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.
599 lines
22 KiB
Python
599 lines
22 KiB
Python
"""Reference-value tests that pin numerical behaviour from the Python side."""
|
||
|
||
from __future__ import annotations
|
||
|
||
import math
|
||
|
||
import numpy as np
|
||
import pytest
|
||
|
||
import wickra as ta
|
||
|
||
|
||
def test_sma_constant_series():
|
||
out = ta.SMA(5).batch(np.full(20, 42.0, dtype=np.float64))
|
||
# First 4 are warmup -> NaN; rest equal 42.
|
||
assert np.all(np.isnan(out[:4]))
|
||
assert np.allclose(out[4:], 42.0)
|
||
|
||
|
||
def test_sma_known_window():
|
||
# SMA(3) of [2, 4, 6, 8, 10] -> [_, _, 4, 6, 8]
|
||
out = ta.SMA(3).batch(np.array([2.0, 4.0, 6.0, 8.0, 10.0]))
|
||
assert math.isnan(out[0]) and math.isnan(out[1])
|
||
np.testing.assert_allclose(out[2:], [4.0, 6.0, 8.0])
|
||
|
||
|
||
def test_ema_seed_equals_simple_mean_of_first_window():
|
||
# EMA(5) seed = mean([10, 20, 30, 40, 50]) = 30
|
||
out = ta.EMA(5).batch(np.array([10.0, 20.0, 30.0, 40.0, 50.0]))
|
||
assert math.isnan(out[0])
|
||
assert math.isclose(out[4], 30.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_wma_known_window():
|
||
# WMA(4) of [1, 2, 3, 4] = (1*1 + 2*2 + 3*3 + 4*4)/10 = 3
|
||
out = ta.WMA(4).batch(np.array([1.0, 2.0, 3.0, 4.0]))
|
||
assert math.isnan(out[0]) and math.isnan(out[1]) and math.isnan(out[2])
|
||
assert math.isclose(out[3], 3.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_rsi_pure_uptrend_is_100():
|
||
out = ta.RSI(14).batch(np.arange(1.0, 21.0, dtype=np.float64))
|
||
np.testing.assert_allclose(out[14:], 100.0)
|
||
|
||
|
||
def test_rsi_pure_downtrend_is_0():
|
||
out = ta.RSI(14).batch(np.arange(20.0, 0.0, -1.0))
|
||
np.testing.assert_allclose(out[14:], 0.0)
|
||
|
||
|
||
def test_rsi_flat_series_is_50():
|
||
out = ta.RSI(14).batch(np.full(30, 100.0))
|
||
np.testing.assert_allclose(out[14:], 50.0)
|
||
|
||
|
||
def test_rsi_wilder_textbook_first_value():
|
||
"""Wilder's original 14-period example, ~70.46 at the first emit."""
|
||
prices = np.array(
|
||
[
|
||
44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42, 45.84, 46.08,
|
||
45.89, 46.03, 45.61, 46.28, 46.28,
|
||
],
|
||
dtype=np.float64,
|
||
)
|
||
out = ta.RSI(14).batch(prices)
|
||
assert math.isclose(out[14], 70.464, abs_tol=0.05)
|
||
|
||
|
||
def test_inertia_constant_rvi_passes_through_linreg():
|
||
# Every bar identical (open, high, low, close) = (10, 11, 9, 10.5):
|
||
# RVI = (c-o) / (h-l) = 0.5 / 2 = 0.25 every bar. LinReg of a constant
|
||
# series equals that constant after warmup.
|
||
n = 60
|
||
out = ta.Inertia(3, 4).batch(
|
||
np.full(n, 10.0), np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.5)
|
||
)
|
||
# warmup_period = 3 + 4 - 1 = 6.
|
||
np.testing.assert_allclose(out[5:], 0.25, atol=1e-12)
|
||
|
||
|
||
def test_connors_rsi_output_is_bounded():
