Preventing subplots

Emin Ozkan

Hello,
I am interested in having separate plots for equity line and drawdown lines. How does one go about doing this?
I have set cerebro = bt.Cerebro(stdstats=False) and then added an observer for drawdown like this:
cerebro.addobserver(bt.observers.DrawDown,plot=True, subplot=False)
but cerebro.plot() only plots datafeed and the indicators.(i.e. doesn't plot newly added drawdown observer).

backtrader

@emin-ozkan said in Preventing subplots:

I am interested in having separate plots for equity line and drawdown lines

Don't really know what you mean. The "equity line" (known as "value" in backtrader) is not plotted in the same subplot as the `DrawDown´ observer (for starters, the latter is not part of the standard observers included in a non-customized plot)

They do for sure plot separately.

@emin-ozkan said in Preventing subplots:

cerebro.addobserver(bt.observers.DrawDown,plot=True, subplot=False)

This seems like a strange move. Why would you plot the DrawDown observer along with the data feed? The values are completely unrelated and the values of the observer are possible the chart, but cannot be seen due to the huge difference with the data.

Because you don't show a chart (strange since you talk about charting), the bet is that the label for the DrawDown is there in the subplot of the data.

Emin Ozkan

I am sorry that I wasn't clear. I have attached an image to see what I get currently:

What I want to get is
.
I think this is called Broker observer? But the relevant part is I want an observer such as Value or Broker in its own independent plot without including datafeed and indicators etc. (rather than a subplot). Is this possible? Thanks,

backtrader

A subplot is a different plot within a figure (a chart) and not an independent figure (chart).

You are looking for a tear sheet, which implements independent figures (charts).

No, that's not implemented. The implemented charting facility is a meant as visual aid and not as a reporting tool.

You may try disabling the plotting of the data feed with plotinfo=False and see what happens (unknown if someone has tried that and succeeded)

Emin Ozkan

@backtrader Thanks actually I got what I need by adding plot=False to datafeed and the indicators.

Emin Ozkan

Here is another way to plot an independent equity curve based on bokeh. I apologize for shitty code. This way, I don't have to turn off cerebro's built in
pretty plotting such as buysell observers.

cerebro.run()
# This assumes that you have added a strategy
# the value observer 
plot_observer(cerebro.runstrats[0][0])

from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from matplotlib.dates import num2date
import seaborn as sns
from bokeh.io import show
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, RangeTool
from bokeh.plotting import figure, output_file

def plot_observer(strategy,alias='value'):
    for observer in strategy.getobservers():
        linealias = observer.lines._getlinealias(0)
        if linealias ==alias:
            dates = np.array(strategy.datetime.plot())
            ydata = np.array(observer.line.plot())
            dates = num2date(dates)
            plot_bokeh(dates=dates,values=ydata)

    return

def plot_bokeh(dates,values):

    output_file("equity.html", title="equity line")

    TOOLS = "pan,wheel_zoom,box_zoom,reset,save"

    source = ColumnDataSource(data=dict(date=dates, value=values))

    p = figure(plot_height=300, plot_width=600, tools=TOOLS,
               x_axis_type="datetime", x_axis_location="above",
               background_fill_color="#efefef", x_range=(dates[0], dates[-1]))

    p.line('date', 'value', source=source)
    p.yaxis.axis_label = 'value'

    select = figure(title="Drag the middle and edges of the selection box to change the range above",
                    plot_height=130, plot_width=600, y_range=p.y_range,
                    x_axis_type="datetime", y_axis_type=None,
                    tools="", toolbar_location=None, background_fill_color="#efefef")

    range_rool = RangeTool(x_range=p.x_range)
    range_rool.overlay.fill_color = "navy"
    range_rool.overlay.fill_alpha = 0.2

    select.line('date', 'value', source=source)
    select.ygrid.grid_line_color = None
    select.add_tools(range_rool)
    select.toolbar.active_multi = range_rool

    show(column(p, select))