponential family
## Momentum Oscillators
Recursive smoothing with one or more chained EMAs. Lag reduction grows as
you stack more EMAs, but so does responsiveness to noise.
Measure the *rate* of price change. Several are bounded by construction
(0–100 / ±100 oscillators), the rest are difference-driven.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `Ema` | EMA with `α = 2 / (period + 1)`, seeded from the SMA of the first `period` inputs. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Ema.md](indicators/trend/Indicator-Ema.md) |
| `Dema` | Mulloy's `2·EMA − EMA(EMA)`; removes first-order EMA lag. | `f64` | `f64` | unbounded (price scale) | `period` | `2·period − 1` | [Indicator-Dema.md](indicators/trend/Indicator-Dema.md) |
| `Tema` | Mulloy's `3·EMA − 3·EMA(EMA) + EMA(EMA(EMA))`; removes more lag than DEMA. | `f64` | `f64` | unbounded (price scale) | `period` | `3·period − 2` | [Indicator-Tema.md](indicators/trend/Indicator-Tema.md) |
| `Smma` | Wilder's RMA: an SMA-seeded exponential average with the slow `1/period` factor. | `f64` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Smma.md](indicators/trend/Indicator-Smma.md) |
| `Zlema` | EMA of the de-lagged series `2·price − price[lag]`; near-zero group delay. | `f64` | `f64` | unbounded (price scale) | `period` | `lag + period` | [Indicator-Zlema.md](indicators/trend/Indicator-Zlema.md) |
| `T3` | Tillson's six-EMA cascade recombined with a volume factor `v`. | `f64` | `f64` | unbounded (price scale) | `(period, v=0.7)` (Python) | `6·period − 5` | [Indicator-T3.md](indicators/trend/Indicator-T3.md) |
| `Rsi` | Wilder's RSI; smoothed `gain / (gain + loss) × 100`. | `f64` | `f64` | `[0, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-Rsi.md](indicators/momentum-oscillators/Indicator-Rsi.md) |
| `Stochastic` | `%K = (close − low_n)/(high_n − low_n) × 100`, smoothed into `%D`. | `Candle` | `(k, d)` | each in `[0, 100]` | `(k_period=14, d_period=3)` (Python) | `k_period + d_period − 1` | [Indicator-Stochastic.md](indicators/momentum-oscillators/Indicator-Stochastic.md) |
| `Cci` | `(typical − SMA(typical)) / (0.015 · mean_dev)`. | `Candle` | `f64` | unbounded (typically `±100`–`±200`) | `period = 20` (Python) | `period` | [Indicator-Cci.md](indicators/momentum-oscillators/Indicator-Cci.md) |
| `Roc` | `(price − price_n) / price_n × 100`; raw percentage change. | `f64` | `f64` | unbounded around zero | `period` | `period + 1` | [Indicator-Roc.md](indicators/momentum-oscillators/Indicator-Roc.md) |
| `WilliamsR` | `−100 × (high_n − close) / (high_n − low_n)`. | `Candle` | `f64` | `[−100, 0]` | `period = 14` (Python) | `period` | [Indicator-WilliamsR.md](indicators/momentum-oscillators/Indicator-WilliamsR.md) |
| `Mfi` | "Volume-weighted RSI": Wilder smoothing of money-flow ratios. | `Candle` | `f64` | `[0, 100]` | `period = 14` (Python) | `period` | [Indicator-Mfi.md](indicators/momentum-oscillators/Indicator-Mfi.md) |
| `AwesomeOscillator` | `SMA(median, fast) − SMA(median, slow)`; zero-line crossover. | `Candle` | `f64` | unbounded around zero | `(fast=5, slow=34)` (Python) | `slow_period` | [Indicator-AwesomeOscillator.md](indicators/momentum-oscillators/Indicator-AwesomeOscillator.md) |
| `Mom` | `price − price[period]`; raw price-difference momentum. | `f64` | `f64` | unbounded around zero | `period = 10` (Python) | `period + 1` | [Indicator-Mom.md](indicators/momentum-oscillators/Indicator-Mom.md) |
| `Cmo` | Chande Momentum Oscillator; `100·(Σgain − Σloss)/(Σgain + Σloss)`. | `f64` | `f64` | `[−100, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-Cmo.md](indicators/momentum-oscillators/Indicator-Cmo.md) |
| `Tsi` | True Strength Index; double-EMA-smoothed momentum ratio. | `f64` | `f64` | ≈ `[−100, 100]` | `(long=25, short=13)` (Python) | `long + short` | [Indicator-Tsi.md](indicators/momentum-oscillators/Indicator-Tsi.md) |
| `Pmo` | DecisionPoint Price Momentum Oscillator; doubly-smoothed ROC. | `f64` | `f64` | unbounded around zero | `(smoothing1=35, smoothing2=20)` (Python) | `2` | [Indicator-Pmo.md](indicators/momentum-oscillators/Indicator-Pmo.md) |
| `StochRsi` | Stochastic Oscillator applied to the RSI series. | `f64` | `f64` | `[0, 100]` | `(rsi_period=14, stoch_period=14)` (Python) | `rsi_period + stoch_period` | [Indicator-StochRsi.md](indicators/momentum-oscillators/Indicator-StochRsi.md) |
| `UltimateOscillator` | Larry Williams' weighted three-timeframe buying-pressure oscillator. | `Candle` | `f64` | `[0, 100]` | `(short=7, mid=14, long=28)` (Python) | `max(short,mid,long) + 1` | [Indicator-UltimateOscillator.md](indicators/momentum-oscillators/Indicator-UltimateOscillator.md) |
`Trix` is also built from a triple-smoothed EMA, but it is a *momentum
oscillator* — it emits the rate of change of that EMA, not a price-scale
trend line — so it is listed under [Momentum](#momentum), matching the
`indicators/momentum/` source layout.
