ExponentialMovingAverage

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class backtide.indicators.ExponentialMovingAverage(period=14)

Exponential Moving Average (EMA).

A weighted moving average that gives exponentially more weight to recent prices, making it more responsive to new information than the SMA. Useful for faster trend detection, reducing lag in crossover systems, and as a building block for other indicators (MACD, ADX).

Formula:

\[EMA_t = \alpha \cdot C_t + (1 - \alpha) \cdot EMA_{t-1}\]

where \(\alpha = \frac{2}{n + 1}\). Read more on Wikipedia.

Parameters	period : int, default=14 Look-back window length.
Attributes	acronym : str Short ticker-style acronym. name : str Human-readable indicator name.

See Also

MovingAverageConvergenceDivergence

Moving Average Convergence Divergence (MACD).

SimpleMovingAverage

Simple Moving Average (SMA).

WeightedMovingAverage

Weighted Moving Average (WMA).

Methods

compute	Compute the indicator on a dataset.
description	Short explanation of what the indicator measures.

method backtide.indicatorscompute(data)

Compute the indicator on a dataset.

Parameters	data : np.ndarray | pd.DataFrame | pl.DataFrame Historical OHLCV data.
Returns	np.ndarray | pd.Series | pl.Series | pd.DataFrame | pl.DataFrame The computed values. For multi-output indicators (e.g., upper and lower bounds), return a 2d structure.

method backtide.indicatorsdescription()

Short explanation of what the indicator measures.

Returns

str The description.