* feat(rvi): add Relative Vigor Index
Dorsey's RVI = SMA(close - open, period) / SMA(high - low, period) over
a rolling window of period candles. Candle input, single parameter
period (default 10). Positive on average-bullish windows, negative on
average-bearish. Holds the previous value if the entire window has
zero range (denominator undefined).
Reference: Donald Dorsey, also pandas-ta rvi.
Touchpoints: rvi.rs + mod.rs + lib.rs re-export, PyRvi + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values reference,
RviNode (4-column OHLC batch) + index.d.ts/index.js + indicators.test
.js factory + reference, WasmRvi + make_candle_ohlc helper, candle-fuzz
target + criterion bench, README + CHANGELOG.
* feat(pgo): add Pretty Good Oscillator
Mark Johnson's PGO = (close - SMA(close, period)) / EMA(TR, period).
Counts roughly how many ATR-equivalents the close sits from its
period-bar mean. Candle input, single parameter period (default 14).
Johnson's heuristic uses +3/-3 crossings as entry signals.
Touchpoints: pgo.rs + mod.rs + lib.rs re-export, PyPgo + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values flat-close
reference, PgoNode (h/l/c) + index.d.ts/index.js + indicators.test.js
factory + reference, WasmPgo, candle-fuzz target + bench, README +
CHANGELOG.
* feat(kst): add Know Sure Thing (Pring)
Pring's long-horizon momentum oscillator: weighted sum of four
SMA-smoothed ROC series with fixed weights 1, 2, 3, 4, plus an SMA
signal line. Nine parameters (four ROC periods, four SMA periods, one
signal period); classic() applies Pring's recommended defaults.
Multi-output indicator emitting KstOutput { kst, signal }.
Touchpoints: kst.rs + mod.rs + lib.rs re-export, PyKst + __init__.py
+ test_new_indicators MULTI + test_known_values flat-input reference,
KstNode + KstValue + index.d.ts/index.js + indicators.test.js multi
factory + reference, WasmKst (manual JsValue object), scalar-fuzz
target (handled outside the f64-output drive helper), README +
CHANGELOG.
* feat(smi): add Stochastic Momentum Index (Blau)
Blau's doubly-EMA-smoothed bounded oscillator: measures the close's
displacement from the centre of the recent high-low range, scaled by
the smoothed range. Candle input, three parameters (period, d_period,
d2_period) with defaults 5 / 3 / 3.
Internally feeds both the displacement-EMA stack and the range-EMA
stack on every candle so they warm up in parallel (gating either
behind the other starves the second by one input).
Touchpoints: smi.rs + mod.rs + lib.rs re-export, PySmi + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values flat-input
reference, SmiNode + index.d.ts/index.js + indicators.test.js factory
+ reference, WasmSmi, candle-fuzz target, README + CHANGELOG.
* feat(laguerre-rsi): add Ehlers Laguerre RSI
Four-stage Laguerre polynomial filter wrapped in an RSI-style up/down
accumulator. Single gamma in [0, 1] (default 0.5) trades lag for
smoothness. State is seeded by setting all four L_i to the first input
so a constant series stays at the neutral 50. Output clamped to
[0, 100] to absorb floating-point rounding.
Reference: Ehlers, Time Warp - Without Space Travel, 2002.
Touchpoints: laguerre_rsi.rs + mod.rs + lib.rs re-export, PyLaguerreRsi
+ __init__.py + test_new_indicators SCALAR + test_known_values neutral
reference, LaguerreRsiNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmLaguerreRsi via scalar macro, scalar-fuzz
target, README + CHANGELOG.
* feat(connors-rsi): add Connors RSI (CRSI)
Larry Connors' 3-component aggregate: RSI(close), RSI(streak), and
PercentRank of the 1-period return over the last period_rank returns.
Each component is bounded in [0, 100] so the aggregate is too.