## Trend & Directional
### Adaptive & hybrid
These two adjust their effective smoothing on the fly. They are the
"smart" trend filters; both also live in Trend by directory placement.
Answer whether a trend exists and which way it points — directional systems,
crossover packages and trend-versus-range filters.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `Hma` | Hull's `WMA(2·WMA(n/2) − WMA(n), √n)`; near-zero lag with a built-in noise filter. | `f64` | `f64` | unbounded (price scale) | `period` | `period + round(√period) − 1` (see notes) | [Indicator-Hma.md](indicators/trend/Indicator-Hma.md) |
| `Kama` | Kaufman's adaptive average: efficiency ratio picks an α between a fast and slow EMA per bar. | `f64` | `f64` | unbounded (price scale) | `(er_period=10, fast=2, slow=30)` | `er_period + 1` (see notes) | [Indicator-Kama.md](indicators/trend/Indicator-Kama.md) |
| `MacdIndicator` | `EMA(fast) − EMA(slow)` plus a signal EMA and the histogram. | `f64` | `(macd, signal, histogram)` | unbounded around zero | `(fast=12, slow=26, signal=9)` (Python) | `slow + signal − 1` | [Indicator-MacdIndicator.md](indicators/trend-directional/Indicator-MacdIndicator.md) |
| `Adx` | Wilder's directional system: `+DI`, `−DI` and the `ADX` strength index. | `Candle` | `(plus_di, minus_di, adx)` | each in `[0, 100]` | `period = 14` (Python) | `2·period` | [Indicator-Adx.md](indicators/trend-directional/Indicator-Adx.md) |
| `Aroon` | Bars-since-high and bars-since-low scaled to `[0, 100]`. | `Candle` | `(up, down)` | each in `[0, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-Aroon.md](indicators/trend-directional/Indicator-Aroon.md) |
| `Trix` | Rate of change of a triple-smoothed EMA, `× 10000`. | `f64` | `f64` | unbounded around zero | `period = 15` (Python) | `3·period − 1` | [Indicator-Trix.md](indicators/trend-directional/Indicator-Trix.md) |
| `AroonOscillator` | `AroonUp − AroonDown`; the two Aroon lines as one gauge. | `Candle` | `f64` | `[−100, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-AroonOscillator.md](indicators/trend-directional/Indicator-AroonOscillator.md) |
| `Vortex` | Vortex Indicator `VI+` / `VI−`; crossings mark trend onset. | `Candle` | `(plus, minus)` | each `>= 0` | `period = 14` (Python) | `period + 1` | [Indicator-Vortex.md](indicators/trend-directional/Indicator-Vortex.md) |
| `MassIndex` | Dorsey's range-expansion sum of the EMA-of-range ratio. | `Candle` | `f64` | `> 0` | `(ema_period=9, sum_period=25)` (Python) | `2·ema_period + sum_period − 2` | [Indicator-MassIndex.md](indicators/trend-directional/Indicator-MassIndex.md) |
| `ChoppinessIndex` | Summed true range over the high-low span, log-scaled. | `Candle` | `f64` | `[0, 100]` | `period = 14` (Python) | `period` | [Indicator-ChoppinessIndex.md](indicators/trend-directional/Indicator-ChoppinessIndex.md) |
| `VerticalHorizontalFilter` | Net price move divided by total move over `period`. | `f64` | `f64` | `[0, 1]` | `period = 28` (Python) | `period + 1` | [Indicator-VerticalHorizontalFilter.md](indicators/trend-directional/Indicator-VerticalHorizontalFilter.md) |
## Momentum
## Price Oscillators
Momentum indicators measure the *rate* of price change, not the level.
Several are bounded by construction (0–100 oscillators); others are
unbounded; one (`Adx`) is directional and bundles three values.
