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Indicator for multiple Datafeeds
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The Bollinger Bands work for the first Data File, so it´s the only one where Orders are created.
Additionally the buys and sells dont get displayed, Where is the mistake?
Thanks for your help :)import backtrader as bt import backtrader.feeds as btfeed from datetime import datetime class dataFeed(btfeed.GenericCSVData): params = ( ('dtformat', '%Y-%m-%d %H:%M:%S'), ('datetime', 0), ('open', 1), ('high', 2), ('low', 3), ('close', 4), ('volume', 5), ('openinterest', -1) ) class BollingerBands(bt.Indicator): lines = ('topband', 'botband') params = (('period', 21), ('devfactor', 2.0), ('movav', bt.ind.MovAv.Simple),) plotinfo = dict(subplot=False) def _plotlabel(self): plabels = [self.p.period, self.p.devfactor] plabels += [self.p.movav] * self.p.notdefault('movav') return plabels def __init__(self): bb = bt.ind.BollingerBands( period=self.p.period, devfactor=self.p.devfactor, movav=self.p.movav) class firstStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): self.dataclose = self.datas[0].close bb = bt.ind.BollingerBands( period=21, devfactor=2.0, movav=bt.ind.MovAv.Simple) self.lines.topband = bb.top self.lines.botband = bb.bot def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log('BUY EXECUTED, %.2f' % order.executed.price) elif order.issell(): self.log('SELL EXECUTED, %.2f' % order.executed.price) 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 next(self): for d in self.datas: dt, dn = self.datetime.date(), d._name pos = self.getposition(d).size # Simply log the closing price of the series from the reference self.log('Close, %.2f' % d.close[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one #if self.order: # return # Check if we are in the market if not self.position: if self.dataclose[0] < self.lines.botband[0]: # current close less than previous close # if self.dataclose[-1] < self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() # Already in the market ... we might sell if self.dataclose[0] > self.lines.topband[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() startcash = 10000 # Add a strategy cerebro.addstrategy(firstStrategy) cerebro.addindicator(BollingerBands) datalist = [("AM.ATVI.csv"), ("AM.MO.csv"), ("AM.GM.csv"), ("AM.CL.csv")] """ , ("AM.FDX.csv"), ("AM.NAKD.csv"), ("AM.NVDA.csv"), ("AM.OCGN.csv"), ("AM.ON.csv") , ("AM.PDD.csv"), ("AM.PLUG.csv"), ("AM.QCOM.csv"), ("AM.MDLZ.csv"), ("AM.ADP.csv") , ("AM.C.csv"), ("AM.AZN.csv"), ("AM.PEP.csv"), ("AM.ORCL.csv"), ("AM.QTT.csv") , ("AM.RUN.csv"), ("AM.SABR.csv"), ("AM.SNDL.csv"), ("AM.TSLA.csv"), ("AM.UAL.csv") , ("AM.UXIN.csv"), ("AM.WEN.csv"), ("AM.WFC.csv"), ("AM.YY.csv"), ("AM.ZNGA.csv") , ("AM.MSFT.csv"), ("AM.AAPL.csv"), ("AM.FB.csv"), ("AM.BABA.csv"), ("AM.TSM.csv") , ("AM.V.csv"), ("AM.JPM.csv"), ("AM.JNJ.csv") , ("AM.PG.csv"), ("AM.BAC.csv"), ("AM.INTC.csv"), ("AM.VZ.csv"), ("AM.NKE.csv") , ("AM.XOM.csv"), ("AM.KO.csv"), ("AM.T.csv"), ("AM.PFE.csv") , ("AM.MRK.csv"), ("AM.MS.csv"), ("AM.AAL.csv"), ("AM.GT.csv"), ("AM.UBER.csv") , ("AM.AMD.csv"), ("AM.PDD.csv"), ("AM.CVX.csv")]""" #the files are located in my python project for i in range(len(datalist)): data = dataFeed(dataname=datalist[i], timeframe=bt.TimeFrame.Minutes, compression=60) cerebro.adddata(data, name=datalist[i]) # Set our desired cash start cerebro.broker.setcash(startcash) # Set the commission #cerebro.broker.setcommission(commission=0.0005) # Add a sizer #cerebro.addsizer(bt.sizers.PercentSizer, percents=50) cerebro.run() # Print out the starting conditions print('Starting Portfolio Value: %.2f' % startcash) # Get final portfolio Value portvalue = cerebro.broker.getvalue() pnl = portvalue - startcash # Print out the final result print('Final Portfolio Value: ${}'.format(portvalue)) print('P/L: ${}'.format(pnl)) cerebro.plot() -
@hexabraum I think the answer depends on what you are trying to do. Are you trying to test each security independently, eg four securities give you four backtest and four results, or are you trying to test all teh securities at once, in the same portfolio, so four securities equal one backtest.
Could you try to describe your situation more fully? Thanks.
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@run-out 4 securities equal one backtest, however each datafeed gets a technical analysis and therefore buy or sell signals for this exact security. I want the backtrader to backtest my strategy on multiple datafeeds. I hope that i got the point :D
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@run-out Thanks but that doesn´t really help me. My problem is that the Bollinger Bands only work for my first data feed and also only get plotted for my first datafeed. Therefore the other securities are not in consideration to be bought or sold.
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I made some modifications to your code.
- Removed bollinger band class since it appears you are just using the standard class. You can re-introduce this if I'm mistaken.