|
||
# CRSI is the average of three [0, 100] components, so the aggregate must
|
||
# also sit in [0, 100] after warmup.
|
||
prices = 100.0 + 20.0 * np.sin(np.linspace(0, 30, 250))
|
||
out = ta.ConnorsRSI(3, 2, 100).batch(prices.astype(np.float64))
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
assert ready.min() >= 0.0
|
||
assert ready.max() <= 100.0
|
||
|
||
|
||
def test_laguerre_rsi_constant_series_stays_at_mid_band():
|
||
# All four Laguerre stages seed to the first input, so subsequent flat
|
||
# inputs keep them equal and the up/down accumulator is 0 — Wickra maps
|
||
# that to the neutral 50.
|
||
out = ta.LaguerreRSI(0.5).batch(np.full(40, 42.0, dtype=np.float64))
|
||
np.testing.assert_allclose(out, 50.0, atol=1e-12)
|
||
|
||
|
||
def test_smi_close_at_centre_yields_zero():
|
||
# Close at the midpoint of a flat high/low range -> displacement is
|
||
# always zero -> SMI converges to 0.
|
||
n = 60
|
||
out = ta.SMI(5, 3, 3).batch(np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.0))
|
||
# warmup_period = 5 + 3 + 3 - 2 = 9.
|
||
np.testing.assert_allclose(out[8:], 0.0, atol=1e-12)
|
||
|
||
|
||
def test_kst_constant_series_yields_zero():
|
||
# ROC is zero on a flat input, so every RCMA is zero, so KST and its
|
||
# signal SMA are both zero after warmup.
|
||
kst = ta.KST(10, 15, 20, 30, 10, 10, 10, 15, 9)
|
||
out = kst.batch(np.full(80, 42.0, dtype=np.float64))
|
||
warmup = kst.warmup_period()
|
||
# Use NaN-safe comparison on the post-warmup tail.
|
||
tail = out[warmup - 1 :]
|
||
assert np.all(np.isfinite(tail))
|
||
np.testing.assert_allclose(tail, 0.0, atol=1e-12)
|
||
|
||
|
||
def test_pgo_flat_close_yields_zero():
|
||
# On a constant close the numerator (close − SMA) is zero, so PGO emits 0
|
||
# regardless of the TR-EMA in the denominator.
|
||
n = 20
|
||
high = np.full(n, 11.0)
|
||
low = np.full(n, 9.0)
|
||
close = np.full(n, 10.0)
|
||
out = ta.PGO(5).batch(high, low, close)
|
||
assert np.all(np.isnan(out[:4]))
|
||
np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
|
||
|
||
|
||
def test_rvi_reference_value_period_2():
|
||
# Two bars: (open, high, low, close) = (10, 11, 9, 10.5), (10.5, 11.5, 10, 11).
|
||
# num = (0.5 + 0.5) = 1.0; den = (2.0 + 1.5) = 3.5; RVI = 1 / 3.5.
|
||
out = ta.RVI(2).batch(
|
||
np.array([10.0, 10.5]),
|
||
np.array([11.0, 11.5]),
|
||
np.array([9.0, 10.0]),
|
||
np.array([10.5, 11.0]),
|
||
)
|
||
assert math.isnan(out[0])
|
||
assert math.isclose(out[1], 1.0 / 3.5, abs_tol=1e-12)
|
||
|
||
|
||
def test_alma_constant_series_yields_the_constant():
|
||
# ALMA's Gaussian weights are normalised, so any constant series is
|
||
# reproduced exactly after warmup.
|
||
out = ta.ALMA(9, 0.85, 6.0).batch(np.full(30, 42.0, dtype=np.float64))
|
||
assert np.all(np.isnan(out[:8]))
|
||
np.testing.assert_allclose(out[8:], 42.0, atol=1e-12)
|
||
|
||
|
||
def test_alma_reference_value_period_3():
