@backtrader

You are absolutely right, the problem is not with backtrader, but rather with matplotlib. Taking a look at the (excellent) documentation and previous questions have highlighted that for me.

I will post my workaround as soon as I am done.
For others who are stuggeling here, take a look at:

https://www.backtrader.com/docu/observers-reference.html?highlight=buysell#backtrader.observers.BuySell
https://www.backtrader.com/blog/posts/2016-12-10-buysellarrows/buysellarrows.html
https://community.backtrader.com/topic/8/change-buysell-to-trade-arrows-in-charts-213/3

Hello,

After a while of trying to trace the source of my missing BuySell indicators, I think the issue is with backtrader.
Consider the following example:

class St(bt.Strategy):

	def __init__(self):
		self.dataclose = self.datas[0].close

	def next(self):
		if self.dataclose[0] > 2944:
			self.order = self.sell()


data = bt.feeds.BacktraderCSVData(dataname='data.csv')

cerebro = bt.Cerebro()
cerebro.adddata(data)
cerebro.addstrategy(St)
cerebro.run()
cerebro.plot()

With data.csv

Date,Open,High,Low,Close,Volume,OpenInterest
2005-01-03,2952.29,2989.61,2946.80,2970.02,0,0
2005-01-04,2969.78,2979.88,2961.14,2971.12,0,0
2005-01-05,2969.00,2969.00,2942.69,2947.19,0,0
2005-01-06,2947.44,2967.65,2947.44,2966.24,0,0
2005-01-07,2965.54,2988.99,2964.48,2979.82,0,0
2005-01-10,2980.30,2986.07,2967.79,2977.21,0,0
2005-01-11,2977.14,2981.51,2943.42,2949.29,0,0
2005-01-12,2948.89,2952.79,2914.00,2924.01,0,0
2005-01-13,2925.60,2943.09,2922.91,2936.32,0,0
2005-01-14,2933.58,2951.77,2925.13,2948.22,0,0
2005-01-17,2948.11,2963.22,2948.11,2963.06,0,0
2005-01-18,2962.28,2965.60,2940.77,2962.50,0,0
2005-01-19,2963.91,2976.33,2957.21,2959.90,0,0
2005-01-20,2957.55,2957.55,2929.78,2937.71,0,0
2005-01-21,2937.27,2944.92,2927.62,2940.87,0,0
2005-01-24,2939.68,2940.02,2916.08,2937.83,0,0
2005-01-25,2937.66,2960.90,2932.56,2958.61,0,0
2005-01-26,2958.31,2967.62,2954.36,2956.43,0,0
2005-01-27,2956.32,2970.81,2948.55,2970.81,0,0
2005-01-28,2969.91,2977.99,2950.68,2955.89,0,0
2005-01-31,2958.07,2987.88,2958.07,2984.75,0,0
2005-02-01,2984.63,3008.85,2982.06,3008.85,0,0
2005-02-02,3009.08,3022.87,3008.92,3021.95,0,0
2005-02-03,3021.94,3023.85,3001.87,3010.39,0,0
2005-02-04,3011.58,3038.31,3011.58,3037.13,0,0

This yields:

But delete the last 6 dates from data.csv and you will end up with this:

In addition, if my data is on hourly resolution, all BuySell markers disappear, no matter how many hours I include.

Any idea what to do?

Big Thanks to @backtrader

Dynamic subclassing did the job. Absolutely fantastic!

@Robin-Dhillon

Thanks for your input. May problem was though that I had a no information about the column names of my data, hence I did not know upfront if there is a e.g. "closed" column.

@Robin-Dhillon

Sorry. Edit is not working.

Can you specify how you initialize a new line in a a strategy based on a column in the existing dataframe?

Apparently I should have given it a second try when using setattr().
Adding:

setattr(BacktraderDataDefintion.params,"col_a",-1)

Seems to work.

class BacktraderDataDefintion(bt.feeds.PandasData):
      
    lines = ("col_a",)

    params = (
        ('datetime', None),
        ('open', -1),
        # ('high', None),
        # ('low', None),
        ('close', -1),
        # ('volume', None),
    )

 if __name__ == '__main__':
      
          cerebro = bt.Cerebro()
      
          cerebro.addstrategy(TestStrategy)
      
          cerebro.broker.setcommission(commission=0.001)
      
      
      
          #Add params
      
          setattr(BacktraderDataDefintion.params,"col_a",-1)
      
          # Create a Data Feed
      
          data = BacktraderDataDefintion(dataname=createSampleData())
      
      
      
          cerebro.adddata(data)
      
          cerebro.broker.setcash(10000.0)
      
          print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
      
          cerebro.run()
      
          print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())  

However, I am lost on the "lines" class attribute. Especially because of this:

dir(BacktraderDataDefintion.lines)


prints
['__class__',
'__delattr__',
..
'buflen',
'close',
'col_a',
'datetime',
'extend',
...
'size',
'volume']

but:

getattr(BacktraderDataDefintion.lines,"col_a")

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
...

