F-Abschluss: wire the Python package, refresh docs and extend the test suites
Finalises the F1-F12 indicator expansion (25 -> 63 indicators). - Python `wickra/__init__.py`: import and re-export all 63 indicators, grouped by family, with a matching `__all__`. The package previously exposed only the original 25 even though the compiled module and the `.pyi` stubs already carried the rest. - Docs: `Home.md` and `README.md` indicator counts and family tables updated to 63; `Indicators-Overview.md` already restructured per family in F10-F12; `Warmup-Periods.md` gains all 38 new indicators across the single- and multi-output tables (and the stale two-arg `Psar::new` example is corrected to three args); `CHANGELOG.md` `[Unreleased]` lists every new indicator by family. - Tests: `bindings/node/__tests__/indicators.test.js` covers all 63 indicators (streaming==batch plus four new reference-value checks), 80/80 green; new `bindings/python/tests/test_new_indicators.py` covers the 38 additions (streaming==batch, shapes, reference values, lifecycle), Python suite 105/105 green. - `bindings/node/index.js` regenerated by `napi build`. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 454 core tests, 25 data tests, 66 doctests, 80 Node tests and 105 Python tests green; `cargo check -p wickra-wasm --tests` green.
This commit is contained in:
kingchenc
@@ -25,58 +25,144 @@ from __future__ import annotations
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from ._wickra import (
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__version__,
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ADX,
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ATR,
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Aroon,
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AwesomeOscillator,
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BollingerBands,
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CCI,
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DEMA,
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Donchian,
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# Trend
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SMA,
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EMA,
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WMA,
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DEMA,
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TEMA,
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HMA,
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KAMA,
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Keltner,
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MACD,
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MFI,
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OBV,
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PSAR,
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ROC,
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SMMA,
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TRIMA,
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ZLEMA,
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T3,
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VWMA,
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# Momentum
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RSI,
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SMA,
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MACD,
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Stochastic,
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TEMA,
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TRIX,
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VWAP,
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CCI,
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ROC,
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WilliamsR,
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WMA,
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ADX,
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MFI,
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TRIX,
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AwesomeOscillator,
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Aroon,
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MOM,
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CMO,
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TSI,
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PMO,
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StochRSI,
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UltimateOscillator,
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PPO,
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DPO,
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Coppock,
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AroonOscillator,
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Vortex,
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MassIndex,
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# Volatility
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BollingerBands,
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ATR,
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Keltner,
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Donchian,
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PSAR,
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NATR,
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StdDev,
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UlcerIndex,
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HistoricalVolatility,
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BollingerBandwidth,
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PercentB,
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SuperTrend,
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ChandelierExit,
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ChandeKrollStop,
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AtrTrailingStop,
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# Volume
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OBV,
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VWAP,
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ADL,
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VolumePriceTrend,
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ChaikinMoneyFlow,
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ChaikinOscillator,
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ForceIndex,
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EaseOfMovement,
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# Statistics
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TypicalPrice,
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MedianPrice,
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WeightedClose,
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LinearRegression,
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LinRegSlope,
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)
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__all__ = [
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"__version__",
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# Trend
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"SMA",
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"EMA",
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"WMA",
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"RSI",
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"MACD",
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"BollingerBands",
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"ATR",
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"Stochastic",
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"OBV",
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"DEMA",
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"TEMA",
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"HMA",
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"KAMA",
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"SMMA",
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"TRIMA",
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"ZLEMA",
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"T3",
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"VWMA",
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# Momentum
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"RSI",
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"MACD",
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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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"MFI",
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"TRIX",
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"PSAR",
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"Keltner",
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"Donchian",
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"VWAP",
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"AwesomeOscillator",
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"Aroon",
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"MOM",
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"CMO",
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"TSI",
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"PMO",
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"StochRSI",
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"UltimateOscillator",
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"PPO",
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"DPO",
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"Coppock",
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"AroonOscillator",
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"Vortex",
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"MassIndex",
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# Volatility
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"BollingerBands",
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"ATR",
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"Keltner",
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"Donchian",
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"PSAR",
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"NATR",
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"StdDev",
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"UlcerIndex",
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"HistoricalVolatility",
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"BollingerBandwidth",
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"PercentB",
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"SuperTrend",
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"ChandelierExit",
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"ChandeKrollStop",
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"AtrTrailingStop",
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# Volume
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"OBV",
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"VWAP",
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"ADL",
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"VolumePriceTrend",
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"ChaikinMoneyFlow",
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"ChaikinOscillator",
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"ForceIndex",
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"EaseOfMovement",
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# Statistics
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"TypicalPrice",
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"MedianPrice",
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"WeightedClose",
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"LinearRegression",
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"LinRegSlope",
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]
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@@ -0,0 +1,253 @@
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"""Streaming-vs-batch, shape and reference-value tests for the F1-F12 families.
