kingchenc
ec269d8aeb
EMA(close, fast) - EMA(close, slow). Like MACD without the signal EMA. Defaults to (fast = 12, slow = 26); fast must be strictly less than slow. Touchpoints: apo.rs + mod.rs + lib.rs re-export, PyApo + __init__.py + test_new_indicators SCALAR + test_known_values flat reference, ApoNode + index.d.ts/index.js + indicators.test.js factory + reference, WasmApo via scalar macro, scalar-fuzz target, README + CHANGELOG.
305 lines
9.5 KiB
Python
305 lines
9.5 KiB
Python
"""Streaming-vs-batch, shape and reference-value tests for the F1-F12 families.
|
|
|
|
Every indicator added since the original 25 is exercised here. The central
|
|
contract is the same as the rest of the suite: ``batch(...)`` must equal
|
|
repeated streaming ``update(...)`` across the whole warmup -> steady-state
|
|
transition, and batch shapes must match the input length.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import math
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import wickra as ta
|
|
|
|
|
|
def _eq_nan(a: np.ndarray, b: np.ndarray, tol: float = 1e-9) -> bool:
|
|
"""Compare two float arrays treating NaN positions as equal."""
|
|
a = np.asarray(a, dtype=np.float64)
|
|
b = np.asarray(b, dtype=np.float64)
|
|
if a.shape != b.shape:
|
|
return False
|
|
both_nan = np.isnan(a) & np.isnan(b)
|
|
return bool(np.all(np.where(both_nan, 0.0, np.abs(a - b)) <= tol))
|
|
|
|
|
|
@pytest.fixture
|
|
def ohlcv() -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
|
|
"""Synthetic high / low / close / volume series, 200 bars."""
|
|
t = np.arange(200, dtype=np.float64)
|
|
close = 100.0 + np.sin(t * 0.15) * 8.0 + np.cos(t * 0.32) * 3.0
|
|
spread = 0.5 + np.abs(np.sin(t * 0.07))
|
|
high = close + spread
|
|
low = close - spread
|
|
volume = 1000.0 + (t % 7) * 50.0
|
|
return high, low, close, volume
|
|
|
|
|
|
# --- Scalar (f64 -> f64) indicators ---------------------------------------
|
|
|
|
SCALAR = [
|
|
(ta.SMMA, (14,)),
|
|
(ta.TRIMA, (20,)),
|
|
(ta.ZLEMA, (14,)),
|
|
(ta.T3, (5, 0.7)),
|
|
(ta.MOM, (10,)),
|
|
(ta.CMO, (14,)),
|
|
(ta.TSI, (25, 13)),
|
|
(ta.PMO, (35, 20)),
|
|
(ta.StochRSI, (14, 14)),
|
|
(ta.PPO, (12, 26)),
|
|
(ta.APO, (12, 26)),
|
|
(ta.DPO, (20,)),
|
|
(ta.Coppock, (14, 11, 10)),
|
|
(ta.StdDev, (20,)),
|
|
(ta.UlcerIndex, (14,)),
|
|
(ta.HistoricalVolatility, (20, 252)),
|
|
(ta.BollingerBandwidth, (20, 2.0)),
|
|
(ta.PercentB, (20, 2.0)),
|
|
(ta.LinearRegression, (14,)),
|
|
(ta.LinRegSlope, (14,)),
|
|
(ta.VerticalHorizontalFilter, (28,)),
|
|
(ta.ZScore, (20,)),
|
|
(ta.LinRegAngle, (14,)),
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize("cls, args", SCALAR, ids=[c.__name__ for c, _ in SCALAR])
|
|
def test_scalar_streaming_matches_batch(cls, args, sine_prices):
|
|
batch = cls(*args).batch(sine_prices)
|
|
assert batch.shape == sine_prices.shape
|
|
assert batch.dtype == np.float64
|
|
|
|
streamer = cls(*args)
|
|
streamed = []
|
|
for p in sine_prices:
|
|
v = streamer.update(float(p))
|
|
streamed.append(math.nan if v is None else float(v))
|
|
assert _eq_nan(batch, np.array(streamed, dtype=np.float64))