Three parameters (period_rsi, period_streak, period_rank) with
defaults 3 / 2 / 100. Streak tracks consecutive up/down runs (resets
to 0 on unchanged close).
Touchpoints: connors_rsi.rs + mod.rs + lib.rs re-export, PyConnorsRsi
+ __init__.py + test_new_indicators SCALAR + test_known_values bounded
reference, ConnorsRsiNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmConnorsRsi via scalar macro, scalar-fuzz
target, README + CHANGELOG.
* feat(inertia): add Dorsey Inertia (RVI + LinReg)
Donald Dorsey's Inertia — a LinearRegression smoothing of the RVI
series. Endpoint of an n-bar least-squares fit of RVI is the indicator
reading. Preserves trend direction while damping the ratio. Candle
input, two parameters (rvi_period, linreg_period) with defaults 14 / 20.
Touchpoints: inertia.rs + mod.rs + lib.rs re-export, PyInertia +
__init__.py + test_new_indicators CANDLE_SCALAR + test_known_values
constant reference, InertiaNode (4-column OHLC batch) + index.d.ts /
index.js + indicators.test.js factory + reference, WasmInertia,
candle-fuzz target, README + CHANGELOG.
* test(kst): Move KST out of MULTI dict (it is scalar-input)
KST sits in the MULTI dict (candle-input, multi-output) but its
update() takes a single f64, not a candle tuple. The shared streaming
loop in test_multi_streaming_matches_batch fed the OHLCV tuple in,
which crashed with `TypeError: argument 'value': must be real number,
not tuple` on every Python matrix entry.
Split into a new MULTI_SCALAR_INPUT dict with its own test function
that feeds the close-price stream as floats. KST is currently the
only such indicator; structure is ready for future scalar-input
multi-output additions (e.g. some MACD-shaped indicators).
* test(coverage): Cover SMI zero-range and ConnorsRsi zero-prev cold paths
codecov/patch on PR 40 flagged two uncovered defensive branches:
- SMI returns self.current early when the smoothed range collapses to
zero (`r2 <= 0.0`) so the formula stays defined. Exercised by feeding
bars where high == low.
- ConnorsRsi skips the ROC ring-buffer update when the previous price
is exactly zero so the divide-by-zero in `(input - prev) / prev` is
impossible. Exercised by seeding the first bar at 0.0.
* 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.
* chore(docs): rename benchmark CPU from 7950X3D to 9950X
The "Reproduced on" line in the umbrella + binding READMEs and the
benchmark page on the site listed the wrong AMD CPU. The benchmarks
were actually produced on a Ryzen 9 9950X, not a 7950X3D. Same
column for absolute µs values applies — the speedup ratios in the
tables are unchanged either way because they're relative across
libraries on the same machine.
The performance-regression issue template's CPU example also
updated for consistency (it was a generic placeholder, but matching
the canonical machine makes the example concrete).
* chore(npm): restore Windows ARM64 sub-package + napi matrix entry
npm Support unblocked the `wickra-win32-arm64-msvc` package name and
transferred write access to @kingchenc (placeholder 0.0.1-security
was published from their side; we ship our first real version on
top of that). This re-enables every change 8aa74cb temporarily
backed out for 0.2.1:
- bindings/node/package.json: re-add `aarch64-pc-windows-msvc` to
napi.triples.additional and `wickra-win32-arm64-msvc` to
optionalDependencies.
- bindings/node/npm/win32-arm64-msvc/package.json: restored — name,
cpu = arm64, os = win32, version pinned to the workspace.
- .github/workflows/release.yml: re-enable the
`windows-11-arm / aarch64-pc-windows-msvc` row in the node-build
matrix and drop the "temporarily skipped" comment block.
After the next tag-push this binding will be published alongside
the other five platforms and `npm install wickra` on Windows ARM64
will resolve to a native build instead of failing the loader's
optional-dep lookup.
