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feat: add DeMark deepening (B12, 7 indicators) (#204)

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B12 of the family-deepening roadmap — seven Tom DeMark indicators (467 -> 474).

**Candle -> +1/0 qualifier patterns (candlestick macro bindings):**
- **TD Camouflage** — hidden intrabar strength/weakness against the prior close.
- **TD Clop** — two-bar open/close engulfing reversal.
- **TD Clopwin** — the inside-body cousin of TD Clop (compression bar).
- **TD Propulsion** — continuation thrust closing beyond the prior extreme.
- **TD Trap** — inside ("trap") bar followed by a range breakout.

**Hand-bound:**
- **TD D-Wave** — streaming Elliott-style 1-5 / A-C swing-wave counter (candle -> f64, `strength` param).
- **TD Moving Averages** — ST1/ST2 median-price trend ribbon (candle -> struct {st1, st2}).

All seven join the existing **DeMark** family. Patterns follow the house-style
+1/0 candle-pattern convention (neutral 0.0 during warmup). Public binding names
use the family-consistent `TD...` casing.

Wiring complete across core, Python, Node, WASM, fuzz, tests, README + docs
counter (474) and CHANGELOG. Verified: core 3874 + doc 427, clippy clean,
node 549, python 903.
This commit is contained in:
kingchenc
2026-06-08 01:12:46 +02:00
committed by GitHub
parent ed01604a18
commit 8431b1400c
21 changed files with 1899 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files
bindings/python/python/wickra/__init__.py
+14
View File
@@ -322,6 +322,13 @@ from ._wickra import (
WilliamsFractals,
ZigZag,
# DeMark
TDMovingAverage,
TDDWave,
TDTrap,
TDPropulsion,
TDClopwin,
TDClop,
TDCamouflage,
TDSetup,
TDSequential,
TDDeMarker,
@@ -819,6 +826,13 @@ __all__ = [
"WilliamsFractals",
"ZigZag",
# DeMark
"TDMovingAverage",
"TDDWave",
"TDTrap",
"TDPropulsion",
"TDClopwin",
"TDClop",
"TDCamouflage",
"TDSetup",
"TDSequential",
"TDDeMarker",
bindings/python/src/lib.rs
+137
View File
@@ -14086,6 +14086,131 @@ impl PyTdCombo {
}
}
// ============================== TD D-Wave ==============================
#[pyclass(name = "TDDWave", module = "wickra._wickra", skip_from_py_object)]
#[derive(Clone)]
struct PyTdDWave {
inner: wc::TdDWave,
}
#[pymethods]
impl PyTdDWave {
#[new]
#[pyo3(signature = (strength=2))]
fn new(strength: usize) -> PyResult<Self> {
Ok(Self {
inner: wc::TdDWave::new(strength).map_err(map_err)?,
})
}
fn update(&mut self, candle: &Bound<'_, PyAny>) -> PyResult<Option<f64>> {
let c = extract_candle(candle)?;
Ok(self.inner.update(c))
}
fn batch<'py>(
&mut self,
py: Python<'py>,
high: PyReadonlyArray1<'py, f64>,
low: PyReadonlyArray1<'py, f64>,
close: PyReadonlyArray1<'py, f64>,
) -> PyResult<Bound<'py, PyArray1<f64>>> {
let h = high
.as_slice()
.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
let l = low
.as_slice()
.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
let c = close
.as_slice()
.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
if h.len() != l.len() || l.len() != c.len() {
return Err(PyValueError::new_err(
