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kingchenc d2f99efd78 F13c: restructure the indicator catalogue into eight families

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.

2026-05-22 21:21:56 +02:00

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Indicators Overview

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 number | null (Node). Inputs are either a f64 close price or an OHLCV Candle (Rust) / dict-or-tuple (Python) / column arrays (Node). The full trait surface and warmup-period semantics are covered in Quickstart: Rust and Warmup Periods.

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).

The eight families:

#	Family	Count	What it answers
1	Moving Averages	12	Where is the smoothed trend line?
2	Momentum Oscillators	13	How fast is price changing; is it overbought?
3	Trend & Directional	9	Is there a trend, and which way?
4	Price Oscillators	5	Difference-of-averages momentum around zero.
5	Volatility & Bands	12	How wide is the range; where are the envelopes?
6	Trailing Stops	5	Where is the stop-loss for this trend?
7	Volume	9	Is volume confirming the move?
8	Price Statistics	7	Per-bar price transforms and rolling regressions.

Moving 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`	`period`	Indicator-Sma.md
`Ema`	EMA with `α = 2 / (period + 1)`, SMA-seeded.	`f64`	`f64`	unbounded (price scale)	`period`	`period`	Indicator-Ema.md
`Wma`	Linear weights `1, 2, …, period`; newest bar matters most.	`f64`	`f64`	unbounded (price scale)	`period`	`period`	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
`Tema`	Mulloy's `3·EMA − 3·EMA(EMA) + EMA(EMA(EMA))`.	`f64`	`f64`	unbounded (price scale)	`period`	`3·period − 2`	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
`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
`Smma`	Wilder's RMA: SMA-seeded exponential average, `1/period` factor.	`f64`	`f64`	unbounded (price scale)	`period`	`period`	Indicator-Smma.md
`Trima`	A `period`-window SMA applied twice; triangular weights.	`f64`	`f64`	unbounded (price scale)	`period`	`period`	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
`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
`Vwma`	Rolling mean of closes weighted by each bar's volume.	`Candle`	`f64`	unbounded (price scale)	`period`	`period`	Indicator-Vwma.md

Momentum Oscillators

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
`Rsi`	Wilder's RSI; smoothed `gain / (gain + loss) × 100`.	`f64`	`f64`	`[0, 100]`	`period = 14` (Python)	`period + 1`	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
`Cci`	`(typical − SMA(typical)) / (0.015 · mean_dev)`.	`Candle`	`f64`	unbounded (typically `±100`–`±200`)	`period = 20` (Python)	`period`	Indicator-Cci.md
`Roc`	`(price − price_n) / price_n × 100`; raw percentage change.	`f64`	`f64`	unbounded around zero	`period`	`period + 1`	Indicator-Roc.md
`WilliamsR`	`−100 × (high_n − close) / (high_n − low_n)`.	`Candle`	`f64`	`[−100, 0]`	`period = 14` (Python)	`period`	Indicator-WilliamsR.md
`Mfi`	"Volume-weighted RSI": Wilder smoothing of money-flow ratios.	`Candle`	`f64`	`[0, 100]`	`period = 14` (Python)	`period`	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
`Mom`	`price − price[period]`; raw price-difference momentum.	`f64`	`f64`	unbounded around zero	`period = 10` (Python)	`period + 1`	Indicator-Mom.md
`Cmo`	Chande Momentum Oscillator; `100·(Σgain − Σloss)/(Σgain + Σloss)`.	`f64`	`f64`	`[−100, 100]`	`period = 14` (Python)	`period + 1`	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
`Pmo`	DecisionPoint Price Momentum Oscillator; doubly-smoothed ROC.	`f64`	`f64`	unbounded around zero	`(smoothing1=35, smoothing2=20)` (Python)	`2`	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
`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

Trend & Directional

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
`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
`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
`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
`Trix`	Rate of change of a triple-smoothed EMA, `× 10000`.	`f64`	`f64`	unbounded around zero	`period = 15` (Python)	`3·period − 1`	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
`Vortex`	Vortex Indicator `VI+` / `VI−`; crossings mark trend onset.	`Candle`	`(plus, minus)`	each `>= 0`	`period = 14` (Python)	`period + 1`	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
`ChoppinessIndex`	Summed true range over the high-low span, log-scaled.	`Candle`	`f64`	`[0, 100]`	`period = 14` (Python)	`period`	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

Price Oscillators

Difference-of-averages and intrabar oscillators that swing around a zero line.

