feat: TA-Lib parity — 19 standalone indicators (DM components, price transforms, ROC/LinReg/MACD/SAR variants, Hilbert outputs) (#148)
Closes the remaining TA-Lib function-name gap by shipping each missing or bundled-only function as a real, standalone, fully-covered indicator. 19 new indicators across 5 families; mod-count 295 -> 314. ### Trend & Directional — Directional Movement components - `PlusDm` (`PLUS_DM`), `MinusDm` (`MINUS_DM`) — Wilder-smoothed ±DM. - `PlusDi` (`PLUS_DI`), `MinusDi` (`MINUS_DI`) — `100·smoothed(±DM)/ATR`. - `Dx` (`DX`) — `100·|+DI−−DI|/(+DI+−DI)`. ### Price Statistics - `AvgPrice` (`AVGPRICE`) — `(O+H+L+C)/4`. - `MidPoint` (`MIDPOINT`) — `(max+min)/2` of a scalar series over N. - `MidPrice` (`MIDPRICE`) — `(highestHigh+lowestLow)/2` over N. - `LinRegIntercept` (`LINEARREG_INTERCEPT`) — OLS intercept. - `Tsf` (`TSF`) — time series forecast `a + b·period`. ### Momentum Oscillators - `Rocp` (`ROCP`), `Rocr` (`ROCR`), `Rocr100` (`ROCR100`) — ROC ratio forms. ### Trailing Stops - `SarExt` (`SAREXT`) — Parabolic SAR with start value, reversal offset, separate long/short acceleration, signed output. ### Trend & Directional — MACD variants - `MacdFix` (`MACDFIX`) — MACD fixed 12/26. - `MacdExt` (`MACDEXT`) — MACD with a selectable moving-average type per line (new public `MaType` enum: SMA/EMA/WMA/DEMA/TEMA/TRIMA). ### Ehlers / Cycle (DSP) — Hilbert transform outputs - `HtPhasor` (`HT_PHASOR`) — in-phase / quadrature components. - `HtDcPhase` (`HT_DCPHASE`) — dominant-cycle phase (degrees). - `HtTrendMode` (`HT_TRENDMODE`) — trend (1) vs cycle (0) classification. Each indicator ships the full chain: core + every-branch unit tests, Python / Node / WASM bindings, fuzz coverage, README counter + family rows, CHANGELOG. `cargo test`, doctests, `clippy -D warnings`, `npm test` and pytest all green locally; mod-count == lib-block == README counter (314), FAMILIES total 309.
This commit is contained in:
kingchenc
@@ -25,6 +25,17 @@ from __future__ import annotations
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from ._wickra import (
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__version__,
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TSF,
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LINEARREG_INTERCEPT,
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ROCR100,
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ROCR,
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ROCP,
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AVGPRICE,
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MIDPOINT,
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MIDPRICE,
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DX,
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MINUS_DI,
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PLUS_DI,
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# Trend
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SMA,
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EMA,
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@@ -49,12 +60,16 @@ from ._wickra import (
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RSI,
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AnchoredRSI,
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MACD,
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MACDFIX,
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MACDEXT,
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Stochastic,
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CCI,
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ROC,
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WilliamsR,
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ADX,
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ADXR,
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PLUS_DM,
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MINUS_DM,
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MFI,
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TRIX,
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AwesomeOscillator,
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@@ -98,6 +113,7 @@ from ._wickra import (
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Keltner,
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Donchian,
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PSAR,
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SAREXT,
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NATR,
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StdDev,
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UlcerIndex,
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@@ -181,6 +197,9 @@ from ._wickra import (
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EhlersStochastic,
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EmpiricalModeDecomposition,
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HilbertDominantCycle,
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HT_DCPHASE,
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HT_PHASOR,
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HT_TRENDMODE,
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AdaptiveCycle,
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SineWave,
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MAMA,
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@@ -343,6 +362,17 @@ from ._wickra import (
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)
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__all__ = [
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"TSF",
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"LINEARREG_INTERCEPT",
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"ROCR100",
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"ROCR",
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"ROCP",
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"AVGPRICE",
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"MIDPOINT",
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"MIDPRICE",
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"DX",
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"MINUS_DI",
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"PLUS_DI",
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"__version__",
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# Trend
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"SMA",
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@@ -368,12 +398,16 @@ __all__ = [
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"RSI",
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"AnchoredRSI",
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"MACD",
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"MACDFIX",
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"MACDEXT",
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"Stochastic",
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"CCI",
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"ROC",
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"WilliamsR",