* release: bump workspace + bindings to 0.2.7
Workspace, every binding (Python, Node, six platform stubs incl. the
restored win32-arm64-msvc), and the CHANGELOG all move together to
0.2.7. wickra-win32-arm64-msvc is now part of the standard publish
matrix and will land on npm alongside the other five binaries.
The 0.2.7 CHANGELOG entry consolidates the two changes this cycle:
- Windows ARM64 binding restored (npm Support unblocked the name).
- Benchmark CPU label corrected (Ryzen 9 9950X, not 7950X3D).
* fix(docs-rs): rename `doc_auto_cfg` to `doc_cfg` after Rust 1.92 merge
`doc_auto_cfg` was removed in Rust 1.92.0 and folded back into
`doc_cfg` (rust-lang/rust#138907). docs.rs builds with the latest
nightly and sets `--cfg docsrs`, so the previous
#![cfg_attr(docsrs, feature(doc_auto_cfg))]
aborts compilation with E0557 on every published 0.2.x. GitHub CI
never tripped this — stable rustc ignores the line because nothing
sets the `docsrs` cfg there.
Switch all three published library crates (`wickra`, `wickra-core`,
`wickra-data`) to the merged-into `doc_cfg` gate. Same intent, same
on-docs.rs output, builds again on nightly.
* docs(readme): float Wickra to the top of the comparison tables
Reorders the "Why Wickra exists" library-comparison table and the two
benchmark headers so Wickra is the first row (with a ★ marker) instead
of the last. The previous order placed Wickra at the bottom, which
buries the only row a reader landing on the README is here to compare
against. Same column data, same ★/winner annotations, just the row
order flipped and a ★ prefix on the Wickra label.
Mirrored across the umbrella README and every binding README so the
crates.io / PyPI / npm landing pages stay in sync.
* release: bump workspace + bindings to 0.2.6
Workspace, every binding (Python, Node, Node platform stubs), the
release.yml comment and the CHANGELOG all move together to 0.2.6 so
the next tagged release lines every artefact up.
0.2.6 carries two changes from the [0.2.6] CHANGELOG entry:
- fix(docs-rs): swap the now-removed `doc_auto_cfg` feature gate for
the merged-into `doc_cfg` so docs.rs nightly builds resume.
- docs(readme): float ★ Wickra to the top of every comparison table
across the umbrella + binding READMEs.
wickra-win32-arm64-msvc stays excluded for this release with the same
npm spam-filter rationale that held for 0.2.5.
Three separate README files (root, bindings/node, bindings/python) had
been drifting independently — each registry showed a different project
page, which is exactly the consistency debt I want to avoid.
Single source of truth: /README.md. The three binding READMEs are
overwritten with the root README content as a baseline, and release.yml
gets a one-line cp step right before every publishing call so future
edits to /README.md propagate automatically:
- python-wheels job: cp README.md bindings/python/README.md before
PyO3/maturin-action runs the wheel build
- python-sdist job: same, before the sdist build
- node-publish job: cp ../../README.md README.md (working-directory
bindings/node) before the main 'npm publish wickra'
- wasm-publish job: cp README.md bindings/wasm/README.md before
wasm-pack build (which copies the crate README into pkg/ on its own)
Cargo crates (wickra, wickra-core, wickra-data) already inherit
readme.workspace = true pointing at /README.md, so crates.io was already
correct — no change needed there.
The per-platform npm subpackages (bindings/node/npm/<target>/) keep
their tiny package.json with no README; they are install-time
optionalDependencies that the loader reads through, never user-facing
on the registry.
Effect: same README on github.com/kingchenc/wickra, crates.io/crates/wickra,
pypi.org/project/wickra, and npmjs.com/package/wickra. Will be live on
the registries with the next tag-push.
The rolling-window VWAP indicator (`wickra_core::RollingVwap`) was only
available in the Rust crate, even though the README's Volume-family
table already advertised "VWAP (cumulative + rolling)" as a cross-
language feature. Users on Python, Node or in the browser had to fall
back to the cumulative `VWAP` or re-implement the rolling variant
themselves.