|
||
# ALMA(period=3, offset=0.85, sigma=6) on [10, 20, 30].
|
||
# m = 0.85 * 2 = 1.7; s = 3 / 6 = 0.5; 2*s^2 = 0.5.
|
||
out = ta.ALMA(3, 0.85, 6.0).batch(np.array([10.0, 20.0, 30.0]))
|
||
assert math.isnan(out[0]) and math.isnan(out[1])
|
||
# Independently compute the expected Gaussian-weighted sum.
|
||
w = np.exp(-((np.arange(3, dtype=np.float64) - 1.7) ** 2) / 0.5)
|
||
expected = float(np.dot([10.0, 20.0, 30.0], w) / w.sum())
|
||
assert math.isclose(out[2], expected, abs_tol=1e-12)
|
||
# Sanity: heavy offset toward the newest sample lifts the average above
|
||
# the simple mean of 20.
|
||
assert out[2] > 20.0
|
||
|
||
|
||
def test_mcginley_dynamic_constant_series_yields_the_constant():
|
||
# ratio = 1, so the recurrence collapses to MD + 0 / divisor = MD.
|
||
out = ta.McGinleyDynamic(5).batch(np.full(30, 42.0, dtype=np.float64))
|
||
assert np.all(np.isnan(out[:4]))
|
||
np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
|
||
|
||
|
||
def test_mcginley_dynamic_reference_value():
|
||
# Period 3, seed = SMA([10, 20, 30]) = 20.0. Next price 40.0:
|
||
# ratio = 2; divisor = 0.6 * 3 * 16 = 28.8; next = 20 + 20/28.8.
|
||
out = ta.McGinleyDynamic(3).batch(np.array([10.0, 20.0, 30.0, 40.0]))
|
||
assert math.isnan(out[0]) and math.isnan(out[1])
|
||
assert math.isclose(out[2], 20.0, abs_tol=1e-12)
|
||
expected = 20.0 + 20.0 / (0.6 * 3.0 * 16.0)
|
||
assert math.isclose(out[3], expected, abs_tol=1e-12)
|
||
|
||
|
||
def test_frama_constant_series_yields_the_constant():
|
||
# Flat input -> degenerate ranges -> alpha clamps to 0.01 and the EMA
|
||
# recurrence holds the seed value.
|
||
out = ta.FRAMA(4).batch(np.full(20, 42.0, dtype=np.float64))
|
||
assert np.all(np.isnan(out[:3]))
|
||
np.testing.assert_allclose(out[3:], 42.0, atol=1e-12)
|
||
|
||
|
||
def test_frama_pure_uptrend_hugs_latest():
|
||
# Monotonic uptrend -> alpha pushed toward 1.0, FRAMA tracks close.
|
||
out = ta.FRAMA(4).batch(np.arange(1.0, 9.0, dtype=np.float64))
|
||
assert math.isclose(out[-1], 8.0, abs_tol=0.05)
|
||
|
||
|
||
def test_jma_constant_series_yields_the_constant():
|
||
# JMA seeds e0 and the output to the first input, so a constant series
|
||
# is reproduced exactly from the first sample.
|
||
out = ta.JMA(14, 0.0, 2).batch(np.full(30, 42.0, dtype=np.float64))
|
||
np.testing.assert_allclose(out, 42.0, atol=1e-12)
|
||
|
||
|
||
def test_evwma_reference_value_period_2():