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"ADX",
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"ADXR",
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"PLUS_DM",
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"MINUS_DM",
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"MFI",
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"TRIX",
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"AwesomeOscillator",
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@@ -417,6 +451,7 @@ __all__ = [
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"Keltner",
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"Donchian",
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"PSAR",
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"SAREXT",
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"NATR",
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"StdDev",
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"UlcerIndex",
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@@ -500,6 +535,9 @@ __all__ = [
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"EhlersStochastic",
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"EmpiricalModeDecomposition",
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"HilbertDominantCycle",
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"HT_DCPHASE",
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"HT_PHASOR",
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"HT_TRENDMODE",
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"AdaptiveCycle",
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"SineWave",
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"MAMA",
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File diff suppressed because it is too large
Load Diff
@@ -45,6 +45,12 @@ def ohlcv() -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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# --- Scalar (f64 -> f64) indicators ---------------------------------------
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SCALAR = [
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(ta.TSF, (14,)),
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(ta.LINEARREG_INTERCEPT, (14,)),
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(ta.ROCR100, (10,)),
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(ta.ROCR, (10,)),
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(ta.ROCP, (10,)),
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(ta.MIDPOINT, (14,)),
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(ta.SMMA, (14,)),
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(ta.TRIMA, (20,)),
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(ta.ZLEMA, (14,)),
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@@ -96,6 +102,8 @@ SCALAR = [
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(ta.EhlersStochastic, (20,)),
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(ta.EmpiricalModeDecomposition, (20, 0.5)),
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(ta.HilbertDominantCycle, ()),
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(ta.HT_DCPHASE, ()),
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(ta.HT_TRENDMODE, ()),
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(ta.AdaptiveCycle, ()),
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(ta.SineWave, ()),
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(ta.FAMA, (0.5, 0.05)),
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@@ -136,6 +144,9 @@ SCALAR_MULTI = {
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"LinRegChannel": (lambda: ta.LinRegChannel(20, 2.0), 3),
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"StandardErrorBands": (lambda: ta.StandardErrorBands(21, 2.0), 3),
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"DoubleBollinger": (lambda: ta.DoubleBollinger(20, 1.0, 2.0), 5),
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"MacdFix": (lambda: ta.MACDFIX(9), 3),
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"MacdExt": (lambda: ta.MACDEXT(12, 0, 26, 0, 9, 0), 3),
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"HtPhasor": (lambda: ta.HT_PHASOR(), 2),
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}
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@@ -275,7 +286,15 @@ 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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"MIDPRICE": (lambda: ta.MIDPRICE(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"AVGPRICE": (lambda: ta.AVGPRICE(), lambda ind, h, l, c, v: ind.batch(c, h, l, c)),
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"DX": (lambda: ta.DX(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"MINUS_DI": (lambda: ta.MINUS_DI(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"PLUS_DI": (lambda: ta.PLUS_DI(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"VWMA": (lambda: ta.VWMA(20), lambda ind, h, l, c, v: ind.batch(c, v)),
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"SAREXT": (lambda: ta.SAREXT(), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"PLUS_DM": (lambda: ta.PLUS_DM(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"MINUS_DM": (lambda: ta.MINUS_DM(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"RVI": (
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# extract_candle pulls the open price from index 0 of the tuple; the
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# streaming test below already builds candles with open == close, so
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@@ -1171,6 +1190,99 @@ def test_weighted_close_reference():
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)
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def test_plus_dm_reference():
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# Highs rise by 1 (up = +1) while lows rise by 0.5, so every raw +DM equals
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# the up-move (1.0). Period 3: seed = 3 * 1 = 3.0, then the Wilder step holds it.
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high = np.array([11.0, 12.0, 13.0, 14.0, 15.0])
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low = np.array([9.0, 9.5, 10.0, 10.5, 11.0])
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close = np.array([10.0, 11.0, 12.0, 13.0, 14.0])
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out = ta.PLUS_DM(3).batch(high, low, close)
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assert math.isnan(out[0]) and math.isnan(out[2])
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assert out[3] == pytest.approx(3.0)
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assert out[4] == pytest.approx(3.0)
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def test_minus_dm_reference():
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# Lows fall by 1 (down = +1) while highs fall by 0.5, so every raw -DM equals
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# the down-move (1.0). Period 3: seed = 3 * 1 = 3.0, then the Wilder step holds it.
