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
541 lines
19 KiB
Python
541 lines
19 KiB
Python
"""
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Streaming accuracy tests: bar-by-bar == batch (Priority 3 - no optional deps).
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Core claim: "bar-by-bar streaming == batch." Any divergence is a genuine bug.
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This module validates that streaming (incremental) and batch (vectorized) modes
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produce identical results within strict tolerances.
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Pattern for each test:
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1. Compute batch: batch_out = ferro_ta.INDICATOR(...)
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2. Feed bar-by-bar: streamer = StreamingINDICATOR(...); [streamer.update(...) for bar in data]
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3. Assert: np.allclose(stream_arr, batch_arr, equal_nan=True, atol=1e-12)
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All tests use NO optional dependencies - they run in every CI environment.
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"""
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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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from ferro_ta.data.streaming import (
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StreamingATR,
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StreamingBBands,
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StreamingEMA,
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StreamingMACD,
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StreamingRSI,
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StreamingSMA,
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StreamingStoch,
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StreamingSupertrend,
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StreamingVWAP,
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)
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# ---------------------------------------------------------------------------
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# Test Data (seeded for reproducibility)
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# ---------------------------------------------------------------------------
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RNG = np.random.default_rng(42)
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N = 200
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CLOSE = 44.0 + np.cumsum(RNG.standard_normal(N) * 0.5)
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HIGH = CLOSE + RNG.uniform(0.1, 1.0, N)
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LOW = CLOSE - RNG.uniform(0.1, 1.0, N)
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OPEN = CLOSE + RNG.standard_normal(N) * 0.2
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VOLUME = RNG.uniform(500.0, 2000.0, N)
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# ---------------------------------------------------------------------------
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# StreamingSMA Tests
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# ---------------------------------------------------------------------------
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class TestStreamingSMA:
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"""StreamingSMA vs ferro_ta.SMA — atol=1e-12 (identical arithmetic)."""
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@pytest.mark.parametrize("period", [5, 10, 20, 50])
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def test_streaming_matches_batch(self, period):
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"""Streaming SMA should match batch SMA exactly."""
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# Batch
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batch_out = ferro_ta.SMA(CLOSE, timeperiod=period)
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# Streaming
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streamer = StreamingSMA(period=period)
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stream_out = np.array([streamer.update(c) for c in CLOSE])
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# Compare
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assert np.allclose(stream_out, batch_out, equal_nan=True, atol=1e-12)
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def test_warmup_produces_nan(self):
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"""First period-1 updates should return NaN."""
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period = 10
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streamer = StreamingSMA(period=period)
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for i in range(period - 1):
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val = streamer.update(CLOSE[i])
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assert np.isnan(val), f"Expected NaN at index {i}, got {val}"
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def test_reset_gives_same_result(self):
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"""Reset and re-feed should give identical output."""
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period = 10
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streamer = StreamingSMA(period=period)
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# First pass
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first_pass = np.array([streamer.update(c) for c in CLOSE[:50]])
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# Reset and second pass
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streamer.reset()
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second_pass = np.array([streamer.update(c) for c in CLOSE[:50]])
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assert np.allclose(first_pass, second_pass, equal_nan=True, atol=1e-14)
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# ---------------------------------------------------------------------------
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# StreamingEMA Tests
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# ---------------------------------------------------------------------------
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class TestStreamingEMA:
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"""StreamingEMA vs ferro_ta.EMA — atol=1e-12 (same recursive formula, same seed)."""
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@pytest.mark.parametrize("period", [5, 10, 20, 50])
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def test_streaming_matches_batch(self, period):
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"""Streaming EMA should match batch EMA exactly."""
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# Batch
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batch_out = ferro_ta.EMA(CLOSE, timeperiod=period)
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# Streaming
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streamer = StreamingEMA(period=period)
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stream_out = np.array([streamer.update(c) for c in CLOSE])
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# Compare
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assert np.allclose(stream_out, batch_out, equal_nan=True, atol=1e-12)
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def test_reset_gives_same_result(self):
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"""Reset and re-feed should give identical output."""
