kingchenc
d9d3ad18aa
* feat(apo): add Absolute Price Oscillator 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. * fix(apo): add PyApo + ApoNode + WasmApo bindings missed fromec269d8The previous APO commit (ec269d8) only registered APO in the Python __init__.py / Node index.js / Node index.d.ts / fuzz / tests / docs. The actual PyApo pyclass, ApoNode napi class, and WasmApo wasm class edits silently no-op'd because the underlying lib.rs files had been touched by a branch switch between Read and Edit. The bindings were therefore advertising APO from the Python module / Node package / WASM module but not actually exposing it. Fix: insert PyApo block + add_class call in bindings/python/src/lib.rs, ApoNode block in bindings/node/src/lib.rs, WasmApo macro line in bindings/wasm/src/lib.rs. cargo test workspace stays at 615 (no new tests added; the existing test_known_values + indicators.test.js references would have failed at import once the bindings rebuilt without these classes). * feat(ao-histogram): add Awesome Oscillator Histogram AO - SMA(AO, sma_period). A configurable variant of the existing AcceleratorOscillator (which fixes fast=5, slow=34, sma=5). Three parameters; defaults match Bill Williams' Accelerator. Touchpoints: awesome_oscillator_histogram.rs + mod.rs + lib.rs re-export, PyAoHist + __init__.py + test_new_indicators CANDLE_SCALAR + test_known_values flat reference, AwesomeOscillatorHistogramNode + index.d.ts/index.js + indicators.test.js factory + reference, WasmAoHist, candle-fuzz target, README + CHANGELOG. * feat(cfo): add Chande Forecast Oscillator 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. * fix(cfo): add WasmCfo binding missed from733afd9* feat(zero-lag-macd): add Zero-Lag MACD Classic MACD topology with ZLEMA substituted for EMA everywhere: faster reaction to trend changes at the cost of slightly noisier readings. Multi-output ZeroLagMacdOutput { macd, signal, histogram }. Three parameters (fast = 12, slow = 26, signal = 9); fast must be strictly less than slow. Touchpoints: zero_lag_macd.rs + mod.rs + lib.rs re-export, PyZeroLagMacd + __init__.py + test_new_indicators MULTI + test_known_values flat reference, ZeroLagMacdNode + ZeroLagMacdValue + index.d.ts/index.js + indicators.test.js multi factory + reference, WasmZeroLagMacd, scalar fuzz with hand-rolled drive (multi-output bypasses the f64-only helper), README + CHANGELOG. * feat(elder-impulse): add Alexander Elder Impulse System Tri-state momentum gauge: +1 (green/buy) when EMA trend and MACD histogram both rise, -1 (red/sell) when both fall, 0 (blue/neutral) on disagreement. Four parameters (ema_period, macd_fast, macd_slow, macd_signal); defaults (13, 12, 26, 9) match Elder. Internally feeds both branches on every input so they warm in parallel; needs one bar past the slowest branch to seed direction state. Touchpoints: elder_impulse.rs + mod.rs + lib.rs re-export, PyElderImpulse + __init__.py + test_new_indicators SCALAR + test_known_values neutral reference, ElderImpulseNode + index.d.ts/index.js + indicators.test.js factory + reference, WasmElderImpulse via scalar macro, scalar-fuzz target, README + CHANGELOG. * feat(stc): add Schaff Trend Cycle Doug Schaff's doubly-Stochastic-smoothed MACD. Bounded [0, 100] reading that reacts faster than MACD by extracting the percentile of MACD within a recent window, half-EMA-smoothing it, and re-stochasing the smoothed series. Four parameters (fast = 23, slow = 50, schaff_period = 10, factor = 0.5); fast must be strictly less than slow and factor must lie in (0, 1]. Output clamped to [0, 100] to absorb floating-point rounding. The stochastic stages clamp to 0 when their rolling range collapses (flat input or perfectly monotone trend), so a flat series settles deterministically at 0 after warmup. Touchpoints: stc.rs + mod.rs + lib.rs re-export, PyStc + __init__.py + test_new_indicators SCALAR + test_known_values flat reference, StcNode + index.d.ts/index.js + indicators.test.js factory + reference, WasmStc via scalar macro, scalar-fuzz target, README + CHANGELOG. * fix(stc): rename last_stc -> last_value to satisfy clippy * ci: Retry setup-node and setup-python on CDN flakes Setup-node on Windows runners and setup-python across all OSes occasionally fail with a silent hang or 5xx mid-download ("Attempting to download 18..." → fail in <1s) — pure upstream CDN flake. The fix ran on this branch's previous merge commit (24e723f) had to be re-triggered manually via `gh run rerun --failed`. Wrap both setup actions with continue-on-error and a follow-up retry step that waits 30s and re-runs the same setup. The retry only fires when the first attempt failed (steps.