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high = np.array([20.0, 19.5, 19.0, 18.5, 18.0])
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low = np.array([18.0, 17.0, 16.0, 15.0, 14.0])
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close = np.array([19.0, 18.0, 17.0, 16.0, 15.0])
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out = ta.MINUS_DM(3).batch(high, low, close)
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assert math.isnan(out[0]) and math.isnan(out[2])
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assert out[3] == pytest.approx(3.0)
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assert out[4] == pytest.approx(3.0)
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def test_plus_di_reference():
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# Strict uptrend -> +DI dominates and stays within (0, 100].
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high = np.array([101.0, 103.0, 105.0, 107.0, 109.0, 111.0])
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low = np.array([99.5, 101.5, 103.5, 105.5, 107.5, 109.5])
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close = np.array([100.5, 102.5, 104.5, 106.5, 108.5, 110.5])
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out = ta.PLUS_DI(3).batch(high, low, close)
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assert 0.0 < out[-1] <= 100.0
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def test_minus_di_reference():
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# Strict downtrend -> -DI dominates and stays within (0, 100].
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high = np.array([111.0, 109.0, 107.0, 105.0, 103.0, 101.0])
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low = np.array([109.5, 107.5, 105.5, 103.5, 101.5, 99.5])
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close = np.array([110.5, 108.5, 106.5, 104.5, 102.5, 100.5])
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out = ta.MINUS_DI(3).batch(high, low, close)
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assert 0.0 < out[-1] <= 100.0
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def test_dx_reference():
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# Strict trend -> one-sided directional movement -> DX is large, in (0, 100].
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high = np.array([101.0, 103.0, 105.0, 107.0, 109.0, 111.0])
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low = np.array([99.5, 101.5, 103.5, 105.5, 107.5, 109.5])
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close = np.array([100.5, 102.5, 104.5, 106.5, 108.5, 110.5])
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out = ta.DX(3).batch(high, low, close)
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assert 50.0 < out[-1] <= 100.0
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def test_mid_price_reference():
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# Window highs {12, 14, 16}, lows {8, 9, 10}: (16 + 8) / 2 = 12.
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high = np.array([12.0, 14.0, 16.0])
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low = np.array([8.0, 9.0, 10.0])
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close = np.array([10.0, 11.0, 12.0])
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out = ta.MIDPRICE(3).batch(high, low, close)
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assert out[-1] == pytest.approx(12.0)
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def test_mid_point_reference():
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# Window {8, 12, 10}: (12 + 8) / 2 = 10.
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out = ta.MIDPOINT(3).batch(np.array([8.0, 12.0, 10.0]))
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assert out[-1] == pytest.approx(10.0)
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def test_avg_price_reference():
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# (open + high + low + close) / 4 = (10 + 14 + 6 + 12) / 4 = 10.5.
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assert ta.AVGPRICE().update((10.0, 14.0, 6.0, 12.0, 1.0, 0)) == pytest.approx(10.5)
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def test_roc_ratio_variants_reference():
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# period 1 over [10, 11]: ROCP = 0.1, ROCR = 1.1, ROCR100 = 110.
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assert ta.ROCP(1).batch(np.array([10.0, 11.0]))[-1] == pytest.approx(0.1)
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assert ta.ROCR(1).batch(np.array([10.0, 11.0]))[-1] == pytest.approx(1.1)
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assert ta.ROCR100(1).batch(np.array([10.0, 11.0]))[-1] == pytest.approx(110.0)
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def test_linreg_intercept_and_tsf_reference():
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# period 3 over [1, 2, 9]: fit y = 0 + 4x. intercept = 0; forecast at x=3 = 12.
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data = np.array([1.0, 2.0, 9.0])
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assert ta.LINEARREG_INTERCEPT(3).batch(data)[-1] == pytest.approx(0.0, abs=1e-9)
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assert ta.TSF(3).batch(data)[-1] == pytest.approx(12.0)
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def test_macdfix_matches_macd():
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# MACDFIX(signal) is exactly MACD(12, 26, signal).
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prices = 100.0 + np.sin(np.arange(80) * 0.3) * 5.0
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fix = ta.MACDFIX(9).batch(prices)
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classic = ta.MACD(12, 26, 9).batch(prices)
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np.testing.assert_allclose(fix, classic, equal_nan=True)
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def test_nvi_reference():
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# closes [10, 11], volumes [200, 100]: volume contracts -> NVI absorbs +10%.
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# 1000 * (1 + 0.1) = 1100.
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