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kingchenc b3ddbea584 E14: remove hardcoded local paths from the docs

Indicators-Overview.md referenced the absolute author-machine paths
D:\Coding\Wickra\crates\... and D:\Coding\Wickra\bindings\... in its
"Source-of-truth files" section, and seven trend-indicator pages had
Node examples that did require('D:/Coding/Wickra/bindings/node').

Replace the overview paths with repo-relative GitHub links and change
the Node examples to require('wickra'), the published npm package name
a reader would actually use. No D:/Coding path remains anywhere in docs.

2026-05-22 16:18:48 +02:00

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Indicators Overview

Wickra ships 25 indicators, organised in source under the four classical families — trend, momentum, volatility, volume — that map directly to the directory structure of crates/wickra-core/src/indicators/. The same family labels are used here, plus 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).

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 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; for two indicators (Hma, Kama) the practical first-emission index can lag the reported number because of stacked sub-indicator warmups — those discrepancies are noted on the deep-dive pages.

Trend

Trend indicators smooth the price series to surface direction. They are all single-input, single-output (f64 → f64).

Simple averages

Pure linear weighting. Mostly used as fast baselines or as comparison benchmarks against fancier averages.

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
`Wma`	Linear weights `1, 2, …, period` so the newest bar matters most.	`f64`	`f64`	unbounded (price scale)	`period`	`period`	Indicator-Wma.md

Exponential family

Recursive smoothing with one or more chained EMAs. Lag reduction grows as you stack more EMAs, but so does responsiveness to noise.

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
`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))`; removes more lag than DEMA.	`f64`	`f64`	unbounded (price scale)	`period`	`3·period − 2`	Indicator-Tema.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`	(see source `crates/wickra-core/src/indicators/trix.rs`)

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.

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
`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

Momentum

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
`Rsi`	Wilder's RSI; smoothed `gain / (gain + loss) × 100`.	`f64`	`f64`	`[0, 100]`	`period = 14` (Python)	`period + 1`
`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`
`Mfi`	"Volume-weighted RSI": Wilder smoothing of money-flow ratios.	`Candle`	`f64`	`[0, 100]`	`period = 14` (Python)	`period`
`Aroon`	Bars-since-high and bars-since-low scaled to `[0, 100]`.	`Candle`	`(up, down)`	each in `[0, 100]`	`period = 14` (Python)	`period + 1`

Unbounded oscillators

Centered on zero or driven by raw price differences; no fixed cap.

Indicator	One-liner	Input	Output	Range	Defaults	Warmup
`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`
`Cci`	`(typical − SMA(typical)) / (0.015 · mean_dev)`; unbounded but typically `±100`.	`Candle`	`f64`	unbounded (typically `±100` to `±200`)	`period = 20` (Python)	`period`
`Roc`	`(price − price_n) / price_n × 100`; raw percentage change over `period` bars.	`f64`	`f64`	unbounded around zero	`period`	`period + 1`
`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`
`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`

Directional

Indicator	One-liner	Input	Output	Range	Defaults	Warmup
`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`

Volatility

Volatility indicators sit in two functional groups: those that draw an envelope around price, and those that report a scalar dispersion/range. PSAR is a special case — a trailing-stop tracker rather than a width measure — that lives in the volatility module by source convention.

Envelopes

Indicator	One-liner	Input	Output	Range	Defaults	Warmup
`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`
`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)`
`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`

Range-average

Indicator	One-liner	Input	Output	Range	Defaults	Warmup
`Atr`	Wilder-smoothed True Range; per-bar absolute volatility.	`Candle`	`f64`	`[0, ∞)` (price scale)	`period = 14` (Python)	`period`

Trailing stop

Indicator	One-liner	Input	Output	Range	Defaults	Warmup
`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`

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
`Obv`	On-Balance Volume: cumulative signed volume driven by close-vs-prior-close sign.	`Candle`	`f64`	unbounded (drifts with cumulative volume)	(no parameters)	`1`
`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`

Rolling

Indicator	One-liner	Input	Output	Range	Defaults	Warmup
`RollingVwap`	VWAP over a sliding window instead of since-start; useful for session-independent VWAP.	`Candle`	`f64`	unbounded (price scale)	`period`	`period`

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.

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. in tables above) come from the #[pyo3(signature = …)] attributes in bindings/python/src/lib.rs; indicators not listed with a Python default require an explicit period argument.

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