### Bounded oscillators (0 – 100)
These all share the "overbought above 70/80, oversold below 30/20"
mental model, though the exact thresholds differ in the literature.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|--------------|-----------|-------|--------|-------|----------|--------|-----------|
| `Rsi` | Wilder's RSI; smoothed `gain / (gain + loss) × 100`. | `f64` | `f64` | `[0, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-Rsi.md](indicators/momentum/Indicator-Rsi.md) |
| `Stochastic` | `%K = (close − low_n)/(high_n − low_n) × 100`, smoothed into `%D`. | `Candle` | `(k, d)` | each in `[0, 100]` | `(k_period=14, d_period=3)` (Python) | `k_period + d_period − 1` | [Indicator-Stochastic.md](indicators/momentum/Indicator-Stochastic.md) |
| `Mfi` | "Volume-weighted RSI": Wilder smoothing of money-flow ratios. | `Candle` | `f64` | `[0, 100]` | `period = 14` (Python) | `period` | [Indicator-Mfi.md](indicators/momentum/Indicator-Mfi.md) |
| `Aroon` | Bars-since-high and bars-since-low scaled to `[0, 100]`. | `Candle` | `(up, down)` | each in `[0, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-Aroon.md](indicators/momentum/Indicator-Aroon.md) |
| `StochRsi` | Stochastic Oscillator applied to the RSI series; sharpens RSI extremes. | `f64` | `f64` | `[0, 100]` | `(rsi_period=14, stoch_period=14)` (Python) | `rsi_period + stoch_period` | [Indicator-StochRsi.md](indicators/momentum/Indicator-StochRsi.md) |
| `UltimateOscillator` | Larry Williams' weighted three-timeframe buying-pressure oscillator. | `Candle` | `f64` | `[0, 100]` | `(short=7, mid=14, long=28)` (Python) | `max(short,mid,long) + 1` | [Indicator-UltimateOscillator.md](indicators/momentum/Indicator-UltimateOscillator.md) |
### Unbounded oscillators
Centered on zero or driven by raw price differences; no fixed cap.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|---------------------|-----------|-------|--------|-------|----------|--------|-----------|
| `MacdIndicator` | `EMA(fast) − EMA(slow)` plus a signal-line EMA and the difference histogram. | `f64` | `(macd, signal, histogram)` | unbounded around zero | `(fast=12, slow=26, signal=9)` (Python) | `slow + signal − 1` | [Indicator-MacdIndicator.md](indicators/momentum/Indicator-MacdIndicator.md) |
| `Cci` | `(typical − SMA(typical)) / (0.015 · mean_dev)`; unbounded but typically `±100`. | `Candle` | `f64` | unbounded (typically `±100` to `±200`) | `period = 20` (Python) | `period` | [Indicator-Cci.md](indicators/momentum/Indicator-Cci.md) |
| `Roc` | `(price − price_n) / price_n × 100`; raw percentage change over `period` bars. | `f64` | `f64` | unbounded around zero | `period` | `period + 1` | [Indicator-Roc.md](indicators/momentum/Indicator-Roc.md) |
| `AwesomeOscillator` | `SMA(median, fast) − SMA(median, slow)`; Bill Williams' zero-line crossover oscillator. | `Candle` | `f64` | unbounded around zero | `(fast=5, slow=34)` (Python) | `slow_period` | [Indicator-AwesomeOscillator.md](indicators/momentum/Indicator-AwesomeOscillator.md) |
| `WilliamsR` | `−100 × (high_n − close) / (high_n − low_n)`; same family as Stochastic but inverted to `[−100, 0]`. | `Candle` | `f64` | `[−100, 0]` | `period = 14` (Python) | `period` | [Indicator-WilliamsR.md](indicators/momentum/Indicator-WilliamsR.md) |
| `Trix` | `(EMA(EMA(EMA(price))).pct_change × 10000)`; oscillator built from a triple-smoothed EMA. | `f64` | `f64` | unbounded around zero | `period = 15` (Python) | `3·period − 1` | [Indicator-Trix.md](indicators/momentum/Indicator-Trix.md) |
| `Mom` | `price − price[period]`; raw price-difference momentum. | `f64` | `f64` | unbounded around zero | `period = 10` (Python) | `period + 1` | [Indicator-Mom.md](indicators/momentum/Indicator-Mom.md) |
| `Cmo` | Chande Momentum Oscillator; `100·(Σgain − Σloss)/(Σgain + Σloss)` over `period` changes. | `f64` | `f64` | `[−100, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-Cmo.md](indicators/momentum/Indicator-Cmo.md) |
| `Tsi` | True Strength Index; ratio of double-EMA-smoothed momentum to its absolute value. | `f64` | `f64` | ≈ `[−100, 100]` around zero | `(long=25, short=13)` (Python) | `long + short` | [Indicator-Tsi.md](indicators/momentum/Indicator-Tsi.md) |
| `Pmo` | DecisionPoint Price Momentum Oscillator; doubly-smoothed rate of change. | `f64` | `f64` | unbounded around zero | `(smoothing1=35, smoothing2=20)` (Python) | `2` | [Indicator-Pmo.md](indicators/momentum/Indicator-Pmo.md) |
| `Ppo` | Percentage Price Oscillator; `100·(EMA_fast − EMA_slow)/EMA_slow`. | `f64` | `f64` | unbounded around zero (percent) | `(fast=12, slow=26)` (Python) | `slow` | [Indicator-Ppo.md](indicators/momentum/Indicator-Ppo.md) |
