Backtrader - 绘图

以下内容皆参考 Backtrader 官网

有了策略可以让我们进行评估，有了历史资料可以进行回测，加上 Backtrader 有 trade_notify, order_notify，我们可以看到每笔交易的价格，如果要进行策略优化，也有 stop 这个事件，可以在执行完策回测後进行回报成果，基本上这样就结束了...吗？如果只是程序在跑，的确是结束了，不过人是视觉的动物，所以补上绘图这一块才算是完整。

在之前就有大概介绍过如何进行绘图，今天再说明仔细一点，Backtrader 的绘图指令很简单，只有一行:

cerebro.plot()

它就会自动绘出以下的资料:

• datafeed 的资料集
• indicators 指标
• observers - 最基本的就是 现金, 收益, 买进/卖出 这 3 个

datafeed

datafeed 的部份，不管是使用 adddata, resampledata, replaydata 都会新增一笔资料，也会在绘图的时候一并显示。

indicators

指标的话，就是在 init 的地方，我们可以新增指标，不管有没有指定变数，只要我们有初始化，那就会显示

def __init__(self):
    self.sma_short = btind.SimpleMovingAverage(period = 5)
    self.sma_long = btind.SimpleMovingAverage(period = 30)
    # 或者不指定变数
    # btind.SimpleMovingAverage(period = 5)
    # btind.SimpleMovingAverage(period = 30)

observers

如果还有记得第一天，我们在初始化 cerebro 的时候是这样子的

cerebro = bt.Cerebro(stdstats=False)

# 也可以在执行的时候指定
# cerebro.run(stdstats=False)

stdstats 指的就是上面 3 个基本的 observers，所以第一天画出来的图，并没有 observers 的资讯。

我们以一段简单的策略来示范:

import backtrader as bt
import backtrader.indicators as btind
import backtrader.feeds as btfeeds
class St(bt.Strategy):
    def __init__(self):
        self.sma_short = btind.SimpleMovingAverage(period = 5)
        self.sma_long = btind.SimpleMovingAverage(period = 30)

data = btfeeds.PandasData(dataname=df, timeframe=bt.TimeFrame.Minutes)

cerebro = bt.Cerebro(stdstats=True)

cerebro.resampledata(data, timeframe=bt.TimeFrame.Days)

cerebro.addstrategy(St)
cerebro.run()
cerebro.plot()

上面的2个图就是，现金/收益, 买进/卖出，下面最主要的图就是 datafeed/指标，之前说过指标也是以 line 的型式在储存资料，所以可以在这上面画出一条线来，要加画线的话，可以在 init 增加我们的指标，而且不一定要指定变数。像是这样

indicators, observers 绘图设定

在绘制 indicators 和 observers 的时候，有一些选项可以进行细部设定选项如下所例:

• plot (bool): 是否要绘制
• subplot (bool): 是否为 datafeed 的副图
• plotname (string): 图形名称
• plotabove (bool): 在 subplot = true 时，是否要绘制在 datafeed 的图形之上
• plotlinelabels (bool): subplot = False，图例是否显示 plotname
• plotlinevalues (bool): subplot = False，图例是否显示 值
• plotvaluetags (bool): 是否显示最後数值的标签
• plotymargin (dicimal): 每个图示之间的间距
• plothlines (数组): 根据数组的值绘制一横线
• plotyticks (数组): 根据数组的值绘制一直线
• plotylines (数组): 根据数组的值绘制一横线
• plotforce (bool): 某些时候可能会绘制失败，可以试试这个参数，强制它一定要绘图
• plotmaster (line): 要绘制在哪个主视图
• plotylimited (bool): 目前只适用 datafeed 的图，控制是否变更比例(如果不变更比例，超出的部份会无水显示)

设定的方法有 2 种：

# 在初始化 indicators 时指定 kwargs
btind.SimpleMovingAverage(period = 5, plot = False)

# 在初始化後，依照指定的变数设定
sma = btind.SimpleMovingAverage(period = 5)
sma.plotinfo.plot = False

indicators|observers 系统层设定

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

• plotter: 系统层设定的物件，如果是 None 的话，就使用预设的 PlotScheme 物件
• numfigs: 要拆分多少张图显示
• iplot: 自动在 jupyter notebook 使用 plot inline
• **kwaargs: 变更 plotter 的选项

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

另外，绘图的设定选项还有在 indicators|observers 里设定，不过这里要知道怎麽自订义 indicators|observers，因为我还没有分享到这，所以暂时就不分享这部份，之後如果有时间，再来补充