|
||
# EVWMA(2). Bars: (close, volume) = (10, 1), (20, 3), (30, 1).
|
||
# Bar 2: sum_v = 4, seeded prev = 20, EVWMA = (1*20 + 3*20)/4 = 20.
|
||
# Bar 3: sum_v = 4 (drops 1, gains 1), EVWMA = (3*20 + 1*30)/4 = 22.5.
|
||
out = ta.EVWMA(2).batch(np.array([10.0, 20.0, 30.0]), np.array([1.0, 3.0, 1.0]))
|
||
assert math.isnan(out[0])
|
||
assert math.isclose(out[1], 20.0, abs_tol=1e-12)
|
||
assert math.isclose(out[2], 22.5, abs_tol=1e-12)
|
||
|
||
|
||
def test_alligator_constant_series_holds_at_median_price():
|
||
# Median price = (11 + 9) / 2 = 10 on every candle, so all three SMMAs
|
||
# seed at 10 and stay there.
|
||
n = 30
|
||
high = np.full(n, 11.0)
|
||
low = np.full(n, 9.0)
|
||
out = ta.Alligator(13, 8, 5).batch(high, low)
|
||
assert out.shape == (n, 3)
|
||
for row in out[12:]:
|
||
assert math.isclose(row[0], 10.0, abs_tol=1e-12)
|
||
assert math.isclose(row[1], 10.0, abs_tol=1e-12)
|
||
assert math.isclose(row[2], 10.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_vidya_constant_series_holds_seed():
|
||
# CMO = 0 on a flat series -> alpha = 0 -> VIDYA holds its seed value.
|
||
out = ta.VIDYA(14, 4).batch(np.full(20, 42.0, dtype=np.float64))
|
||
assert np.all(np.isnan(out[:4]))
|
||
np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
|
||
|
||
|
||
def test_zero_lag_macd_constant_series_converges_to_zero():
|
||
# Each inner ZLEMA reproduces a constant, so macd, signal and histogram
|
||
# are all 0 once the slowest branch warms up.
|
||
out = ta.ZeroLagMACD(3, 5, 3).batch(np.full(60, 42.0, dtype=np.float64))
|
||
# Take the last row and verify all three columns are 0.
|
||
last = out[-1]
|
||
assert math.isclose(last[0], 0.0, abs_tol=1e-12)
|
||
assert math.isclose(last[1], 0.0, abs_tol=1e-12)
|
||
assert math.isclose(last[2], 0.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_awesome_oscillator_histogram_flat_series_converges_to_zero():
|
||
# Flat median price -> AO = 0 -> SMA(AO) = 0 -> AOHist = 0.
|
||
n = 50
|
||
high = np.full(n, 11.0)
|
||
low = np.full(n, 9.0)
|
||
out = ta.AwesomeOscillatorHistogram(3, 5, 3).batch(high, low)
|
||
# warmup = slow + sma - 1 = 5 + 3 - 1 = 7.
|
||
np.testing.assert_allclose(out[6:], 0.0, atol=1e-12)
|
||
|
||
|
||
def test_stc_constant_series_yields_zero():
|
||
# Flat input collapses both stochastic stages to zero -> STC stays at 0.
|
||
out = ta.STC(3, 5, 4, 0.5).batch(np.full(60, 42.0, dtype=np.float64))
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
np.testing.assert_array_equal(ready[-5:], np.zeros(5))
|
||
|
||
|
||
def test_elder_impulse_constant_series_is_neutral():
|
||
# Flat input -> neither EMA nor MACD histogram moves -> Impulse stays at 0.
|
||
out = ta.ElderImpulse(13, 12, 26, 9).batch(np.full(120, 42.0, dtype=np.float64))
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
np.testing.assert_array_equal(ready, np.zeros_like(ready))
|
||
|
||
|
||
def test_cfo_perfect_linear_series_yields_zero():
|
||
# LinReg of a perfectly linear series fits exactly, so CFO = 0 after warmup.
|
||
out = ta.CFO(5).batch(np.arange(1.0, 21.0, dtype=np.float64) * 2.0)
|
||
np.testing.assert_allclose(out[4:], 0.0, atol=1e-9)
|
||
|
||
|
||
def test_apo_constant_series_converges_to_zero():