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Every indicator added since the original 25 is exercised here. The central
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contract is the same as the rest of the suite: ``batch(...)`` must equal
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repeated streaming ``update(...)`` across the whole warmup -> steady-state
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transition, and batch shapes must match the input length.
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"""
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from __future__ import annotations
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import math
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import numpy as np
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import pytest
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import wickra as ta
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def _eq_nan(a: np.ndarray, b: np.ndarray, tol: float = 1e-9) -> bool:
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"""Compare two float arrays treating NaN positions as equal."""
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a = np.asarray(a, dtype=np.float64)
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b = np.asarray(b, dtype=np.float64)
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if a.shape != b.shape:
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return False
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both_nan = np.isnan(a) & np.isnan(b)
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return bool(np.all(np.where(both_nan, 0.0, np.abs(a - b)) <= tol))
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@pytest.fixture
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def ohlcv() -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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"""Synthetic high / low / close / volume series, 200 bars."""
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t = np.arange(200, dtype=np.float64)
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close = 100.0 + np.sin(t * 0.15) * 8.0 + np.cos(t * 0.32) * 3.0
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spread = 0.5 + np.abs(np.sin(t * 0.07))
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high = close + spread
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low = close - spread
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volume = 1000.0 + (t % 7) * 50.0
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return high, low, close, volume
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# --- Scalar (f64 -> f64) indicators ---------------------------------------
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SCALAR = [
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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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(ta.T3, (5, 0.7)),
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(ta.MOM, (10,)),
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(ta.CMO, (14,)),
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(ta.TSI, (25, 13)),
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(ta.PMO, (35, 20)),
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(ta.StochRSI, (14, 14)),
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(ta.PPO, (12, 26)),
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(ta.DPO, (20,)),
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(ta.Coppock, (14, 11, 10)),
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(ta.StdDev, (20,)),
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(ta.UlcerIndex, (14,)),
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(ta.HistoricalVolatility, (20, 252)),
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(ta.BollingerBandwidth, (20, 2.0)),
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(ta.PercentB, (20, 2.0)),
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(ta.LinearRegression, (14,)),
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(ta.LinRegSlope, (14,)),
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]
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@pytest.mark.parametrize("cls, args", SCALAR, ids=[c.__name__ for c, _ in SCALAR])
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def test_scalar_streaming_matches_batch(cls, args, sine_prices):
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batch = cls(*args).batch(sine_prices)
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assert batch.shape == sine_prices.shape
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assert batch.dtype == np.float64
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streamer = cls(*args)
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streamed = []
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for p in sine_prices:
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v = streamer.update(float(p))
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streamed.append(math.nan if v is None else float(v))
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assert _eq_nan(batch, np.array(streamed, dtype=np.float64))
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# --- Candle-input, single-output indicators -------------------------------
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#
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# Each entry is (factory, batch-call). Streaming always feeds the full
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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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"VWMA": (lambda: ta.VWMA(20), lambda ind, h, l, c, v: ind.batch(c, v)),
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"UltimateOscillator": (
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lambda: ta.UltimateOscillator(7, 14, 28),
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lambda ind, h, l, c, v: ind.batch(h, l, c),
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),
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"AroonOscillator": (
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lambda: ta.AroonOscillator(14),
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lambda ind, h, l, c, v: ind.batch(h, l),
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),
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"NATR": (lambda: ta.NATR(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"MassIndex": (lambda: ta.MassIndex(9, 25), lambda ind, h, l, c, v: ind.batch(h, l)),
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"ADL": (lambda: ta.ADL(), lambda ind, h, l, c, v: ind.batch(h, l, c, v)),
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"VolumePriceTrend": (
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lambda: ta.VolumePriceTrend(),
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lambda ind, h, l, c, v: ind.batch(c, v),
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),
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"ChaikinMoneyFlow": (
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lambda: ta.ChaikinMoneyFlow(20),
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lambda ind, h, l, c, v: ind.batch(h, l, c, v),
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),
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"ChaikinOscillator": (
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lambda: ta.ChaikinOscillator(3, 10),
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lambda ind, h, l, c, v: ind.batch(h, l, c, v),
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),
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"ForceIndex": (
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lambda: ta.ForceIndex(13),
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lambda ind, h, l, c, v: ind.batch(c, v),
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),
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"EaseOfMovement": (
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lambda: ta.EaseOfMovement(14),
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lambda ind, h, l, c, v: ind.batch(h, l, v),
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),
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"AtrTrailingStop": (
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lambda: ta.AtrTrailingStop(14, 3.0),
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lambda ind, h, l, c, v: ind.batch(h, l, c),
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),
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"TypicalPrice": (
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lambda: ta.TypicalPrice(),