| `Dpo` | Detrended Price Oscillator; `price[t − period/2 − 1] − SMA(period)`. | `f64` | `f64` | unbounded around zero | `period = 20` (Python) | `max(period, period/2 + 2)` | [Indicator-Dpo.md](indicators/momentum/Indicator-Dpo.md) |
| `Coppock` | Coppock Curve; `WMA(ROC(long) + ROC(short), wma_period)`. | `f64` | `f64` | unbounded around zero | `(roc_long=14, roc_short=11, wma_period=10)` (Python) | `max(roc_long, roc_short) + wma_period` | [Indicator-Coppock.md](indicators/momentum/Indicator-Coppock.md) |
### Directional
Difference-of-averages and intrabar oscillators that swing around a zero line.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `Adx` | Wilder's directional system: `+DI`, `−DI` (each `[0, 100]`) and `ADX` trend-strength index. | `Candle` | `(plus_di, minus_di, adx)` | each in `[0, 100]` | `period = 14` (Python) | `2·period` | [Indicator-Adx.md](indicators/momentum/Indicator-Adx.md) |
| `AroonOscillator` | `AroonUp − AroonDown`; the two Aroon lines as one trend gauge. | `Candle` | `f64` | `[−100, 100]` | `period = 14` (Python) | `period + 1` | [Indicator-AroonOscillator.md](indicators/momentum/Indicator-AroonOscillator.md) |
| `Vortex` | Vortex Indicator `VI+` / `VI−`; crossings mark trend onset. | `Candle` | `(plus, minus)` | each `>= 0` | `period = 14` (Python) | `period + 1` | [Indicator-Vortex.md](indicators/momentum/Indicator-Vortex.md) |
| `MassIndex` | Dorsey's range-expansion sum of the EMA-of-range ratio. | `Candle` | `f64` | `> 0` (around `sum_period`) | `(ema_period=9, sum_period=25)` (Python) | `2·ema_period + sum_period − 2` | [Indicator-MassIndex.md](indicators/momentum/Indicator-MassIndex.md) |
| `Ppo` | Percentage Price Oscillator; `100·(EMA_fast − EMA_slow)/EMA_slow`. | `f64` | `f64` | unbounded around zero (percent) | `(fast=12, slow=26)` (Python) | `slow` | [Indicator-Ppo.md](indicators/price-oscillators/Indicator-Ppo.md) |
| `Dpo` | Detrended Price Oscillator; `price[t − period/2 − 1] − SMA(period)`. | `f64` | `f64` | unbounded around zero | `period = 20` (Python) | `max(period, period/2 + 2)` | [Indicator-Dpo.md](indicators/price-oscillators/Indicator-Dpo.md) |
| `Coppock` | Coppock Curve; `WMA(ROC(long) + ROC(short), wma_period)`. | `f64` | `f64` | unbounded around zero | `(roc_long=14, roc_short=11, wma_period=10)` (Python) | `max(roc_long, roc_short) + wma_period` | [Indicator-Coppock.md](indicators/price-oscillators/Indicator-Coppock.md) |
| `AcceleratorOscillator` | `AO − SMA(AO, signal)`; the acceleration of momentum. | `Candle` | `f64` | unbounded around zero | `(ao_fast=5, ao_slow=34, signal_period=5)` (Python) | `ao_slow + signal_period − 1` | [Indicator-AcceleratorOscillator.md](indicators/price-oscillators/Indicator-AcceleratorOscillator.md) |
| `BalanceOfPower` | `(close − open) / (high − low)`; intrabar buyer/seller control. | `Candle` | `f64` | `[−1, +1]` | (no parameters) | `1` | [Indicator-BalanceOfPower.md](indicators/price-oscillators/Indicator-BalanceOfPower.md) |
## Volatility
## Volatility & Bands
Volatility indicators sit in three functional groups: those that draw an
envelope around price, those that report a scalar dispersion/range, and a
set of trailing stops — ATR-driven stop-loss trackers rather than width
measures — that live in the volatility module by source convention.
### Envelopes
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-------------------|-----------|-------|--------|-------|----------|--------|-----------|
| `BollingerBands` | SMA middle band with `±multiplier × population_stddev` upper/lower bands. | `f64` | `(upper, middle, lower, stddev)` | unbounded (price scale) | `(period=20, multiplier=2.0)` (Python) | `period` | [Indicator-BollingerBands.md](indicators/volatility/Indicator-BollingerBands.md) |
| `Keltner` | EMA middle band with `±multiplier × ATR` upper/lower bands. | `Candle` | `(upper, middle, lower)` | unbounded (price scale) | `(ema_period=20, atr_period=10, multiplier=2.0)` (Python) | `max(ema_period, atr_period)` | [Indicator-Keltner.md](indicators/volatility/Indicator-Keltner.md) |
| `Donchian` | Highest high and lowest low over `period` bars; middle = mean of the two. | `Candle` | `(upper, middle, lower)` | unbounded (price scale) | `period = 20` (Python) | `period` | [Indicator-Donchian.md](indicators/volatility/Indicator-Donchian.md) |
| `BollingerBandwidth` | `(upper − lower) / middle` of the Bollinger Bands; the "squeeze" gauge. | `f64` | `f64` | `[0, ∞)` | `(period=20, multiplier=2.0)` (Python) | `period` | [Indicator-BollingerBandwidth.md](indicators/volatility/Indicator-BollingerBandwidth.md) |