|
||
# Both EMAs reproduce a constant exactly, so APO = 0 after warmup.
|
||
out = ta.APO(3, 5).batch(np.full(30, 42.0, dtype=np.float64))
|
||
assert np.all(np.isnan(out[:4]))
|
||
np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
|
||
|
||
|
||
def test_macd_constant_series_converges_to_zero():
|
||
out = ta.MACD().batch(np.full(200, 100.0))
|
||
# Last row's MACD and signal must be ~0.
|
||
last = out[-1]
|
||
assert math.isclose(last[0], 0.0, abs_tol=1e-9)
|
||
assert math.isclose(last[1], 0.0, abs_tol=1e-9)
|
||
assert math.isclose(last[2], 0.0, abs_tol=1e-9)
|
||
|
||
|
||
def test_bollinger_constant_series_zero_width():
|
||
out = ta.BollingerBands(20, 2.0).batch(np.full(50, 100.0))
|
||
row = out[-1]
|
||
np.testing.assert_allclose(row, [100.0, 100.0, 100.0, 0.0], atol=1e-12)
|
||
|
||
|
||
def test_bollinger_upper_middle_lower_ordering():
|
||
out = ta.BollingerBands(20, 2.0).batch(np.linspace(50.0, 150.0, 100))
|
||
ready = out[~np.isnan(out[:, 0])]
|
||
assert np.all(ready[:, 0] >= ready[:, 1])
|
||
assert np.all(ready[:, 1] >= ready[:, 2])
|
||
assert np.all(ready[:, 3] >= 0.0)
|
||
|
||
|
||
def test_atr_constant_range_constant_output():
|
||
high = np.full(30, 11.0)
|
||
low = np.full(30, 9.0)
|
||
close = np.full(30, 10.0)
|
||
out = ta.ATR(14).batch(high, low, close)
|
||
# Once seeded, ATR equals the constant TR of 2.
|
||
np.testing.assert_allclose(out[13:], 2.0, atol=1e-12)
|
||
|
||
|
||
def test_stochastic_extremes():
|
||
# Close at the top of a 3-period range -> %K = 100.
|
||
high = np.array([10.0, 11.0, 12.0])
|
||
low = np.array([8.0, 9.0, 10.0])
|
||
close = np.array([9.0, 10.0, 12.0])
|
||
out = ta.Stochastic(3, 1).batch(high, low, close)
|
||
assert math.isclose(out[2, 0], 100.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_obv_cumulative_known_sequence():
|
||
close = np.array([10.0, 11.0, 10.5, 10.5, 12.0])
|
||
volume = np.array([100.0, 20.0, 30.0, 40.0, 10.0])
|
||
out = ta.OBV().batch(close, volume)
|
||
np.testing.assert_allclose(out, [0.0, 20.0, -10.0, -10.0, 0.0])
|
||
|
||
|
||
# --- Family 10 — Ehlers / Cycle reference values ---
|
||
|
||
|
||
def test_inverse_fisher_saturates_for_large_input():
|
||
# tanh(10) ~ 0.99999996; very close to +1 without exceeding.
|
||
v = ta.InverseFisherTransform(1.0).batch(np.array([10.0]))[0]
|
||
assert v < 1.0
|
||
assert v > 0.999
|
||
|
||
|
||
def test_super_smoother_constant_input_is_constant():
|
||
out = ta.SuperSmoother(20).batch(np.full(200, 50.0))
|
||
# Steady-state gain is 1, so a flat input stays flat.
|
||
np.testing.assert_allclose(out[-50:], 50.0, atol=1e-9)
|
||
|
||
|
||
def test_decycler_oscillator_flat_series_is_zero():
|
||
out = ta.DecyclerOscillator(10, 30).batch(np.full(80, 42.0))
|
||
ready = out[~np.isnan(out)]
|
||
np.testing.assert_allclose(ready, 0.0, atol=1e-9)
|
||
|
||
|
||
def test_mama_constant_series_both_lines_converge_to_price():
|
||
out = ta.MAMA().batch(np.full(200, 100.0))
|
||
last = out[-1]