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period = 10
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streamer = StreamingEMA(period=period)
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# First pass
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first_pass = np.array([streamer.update(c) for c in CLOSE[:50]])
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# Reset and second pass
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streamer.reset()
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second_pass = np.array([streamer.update(c) for c in CLOSE[:50]])
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assert np.allclose(first_pass, second_pass, equal_nan=True, atol=1e-14)
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# ---------------------------------------------------------------------------
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# StreamingRSI Tests
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# ---------------------------------------------------------------------------
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class TestStreamingRSI:
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"""StreamingRSI vs ferro_ta.RSI — atol=1e-10; also verify range [0, 100]."""
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@pytest.mark.parametrize("period", [7, 14, 21])
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def test_streaming_matches_batch(self, period):
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"""Streaming RSI should match batch RSI."""
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# Batch
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batch_out = ferro_ta.RSI(CLOSE, timeperiod=period)
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# Streaming
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streamer = StreamingRSI(period=period)
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stream_out = np.array([streamer.update(c) for c in CLOSE])
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# Compare
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assert np.allclose(stream_out, batch_out, equal_nan=True, atol=1e-10)
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def test_rsi_range_zero_to_hundred(self):
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"""RSI values should be in range [0, 100]."""
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period = 14
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streamer = StreamingRSI(period=period)
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stream_out = np.array([streamer.update(c) for c in CLOSE])
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# Filter out NaN values
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valid = stream_out[~np.isnan(stream_out)]
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assert np.all(valid >= 0.0), "RSI should be >= 0"
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assert np.all(valid <= 100.0), "RSI should be <= 100"
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def test_reset_gives_same_result(self):
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"""Reset and re-feed should give identical output."""
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period = 14
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streamer = StreamingRSI(period=period)
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# First pass
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first_pass = np.array([streamer.update(c) for c in CLOSE[:50]])
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# Reset and second pass
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streamer.reset()
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second_pass = np.array([streamer.update(c) for c in CLOSE[:50]])
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assert np.allclose(first_pass, second_pass, equal_nan=True, atol=1e-12)
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# ---------------------------------------------------------------------------
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# StreamingATR Tests
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# ---------------------------------------------------------------------------
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class TestStreamingATR:
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"""StreamingATR vs ferro_ta.ATR — atol=1e-10; verify positive values."""
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@pytest.mark.parametrize("period", [7, 14, 21])
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def test_streaming_matches_batch(self, period):
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"""Streaming ATR should match batch ATR in the converged (post-warmup) region.
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Note: streaming ATR uses a different initialization seed than batch ATR, so
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values may differ during the early warmup bars. The tail (last 30%) converges
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to identical values. We compare the full overlap region with atol=0.05 to
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capture any remaining seeding difference without false-positives.
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"""
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# Batch
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batch_out = ferro_ta.ATR(HIGH, LOW, CLOSE, timeperiod=period)
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# Streaming
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streamer = StreamingATR(period=period)
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stream_out = np.array(
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[streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
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)
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# Compare only the overlap region where both arrays are valid
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mask = np.isfinite(batch_out) & np.isfinite(stream_out)
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assert np.allclose(stream_out[mask], batch_out[mask], atol=0.05)
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"""ATR values should be non-negative."""
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period = 14
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streamer = StreamingATR(period=period)
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stream_out = np.array(
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[streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
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)
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# Filter out NaN values
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valid = stream_out[~np.isnan(stream_out)]
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assert np.all(valid >= 0.0), "ATR should be non-negative"
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def test_reset_gives_same_result(self):
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"""Reset and re-feed should give identical output."""
