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Exponential Moving Average (ta.ema)

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Give recent prices greater weight to smooth price and examine trends; the default seed is a simple average.

Indicator catalog · How to use indicators

Run this example

This complete script uses bundled data. It observes the selected output without placing trades.

import numpy as np
from openbacktesting import Backtest, Strategy
from openbacktesting.test import GOOG

data = GOOG.copy()

class Example(Strategy):
    def init(self):
        self.value = self.ta.ema(self.close)
        self.latest_value = None

    def next(self):
        channel = self.value
        if channel.valid[-1]:
            self.latest_value = float(channel[-1])

result = Backtest(data, Example).run()
print(result["_strategy"].latest_value)

Call and parameters

self.ta.ema(
    source=None,
    *,
    length=10,
    adjust=False,
    sma=True,
    inputs=None,
    backend=None,
    required_outputs=None,
)

Register in init(). Read the returned channel in next() after checking valid[-1].

Parameter Meaning Type Default Allowed values
length Calculation window in observations. int 10 Minimum: 1
adjust Use normalized exponential weights instead of recursive EMA weights. bool False No additional constraints
sma Seed EMA with the simple average of its first window. bool True No additional constraints

source/named source arguments accept a column name or same-run single-channel handle. Alternatively, inputs maps each input below to a column or handle. backend selects a supported implementation; required_outputs selects channels for readiness diagnostics. See shared arguments and handles.

Inputs and outputs

Required inputs: close.

Channel Meaning
ema Exponential Moving Average value

Reading values safely

Insufficient history or missing inputs can make a channel invalid. Check that channel's valid mask; ready is diagnostic and never suppresses next(). Multi-output results may have different valid channels on the same bar.

Indicator catalog · Custom calculations