| `PercentB` | `(price − lower) / (upper − lower)`; price position within the bands. | `f64` | `f64` | unbounded (`0`–`1` inside the bands) | `(period=20, multiplier=2.0)` (Python) | `period` | [Indicator-PercentB.md](indicators/volatility/Indicator-PercentB.md) |
### Range-average
Indicators that measure dispersion / range and those that draw an envelope
around price.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `Atr` | Wilder-smoothed True Range; per-bar absolute volatility. | `Candle` | `f64` | `[0, ∞)` (price scale) | `period = 14` (Python) | `period` | [Indicator-Atr.md](indicators/volatility/Indicator-Atr.md) |
| `Natr` | `100·ATR/close`; ATR as a percentage, comparable across instruments. | `Candle` | `f64` | `[0, ∞)` (percent) | `period = 14` (Python) | `period` | [Indicator-Natr.md](indicators/volatility/Indicator-Natr.md) |
| `StdDev` | Rolling population standard deviation of price. | `f64` | `f64` | `[0, ∞)` (price scale) | `period = 20` (Python) | `period` | [Indicator-StdDev.md](indicators/volatility/Indicator-StdDev.md) |
| `UlcerIndex` | RMS of trailing-high drawdowns; downside-only risk. | `f64` | `f64` | `[0, ∞)` (percent) | `period = 14` (Python) | `2·period − 1` | [Indicator-UlcerIndex.md](indicators/volatility/Indicator-UlcerIndex.md) |
| `HistoricalVolatility` | Annualised sample stddev of log returns. | `f64` | `f64` | `[0, ∞)` (annualised percent) | `(period=20, trading_periods=252)` (Python) | `period + 1` | [Indicator-HistoricalVolatility.md](indicators/volatility/Indicator-HistoricalVolatility.md) |
| `Atr` | Wilder-smoothed True Range; per-bar absolute volatility. | `Candle` | `f64` | `[0, ∞)` (price scale) | `period = 14` (Python) | `period` | [Indicator-Atr.md](indicators/volatility-bands/Indicator-Atr.md) |
| `BollingerBands` | SMA middle band with `±multiplier × population_stddev` bands. | `f64` | `(upper, middle, lower, stddev)` | unbounded (price scale) | `(period=20, multiplier=2.0)` (Python) | `period` | [Indicator-BollingerBands.md](indicators/volatility-bands/Indicator-BollingerBands.md) |
| `Keltner` | EMA middle band with `±multiplier × ATR` bands. | `Candle` | `(upper, middle, lower)` | unbounded (price scale) | `(ema_period=20, atr_period=10, multiplier=2.0)` (Python) | `max(ema_period, atr_period)` | [Indicator-Keltner.md](indicators/volatility-bands/Indicator-Keltner.md) |
| `Donchian` | Highest high and lowest low over `period` bars. | `Candle` | `(upper, middle, lower)` | unbounded (price scale) | `period = 20` (Python) | `period` | [Indicator-Donchian.md](indicators/volatility-bands/Indicator-Donchian.md) |
| `Natr` | `100·ATR/close`; ATR as a percentage. | `Candle` | `f64` | `[0, ∞)` (percent) | `period = 14` (Python) | `period` | [Indicator-Natr.md](indicators/volatility-bands/Indicator-Natr.md) |
| `StdDev` | Rolling population standard deviation of price. | `f64` | `f64` | `[0, ∞)` (price scale) | `period = 20` (Python) | `period` | [Indicator-StdDev.md](indicators/volatility-bands/Indicator-StdDev.md) |
| `UlcerIndex` | RMS of trailing-high drawdowns; downside-only risk. | `f64` | `f64` | `[0, ∞)` (percent) | `period = 14` (Python) | `2·period − 1` | [Indicator-UlcerIndex.md](indicators/volatility-bands/Indicator-UlcerIndex.md) |
| `HistoricalVolatility` | Annualised sample stddev of log returns. | `f64` | `f64` | `[0, ∞)` (annualised percent) | `(period=20, trading_periods=252)` (Python) | `period + 1` | [Indicator-HistoricalVolatility.md](indicators/volatility-bands/Indicator-HistoricalVolatility.md) |
| `BollingerBandwidth` | `(upper − lower) / middle` of the Bollinger Bands. | `f64` | `f64` | `[0, ∞)` | `(period=20, multiplier=2.0)` (Python) | `period` | [Indicator-BollingerBandwidth.md](indicators/volatility-bands/Indicator-BollingerBandwidth.md) |
| `PercentB` | `(price − lower) / (upper − lower)`; price position in the bands. | `f64` | `f64` | unbounded (`0`–`1` inside) | `(period=20, multiplier=2.0)` (Python) | `period` | [Indicator-PercentB.md](indicators/volatility-bands/Indicator-PercentB.md) |
| `TrueRange` | `max(H−L, |H−prevC|, |L−prevC|)`; raw single-bar volatility. | `Candle` | `f64` | `[0, ∞)` (price scale) | (no parameters) | `1` | [Indicator-TrueRange.md](indicators/volatility-bands/Indicator-TrueRange.md) |
| `ChaikinVolatility` | Rate of change of an EMA-smoothed high-low spread. | `Candle` | `f64` | unbounded around zero (percent) | `(ema_period=10, roc_period=10)` (Python) | `ema_period + roc_period` | [Indicator-ChaikinVolatility.md](indicators/volatility-bands/Indicator-ChaikinVolatility.md) |
### Trailing stop
## Trailing Stops
ATR-driven stop-loss trackers: per-bar levels that follow a trend and flip
