Using Indicators

Indicators can be used in two places in the platform:

  • Inside Strategies

  • Inside other Indicators

Indicators in action

  1. Indicators are always instantiated during __init__ in the Strategy

  2. Indicator values (or values thereof derived) are used/checked during next

There is an important axiom to take into account:

  • Any Indicator (or value thereof derived) declared during __init__ will be precalculated before next is called.

Let’s go for the differences an operation modes.

__init__ vs next

Things works as follows:

  • Any operation involving lines objects during __init__ generates another lines object

  • Any operation involving lines objects during next yields regular Python types like floats and bools.

During __init__

Example of an operation during __init__:

hilo_diff = -

The variable hilo_diff holds a reference to a lines object which is precalculated before calling next and can be accessed using the standard array notation []

It does obviously contains for each bar of the data feed the difference between the high and the low.

This also works when mixing simple lines (like those in the Data Feed) and complex ones like indicators:

sma = bt.SimpleMovingAverage(
close_sma_diff = - sma

Now close_sma_diff contains again a line object.

Using logical operatorss:

close_over_sma = > sma

Now the generated lines object will contain an array of booleans.

During next

Example of an operation (logical operator):

close_over_sma = > self.sma

Using the equivalent array (index 0 based notation):

close_over_sma =[0] > self.sma[0]

In this case close_over_sma yields a boolen which is the result of comparing two floating point values, the ones returned by the [0] operator applied to and self.sma

The __init__ vs next why

Logic simplification (and with it ease of use) is the key. Calculations and most of the associated logic can be declared during __init__ keeping the actual operational logic to a minimum during next.

There is actually a side benefit: speed (due to the precalculation explained at the beginning)

A complete example which generates a buy signal during __init__:

class MyStrategy(bt.Strategy):

    def __init__(self):

        sma1 = btind.SimpleMovingAverage(
        ema1 = btind.ExponentialMovingAverage()

        close_over_sma = > sma1
        close_over_ema = > ema1
        sma_ema_diff = sma1 - ema1

        buy_sig = bt.And(close_over_sma, close_over_ema, sma_ema_diff > 0)

    def next(self):

        if buy_sig:


Python’s and operator cannot be overriden, forcing the platform to define its own And. The same applies to other constructs like Or and If

It should be obvious that the “declarative” approach during __init__ keeps the bloating of next (where the actual strategy work happens) to a minimum.

(Don’t forget there is also a speed up factor)


When the logic gets really complicated and involves several operations it is usually much better to encapsulate that inside an Indicator.

Some notes

In the example above there are two things which have been simplified in backtrader when compared to other platforms:

  • Declared Indicators are neither getting a parent parameter (like the strategy in which they are being created nor is any kind of “register” method/function being called.

    And in spite of it the strategy will kick the calculation of the Indicators and any lines object generated because of operations (like sma - ema)

  • ExponentialMovingAverage is being instantiated without

    This is intentional. If no data is passed, the 1st data of the parent (in this case the Strategy in which is being created) will be automatically passed in the background

Indicator Plotting

First and foremost:

  • Declared Indicators get automatically plotted (if cerebro.plot is called)

  • lines objects from operations DO NOT GET plotted (like close_over_sma = > self.sma)

    There is an auxiliary LinePlotterIndicator which plots such operations if wished with the following approach:

    close_over_sma = > self.sma
    LinePlotterIndicator(close_over_sma, name='Close_over_SMA')

    The name parameter gives name to the single line held by this indicator.

Controlling plotting

During the development of an Indicator a plotinfo declaration can be added. It can be a tuple of tuples (2 elements), a dict or an OrderedDict. It looks like:

class MyIndicator(bt.Indicator):

    plotinfo = dict(subplot=False)

The value can be later accessed (and set) as follows (if needed):

myind = MyIndicator(, someparam=value)
myind.plotinfo.subplot = True

The value can even be set during instantiation:

myind = MyIndicator(, someparams=value, subplot=True)

The subplot=True will be passed to the (behind the scenes) intantiated member variable plotinfo for the indicator.

The plotinfo offers the following parameters to control plotting behavior:

  • plot (default: True)

    Whether the indicator is to be plotted or not

  • subplot (default: True)

    Whether to plot the indicator in a different window. For indicators like moving averages the default is changed to False

  • plotname (default: '')

    Sets the plotname to show on the plot. The empty value means the canonical name of the indicator (class.__name__) will be used. This has some limitations because Python identifiers cannot use for example arithmetic operators.

    An indicator like DI+ will be declared as follows:

    class DIPlus(bt.Indicator):

    Making the plot “nicer”

  • plotabove (default: False)

    Indicators are usually plotted (those with subplot=True) below the data they have operated on. Setting this to True will make the indicator be plotted above the data.

  • plotlinelabels (default: False)

    Meant for “indicators” on “indicators”. If one calculates the SimpleMovingAverage of the RSI the plot will usually show the name “SimpleMovingAverage” for the corresponding plotted line. This is the name of the “Indicator” and not the actual line being plotted.

    This default behavior makes sense because the user wants to usually see that a SimpleMovingAverage has been created using the RSI.

    if the value is set to True the actual name of the line inside the SimpleMovingAverage will be used.

  • plotymargin (default: 0.0)

    Amount of margin to leave at the top and bottom of the indicator (0.15 -> 15%). Sometimes the matplotlib plots go too far to the top/bottom of the axis and a margin may be wished

  • plotyticks (default: [])

    Used to control the drawn y scale ticks

    If an empty list is passed the “y ticks” will be automatically calculated. For something like a Stochastic it may make sense to set this to well-known idustry standards like: [20.0, 50.0, 80.0]

    Some indicators offer parameters like upperband and lowerband that are actually used to manipulate the y ticks

  • plothlines (default: [])

    Used to control the drawing of horizontal lines along the indicator axis.

    If an empty list is passed no horizontal lines will drawn.

    For something like a Stochastic it may make sense to draw lines for well-known idustry standards like: [20.0, 80.0]

    Some indicators offer parameters like upperband and lowerband that are actually used to manipulate the horizontal lines

  • plotyhlines (default: [])

    Used to simultaneously control plotyticks and plothlines using a single parameter.

  • plotforce (default: False)

    If for some reason you believe an indicator should be plotting and it is not plotting … set this to True as a last resort.