AttributeError: 'NoneType' object has no attribute 'lines'

How is this possible?

@Robin-Dhillon Manipulating the *.csv is not an option.

Hello,

I am very new to backtrader and hope to get some advise here.

Here is what I am trying to do:
Assuming I get data as pandas dataframe. One or more columns are to be used in the strategy, but I don't know their name. How can I dynamically change the "params" attribute of bt.feeds.PandasData? Note that the resulting complications with the strategy are irrelevant at that piont.

Consider the following reproducible example:
import datetime
import backtrader as bt
import numpy as np
import pandas as pd

class TestStrategy(bt.Strategy):
	def log(self, txt, dt=None):
		dt = dt or self.datas[0].datetime.date(0)
		print('%s, %s' % (dt.isoformat(), txt))

	def __init__(self):
		self.dataclose = self.datas[0].close
		self.col_a = self.datas[0].col_a
		self.order = None
		self.buyprice = None
		self.buycomm = None
		
	def notify_order(self, order):
		if order.status in [order.Submitted, order.Accepted]:
			return

		if order.status in [order.Completed]:
			if order.isbuy():
				self.log(
					'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
					(order.executed.price,
					 order.executed.value,
					 order.executed.comm))

				self.buyprice = order.executed.price
				self.buycomm = order.executed.comm
			else:  # Sell
				self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
						 (order.executed.price,
						  order.executed.value,
						  order.executed.comm))

			self.bar_executed = len(self)

		elif order.status in [order.Canceled, order.Margin, order.Rejected]:
			self.log('Order Canceled/Margin/Rejected')

		# Write down: no pending order
		self.order = None

	def notify_trade(self, trade):
		if not trade.isclosed:
			return

		self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
				 (trade.pnl, trade.pnlcomm))

	def next(self):
		self.log('Close, %.2f' % self.dataclose[0])
		if self.order:
			return

		if not self.position:
			if (self.col_a[0] < 1):
				self.log('BUY CREATE, %.2f' % self.dataclose[0])
				self.order = self.buy(size=500)

		else:
			if (self.col_a[0] > 1):
				self.log('SELL CREATE, %.2f' % self.dataclose[0])
				self.order = self.sell(size=500)

class BacktraderDataDefintion(bt.feeds.PandasData):
	  
	lines = ("col_a",)

	params = (
		('datetime', None),
		('open', -1),
		# ('high', None),
		# ('low', None),
		('close', -1),
		# ('volume', None),
		('col_a', -1),
	)                
def createSampleData():
	
	a = np.random.randint(0,4,size=15)
	open = np.random.randint(0,10,size=15)
	close = np.random.randint(0,10,size=15)
	df = pd.DataFrame({"Open":open,"Close":close,"col_a" if a[0] <= 1 else "col_b":a}, index=pd.date_range('2018-01-01', periods=15, freq='H'))
	return df

if __name__ == '__main__':
	cerebro = bt.Cerebro()
	cerebro.addstrategy(TestStrategy)
	cerebro.broker.setcommission(commission=0.001)

	# Create a Data Feed
	data = BacktraderDataDefintion(dataname=createSampleData())

	cerebro.adddata(data)
	cerebro.broker.setcash(10000.0)
	print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
	cerebro.run()
	print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

As you can see the data sometimes has a column called "col_a" or "col_b". How can I make sure that the attribute tuple "lines" and "params" in class "BacktraderDataDefintion" get adjusted automatically, depending on the pandas DataFrame?

The expected output would be:

lines = ("col_a",)

params = (
	('datetime', None),
	('open', -1),
	# ('high', None),
	# ('low', None),
	('close', -1),
	# ('volume', None),
	('col_a', -1),
) 

or

lines = ("col_b",)

params = (
	('datetime', None),
	('open', -1),
	# ('high', None),
	# ('low', None),
	('close', -1),
	# ('volume', None),
	('col_b', -1),
) 

depending on the columns in dataframe "df".

Hopefully someone has an idea.