* 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.
R13 — `HistoricalVolatility::update` previously substituted `0.0` for
the log-return whenever `prev <= 0` or `input <= 0`. The log-return is
undefined there, and silently treating bad ticks as "no movement"
underreports realised volatility on broken data feeds. The fix skips
non-positive prices entirely: `self.last` is returned, state is left
untouched, and the next real tick re-anchors against the previous
*valid* `prev_price`. This matches how every other indicator handles
invalid inputs (SMA / EMA / ROC / Bollinger).
A new test `skips_non_positive_prices` proves the invariant: after a
warmed-up indicator, two consecutive bad ticks (`-5.0` and `0.0`) must
return the baseline value, and a subsequent real positive tick must
produce the same output as a control indicator that simply never saw
the bad ticks.
R14 — `Tick::new` previously returned `Error::InvalidCandle` for
negative volume. A tick is not a candle; downstream tick-stream
pipelines should be able to match on a semantically-correct error. A
new `Error::InvalidTick { message }` variant is added; the existing
test is updated to assert against it. Python's `map_err` is extended
to forward the new variant as `PyValueError`; the Node and WASM
bindings format via `Error::to_string()` and pick the new variant up
automatically without source changes.
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.
Bumps the Python binding from pyo3 0.22 / numpy 0.22 to 0.28 / 0.28,
which resolves RUSTSEC-2025-0020 — a buffer overflow in
`PyString::from_object` that affected every published Python wheel.
Migration:
- `into_pyarray_bound(py)` → `into_pyarray(py)` (numpy 0.23 dropped the
`_bound` transitional suffix; the method now returns `Bound<'py, _>`
directly).
- `downcast::<PyDict>` → `cast::<PyDict>` (pyo3 renamed the method on
`PyAnyMethods`).
- Every `#[pyclass]` declares `skip_from_py_object` to opt out of the
now-deprecated automatic `FromPyObject` derive for `Clone` types.
Indicators are stateful — silently extracting them by value-clone is
never the intended FFI semantics.
- Workspace clippy gains `unused_self = "allow"` on the python crate
only: Python's `__repr__` protocol forces `&self` even for parameter-
less indicators where the body does not read state.
- `map_err` arms collapsed into a single `PyValueError` arm
(clippy::match_same_arms).
`deny.toml` no longer suppresses RUSTSEC-2025-0020; `cargo deny check`
is green on advisories, bans, licenses and sources without exceptions.
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.
Every Python batch() did prices.as_slice().expect("contiguous"), so a
non-contiguous NumPy input (e.g. a strided view) aborted with a Rust
panic instead of a catchable exception. as_slice() failures now map to a
PyValueError pointing at np.ascontiguousarray; the scalar / MACD /
Bollinger batch methods that returned a bare array were lifted to
PyResult so the error can propagate. Adds input-validation tests
(non-contiguous arrays, unequal-length candle batches, ROC/TRIX
defaults). All 60 Python tests pass against the freshly built wheel.
Every other Python momentum indicator (RSI, CCI, ...) carries a
#[pyo3(signature)] default, but ROC and TRIX required an explicit
period. Both now default to the TA-Lib convention (ROC period=10, TRIX
period=30), and the .pyi stubs reflect the defaults. Node and WASM
constructors deliberately stay explicit-only -- napi-rs and
wasm-bindgen do not support default arguments, and every constructor in
those bindings is uniformly explicit.
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.
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