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feat: Family 03 MACD & Price Oscillators — APO / AO-Hist / CFO / Zero-Lag MACD / Elder Impulse / STC (#41)

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* feat(apo): add Absolute Price Oscillator

EMA(close, fast) - EMA(close, slow). Like MACD without the signal EMA.
Defaults to (fast = 12, slow = 26); fast must be strictly less than
slow.

Touchpoints: apo.rs + mod.rs + lib.rs re-export, PyApo + __init__.py
+ test_new_indicators SCALAR + test_known_values flat reference,
ApoNode + index.d.ts/index.js + indicators.test.js factory + reference,
WasmApo via scalar macro, scalar-fuzz target, README + CHANGELOG.

* fix(apo): add PyApo + ApoNode + WasmApo bindings missed from ec269d8

The previous APO commit (ec269d8) only registered APO in the Python
__init__.py / Node index.js / Node index.d.ts / fuzz / tests / docs.
The actual PyApo pyclass, ApoNode napi class, and WasmApo wasm class
edits silently no-op'd because the underlying lib.rs files had been
touched by a branch switch between Read and Edit. The bindings were
therefore advertising APO from the Python module / Node package /
WASM module but not actually exposing it.

Fix: insert PyApo block + add_class call in bindings/python/src/lib.rs,
ApoNode block in bindings/node/src/lib.rs, WasmApo macro line in
bindings/wasm/src/lib.rs. cargo test workspace stays at 615 (no new
tests added; the existing test_known_values + indicators.test.js
references would have failed at import once the bindings rebuilt
without these classes).

* feat(ao-histogram): add Awesome Oscillator Histogram

AO - SMA(AO, sma_period). A configurable variant of the existing
AcceleratorOscillator (which fixes fast=5, slow=34, sma=5).
Three parameters; defaults match Bill Williams' Accelerator.

Touchpoints: awesome_oscillator_histogram.rs + mod.rs + lib.rs
re-export, PyAoHist + __init__.py + test_new_indicators CANDLE_SCALAR
+ test_known_values flat reference, AwesomeOscillatorHistogramNode +
index.d.ts/index.js + indicators.test.js factory + reference,
WasmAoHist, candle-fuzz target, README + CHANGELOG.

* feat(cfo): add Chande Forecast Oscillator

100 * (close - LinReg(close, period)) / close. Positive when close
overshoots the linear forecast, negative when it undershoots. Holds
the previous value if the close is zero (percentage form undefined).
Single param period (default 14).

Touchpoints: cfo.rs + mod.rs + lib.rs re-export, PyCfo + __init__.py
+ test_new_indicators SCALAR + test_known_values linear reference,
CfoNode + index.d.ts/index.js + indicators.test.js factory + reference,
WasmCfo via scalar macro, scalar-fuzz target, README + CHANGELOG.

* fix(cfo): add WasmCfo binding missed from 733afd9

* feat(zero-lag-macd): add Zero-Lag MACD

Classic MACD topology with ZLEMA substituted for EMA everywhere:
faster reaction to trend changes at the cost of slightly noisier
readings. Multi-output ZeroLagMacdOutput { macd, signal, histogram }.
Three parameters (fast = 12, slow = 26, signal = 9); fast must be
strictly less than slow.

Touchpoints: zero_lag_macd.rs + mod.rs + lib.rs re-export, PyZeroLagMacd
+ __init__.py + test_new_indicators MULTI + test_known_values flat
reference, ZeroLagMacdNode + ZeroLagMacdValue + index.d.ts/index.js +
indicators.test.js multi factory + reference, WasmZeroLagMacd, scalar
fuzz with hand-rolled drive (multi-output bypasses the f64-only
helper), README + CHANGELOG.

* feat(elder-impulse): add Alexander Elder Impulse System

Tri-state momentum gauge: +1 (green/buy) when EMA trend and MACD
histogram both rise, -1 (red/sell) when both fall, 0 (blue/neutral)
on disagreement. Four parameters (ema_period, macd_fast, macd_slow,
macd_signal); defaults (13, 12, 26, 9) match Elder.

