kingchenc
466faddd87
* feat(alma): add Arnaud Legoux Moving Average
Gaussian-weighted moving average with configurable centre (offset in
[0, 1]) and kernel width (sigma > 0). Pre-computes normalised weights
at construction so each update is a single rolling window dot product.
Reference: Arnaud Legoux and Dimitrios Kouzis-Loukas, 2009.
Touchpoints:
- crates/wickra-core: alma.rs + mod.rs + lib.rs re-export
- bindings/python: PyAlma + __init__.py + test_new_indicators +
test_known_values reference
- bindings/node: AlmaNode + index.d.ts/index.js + indicators.test.js
factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers ALMA(9, 0.85, 6.0)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry
* feat(mcginley): add McGinley Dynamic moving average
John McGinley's self-adjusting moving average with the recurrence
MD + (price - MD) / (0.6 * period * (price / MD)^4). Speeds up when
price falls below the indicator and damps when price runs above the
indicator. Seeded with the simple average of the first period inputs.
Reference: McGinley, Technical Analysis of Stocks & Commodities, 1990.
Touchpoints:
- crates/wickra-core: mcginley_dynamic.rs + mod.rs + lib.rs re-export
- bindings/python: PyMcGinleyDynamic + __init__.py + test_new_indicators
+ test_known_values reference
- bindings/node: McGinleyDynamicNode (scalar macro) + index.d.ts/index.js
+ indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers McGinleyDynamic(10)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry
* feat(frama): add Fractal Adaptive Moving Average
Ehlers' FRAMA adapts its smoothing constant to the fractal dimension of
the recent window: tight tracking in trends, heavy smoothing in chop.
Uses the close-only variant where max/min over each window half drive
the dimension estimate. Period must be even (default 16).
Reference: Ehlers, Fractal Adaptive Moving Average, 2005.
Touchpoints:
- crates/wickra-core: frama.rs + mod.rs + lib.rs re-export
- bindings/python: PyFrama + __init__.py + test_new_indicators +
test_known_values reference (constant series + uptrend tracking)
- bindings/node: FramaNode (scalar macro) + index.d.ts/index.js +
indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers Frama(16)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry
* feat(vidya): add Variable Index Dynamic Average
Chande's VIDYA — an EMA whose alpha scales with |CMO(cmo_period)| / 100.
Strong directional momentum lifts the smoothing constant toward the
EMA-of-period rate; flat or choppy windows shrink it toward zero so
VIDYA coasts on its previous value. Two parameters: period (14) and
cmo_period (9). Reuses the existing wickra-core Cmo internally.
Reference: Chande, Stocks & Commodities, 1992.
Also fixes a silent gap from d37fbd1 (feat(frama)): the PyFrama Python
class wrapper and its add_class registration were dropped because the
two edits hit "File has not been read yet" errors that scrolled past
in a batch. Adds them here alongside VIDYA's bindings.
Touchpoints (VIDYA): vidya.rs + mod.rs + lib.rs re-export, PyVidya +
__init__.py + test_new_indicators + test_known_values reference,
VidyaNode (manual two-param binding) + index.d.ts/index.js +
indicators.test.js factory + reference, wasm_scalar_indicator! macro,
fuzz target, bench, README + CHANGELOG.
* feat(jma): add Jurik Moving Average
Three-stage filter reconstruction of Mark Jurik's adaptive MA (the
algorithm is proprietary; this is the form used by most open-source
ports since the 1999 TASC article). Parameters: period (14), phase in
[-100, 100] (0), power in 1..=4 (2). State is seeded by setting
e0 = JMA = first input so a constant input stream is reproduced exactly.
Touchpoints: jma.rs + mod.rs + lib.rs re-export, PyJma + __init__.py +
test_new_indicators + test_known_values reference, JmaNode (manual
three-param binding) + index.d.ts/index.js + indicators.test.js factory
+ reference, wasm_scalar_indicator! macro, fuzz target, bench, README +
CHANGELOG.
