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.
68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
"""Smoke tests: every public class can be constructed and emits the right shape."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import wickra as ta
|
|
|
|
|
|
def test_version_is_a_nonempty_string():
|
|
assert isinstance(ta.__version__, str)
|
|
assert ta.__version__
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"cls, args",
|
|
[
|
|
(ta.SMA, (14,)),
|
|
(ta.EMA, (14,)),
|
|
(ta.WMA, (14,)),
|
|
(ta.RSI, (14,)),
|
|
],
|
|
)
|
|
def test_scalar_batch_returns_same_length(cls, args, sine_prices):
|
|
out = cls(*args).batch(sine_prices)
|
|
assert out.shape == sine_prices.shape
|
|
assert out.dtype == np.float64
|
|
|
|
|
|
def test_macd_batch_returns_n_by_3(sine_prices):
|
|
out = ta.MACD().batch(sine_prices)
|
|
assert out.shape == (sine_prices.size, 3)
|
|
|
|
|
|
def test_bollinger_batch_returns_n_by_4(sine_prices):
|
|
out = ta.BollingerBands().batch(sine_prices)
|
|
assert out.shape == (sine_prices.size, 4)
|
|
|
|
|
|
def test_atr_batch_shape(ohlc_series):
|
|
high, low, close = ohlc_series
|
|
out = ta.ATR(14).batch(high, low, close)
|
|
assert out.shape == close.shape
|
|
|
|
|
|
def test_stochastic_batch_shape(ohlc_series):
|
|
high, low, close = ohlc_series
|
|
out = ta.Stochastic(14, 3).batch(high, low, close)
|
|
assert out.shape == (close.size, 2)
|
|
|
|
|
|
def test_obv_batch_shape(ohlc_series):
|
|
_, _, close = ohlc_series
|
|
volume = np.ones_like(close)
|
|
out = ta.OBV().batch(close, volume)
|
|
assert out.shape == close.shape
|
|
|
|
|
|
def test_ehlers_super_smoother_batch_shape(sine_prices):
|
|
out = ta.SuperSmoother(10).batch(sine_prices)
|
|
assert out.shape == sine_prices.shape
|
|
|
|
|
|
def test_mama_batch_shape(sine_prices):
|
|
out = ta.MAMA().batch(sine_prices)
|
|
assert out.shape == (sine_prices.size, 2)
|