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Important Note!

Backtrader_Bokeh has been fully upgraded to Noba which has richer feature, such as ioc container, event system, pipeline system, and a database abstraction layer

Perhaps you should not use this Backtrader_Bokeh again, but if you insist on using it, there is no problem, and we also provide support.

The following is wiki to the Backtrader_Bokeh.

Regards

---

Welcome to the backtrader_bokeh wiki!

Everyone who has used backtrader knows that it's plot backend is Matplotlib. The advantage is that Matplotlib is the default backend of backtrader, but the disadvantage is that Matplotlib is relatively weak in interaction and other aspects. How can the strategy data and analysis results be display in the browser? The answer is Backtrader_Bokeh which combined Backtrader and Bokeh。 Check the example , you can see the plot effect via Backtrader_Bokeh. * just present part of demos, all demos pls run *.py in demos

Backtrader_Bokeh inherited from backtrader_plotting and btplotting. In the meantime, corrected their problems and plan to launch a series of new features more suitable for the quantitative framework backtrader. Welcome to GitHub for issue and discussion, and star is also important. If you want to support the development of Backtrader_Bokeh, consider to support this project via ETH: 0x0275779f70179748C6fCe1Fe5D7638DfA7e3F986

* Advanced feature of Backtrader_Bokeh are not free (advanced feature refer to New Feature). You can use most of the free functions without token, and can get token by paying or participating in activities(get token)

Telegram Channel: Aui_Say
Discord Server: Aui and Friends

Installation

Option 1:

pip install backtrader_bokeh

* If you delete the official source of pip, you may not find the backtrader_ bokeh。 You can: pip install -i https://pypi.org/simple backtrader_bokeh

Option 2:

pip install git+https://github.com/iniself/backtrader_bokeh

* If there is an error prompt during the installation and use of dependent packages，It is recommended to create a new Python environment and install backtrader_ Bokeh。Do not install like:

pip install --force-reinstall git+https://github.com/iniself/backtrader_bokeh

Quickstart

Backtrader_Bokeh is very easy. You only need to import Backtrader_Bokeh in your Python file as follows. That's all , then you can to get Backtrader_Bokeh brings many benefits, include:

1. Get a powerful backend via Bokeh
2. Enhance Backtrader and fix many bugs of Backtrader through Backtrader_Bokeh's patch, instead of modifying the source code of backtrader
3. A set of easier and clearer Api

from backtrader_bokeh import bt
# import backtraer as bt  # no need anymore, don't do this

There are many ways to use Backtrader_Bokeh. This wiki only introduces three kinds, and you can refer to Demo for more information:

Use Backtrader_Bokeh as analyzers（only Live Mode）

• Default 80 port:

  from backtrader_bokeh import bt
    ...
    ...
  
  cerebro = bt.Cerebro()
  cerebro.addstrategy(MyStrategy)
  cerebro.adddata(LiveDataStream()) # Note! Data is must Live Data
  cerebro.addanalyzer(bt.analyzers.Live, force_plot_legend=True, autostart=True)
  cerebro.run()

• If 80 port is not available, you can use other port:

  cerebro.addanalyzer(bt.analyzers.Live, address="localhost", port=8889, force_plot_legend=True, autostart=True)

Use Backtrader_Bokeh as plot object

• Normal Mode is the backtest which has only a set of strategy's argument:

  from backtrader_bokeh import bt
    ...
    ...
  cerebro = bt.Cerebro()
  cerebro.run()
  plot = bt.Bokeh(style = 'bar', scheme=bt.schemes.Black(), force_plot_legend=True) # bt.schemes.Black is style of scheme
  cerebro.plot(plot, iplot=False) # if run in Jupter, need to pass 'iplot' argument in there

• Optstrategy Mode is the backtest which has multi sets of strategy's argument:

  from backtrader_bokeh import bt
    ...
    ...
  	
  cerebro = bt.Cerebro()
  cerebro.optstrategy(MyStrategy, buydate=range(40, 180, 30))	
  result = cerebro.run(optreturn=False)
  
  b = bt.Bokeh(style='bar', scheme=bt.schemes.White(), force_plot_legend=True)
  browser = bt.Opt(b, result, address='localhost', port=8889, autostart= True)
  browser.start()

