mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
e3e9555fc1
Single-function API: ema(source, period=10, *, alpha=None) - Bias-compensated EMA matching C# Ema.Batch exactly - NaN/Inf safe with last-valid substitution - 65 lines, pseudocode-readable reference implementation - Co-located ema_test.py (15 tests) + tests/test_ema.py (37 tests) - All 50 tests pass, 2 cross-validation skipped (no native lib)
144 lines
4.1 KiB
Python
144 lines
4.1 KiB
Python
"""Unit tests for pure-Python EMA implementation.
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Per PYTHON_FALLBACK_SPEC §5: co-located <indicator>_test.py
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Run::
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pytest lib/trends_IIR/ema/ema_test.py -v
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"""
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import math
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import sys
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from pathlib import Path
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import numpy as np
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import pytest
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# Import from co-located ema.py
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sys.path.insert(0, str(Path(__file__).parent))
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from ema import ema # noqa: E402
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# ── basic correctness ──
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def test_known_values():
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"""Hand-calculated EMA(3) with bias compensation.
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alpha=0.5, decay=0.5:
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Bar 0: acc = 0.5*10 = 5, E = 0.5, result = 5/0.5 = 10.0
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Bar 1: acc = 5*0.5 + 0.5*20 = 12.5, E = 0.25, result = 12.5/0.75 ≈ 16.667
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Bar 2: acc = 12.5*0.5 + 0.5*30 = 21.25, E = 0.125, result = 21.25/0.875 ≈ 24.286
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"""
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data = np.array([10.0, 20.0, 30.0, 40.0, 50.0])
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result = ema(data, period=3)
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assert result[0] == pytest.approx(10.0, rel=1e-12)
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assert result[1] == pytest.approx(16.666666666666668, rel=1e-10)
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assert result[2] == pytest.approx(24.285714285714285, rel=1e-10)
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def test_period_1():
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"""Period=1 → alpha=1.0 → output equals input (no smoothing)."""
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data = np.array([10.0, 20.0, 30.0, 40.0, 50.0])
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result = ema(data, period=1)
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np.testing.assert_array_equal(result, data)
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def test_constant_input():
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"""Constant 100.0 through any EMA → always 100.0."""
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data = np.full(100, 100.0)
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result = ema(data, period=20)
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np.testing.assert_allclose(result, 100.0, rtol=1e-12)
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# ── edge cases ──
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def test_empty_array():
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"""Empty input → raises ValueError."""
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with pytest.raises(ValueError, match="must not be empty"):
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ema(np.array([]), period=5)
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def test_invalid_period():
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"""Period ≤ 0 → raises ValueError."""
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with pytest.raises(ValueError, match="period must be > 0"):
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ema(np.array([1.0, 2.0, 3.0]), period=0)
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with pytest.raises(ValueError, match="period must be > 0"):
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ema(np.array([1.0, 2.0, 3.0]), period=-1)
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def test_single_element():
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"""Single-element input returns that element."""
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result = ema([42.0], period=10)
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assert len(result) == 1
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assert result[0] == pytest.approx(42.0, rel=1e-12)
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# ── NaN handling ──
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def test_nan_in_input():
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"""NaN replaced with last valid value → output stays finite."""
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data = np.array([10.0, 20.0, np.nan, 40.0, 50.0])
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result = ema(data, period=3)
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assert all(math.isfinite(v) for v in result)
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def test_all_nan_returns_nan():
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"""All-NaN input → all-NaN output."""
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data = np.full(5, np.nan)
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result = ema(data, period=3)
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assert all(math.isnan(v) for v in result)
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def test_inf_handled():
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"""Inf replaced with last valid value."""
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data = np.array([10.0, 20.0, np.inf, 40.0, 50.0])
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result = ema(data, period=3)
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assert all(math.isfinite(v) for v in result)
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# ── output shape & warmup ──
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def test_output_length_matches_input():
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"""len(output) == len(input)."""
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data = np.random.default_rng(42).normal(100, 5, size=200)
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result = ema(data, period=14)
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assert len(result) == 200
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def test_first_bar_always_valid():
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"""Bias compensation means EMA produces valid output from bar 0."""
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data = np.random.default_rng(42).normal(100, 5, size=50)
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result = ema(data, period=50)
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assert math.isfinite(result[0])
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# ── numerical precision ──
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def test_large_values():
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"""No overflow with 1e300 values."""
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data = np.full(100, 1e300)
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result = ema(data, period=10)
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np.testing.assert_allclose(result, 1e300, rtol=1e-10)
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def test_tiny_values():
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"""No underflow with 1e-300 values."""
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data = np.full(100, 1e-300)
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result = ema(data, period=10)
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np.testing.assert_allclose(result, 1e-300, rtol=1e-10)
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def test_10k_series_all_finite():
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"""Long series stays finite (no drift)."""
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data = np.random.default_rng(42).normal(100, 10, size=10_000)
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result = ema(data, period=20)
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assert np.all(np.isfinite(result))
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def test_alpha_from_period_equivalence():
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"""ema(period=N) == ema(alpha=2/(N+1))."""
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data = np.random.default_rng(42).normal(100, 5, size=200)
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r1 = ema(data, period=14)
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r2 = ema(data, alpha=2.0 / 15.0)
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np.testing.assert_allclose(r1, r2, rtol=1e-14)
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