Indicator	One-liner	Input	Output	Range	Defaults	Warmup	Deep dive
`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
`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
`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
`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
`BalanceOfPower`	`(close − open) / (high − low)`; intrabar buyer/seller control.	`Candle`	`f64`	`[−1, +1]`	(no parameters)	`1`	Indicator-BalanceOfPower.md

Volatility & Bands

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
`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
`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
`Donchian`	Highest high and lowest low over `period` bars.	`Candle`	`(upper, middle, lower)`	unbounded (price scale)	`period = 20` (Python)	`period`	Indicator-Donchian.md
`Natr`	`100·ATR/close`; ATR as a percentage.	`Candle`	`f64`	`[0, ∞)` (percent)	`period = 14` (Python)	`period`	Indicator-Natr.md
`StdDev`	Rolling population standard deviation of price.	`f64`	`f64`	`[0, ∞)` (price scale)	`period = 20` (Python)	`period`	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
`HistoricalVolatility`	Annualised sample stddev of log returns.	`f64`	`f64`	`[0, ∞)` (annualised percent)	`(period=20, trading_periods=252)` (Python)	`period + 1`	Indicator-HistoricalVolatility.md
`BollingerBandwidth`	`(upper − lower) / middle` of the Bollinger Bands.	`f64`	`f64`	`[0, ∞)`	`(period=20, multiplier=2.0)` (Python)	`period`	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
`TrueRange`	`max(H−L,	H−prevC	,	L−prevC	)`; raw single-bar volatility.	`Candle`	`f64`
`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

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; 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
`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
`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
`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
`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

Volume

Price moves weighted or confirmed by traded volume. All take Candle input.

Indicator	One-liner	Input	Output	Range	Defaults	Warmup	Deep dive
`Obv`	On-Balance Volume: cumulative signed volume.	`Candle`	`f64`	unbounded (drifts with volume)	(no parameters)	`1`	Indicator-Obv.md
`Vwap`	Cumulative volume-weighted average price from the stream start.	`Candle`	`f64`	unbounded (price scale)	(no parameters)	`1`	Indicator-Vwap.md
`RollingVwap`	VWAP over a sliding window instead of since-start.	`Candle`	`f64`	unbounded (price scale)	`period`	`period`	Indicator-Vwap.md → RollingVwap
`Adl`	Accumulation/Distribution Line; cumulative range-weighted volume.	`Candle`	`f64`	unbounded (drifts with volume)	(no parameters)	`1`	Indicator-Adl.md
`VolumePriceTrend`	Cumulative `volume · ROC`; volume weighted by percentage move.	`Candle`	`f64`	unbounded (drifts with volume)	(no parameters)	`1`	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
`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
`ForceIndex`	`EMA((close − prev_close) · volume, period)`.	`Candle`	`f64`	unbounded around zero	`period = 13` (Python)	`period + 1`	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

Price Statistics

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
`MedianPrice`	`(high + low) / 2`.	`Candle`	`f64`	unbounded (price scale)	(no parameters)	`1`	Indicator-MedianPrice.md
`WeightedClose`	`(high + low + 2·close) / 4`.	`Candle`	`f64`	unbounded (price scale)	(no parameters)	`1`	Indicator-WeightedClose.md
`LinearRegression`	Endpoint of the rolling least-squares line.	`f64`	`f64`	unbounded (price scale)	`period = 14` (Python)	`period`	Indicator-LinearRegression.md
`LinRegSlope`	Slope of the rolling least-squares line.	`f64`	`f64`	unbounded around zero	`period = 14` (Python)	`period`	Indicator-LinRegSlope.md
`ZScore`	`(price − SMA(n)) / population_stddev(n)`.	`f64`	`f64`	unbounded around zero	`period = 20` (Python)	`period`	Indicator-ZScore.md
`LinRegAngle`	The rolling regression slope as a degree angle.	`f64`	`f64`	`(−90°, +90°)`	`period = 14` (Python)	`period`	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. 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/ — one file per indicator. The Rust unit tests inside each module are the ground truth for sample values. Python defaults (the period = 14 etc.) come from the #[pyo3(signature = …)] attributes in bindings/python/src/lib.rs; indicators without a Python default require an explicit argument.

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