<id>.outcome == 'failure'), so a green setup costs nothing extra. The retry uses the identical pinned SHA so we still get supply-chain verification on both attempts. Applied to ci.yml (Python matrix and Node matrix). release.yml has the same setup-node / setup-python steps but is rarely re-run, so the existing manual rerun pattern stays sufficient for now. * test(zero-lag-macd): Fix MULTI dict shape mismatch + cover warmup_period ZeroLagMACD was registered in the Python MULTI dict (which asserts a (n, 2) batch shape) but actually emits (n, 3) — macd, signal, histogram — like MACD. Moved out into its own standalone test test_zero_lag_macd_streaming_matches_batch (3-tuple shape), and included in the lifecycle sweep. Mirrors the existing Alligator pattern for 3-output candle indicators. Also adds a unit test for ZeroLagMacd::warmup_period that pins both the (12, 26, 9) classic case and a small-period config — these four lines were the codecov/patch miss on PR 41.
333 lines
12 KiB
Python
333 lines
12 KiB
Python
"""Reference-value tests that pin numerical behaviour from the Python side."""
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from __future__ import annotations
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import math
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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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def test_sma_constant_series():
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out = ta.SMA(5).batch(np.full(20, 42.0, dtype=np.float64))
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# First 4 are warmup -> NaN; rest equal 42.
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assert np.all(np.isnan(out[:4]))
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assert np.allclose(out[4:], 42.0)
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def test_sma_known_window():
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# SMA(3) of [2, 4, 6, 8, 10] -> [_, _, 4, 6, 8]
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out = ta.SMA(3).batch(np.array([2.0, 4.0, 6.0, 8.0, 10.0]))
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assert math.isnan(out[0]) and math.isnan(out[1])
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np.testing.assert_allclose(out[2:], [4.0, 6.0, 8.0])
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def test_ema_seed_equals_simple_mean_of_first_window():
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# EMA(5) seed = mean([10, 20, 30, 40, 50]) = 30
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out = ta.EMA(5).batch(np.array([10.0, 20.0, 30.0, 40.0, 50.0]))
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assert math.isnan(out[0])
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assert math.isclose(out[4], 30.0, abs_tol=1e-12)
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def test_wma_known_window():
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# WMA(4) of [1, 2, 3, 4] = (1*1 + 2*2 + 3*3 + 4*4)/10 = 3
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out = ta.WMA(4).batch(np.array([1.0, 2.0, 3.0, 4.0]))
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assert math.isnan(out[0]) and math.isnan(out[1]) and math.isnan(out[2])
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assert math.isclose(out[3], 3.0, abs_tol=1e-12)
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def test_rsi_pure_uptrend_is_100():
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out = ta.RSI(14).batch(np.arange(1.0, 21.0, dtype=np.float64))
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np.testing.assert_allclose(out[14:], 100.0)
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def test_rsi_pure_downtrend_is_0():
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out = ta.RSI(14).batch(np.arange(20.0, 0.0, -1.0))
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np.testing.assert_allclose(out[14:], 0.0)
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def test_rsi_flat_series_is_50():
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out = ta.RSI(14).batch(np.full(30, 100.0))
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np.testing.assert_allclose(out[14:], 50.0)
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def test_rsi_wilder_textbook_first_value():
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"""Wilder's original 14-period example, ~70.46 at the first emit."""
