Indicators¶

const lrvx = require('@lrvx/lrvx');

Batch functions¶

Input arrays are Float64Array. Single-output functions return Float64Array.

const ema  = lrvx.ema(closes, 20);
const macd = lrvx.macd(closes, 12, 26, 9);       // { line, signal, histogram }
const bb   = lrvx.bollinger(closes, 20, 2.0);     // { upper, middle, lower }
const st   = lrvx.stochastic(hi, lo, cl, 14, 3); // { k, d }
const adx  = lrvx.adx(hi, lo, cl, 14);           // { adx, plusDi, minusDi }

Single value — (input, period):

sma, ema, rma, rsi, dema, tema, slope

kama(input, period, fast?, slow?) takes two extra optional smoothing constants.

OHLC input:

atr(high, low, close, period), cci(high, low, close, period), chop(high, low, close, period), parkinson_vol(high, low, period), rogers_satchell_vol(open, high, low, close, period)

Statistical — (input, period):

skewness, kurtosis, rolling_zscore, shannon_entropy(input, period, bins), rollingCorrelation(x, y, period)

lrvx.correlation(x, y) (no period) is the single-number Pearson coefficient — see Statistics.

Volume:

obv(close, volume), vwap(close, volume, window), cvd(open, high, low, close, volume)

Streaming classes¶

All streaming indicators share the same interface:

const ind = new lrvx.EMA(14);
ind.update(price);   // returns current value (null during warmup)
ind.value            // current value
ind.ready            // true once warmed up
ind.reset()          // clear state, keep config

Single value — update(value):

SMA(period), EMA(period), RMA(period), RSI(period), DEMA(period), TEMA(period), KAMA(period, fast?, slow?), Slope(length), Skewness(period), Kurtosis(period), RollingZScore(period), ShannonEntropy(period, bins), AutoCorrelation(window, lag)

Multi-output — update(value), named properties instead of .value:

MACD(fast?, slow?, signal?) — defaults 12/26/9 → .line, .signal, .histogram
Bollinger(period, stdDev?) — default stdDev 2.0 → .upper, .middle, .lower

OHLC / multi-input:

ATR(period) — update(high, low, close)
Stochastic(kPeriod, dPeriod?) — update(high, low, close) → .k, .d
CCI(period) — update(high, low, close)
ParkinsonVol(period) — update(high, low)
RogersSatchellVol(period) — update(open, high, low, close)
Correlation(period) — update(x, y)

Volume indicators (obv, vwap, cvd) exist as batch functions only — there are no streaming classes for them.

Indicator catalog¶

Every indicator below is one Node.js class with both a batch compute() method and streaming update() / value / ready / reset(). Same instance, two ways to use it:

const lrvx = require('@lrvx/lrvx');
const ema = new lrvx.EMA(10);
const out = ema.compute(prices);            // batch
for (const v of stream) {
  ema.update(v);
  if (ema.ready) console.log(ema.value);    // streaming on the same instance
}

Indicator	Constructor	Kind
`EMA`	`new lrvx.EMA(period)`	SingleInput
`SMA`	`new lrvx.SMA(period)`	SingleInput
`RMA`	`new lrvx.RMA(period)`	SingleInput
`RSI`	`new lrvx.RSI(period)`	SingleInput
`KAMA`	`new lrvx.KAMA(period, fast, slow)`	SingleInput
`DEMA`	`new lrvx.DEMA(period)`	SingleInput
`TEMA`	`new lrvx.TEMA(period)`	SingleInput
`Slope`	`new lrvx.Slope(length)`	SingleInput
`Skewness`	`new lrvx.Skewness(period)`	SingleInput
`Kurtosis`	`new lrvx.Kurtosis(period)`	SingleInput
`RollingZScore`	`new lrvx.RollingZScore(period)`	SingleInput
`ShannonEntropy`	`new lrvx.ShannonEntropy(period, bins)`	SingleInput
`AutoCorrelation`	`new lrvx.AutoCorrelation(window, lag)`	SingleInput
`ATR`	`new lrvx.ATR(period)`	BarInput
`CCI`	`new lrvx.CCI(period)`	BarInput
`Stochastic`	`new lrvx.Stochastic(k_period, d_period)`	BarInput
`ParkinsonVol`	`new lrvx.ParkinsonVol(period)`	HighLowInput
`RogersSatchellVol`	`new lrvx.RogersSatchellVol(period)`	OhlcInput
`Correlation`	`new lrvx.Correlation(period)`	PairInput
`MACD`	`new lrvx.MACD(fast, slow, signal)`	MultiOutput
`Bollinger`	`new lrvx.Bollinger(period, stddev)`	MultiOutput