|
|
|
|
|
|
# --- Candle-input, single-output indicators -------------------------------
|
|
#
|
|
# Each entry is (factory, batch-call). Streaming always feeds the full
|
|
# 6-tuple candle; the batch helper takes only the columns it needs.
|
|
|
|
CANDLE_SCALAR = {
|
|
"VWMA": (lambda: ta.VWMA(20), lambda ind, h, l, c, v: ind.batch(c, v)),
|
|
"UltimateOscillator": (
|
|
lambda: ta.UltimateOscillator(7, 14, 28),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"AroonOscillator": (
|
|
lambda: ta.AroonOscillator(14),
|
|
lambda ind, h, l, c, v: ind.batch(h, l),
|
|
),
|
|
"NATR": (lambda: ta.NATR(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
|
|
"MassIndex": (lambda: ta.MassIndex(9, 25), lambda ind, h, l, c, v: ind.batch(h, l)),
|
|
"ADL": (lambda: ta.ADL(), lambda ind, h, l, c, v: ind.batch(h, l, c, v)),
|
|
"VolumePriceTrend": (
|
|
lambda: ta.VolumePriceTrend(),
|
|
lambda ind, h, l, c, v: ind.batch(c, v),
|
|
),
|
|
"ChaikinMoneyFlow": (
|
|
lambda: ta.ChaikinMoneyFlow(20),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c, v),
|
|
),
|
|
"ChaikinOscillator": (
|
|
lambda: ta.ChaikinOscillator(3, 10),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c, v),
|
|
),
|
|
"ForceIndex": (
|
|
lambda: ta.ForceIndex(13),
|
|
lambda ind, h, l, c, v: ind.batch(c, v),
|
|
),
|
|
"EaseOfMovement": (
|
|
lambda: ta.EaseOfMovement(14),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, v),
|
|
),
|
|
"AtrTrailingStop": (
|
|
lambda: ta.AtrTrailingStop(14, 3.0),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"TypicalPrice": (
|
|
lambda: ta.TypicalPrice(),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"MedianPrice": (
|
|
lambda: ta.MedianPrice(),
|
|
lambda ind, h, l, c, v: ind.batch(h, l),
|
|
),
|
|
"WeightedClose": (
|
|
lambda: ta.WeightedClose(),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"AcceleratorOscillator": (
|
|
lambda: ta.AcceleratorOscillator(5, 34, 5),
|
|
lambda ind, h, l, c, v: ind.batch(h, l),
|
|
),
|
|
"BalanceOfPower": (
|
|
# The streaming 6-tuple feeds open == close, so batch matches with
|
|
# the close column standing in for open.
|
|
lambda: ta.BalanceOfPower(),
|
|
lambda ind, h, l, c, v: ind.batch(c, h, l, c),
|
|
),
|
|
"ChoppinessIndex": (
|
|
lambda: ta.ChoppinessIndex(14),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"TrueRange": (
|
|
lambda: ta.TrueRange(),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"ChaikinVolatility": (
|
|
lambda: ta.ChaikinVolatility(10, 10),
|
|
lambda ind, h, l, c, v: ind.batch(h, l),
|
|
),
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("name", list(CANDLE_SCALAR))
|
|
def test_candle_scalar_streaming_matches_batch(name, ohlcv):
|
|
high, low, close, volume = ohlcv
|
|
make, batch_call = CANDLE_SCALAR[name]
|
|
|
|
batch = batch_call(make(), high, low, close, volume)
|
|
assert batch.shape == close.shape
|
|
|
|
streamer = make()
|
|
streamed = []
|
|
for i in range(close.size):
|
|
candle = (
|
|
float(close[i]),
|
|
float(high[i]),
|
|
float(low[i]),
|
|
float(close[i]),
|
|
float(volume[i]),
|
|
i,
|
|
)
|
|
v = streamer.update(candle)
|
|
streamed.append(math.nan if v is None else float(v))
|
|
assert _eq_nan(batch, np.array(streamed, dtype=np.float64)), f"{name} mismatch"
|
|
|
|
|
|
# --- Candle-input, multi-output indicators --------------------------------
|
|
|
|
MULTI = {
|
|
"Vortex": (lambda: ta.Vortex(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
|
|
"SuperTrend": (
|
|
lambda: ta.SuperTrend(10, 3.0),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"ChandelierExit": (
|
|
lambda: ta.ChandelierExit(22, 3.0),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
"ChandeKrollStop": (
|
|
lambda: ta.ChandeKrollStop(10, 1.0, 9),
|
|
lambda ind, h, l, c, v: ind.batch(h, l, c),
|
|
),
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("name", list(MULTI))
|
|
def test_multi_streaming_matches_batch(name, ohlcv):
|
|
high, low, close, volume = ohlcv
|
|
make, batch_call = MULTI[name]
|
|
|
|
batch = batch_call(make(), high, low, close, volume)
|
|
assert batch.shape == (close.size, 2)
|
|
|
|
streamer = make()
|
|
rows = []
|
|
for i in range(close.size):
|
|
candle = (
|
|
float(close[i]),
|
|
float(high[i]),
|
|
float(low[i]),
|
|
float(close[i]),
|
|
float(volume[i]),
|
|
i,
|
|
)
|
|
v = streamer.update(candle)
|
|
rows.append([math.nan, math.nan] if v is None else list(v))
|
|
assert _eq_nan(batch, np.array(rows, dtype=np.float64)), f"{name} mismatch"
|
|
|
|
|
|
# --- Reference values -----------------------------------------------------
|
|
|
|
|
|
def test_typical_price_reference():