New Feature

We will put some content into there that are not easy to put into other chapter

• How can additional DataFeeds be plotted:
  By inheriting the method of datafeeds class and modifying the lines parameter, you can add datafeeds lines. But backtrader will not plot these lines, you just can use it in strategy. Since backtrader_ Bokeh v0.0.9 , you can do it without additional work

  from backtrader_bokeh import bt
  class MyYahooData(bt.feeds.YahooFinanceCSVData):
    lines = ('extradata',) #Add additional datafeed lines, which is backtrader's job
    extradataline = {  #Note! 'extradataline' is 'extradata' + 'line'. If wrong or is not set, line still be plotted according to the default method
        # All options in 'plotinfo' can be set in the following
        'plotname':"linename",
        'subplot':True
        ...
    }    
    ...

  As above, plotting additional data lines does not require you to do any additional work, unless you want to customize the plot of line via the option of 'extradataline'. In generally, default is ok

• Set special transaction rules: Different securities markets have different rules. For example, stock market in Chinese Mainland have a daily limit. After v0.1.0 , these transaction rules can be set. At present, what can be set is the "daily limit", "short", and "the minimum number of purchases". If the following settings are not made, the default rule is no daily limit, can short and no minimum purchase limit

  cerebro.broker.set_rule({
    'limit':0.2, #20% for daily limit
    'short':False, #can't short
    'least':100, #minimum purchase limit is 100
  })

  • If 'limit': 0.2, when you encounter the limit-up, you can't buy stocks, and if you encounter limit-down, you can't sell stocks. * Although stocks cannot be traded on the day, the order will remain valid. If you want to limit the validity period of the order, pass in the valid option, for example: self buy(size=1000, valid=timedelta(3))

  • If 'short':false, securities can only be sold within the position size. For example: if your position is 10000, but your sales order is 15000, the transaction will be carried out automatically to 10000, and the extra 5000 will be automatically canceled. If carried amount is zero, the whole sales order will be canceled. * when printing order, the 'adj size' should display the adjusted order amount

  • If 'least':100, the purchase amount can only be an integer multiple of this parameter. For example: if the amount of orders is 680, the final amount of transactions will be 600, and the extra 80 will be automatically canceled. * when printing order, the 'adj size' should display the adjusted order amount

• LogTab:
  In addition to plot, we often need to some additional informations. At this time, the usual method is to use print to print the information in the terminal. However, this kind of method is very unfriendly, so we put these informations to the Logtab web page instead of treminal:

  • Different print contents will be displayed in different tables on the Logtab page
  • Support to display other relevant information, such as the log title
  • Support different levels of log control. *CRITICAL, FATAL, ERROR, WARNING, WARN, INFO, DEBUG, NOTSET
  • Can sort the log table etc.
  • Others

  How to use LogTab：

  from backtrader_bokeh import bt
  
  class MyStrategy(bt.Strategy):
      def next(self):
          open([self.data.open[0]]) # default info level
          close.info([self.data.open[0]])
  
  if __name__ == '__main__':
      # get logger with default log level INFO
      open = bt.getlogger(['open'], name='Open Price') # name is the title of log table
      close = bt.getlogger(['close'], name='Close Price',  stdout=True, level=logging.DEBUG) # Stdout controls whether the results are printed in the terminal at the same time, default is false. Level is the  log level. For details, see the logging module
      ...
      cerebro.run()
  
      p = bt.Bokeh(style='bar', use_default_tabs=False,  tabs=[bt.tabs.LogTabs(2)]) # number 2 is the how many table in one row
      cerebro.plot(p)    

• ConfigTab:
  Due to backtrader_ Bokeh has many configurable options, so users often need to pass in a large number of parameters when calling functions. Although we set lots of initial values, but not enough. So we develop the ConfigTab : one configuration panel. These configuration in ConfigTab is global. Once you configure it, all new projects will adopt this configuration by default. Users should concentrate on their strategy research, and the configuration is left to ConfigTab

