kingchenc
7a18a26daf
Implements Family 10 (Ehlers / Cycle) end-to-end across Rust core,
Python / Node / WASM bindings, fuzz, tests, benches and docs. This
is an entirely new family covering John Ehlers' digital-signal-
processing school of cycle analytics — a strong differentiator
versus TA-Lib and pandas-ta, which ship only fragments.
Indicators:
- MAMA (Mesa Adaptive MA) — multi-output { mama, fama }
- FAMA (Following Adaptive MA) — scalar wrapper around MAMA's slow line
- Fisher Transform — Gaussian-normalising price transform
- Inverse Fisher Transform — bounded oscillator (tanh-based)
- SuperSmoother — 2-pole Butterworth lowpass
- Roofing Filter — high-pass + SuperSmoother bandpass
- Decycler — price minus 2-pole high-pass (lag-free trend)
- Decycler Oscillator — fast / slow Decycler difference (MACD-like)
- Hilbert Dominant Cycle — phase-derived period estimator [6, 50]
- Sine Wave Indicator — sin(phase) with 45° lead companion
- Adaptive Cycle Indicator — half-period driver for adaptive oscillators
- Center of Gravity Oscillator — weighted-mass momentum
- Cybernetic Cycle Component — EasyLanguage classic
- Empirical Mode Decomposition — bandpass + envelope mean
- Ehlers Stochastic — Stochastic on Roofing Filter input, [-1, +1]
- Instantaneous Trendline — Ehlers 2-pole lag-free trend
Indicator count rises 71 -> 87 across nine families (was eight).
All sixteen pass batch == streaming equivalence, expose the standard
Indicator surface (update / batch / reset / is_ready / warmup_period
/ name), are fuzz-tested, benchmarked against the checked-in BTCUSDT
1-minute dataset and reach across all four bindings.
Wiki deep-dive drafts for every indicator + Sidebar / Overview /
Home / Warmup updates are staged under indicator-ideas/families/
wiki/family-10-ehlers-cycle/ in the main repo (ghost-ignored) for
the maintainer to publish to the wiki repo manually.
106 lines
2.7 KiB
Python
106 lines
2.7 KiB
Python
"""Tests for the indicator lifecycle methods: reset, is_ready, warmup_period, repr."""
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from __future__ import annotations
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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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SCALAR_INDICATORS = [
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(ta.SMA, (14,)),
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(ta.EMA, (14,)),
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(ta.WMA, (14,)),
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(ta.RSI, (14,)),
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(ta.MACD, ()),
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(ta.BollingerBands, ()),
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]
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@pytest.mark.parametrize("cls, args", SCALAR_INDICATORS)
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def test_is_ready_transitions_after_warmup(cls, args):
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ind = cls(*args)
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assert not ind.is_ready()
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series = np.linspace(1.0, 200.0, 200)
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ind.batch(series)
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assert ind.is_ready()
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@pytest.mark.parametrize("cls, args", SCALAR_INDICATORS)
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def test_reset_returns_to_initial_state(cls, args):
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ind = cls(*args)
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ind.batch(np.linspace(1.0, 200.0, 200))
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assert ind.is_ready()
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ind.reset()
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assert not ind.is_ready()
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@pytest.mark.parametrize(
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"cls, args, period",
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[
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(ta.SMA, (14,), 14),
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(ta.EMA, (14,), 14),
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(ta.WMA, (14,), 14),
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(ta.RSI, (14,), 15),
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(ta.BollingerBands, (20, 2.0), 20),
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],
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)
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def test_warmup_period(cls, args, period):
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assert cls(*args).warmup_period() == period
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def test_repr_contains_class_and_parameters():
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assert "SMA" in repr(ta.SMA(14))
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assert "14" in repr(ta.SMA(14))
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assert "BollingerBands" in repr(ta.BollingerBands(20, 2.0))
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def test_constructor_rejects_zero_period():
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with pytest.raises(ValueError):
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ta.SMA(0)
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with pytest.raises(ValueError):
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ta.RSI(0)
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def test_macd_rejects_fast_geq_slow():
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with pytest.raises(ValueError):
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ta.MACD(fast=26, slow=12, signal=9)
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def test_bollinger_rejects_non_positive_multiplier():
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with pytest.raises(ValueError):
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ta.BollingerBands(20, 0.0)
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with pytest.raises(ValueError):
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ta.BollingerBands(20, -1.0)
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def test_candle_dict_input_supported():
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atr = ta.ATR(2)
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atr.update({"open": 10.0, "high": 11.0, "low": 9.0, "close": 10.5, "volume": 1.0})
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v = atr.update({"open": 10.5, "high": 12.0, "low": 10.0, "close": 11.0, "volume": 1.0})
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assert v is not None
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def test_candle_tuple_input_supported():
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atr = ta.ATR(2)
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atr.update((10.0, 11.0, 9.0, 10.5, 1.0, 0))
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v = atr.update((10.5, 12.0, 10.0, 11.0, 1.0, 1))
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assert v is not None
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def test_ehlers_indicators_lifecycle():
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# Spot-check a few Family-10 entries beyond what test_new_indicators covers.
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series = np.linspace(1.0, 200.0, 200) + np.sin(np.arange(200) * 0.3) * 5.0
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for ind in [
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ta.SuperSmoother(10),
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ta.FisherTransform(10),
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ta.MAMA(),
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ta.HilbertDominantCycle(),
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ta.SineWave(),
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]:
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assert not ind.is_ready()
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ind.batch(series)
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assert ind.is_ready()
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ind.reset()
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assert not ind.is_ready()
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