Indicators Overview

Indicators Overview

Wickra ships 105 indicators organised into nine 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 nine families:

#	Family	Count	What it answers
1	Moving Averages	19	Where is the smoothed trend line?
2	Momentum Oscillators	20	How fast is price changing; is it overbought?
3	Trend & Directional	13	Is there a trend, and which way?
4	Price Oscillators	5	Difference-of-averages momentum around zero.
5	Volatility & Bands	17	How wide is the range; where are the envelopes?
6	Bands & Channels	11	Price-envelope overlays beyond the volatility staples.
7	Trailing Stops	5	Where is the stop-loss for this trend?
8	Volume	8	Is volume confirming the move?
9	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
Ema	EMA with α = 2 / (period + 1), SMA-seeded.	f64	f64	unbounded (price scale)	period	period	Indicator-Ema
Wma	Linear weights 1, 2, …, period; newest bar matters most.	f64	f64	unbounded (price scale)	period	period	Indicator-Wma
Dema	Mulloy's 2·EMA − EMA(EMA); removes first-order EMA lag.	f64	f64	unbounded (price scale)	period	2·period − 1	Indicator-Dema
Tema	Mulloy's 3·EMA − 3·EMA(EMA) + EMA(EMA(EMA)).	f64	f64	unbounded (price scale)	period	3·period − 2	Indicator-Tema
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
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
Smma	Wilder's RMA: SMA-seeded exponential average, 1/period factor.	f64	f64	unbounded (price scale)	period	period	Indicator-Smma
Trima	A period-window SMA applied twice; triangular weights.	f64	f64	unbounded (price scale)	period	period	Indicator-Trima
Zlema	EMA of the de-lagged series 2·price − price[lag].	f64	f64	unbounded (price scale)	period	lag + period	Indicator-Zlema
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
Vwma	Rolling mean of closes weighted by each bar's volume.	Candle	f64	unbounded (price scale)	period	period	Indicator-Vwma
Alma	Gaussian-weighted MA, kernel centred at offset · (period − 1).	f64	f64	unbounded (price scale)	(period=9, offset=0.85, sigma=6.0)	period	Indicator-Alma
McGinleyDynamic	Self-adjusting MA, MD + (price − MD) / (0.6 · period · (price / MD)⁴).	f64	f64	unbounded (price scale)	period (10 in Python)	period	Indicator-McGinleyDynamic
Frama	Ehlers' fractal-dimension-adaptive EMA over close-only window halves.	f64	f64	unbounded (price scale)	period=16 (even)	period	Indicator-Frama
Vidya	EMA whose alpha scales with |CMO(cmo_period)| / 100.	f64	f64	unbounded (price scale)	(period=14, cmo_period=9)	cmo_period + 1	Indicator-Vidya
Jma	Jurik MA — three-stage filter reconstruction.	f64	f64	unbounded (price scale)	(period=14, phase=0, power=2)	1	Indicator-Jma
Alligator	Three Smmas of (high + low) / 2: Jaw / Teeth / Lips.	Candle	AlligatorOutput (3)	unbounded (price scale)	(jaw=13, teeth=8, lips=5)	max(jaw, teeth, lips)	Indicator-Alligator
Evwma	Elastic volume-weighted recurrence over a rolling window.	Candle	f64	unbounded (price scale)	period (20 in Python)	period	Indicator-Evwma