  • Newly installed backtrader_ Bokeh will enable ConfigTab by default. You can customize your own backtrader through configuration_ Bokeh
  • In the configuration panel, you can click to close configtab. So the next time when you start Backtrader_Bokeh, Configtab will not be loaded automatically
  • After close ConfigTab, if you want to reconfigure, just pass in the parameter as below * in the following way. This is actually the content in "system and theme drawing options" in this document

    bt.Bokeh(tabs=[bt.tabs.ConfigTab])

  • The global configuration priority is lower than the function parameter. That is to say, after global configuration through configtab, the backtrader can still be changed in a specific project through function parameter
  • And more you can explore by yourself

• Support keyboard operation:
  Since v0.6.0, Backtrader_Bokeh began to support keyboard operation step by step. Keyboard operation can make you operate your backtest results more accurately and quickly. This feature does not need any configuration and is available out of the box

  • Crosshair and Tooltip can be operated by left and right keys and key combinations. You have three operation speeds: the slowest is ← →, the faster is Option + ← →, and the fastest is Shift + ← →
  • Plot can be translated through Shift + Control + ← →. * Rolling the mouse wheel can also translate plot. But first need to check Wheel Pan tool
  • Zoom out and zoom in through key combination. There are four modes:
    1. Shift + ↑ ↓ quickly zoom in (or out) along the horizontal axis
    2. Shift + Option + ↑ ↓ zoom in (or out) slowly along the horizontal axis
    3. Shift + Ctrl + ↑ ↓ quickly zoom in (or out) along the horizontal and vertical axes
    4. Shift + Ctrl + Option + ↑ ↓ slowly zoom in (or out) along the horizontal and vertical axes

• Draw line:
  Since v0.8.0, Backtrader_Bokeh began to support the feature of drawing lines. It includes two drawing tools: Straightline Draw Tool and Freehand Draw Tool. "Straightline Draw Tool" is used to draw straight lines, and "Freehand Draw Tool" can draw any line you want. This feature does not need any configuration and is available out of the box

  • Click Straightline Draw Tool or Freehand Draw Tool icon to enable the feature
  • When "Straightline Draw Tool" is enabled, double-click the left mouse button and move mouse to start drawing, and click the left mouse button when turning is required. During drawing, press ESC or double-click the left mouse button to stop drawing
  • When "Freehand Draw Tool" is enabled, one-click the left mouse button and move mouse to start drawing. During drawing, press ESC or one-click the left mouse button to stop drawing
  • Click the left mouse button on one line to select the line. After selecting, you can move the line or press BACKSPACE to delete the line. * When you press "shift", you can select multiple lines at the same time

List of Options

First, introduce some functions that need to pass in arguments:

• Live Mode

  cerebro.addanalyzer(...)

• Normal Mode

  bt.Bokeh(...)

• Optstrategy Mode

  bt.Bokeh(...)
  bt.Opt(...)

This wiki will introduce Backtrader_Bokeh parameters from the following aspects:

• Type of parameters
• Definition of Parameter
• Example. * In addition to special statements, the parameters suitable for bt. Bokeh() are also suitable for cerebro addanalyzer()

Pre knowledge

Back to Backtrader's plot options:

• Options affecting the plotting behavior of the entire object
• Options affecting the plotting behavior of individual lines
• Options affecting the SYSTEM wide plotting options

Backtrader_Bokeh also configures plot's options like above. Backtrader_Bokeh's option inherit most of the Backtrader's option. Besides this, Backtrader_Bokeh has also added a large of options according to Bokeh's needs. In short，Backtrader_Bokeh Options = Backtrader Options + Bokeh Options

System and Scheme Options

1. style
   • str
   • Controls the type of display of the main plot: Single only shows the line chart of closing price, bar or candle shows the bar chart including opening price, closing price, highest price and lowest price. * since v0.0.7, you can customize the style of each data like following:

     data = bt.feeds.YahooFinanceCSVData(...)
     data.plotinfo.plotstyle = 'bar'      