|
||
# MAMA and FAMA both track price closely on a flat series.
|
||
assert abs(last[0] - 100.0) < 1.0
|
||
assert abs(last[1] - 100.0) < 1.0
|
||
|
||
|
||
# --- DeMark family ---------------------------------------------------------
|
||
|
||
|
||
def test_td_setup_buy_setup_completes_at_minus_9_uptrend():
|
||
# Strictly rising closes -> every bar has close > close[-4] (sell setup);
|
||
# the streak hits -9 at index 12 and caps there.
|
||
h = np.arange(2.0, 22.0)
|
||
l = h - 1.0
|
||
c = h - 0.5
|
||
out = ta.TDSetup(4, 9).batch(h, l, c)
|
||
assert out[12] == pytest.approx(-9.0)
|
||
assert out[-1] == pytest.approx(-9.0)
|
||
|
||
|
||
def test_td_demarker_downtrend_pegs_at_zero():
|
||
n = 20
|
||
h = np.arange(30.0, 30.0 - n, -1.0)
|
||
l = h - 2.0
|
||
out = ta.TDDeMarker(5).batch(h, l)
|
||
assert out[-1] == pytest.approx(0.0)
|
||
|
||
|
||
def test_td_pressure_pure_bearish_yields_minus_100():
|
||
n = 20
|
||
open_ = np.full(n, 11.0)
|
||
high = np.full(n, 11.0)
|
||
low = np.full(n, 9.0)
|
||
close = np.full(n, 9.0)
|
||
volume = np.full(n, 100.0)
|
||
out = ta.TDPressure(5).batch(open_, high, low, close, volume)
|
||
assert out[-1] == pytest.approx(-100.0)
|
||
|
||
|
||
def test_td_combo_uptrend_completes_to_minus_13():
|
||
# Pure uptrend -> setup completes, then combo conditions (close>=high[-2],
|
||
# high>=prev.high, close>prev.close) all hold for every subsequent bar
|
||
# -> sell combo saturates at -13.
|
||
n = 40
|
||
high = np.arange(1.0, 1.0 + n) + 0.5
|
||
low = high - 1.0
|
||
close = high - 0.5
|
||
out = ta.TDCombo().batch(high, low, close)
|
||
assert out[-1] == pytest.approx(-13.0)
|
||
|
||
|
||
def test_td_countdown_uptrend_completes_to_minus_13():
|
||
n = 40
|
||
high = np.arange(1.0, 1.0 + n) + 0.5
|
||
low = high - 1.0
|
||
close = high - 0.5
|
||
out = ta.TDCountdown().batch(high, low, close)
|
||
assert out[-1] == pytest.approx(-13.0)
|
||
|
||
|
||
def test_td_range_projection_doji_reference():
|
||
# open=close=10, high=12, low=9 -> doji branch.
|
||
# pivot_sum = 12 + 9 + 2*10 = 41; half = 20.5.
|
||
# projHigh = 20.5 - 9 = 11.5; projLow = 20.5 - 12 = 8.5.
|
||
out = ta.TDRangeProjection().batch(
|
||
np.array([10.0]), np.array([12.0]), np.array([9.0]), np.array([10.0])
|
||
)
|
||
assert out[0, 0] == pytest.approx(11.5)
|
||
assert out[0, 1] == pytest.approx(8.5)
|
||
|
||
|
||
def test_td_open_sell_signal_reference():
|
||
# Prev high=12. Curr open=13 > 12, curr low=11 < 12 -> -1.
|
||
td = ta.TDOpen()
|
||
assert td.update((10.0, 12.0, 9.0, 11.0, 1.0, 0)) is None
|
||
assert td.update((13.0, 13.5, 11.0, 11.5, 1.0, 1)) == pytest.approx(-1.0)
|
||
|
||
|
||
def test_td_differential_sell_signal_reference():