Internally feeds both branches on every input so they warm in parallel;
needs one bar past the slowest branch to seed direction state.

Touchpoints: elder_impulse.rs + mod.rs + lib.rs re-export, PyElderImpulse
+ __init__.py + test_new_indicators SCALAR + test_known_values neutral
reference, ElderImpulseNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmElderImpulse via scalar macro, scalar-fuzz
target, README + CHANGELOG.

* feat(stc): add Schaff Trend Cycle

Doug Schaff's doubly-Stochastic-smoothed MACD. Bounded [0, 100]
reading that reacts faster than MACD by extracting the percentile of
MACD within a recent window, half-EMA-smoothing it, and re-stochasing
the smoothed series. Four parameters (fast = 23, slow = 50,
schaff_period = 10, factor = 0.5); fast must be strictly less than
slow and factor must lie in (0, 1].

Output clamped to [0, 100] to absorb floating-point rounding. The
stochastic stages clamp to 0 when their rolling range collapses (flat
input or perfectly monotone trend), so a flat series settles
deterministically at 0 after warmup.

Touchpoints: stc.rs + mod.rs + lib.rs re-export, PyStc + __init__.py
+ test_new_indicators SCALAR + test_known_values flat reference,
StcNode + index.d.ts/index.js + indicators.test.js factory + reference,
WasmStc via scalar macro, scalar-fuzz target, README + CHANGELOG.

* fix(stc): rename last_stc -> last_value to satisfy clippy

* ci: Retry setup-node and setup-python on CDN flakes

Setup-node on Windows runners and setup-python across all OSes
occasionally fail with a silent hang or 5xx mid-download ("Attempting
to download 18..." → fail in <1s) — pure upstream CDN flake. The fix
ran on this branch's previous merge commit (24e723f) had to be
re-triggered manually via `gh run rerun --failed`.

Wrap both setup actions with continue-on-error and a follow-up retry
step that waits 30s and re-runs the same setup. The retry only fires
when the first attempt failed (steps.<id>.outcome == 'failure'), so a
green setup costs nothing extra. The retry uses the identical pinned
SHA so we still get supply-chain verification on both attempts.

Applied to ci.yml (Python matrix and Node matrix). release.yml has
the same setup-node / setup-python steps but is rarely re-run, so
the existing manual rerun pattern stays sufficient for now.

* test(zero-lag-macd): Fix MULTI dict shape mismatch + cover warmup_period

ZeroLagMACD was registered in the Python MULTI dict (which asserts a
(n, 2) batch shape) but actually emits (n, 3) — macd, signal,
histogram — like MACD. Moved out into its own standalone test
test_zero_lag_macd_streaming_matches_batch (3-tuple shape), and
included in the lifecycle sweep. Mirrors the existing Alligator
pattern for 3-output candle indicators.