   • bt.Bokeh(style='bar')
2. resources
   • str
   • Unless this option is passed in as follows, backtrader_Bokeh loads the local bokeh resource file by default instead of through the CDN network
   • bt.Bokeh(resources='cdn')
3. scheme
   • object
   • Plot Scheme. There are currently two schemes: Black (dark theme) and White (light theme). Default is White
   • bt.Bokeh(scheme=bt.schemes.Black())
4. filename
   • str
   • In Normal Mode, the specified file name is used instead of the Backtrader_Bokeh default temporary file name. * This option is only applicable to static web pages, so it is invalid in "Live Mode" and "Optstrategy Mode"
   • bt.Bokeh(filename='yourfile.html')
5. output_mode
   • str
   • only Normal Mode:
     save: save the file without opening the browser
     show: save the file and opening the browser
     memory: do not save the file, but return to the model
6. use_default_tabs
   • bool
   • If true, the default web tabs will be added
   • bt.Bokeh(use_default_tabs=False)
7. tabs
   • list
   • Tabs you want to add in the web page. only be effectual when use_default_tabs=False

   • bt.Bokeh(tabs=[bt.tabs.AnalyzerTab])

8. show_headline
   • bool
   • Headline show or not show
   • bt.Bokeh(show_headline=False)
9. headline
   • str
   • Change headline content. Default is "Backtrader Results"
   • bt.Bokeh(headline='Your backtrader')
10. force_plot_legend
    • bool
    • If True (default is True), all legends will be forced to plot. * Set to true when legends dont be ploted
    • bt.Bokeh(force_plot_legend=True)
11. hover_tooltip_config
    • str
    • Decide what is included in the tooltip. When this parameter is not passed in, tooltip is default (data, indicators, observer) . For example, data feed will display time, opening price, closing price, highest price, lowest price and trading volume. But if you want to display additional info, you need this option
    • IND-DATA: Add the indicators info to the tooltip in the figure of main Data Feed
    • DATA-OBS: Add the Data feed info to the Observer
    • IND-OBS: Add the Indicators info to the Observer
    • ……
12. plotconfig
    • dict
    • Object-Wide plotting options (detail on Object-Wide plotting options）。Backtrader_Bokeh's plotconfig is equivalent to Plotting - Backtrader

    • plotconfig = {
          'id: sm5': dict(
              subplot=False,
              plotname='sm5 indicator'
          )
      }
      
      bt.Bokeh(plotconfig=plotconfig)

13. usercolumns
    • dict
    • Custom columns can be added to the results list to display special attributes of the results. To use it, you need to pass a dictionary, where the key is the label of the column, and the value is an callable value, which needs an optimization result to calculate the attribute. This option is only applicable to Optstrategy Mode

    • def get_pnl_gross(strats):
          a = strats[0].analyzers.tradeanalyzer.get_analysis()
          return a.pnl.gross.total if 'pnl' in a else 0
      
      b = bt.Bokeh(style='bar', scheme=bt.schemes.White())
      browser = bt.Opt(b, result, usercolumns=dict(pnl=get_pnl_gross), sortcolumn='pnl', sortasc=False)
      browser.start()

14. Other Scheme Options

    •     def _set_params(self):
              self.multiple_tabs = False
              self.show_headline = True
              self.headline = ''
              self.hover_tooltip_config = ''
      
              self.barup_wick = self.barup
              self.bardown_wick = self.bardown
      
              self.barup_outline = self.barup
              self.bardown_outline = self.bardown
      
              self.crosshair_line_color = '#999999'
      
              self.legend_background_color = '#3C3F41'
              self.legend_text_color = 'lightgrey'
              self.legend_location = 'top_left'
              self.legend_orientation = 'vertical'
      
              self.loc = 'lightgray'
              self.background_fill = '#222222'
              self.body_background_color = 'white'
              self.border_fill = '#3C3F41'
              self.legend_click = 'hide'  # or 'mute'
              self.axis_line_color = 'darkgrey'
              self.tick_line_color = self.axis_line_color
              self.grid_line_color = '#444444'
              self.axis_text_color = 'lightgrey'
              self.plot_title_text_color = 'darkgrey'
              self.axis_label_text_color = 'darkgrey'
              self.tag_pre_background_color = 'lightgrey'
              self.tag_pre_text_color = 'black'
      
              self.xaxis_pos = 'all'  # 'all' or 'bottom'
      
              self.table_color_even = '#404040'
              self.table_color_odd = '#333333'
              self.table_header_color = '#7a7a7a'
      