when price closes through them.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `Psar` | Wilder's Parabolic Stop-and-Reverse; per-bar stop level that flips sides on price crossing. | `Candle` | `f64` | unbounded (price scale) | `(af_start=0.02, af_step=0.02, af_max=0.20)` (Python) | `2` | [Indicator-Psar.md](indicators/volatility/Indicator-Psar.md) |
| `SuperTrend` | ATR-banded trailing stop that flips on a close through the band; reports the line and the trend direction. | `Candle` | `(value, direction)` | `value` price scale; `direction` `±1` | `(atr_period=10, multiplier=3.0)` (Python) | `atr_period` | [Indicator-SuperTrend.md](indicators/volatility/Indicator-SuperTrend.md) |
| `ChandelierExit` | `highest_high − k·ATR` (long stop) and `lowest_low + k·ATR` (short stop). | `Candle` | `(long_stop, short_stop)` | unbounded (price scale) | `(period=22, multiplier=3.0)` (Python) | `period` | [Indicator-ChandelierExit.md](indicators/volatility/Indicator-ChandelierExit.md) |
| `ChandeKrollStop` | Two-stage ATR stop: an extreme-based stop, then smoothed over a shorter window. | `Candle` | `(stop_long, stop_short)` | unbounded (price scale) | `(atr_period=10, atr_multiplier=1.0, stop_period=9)` (Python) | `atr_period + stop_period − 1` | [Indicator-ChandeKrollStop.md](indicators/volatility/Indicator-ChandeKrollStop.md) |
| `AtrTrailingStop` | A single line trailing the close by `k·ATR`, ratcheting toward the trend and flipping on a cross. | `Candle` | `f64` | unbounded (price scale) | `(atr_period=14, multiplier=3.0)` (Python) | `atr_period` | [Indicator-AtrTrailingStop.md](indicators/volatility/Indicator-AtrTrailingStop.md) |
| `Psar` | Wilder's Parabolic Stop-and-Reverse; flips sides on a crossing. | `Candle` | `f64` | unbounded (price scale) | `(af_start=0.02, af_step=0.02, af_max=0.20)` (Python) | `2` | [Indicator-Psar.md](indicators/trailing-stops/Indicator-Psar.md) |
| `SuperTrend` | ATR-banded trailing stop with explicit flip logic. | `Candle` | `(value, direction)` | `value` price scale; `direction` `±1` | `(atr_period=10, multiplier=3.0)` (Python) | `atr_period` | [Indicator-SuperTrend.md](indicators/trailing-stops/Indicator-SuperTrend.md) |
| `ChandelierExit` | `highest_high − k·ATR` (long) and `lowest_low + k·ATR` (short). | `Candle` | `(long_stop, short_stop)` | unbounded (price scale) | `(period=22, multiplier=3.0)` (Python) | `period` | [Indicator-ChandelierExit.md](indicators/trailing-stops/Indicator-ChandelierExit.md) |
| `ChandeKrollStop` | Two-stage ATR stop: extreme-based, then smoothed. | `Candle` | `(stop_long, stop_short)` | unbounded (price scale) | `(atr_period=10, atr_multiplier=1.0, stop_period=9)` (Python) | `atr_period + stop_period − 1` | [Indicator-ChandeKrollStop.md](indicators/trailing-stops/Indicator-ChandeKrollStop.md) |
| `AtrTrailingStop` | A single line trailing the close by `k·ATR`, ratcheting. | `Candle` | `f64` | unbounded (price scale) | `(atr_period=14, multiplier=3.0)` (Python) | `atr_period` | [Indicator-AtrTrailingStop.md](indicators/trailing-stops/Indicator-AtrTrailingStop.md) |
## Volume
Volume indicators all take `Candle` input because they need `close` and
`volume` together (some also need `high`/`low`).
### Cumulative
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|---------------|-----------|-------|--------|-------|----------|--------|-----------|
| `Obv` | On-Balance Volume: cumulative signed volume driven by close-vs-prior-close sign. | `Candle` | `f64` | unbounded (drifts with cumulative volume) | (no parameters) | `1` | [Indicator-Obv.md](indicators/volume/Indicator-Obv.md) |
| `Vwap` | Cumulative volume-weighted average price from the start of the stream (intraday reset is your responsibility). | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-Vwap.md](indicators/volume/Indicator-Vwap.md) |
| `Adl` | Accumulation/Distribution Line; cumulative range-weighted volume. | `Candle` | `f64` | unbounded (drifts with volume) | (no parameters) | `1` | [Indicator-Adl.md](indicators/volume/Indicator-Adl.md) |
| `VolumePriceTrend` | Cumulative `volume · ROC`; volume flow weighted by percentage move. | `Candle` | `f64` | unbounded (drifts with volume) | (no parameters) | `1` | [Indicator-VolumePriceTrend.md](indicators/volume/Indicator-VolumePriceTrend.md) |
### Rolling
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|---------------|-----------|-------|--------|-------|----------|--------|-----------|
| `RollingVwap` | VWAP over a sliding window instead of since-start; useful for session-independent VWAP. | `Candle` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Vwap.md → RollingVwap](indicators/volume/Indicator-Vwap.md#rollingvwap-finite-window) |
### Oscillators
Volume-flow oscillators: bounded or zero-centred readings derived from where
price closes within each bar and how much volume backed the move.
