feat(patterns): add the Harmonic Patterns family (8 XABCD detectors) (#169)
## Summary Adds a new **Harmonic Patterns** indicator family (counter 359 → 367, families 22 → 23) — the second half of the A4 roadmap item, following the Chart Patterns family in #166. Eight Fibonacci-ratio detectors built on the shared swing-pivot tracker (`indicators::pattern_swing`) plus two new helpers there — `xabcd` (reads the last five pivots as X-A-B-C-D) and `ratios_in` (checks a list of `(value, low, high)` Fibonacci windows in one expression, no multi-line `&&` coverage gaps). Each consumes candles and emits the uniform pattern sign convention — `+1.0` bullish (terminal point D a swing low), `-1.0` bearish (D a swing high), `0.0` otherwise, never `None`. Parameter-free, with the Fibonacci windows documented as constants per detector. ## Detectors | Indicator | Defining ratio | |-----------|----------------| | `Abcd` | four-point AB=CD (BC retraces AB, CD ≈ AB) | | `Gartley` | AD/XA ≈ 0.786 | | `Butterfly` | AD/XA ∈ 1.27–1.618 (extended D) | | `Bat` | AD/XA ≈ 0.886, shallow B | | `Crab` | AD/XA ≈ 1.618 (deepest D) | | `Shark` | expansion AB, AD/XA 0.886–1.13 | | `Cypher` | BC on XA, CD/XC ≈ 0.786 | | `ThreeDrives` | two symmetric extension drives | ## Touchpoints Core modules + `FAMILIES` group/assert, crate root re-exports, Python/Node/WASM bindings via the candle-pattern macros (Node `index.d.ts`/`index.js` regenerated), the candle fuzz target (`// --- Harmonic Patterns ---` section), Python reference + `CANDLE_SCALAR` registry tests and the Node candle-scalar factory, README catalogue counter + banner cache-buster + family table row + family-count word, `docs/README.md` counter, and the changelog. ## Verification - `cargo test -p wickra-core --lib` — 2966 passed - `cargo test -p wickra-core --doc` — 335 passed - `cargo clippy --workspace --all-targets --all-features -- -D warnings` — clean - Node `npm run build && npm test` — 444 passed - Python `maturin develop --release` + `pytest` — 748 passed Every detector branch is unit-tested, including a bullish and a bearish match per pattern to cover both output arms, plus an out-of-ratio non-match. Fibonacci windows use standard harmonic-trading ranges with documented tolerance bands.
This commit is contained in:
kingchenc
@@ -330,6 +330,15 @@ from ._wickra import (
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HeadAndShoulders,
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TripleTopBottom,
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DoubleTopBottom,
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# Harmonic patterns
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ThreeDrives,
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Cypher,
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Shark,
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Crab,
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Bat,
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Butterfly,
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Gartley,
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Abcd,
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# Microstructure: order book
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OrderBookImbalanceTop1,
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OrderBookImbalanceTopN,
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@@ -716,6 +725,15 @@ __all__ = [
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"HeadAndShoulders",
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"TripleTopBottom",
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"DoubleTopBottom",
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# Harmonic patterns
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"ThreeDrives",
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"Cypher",
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"Shark",
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"Crab",
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"Bat",
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"Butterfly",
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"Gartley",
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"Abcd",
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# Microstructure: order book
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"OrderBookImbalanceTop1",
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"OrderBookImbalanceTopN",
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@@ -13683,6 +13683,14 @@ candle_pattern_no_param!(PyWedge, wc::Wedge, "Wedge");
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candle_pattern_no_param!(PyFlagPennant, wc::FlagPennant, "FlagPennant");
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candle_pattern_no_param!(PyRectangleRange, wc::RectangleRange, "RectangleRange");
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candle_pattern_no_param!(PyCupAndHandle, wc::CupAndHandle, "CupAndHandle");
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candle_pattern_no_param!(PyAbcd, wc::Abcd, "Abcd");
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candle_pattern_no_param!(PyGartley, wc::Gartley, "Gartley");
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candle_pattern_no_param!(PyButterfly, wc::Butterfly, "Butterfly");
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candle_pattern_no_param!(PyBat, wc::Bat, "Bat");
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candle_pattern_no_param!(PyCrab, wc::Crab, "Crab");
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candle_pattern_no_param!(PyShark, wc::Shark, "Shark");
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candle_pattern_no_param!(PyCypher, wc::Cypher, "Cypher");
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candle_pattern_no_param!(PyThreeDrives, wc::ThreeDrives, "ThreeDrives");
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// ============================== Microstructure: Order Book ==============================
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//
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// Order-book indicators consume a depth snapshot rather than OHLCV. Streaming
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@@ -18212,5 +18220,13 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
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m.add_class::<PyFlagPennant>()?;
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m.add_class::<PyRectangleRange>()?;
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m.add_class::<PyCupAndHandle>()?;
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m.add_class::<PyAbcd>()?;
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m.add_class::<PyGartley>()?;
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m.add_class::<PyButterfly>()?;
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m.add_class::<PyBat>()?;
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m.add_class::<PyCrab>()?;
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m.add_class::<PyShark>()?;
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m.add_class::<PyCypher>()?;
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m.add_class::<PyThreeDrives>()?;
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Ok(())
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}
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@@ -330,6 +330,38 @@ def test_relative_strength_streaming_matches_batch():
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# 6-tuple candle; the batch helper takes only the columns it needs.