              # Plot a title above the plot figure
              self.plot_title = True
              # Number of columns on the analyzer tab
              self.analyzer_tab_num_cols = 1
              # Number of columns on the metadata tab
              self.metadata_tab_num_cols = 3
              # Sizing mode for plot figures
              self.plot_sizing_mode = 'scale_width'
              # Aspect ratios for different figure types
              self.data_aspectratio = 2.5
              self.vol_aspectratio = 5.0
              self.obs_aspectratio = 5.0
              self.ind_aspectratio = 5.0
              # output backend mode ("canvas", "svg", "webgl")
              self.output_backend = 'canvas'
      
              self.toolbar_location = 'right'
      
              self.tooltip_background_color = '#4C4F51'
              self.tooltip_text_label_color = '#848EFF'
              self.tooltip_text_value_color = '#aaaaaa'
      
              self.tab_active_background_color = '#333333'
              self.tab_active_color = '#4C4F51'
      
              self.text_color = 'lightgrey'
      
              # https://docs.bokeh.org/en/latest/docs/reference/models/formatters.html#bokeh.models.formatters.DatetimeTickFormatter
              self.hovertool_timeformat = '%F %R'
      
              self.number_format = '0,0[.]00[000000]'
              self.number_format_volume = '0.00 a'
      
              # https://docs.bokeh.org/en/latest/docs/reference/models/formatters.html
              self.axis_tickformat_days = '%d %b %R'
              self.axis_tickformat_hourmin = '%H:%M:%S'
              self.axis_tickformat_hours = '%d %b %R'
              self.axis_tickformat_minsec = '%H:%M:%S'
              self.axis_tickformat_minutes = '%H:%M'
              self.axis_tickformat_months = '%d/%m/%y'
              self.axis_tickformat_seconds = '%H:%M:%S'
              self.axis_tickformat_years = '%Y %b'
      
              # used to add padding on the y-axis for all data except volume
              self.y_range_padding = 0.5
              # position of y axis for volume
              self.vol_axis_location = 'right'

    • Scheme Options can be directly pass into cerebro Addanalyzer() or bt. bokeh() as arguments. Or you can pass into the construct function of the scheme class

      • bt.Bokeh(hovertool_timeformat='%F %R:%S')

      • bt.Bokeh(scheme=bt.schemes.Black(hovertool_timeformat='%F %R:%S'))

Object-Wide plotting options

It has been said in pre knowledge, Object-Wide plotting options are the settings of plotinfo and plotlines for each object (such as an indicator). There are three ways to configure this option in backtrader:

1. Inheriting:

   class MY_SMA(bt.indicators.SMA):
      params = (('barplot', True), ('bardist', 0.02))
      plotinfo = dict(
        plotname = "MySMA"
      )
      plotlines = dict(
   		...
     )
   class MyStrategy(bt.Strategy):
     def __init__(self):
       self.sma5 = MY_SMA(period=15)
     ....
     
   cerebro.addstrategy(MyStrategy)

2. Pass in argument:

   class MyStrategy(bt.Strategy):
     def __init__(self):
   		self.sma5 = bt.indicators.SMA(period=15, plotname = "MySMA")
     ....
     
   cerebro.addstrategy(MyStrategy)

   class MyStrategy(bt.Strategy):
     def __init__(self):
   		self.sma5 = bt.indicators.SMA(period=15)
   		self.sma5.plotinfo.plotname = "MySMA"
     ....
     
   cerebro.addstrategy(MyStrategy)

3. Backtrader_Bokeh adds a way to handle all Object-Wide plotting options in one place:

   class MyStrategy(bt.Strategy):
     def __init__(self):
        self.sma5 = bt.indicators.SMA(period=15)
        self.sma5.plotinfo.plotid='sm5'
     ....
   plotconfig = {
       'id:sm5': dict(
           plotname='MySMA'
       )
   }  
   cerebro.addstrategy(MyStrategy)
   b = bt.Bokeh(plotconfig=plotconfig)
   cerebro.plot(b)

Example of Object-Wide plotting options * just part of plotinfo，more plotinfo and plotlines pls refer Backtrader :