|
|
# (high + low + close) / 3 = (12 + 6 + 9) / 3 = 9.
|
|
assert ta.TypicalPrice().update((9.0, 12.0, 6.0, 9.0, 1.0, 0)) == pytest.approx(9.0)
|
|
|
|
|
|
def test_median_price_reference():
|
|
# (high + low) / 2 = (12 + 8) / 2 = 10.
|
|
assert ta.MedianPrice().update((10.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(10.0)
|
|
|
|
|
|
def test_weighted_close_reference():
|
|
# (high + low + 2*close) / 4 = (12 + 8 + 22) / 4 = 10.5.
|
|
assert ta.WeightedClose().update((10.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(
|
|
10.5
|
|
)
|
|
|
|
|
|
def test_chaikin_money_flow_reference():
|
|
cmf = ta.ChaikinMoneyFlow(2)
|
|
assert cmf.update((8.0, 10.0, 8.0, 10.0, 100.0, 0)) is None
|
|
assert cmf.update((10.0, 12.0, 8.0, 10.0, 100.0, 1)) == pytest.approx(0.5)
|
|
|
|
|
|
def test_linear_regression_reference():
|
|
out = ta.LinearRegression(3).batch(np.array([1.0, 2.0, 9.0]))
|
|
assert math.isnan(out[0]) and math.isnan(out[1])
|
|
assert out[2] == pytest.approx(8.0)
|
|
|
|
|
|
def test_linreg_slope_reference():
|
|
out = ta.LinRegSlope(3).batch(np.array([1.0, 2.0, 9.0]))
|
|
assert math.isnan(out[0]) and math.isnan(out[1])
|
|
assert out[2] == pytest.approx(4.0)
|
|
|
|
|
|
def test_balance_of_power_reference():
|
|
# (close - open) / (high - low) = (12 - 10) / (14 - 10) = 0.5.
|
|
bop = ta.BalanceOfPower()
|
|
assert bop.update((10.0, 14.0, 10.0, 12.0, 1.0, 0)) == pytest.approx(0.5)
|
|
|
|
|
|
def test_true_range_reference():
|
|
tr = ta.TrueRange()
|
|
assert tr.update((11.0, 12.0, 8.0, 11.0, 1.0, 0)) == pytest.approx(4.0)
|
|
assert tr.update((9.5, 10.0, 9.0, 9.5, 1.0, 1)) == pytest.approx(2.0)
|
|
|
|
|
|
def test_linreg_angle_reference():
|
|
# A series rising by 1 per step has slope 1, and atan(1) = 45 degrees.
|
|
out = ta.LinRegAngle(5).batch(np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]))
|
|
assert out[4] == pytest.approx(45.0)
|
|
|
|
|
|
def test_z_score_reference():
|
|
# Window [1, 3]: mean 2, population stddev 1; latest 3 -> z = 1.
|
|
out = ta.ZScore(2).batch(np.array([1.0, 3.0]))
|
|
assert math.isnan(out[0])
|
|
assert out[1] == pytest.approx(1.0)
|
|
|
|
|
|
# --- Lifecycle ------------------------------------------------------------
|
|
|
|
|
|
def test_new_indicators_expose_lifecycle():
|
|
instances = [make() for make, _ in CANDLE_SCALAR.values()]
|
|
instances += [make() for make, _ in MULTI.values()]
|
|
instances += [cls(*args) for cls, args in SCALAR]
|
|
for ind in instances:
|
|
assert ind.is_ready() is False
|
|
assert ind.warmup_period() >= 1
|
|
ind.reset()
|
|
assert ind.is_ready() is False
|