Add B9 Price Statistics deepening (5 indicators) (#197)
Deepens the **Price Statistics** family (B9) with five rolling-statistics indicators (447 -> 452): - **ShannonEntropy** — Shannon entropy of a binned rolling value distribution. - **SampleEntropy** — Richman-Moorman sample entropy (regularity/complexity of a window). - **KendallTau** — Kendall rank correlation (tau-b) over paired observations (pairwise; distinct from Pearson/Spearman). - **JarqueBera** — Jarque-Bera normality test statistic over a rolling window. - **RollingMinMaxScaler** — maps the latest value to 0..1 over a rolling window. All scalar f64 input except KendallTau (pairwise). Multi-arg scalars (Shannon/Sample entropy) use hand-written Python/Node bindings + the variadic wasm macro; KendallTau uses the pair macros. Verified locally: 3668 core lib + 410 doc tests, clippy clean, 527 node tests, 871 pytest, counter 452.
This commit is contained in:
kingchenc
@@ -3700,6 +3700,102 @@ impl PyTimeBasedStop {
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}
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}
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// ============================== JarqueBera ==============================
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#[pyclass(name = "JARQUEBERA", module = "wickra._wickra", skip_from_py_object)]
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#[derive(Clone)]
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struct PyJarqueBera {
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inner: wc::JarqueBera,
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}
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#[pymethods]
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impl PyJarqueBera {
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#[new]
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#[pyo3(signature = (period=20))]
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fn new(period: usize) -> PyResult<Self> {
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Ok(Self {
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inner: wc::JarqueBera::new(period).map_err(map_err)?,
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})
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}
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fn update(&mut self, value: f64) -> Option<f64> {
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self.inner.update(value)
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}
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fn batch<'py>(
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&mut self,
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py: Python<'py>,
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prices: PyReadonlyArray1<'py, f64>,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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let s = prices
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.as_slice()
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.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
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Ok(flatten(self.inner.batch(s)).into_pyarray(py))
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}
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#[getter]
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fn period(&self) -> usize {
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self.inner.period()
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}
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fn reset(&mut self) {
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self.inner.reset();
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}
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fn is_ready(&self) -> bool {
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self.inner.is_ready()
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}
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fn warmup_period(&self) -> usize {
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self.inner.warmup_period()
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}
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fn __repr__(&self) -> String {
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format!("JARQUEBERA(period={})", self.inner.period())
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}
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}
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// ============================== RollingMinMaxScaler ==============================
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#[pyclass(name = "ROLLINGMINMAX", module = "wickra._wickra", skip_from_py_object)]
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#[derive(Clone)]
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struct PyRollingMinMaxScaler {
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inner: wc::RollingMinMaxScaler,
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}
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#[pymethods]
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impl PyRollingMinMaxScaler {
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#[new]
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#[pyo3(signature = (period=20))]
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fn new(period: usize) -> PyResult<Self> {
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Ok(Self {
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inner: wc::RollingMinMaxScaler::new(period).map_err(map_err)?,
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})
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}
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fn update(&mut self, value: f64) -> Option<f64> {
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self.inner.update(value)
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}
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fn batch<'py>(
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&mut self,
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py: Python<'py>,
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prices: PyReadonlyArray1<'py, f64>,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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let s = prices
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.as_slice()
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.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
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Ok(flatten(self.inner.batch(s)).into_pyarray(py))
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}
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#[getter]
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fn period(&self) -> usize {
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self.inner.period()
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}
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fn reset(&mut self) {
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self.inner.reset();
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}
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fn is_ready(&self) -> bool {
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self.inner.is_ready()
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}
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fn warmup_period(&self) -> usize {
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self.inner.warmup_period()
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}
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fn __repr__(&self) -> String {
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format!("ROLLINGMINMAX(period={})", self.inner.period())
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}
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}
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// ============================== Stochastic ==============================
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#[pyclass(name = "IMI", module = "wickra._wickra", skip_from_py_object)]
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@@ -22735,6 +22831,176 @@ impl PyVolumeWeightedMacd {
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}
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}
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// ============================== Shannon Entropy ==============================
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#[pyclass(name = "SHANNONENT", module = "wickra._wickra", skip_from_py_object)]
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#[derive(Clone)]
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struct PyShannonEntropy {
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inner: wc::ShannonEntropy,
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}
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#[pymethods]
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impl PyShannonEntropy {
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#[new]
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#[pyo3(signature = (period=20, bins=8))]
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fn new(period: usize, bins: usize) -> PyResult<Self> {
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Ok(Self {
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inner: wc::ShannonEntropy::new(period, bins).map_err(map_err)?,
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})
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}
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fn update(&mut self, value: f64) -> Option<f64> {
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self.inner.update(value)
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}
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fn batch<'py>(
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&mut self,
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py: Python<'py>,
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prices: PyReadonlyArray1<'py, f64>,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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let slice = prices
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.as_slice()
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.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
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Ok(flatten(self.inner.batch(slice)).into_pyarray(py))
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}
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#[getter]
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fn params(&self) -> (usize, usize) {
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self.inner.params()
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}
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#[getter]
