release: cut v1.0.0
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
This commit is contained in:
Pratik Bhadane
@@ -11,7 +11,6 @@ All tests use NO optional dependencies - they run in every CI environment.
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from __future__ import annotations
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import numpy as np
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import pytest
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import ferro_ta
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@@ -81,7 +80,7 @@ class TestEMAKnownValues:
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# After warmup, EMA should be strictly increasing
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for i in range(20, len(result) - 1):
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assert result[i + 1] > result[i], (
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f"EMA not increasing at index {i}: {result[i]} >= {result[i+1]}"
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f"EMA not increasing at index {i}: {result[i]} >= {result[i + 1]}"
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)
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@@ -195,9 +194,9 @@ class TestRSIKnownValues:
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valid_values = result[~np.isnan(result)]
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if len(valid_values) > 0:
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# Should be either NaN everywhere or 100 everywhere
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assert np.all(np.abs(valid_values - 100.0) < 1e-10) or np.all(np.abs(valid_values - 50.0) < 5.0), (
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"RSI of constant series should be 100 (no down movement) or close to 50"
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)
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assert np.all(np.abs(valid_values - 100.0) < 1e-10) or np.all(
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np.abs(valid_values - 50.0) < 5.0
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), "RSI of constant series should be 100 (no down movement) or close to 50"
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# ---------------------------------------------------------------------------
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@@ -229,12 +228,63 @@ class TestATRKnownValues:
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# Bar 0: H=11, L=9, C=10
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# Bar 1: H=13, L=10, C=12 → TR = max(13-10, |13-10|, |10-10|) = 3
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# Bar 2: H=14, L=11, C=13 → TR = max(14-11, |14-12|, |11-12|) = 3
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high = np.array([11.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0,
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22.0, 23.0, 24.0, 25.0, 26.0])
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low = np.array([9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
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19.0, 20.0, 21.0, 22.0, 23.0])
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close = np.array([10.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0,
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21.0, 22.0, 23.0, 24.0, 25.0])
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high = np.array(
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[
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11.0,
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13.0,
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14.0,
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15.0,
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16.0,
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17.0,
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18.0,
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19.0,
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20.0,
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21.0,
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22.0,
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23.0,
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24.0,
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25.0,
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26.0,
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]
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)
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low = np.array(
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[
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9.0,
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10.0,
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11.0,
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12.0,
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13.0,
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14.0,
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15.0,
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16.0,
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17.0,
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18.0,
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19.0,
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20.0,
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21.0,
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22.0,
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23.0,
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]
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)
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close = np.array(
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[
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10.0,
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12.0,
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13.0,
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14.0,
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15.0,
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16.0,
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17.0,
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18.0,
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19.0,
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20.0,
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21.0,
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22.0,
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23.0,
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24.0,
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25.0,
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]
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)
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# For period=1, ATR still has warmup. Use TRANGE to check TR values directly
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tr = ferro_ta.TRANGE(high, low, close)
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@@ -263,7 +313,7 @@ class TestMOMKnownValues:
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assert np.isnan(result[0])
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assert np.isnan(result[1])
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assert np.abs(result[2] - 5.0) < 1e-10 # 15 - 10 = 5
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assert np.abs(result[2] - 5.0) < 1e-10 # 15 - 10 = 5
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assert np.abs(result[3] - (-1.0)) < 1e-10 # 11 - 12 = -1
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@@ -295,7 +345,9 @@ class TestMACDKnownValues:
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def test_macd_histogram_identity(self):
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"""histogram should always equal macd - signal."""
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data = np.arange(1.0, 51.0)
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macd, signal, histogram = ferro_ta.MACD(data, fastperiod=12, slowperiod=26, signalperiod=9)
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macd, signal, histogram = ferro_ta.MACD(
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data, fastperiod=12, slowperiod=26, signalperiod=9
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)
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# histogram = macd - signal (within floating-point tolerance)
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expected_histogram = macd - signal
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@@ -338,9 +390,9 @@ class TestVWAPKnownValues:
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expected_0 = typ[0]
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expected_1 = (typ[0] * volume[0] + typ[1] * volume[1]) / (volume[0] + volume[1])
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expected_2 = (
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typ[0] * volume[0] + typ[1] * volume[1] + typ[2] * volume[2]
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) / (volume[0] + volume[1] + volume[2])
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expected_2 = (typ[0] * volume[0] + typ[1] * volume[1] + typ[2] * volume[2]) / (
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volume[0] + volume[1] + volume[2]
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)
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assert np.abs(result[0] - expected_0) < 1e-10
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assert np.abs(result[1] - expected_1) < 1e-10
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@@ -359,7 +411,6 @@ class TestDONCHIANKnownValues:
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"""upper == MAX(high), lower == MIN(low), middle == (upper+lower)/2."""
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high = np.array([11.0, 13.0, 14.0, 12.0, 15.0])
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low = np.array([9.0, 10.0, 11.0, 10.0, 12.0])
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close = np.array([10.0, 12.0, 13.0, 11.0, 14.0])
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period = 3
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upper, middle, lower = ferro_ta.DONCHIAN(high, low, timeperiod=period)
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@@ -394,7 +445,9 @@ class TestPIVOT_POINTSKnownValues:
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low = np.array([90.0, 90.0])
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close = np.array([100.0, 100.0])
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pivot, r1, s1, r2, s2 = ferro_ta.PIVOT_POINTS(high, low, close, method="classic")
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pivot, r1, s1, r2, s2 = ferro_ta.PIVOT_POINTS(
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high, low, close, method="classic"
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)
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# Check last bar (index 1) which has full history
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# P = (110 + 90 + 100) / 3 = 100
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@@ -458,7 +511,6 @@ class TestPatternKnownValues:
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def test_doji_known_sequence(self):
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"""Construct a perfect doji: open == close, small body."""
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# Doji: open == close (or very close), H and L have range
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n = 5
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high = np.array([11.0, 11.0, 11.0, 11.0, 11.0])
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low = np.array([9.0, 9.0, 9.0, 9.0, 9.0])
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close = np.array([10.0, 10.0, 10.0, 10.0, 10.0])
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@@ -507,3 +559,4 @@ class TestPatternKnownValues:
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# Last bar has hammer characteristics
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# (actual detection may vary based on implementation)
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assert result.shape == close.shape
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