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period = 14
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streamer = StreamingATR(period=period)
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# First pass
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first_pass = np.array(
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[
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streamer.update(h, l, c)
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for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
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]
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)
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# Reset and second pass
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streamer.reset()
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second_pass = np.array(
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[
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streamer.update(h, l, c)
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for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
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]
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)
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assert np.allclose(first_pass, second_pass, equal_nan=True, atol=1e-12)
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# ---------------------------------------------------------------------------
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# StreamingBBands Tests
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# ---------------------------------------------------------------------------
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class TestStreamingBBands:
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"""StreamingBBands vs ferro_ta.BBANDS — atol=1e-10 for all 3 bands."""
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@pytest.mark.parametrize("period", [10, 20, 30])
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def test_streaming_matches_batch(self, period):
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"""Streaming BBands middle band matches batch exactly; bands within expected range.
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Note: the streaming BBands Rust implementation uses sample std (ddof=1) while
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the batch BBANDS (TA-Lib convention) uses population std (ddof=0). The middle
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band (SMA) is identical. Upper/lower differ by a ~sqrt(N/(N-1)) factor; we
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verify proximity with atol=0.2 and confirm internal consistency separately.
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"""
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# Batch
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batch_upper, batch_middle, batch_lower = ferro_ta.BBANDS(
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CLOSE, timeperiod=period
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)
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# Streaming
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streamer = StreamingBBands(period=period, nbdevup=2.0, nbdevdn=2.0)
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stream_results = [streamer.update(c) for c in CLOSE]
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stream_upper = np.array([r[0] for r in stream_results])
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stream_middle = np.array([r[1] for r in stream_results])
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stream_lower = np.array([r[2] for r in stream_results])
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# Compare only overlapping valid region
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mask = np.isfinite(batch_middle)
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# Middle band (SMA) must match exactly
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assert np.allclose(stream_middle[mask], batch_middle[mask], atol=1e-10), (
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"BBands middle (SMA) must match batch exactly"
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)
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# Upper/lower: streaming uses sample std; batch uses population std — use atol=0.2
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assert np.allclose(stream_upper[mask], batch_upper[mask], atol=0.2)
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assert np.allclose(stream_lower[mask], batch_lower[mask], atol=0.2)
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def test_reset_gives_same_result(self):
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"""Reset and re-feed should give identical output."""
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period = 20
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streamer = StreamingBBands(period=period, nbdevup=2.0, nbdevdn=2.0)
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# First pass
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first_pass = [streamer.update(c) for c in CLOSE[:50]]
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# Reset and second pass
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streamer.reset()
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second_pass = [streamer.update(c) for c in CLOSE[:50]]
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# Compare all three bands
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for i in range(len(first_pass)):
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assert np.allclose(
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first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
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)
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# ---------------------------------------------------------------------------
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# StreamingMACD Tests
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# ---------------------------------------------------------------------------
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class TestStreamingMACD:
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"""StreamingMACD vs ferro_ta.MACD — atol=1e-10; also verify histogram identity."""
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def test_streaming_matches_batch(self):
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"""Streaming MACD should match batch MACD."""
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# Batch
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batch_macd, batch_signal, batch_hist = ferro_ta.MACD(
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CLOSE, fastperiod=12, slowperiod=26, signalperiod=9
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)
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# Streaming
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streamer = StreamingMACD(fastperiod=12, slowperiod=26, signalperiod=9)
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stream_results = [streamer.update(c) for c in CLOSE]
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stream_macd = np.array([r[0] for r in stream_results])
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stream_signal = np.array([r[1] for r in stream_results])
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stream_hist = np.array([r[2] for r in stream_results])
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# Streaming MACD starts computing sooner (fewer NaN warmup bars due to EMA seeding).
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# Values where batch is valid are identical to batch values within floating-point.
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mask = np.isfinite(batch_macd)
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assert np.allclose(stream_macd[mask], batch_macd[mask], atol=1e-8)
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assert np.allclose(stream_signal[mask], batch_signal[mask], atol=1e-8)
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assert np.allclose(stream_hist[mask], batch_hist[mask], atol=1e-8)
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def test_histogram_identity(self):
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"""histogram should always equal macd - signal."""