This commit closes the gap end-to-end:
- Python: `wickra.RollingVWAP(period)` — same constructor / `update` /
`batch` / `reset` / `is_ready` / `warmup_period` surface as `VWAP`,
plus a `period` property and a typed `__repr__`. The `__init__.py`
re-exports it and `__all__` lists it; the `.pyi` stub matches.
- Node: `RollingVWAP(period)` — napi class with the same lifecycle,
exported from `index.js` and declared in `index.d.ts`.
- WASM: `RollingVWAP(period)` — wasm-bindgen class with the same
`Float64Array` I/O as `VWAP`.
Tests added:
- Python: `test_rolling_vwap_streaming_matches_batch` — exercises
`update == batch` plus the full lifecycle on the shared OHLC fixture.
- Node: `RollingVWAP` row in the `candleScalar` parity table — covered
by the generic streaming-vs-batch + lifecycle harness.
- WASM: dedicated `wasm-bindgen-test` mirrors the Python test.
The wiki page `Indicator-Vwap.md` drops the "Rust-only" caveat and
gains Python / Node / WASM examples.
Twelve WASM classes previously exposed only `batch()` (and not even
`reset()` for ten of them): ADX, WilliamsR, CCI, MFI, PSAR, Keltner,
Donchian, VWAP, AwesomeOscillator, Aroon, Stochastic, OBV. Browser
consumers wanting per-tick updates had to replay `batch()` on every new
candle — the opposite of the library's streaming-first promise.
Each class now exposes:
- `update(...)` — per-tick streaming update with the same column inputs
as `batch()`. Single-output indicators return `Option<f64>`. Multi-
output indicators (ADX, Keltner, Donchian, Aroon, Stochastic) return a
named JS object (`{ plusDi, minusDi, adx }`, `{ upper, middle, lower }`,
`{ up, down }`, `{ k, d }`) once warm, or `null` during warmup. This
matches the existing `SuperTrend` convention so JS code can treat all
multi-output WASM indicators uniformly.
- `reset()`, `isReady()`, `warmupPeriod()` — bring the lifecycle API to
full parity with Python and Node.
`WasmKama` also gains the previously missing `warmupPeriod()` (R8). A
single new `wasm-bindgen-test` exercises every newly wired class against
a deterministic 40-bar synthetic OHLCV stream, asserting that
streaming `update` matches `batch` value-by-value and that the lifecycle
contract behaves the same as the core indicator.
Finish the per-language `examples/<lang>/` restructure by relocating the
WASM browser demo from bindings/wasm/examples/ to examples/wasm/.
* `examples/wasm/index.html` is the moved file; its WASM module import
becomes `../../bindings/wasm/pkg/wickra_wasm.js` so the demo still loads
the wasm-pack output without copying it.
* bindings/wasm/README.md, Quickstart-WASM.md, examples/README.md and the
root README "Languages" + project-layout block all point at the new
path. The serve command in the docs now says "serve the repository root
and open examples/wasm/index.html".
`bindings/wasm/examples/` is empty after the move; the now-empty
directory is removed.
Second half of the eight indicators that fill out the new family taxonomy.
- Rust core: true_range.rs (TrueRange — the raw single-bar volatility ATR
averages), chaikin_volatility.rs (ChaikinVolatility — rate of change of a
smoothed high-low spread), z_score.rs (ZScore — price normalised against
its rolling mean and standard deviation) and linreg_angle.rs (LinRegAngle
— the rolling regression slope as a degree angle). Each with a full
Indicator impl, runnable doctest and reference / property / warmup /
reset / batch==streaming tests.
- Python / Node / WASM: classes wired through all three bindings (ZScore
and LinRegAngle ride the scalar macros where possible) plus .pyi stubs
and __init__.py / __all__ entries.
- Wiki: four new Indicator-*.md pages.