* feat(alligator): add Bill Williams Alligator
Three SMMA lines (Jaw / Teeth / Lips) over the median price
(high + low) / 2 with default periods 13 / 8 / 5. Multi-output
indicator returning AlligatorOutput { jaw, teeth, lips }. The
original chart variant shifts each line forward for display; we
publish the unshifted SMMA values and leave the visual shift to
the consumer.
Reference: Bill Williams, Trading Chaos, 1995.
Touchpoints: alligator.rs + mod.rs + lib.rs re-export, PyAlligator
(Candle input, returns 3-tuple, ndarray (n, 3) batch) + __init__.py
+ test_new_indicators + test_known_values reference, AlligatorNode +
AlligatorValue + index.d.ts/index.js + indicators.test.js multi
factory + reference, WasmAlligator (manual JsValue object) +
candle-fuzz target + README + CHANGELOG.
* feat(evwma): add Elastic Volume-Weighted Moving Average
Christian P. Fries' elastic recurrence where the smoothing weight is the
bar's volume relative to the running window total:
V_sum_t = sum of volumes over the last period candles
EVWMA_t = ((V_sum_t - v_t) * EVWMA_{t-1} + v_t * close_t) / V_sum_t
A bar whose volume is small barely moves the average; a bar that
dominates the window pulls it strongly toward that bar's close. Seeded
with the close of the first full window; holds its previous value if
the entire window has zero volume.
Reference: Fries, Wilmott Magazine, 2001.
Touchpoints: evwma.rs + mod.rs + lib.rs re-export, PyEvwma (close +
volume batch) + __init__.py + test_new_indicators CANDLE_SCALAR +
test_known_values reference, EvwmaNode + index.d.ts/index.js +
indicators.test.js candleScalar factory + reference, WasmEvwma,
candle-fuzz target + README + CHANGELOG.
* ci: Force local wheel install in Python jobs
Use --no-index --no-deps so the Python matrix installs the freshly
built wheel from dist/ and never falls back to PyPI. Previously pip
sometimes picked the released 0.2.x wheel on macOS / Windows when its
platform tag was a wider match than the local build, which made the
job test the released package and miss any new symbols added in the
PR (e.g. AttributeError: module 'wickra' has no attribute 'ALMA').
numpy is already installed by the preceding pip step, so --no-deps
is safe.
324 lines
17 KiB
JavaScript
324 lines
17 KiB
JavaScript
// Comprehensive tests for the Wickra Node bindings: streaming-vs-batch
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// equivalence, reference values, and lifecycle methods across all 71
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// indicators. Ported from the Python test_streaming_vs_batch / test_known_values
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// suites.
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const test = require('node:test');
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const assert = require('node:assert/strict');
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const wickra = require('..');
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// Synthetic OHLCV series long enough to warm up every indicator.
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const N = 120;
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const close = Array.from({ length: N }, (_, i) => 100 + Math.sin(i * 0.2) * 10 + i * 0.1);
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const high = close.map((c) => c + 1.5);
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const low = close.map((c) => c - 1.5);
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const volume = Array.from({ length: N }, (_, i) => 1000 + (i % 7) * 50);
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const open = close.map((c) => c - 0.5);
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function eq(a, b) {
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if (Number.isNaN(a)) return Number.isNaN(b);
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return Math.abs(a - b) < 1e-9;
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}
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function num(v) {
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return v === null || v === undefined ? NaN : v;
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}
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// --- Scalar indicators: update(value) vs batch(prices) ---
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const scalarFactories = {
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SMA: () => new wickra.SMA(14),
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EMA: () => new wickra.EMA(14),
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WMA: () => new wickra.WMA(14),
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RSI: () => new wickra.RSI(14),
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DEMA: () => new wickra.DEMA(10),
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TEMA: () => new wickra.TEMA(10),
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HMA: () => new wickra.HMA(9),
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ROC: () => new wickra.ROC(12),
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TRIX: () => new wickra.TRIX(9),
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KAMA: () => new wickra.KAMA(10, 2, 30),
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ALMA: () => new wickra.ALMA(9, 0.85, 6.0),
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McGinleyDynamic: () => new wickra.McGinleyDynamic(10),
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FRAMA: () => new wickra.FRAMA(16),
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VIDYA: () => new wickra.VIDYA(14, 9),
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JMA: () => new wickra.JMA(14, 0, 2),
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SMMA: () => new wickra.SMMA(14),
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TRIMA: () => new wickra.TRIMA(20),
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ZLEMA: () => new wickra.ZLEMA(14),
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T3: () => new wickra.T3(5, 0.7),
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MOM: () => new wickra.MOM(10),
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CMO: () => new wickra.CMO(14),
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TSI: () => new wickra.TSI(25, 13),
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PMO: () => new wickra.PMO(35, 20),
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StochRSI: () => new wickra.StochRSI(14, 14),
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PPO: () => new wickra.PPO(12, 26),
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DPO: () => new wickra.DPO(20),
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Coppock: () => new wickra.Coppock(14, 11, 10),
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StdDev: () => new wickra.StdDev(20),
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UlcerIndex: () => new wickra.UlcerIndex(14),
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HistoricalVolatility: () => new wickra.HistoricalVolatility(20, 252),
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BollingerBandwidth: () => new wickra.BollingerBandwidth(20, 2),