"high, low, close must be equal length",
));
}
let mut out = Vec::with_capacity(h.len());
for i in 0..h.len() {
let candle = wc::Candle::new(c[i], h[i], l[i], c[i], 0.0, 0).map_err(map_err)?;
out.push(self.inner.update(candle).unwrap_or(f64::NAN));
}
Ok(out.into_pyarray(py))
}
fn reset(&mut self) {
self.inner.reset();
}
fn is_ready(&self) -> bool {
self.inner.is_ready()
}
fn warmup_period(&self) -> usize {
self.inner.warmup_period()
}
}
// ============================== TD Moving Averages ==============================
#[pyclass(
name = "TDMovingAverage",
module = "wickra._wickra",
skip_from_py_object
)]
#[derive(Clone)]
struct PyTdMovingAverage {
inner: wc::TdMovingAverage,
}
#[pymethods]
impl PyTdMovingAverage {
#[new]
#[pyo3(signature = (period_st1=5, period_st2=13))]
fn new(period_st1: usize, period_st2: usize) -> PyResult<Self> {
Ok(Self {
inner: wc::TdMovingAverage::new(period_st1, period_st2).map_err(map_err)?,
})
}
/// Returns `(st1, st2)`.
fn update(&mut self, candle: &Bound<'_, PyAny>) -> PyResult<Option<(f64, f64)>> {
let c = extract_candle(candle)?;
Ok(self.inner.update(c).map(|o| (o.st1, o.st2)))
}
fn batch<'py>(
&mut self,
py: Python<'py>,
high: PyReadonlyArray1<'py, f64>,
low: PyReadonlyArray1<'py, f64>,
) -> PyResult<Bound<'py, PyArray2<f64>>> {
let h = high
.as_slice()
.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
let l = low
.as_slice()
.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
if h.len() != l.len() {
return Err(PyValueError::new_err("high, low must be equal length"));
}
let n = h.len();
let mut out = vec![f64::NAN; n * 2];
for i in 0..n {
let candle = wc::Candle::new(l[i], h[i], l[i], l[i], 0.0, 0).map_err(map_err)?;
if let Some(o) = self.inner.update(candle) {
out[i * 2] = o.st1;
out[i * 2 + 1] = o.st2;
}
}
Ok(numpy::ndarray::Array2::from_shape_vec((n, 2), out)
.expect("shape consistent")
.into_pyarray(py))
}
fn reset(&mut self) {
self.inner.reset();
}
fn is_ready(&self) -> bool {
self.inner.is_ready()
}
fn warmup_period(&self) -> usize {
self.inner.warmup_period()
}
}
// ============================== TD Countdown ==============================
#[pyclass(name = "TDCountdown", module = "wickra._wickra", skip_from_py_object)]
@@ -17703,6 +17828,11 @@ candle_pattern_no_param!(PyCrab, wc::Crab, "Crab");
candle_pattern_no_param!(PyShark, wc::Shark, "Shark");
candle_pattern_no_param!(PyCypher, wc::Cypher, "Cypher");
candle_pattern_no_param!(PyThreeDrives, wc::ThreeDrives, "ThreeDrives");
candle_pattern_no_param!(PyTdCamouflage, wc::TdCamouflage, "TDCamouflage");
candle_pattern_no_param!(PyTdClop, wc::TdClop, "TDClop");
candle_pattern_no_param!(PyTdClopwin, wc::TdClopwin, "TDClopwin");
candle_pattern_no_param!(PyTdPropulsion, wc::TdPropulsion, "TDPropulsion");
candle_pattern_no_param!(PyTdTrap, wc::TdTrap, "TDTrap");
// ============================== Microstructure: Order Book ==============================
//
// Order-book indicators consume a depth snapshot rather than OHLCV. Streaming
@@ -24028,6 +24158,8 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
m.add_class::<PyTdRei>()?;