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CANDLE_SCALAR = {
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"ThreeDrives": (
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lambda: ta.ThreeDrives(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"Cypher": (
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lambda: ta.Cypher(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"Shark": (
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lambda: ta.Shark(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"Crab": (
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lambda: ta.Crab(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"Bat": (
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lambda: ta.Bat(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"Butterfly": (
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lambda: ta.Butterfly(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"Gartley": (
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lambda: ta.Gartley(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"Abcd": (
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lambda: ta.Abcd(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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),
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"CupAndHandle": (
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lambda: ta.CupAndHandle(),
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lambda ind, h, l, c, v: ind.batch(c, h, l, c),
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@@ -2461,6 +2493,91 @@ def test_cup_and_handle_reference():
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assert t.update((110.0, 119.79, 110.0, 110.0, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((111.1, 121.0, 111.1, 111.1, 1.0, 4)) == pytest.approx(1.0)
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def test_abcd_reference():
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t = ta.Abcd()
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assert t.update((139.86, 140.0, 139.86, 139.86, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 138.6, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 124.7, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((84.7, 123.453, 84.7, 84.7, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((85.547, 93.17, 85.547, 85.547, 1.0, 4)) == pytest.approx(1.0)
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def test_gartley_reference():
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t = ta.Gartley()
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assert t.update((149.85, 150.0, 149.85, 149.85, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 148.5, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 140.0, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((115.3, 138.6, 115.3, 115.3, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((116.453, 127.65, 116.453, 116.453, 1.0, 4)) == pytest.approx(0.0)
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assert t.update((108.56, 126.3735, 108.56, 108.56, 1.0, 5)) == pytest.approx(0.0)
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assert t.update((109.6456, 119.416, 109.6456, 109.6456, 1.0, 6)) == pytest.approx(1.0)
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def test_butterfly_reference():
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t = ta.Butterfly()
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assert t.update((149.85, 150.0, 149.85, 149.85, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 148.5, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 140.0, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((108.6, 138.6, 108.6, 108.6, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((109.686, 128.0, 109.686, 109.686, 1.0, 4)) == pytest.approx(0.0)
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assert t.update((79.8, 126.72, 79.8, 79.8, 1.0, 5)) == pytest.approx(0.0)
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assert t.update((80.598, 87.78, 80.598, 80.598, 1.0, 6)) == pytest.approx(1.0)
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def test_bat_reference():
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t = ta.Bat()
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assert t.update((149.85, 150.0, 149.85, 149.85, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 148.5, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 140.0, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((122.0, 138.6, 122.0, 122.0, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((123.22, 137.0, 123.22, 123.22, 1.0, 4)) == pytest.approx(0.0)
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assert t.update((104.56, 135.63, 104.56, 104.56, 1.0, 5)) == pytest.approx(0.0)
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assert t.update((105.6056, 115.016, 105.6056, 105.6056, 1.0, 6)) == pytest.approx(1.0)
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def test_crab_reference():
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t = ta.Crab()
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assert t.update((149.85, 150.0, 149.85, 149.85, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 148.5, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 140.0, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((120.0, 138.6, 120.0, 120.0, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((121.2, 137.5, 121.2, 121.2, 1.0, 4)) == pytest.approx(0.0)
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assert t.update((75.3, 136.125, 75.3, 75.3, 1.0, 5)) == pytest.approx(0.0)
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assert t.update((76.053, 82.83, 76.053, 76.053, 1.0, 6)) == pytest.approx(1.0)
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def test_shark_reference():
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t = ta.Shark()
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assert t.update((149.85, 150.0, 149.85, 149.85, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 148.5, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 140.0, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((88.0, 138.6, 88.0, 88.0, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((88.88, 186.8, 88.88, 88.88, 1.0, 4)) == pytest.approx(0.0)
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assert t.update((100.0, 184.932, 100.0, 100.0, 1.0, 5)) == pytest.approx(0.0)
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assert t.update((101.0, 110.0, 101.0, 101.0, 1.0, 6)) == pytest.approx(1.0)
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def test_cypher_reference():
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t = ta.Cypher()
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assert t.update((149.85, 150.0, 149.85, 149.85, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 148.5, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 140.0, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((120.0, 138.6, 120.0, 120.0, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((121.2, 168.0, 121.2, 121.2, 1.0, 4)) == pytest.approx(0.0)
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assert t.update((114.55, 166.32, 114.55, 114.55, 1.0, 5)) == pytest.approx(0.0)
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assert t.update((115.6955, 126.005, 115.6955, 115.6955, 1.0, 6)) == pytest.approx(1.0)
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def test_three_drives_reference():
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t = ta.ThreeDrives()
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assert t.update((119.88, 120.0, 119.88, 119.88, 1.0, 0)) == pytest.approx(0.0)
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assert t.update((100.0, 118.8, 100.0, 100.0, 1.0, 1)) == pytest.approx(0.0)
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assert t.update((101.0, 128.0, 101.0, 101.0, 1.0, 2)) == pytest.approx(0.0)
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assert t.update((108.0, 126.72, 108.0, 108.0, 1.0, 3)) == pytest.approx(0.0)
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assert t.update((109.08, 136.0, 109.08, 109.08, 1.0, 4)) == pytest.approx(0.0)
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assert t.update((122.4, 134.64, 122.4, 122.4, 1.0, 5)) == pytest.approx(-1.0)
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# --- Lifecycle ------------------------------------------------------------
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