The eight-family taxonomy restructure (Overview / Home / README / folder
layout) lands next in F13c.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests,
25 data tests and 74 doctests green.
First half of the eight indicators that fill out the new family taxonomy.
- Rust core: accelerator_oscillator.rs (AcceleratorOscillator — AO minus a
short SMA of itself), balance_of_power.rs (BalanceOfPower — per-bar
(close-open)/(high-low)), choppiness_index.rs (ChoppinessIndex — summed
true range over the high-low span, log-scaled) and
vertical_horizontal_filter.rs (VerticalHorizontalFilter — net move over
total move). Each with a full Indicator impl, runnable doctest and
reference / property / warmup / reset / batch==streaming tests.
- Python / Node / WASM: classes wired through all three bindings
(BalanceOfPower carries an explicit open column; VHF rides the scalar
macros) plus .pyi stubs and __init__.py / __all__ entries.
- Wiki: four new Indicator-*.md pages.
The eight-family taxonomy restructure (Overview / Home / README / folder
layout) lands in F13c once F13b's four indicators are in.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 481 core tests,
25 data tests and 70 doctests green.
- Rust core: cmf.rs (Chaikin Money Flow — summed money-flow volume over
summed volume, bounded to [-1, +1]), chaikin_oscillator.rs (Chaikin
Oscillator — the MACD of the ADL, EMA(ADL, fast) - EMA(ADL, slow)),
force_index.rs (Elder's Force Index — EMA of price change scaled by
volume), ease_of_movement.rs (Arms' Ease of Movement — SMA of distance
travelled per unit of volume). Each with a full Indicator impl,
runnable doctest and reference / property / warmup / reset /
batch==streaming tests.
- Python: PyChaikinMoneyFlow / PyChaikinOscillator / PyForceIndex /
PyEaseOfMovement PyO3 classes + module registration + .pyi stubs.
- Node: explicit ChaikinMoneyFlowNode / ChaikinOscillatorNode /
ForceIndexNode / EaseOfMovementNode; index.d.ts and index.js updated.
- WASM: WasmChaikinMoneyFlow / WasmChaikinOscillator / WasmForceIndex /
WasmEaseOfMovement.
- Wiki: Indicator-ChaikinMoneyFlow/ChaikinOscillator/ForceIndex/
EaseOfMovement.md plus a new "Oscillators" sub-table in
Indicators-Overview.md and entries in Home.md.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 402 core tests,
25 data tests and 57 doctests green.
Completes the F9 family (Cumulative volume) end to end:
- Rust core: adl.rs (Accumulation/Distribution Line — cumulative
range-weighted volume) and vpt.rs (Volume-Price Trend — cumulative
volume scaled by percentage price change). Each with a full Indicator
impl, runnable doctest and reference / cumulative-property / warmup /
reset / batch==streaming tests.
- Python: PyAdl / PyVolumePriceTrend PyO3 classes + module registration
+ .pyi stubs (no parameters, like OBV/VWAP).
- Node: explicit AdlNode and VolumePriceTrendNode; index.d.ts and
index.js updated.
- WASM: WasmAdl and WasmVolumePriceTrend.
- Wiki: Indicator-Adl.md and Indicator-VolumePriceTrend.md plus rows in
Indicators-Overview.md and entries in Home.md.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 373 core tests,
25 data tests and 53 doctests green.
Completes the F7 family (Volatility) end to end:
- Rust core: natr.rs (ATR as a percentage of close), std_dev.rs
(rolling population standard deviation), ulcer_index.rs (RMS of
trailing-high drawdowns — downside-only risk), historical_volatility.rs
(annualised sample stddev of log returns). Each with a full Indicator
impl, runnable doctest and reference / constant-series / warmup /
reset / batch==streaming tests.
- Python: PyNatr / PyStdDev / PyUlcerIndex / PyHistoricalVolatility
PyO3 classes + module registration + .pyi stubs.