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PercentB: () => new wickra.PercentB(20, 2),
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LinearRegression: () => new wickra.LinearRegression(14),
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LinRegSlope: () => new wickra.LinRegSlope(14),
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VerticalHorizontalFilter: () => new wickra.VerticalHorizontalFilter(28),
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ZScore: () => new wickra.ZScore(20),
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LinRegAngle: () => new wickra.LinRegAngle(14),
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};
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for (const [name, make] of Object.entries(scalarFactories)) {
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test(`${name}: streaming update matches batch`, () => {
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const batch = make().batch(close);
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const streaming = make();
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assert.equal(batch.length, N);
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for (let i = 0; i < N; i++) {
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const s = num(streaming.update(close[i]));
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assert.ok(eq(s, batch[i]), `${name} mismatch at ${i}: ${s} vs ${batch[i]}`);
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}
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});
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}
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// --- Scalar-output candle indicators: update(...) vs batch(...) ---
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const candleScalar = {
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ATR: { make: () => new wickra.ATR(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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CCI: { make: () => new wickra.CCI(20), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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WilliamsR: { make: () => new wickra.WilliamsR(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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PSAR: { make: () => new wickra.PSAR(0.02, 0.02, 0.2), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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MFI: { make: () => new wickra.MFI(14), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
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VWAP: { make: () => new wickra.VWAP(), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
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RollingVWAP: { make: () => new wickra.RollingVWAP(20), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
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AwesomeOscillator: { make: () => new wickra.AwesomeOscillator(5, 34), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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OBV: { make: () => new wickra.OBV(), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
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VWMA: { make: () => new wickra.VWMA(20), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
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EVWMA: { make: () => new wickra.EVWMA(20), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
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UltimateOscillator: { make: () => new wickra.UltimateOscillator(7, 14, 28), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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AroonOscillator: { make: () => new wickra.AroonOscillator(14), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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NATR: { make: () => new wickra.NATR(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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MassIndex: { make: () => new wickra.MassIndex(9, 25), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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ADL: { make: () => new wickra.ADL(), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
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VolumePriceTrend: { make: () => new wickra.VolumePriceTrend(), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
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ChaikinMoneyFlow: { make: () => new wickra.ChaikinMoneyFlow(20), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
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ChaikinOscillator: { make: () => new wickra.ChaikinOscillator(3, 10), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
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ForceIndex: { make: () => new wickra.ForceIndex(13), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
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EaseOfMovement: { make: () => new wickra.EaseOfMovement(14, 1e8), step: (ind, i) => ind.update(high[i], low[i], volume[i]), batch: (ind) => ind.batch(high, low, volume) },
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AtrTrailingStop: { make: () => new wickra.AtrTrailingStop(14, 3), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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TypicalPrice: { make: () => new wickra.TypicalPrice(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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MedianPrice: { make: () => new wickra.MedianPrice(), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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WeightedClose: { make: () => new wickra.WeightedClose(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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AcceleratorOscillator: { make: () => new wickra.AcceleratorOscillator(5, 34, 5), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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BalanceOfPower: { make: () => new wickra.BalanceOfPower(), step: (ind, i) => ind.update(open[i], high[i], low[i], close[i]), batch: (ind) => ind.batch(open, high, low, close) },
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ChoppinessIndex: { make: () => new wickra.ChoppinessIndex(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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TrueRange: { make: () => new wickra.TrueRange(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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ChaikinVolatility: { make: () => new wickra.ChaikinVolatility(10, 10), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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};
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for (const [name, d] of Object.entries(candleScalar)) {
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test(`${name}: streaming update matches batch`, () => {
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const batch = d.batch(d.make());
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const streaming = d.make();
