Pratik Bhadane
307beeca02
Prepare the first public 1.0.0 release and finish the remaining CI hardening work. Highlights: - align Python, Rust, WASM, Conda, API, MCP, and docs version metadata to 1.0.0 - promote package metadata to Production/Stable and update stability/versioning docs for the stable series - move the accumulated Unreleased notes into a dated 1.0.0 changelog section and keep a fresh top-level Unreleased block - strengthen the changelog checker so it validates a single top-level Unreleased section - fix the CI/package support mismatch by declaring Python >=3.10 consistently and gating pandas-ta extras to Python 3.12+ - restore Sphinx autodoc compatibility for documented ferro_ta.<module> imports by registering module aliases - make the TA-Lib benchmark guardrail less flaky by checking median and tail-percentile speedups instead of failing on a single mild outlier - switch PyPI publishing to OIDC-only trusted publishing and wire the changelog check into the required CI gate - apply the Ruff-driven cleanup across the Python and test tree and refresh uv/cargo lockfiles Validated locally: - python3 scripts/check_changelog.py - uv run --with ruff ruff check python tests - uv run --with ruff ruff format --check python tests - uv lock --check - sphinx-build -b html docs docs/_build -W --keep-going - build/install the ferro_ta 1.0.0 wheel successfully
114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
"""Unit tests for ferro_ta.indicators.price_transform"""
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import numpy as np
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from ferro_ta.indicators.price_transform import AVGPRICE, MEDPRICE, TYPPRICE, WCLPRICE
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# ---------------------------------------------------------------------------
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# Shared fixtures
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# ---------------------------------------------------------------------------
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O = np.array([10.0, 11.0, 12.0, 13.0])
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H = np.array([12.0, 13.0, 14.0, 15.0])
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L = np.array([9.0, 10.0, 11.0, 12.0])
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C = np.array([11.0, 12.0, 13.0, 14.0])
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# ---------------------------------------------------------------------------
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# AVGPRICE
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# ---------------------------------------------------------------------------
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class TestAVGPRICE:
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def test_known_formula(self):
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result = AVGPRICE(O, H, L, C)
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expected = (O + H + L + C) / 4.0
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np.testing.assert_allclose(result, expected, rtol=1e-10)
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def test_first_bar(self):
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result = AVGPRICE(O, H, L, C)
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np.testing.assert_allclose(result[0], (10 + 12 + 9 + 11) / 4.0, rtol=1e-10)
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def test_no_nan(self):
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result = AVGPRICE(O, H, L, C)
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assert np.all(np.isfinite(result))
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def test_length(self):
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assert len(AVGPRICE(O, H, L, C)) == len(O)
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# ---------------------------------------------------------------------------
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# MEDPRICE
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# ---------------------------------------------------------------------------
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class TestMEDPRICE:
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def test_known_formula(self):
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result = MEDPRICE(H, L)
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expected = (H + L) / 2.0
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np.testing.assert_allclose(result, expected, rtol=1e-10)
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def test_first_bar(self):
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result = MEDPRICE(H, L)
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np.testing.assert_allclose(result[0], (12 + 9) / 2.0, rtol=1e-10)
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def test_no_nan(self):
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result = MEDPRICE(H, L)
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assert np.all(np.isfinite(result))
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def test_length(self):
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assert len(MEDPRICE(H, L)) == len(H)
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# ---------------------------------------------------------------------------
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# TYPPRICE
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# ---------------------------------------------------------------------------
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class TestTYPPRICE:
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def test_known_formula(self):
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result = TYPPRICE(H, L, C)
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expected = (H + L + C) / 3.0
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np.testing.assert_allclose(result, expected, rtol=1e-10)
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def test_first_bar(self):
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result = TYPPRICE(H, L, C)
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np.testing.assert_allclose(result[0], (12 + 9 + 11) / 3.0, rtol=1e-10)
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def test_no_nan(self):
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result = TYPPRICE(H, L, C)
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assert np.all(np.isfinite(result))
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def test_length(self):
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assert len(TYPPRICE(H, L, C)) == len(H)
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# ---------------------------------------------------------------------------
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# WCLPRICE
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# ---------------------------------------------------------------------------
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class TestWCLPRICE:
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def test_known_formula(self):
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result = WCLPRICE(H, L, C)
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expected = (H + L + 2.0 * C) / 4.0
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np.testing.assert_allclose(result, expected, rtol=1e-10)
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def test_first_bar(self):
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result = WCLPRICE(H, L, C)
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np.testing.assert_allclose(result[0], (12 + 9 + 2 * 11) / 4.0, rtol=1e-10)
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def test_no_nan(self):
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result = WCLPRICE(H, L, C)
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assert np.all(np.isfinite(result))
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def test_close_weight_double(self):
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# WCLPRICE weights close twice vs TYPPRICE
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wcl = WCLPRICE(H, L, C)
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# On a rising series (H > L > 0), WCLPRICE > TYPPRICE when C > (H+L)/2
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# Just verify formula correctness already done above
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assert np.all(np.isfinite(wcl))
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def test_length(self):
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assert len(WCLPRICE(H, L, C)) == len(H)
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