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
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
Cci	(typical − SMA(typical)) / (0.015 · mean_dev).	Candle	f64	unbounded (typically ±100–±200)	period = 20 (Python)	period	Indicator-Cci
Roc	(price − price_n) / price_n × 100; raw percentage change.	f64	f64	unbounded around zero	period	period + 1	Indicator-Roc
WilliamsR	−100 × (high_n − close) / (high_n − low_n).	Candle	f64	[−100, 0]	period = 14 (Python)	period	Indicator-WilliamsR
Mfi	"Volume-weighted RSI": Wilder smoothing of money-flow ratios.	Candle	f64	[0, 100]	period = 14 (Python)	period	Indicator-Mfi
AwesomeOscillator	SMA(median, fast) − SMA(median, slow); zero-line crossover.	Candle	f64	unbounded around zero	(fast=5, slow=34) (Python)	slow_period	Indicator-AwesomeOscillator
Mom	price − price[period]; raw price-difference momentum.	f64	f64	unbounded around zero	period = 10 (Python)	period + 1	Indicator-Mom
Cmo	Chande Momentum Oscillator; 100·(Σgain − Σloss)/(Σgain + Σloss).	f64	f64	[−100, 100]	period = 14 (Python)	period + 1	Indicator-Cmo
Tsi	True Strength Index; double-EMA-smoothed momentum ratio.	f64	f64	≈ [−100, 100]	(long=25, short=13) (Python)	long + short	Indicator-Tsi
Pmo	DecisionPoint Price Momentum Oscillator; doubly-smoothed ROC.	f64	f64	unbounded around zero	(smoothing1=35, smoothing2=20) (Python)	2	Indicator-Pmo
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
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
Rvi	SMA(close − open, period) / SMA(high − low, period).	Candle	f64	unbounded (typically (−1, 1))	period = 10 (Python)	period	Indicator-Rvi
Pgo	(close − SMA(close, period)) / EMA(TR, period).	Candle	f64	unbounded	period = 14 (Python)	period	Indicator-Pgo
Kst	Pring's 1·RCMA_1 + 2·RCMA_2 + 3·RCMA_3 + 4·RCMA_4, plus SMA signal.	f64	(kst, signal)	unbounded	9 periods, see deep-dive	longest roc_i + sma_i + signal − 1	Indicator-Kst
Smi	Blau's doubly-EMA-smoothed close-vs-range displacement.	Candle	f64	[−100, 100]	(period=5, d=3, d2=3)	period + d + d2 − 2	Indicator-Smi
LaguerreRsi	Ehlers' 4-stage Laguerre filter with RSI up/down accumulator.	f64	f64	[0, 100] (clamped)	gamma = 0.5	1	Indicator-LaguerreRSI
ConnorsRsi	Average of RSI(close), RSI(streak), percentile-rank of returns.	f64	f64	[0, 100]	(3, 2, 100)	max(period_rsi+1, period_streak+2, period_rank+1)	Indicator-ConnorsRSI
Inertia	LinearRegression(RVI(rvi_period), linreg_period).	Candle	f64	unbounded	(rvi=14, linreg=20)	rvi_period + linreg_period − 1	Indicator-Inertia

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
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
Adxr	Wilder's ADX-rating: average of ADX_t and ADX_{t − (period − 1)}.	Candle	f64	[0, 100]	period = 14 (Python)	3·period − 1	Indicator-Adxr
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
Trix	Rate of change of a triple-smoothed EMA, × 10000.	f64	f64	unbounded around zero	period = 15 (Python)	3·period − 1	Indicator-Trix
AroonOscillator	AroonUp − AroonDown; the two Aroon lines as one gauge.	Candle	f64	[−100, 100]	period = 14 (Python)	period + 1	Indicator-AroonOscillator
Vortex	Vortex Indicator VI+ / VI−; crossings mark trend onset.	Candle	(plus, minus)	each >= 0	period = 14 (Python)	period + 1	Indicator-Vortex
Rwi	Poulos' Random Walk Index: actual displacement vs ATR_i · sqrt(i).	Candle	(high, low)	each >= 0	period = 14 (Python)	period	Indicator-Rwi
Tii	Share of recent SMA-deviations that are positive, scaled to [0, 100].	f64	f64	[0, 100]	(sma_period=60, dev_period=30) (Python)	sma_period + dev_period − 1	Indicator-Tii
WaveTrend	LazyBear: 3-stage EMA cascade through the typical-price channel index.	Candle	(wt1, wt2)	typically [-100, +100]	(channel=10, average=21, signal=4) (Python)	2·channel + average + signal − 3	Indicator-WaveTrend
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
ChoppinessIndex	Summed true range over the high-low span, log-scaled.	Candle	f64	[0, 100]	period = 14 (Python)	period	Indicator-ChoppinessIndex
VerticalHorizontalFilter	Net price move divided by total move over period.	f64	f64	[0, 1]	period = 28 (Python)	period + 1	Indicator-VerticalHorizontalFilter