Price moves weighted or confirmed by traded volume. All take `Candle` input.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `ChaikinMoneyFlow` | Summed money-flow volume divided by summed volume over `period` bars. | `Candle` | `f64` | `[−1, +1]` | `period = 20` (Python) | `period` | [Indicator-ChaikinMoneyFlow.md](indicators/volume/Indicator-ChaikinMoneyFlow.md) |
| `Obv` | On-Balance Volume: cumulative signed volume. | `Candle` | `f64` | unbounded (drifts with volume) | (no parameters) | `1` | [Indicator-Obv.md](indicators/volume/Indicator-Obv.md) |
| `Vwap` | Cumulative volume-weighted average price from the stream start. | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-Vwap.md](indicators/volume/Indicator-Vwap.md) |
| `RollingVwap` | VWAP over a sliding window instead of since-start. | `Candle` | `f64` | unbounded (price scale) | `period` | `period` | [Indicator-Vwap.md → RollingVwap](indicators/volume/Indicator-Vwap.md#rollingvwap-finite-window) |
| `Adl` | Accumulation/Distribution Line; cumulative range-weighted volume. | `Candle` | `f64` | unbounded (drifts with volume) | (no parameters) | `1` | [Indicator-Adl.md](indicators/volume/Indicator-Adl.md) |
| `VolumePriceTrend` | Cumulative `volume · ROC`; volume weighted by percentage move. | `Candle` | `f64` | unbounded (drifts with volume) | (no parameters) | `1` | [Indicator-VolumePriceTrend.md](indicators/volume/Indicator-VolumePriceTrend.md) |
| `ChaikinMoneyFlow` | Summed money-flow volume over summed volume across `period` bars. | `Candle` | `f64` | `[−1, +1]` | `period = 20` (Python) | `period` | [Indicator-ChaikinMoneyFlow.md](indicators/volume/Indicator-ChaikinMoneyFlow.md) |
| `ChaikinOscillator` | `EMA(ADL, fast) − EMA(ADL, slow)`; the MACD of the ADL. | `Candle` | `f64` | unbounded around zero | `(fast=3, slow=10)` (Python) | `slow` | [Indicator-ChaikinOscillator.md](indicators/volume/Indicator-ChaikinOscillator.md) |
| `ForceIndex` | `EMA((close − prev_close) · volume, period)`; the conviction behind a move. | `Candle` | `f64` | unbounded around zero | `period = 13` (Python) | `period + 1` | [Indicator-ForceIndex.md](indicators/volume/Indicator-ForceIndex.md) |
| `EaseOfMovement` | `SMA` of distance travelled per unit of volume. | `Candle` | `f64` | unbounded around zero | `(period=14, divisor=1e8)` (Python) | `period + 1` | [Indicator-EaseOfMovement.md](indicators/volume/Indicator-EaseOfMovement.md) |
| `ForceIndex` | `EMA((close − prev_close) · volume, period)`. | `Candle` | `f64` | unbounded around zero | `period = 13` (Python) | `period + 1` | [Indicator-ForceIndex.md](indicators/volume/Indicator-ForceIndex.md) |
| `EaseOfMovement` | `SMA` of distance travelled per unit of volume. | `Candle` | `f64` | unbounded around zero | `(period=14, divisor=1e8)` (Python) | `period + 1` | [Indicator-EaseOfMovement.md](indicators/volume/Indicator-EaseOfMovement.md) |
## Statistics
## Price Statistics
Price transforms and rolling regressions. The transforms collapse a full
OHLC bar to a single representative price; the regressions fit a
least-squares line to a sliding window of prices.
### Price transforms
Stateless per-bar reductions of an OHLC candle to one price. Each emits from
the very first candle (`warmup = 1`).
Per-bar price transforms and rolling least-squares regressions.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `TypicalPrice` | `(high + low + close) / 3`. | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-TypicalPrice.md](indicators/statistics/Indicator-TypicalPrice.md) |
| `MedianPrice` | `(high + low) / 2`. | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-MedianPrice.md](indicators/statistics/Indicator-MedianPrice.md) |
| `WeightedClose` | `(high + low + 2·close) / 4`. | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-WeightedClose.md](indicators/statistics/Indicator-WeightedClose.md) |
### Regression
Rolling ordinary-least-squares fits over the last `period` prices.
| Indicator | One-liner | Input | Output | Range | Defaults | Warmup | Deep dive |
|-----------|-----------|-------|--------|-------|----------|--------|-----------|
| `LinearRegression` | Endpoint of the rolling least-squares line — a low-lag smoothed price. | `f64` | `f64` | unbounded (price scale) | `period = 14` (Python) | `period` | [Indicator-LinearRegression.md](indicators/statistics/Indicator-LinearRegression.md) |
| `LinRegSlope` | Slope of the rolling least-squares line — trend steepness per bar. | `f64` | `f64` | unbounded around zero | `period = 14` (Python) | `period` | [Indicator-LinRegSlope.md](indicators/statistics/Indicator-LinRegSlope.md) |
| `TypicalPrice` | `(high + low + close) / 3`. | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-TypicalPrice.md](indicators/price-statistics/Indicator-TypicalPrice.md) |
| `MedianPrice` | `(high + low) / 2`. | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-MedianPrice.md](indicators/price-statistics/Indicator-MedianPrice.md) |
| `WeightedClose` | `(high + low + 2·close) / 4`. | `Candle` | `f64` | unbounded (price scale) | (no parameters) | `1` | [Indicator-WeightedClose.md](indicators/price-statistics/Indicator-WeightedClose.md) |
| `LinearRegression` | Endpoint of the rolling least-squares line. | `f64` | `f64` | unbounded (price scale) | `period = 14` (Python) | `period` | [Indicator-LinearRegression.md](indicators/price-statistics/Indicator-LinearRegression.md) |
| `LinRegSlope` | Slope of the rolling least-squares line. | `f64` | `f64` | unbounded around zero | `period = 14` (Python) | `period` | [Indicator-LinRegSlope.md](indicators/price-statistics/Indicator-LinRegSlope.md) |
| `ZScore` | `(price − SMA(n)) / population_stddev(n)`. | `f64` | `f64` | unbounded around zero | `period = 20` (Python) | `period` | [Indicator-ZScore.md](indicators/price-statistics/Indicator-ZScore.md) |
| `LinRegAngle` | The rolling regression slope as a degree angle. | `f64` | `f64` | `(−90°, +90°)` | `period = 14` (Python) | `period` | [Indicator-LinRegAngle.md](indicators/price-statistics/Indicator-LinRegAngle.md) |
## Pick the right indicator for…
A short cheat-sheet of "I want X, which indicator?" answers, grounded in
what each indicator actually computes.
