Indicator¶

The Indicator class is the base class for all technical indicators.

Backtrader Indicator Module.

This module provides the base Indicator class and related infrastructure for creating and managing technical analysis indicators. It replaces the metaclass-based approach with explicit inheritance and registration.

The Indicator class serves as the foundation for all technical indicators in backtrader, managing line data, minimum periods, and calculation logic.

class backtrader.indicator.IndicatorRegistry[source]

Bases: object

Registry to manage indicator classes and provide caching functionality.

This class replaces the metaclass-based indicator registration and caching mechanism from the original backtrader implementation.

classmethod register(name, indicator_cls)[source]

Parameters:

name – Name of the indicator class
indicator_cls – The indicator class to register

classmethod cleancache()[source]: Clear the indicator cache.

classmethod usecache(onoff)[source]

Enable or disable indicator caching.

Parameters:: onoff – If True, enable caching; if False, disable it

classmethod get_cached_or_create(indicator_cls, *args, **kwargs)[source]

Get cached indicator instance or create new one.

Parameters:

indicator_cls – The indicator class to instantiate
*args – Positional arguments for the indicator
**kwargs – Keyword arguments for the indicator

Returns:

Cached indicator instance if available and caching enabled, otherwise a new indicator instance

class backtrader.indicator.Indicator[source]

Bases: IndicatorBase

Base class for all technical indicators in Backtrader.

This class provides the foundation for creating custom indicators. It manages line data, minimum periods, and calculation logic. Indicators inherit from IndicatorBase and integrate with the LineIterator system for data flow.

_ltype: Line type set to IndType (0) for indicators

csv: Whether to output this indicator to CSV (default: False)

aliased: Whether this indicator has an alias name

csv = False

__getitem__(ago)[source]

CRITICAL FIX: Forward item access to the first line (e.g., sma line)

For indicators with named lines like SMA (which has lines.sma), accessing indicator[0] should return the value from the first line, not the indicator’s own array.

aliased = False

classmethod __init_subclass__(**kwargs)[source]

Handle subclass registration and initialization.

This method is called when a subclass of Indicator is created. It performs: 1. Lines creation using Lines infrastructure 2. Automatic registration in IndicatorRegistry 3. Alias handling for module-level access 4. next/once method setup for calculation modes

Parameters:: **kwargs – Additional keyword arguments

classmethod cleancache()[source]: Clear the indicator cache

classmethod usecache(onoff)[source]: Enable or disable caching

advance(size=1)[source]

Advance indicator lines when data length is less than clock length.

Also advances sub-indicators so that during _oncepost() replay every level of the indicator tree stays in sync (fixes runonce ATR/SMMA index mismatch when an indicator uses sub_ind[0] in next()).

Parameters:: size – Number of steps to advance (default: 1)

preonce_via_prenext(start, end)[source]

Implement preonce using prenext for batch calculation.

This is a generic implementation if prenext is overridden but preonce is not. It loops through the range and calls prenext for each step.

Parameters:

start – Starting index
end – Ending index

oncestart_via_nextstart(start, end)[source]

Implement oncestart using nextstart for batch calculation.

This is used when nextstart is overridden but oncestart is not.

Parameters:

start – Starting index
end – Ending index

once_via_next(start, end)[source]

Implement once using next for batch calculation.

This is used when next is overridden but once is not. It loops through the range and calls next for each step.

Parameters:

start – Starting index
end – Ending index

frompackages = ()

packages = ()

class backtrader.indicator.LinePlotterIndicatorBase[source]

Bases: type

Base class for indicators that plot multiple lines.

Note: These classes are not currently used in the project. They are kept for compatibility with the original backtrader.

donew(*args, **kwargs)[source]

Create a new LinePlotterIndicator instance.

Parameters:

*args – Positional arguments
**kwargs – Keyword arguments, must include ‘name’

Returns:

(created_object, args, kwargs)

Return type:

tuple

class backtrader.indicator.LinePlotterIndicator[source]

Bases: Indicator, LinePlotterIndicatorBase

Indicator that plots multiple lines.

Note: This class is not currently used in the project.

frompackages = ()

packages = ()

Overview¶

Indicators calculate values from data feeds and can be used in strategies for trading decisions. Backtrader includes many built-in indicators and supports custom indicator creation.

Built-in Indicators¶

Moving Averages¶

SMA: Simple Moving Average
EMA: Exponential Moving Average
WMA: Weighted Moving Average
SMMA: Smoothed Moving Average
DEMA: Double Exponential Moving Average
TEMA: Triple Exponential Moving Average

Momentum¶

RSI: Relative Strength Index
MACD: Moving Average Convergence Divergence
Stochastic: Stochastic Oscillator
ROC: Rate of Change
Momentum: Momentum indicator
CCI: Commodity Channel Index

Volatility¶

ATR: Average True Range
BollingerBands: Bollinger Bands
StandardDeviation: Standard Deviation

Trend¶

ADX: Average Directional Index
Aroon: Aroon Indicator
ParabolicSAR: Parabolic SAR

Volume¶

OBV: On Balance Volume
VWAP: Volume Weighted Average Price

Creating Custom Indicators¶

class MyIndicator(bt.Indicator):
    lines = ('myline',)
    params = (('period', 20),)

    def __init__(self):
        self.addminperiod(self.params.period)

    def next(self):
        # Calculate indicator value
        self.lines.myline[0] = sum(
            self.data.close.get(size=self.params.period)
        ) / self.params.period

Using Indicators¶

class MyStrategy(bt.Strategy):
    def __init__(self):
        # Create indicators
        self.sma = bt.indicators.SMA(self.data.close, period=20)
        self.rsi = bt.indicators.RSI(self.data.close)
        self.macd = bt.indicators.MACD(self.data.close)

        # Combine indicators
        self.crossover = bt.indicators.CrossOver(
            self.data.close, self.sma
        )

    def next(self):
        if self.crossover > 0 and self.rsi < 30:
            self.buy()