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
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
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
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
BalanceOfPower	(close − open) / (high − low); intrabar buyer/seller control.	Candle	f64	[−1, +1]	(no parameters)	1	Indicator-BalanceOfPower

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
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
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
Donchian	Highest high and lowest low over period bars.	Candle	(upper, middle, lower)	unbounded (price scale)	period = 20 (Python)	period	Indicator-Donchian
Natr	100·ATR/close; ATR as a percentage.	Candle	f64	[0, ∞) (percent)	period = 14 (Python)	period	Indicator-Natr
StdDev	Rolling population standard deviation of price.	f64	f64	[0, ∞) (price scale)	period = 20 (Python)	period	Indicator-StdDev
UlcerIndex	RMS of trailing-high drawdowns; downside-only risk.	f64	f64	[0, ∞) (percent)	period = 14 (Python)	2·period − 1	Indicator-UlcerIndex
HistoricalVolatility	Annualised sample stddev of log returns.	f64	f64	[0, ∞) (annualised percent)	(period=20, trading_periods=252) (Python)	period + 1	Indicator-HistoricalVolatility
BollingerBandwidth	(upper − lower) / middle of the Bollinger Bands.	f64	f64	[0, ∞)	(period=20, multiplier=2.0) (Python)	period	Indicator-BollingerBandwidth
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
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
RVIVolatility	RSI-shaped volatility direction gauge built on rolling stddev.	f64	f64	[0, 100]	period = 10 (Python)	2·period − 1	Indicator-RviVolatility
ParkinsonVolatility	High-low realised vol; ~5× more efficient than C2C stddev.	Candle	f64	[0, ∞) (annualised percent)	(period=20, trading_periods=252) (Python)	period	Indicator-ParkinsonVolatility
GarmanKlassVolatility	OHLC realised vol; ~7.4× efficient, biased under drift.	Candle	f64	[0, ∞) (annualised percent)	(period=20, trading_periods=252) (Python)	period	Indicator-GarmanKlassVolatility
RogersSatchellVolatility	Drift-free OHLC realised vol; exact under arbitrary Brownian drift.	Candle	f64	[0, ∞) (annualised percent)	(period=20, trading_periods=252) (Python)	period	Indicator-RogersSatchellVolatility
YangZhangVolatility	Drift- and gap-robust OHLC blend of overnight, open-close and Rogers-Satchell.	Candle	f64	[0, ∞) (annualised percent)	(period=20, trading_periods=252) (Python)	period + 1	Indicator-YangZhangVolatility

Bands & Channels

Price-envelope overlays beyond the volatility-housed Bollinger / Keltner / Donchian trio. Eleven additional bands organised by what drives their width: fixed percent, ATR, range, regression-residual stddev, fractal swings, or volume-weighted stddev.