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assert.equal(batch.length, N);
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for (let i = 0; i < N; i++) {
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const s = num(d.step(streaming, i));
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assert.ok(eq(s, batch[i]), `${name} mismatch at ${i}: ${s} vs ${batch[i]}`);
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}
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});
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}
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// --- Multi-output indicators: object update vs interleaved batch ---
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const multi = {
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Alligator: { make: () => new wickra.Alligator(13, 8, 5), fields: ['jaw', 'teeth', 'lips'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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MACD: { make: () => new wickra.MACD(12, 26, 9), fields: ['macd', 'signal', 'histogram'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
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BollingerBands: { make: () => new wickra.BollingerBands(20, 2), fields: ['upper', 'middle', 'lower', 'stddev'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
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Stochastic: { make: () => new wickra.Stochastic(14, 3), fields: ['k', 'd'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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ADX: { make: () => new wickra.ADX(14), fields: ['plusDi', 'minusDi', 'adx'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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Keltner: { make: () => new wickra.Keltner(20, 10, 2), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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Donchian: { make: () => new wickra.Donchian(20), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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Aroon: { make: () => new wickra.Aroon(14), fields: ['up', 'down'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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Vortex: { make: () => new wickra.Vortex(14), fields: ['plus', 'minus'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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SuperTrend: { make: () => new wickra.SuperTrend(10, 3), fields: ['value', 'direction'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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ChandelierExit: { make: () => new wickra.ChandelierExit(22, 3), fields: ['longStop', 'shortStop'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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ChandeKrollStop: { make: () => new wickra.ChandeKrollStop(10, 1, 9), fields: ['stopLong', 'stopShort'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
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};
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for (const [name, d] of Object.entries(multi)) {
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test(`${name}: streaming update matches interleaved batch`, () => {
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const k = d.fields.length;
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const batch = d.batch(d.make());
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const streaming = d.make();
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assert.equal(batch.length, N * k);
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for (let i = 0; i < N; i++) {
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const o = d.step(streaming, i);
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d.fields.forEach((field, j) => {
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const s = o === null || o === undefined ? NaN : o[field];
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assert.ok(eq(s, batch[i * k + j]), `${name}.${field} mismatch at ${i}`);
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});
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}
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});
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}
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// --- Lifecycle: every indicator exposes reset / isReady / warmupPeriod ---
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test('every indicator exposes reset, isReady and warmupPeriod', () => {
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const all = [
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...Object.values(scalarFactories).map((f) => f()),
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...Object.values(candleScalar).map((d) => d.make()),
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...Object.values(multi).map((d) => d.make()),
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];
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for (const ind of all) {
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assert.equal(typeof ind.reset, 'function');
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assert.equal(typeof ind.isReady, 'function');
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assert.equal(typeof ind.warmupPeriod, 'function');
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assert.equal(ind.isReady(), false);
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assert.ok(ind.warmupPeriod() >= 1);
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}
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});
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test('reset returns an indicator to its un-warmed state', () => {
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const sma = new wickra.SMA(5);
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sma.batch([1, 2, 3, 4, 5]);
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assert.equal(sma.isReady(), true);
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sma.reset();
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assert.equal(sma.isReady(), false);
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assert.equal(sma.update(10), null);
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});
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// --- Reference values ---
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test('SMA(3) reference values', () => {
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const out = new wickra.SMA(3).batch([2, 4, 6, 8, 10]);
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assert.ok(Number.isNaN(out[0]) && Number.isNaN(out[1]));
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assert.equal(out[2], 4);
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assert.equal(out[3], 6);
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assert.equal(out[4], 8);
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});
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test('MFI(2) reference value equals 1200/23', () => {
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// Candle 1 seeds; candle 2 (tp 12 > 10) +mf 1200; candle 3 (tp 11 < 12) -mf 1100.