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prices = np.array(
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[
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44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42, 45.84, 46.08,
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45.89, 46.03, 45.61, 46.28, 46.28,
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],
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dtype=np.float64,
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)
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out = ta.RSI(14).batch(prices)
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assert math.isclose(out[14], 70.464, abs_tol=0.05)
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def test_inertia_constant_rvi_passes_through_linreg():
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# Every bar identical (open, high, low, close) = (10, 11, 9, 10.5):
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# RVI = (c-o) / (h-l) = 0.5 / 2 = 0.25 every bar. LinReg of a constant
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# series equals that constant after warmup.
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n = 60
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out = ta.Inertia(3, 4).batch(
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np.full(n, 10.0), np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.5)
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)
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# warmup_period = 3 + 4 - 1 = 6.
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np.testing.assert_allclose(out[5:], 0.25, atol=1e-12)
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def test_connors_rsi_output_is_bounded():
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# CRSI is the average of three [0, 100] components, so the aggregate must
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# also sit in [0, 100] after warmup.
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prices = 100.0 + 20.0 * np.sin(np.linspace(0, 30, 250))
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out = ta.ConnorsRSI(3, 2, 100).batch(prices.astype(np.float64))
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ready = out[~np.isnan(out)]
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assert ready.size > 0
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assert ready.min() >= 0.0
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assert ready.max() <= 100.0
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def test_laguerre_rsi_constant_series_stays_at_mid_band():
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# All four Laguerre stages seed to the first input, so subsequent flat
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# inputs keep them equal and the up/down accumulator is 0 — Wickra maps
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# that to the neutral 50.
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out = ta.LaguerreRSI(0.5).batch(np.full(40, 42.0, dtype=np.float64))
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np.testing.assert_allclose(out, 50.0, atol=1e-12)
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def test_smi_close_at_centre_yields_zero():
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# Close at the midpoint of a flat high/low range -> displacement is
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# always zero -> SMI converges to 0.
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n = 60
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out = ta.SMI(5, 3, 3).batch(np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.0))
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# warmup_period = 5 + 3 + 3 - 2 = 9.
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np.testing.assert_allclose(out[8:], 0.0, atol=1e-12)
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def test_kst_constant_series_yields_zero():
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# ROC is zero on a flat input, so every RCMA is zero, so KST and its
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# signal SMA are both zero after warmup.
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kst = ta.KST(10, 15, 20, 30, 10, 10, 10, 15, 9)
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out = kst.batch(np.full(80, 42.0, dtype=np.float64))
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warmup = kst.warmup_period()
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# Use NaN-safe comparison on the post-warmup tail.
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tail = out[warmup - 1 :]
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assert np.all(np.isfinite(tail))
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np.testing.assert_allclose(tail, 0.0, atol=1e-12)
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def test_pgo_flat_close_yields_zero():
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# On a constant close the numerator (close − SMA) is zero, so PGO emits 0
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# regardless of the TR-EMA in the denominator.
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n = 20
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high = np.full(n, 11.0)
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low = np.full(n, 9.0)
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close = np.full(n, 10.0)
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out = ta.PGO(5).batch(high, low, close)
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assert np.all(np.isnan(out[:4]))
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np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
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def test_rvi_reference_value_period_2():
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# Two bars: (open, high, low, close) = (10, 11, 9, 10.5), (10.5, 11.5, 10, 11).
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# num = (0.5 + 0.5) = 1.0; den = (2.0 + 1.5) = 3.5; RVI = 1 / 3.5.