m.add_class::<PyTdPressure>()?;
m.add_class::<PyTdCombo>()?;
m.add_class::<PyTdDWave>()?;
m.add_class::<PyTdMovingAverage>()?;
m.add_class::<PyTdCountdown>()?;
m.add_class::<PyTdLines>()?;
m.add_class::<PyTdRangeProjection>()?;
@@ -24336,5 +24468,10 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
m.add_class::<PyAdaptiveRsi>()?;
m.add_class::<PyUniversalOscillator>()?;
m.add_class::<PyAdaptiveCci>()?;
m.add_class::<PyTdCamouflage>()?;
m.add_class::<PyTdClop>()?;
m.add_class::<PyTdClopwin>()?;
m.add_class::<PyTdPropulsion>()?;
m.add_class::<PyTdTrap>()?;
Ok(())
}
bindings/python/tests/test_new_indicators.py
+61
View File
@@ -382,6 +382,30 @@ def test_relative_strength_streaming_matches_batch():
# 6-tuple candle; the batch helper takes only the columns it needs.
CANDLE_SCALAR = {
"TDDWave": (
lambda: ta.TDDWave(2),
lambda ind, h, l, c, v: ind.batch(h, l, c),
),
"TDTrap": (
lambda: ta.TDTrap(),
lambda ind, h, l, c, v: ind.batch(c, h, l, c),
),
"TDPropulsion": (
lambda: ta.TDPropulsion(),
lambda ind, h, l, c, v: ind.batch(c, h, l, c),
),
"TDClopwin": (
lambda: ta.TDClopwin(),
lambda ind, h, l, c, v: ind.batch(c, h, l, c),
),
"TDClop": (
lambda: ta.TDClop(),
lambda ind, h, l, c, v: ind.batch(c, h, l, c),
),
"TDCamouflage": (
lambda: ta.TDCamouflage(),
lambda ind, h, l, c, v: ind.batch(c, h, l, c),
),
"PivotReversal": (
lambda: ta.PivotReversal(1, 1),
lambda ind, h, l, c, v: ind.batch(h, l, c),
@@ -952,6 +976,11 @@ def test_candle_scalar_streaming_matches_batch(name, ohlcv):
# --- Candle-input, multi-output indicators --------------------------------
MULTI = {
"TDMovingAverage": (
lambda: ta.TDMovingAverage(5, 13),
lambda ind, h, l, c, v: ind.batch(h, l),
2,
),
"VolumeWeightedSr": (
lambda: ta.VolumeWeightedSr(3),
lambda ind, h, l, c, v: ind.batch(h, l, v),
@@ -3174,6 +3203,38 @@ def test_pivot_reversal_reference():
assert t.update((13.0, 14.0, 12.5, 13.0, 1.0, 4)) == pytest.approx(1.0)
def test_td_camouflage_reference():
t = ta.TDCamouflage()
assert t.update((10.0, 11.0, 8.0, 10.0, 1.0, 0)) == pytest.approx(0.0)
assert t.update((9.0, 10.0, 7.0, 9.5, 1.0, 1)) == pytest.approx(1.0)
def test_td_clop_reference():
t = ta.TDClop()
assert t.update((10.0, 12.0, 9.0, 11.0, 1.0, 0)) == pytest.approx(0.0)
assert t.update((9.0, 13.0, 8.0, 12.0, 1.0, 1)) == pytest.approx(1.0)
def test_td_clopwin_reference():
t = ta.TDClopwin()
assert t.update((10.0, 15.0, 9.0, 14.0, 1.0, 0)) == pytest.approx(0.0)
assert t.update((11.0, 14.0, 10.0, 13.0, 1.0, 1)) == pytest.approx(1.0)
def test_td_propulsion_reference():
t = ta.TDPropulsion()
assert t.update((9.5, 11.0, 9.0, 10.0, 1.0, 0)) == pytest.approx(0.0)
assert t.update((10.5, 12.0, 10.0, 11.5, 1.0, 1)) == pytest.approx(1.0)
def test_td_trap_reference():
t = ta.TDTrap()
assert t.update((100.0, 110.0, 90.0, 100.0, 1.0, 0)) == pytest.approx(0.0)
assert t.update((101.5, 108.0, 95.0, 102.0, 1.0, 1)) == pytest.approx(0.0)
assert t.update((106.0, 112.0, 100.0, 109.0, 1.0, 2)) == pytest.approx(1.0)
# --- Lifecycle ------------------------------------------------------------