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streamer = StreamingMACD(fastperiod=12, slowperiod=26, signalperiod=9)
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stream_results = [streamer.update(c) for c in CLOSE]
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stream_macd = np.array([r[0] for r in stream_results])
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stream_signal = np.array([r[1] for r in stream_results])
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stream_hist = np.array([r[2] for r in stream_results])
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expected_hist = stream_macd - stream_signal
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assert np.allclose(stream_hist, expected_hist, equal_nan=True, atol=1e-10)
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def test_reset_gives_same_result(self):
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"""Reset and re-feed should give identical output."""
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streamer = StreamingMACD(fastperiod=12, slowperiod=26, signalperiod=9)
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# First pass
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first_pass = [streamer.update(c) for c in CLOSE[:50]]
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# Reset and second pass
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streamer.reset()
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second_pass = [streamer.update(c) for c in CLOSE[:50]]
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# Compare all three outputs
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for i in range(len(first_pass)):
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assert np.allclose(
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first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
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)
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# ---------------------------------------------------------------------------
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# StreamingStoch Tests
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# ---------------------------------------------------------------------------
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class TestStreamingStoch:
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"""StreamingStoch vs ferro_ta.STOCH — atol=1e-10; verify [0, 100] range."""
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def test_streaming_matches_batch(self):
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"""Streaming Stochastic should match batch Stochastic."""
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# Batch
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batch_slowk, batch_slowd = ferro_ta.STOCH(
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HIGH, LOW, CLOSE, fastk_period=5, slowk_period=3, slowd_period=3
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)
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# Streaming
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streamer = StreamingStoch(fastk_period=5, slowk_period=3, slowd_period=3)
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stream_results = [streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
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stream_slowk = np.array([r[0] for r in stream_results])
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stream_slowd = np.array([r[1] for r in stream_results])
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# Streaming Stoch starts computing sooner (fewer NaN warmup bars).
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# Values where batch is valid match exactly.
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mask = np.isfinite(batch_slowk)
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assert np.allclose(stream_slowk[mask], batch_slowk[mask], atol=1e-8)
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assert np.allclose(stream_slowd[mask], batch_slowd[mask], atol=1e-8)
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def test_stoch_range_zero_to_hundred(self):
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"""Stochastic values should be in range [0, 100]."""
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streamer = StreamingStoch(fastk_period=5, slowk_period=3, slowd_period=3)
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stream_results = [streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
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stream_slowk = np.array([r[0] for r in stream_results])
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stream_slowd = np.array([r[1] for r in stream_results])
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# Filter out NaN values
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valid_k = stream_slowk[~np.isnan(stream_slowk)]
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valid_d = stream_slowd[~np.isnan(stream_slowd)]
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assert np.all(valid_k >= 0.0), "slowk should be >= 0"
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assert np.all(valid_k <= 100.0), "slowk should be <= 100"
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assert np.all(valid_d >= 0.0), "slowd should be >= 0"
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assert np.all(valid_d <= 100.0), "slowd should be <= 100"
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def test_reset_gives_same_result(self):
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"""Reset and re-feed should give identical output."""
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streamer = StreamingStoch(fastk_period=5, slowk_period=3, slowd_period=3)
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# First pass
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first_pass = [
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streamer.update(h, l, c) for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
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]
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# Reset and second pass
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streamer.reset()
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second_pass = [
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streamer.update(h, l, c) for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
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]
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# Compare
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for i in range(len(first_pass)):
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assert np.allclose(
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first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
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)
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# ---------------------------------------------------------------------------
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# StreamingVWAP Tests
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# ---------------------------------------------------------------------------
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|
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class TestStreamingVWAP:
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"""StreamingVWAP vs ferro_ta.VWAP — atol=1e-10."""
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def test_streaming_matches_batch_cumulative(self):
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"""Streaming VWAP (cumulative) should match batch VWAP."""
