量化交易学习（四十）backtrader文档——绘图（3）

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今天这篇是backtrader文档的学习笔记。主要介绍了绘图。

官方文档链接：https://www.backtrader.com/docu/plotting/plotting/

控制绘图的方法

在处理指标和观察者时，支持以下方法来进一步控制绘图：

  • _plotlabel(self)

它应该返回一个列表，以符合标签，这些标签将放置在指标或观察者名称后面的括号之间

RSI指标示例：

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def _plotlabel(self):
    plabels = [self.p.period]
    plabels += [self.p.movav] * self.p.notdefault('movav')
    return plabels

可以看出该方法返回：

  • 返回一个表示RSI的周期的int变量，以及如果默认移动平均线已变化，返回对应的类

在后台，两者都将转换为字符串。对于类，将努力只打印类的名称而不是完整的module.name组合。

  • _plotinit(self)

在绘图开始时调用它来执行指标可能需要的任何特定初始化。再用RSI举一个例子：

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def _plotinit(self):
    self.plotinfo.plotyhlines = [self.p.upperband, self.p.lowerband]

这里，代码给plotyhlines赋值，以在特定y值处绘制水平线（hlines部分）。

参数值upperband和lowerband用于此目的，无法提前知道，因为参数可以由最终用户更改

系统范围的绘图选项

首先是cerebro内的签名：plot

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def plot(self, plotter=None, numfigs=1, iplot=True, **kwargs):

简单介绍下：

  • plotter：一个对象/类，属性包含控制系统范围绘图的选项

如果设为None，则一个默认的PlotScheme对象（见下文）将被实例化

  • numfigs：一个图必须被分成多少个独立图表进行绘制

有时，图表包含太多条形图，如果都在单个图中绘制，则不容易阅读。这会根据要求将其分解为指定份数。

  • iplot：如果在 Jupyter Notebook 中运行，则自动绘制内联图

  • **kwargs: 这些参数将用于更改plotter的属性值，如果没有plotter传入，则创建默认的PlotScheme对象。

PlotScheme类

该对象包含控制系统范围绘图的所有选项。这些选项记录在代码中：

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class PlotScheme(object):
    def __init__(self):
        # to have a tight packing on the chart wether only the x axis or also
        # the y axis have (see matplotlib)
        self.ytight = False

        # y-margin (top/bottom) for the subcharts. This will not overrule the
        # option plotinfo.plotymargin
        self.yadjust = 0.0
        # Each new line is in z-order below the previous one. change it False
        # to have lines paint above the previous line
        self.zdown = True
        # Rotation of the date labes on the x axis
        self.tickrotation = 15

        # How many "subparts" takes a major chart (datas) in the overall chart
        # This is proportional to the total number of subcharts
        self.rowsmajor = 5

        # How many "subparts" takes a minor chart (indicators/observers) in the
        # overall chart. This is proportional to the total number of subcharts
        # Together with rowsmajor, this defines a proportion ratio betwen data
        # charts and indicators/observers charts
        self.rowsminor = 1

        # Distance in between subcharts
        self.plotdist = 0.0

        # Have a grid in the background of all charts
        self.grid = True

        # Default plotstyle for the OHLC bars which (line -> line on close)
        # Other options: 'bar' and 'candle'
        self.style = 'line'

        # Default color for the 'line on close' plot
        self.loc = 'black'
        # Default color for a bullish bar/candle (0.75 -> intensity of gray)
        self.barup = '0.75'
        # Default color for a bearish bar/candle
        self.bardown = 'red'
        # Level of transparency to apply to bars/cancles (NOT USED)
        self.bartrans = 1.0

        # Wether the candlesticks have to be filled or be transparent
        self.barupfill = True
        self.bardownfill = True

        # Wether the candlesticks have to be filled or be transparent
        self.fillalpha = 0.20

        # Wether to plot volume or not. Note: if the data in question has no
        # volume values, volume plotting will be skipped even if this is True
        self.volume = True

        # Wether to overlay the volume on the data or use a separate subchart
        self.voloverlay = True
        # Scaling of the volume to the data when plotting as overlay
        self.volscaling = 0.33
        # Pushing overlay volume up for better visibiliy. Experimentation
        # needed if the volume and data overlap too much
        self.volpushup = 0.00

        # Default colour for the volume of a bullish day
        self.volup = '#aaaaaa'  # 0.66 of gray
        # Default colour for the volume of a bearish day
        self.voldown = '#cc6073'  # (204, 96, 115)
        # Transparency to apply to the volume when overlaying
        self.voltrans = 0.50

        # Transparency for text labels (NOT USED CURRENTLY)
        self.subtxttrans = 0.66
        # Default font text size for labels on the chart
        self.subtxtsize = 9

        # Transparency for the legend (NOT USED CURRENTLY)
        self.legendtrans = 0.25
        # Wether indicators have a leged displaey in their charts
        self.legendind = True
        # Location of the legend for indicators (see matplotlib)
        self.legendindloc = 'upper left'

        # Plot the last value of a line after the Object name
        self.linevalues = True

        # Plot a tag at the end of each line with the last value
        self.valuetags = True

        # Default color for horizontal lines (see plotinfo.plothlines)
        self.hlinescolor = '0.66'  # shade of gray
        # Default style for horizontal lines
        self.hlinesstyle = '--'
        # Default width for horizontal lines
        self.hlineswidth = 1.0

        # Default color scheme: Tableau 10
        self.lcolors = tableau10

        # strftime Format string for the display of ticks on the x axis
        self.fmt_x_ticks = None

        # strftime Format string for the display of data points values
        self.fmt_x_data = None

PlotScheme 中的颜色

PlotScheme类定义了一个可以在子类中重写的方法，该方法返回要使用的下一个颜色：

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def color(self, idx)

idx是当前正在被绘制在单独子图上的线对象的索引。 例如，MACD绘制了 3 条线，因此idx变量将只有以下值：0、1和2。下一个图表（可能是另一个指标）将在0索引再次开始计数。

backtrader默认使用的配色方案（如上所示）是索引按下面修改的Tableau 10 Color Palette：

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tab10_index = [3, 0, 2, 1, 2, 4, 5, 6, 7, 8, 9]

通过重写color方法或将lcolors变量传递给plot（或在PlotScheme的子类中），可以完全更改颜色。

源代码中还包含Tableau 10 Light和Tableau 20调色板的定义。

这一篇就到这里啦。欢迎大家点赞、转发、私信。还没有关注我的朋友可以关注 江达小记

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