- Node: StdDevNode / UlcerIndexNode via the scalar macro, explicit
NatrNode and HistoricalVolatilityNode; index.d.ts and index.js updated.
- WASM: WasmStdDev / WasmUlcerIndex / WasmHistoricalVolatility via the
scalar macro, explicit WasmNatr.
- Wiki: Indicator-Natr/StdDev/UlcerIndex/HistoricalVolatility.md plus
rows in Indicators-Overview.md and entries in Home.md.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 350 core tests,
25 data tests and 49 doctests green.
Completes the F5 family (Price oscillators) end to end:
- Rust core: ppo.rs (Percentage Price Oscillator — MACD as a percentage
of the slow EMA), dpo.rs (Detrended Price Oscillator — shifted price
minus its SMA), coppock.rs (Coppock Curve — WMA of two summed ROCs).
Each with a full Indicator impl, runnable doctest and reference /
constant-series / warmup / reset / batch==streaming / non-finite tests.
- Python: PyPpo / PyDpo / PyCoppock PyO3 classes + module registration
+ .pyi stubs (defaults PPO=(12,26), DPO=20, Coppock=(14,11,10)).
- Node: DpoNode via the scalar macro, explicit PpoNode and CoppockNode;
index.d.ts and index.js updated.
- WASM: WasmDpo / WasmPpo / WasmCoppock via the scalar macro.
- Wiki: Indicator-Ppo/Dpo/Coppock.md plus rows in Indicators-Overview.md
and entries in Home.md.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 300 core tests,
25 data tests and 42 doctests green.
Completes the F4 family (Stochastic oscillators) end to end:
- Rust core: stoch_rsi.rs (Stochastic Oscillator applied to the RSI
series, bounded [0,100]) and ultimate_oscillator.rs (Larry Williams'
weighted three-timeframe buying-pressure oscillator). Each with a full
Indicator impl, runnable doctest and reference / saturation / bounds /
warmup / reset / batch==streaming tests.
- Python: PyStochRsi / PyUltimateOscillator PyO3 classes + module
registration + .pyi stubs (defaults StochRSI=(14,14), UO=(7,14,28)).
- Node: explicit StochRsiNode and UltimateOscillatorNode; index.d.ts
and index.js updated.
- WASM: WasmStochRsi via the scalar macro, explicit
WasmUltimateOscillator.
- Wiki: Indicator-StochRsi.md and Indicator-UltimateOscillator.md plus
rows in Indicators-Overview.md and entries in Home.md.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 278 core tests,
25 data tests and 39 doctests green.
Completes the F2 family (Advanced MAs) end to end:
- Rust core: zlema.rs (Zero-Lag EMA over the de-lagged series
2·price − price[lag]), t3.rs (Tillson's six-EMA cascade with the
volume-factor polynomial), vwma.rs (volume-weighted rolling mean with
a zero-volume fallback to the unweighted mean). Each with a full
Indicator impl, runnable doctest and reference-value / warmup /
reset / batch==streaming / non-finite tests.
- Python: PyZlema / PyT3 / PyVwma PyO3 classes + module registration
+ .pyi stubs (T3 defaults v=0.7).
- Node: ZlemaNode via the scalar macro, explicit T3Node and VwmaNode
classes; index.d.ts and index.js updated.
- WASM: WasmZlema / WasmT3 via the scalar macro, explicit WasmVwma.
- Wiki: Indicator-Zlema.md, Indicator-T3.md, Indicator-Vwma.md plus
rows in Indicators-Overview.md and entries in Home.md.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 232 core tests,
25 data tests and 33 doctests green.
Completes the F1 family (Simple & Weighted MAs). The Rust core for both
SMMA (Wilder's RMA) and TRIMA (triangular MA) already landed; this adds
the remaining Definition-of-Done steps:
- Python: PySmma / PyTrima PyO3 classes + module registration + .pyi stubs.