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out = ta.RVI(2).batch(
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np.array([10.0, 10.5]),
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np.array([11.0, 11.5]),
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np.array([9.0, 10.0]),
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np.array([10.5, 11.0]),
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)
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assert math.isnan(out[0])
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assert math.isclose(out[1], 1.0 / 3.5, abs_tol=1e-12)
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def test_alma_constant_series_yields_the_constant():
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# ALMA's Gaussian weights are normalised, so any constant series is
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# reproduced exactly after warmup.
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out = ta.ALMA(9, 0.85, 6.0).batch(np.full(30, 42.0, dtype=np.float64))
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assert np.all(np.isnan(out[:8]))
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np.testing.assert_allclose(out[8:], 42.0, atol=1e-12)
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def test_alma_reference_value_period_3():
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# ALMA(period=3, offset=0.85, sigma=6) on [10, 20, 30].
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# m = 0.85 * 2 = 1.7; s = 3 / 6 = 0.5; 2*s^2 = 0.5.
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out = ta.ALMA(3, 0.85, 6.0).batch(np.array([10.0, 20.0, 30.0]))
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assert math.isnan(out[0]) and math.isnan(out[1])
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# Independently compute the expected Gaussian-weighted sum.
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w = np.exp(-((np.arange(3, dtype=np.float64) - 1.7) ** 2) / 0.5)
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expected = float(np.dot([10.0, 20.0, 30.0], w) / w.sum())
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assert math.isclose(out[2], expected, abs_tol=1e-12)
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# Sanity: heavy offset toward the newest sample lifts the average above
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# the simple mean of 20.
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assert out[2] > 20.0
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def test_mcginley_dynamic_constant_series_yields_the_constant():
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# ratio = 1, so the recurrence collapses to MD + 0 / divisor = MD.
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out = ta.McGinleyDynamic(5).batch(np.full(30, 42.0, dtype=np.float64))
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assert np.all(np.isnan(out[:4]))
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np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
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def test_mcginley_dynamic_reference_value():
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# Period 3, seed = SMA([10, 20, 30]) = 20.0. Next price 40.0:
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# ratio = 2; divisor = 0.6 * 3 * 16 = 28.8; next = 20 + 20/28.8.
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out = ta.McGinleyDynamic(3).batch(np.array([10.0, 20.0, 30.0, 40.0]))
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assert math.isnan(out[0]) and math.isnan(out[1])
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assert math.isclose(out[2], 20.0, abs_tol=1e-12)
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expected = 20.0 + 20.0 / (0.6 * 3.0 * 16.0)
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assert math.isclose(out[3], expected, abs_tol=1e-12)
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def test_frama_constant_series_yields_the_constant():
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# Flat input -> degenerate ranges -> alpha clamps to 0.01 and the EMA
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# recurrence holds the seed value.
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out = ta.FRAMA(4).batch(np.full(20, 42.0, dtype=np.float64))
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assert np.all(np.isnan(out[:3]))
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np.testing.assert_allclose(out[3:], 42.0, atol=1e-12)
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def test_frama_pure_uptrend_hugs_latest():
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# Monotonic uptrend -> alpha pushed toward 1.0, FRAMA tracks close.
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out = ta.FRAMA(4).batch(np.arange(1.0, 9.0, dtype=np.float64))
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assert math.isclose(out[-1], 8.0, abs_tol=0.05)
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def test_jma_constant_series_yields_the_constant():
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# JMA seeds e0 and the output to the first input, so a constant series
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# is reproduced exactly from the first sample.
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out = ta.JMA(14, 0.0, 2).batch(np.full(30, 42.0, dtype=np.float64))
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np.testing.assert_allclose(out, 42.0, atol=1e-12)
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def test_evwma_reference_value_period_2():
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# EVWMA(2). Bars: (close, volume) = (10, 1), (20, 3), (30, 1).
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# Bar 2: sum_v = 4, seeded prev = 20, EVWMA = (1*20 + 3*20)/4 = 20.
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# Bar 3: sum_v = 4 (drops 1, gains 1), EVWMA = (3*20 + 1*30)/4 = 22.5.
