kingchenc
733afd9064
100 * (close - LinReg(close, period)) / close. Positive when close overshoots the linear forecast, negative when it undershoots. Holds the previous value if the close is zero (percentage form undefined). Single param period (default 14). Touchpoints: cfo.rs + mod.rs + lib.rs re-export, PyCfo + __init__.py + test_new_indicators SCALAR + test_known_values linear reference, CfoNode + index.d.ts/index.js + indicators.test.js factory + reference, WasmCfo via scalar macro, scalar-fuzz target, README + CHANGELOG.
138 lines
4.5 KiB
Python
138 lines
4.5 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_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_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])
|