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const mfi = new wickra.MFI(2);
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assert.equal(mfi.update(10, 10, 10, 100), null);
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assert.equal(mfi.update(12, 12, 12, 100), null);
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const v = mfi.update(11, 11, 11, 100);
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assert.ok(Math.abs(v - 1200 / 23) < 1e-9);
|
|
});
|
|
|
|
test('RSI pure uptrend yields 100', () => {
|
|
const prices = Array.from({ length: 20 }, (_, i) => i + 1);
|
|
const out = new wickra.RSI(14).batch(prices);
|
|
for (let i = 14; i < out.length; i++) {
|
|
assert.equal(out[i], 100);
|
|
}
|
|
});
|
|
|
|
test('MACD histogram equals macd minus signal', () => {
|
|
const macd = new wickra.MACD(12, 26, 9);
|
|
let v = null;
|
|
for (let i = 1; i <= 60; i++) v = macd.update(i);
|
|
assert.ok(v);
|
|
assert.ok(Math.abs(v.histogram - (v.macd - v.signal)) < 1e-9);
|
|
});
|
|
|
|
test('TypicalPrice reference value', () => {
|
|
// (high + low + close) / 3 = (12 + 6 + 9) / 3 = 9.
|
|
assert.equal(new wickra.TypicalPrice().update(12, 6, 9), 9);
|
|
});
|
|
|
|
test('ChaikinMoneyFlow(2) reference value equals 0.5', () => {
|
|
// Bar 1 closes at the high (MFV +100); bar 2 closes mid-range (MFV 0).
|
|
const cmf = new wickra.ChaikinMoneyFlow(2);
|
|
assert.equal(cmf.update(10, 8, 10, 100), null);
|
|
assert.ok(Math.abs(cmf.update(12, 8, 10, 100) - 0.5) < 1e-9);
|
|
});
|
|
|
|
test('LinearRegression(3) reference values', () => {
|
|
// Least-squares line through [1, 2, 9] is y = 4x; endpoint 4·2 = 8.
|
|
const out = new wickra.LinearRegression(3).batch([1, 2, 9]);
|
|
assert.ok(Number.isNaN(out[0]) && Number.isNaN(out[1]));
|
|
assert.ok(Math.abs(out[2] - 8) < 1e-9);
|
|
});
|
|
|
|
test('SuperTrend flat market holds the lower band and an uptrend', () => {
|
|
// Flat candles: ATR 2, hl2 10, lower band 10 - 3·2 = 4.
|
|
const n = 20;
|
|
const out = new wickra.SuperTrend(5, 3).batch(
|
|
Array(n).fill(11),
|
|
Array(n).fill(9),
|
|
Array(n).fill(10),
|
|
);
|
|
assert.ok(Math.abs(out[2 * n - 2] - 4) < 1e-9); // value
|
|
assert.equal(out[2 * n - 1], 1); // direction
|
|
});
|
|
|
|
test('BalanceOfPower reference value', () => {
|
|
// (close - open) / (high - low) = (12 - 10) / (14 - 10) = 0.5.