|
||
# Prev high=10, low=8, close=9: buying=1, selling=1.
|
||
# Curr high=12, low=9.8, close=10.5: close>prev.close, selling=1.5>1,
|
||
# buying=0.7<1 -> sell signal -1.
|
||
td = ta.TDDifferential()
|
||
assert td.update((9.0, 10.0, 8.0, 9.0, 1.0, 0)) is None
|
||
assert td.update((10.5, 12.0, 9.8, 10.5, 1.0, 1)) == pytest.approx(-1.0)
|
||
|
||
|
||
def test_td_lines_uptrend_support_reference():
|
||
# Strictly rising series -> sell setup completes at idx 12, the
|
||
# lowest low across bars 4..=12 is the low at idx 4 = 4.5.
|
||
n = 20
|
||
high = np.arange(1.0, 1.0 + n) + 0.5
|
||
low = high - 1.0
|
||
close = high - 0.5
|
||
out = ta.TDLines().batch(high, low, close)
|
||
assert math.isnan(out[-1, 0])
|
||
assert out[-1, 1] == pytest.approx(4.5)
|
||
|
||
|
||
def test_td_risk_level_uptrend_sell_risk_reference():
|
||
# Strictly rising series -> sell setup completes at idx 12 with high
|
||
# 13.5 and true range 1.5 -> sell_risk = 13.5 + 1.5 = 15.0.
|
||
# Subsequent setups re-ratchet the level, so we check the first emission
|
||
# at idx 12 rather than the latest value.
|
||
n = 20
|
||
high = np.arange(1.0, 1.0 + n) + 0.5
|
||
low = high - 1.0
|
||
close = high - 0.5
|
||
out = ta.TDRiskLevel().batch(high, low, close)
|
||
assert math.isnan(out[12, 0])
|
||
assert out[12, 1] == pytest.approx(15.0)
|
||
|
||
|
||
def test_percentage_trailing_stop_seed_and_ratchet():
|
||
# 10% trail: first close 100 -> stop 90; next 110 -> stop max(90, 99) = 99.
|
||
s = ta.PercentageTrailingStop(10.0)
|
||
assert math.isclose(s.update(100.0), 90.0, abs_tol=1e-12)
|
||
assert math.isclose(s.update(110.0), 99.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_step_trailing_stop_snaps_below_close():
|
||
# step 1: floor((100.4 - 1) / 1) = 99.
|
||
s = ta.StepTrailingStop(1.0)
|
||
assert math.isclose(s.update(100.4), 99.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_renko_trailing_stop_holds_until_full_block():
|
||
# block 1: seed 100 -> stop 99; 100.5 still 99; 101 -> stop 100.
|
||
s = ta.RenkoTrailingStop(1.0)
|
||
assert math.isclose(s.update(100.0), 99.0, abs_tol=1e-12)
|
||
assert math.isclose(s.update(100.5), 99.0, abs_tol=1e-12)
|
||
assert math.isclose(s.update(101.0), 100.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_donchian_stop_window_extremes():
|
||
# 5-bar window of highs 1..5 and lows 0..4.
|
||
high = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
|
||
low = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
|
||
out = ta.DonchianStop(5).batch(high, low)
|
||
# First 4 rows NaN, fifth row: stop_long = 0, stop_short = 5.
|
||
for i in range(4):
|
||
assert math.isnan(out[i, 0])
|
||
assert math.isnan(out[i, 1])
|
||
assert math.isclose(out[4, 0], 0.0, abs_tol=1e-12)
|
||
assert math.isclose(out[4, 1], 5.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_hilo_activator_flat_market_holds_low_sma():