Indicator	One-liner	Input	Output	Range	Defaults	Warmup	Deep dive
MaEnvelope	SMA centerline with fixed-percent envelope.	f64	(upper, middle, lower)	unbounded (price scale)	(period=20, percent=0.025) (Python)	period	Indicator-MaEnvelope
AccelerationBands	Price Headley's momentum-biased bands; width scales with (H − L) / (H + L).	Candle	(upper, middle, lower)	unbounded (price scale)	(period=20, factor=0.001) (Python)	period	Indicator-AccelerationBands
StarcBands	Stoller Average Range Channel — SMA(close) ± k·ATR.	Candle	(upper, middle, lower)	unbounded (price scale)	(sma_period=6, atr_period=15, multiplier=2.0) (Python)	max(sma_period, atr_period)	Indicator-StarcBands
AtrBands	Close-anchored envelope close ± k·ATR; stop/target bracket.	Candle	(upper, middle, lower)	unbounded (price scale)	(period=14, multiplier=3.0) (Python)	period	Indicator-AtrBands
HurstChannel	SMA centerline wrapped by the rolling high-low range.	Candle	(upper, middle, lower)	unbounded (price scale)	(period=10, multiplier=0.5) (Python)	period	Indicator-HurstChannel
LinRegChannel	OLS endpoint ± k · σ of regression residuals.	f64	(upper, middle, lower)	unbounded (price scale)	(period=20, multiplier=2.0) (Python)	period	Indicator-LinRegChannel
StandardErrorBands	OLS endpoint ± k · stderr (n − 2 denominator).	f64	(upper, middle, lower)	unbounded (price scale)	(period=21, multiplier=2.0) (Python)	period	Indicator-StandardErrorBands
DoubleBollinger	Two concentric Bollinger envelopes (±k_inner·σ, ±k_outer·σ).	f64	5 bands	unbounded (price scale)	(period=20, k_inner=1.0, k_outer=2.0) (Python)	period	Indicator-DoubleBollinger
TtmSqueeze	BB-inside-KC squeeze flag + detrended-close momentum (LinReg).	Candle	(squeeze, momentum)	squeeze ∈ {0,1}; momentum unbounded	(period=20, bb_mult=2.0, kc_mult=1.5) (Python)	period	Indicator-TtmSqueeze
FractalChaosBands	Step-function envelope of the latest Bill Williams 5-bar fractals.	Candle	(upper, lower)	unbounded (price scale)	k = 2 (Python)	2k + 1 plus first fractal of each kind	Indicator-FractalChaosBands
VwapStdDevBands	Cumulative VWAP ± k·σ (volume-weighted standard deviation).	Candle	(upper, middle, lower, stddev)	unbounded (price scale)	multiplier = 2.0 (Python)	1	Indicator-VwapStdDevBands

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
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
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
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
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

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
Vwap	Cumulative volume-weighted average price from the stream start; a sibling RollingVwap(period) is exposed for a finite window.	Candle	f64	unbounded (price scale)	(no parameters)	1 (cumulative); period (rolling)	Indicator-Vwap (cumulative + rolling)
Adl	Accumulation/Distribution Line; cumulative range-weighted volume.	Candle	f64	unbounded (drifts with volume)	(no parameters)	1	Indicator-Adl
VolumePriceTrend	Cumulative volume · ROC; volume weighted by percentage move.	Candle	f64	unbounded (drifts with volume)	(no parameters)	1	Indicator-VolumePriceTrend
ChaikinMoneyFlow	Summed money-flow volume over summed volume across period bars.	Candle	f64	[−1, +1]	period = 20 (Python)	period	Indicator-ChaikinMoneyFlow
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
ForceIndex	EMA((close − prev_close) · volume, period).	Candle	f64	unbounded around zero	period = 13 (Python)	period + 1	Indicator-ForceIndex
EaseOfMovement	SMA of distance travelled per unit of volume.	Candle	f64	unbounded around zero	(period=14, divisor=1e8) (Python)	period + 1	Indicator-EaseOfMovement

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
MedianPrice	(high + low) / 2.	Candle	f64	unbounded (price scale)	(no parameters)	1	Indicator-MedianPrice
WeightedClose	(high + low + 2·close) / 4.	Candle	f64	unbounded (price scale)	(no parameters)	1	Indicator-WeightedClose
LinearRegression	Endpoint of the rolling least-squares line.	f64	f64	unbounded (price scale)	period = 14 (Python)	period	Indicator-LinearRegression
LinRegSlope	Slope of the rolling least-squares line.	f64	f64	unbounded around zero	period = 14 (Python)	period	Indicator-LinRegSlope
ZScore	(price − SMA(n)) / population_stddev(n).	f64	f64	unbounded around zero	period = 20 (Python)	period	Indicator-ZScore
LinRegAngle	The rolling regression slope as a degree angle.	f64	f64	(−90°, +90°)	period = 14 (Python)	period	Indicator-LinRegAngle

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.

See also

• Warmup Periods — verified table of every indicator's warmup_period().
• Indicator Chaining — combining indicators with Chain and the stacked-warmup rule.
• Quickstart: Rust, Quickstart: Python, Quickstart: Node — language-specific API surfaces.
• Source: https://github.com/kingchenc/wickra