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fn value(&self) -> Option<f64> {
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self.inner.value()
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}
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fn reset(&mut self) {
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self.inner.reset();
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}
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fn is_ready(&self) -> bool {
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self.inner.is_ready()
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}
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fn warmup_period(&self) -> usize {
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self.inner.warmup_period()
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}
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fn __repr__(&self) -> String {
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let (period, bins) = self.inner.params();
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format!("SHANNONENT(period={period}, bins={bins})")
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}
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}
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// ============================== Sample Entropy ==============================
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#[pyclass(name = "SAMPLEENT", module = "wickra._wickra", skip_from_py_object)]
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#[derive(Clone)]
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struct PySampleEntropy {
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inner: wc::SampleEntropy,
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}
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#[pymethods]
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impl PySampleEntropy {
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#[new]
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#[pyo3(signature = (period=20, m=2, r_factor=0.2))]
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fn new(period: usize, m: usize, r_factor: f64) -> PyResult<Self> {
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Ok(Self {
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inner: wc::SampleEntropy::new(period, m, r_factor).map_err(map_err)?,
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})
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}
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fn update(&mut self, value: f64) -> Option<f64> {
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self.inner.update(value)
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}
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fn batch<'py>(
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&mut self,
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py: Python<'py>,
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prices: PyReadonlyArray1<'py, f64>,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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let slice = prices
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.as_slice()
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.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
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Ok(flatten(self.inner.batch(slice)).into_pyarray(py))
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}
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#[getter]
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fn params(&self) -> (usize, usize, f64) {
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self.inner.params()
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}
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#[getter]
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fn value(&self) -> Option<f64> {
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self.inner.value()
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}
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fn reset(&mut self) {
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self.inner.reset();
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}
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fn is_ready(&self) -> bool {
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self.inner.is_ready()
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}
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fn warmup_period(&self) -> usize {
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self.inner.warmup_period()
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}
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fn __repr__(&self) -> String {
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let (period, m, r_factor) = self.inner.params();
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format!("SAMPLEENT(period={period}, m={m}, r_factor={r_factor})")
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}
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}
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// ============================== Kendall Tau ==============================
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#[pyclass(name = "KendallTau", module = "wickra._wickra", skip_from_py_object)]
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#[derive(Clone)]
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struct PyKendallTau {
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inner: wc::KendallTau,
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}
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#[pymethods]
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impl PyKendallTau {
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#[new]
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#[pyo3(signature = (period=20))]
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fn new(period: usize) -> PyResult<Self> {
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Ok(Self {
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inner: wc::KendallTau::new(period).map_err(map_err)?,
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})
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}
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fn update(&mut self, x: f64, y: f64) -> Option<f64> {
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self.inner.update((x, y))
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}
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/// Batch over two equally-sized numpy arrays.
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fn batch<'py>(
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&mut self,
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py: Python<'py>,
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x: PyReadonlyArray1<'py, f64>,
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y: PyReadonlyArray1<'py, f64>,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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let xs = x
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.as_slice()
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.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
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let ys = y
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.as_slice()
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.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
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if xs.len() != ys.len() {
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return Err(PyValueError::new_err("x and y must be equal length"));
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}
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let mut out = Vec::with_capacity(xs.len());
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for i in 0..xs.len() {
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out.push(self.inner.update((xs[i], ys[i])).unwrap_or(f64::NAN));
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}
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Ok(out.into_pyarray(py))
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}
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#[getter]
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fn period(&self) -> usize {
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self.inner.period()
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}
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#[getter]
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fn value(&self) -> Option<f64> {
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self.inner.value()
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}
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fn reset(&mut self) {
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self.inner.reset();
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}
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fn is_ready(&self) -> bool {
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self.inner.is_ready()
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}
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fn warmup_period(&self) -> usize {
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self.inner.warmup_period()
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}
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fn __repr__(&self) -> String {
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format!("KendallTau(period={})", self.inner.period())
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}
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}
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#[pymodule]
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#[allow(clippy::too_many_lines)]
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fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
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@@ -23198,5 +23464,10 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
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m.add_class::<PyIntradayIntensity>()?;
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m.add_class::<PyBetterVolume>()?;
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m.add_class::<PyVolumeWeightedMacd>()?;
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m.add_class::<PyShannonEntropy>()?;
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m.add_class::<PySampleEntropy>()?;
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m.add_class::<PyKendallTau>()?;
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m.add_class::<PyJarqueBera>()?;
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m.add_class::<PyRollingMinMaxScaler>()?;
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Ok(())
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}
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