1. plot
   • bool
   • Whether the object has to be plotted
   • plot=True
2. subplot
   • bool
   • Whether to plot along the data or in an independent subchart. Moving Averages are an example of plotting over the data. Stochastic and RSI are examples of things plotted in a subchart on a different scale
   • subplot=True
3. plotmaster
   • object
   • An Indicator/Observer has a master which is the data on which is working. In some cases plotting it with a different master may be wished needed

   • class MyStrategy(bt.Strategy):
         def __init__(self):
             self.sma5 = bt.indicators.SMA(period=5, subplot=True)
             self.sma10 = bt.indicators.SMA(period=10, plotmaster=sma5)

4. plotname
   • str
   • Name to use on the chart instead of the class name. As in the example above mysma instead of SimpleMovingAverage
   • plotname='somename'
5. plotorder
   • int
   • The smaller of number, the plot is more upper on the page. Default all 0
     * The following code will let the Observer be ploted above Data Feed (stock price, trading volume and other main charts)

   • class MyBroker(bt.observers.Broker):
         def __init__(self):
             self.plotinfo.plotorder = 5
             
     cerebro.addobserver(MyBroker)

6. The other Object-Wide plotting options

   • plotinfo = dict(plot=True, 
                     subplot=True,
                     plotname='',
                     plotorder=0, 
                     plotlinelabels=False, # whether to plot the names of the individudal lines along the data in the legend on the chart when subplot=False
                     plotlinevalues=True, # controls whether the legend for the lines in indicators and observers has the last plotted value. Can be controlled on a per-line basis with _plotvalue for each line
                     plotvaluetags=True, # controls whether a value tag with the last value is plotted on the right hand side of the line. Can be controlled on a per-line basis with _plotvaluetag for each line
                     plotymargin=0.0, # margin to add to the top and bottom of individual subcharts on the graph. It is a percentage but 1 based. For example: 0.05 -> 5%
                     plothlines=[a,b,...], # an iterable containing values (within the scale) at which horizontal lines have to be plotted
                     plotyticks=[], #  an iterable containing values (within the scale) at which value ticks have to specifically be placed on the scale
                     plotyhlines=[a,b,...], # an iterable containing values (within the scale) at which horizontal lines have to be plotted
                     plotforce=False, # sometimes and thus the complex process of matching data feeds to indicators and bla, bla, bla … a custom indicator may fail to plot. This is a last resort mechanism to try to enforce plotting
                     plotmaster=None, # an Indicator/Observer has a master which is the data on which is working. In some cases plotting it with a different master may be wished needed
                     plotylimited=True, # currently only applies to data feeds. If True (default), other lines on the data plot don’t change the scale. Example: Bollinger Bands (top and bottom) may be far away from the actual absolute minimum/maximum of the data feed. With \plotlimited=True, those bands remain out of the chart, because the data controls the scaling. If set toFalse`, the bands affects the y-scale and become visible on the chart
     )

   • Please try different options by yourself

Browser option

1. autostart
   • bool
   • If true (default is True), open browser automatically. * Suitable for Optstrategy Mode and Live Mode, because browser can't be open automatically under the two Modes
   • bt.Opt(autostrart=True) (Optstrategy Mode), cerebro.addanalyzer(autostrart=True) (Live Mode)
2. address
   • str
   • Hosts address. If run Backtrader_Bokeh locally, the configuration is "localhost" :
   • bt.Opt(address='localhost', port=8889 (Optstrategy Mode), cerebro.addanalyzer(address='localhost', port=9889) (Live Mode)
3. port
   • int
   • Web port. The default is 80 port，If 80 port is not available or want to share the plotting to public, you can change the default port:
   • bt.Opt(address='localhost', port=8889 (Optstrategy Mode), cerebro.addanalyzer(address='localhost', port=9889) (Live Mode)

Datadomains

Datadomains are basically used to group entities that belong together to plot them together on one page. A datadomain is a single string. All entities having an identical string belong to the same datadomain.

Per default each data feed creates one datadomain which is derived from its _name attribute. All entities that are based on this data (like e.g. indiators) will inherit the datadomain of that data. So by default each data and its corresponding indicators and others entities do form a separate datadomain.

Datadomain values can be manually overridden though to change the automtically created grouping. This is done by passing the parameter datadomain to the initializer of an entity.