Also adds a unit test for ZeroLagMacd::warmup_period that pins both
the (12, 26, 9) classic case and a small-period config — these four
lines were the codecov/patch miss on PR 41.
This commit is contained in:
kingchenc
2026-05-25 17:26:46 +02:00
committed by GitHub
parent 7f1a6df202
commit d9d3ad18aa
21 changed files with 2303 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files
bindings/node/__tests__/indicators.test.js
+54
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@@ -52,6 +52,10 @@ const scalarFactories = {
PMO: () => new wickra.PMO(35, 20),
StochRSI: () => new wickra.StochRSI(14, 14),
PPO: () => new wickra.PPO(12, 26),
APO: () => new wickra.APO(12, 26),
CFO: () => new wickra.CFO(14),
ElderImpulse: () => new wickra.ElderImpulse(13, 12, 26, 9),
STC: () => new wickra.STC(23, 50, 10, 0.5),
DPO: () => new wickra.DPO(20),
Coppock: () => new wickra.Coppock(14, 11, 10),
StdDev: () => new wickra.StdDev(20),
@@ -113,6 +117,7 @@ const candleScalar = {
MedianPrice: { make: () => new wickra.MedianPrice(), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
WeightedClose: { make: () => new wickra.WeightedClose(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
AcceleratorOscillator: { make: () => new wickra.AcceleratorOscillator(5, 34, 5), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
AwesomeOscillatorHistogram: { make: () => new wickra.AwesomeOscillatorHistogram(5, 34, 5), step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
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) },
ChoppinessIndex: { make: () => new wickra.ChoppinessIndex(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
TrueRange: { make: () => new wickra.TrueRange(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
@@ -136,6 +141,7 @@ for (const [name, d] of Object.entries(candleScalar)) {
const multi = {
KST: { make: () => new wickra.KST(10, 15, 20, 30, 10, 10, 10, 15, 9), fields: ['kst', 'signal'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
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) },
ZeroLagMACD: { make: () => new wickra.ZeroLagMACD(12, 26, 9), fields: ['macd', 'signal', 'histogram'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
MACD: { make: () => new wickra.MACD(12, 26, 9), fields: ['macd', 'signal', 'histogram'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
BollingerBands: { make: () => new wickra.BollingerBands(20, 2), fields: ['upper', 'middle', 'lower', 'stddev'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
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) },
@@ -273,6 +279,54 @@ test('LinRegAngle of a unit-slope series is 45 degrees', () => {
assert.ok(Math.abs(out[4] - 45) < 1e-9);
});
test('ZeroLagMACD on a flat series converges to zero', () => {
const out = new wickra.ZeroLagMACD(3, 5, 3).batch(Array(60).fill(42));
// Last interleaved row: macd, signal, histogram all 0.
const n = 60;
assert.ok(Math.abs(out[(n - 1) * 3]) < 1e-12);
assert.ok(Math.abs(out[(n - 1) * 3 + 1]) < 1e-12);
assert.ok(Math.abs(out[(n - 1) * 3 + 2]) < 1e-12);
});
test('AwesomeOscillatorHistogram on a flat median converges to zero', () => {
const n = 50;
const out = new wickra.AwesomeOscillatorHistogram(3, 5, 3).batch(
Array(n).fill(11),
Array(n).fill(9),
);
// warmup = 5 + 3 - 1 = 7.
for (let i = 6; i < n; i++) assert.ok(Math.abs(out[i]) < 1e-12);
});
test('STC on a flat series stays at zero', () => {
const out = new wickra.STC(3, 5, 4, 0.5).batch(Array(60).fill(42));
// Latest values must be exactly zero.
for (let i = out.length - 5; i < out.length; i++) {
if (Number.isNaN(out[i])) continue;
assert.equal(out[i], 0);
}
});
test('ElderImpulse on a flat series stays neutral (0)', () => {
const out = new wickra.ElderImpulse(13, 12, 26, 9).batch(Array(120).fill(42));
for (let i = 0; i < out.length; i++) {
if (Number.isNaN(out[i])) continue;
assert.equal(out[i], 0);
}
});
test('CFO(5) on a perfectly linear series yields zero', () => {
const prices = Array.from({ length: 20 }, (_, i) => (i + 1) * 2);
const out = new wickra.CFO(5).batch(prices);
for (let i = 4; i < 20; i++) assert.ok(Math.abs(out[i]) < 1e-9);
});
test('APO(3, 5) on a flat series converges to zero', () => {
const out = new wickra.APO(3, 5).batch(Array(30).fill(42));
for (let i = 0; i < 4; i++) assert.ok(Number.isNaN(out[i]));
for (let i = 4; i < 30; i++) assert.ok(Math.abs(out[i]) < 1e-12);
});
test('Inertia(3, 4) on a constant RVI series equals that RVI', () => {
const n = 60;
// Every bar (open, high, low, close) = (10, 11, 9, 10.5) -> RVI = 0.25.