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Bollinger Upper

BollingerUpper

Bases: IndicatorBase

Bollinger Upper Band indicator.

Computes the upper Bollinger Band as: SMA + (\(k\) * standard deviation) where \(k\) is the number of standard deviations (default 2).

The rolling window is maintained independently for each symbol. Until the window is fully populated, the indicator yields numpy.nan.

name property

Canonical indicator name.

Returns:

Type Description
str

Identifier encoding the indicator type, period, number of standard deviations, and bar field.
Source code in src/onesecondtrader/indicators/bollinger/bollinger_upper.py 
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def name(self) -> str:
    """
    Canonical indicator name.

    Returns:
        Identifier encoding the indicator type, period, number of standard deviations, and bar field.
    """
    return f"BB_UPPER_{self.period}_{self.num_std}_{self.bar_field.name}"

__init__(period=20, num_std=2.0, max_history=100, bar_field=models.BarField.CLOSE, plot_at=0, plot_as=models.PlotStyle.LINE)

Parameters:

Name Type Description Default
period int

Window size used to compute the moving average and standard deviation.

 20
num_std float

Number of standard deviations for the band offset.

 2.0
max_history int

Maximum number of computed indicator values retained per symbol.

 100
bar_field BarField

Bar field used as the input series.

 CLOSE
plot_at int

Opaque plotting identifier forwarded to the charting backend.

 0
plot_as PlotStyle

Visual style used to render the indicator.

 LINE
Source code in src/onesecondtrader/indicators/bollinger/bollinger_upper.py 
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def __init__(
    self,
    period: int = 20,
    num_std: float = 2.0,
    max_history: int = 100,
    bar_field: models.BarField = models.BarField.CLOSE,
    plot_at: int = 0,
    plot_as: models.PlotStyle = models.PlotStyle.LINE,
) -> None:
    """
    Parameters:
        period:
            Window size used to compute the moving average and standard deviation.
        num_std:
            Number of standard deviations for the band offset.
        max_history:
            Maximum number of computed indicator values retained per symbol.
        bar_field:
            Bar field used as the input series.
        plot_at:
            Opaque plotting identifier forwarded to the charting backend.
        plot_as:
            Visual style used to render the indicator.
    """
    super().__init__(max_history=max_history, plot_at=plot_at, plot_as=plot_as)

    self.period: int = max(1, int(period))
    self.num_std: float = float(num_std)
    self.bar_field: models.BarField = bar_field
    self._window: dict[str, collections.deque[float]] = {}

_compute_indicator(incoming_bar)

Compute the upper Bollinger Band for a single received bar.

Parameters:

Name Type Description Default
incoming_bar BarReceived

Market bar used as input for the computation.

 required

Returns:

Type Description
float

Upper Bollinger Band value, or numpy.nan if the rolling window is not yet fully populated.
Source code in src/onesecondtrader/indicators/bollinger/bollinger_upper.py 
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def _compute_indicator(self, incoming_bar: events.market.BarReceived) -> float:
    """
    Compute the upper Bollinger Band for a single received bar.

    Parameters:
        incoming_bar:
            Market bar used as input for the computation.

    Returns:
        Upper Bollinger Band value, or `numpy.nan` if the rolling window is not yet fully populated.
    """
    symbol = incoming_bar.symbol
    if symbol not in self._window:
        self._window[symbol] = collections.deque(maxlen=self.period)

    window = self._window[symbol]
    value = self._extract_field(incoming_bar)
    window.append(value)

    if len(window) < self.period:
        return np.nan

    mean = sum(window) / self.period
    variance = sum((x - mean) ** 2 for x in window) / self.period
    std = np.sqrt(variance)

    return mean + (self.num_std * std)

_extract_field(incoming_bar)

Extract the configured bar field from an incoming bar.

Parameters:

Name Type Description Default
incoming_bar BarReceived

Market bar providing the input data.

 required

Returns:

Type Description
float

Extracted field value, or numpy.nan if unavailable.
Source code in src/onesecondtrader/indicators/bollinger/bollinger_upper.py 
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def _extract_field(self, incoming_bar: events.market.BarReceived) -> float:
    """
    Extract the configured bar field from an incoming bar.

    Parameters:
        incoming_bar:
            Market bar providing the input data.

    Returns:
        Extracted field value, or `numpy.nan` if unavailable.
    """
    match self.bar_field:
        case models.BarField.OPEN:
            return incoming_bar.open
        case models.BarField.HIGH:
            return incoming_bar.high
        case models.BarField.LOW:
            return incoming_bar.low
        case models.BarField.CLOSE:
            return incoming_bar.close
        case models.BarField.VOLUME:
            return (
                float(incoming_bar.volume)
                if incoming_bar.volume is not None
                else np.nan
            )
        case _:
            return incoming_bar.close