When plot zoom, adjust axis of indicators

Serp

I'm currently testing plotting charts with indicators.
I noticed when eg. plotting:

self.adosc = bt.talib.ADOSC(self.datas[0].high, self.datas[0].low, self.datas[0].close, self.datas[0].volume, fastperiod=30, slowperiod=90, plotname='ADOSC')

the axis of this indicator eg. ranges from -2 to 4.
Now I zoom in and the axis of the close price is adjusted accordingly. But the axis of the indicator is not. It is still from -2 to 4, while the highest value in this zoomed box is only ~0.25.

Is it possible to make also indicator axis to react to zooming?

backtrader

That's controlled by matplotlib

Christian Halter

Very late reply but I managed to get it working by lending a code from here:
https://stackoverflow.com/questions/11086724/matplotlib-linked-x-axes-with-autoscaled-y-axes-on-zoom

Nice side effect: it also scales the volume bars, so they to not constantly drown below the main plot on zooming anymore! :D

DISCLAIMER:
I am kind of a beginner in python/matplotlib coding, so no guarantee that it might not introduce a bug or break something.
Upon plotting, it throws the following warning once but it seems to work nevertheless:
RuntimeWarning: All-NaN slice encountered
ylim = min(ylim[0], np.nanmin(yc)), max(ylim[1], np.nanmax(yc))
Feel free to debug this very minor annoyance.

I modified plot.py like so:

Between the library loading and the "class PInfo(object): blocks, add this:

...
from .utils import tag_box_style

def on_xlim_changed(ax):
    xlim = ax.get_xlim()
    for a in ax.figure.axes:
        # shortcuts: last avoids n**2 behavior when each axis fires event
        if a is ax or len(a.lines) == 0 or getattr(a, 'xlim', None) == xlim:
            continue

        ylim = np.inf, -np.inf
        for l in a.lines:
            x, y = l.get_data()
            # faster, but assumes that x is sorted
            start, stop = np.searchsorted(x, xlim)
            yc = y[max(start-1,0):(stop+1)]
            ylim = min(ylim[0], np.nanmin(yc)), max(ylim[1], np.nanmax(yc))

        # x axis: emit=False avoids infinite loop
        a.set_xlim(xlim, emit=False)

        # y axis: set dataLim, make sure that autoscale in 'y' is on 
        corners = (xlim[0], ylim[0]), (xlim[1], ylim[1])
        a.dataLim.update_from_data_xy(corners, ignore=True, updatex=False)
        a.autoscale(enable=True, axis='y')
        # cache xlim to mark 'a' as treated
        a.xlim = xlim

class PInfo(object):
...

And then further down, add this:

        # plot subindicators that were created on self
        for subind in subinds:
            self.plotind(iref, subind, subinds=self.dplotsover[subind],
                         masterax=ax)

        ax.callbacks.connect('xlim_changed', on_xlim_changed)

        if not masterax:
            # adjust margin if requested ... general of particular
            ymargin = ind.plotinfo._get('plotymargin', 0.0)
            ymargin = max(ymargin, self.pinf.sch.yadjust)
            if ymargin:
                ax.margins(y=ymargin)

And here three examples.

Before, full view
Before, zoom (volume out of view and subplot y-axis not zoomed -> way too small)
After "patch" applied -> y-axis auto-zoom

(Zoom is to the one lonely biggest profit point approximately in the middle)