- Node: SmmaNode / TrimaNode via the scalar-indicator macro; index.d.ts
and index.js updated for the two new classes.
- WASM: WasmSmma / WasmTrima via the scalar-indicator macro.
- Wiki: Indicator-Smma.md and Indicator-Trima.md (full pages) plus rows
in Indicators-Overview.md and entries in Home.md.
cargo fmt + clippy (core/wickra/data/wasm/node) clean; 208 core tests,
25 data tests and 31 doctests green.
The WASM binding's test module (added in B6) used `.ok().expect(...)`
on the Result-returning constructors. clippy's ok_expect lint rejects
this under the workspace's `-D warnings`, and the CI rust job lints
wickra-wasm with --all-targets — so the branch would fail CI.
Replace all seven `.ok().expect(...)` with `.expect(...)` directly;
JsError implements Debug, so this compiles and gives a better panic
message. clippy and fmt are now clean for wickra-wasm.
Normalises whitespace in sources committed earlier in this branch
before rustfmt was run over them (Node/WASM bindings, and three core
indicator test modules), and records the wasm-bindgen-test dependency
tree added in B6 into Cargo.lock. No functional change; cargo fmt --all
--check is now clean.
The WASM binding had no tests; CI only checked that artefacts existed.
Adds a wasm-bindgen-test suite covering SMA reference values, EMA
batch==streaming equivalence, RSI pure-uptrend behaviour, fallible
constructors returning JsError, and the unequal-length batch guards from
B3. Wires wasm-pack test --node into the CI wasm job. The suite
type-checks on the host; it executes under wasm-pack in CI (the local
environment is a non-rustup Rust install without the wasm32 target).
Candle batch() methods that index parallel high/low/close/volume arrays
without first checking their lengths panic on a length mismatch. Adds an
equal-length guard returning a clean error to the 11 affected Node
methods (Stochastic, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian,
VWAP, AO, Aroon), the 10 affected WASM methods, and the 8 affected
Python methods (WilliamsR, ADX, MFI, PSAR, Keltner, VWAP, AO, Aroon) --
matching the guard ATR/OBV already had. Verified in Node: mismatched
arrays now throw instead of crashing.
On every v* tag push the release workflow now publishes the project to
all three public registries in parallel:
- crates.io: wickra-core then wickra-data then wickra, with a 45-second
sleep between each so the registry index can refresh before the next
publish step asserts the previous version is available.
- PyPI: maturin-action builds wheels for Linux x86_64 + aarch64, macOS
x86_64 + aarch64, and Windows x64, plus an sdist; all artefacts upload
to PyPI via MATURIN_PYPI_TOKEN.
- npm: napi-rs builds a native binary per platform (linux-x64-gnu,
darwin-x64, darwin-arm64, win32-x64-msvc), publishes each as its own
platform package, then publishes the main `wickra` meta-package
that resolves to the right platform at install time.
- WASM: wasm-pack builds the bundler-target package and publishes it as
`wickra-wasm` on npm, with the auto-generated package.json enriched
with the author/repo/license metadata.
Package metadata was unified so all targets point at kingchenc/wickra
on GitHub; the Node binding is now plain `wickra` (the @wickra/ scope
was unnecessary because the bare name was free). README install table
updated to match.
Two unrelated failures in the post-release CI run:
- Python jobs: `maturin develop` requires an activated virtualenv on
CI runners that don't have a system Python set up that way. Switch
to `maturin build --release --out dist` followed by `pip install
--find-links dist`, which is venv-agnostic and OS-portable.
- WASM job: the wasm-opt bundled with wasm-pack is older than the WASM
features rustc 1.92 enables by default. The fix is to opt every feature
in explicitly via the package metadata — reference-types, multivalue,
bulk-memory, sign-ext, mutable-globals, nontrapping-float-to-int — so
wasm-opt accepts the input.
Same code as before; only the build steps and the wasm-opt config changed.
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
kingchenc