* 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.
The module doc-comment in bindings/node/src/lib.rs still pointed at
@wickra/wickra. The package is published as bare "wickra" — every
README, Quickstart and example already uses that name. Align the
inline doc with the published name.
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.
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.
MacdNode.batch and BollingerNode.batch return flat interleaved arrays
(3*n and 4*n) but index.d.ts only said Array<number>. Adds /// doc
comments describing the layout; napi-rs now propagates them into the
generated index.d.ts as JSDoc.
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.
KAMA and the nine candle indicators (CCI, WilliamsR, MFI, PSAR,
Keltner, Donchian, VWAP, AO, Aroon) exposed none of the three lifecycle
methods; Stochastic/OBV/ADX exposed only reset; MACD/Bollinger/ATR
lacked warmupPeriod. Every indicator now exposes reset(), isReady() and
warmupPeriod(), matching the scalar-macro surface. Verified against the
rebuilt module (e.g. MACD warmupPeriod 34, Stochastic 16, KAMA 11).
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.
The 14 candle and multi-parameter indicator constructors passed raw
parameters through must() (an expect()), so invalid arguments such as
new MACD(0,0,0) or new BollingerBands(20,-1) aborted the whole process
over the napi boundary (the release profile sets panic=abort).
napi-rs 2.16 does accept a #[napi(constructor)] returning
napi::Result<Self> (the old must() comment was wrong), so each now
returns napi::Result<Self> and throws a clean JS error via map_err.
must()/clamp_period stay for the scalar macro, where period is clamped
and the Result is provably Ok. Verified: every invalid constructor
throws, valid ones still build.
Stochastic, OBV, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian,
VWAP, AwesomeOscillator and Aroon previously exposed only batch() in the
Node binding, so a streaming-first library could not actually stream
them from Node. Each now has an update() mirroring the AtrNode pattern,
with the same input arity and output type as its batch() counterpart.
Verified against the rebuilt native module.
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