量化框架backtrader之一文读懂可视化

简介
回测时往往想知道策略中间的运行情况，虽然可以通过最后的统计指标来一窥端倪，但对回测过程进行可视化是最符合人性的，同时通过观察回测过程也可以更好的设计&优化量化策略。Backtrader使用matplotlib库提供可视化能力

使用方法
Backtrader数据可视化非常简单，只需在run()之后调用plot()方法即可。

cerebro.run()
cerebro.plot()

plot(self, plotter=None, numfigs=1, iplot=True, **kwargs) 方法各参数含义如下：

plotter: 包含绘图属性的PlotScheme及其派生类对象。默认为None，如果为None，则默认的PlotScheme对象会被实例化

numfigs ：将图形拆分成多幅图展示，默认为1

iplot ： 在Jupyter Notebook运行则是否自动 plot inline，默认为True。如果不在jupyter中运行，该参数最好设置为False，否则容易出问题

*kwargs：args参数用于改变plotter属性值

可以通过两种办法来系统性控制可视化配置：

直接通过设置plot()方法的args参数，如下所示。

cerebro.plot(iplot=False,
, # 设置主图行情数据的样式为蜡烛图
plotdist=0.1, # 设置图形之间的间距
barup = '#ff9896', bardown='#98df8a', # 设置蜡烛图上涨和下跌的颜色
volup='#ff9896', voldown='#98df8a', # 设置成交量在行情上涨和下跌情况下的颜色
)

2. 自定义 PlotScheme 类修改对应的参数

PlotScheme对象包括了所有的系统级绘图选项，选项如下所示。

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类定义了一个color(self, idx) 方法返回将要使用的颜色，子类可以重载，其idx参数为要绘制的line的当前index。 如MACD 绘制3条线，idx变量有0，1和2共3个值，新的指标idx会从0重新开始。默认的color scheme是Tableau 10 Color Palette ，对应的index是tab10_index = [3, 0, 2, 1, 2, 4, 5, 6, 7, 8, 9] 。可以通过在自定义 PlotScheme类重载color()方法或传递 lcolors 变量给plot 方法来改变要使用的颜色。

def color(self, idx):
colidx = tab10_index[idx % len(tab10_index)]
return self.lcolors[colidx]

可视化组件
Backtrader支持3大部分组件的可视化：

Data feeds数据源：通过 adddata、replaydata和resampledata方法导入cerebro的原始数据

Indicators指标：在策略类中声明或者通过 addindicator 添加的指标

Observers观测器对象：通过addobserver添加的观测器，如Cash和Value对象

在绘制图形时，默认是将data feeds数据源绘制在主图上，Indicators指标有的与 Data Feeds数据源一起绘制在主图上，比如均线，有的则以子图形式绘制；observers 通常绘制在子图上

可视化选项
除了上面说的通过plot()参数和自定义PlotScheme来系统控制可视化选项外，Indicators指标和Observers观测器有一些选项可以控制其绘图形式，一共有3种类型:

Object对象级的可视化选项 —可以影响整个对象的绘制行为，由plotinfo来控制

plotinfo = dict(plot=True, # 是否绘制
subplot=True, # 是否绘制成子图
plotname='', # 图形名称
plotabove=False, # 子图是否绘制在主图的上方
plotlinelabels=False, # 主图上曲线的名称
plotlinevalues=True,
plotvaluetags=True,
plotymargin=0.0,
plotyhlines=[],
plotyticks=[],
plothlines=[],
plotforce=False,
plotmaster=None,
plotylimited=True,
)

有2种方法来访问plotinfo的属性，如下所示：

# 通过参数来设置
sma = bt.indicators.SimpleMovingAverage(self.data, period=15, plotname='mysma')

# 通过属性来设置
sma = bt.indicators.SimpleMovingAverage(self.data, period=15)
sma.plotinfo.plotname = 'mysma'

• Line线相关的可视化选项 — 可以使用plotlines对象来控制lines对象的绘图行为，plotlines中的选项会在绘图时直接传给matplotlib，如下所示。

lines = ('histo',)
plotlines = dict(histo=dict(_method='bar', alpha=0.50, width=1.0))

方法控制的可视化 — 当处理indicator指标和observer观测器时，_plotlabel(self), _plotinit(self)方法可以进一步控制可视化

结论 & 交流
关注微信公众号：诸葛说talk，获取更多内容。同时还能获取邀请加入量化投资群， 与众多投资爱好者、量化从业者、技术大牛一起交流、切磋，在实战中快速提升自己的投资水平。

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原文链接：https://blog.csdn.net/richardzhutalk/article/details/125697148