|
|
assert.ok(Math.abs(new wickra.BalanceOfPower().update(10, 14, 10, 12) - 0.5) < 1e-9);
|
|
});
|
|
|
|
test('TrueRange reference values', () => {
|
|
const tr = new wickra.TrueRange();
|
|
assert.equal(tr.update(12, 8, 11), 4); // no prev close -> high - low
|
|
assert.equal(tr.update(10, 9, 9.5), 2); // prev close 11 -> max(1, 1, 2)
|
|
});
|
|
|
|
test('LinRegAngle of a unit-slope series is 45 degrees', () => {
|
|
const out = new wickra.LinRegAngle(5).batch([1, 2, 3, 4, 5, 6]);
|
|
assert.ok(Math.abs(out[4] - 45) < 1e-9);
|
|
});
|
|
|
|
test('EVWMA(2) reference values on [10, 20, 30] with volumes [1, 3, 1]', () => {
|
|
const out = new wickra.EVWMA(2).batch([10, 20, 30], [1, 3, 1]);
|
|
assert.ok(Number.isNaN(out[0]));
|
|
assert.ok(Math.abs(out[1] - 20) < 1e-12);
|
|
assert.ok(Math.abs(out[2] - 22.5) < 1e-12);
|
|
});
|
|
|
|
test('Alligator on a flat median price seeds to that median', () => {
|
|
const n = 30;
|
|
const out = new wickra.Alligator(13, 8, 5).batch(Array(n).fill(11), Array(n).fill(9));
|
|
// All three SMMAs see median (11 + 9) / 2 = 10 every bar.
|
|
for (let i = 12; i < n; i++) {
|
|
assert.ok(Math.abs(out[i * 3] - 10) < 1e-12, `jaw at ${i}: ${out[i * 3]}`);
|
|
assert.ok(Math.abs(out[i * 3 + 1] - 10) < 1e-12);
|
|
assert.ok(Math.abs(out[i * 3 + 2] - 10) < 1e-12);
|
|
}
|
|
});
|
|
|
|
test('JMA on a flat series reproduces the constant', () => {
|
|
const out = new wickra.JMA(14, 0, 2).batch(Array(30).fill(42));
|
|
for (let i = 0; i < 30; i++) assert.ok(Math.abs(out[i] - 42) < 1e-12);
|
|
});
|
|
|
|
test('VIDYA on a flat series holds the seed', () => {
|
|
const out = new wickra.VIDYA(14, 4).batch(Array(20).fill(42));
|
|
for (let i = 0; i < 4; i++) assert.ok(Number.isNaN(out[i]));
|
|
for (let i = 4; i < 20; i++) assert.ok(Math.abs(out[i] - 42) < 1e-12);
|
|
});
|
|
|
|
test('FRAMA pure uptrend hugs the latest close', () => {
|
|
const out = new wickra.FRAMA(4).batch([1, 2, 3, 4, 5, 6, 7, 8]);
|
|
assert.ok(Math.abs(out[out.length - 1] - 8) < 0.05);
|
|
});
|
|
|
|
test('McGinleyDynamic(3) seeds with SMA and recurses on the next price', () => {
|
|
// Seed = SMA([10, 20, 30]) = 20. On 40: ratio = 2, divisor = 0.6*3*16 = 28.8.
|
|
const out = new wickra.McGinleyDynamic(3).batch([10, 20, 30, 40]);
|
|
assert.ok(Number.isNaN(out[0]) && Number.isNaN(out[1]));
|
|
assert.ok(Math.abs(out[2] - 20) < 1e-12);
|
|
const expected = 20 + 20 / (0.6 * 3 * 16);
|
|
assert.ok(Math.abs(out[3] - expected) < 1e-12);
|
|
});
|
|
|
|
test('ALMA(3, 0.85, 6) reference value on [10, 20, 30]', () => {
|
|
// m = 0.85 * 2 = 1.7; s = 3 / 6 = 0.5; 2*s^2 = 0.5.
|
|
const out = new wickra.ALMA(3, 0.85, 6).batch([10, 20, 30]);
|
|
assert.ok(Number.isNaN(out[0]) && Number.isNaN(out[1]));
|
|
const w = [0, 1, 2].map((i) => Math.exp(-Math.pow(i - 1.7, 2) / 0.5));
|
|
const s = w[0] + w[1] + w[2];
|
|
const expected = (10 * w[0] + 20 * w[1] + 30 * w[2]) / s;
|
|
assert.ok(Math.abs(out[2] - expected) < 1e-12);
|
|
// The heavy offset toward the newest sample lifts the average above the
|
|
// simple mean of 20.
|
|
assert.ok(out[2] > 20);
|
|
});
|