- Removed
self.dataclose = self.datas[0].closefrom init as this is not needed when using multi data. - Added in the following loop to create a bollinger band for each data in a dictionary.
self.bb_inds = dict() for d in self.datas: bb = bt.ind.BollingerBands(d, period=21, devfactor=2.0, movav=bt.ind.MovAv.Simple) self.bb_inds[d] = dict() self.bb_inds[d]["bb_top"] = bb.top self.bb_inds[d]["bb_bot"] = bb.bot- In next used
din your datas loop to retrieve the appropriate bollinger bands for each data. Changed your data references tod.close[0]where appropriate.
if not self.position: if d.close[0] < self.bb_inds[d]["bb_bot"][0]:- Used yahoo daily data for convenience.
Here's the entire code:
import backtrader as bt import backtrader.feeds as btfeed import datetime # from datetime import datetime class dataFeed(btfeed.GenericCSVData): params = ( ('dtformat', '%Y-%m-%d %H:%M:%S'), ('datetime', 0), ('open', 1), ('high', 2), ('low', 3), ('close', 4), ('volume', 5), ('openinterest', -1) ) # class BollingerBands(bt.Indicator): # lines = ('topband', 'botband') # params = (('period', 21), ('devfactor', 2.0), ('movav', bt.ind.MovAv.Simple),) # # plotinfo = dict(subplot=False) # # def _plotlabel(self): # plabels = [self.p.period, self.p.devfactor] # plabels += [self.p.movav] * self.p.notdefault('movav') # return plabels # # def __init__(self): # bb = bt.ind.BollingerBands( # period=self.p.period, devfactor=self.p.devfactor, movav=self.p.movav) # # bb.lines.top_band = bb.top # bb.lines.botband = bb.bot class firstStrategy(bt.Strategy): def log(self, txt, dt=None): ''' Logging function fot this strategy''' 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.bb_inds = dict() for d in self.datas: bb = bt.ind.BollingerBands(d, period=21, devfactor=2.0, movav=bt.ind.MovAv.Simple) self.bb_inds[d] = dict() self.bb_inds[d]["bb_top"] = bb.top self.bb_inds[d]["bb_bot"] = bb.bot def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log('BUY EXECUTED, %.2f' % order.executed.price) elif order.issell(): self.log('SELL EXECUTED, %.2f' % order.executed.price) 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 next(self): for d in self.datas: dt, dn = self.datetime.date(), d._name pos = self.getposition(d).size # Simply log the closing price of the series from the reference self.log('Close, %.2f' % d.close[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one #if self.order: # return # Check if we are in the market if not self.position: if d.close[0] < self.bb_inds[d]["bb_bot"][0]: # current close less than previous close # if self.dataclose[-1] < self.dataclose[-2]: # previous close less than the previous close # BUY, BUY, BUY!!! (with default parameters) self.log('BUY CREATE, %.2f' % d.close[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() # Already in the market ... we might sell if d.close[0] > self.bb_inds[d]["bb_top"][0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % d.close[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': # Create a cerebro entity cerebro = bt.Cerebro() startcash = 10000 # Add a strategy cerebro.addstrategy(firstStrategy) cerebro.addindicator(BollingerBands) # datalist = [("AM.ATVI.csv"), ("AM.MO.csv"), ("AM.GM.csv"), ("AM.CL.csv")] # """ , ("AM.FDX.csv"), ("AM.NAKD.csv"), ("AM.NVDA.csv"), ("AM.OCGN.csv"), ("AM.ON.csv") # , ("AM.PDD.csv"), ("AM.PLUG.csv"), ("AM.QCOM.csv"), ("AM.MDLZ.csv"), ("AM.ADP.csv") # , ("AM.C.csv"), ("AM.AZN.csv"), ("AM.PEP.csv"), ("AM.ORCL.csv"), ("AM.QTT.csv") # , ("AM.RUN.csv"), ("AM.SABR.csv"), ("AM.SNDL.csv"), ("AM.TSLA.csv"), ("AM.UAL.csv") # , ("AM.UXIN.csv"), ("AM.WEN.csv"), ("AM.WFC.csv"), ("AM.YY.csv"), ("AM.ZNGA.csv") # , ("AM.MSFT.csv"), ("AM.AAPL.csv"), ("AM.FB.csv"), ("AM.BABA.csv"), ("AM.TSM.csv") # , ("AM.V.csv"), ("AM.JPM.csv"), ("AM.JNJ.csv") # , ("AM.PG.csv"), ("AM.BAC.csv"), ("AM.INTC.csv"), ("AM.VZ.csv"), ("AM.NKE.csv") # , ("AM.XOM.csv"), ("AM.KO.csv"), ("AM.T.csv"), ("AM.PFE.csv") # , ("AM.MRK.csv"), ("AM.MS.csv"), ("AM.AAL.csv"), ("AM.GT.csv"), ("AM.UBER.csv") # , ("AM.AMD.csv"), ("AM.PDD.csv"), ("AM.CVX.csv")]""" # #the files are located in my python project # for i in range(len(datalist)): # data = dataFeed(dataname=datalist[i], timeframe=bt.TimeFrame.Minutes, compression=60) # cerebro.adddata(data, name=datalist[i]) for ticker in [ "TSLA", "AAPL", "AMZN", ]: data = bt.feeds.YahooFinanceData( dataname=ticker, timeframe=bt.TimeFrame.Days, fromdate=datetime.date(2020, 1, 1), todate=datetime.date(2020, 12, 31), reverse=False, ) cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(startcash) # Set the commission #cerebro.broker.setcommission(commission=0.0005) # Add a sizer #cerebro.addsizer(bt.sizers.PercentSizer, percents=50) cerebro.run() # Print out the starting conditions print('Starting Portfolio Value: %.2f' % startcash) # Get final portfolio Value portvalue = cerebro.broker.getvalue() pnl = portvalue - startcash # Print out the final result print('Final Portfolio Value: ${}'.format(portvalue)) print('P/L: ${}'.format(pnl)) cerebro.plot()
