mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-18 10:38:05 +00:00
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)
69 lines
1.6 KiB
Python
69 lines
1.6 KiB
Python
"""Exponential Moving Average — bias-compensated, NaN-safe.
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Algorithm (mirrors Ema.cs):
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alpha = 2 / (period + 1)
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decay = 1 - alpha
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For each bar:
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ema = ema * decay + alpha * value
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E *= decay
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result = ema / (1 - E) while E > epsilon
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result = ema after warmup
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"""
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import math
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import numpy as np
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__all__ = ["ema"]
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EPSILON = 1e-10 # bias-compensator cutoff
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def ema(source, period: int = 10, *, alpha: float | None = None) -> np.ndarray:
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"""Bias-compensated EMA.
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Use *period* (default) or explicit *alpha* (keyword-only).
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If *alpha* is given, *period* is ignored.
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"""
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if alpha is not None:
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if not (0.0 < alpha <= 1.0):
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raise ValueError(f"alpha must be in (0, 1], got {alpha}")
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else:
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if period <= 0:
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raise ValueError(f"period must be > 0, got {period}")
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alpha = 2.0 / (period + 1)
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src = np.asarray(source, dtype=np.float64)
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if src.ndim == 0 or src.size == 0:
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raise ValueError("source must not be empty")
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src = src.ravel()
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out = np.empty(len(src), dtype=np.float64)
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decay = 1.0 - alpha
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ema_val = 0.0
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e = 1.0
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last_valid = 0.0
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has_valid = False
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for i, v in enumerate(src):
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if math.isfinite(v):
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last_valid = v
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has_valid = True
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elif has_valid:
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v = last_valid
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else:
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out[i] = math.nan
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continue
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ema_val = ema_val * decay + alpha * v
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if e > EPSILON:
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e *= decay
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out[i] = ema_val / (1.0 - e)
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else:
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out[i] = ema_val
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return out
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