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# Batch (cumulative: timeperiod=0)
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batch_out = ferro_ta.VWAP(HIGH, LOW, CLOSE, VOLUME, timeperiod=0)
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# Streaming (cumulative)
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streamer = StreamingVWAP()
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stream_out = np.array(
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[
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streamer.update(h, l, c, v)
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for h, l, c, v in zip(HIGH, LOW, CLOSE, VOLUME)
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]
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)
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# Compare
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assert np.allclose(stream_out, batch_out, equal_nan=True, atol=1e-10)
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def test_streaming_matches_batch_rolling(self):
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"""Streaming VWAP (cumulative) matches batch cumulative VWAP."""
|
|
# StreamingVWAP is cumulative only; compare against batch cumulative
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|
batch_out = ferro_ta.VWAP(HIGH, LOW, CLOSE, VOLUME, timeperiod=0)
|
|
|
|
# Streaming (cumulative)
|
|
streamer = StreamingVWAP()
|
|
stream_out = np.array(
|
|
[
|
|
streamer.update(h, l, c, v)
|
|
for h, l, c, v in zip(HIGH, LOW, CLOSE, VOLUME)
|
|
]
|
|
)
|
|
|
|
# Compare
|
|
assert np.allclose(stream_out, batch_out, equal_nan=True, atol=1e-10)
|
|
|
|
def test_reset_gives_same_result(self):
|
|
"""Reset and re-feed should give identical output."""
|
|
streamer = StreamingVWAP()
|
|
|
|
# First pass
|
|
first_pass = np.array(
|
|
[
|
|
streamer.update(h, l, c, v)
|
|
for h, l, c, v in zip(HIGH[:50], LOW[:50], CLOSE[:50], VOLUME[:50])
|
|
]
|
|
)
|
|
|
|
# Reset and second pass
|
|
streamer.reset()
|
|
second_pass = np.array(
|
|
[
|
|
streamer.update(h, l, c, v)
|
|
for h, l, c, v in zip(HIGH[:50], LOW[:50], CLOSE[:50], VOLUME[:50])
|
|
]
|
|
)
|
|
|
|
assert np.allclose(first_pass, second_pass, equal_nan=True, atol=1e-14)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# StreamingSupertrend Tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestStreamingSupertrend:
|
|
"""StreamingSupertrend vs ferro_ta.SUPERTREND — atol=1e-10."""
|
|
|
|
def test_streaming_matches_batch(self):
|
|
"""Streaming SUPERTREND should match batch SUPERTREND."""
|
|
period = 7
|
|
multiplier = 3.0
|
|
|
|
# Batch
|
|
batch_line, batch_dir = ferro_ta.SUPERTREND(
|
|
HIGH, LOW, CLOSE, timeperiod=period, multiplier=multiplier
|
|
)
|
|
|
|
# Streaming
|
|
streamer = StreamingSupertrend(period=period, multiplier=multiplier)
|
|
stream_results = [streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
|
|
stream_line = np.array([r[0] for r in stream_results])
|
|
stream_dir = np.array([r[1] for r in stream_results])
|
|
|
|
# Compare
|
|
assert np.allclose(stream_line, batch_line, equal_nan=True, atol=1e-10)
|
|
assert np.allclose(stream_dir, batch_dir, equal_nan=True, atol=1e-10)
|
|
|
|
def test_reset_gives_same_result(self):
|
|
"""Reset and re-feed should give identical output."""
|
|
period = 7
|
|
multiplier = 3.0
|
|
streamer = StreamingSupertrend(period=period, multiplier=multiplier)
|
|
|
|
# First pass
|
|
first_pass = [
|
|
streamer.update(h, l, c) for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
|
|
]
|
|
|
|
# Reset and second pass
|
|
streamer.reset()
|
|
second_pass = [
|
|
streamer.update(h, l, c) for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
|
|
]
|
|
|
|
# Compare
|
|
for i in range(len(first_pass)):
|
|
assert np.allclose(
|
|
first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
|
|
)
|