- **Fast trend filter, minimal lag, single line.** `Hma` for smoothness +
responsiveness, `Tema` for further lag reduction at the cost of more
noise. If you want adaptiveness instead of fixed lag, `Kama`.
- **Slow trend filter, smooth as glass.** `Sma` is the simplest; `Ema`
responds slightly faster with the same smoothness budget. For long
trend filters either is appropriate; the difference is mostly aesthetic.
- **Trend-following crossovers.** Two-line crossovers (`Ema(fast)` vs
`Ema(slow)`, or any of the trend pairs) are the textbook entry signal;
`MacdIndicator` packages the same idea with a signal line and histogram.
- **Trend strength (is there a trend at all?).** `Adx` is the canonical
answer: `adx > 25` is "trending", `adx < 20` is "ranging". `Aroon` is
a softer alternative when you want directional confirmation.
- **Overbought / oversold reversal candidate.** `Rsi` is the default;
`Stochastic` for faster signals; `WilliamsR` for the same logic with an
inverted scale; `Mfi` if you have volume and want a volume-aware RSI.
- **Volatility expansion / contraction.** `BollingerBands` width
(`upper − lower`) for relative volatility; `Atr` for absolute per-bar
volatility in price units; `Keltner` to compare price against an
ATR-scaled envelope.
- **Breakout level.** `Donchian` upper/lower bands are the textbook
Turtle-style breakout trigger.
- **Trailing stop.** `Psar` gives you a per-bar stop level that flips
sides as the trend reverses. `Atr · k` (compute `Atr` yourself, multiply
by your preferred `k`) is the common alternative.
- **Volume confirmation.** `Obv` is the simplest; `Mfi` adds price into
the equation; `Vwap` / `RollingVwap` give you the volume-weighted
reference price.
- **Bill Williams setups.** `AwesomeOscillator` for the zero-line cross /
twin-peaks pattern from his suite.
- **Rate-of-change scalar.** `Roc` is the unsmoothed percentage change;
`Trix` is the same idea but on a triple-smoothed EMA.
- **Fast trend filter, minimal lag.** `Hma` for smoothness + responsiveness,
`Tema` for further lag reduction at the cost of noise, `Kama` for
adaptiveness instead of fixed lag.
- **Slow trend filter.** `Sma` is the simplest; `Ema` responds slightly
faster with the same smoothness budget.
- **Trend-following crossovers.** Two-line crossovers are the textbook entry;
`MacdIndicator` packages the idea with a signal line and histogram.
- **Trend strength — is there a trend at all?** `Adx` (`> 25` trending,
`< 20` ranging); `ChoppinessIndex` / `VerticalHorizontalFilter` answer the
same question without a direction.
- **Overbought / oversold.** `Rsi` is the default; `Stochastic` for faster
signals; `WilliamsR` for an inverted scale; `Mfi` for a volume-aware RSI.
- **Volatility level vs. momentum.** `Atr` / `TrueRange` for the level;
`ChaikinVolatility` for whether ranges are expanding or contracting.
- **Breakout level.** `Donchian` upper/lower bands are the Turtle-style
trigger.
- **Trailing stop.** `Psar`, `SuperTrend`, `ChandelierExit`,
`ChandeKrollStop` and `AtrTrailingStop` are a whole family of them.
- **Volume confirmation.** `Obv` is the simplest; `ChaikinMoneyFlow` is a
bounded balance; `Vwap` / `RollingVwap` give a volume-weighted reference.
- **Mean reversion.** `ZScore` flags statistically stretched prices;
`BollingerBandwidth` / `PercentB` locate price within the bands.
## Source-of-truth files
Every claim above can be checked against the source in
[`crates/wickra-core/src/indicators/`](https://github.com/kingchenc/wickra/tree/main/crates/wickra-core/src/indicators)
— one file per indicator. The Rust unit tests inside each module are the
ground truth for sample values. Python defaults (the `period = 14` etc. in
tables above) come from the `#[pyo3(signature = …)]` attributes in
ground truth for sample values. Python defaults (the `period = 14` etc.) come
from the `#[pyo3(signature = …)]` attributes in
[`bindings/python/src/lib.rs`](https://github.com/kingchenc/wickra/blob/main/bindings/python/src/lib.rs);
indicators not listed with a Python default require an explicit `period`
argument.
indicators without a Python default require an explicit argument.
## See also
- [Warmup Periods](Warmup-Periods.md) — full verified table of every
indicator's `warmup_period()`.
- [Warmup Periods](Warmup-Periods.md) — verified table of every indicator's
`warmup_period()`.
- [Indicator Chaining](Indicator-Chaining.md) — combining indicators with
`Chain` and the stacked-warmup rule.
- [Quickstart: Rust](Quickstart-Rust.md), [Quickstart: Python](Quickstart-Python.md),