@run-out I needed a break and coffee. It was silly

@backtrader @tmorgan4 Hey, I am trying to understand this and the question is about programatically extending the data feed when you do not know the additional column names of df. After the discussion, @tmorgan4 posted a code where ('optix_close', 'optix_pess', 'optix_opt',) lines are already known. How is this created programatically? Am I missing something?

thanks you in advance for the response

Hello,

I am currently extending the data feeds using PandasData feeds and have multiple data feeds going into cerebro.

class PandasData(bt.feeds.PandasData):
    lines = ('abc','xyz')
    params = (
        ('abc', 'abc'),
        ('xyz','xyz'),    
    )

for df in datas:
      data=PandasData(dataname=df)
      cerebro.adddata(data, name='example')

and using these lines in init function as:

for i, d in enumerate(self.datas):
       self.inds[d] = dict()
       self.inds[d]['abc'] = d.abc
       self.inds[d]['xyz'] = d.xyz

Now, what if I don't know the name of extra columns 'abc' and 'xyz' that the pandas dataframe would have and would like to extend the dataframe for those data feeds dynamically.

I saw this topic, what I don't understand here is that they already know these 'optix_close', 'optix_pess', 'optix_opt' column names. How is this programatically extending datafeed?

Thanks in advance

@run-out
Thanks for your help. I found out that I was not using the enumerate for loop in init function so that it can run for each data feed

@run-out
Yes, there's misunderstanding here. p/e is just an example. Take a simple moving average strategy. I have a dataframe that already has sma crossover calculated. I am sending that dataframe to custom pandas data function and creating a line. Like this:

class PandasData(bt.feeds.PandasData):
    lines = ('crossover')
    params = (
        ('crossover', 'crossover'),
    )

and feeding it to cerebro

df=PandasData(dataname=current_df)
     cerebro.adddata(df, name=current_df['symbol'][0])

Now, I use this in next as following:

if self.datas[0].crossover > 0:
     buy()
if self.datas[0].crossover < 0:
    sell()

This works perfectly fine. Now I am extending my strategy so that it can run over multiple assets as the same time. I am sending single dataframes to single data feed like:
resp is a list of dataframe where each has crossover already calculated

for df in resp:
      data=PandasData(dataname=df)
      cerebro.adddata(df, name='data0') #name here would be dynamic so I can get the name in next and find which asset/dataframe's order is placed
      cerebro.run()

and then in next:

for i, d in enumerate(self.datas):
        dt, dn = self.datas[0].datetime.date(), d._name
        pos = self.getposition(d).size
        if not pos:

In order to do this in next, I would need multiple lines of crossover from custom pandas data function, one for each asset/datafeed/dataframe

@run-out
I am trying to follow this https://backtest-rookies.com/2017/08/22/backtrader-multiple-data-feeds-indicators/ but instead of using backtrader indicators, I want to use the lines create from custom dataframe in next. I was able to implement this with single data feed. Creating the lines from custom pandas data and using that line in next.

I am having issues extending the same with multiple data feeds

@run-out
I am able to do this. I have custom loader that has extra columns and I am feeding it to cerebro and using it next. The issue I am facing is with applying the same logic to multiple datafeeds.

Multiple data feeds means there would be pe_largcap and pe_international for all data feeds. So, if the cerebro is feed with 2 assets data, it should have 4 lines. and somehow I need to check those 4 lines in next depending on the data feed.

Hello,

I am trying to run strategy on multiple assets that have custom pandas dataframe.

class PandasData(bt.feeds.PandasData):
    lines = ('pe')
    params = (
        ('pe', 'pe'),
    )

There is a list of dataframes that has information for different assets

for data in list:
    df=PandasData(dataname=data)
    cerebro.adddata(df, name='abc')
    cerebro.run()

I am able to use this for single asset in next() using

for i, d in enumerate(self.datas):
    dt, dn = self.datas[0].datetime.date(0)), d._name
    pos = self.getposition(d).size

How can I create multiple 'pe' lines for multiple assets and use it in the next()?