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lambda ind, h, l, c, v: ind.batch(h, l, c),
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),
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"MedianPrice": (
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lambda: ta.MedianPrice(),
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lambda ind, h, l, c, v: ind.batch(h, l),
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),
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"WeightedClose": (
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lambda: ta.WeightedClose(),
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lambda ind, h, l, c, v: ind.batch(h, l, c),
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),
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}
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@pytest.mark.parametrize("name", list(CANDLE_SCALAR))
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def test_candle_scalar_streaming_matches_batch(name, ohlcv):
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high, low, close, volume = ohlcv
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make, batch_call = CANDLE_SCALAR[name]
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batch = batch_call(make(), high, low, close, volume)
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assert batch.shape == close.shape
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streamer = make()
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streamed = []
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for i in range(close.size):
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candle = (
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float(close[i]),
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float(high[i]),
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float(low[i]),
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float(close[i]),
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float(volume[i]),
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i,
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)
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v = streamer.update(candle)
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streamed.append(math.nan if v is None else float(v))
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assert _eq_nan(batch, np.array(streamed, dtype=np.float64)), f"{name} mismatch"
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# --- Candle-input, multi-output indicators --------------------------------
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MULTI = {
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"Vortex": (lambda: ta.Vortex(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
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"SuperTrend": (
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lambda: ta.SuperTrend(10, 3.0),
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lambda ind, h, l, c, v: ind.batch(h, l, c),
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),
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"ChandelierExit": (
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lambda: ta.ChandelierExit(22, 3.0),
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lambda ind, h, l, c, v: ind.batch(h, l, c),
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),
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"ChandeKrollStop": (
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lambda: ta.ChandeKrollStop(10, 1.0, 9),
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lambda ind, h, l, c, v: ind.batch(h, l, c),
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),
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}
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@pytest.mark.parametrize("name", list(MULTI))
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def test_multi_streaming_matches_batch(name, ohlcv):
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high, low, close, volume = ohlcv
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make, batch_call = MULTI[name]
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batch = batch_call(make(), high, low, close, volume)
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assert batch.shape == (close.size, 2)
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streamer = make()
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rows = []
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for i in range(close.size):
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candle = (
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float(close[i]),
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float(high[i]),
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float(low[i]),
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float(close[i]),
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float(volume[i]),
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i,
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)
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v = streamer.update(candle)
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rows.append([math.nan, math.nan] if v is None else list(v))
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assert _eq_nan(batch, np.array(rows, dtype=np.float64)), f"{name} mismatch"
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# --- Reference values -----------------------------------------------------
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def test_typical_price_reference():
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# (high + low + close) / 3 = (12 + 6 + 9) / 3 = 9.
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assert ta.TypicalPrice().update((9.0, 12.0, 6.0, 9.0, 1.0, 0)) == pytest.approx(9.0)
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def test_median_price_reference():
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# (high + low) / 2 = (12 + 8) / 2 = 10.
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assert ta.MedianPrice().update((10.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(10.0)
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def test_weighted_close_reference():
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# (high + low + 2*close) / 4 = (12 + 8 + 22) / 4 = 10.5.
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assert ta.WeightedClose().update((10.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(
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10.5
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)
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def test_chaikin_money_flow_reference():
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cmf = ta.ChaikinMoneyFlow(2)
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assert cmf.update((8.0, 10.0, 8.0, 10.0, 100.0, 0)) is None
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assert cmf.update((10.0, 12.0, 8.0, 10.0, 100.0, 1)) == pytest.approx(0.5)
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def test_linear_regression_reference():
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out = ta.LinearRegression(3).batch(np.array([1.0, 2.0, 9.0]))
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assert math.isnan(out[0]) and math.isnan(out[1])
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assert out[2] == pytest.approx(8.0)
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def test_linreg_slope_reference():
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out = ta.LinRegSlope(3).batch(np.array([1.0, 2.0, 9.0]))
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assert math.isnan(out[0]) and math.isnan(out[1])
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assert out[2] == pytest.approx(4.0)
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# --- Lifecycle ------------------------------------------------------------
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def test_new_indicators_expose_lifecycle():
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instances = [make() for make, _ in CANDLE_SCALAR.values()]
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instances += [make() for make, _ in MULTI.values()]
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instances += [cls(*args) for cls, args in SCALAR]
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for ind in instances:
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assert ind.is_ready() is False
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assert ind.warmup_period() >= 1
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ind.reset()
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assert ind.is_ready() is False
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