|
||
# Flat candles H=11, L=9, C=10 -> close (10) sits between bands, so the
|
||
# initial long seed is preserved: emitted stop = lo_sma = 9.
|
||
h = np.full(15, 11.0)
|
||
l = np.full(15, 9.0)
|
||
c = np.full(15, 10.0)
|
||
out = ta.HiLoActivator(3).batch(h, l, c)
|
||
# warmup_period == period + 1 == 4, so indices 0..2 are NaN; index 3 onwards is 9.
|
||
for i in range(3):
|
||
assert math.isnan(out[i])
|
||
for i in range(3, 15):
|
||
assert math.isclose(out[i], 9.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_volty_stop_flat_market_constant_level():
|
||
# ATR=2, mult=2 -> band 4; anchor stays at close 10 -> stop = 10 - 4 = 6.
|
||
h = np.full(20, 11.0)
|
||
l = np.full(20, 9.0)
|
||
c = np.full(20, 10.0)
|
||
out = ta.VoltyStop(5, 2.0).batch(h, l, c)
|
||
for i in range(4):
|
||
assert math.isnan(out[i])
|
||
for i in range(4, 20):
|
||
assert math.isclose(out[i], 6.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_yoyo_exit_flat_market_constant_level():
|
||
# ATR=2, mult=2 -> band 4; trail = close - band = 10 - 4 = 6 and holds.
|
||
h = np.full(20, 11.0)
|
||
l = np.full(20, 9.0)
|
||
c = np.full(20, 10.0)
|
||
out = ta.YoyoExit(5, 2.0).batch(h, l, c)
|
||
for i in range(4):
|
||
assert math.isnan(out[i])
|
||
for i in range(4, 20):
|
||
assert math.isclose(out[i], 6.0, abs_tol=1e-12)
|
||
|
||
|
||
def test_rvi_volatility_pure_uptrend_saturates_at_one_hundred():
|
||
# Strictly rising closes -> every stddev sample classified as "up" ->
|
||
# RVIVolatility saturates at 100. Renamed from the original ta.RVI in
|
||
# PR 42 to disambiguate from Family 02's Relative Vigor Index, which
|
||
# now owns the short ta.RVI name (candle input).
|
||
out = ta.RVIVolatility(5).batch(np.arange(1.0, 41.0, dtype=np.float64))
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
np.testing.assert_allclose(ready[-10:], 100.0, atol=1e-9)
|
||
|
||
|
||
def test_parkinson_volatility_zero_range_yields_zero():
|
||
# H == L every bar -> ln(H/L) = 0 -> Parkinson sigma is zero.
|
||
h = np.full(30, 10.0)
|
||
l = np.full(30, 10.0)
|
||
out = ta.ParkinsonVolatility(14, 252).batch(h, l)
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
|
||
|
||
|
||
def test_garman_klass_zero_movement_yields_zero():
|
||
# O == H == L == C every bar -> both log terms are zero -> sigma is zero.
|
||
o = np.full(30, 10.0)
|
||
h = np.full(30, 10.0)
|
||
l = np.full(30, 10.0)
|
||
c = np.full(30, 10.0)
|
||
out = ta.GarmanKlassVolatility(14, 252).batch(o, h, l, c)
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
|
||
|
||
|
||
def test_rogers_satchell_zero_movement_yields_zero():
|
||
o = np.full(30, 10.0)
|
||
h = np.full(30, 10.0)
|
||
l = np.full(30, 10.0)
|
||
c = np.full(30, 10.0)
|
||
out = ta.RogersSatchellVolatility(14, 252).batch(o, h, l, c)
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
|
||
|
||
|
||
def test_yang_zhang_zero_movement_yields_zero():
|
||
# O == H == L == C and constant across bars -> every sub-component is
|
||
# zero -> Yang-Zhang sigma is zero.
|
||
o = np.full(30, 10.0)
|
||
h = np.full(30, 10.0)
|
||
l = np.full(30, 10.0)
|
||
c = np.full(30, 10.0)
|
||
out = ta.YangZhangVolatility(14, 252).batch(o, h, l, c)
|
||
ready = out[~np.isnan(out)]
|
||
assert ready.size > 0
|
||
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
|