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out = ta.EVWMA(2).batch(np.array([10.0, 20.0, 30.0]), np.array([1.0, 3.0, 1.0]))
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assert math.isnan(out[0])
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assert math.isclose(out[1], 20.0, abs_tol=1e-12)
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assert math.isclose(out[2], 22.5, abs_tol=1e-12)
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def test_alligator_constant_series_holds_at_median_price():
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# Median price = (11 + 9) / 2 = 10 on every candle, so all three SMMAs
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# seed at 10 and stay there.
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n = 30
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high = np.full(n, 11.0)
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low = np.full(n, 9.0)
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out = ta.Alligator(13, 8, 5).batch(high, low)
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assert out.shape == (n, 3)
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for row in out[12:]:
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assert math.isclose(row[0], 10.0, abs_tol=1e-12)
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assert math.isclose(row[1], 10.0, abs_tol=1e-12)
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assert math.isclose(row[2], 10.0, abs_tol=1e-12)
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def test_vidya_constant_series_holds_seed():
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# CMO = 0 on a flat series -> alpha = 0 -> VIDYA holds its seed value.
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out = ta.VIDYA(14, 4).batch(np.full(20, 42.0, dtype=np.float64))
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assert np.all(np.isnan(out[:4]))
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np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
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def test_zero_lag_macd_constant_series_converges_to_zero():
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# Each inner ZLEMA reproduces a constant, so macd, signal and histogram
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# are all 0 once the slowest branch warms up.
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out = ta.ZeroLagMACD(3, 5, 3).batch(np.full(60, 42.0, dtype=np.float64))
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# Take the last row and verify all three columns are 0.
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last = out[-1]
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assert math.isclose(last[0], 0.0, abs_tol=1e-12)
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assert math.isclose(last[1], 0.0, abs_tol=1e-12)
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assert math.isclose(last[2], 0.0, abs_tol=1e-12)
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def test_awesome_oscillator_histogram_flat_series_converges_to_zero():
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# Flat median price -> AO = 0 -> SMA(AO) = 0 -> AOHist = 0.
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n = 50
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high = np.full(n, 11.0)
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low = np.full(n, 9.0)
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out = ta.AwesomeOscillatorHistogram(3, 5, 3).batch(high, low)
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# warmup = slow + sma - 1 = 5 + 3 - 1 = 7.
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np.testing.assert_allclose(out[6:], 0.0, atol=1e-12)
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def test_stc_constant_series_yields_zero():
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# Flat input collapses both stochastic stages to zero -> STC stays at 0.
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out = ta.STC(3, 5, 4, 0.5).batch(np.full(60, 42.0, dtype=np.float64))
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ready = out[~np.isnan(out)]
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assert ready.size > 0
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np.testing.assert_array_equal(ready[-5:], np.zeros(5))
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def test_elder_impulse_constant_series_is_neutral():
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# Flat input -> neither EMA nor MACD histogram moves -> Impulse stays at 0.
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out = ta.ElderImpulse(13, 12, 26, 9).batch(np.full(120, 42.0, dtype=np.float64))
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ready = out[~np.isnan(out)]
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assert ready.size > 0
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np.testing.assert_array_equal(ready, np.zeros_like(ready))
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def test_cfo_perfect_linear_series_yields_zero():
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# LinReg of a perfectly linear series fits exactly, so CFO = 0 after warmup.
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out = ta.CFO(5).batch(np.arange(1.0, 21.0, dtype=np.float64) * 2.0)
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np.testing.assert_allclose(out[4:], 0.0, atol=1e-9)
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def test_apo_constant_series_converges_to_zero():
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# Both EMAs reproduce a constant exactly, so APO = 0 after warmup.
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out = ta.APO(3, 5).batch(np.full(30, 42.0, dtype=np.float64))
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assert np.all(np.isnan(out[:4]))
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np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
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def test_macd_constant_series_converges_to_zero():
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out = ta.MACD().batch(np.full(200, 100.0))
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# Last row's MACD and signal must be ~0.
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last = out[-1]
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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])
|