Indicators/Strategies/Analyzers

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  self.getposition(d) not equal to self.getpositionbyname(d._name)
  • Rahul Savani  

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  So managed to get an MWE. The problem relates to 0 prices. The problem goes away if those prices as empty in the csv and treated by NaNs by backtrader. This is really as simple as I can get it, hopefully you agree: Contents of input file S1.csv: date,OPEN,CLOSE 1996-01-01,1.0,1.0 1996-01-02,1.0,1.0 1996-01-03,1.0,1.0 Contents of input file S2.csv: date,OPEN,CLOSE 1996-01-01,0.0,0.0 1996-01-02,0.0,0.0 1996-01-03,0.0,0.0 Contents of input file S3.csv: date,OPEN,CLOSE 1996-01-01,, 1996-01-02,, 1996-01-03,, Source code for MWE: import backtrader as bt def add_data(cerebro, symbols): for symbol in symbols: fname = 'DATA_S/%s.csv' % symbol data = bt.feeds.GenericCSVData( dataname=fname, dtformat=('%Y-%m-%d'), datetime=0, open=1, close=2, high=-1, low=-1, volume=-1, openinterest=-1 ) cerebro.adddata(data) class DrStrategy(bt.Strategy): def next(self): print(self.data.datetime.date(0)) for i, d in enumerate(self.datas): if i == 0: self.buy(data=d) print("Datas %s, position size: %s" % (i, self.getposition(d).size)) if __name__ == "__main__": cerebro = bt.Cerebro(cheat_on_open=True) cerebro.addstrategy(DrStrategy) # position reported for datas 1 erroneously same as for datas 0 add_data(cerebro, symbols=['S1', 'S2']) # looks ok (missing entries in S.3 instead of 0s) # add_data(cerebro, symbols=['S1', 'S3']) results = cerebro.run() Output from running it with add_data(cerebro, symbols=['S1', 'S2']), where we get the problem: 1996-01-01 Datas 0, position size: 0 Datas 1, position size: 0 1996-01-02 Datas 0, position size: 1 Datas 1, position size: 1 1996-01-03 Datas 0, position size: 2 Datas 1, position size: 2 Output from running it with add_data(cerebro, symbols=['S1', 'S3']), where we do not get the problem: 1996-01-01 Datas 0, position size: 0 Datas 1, position size: 0 1996-01-02 Datas 0, position size: 1 Datas 1, position size: 0 1996-01-03 Datas 0, position size: 2 Datas 1, position size: 0 Any feedback, comments, suggestions welcome. Happy to investigate anything related that might be useful. Of course, I will personally be careful to avoid 0 prices. Thanks, --Rahul.
• C

  Force exit or exit open position according to risk management
  • codesutras  

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  @codesutras said in Force exit or exit open position according to risk management: Exit the the open position (or Force Exit ) when current value of portfolio drop by rules defined by RMS. Do as indicated by @ab_trader. Get value and exit when it's below your threshold. @codesutras said in Force exit or exit open position according to risk management: If Possible, perform this operation during intraday price move too. (Note: I've tried to achieve this through Cheat_on_open option. But, that's crazy configuration and it has changed performance of my strategy drastically.) @codesutras said in Force exit or exit open position according to risk management: Also, I'm working with csv feed for daily data. You want to exit intraday but you have daily data. There is physical impossibility here. Cheat on open is meant to let you calculate an entry against the opening price (which you may approximately experience during the pre-opening auction). It has nothing to do with intraday
• R

  One for the cookbook: On Balance Volume
  • Rudi  

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• 

  Cross-Sectional Mean Reversion Strategy
  strategy mean reversion universe spy • • teddykoker  

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  B

  Thanks for the contribution and nice post.
• A

  compound hourly return?
  • Alain  

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  and if i calculate the hourly non-compounded returns manually with: def prenext(self): self.last_value = self.broker.getvalue() def next(self) : self.value = self.broker.getvalue() self.hourly_ret += [np.floor((self.value/self.last_value - 1.) * 1e8) / 1e8] self.last_value = self.value i get the correct returns and my value at that candle differs from the one the observer calculates.
• J

  Mismatch between Analysers and Observers
  • jwsmith  

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  See: Community - Release 1.9.72.122
• A

  Question regarding Smoothed Moving Average
  • Alain  

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  See: Community - Release 1.9.72.122 The trivial fix would have helped, but it wasn't the right one. The real fix was somewhere else, being also mostly trivial. The SMMA and EMA indicators rely on the previous value to calculate the new one. As soon as one value is NaN, the result will always be the same and NaN. Non-recursive indicators, mostly every other, will recover after a number of calcualtions which is what made it possible for the error to remain undetected for a long while. The code will work now without having to create null delay object as in class RelativeVolatility(bt.Indicator): alias = ('RV',) lines = ('rv', 'nU', 'nD',) params = dict( p1=10, p2=14, movav=bt.ind.EMA, ) def __init__(self): stddev = bt.ind.StdDev(self.data, period=self.p.p1) u = bt.If(self.data > self.data(-1), stddev, 0.0) d = bt.If(self.data < self.data(-1), stddev, 0.0) self.l.nU = nU = self.p.movav(u, period=self.p.p2) self.l.nD = nD = self.p.movav(d, period=self.p.p2) self.l.rv = 100.0 * nU / (nU + nD)
• 

  ZigZag indicator for live trading.
  • Nikolai  

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• M

  Own Indicator to implement : 1 buy signal /0 sell signal. How to implement it ?
  • Mistigri5  

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  Hi, Thanks very much for your answer. It works now. I should have been a little bit tired Friday. I made corrections on the indicator. You were right, There was a mistake on the name of the indicator. import pandas as pd import backtrader as bt from backtrader.feeds import PandasData import backtrader.indicators as btind # load dataframe dataframe = pd.read_excel("STOXX50 bis.xlsx",names = ["Datetime","Open","High","Low","Close","Volume","signal"]) dataframe['Datetime'] = pd.to_datetime(dataframe['Datetime'],format='%d/%m-/y') class PandasData_Signal(PandasData): # Add a 'action' line to the inherited ones from the base class lines = ('signal',) # add the parameter to the parameters inherited from the base class params = (('signal', 6),) data = PandasData_Signal(dataname=dataframe, datetime=0, open=1, high=2, low=3, close=4, volume=-1, openinterest=-1, signal=6, #openinterest=-1 ) class MLSignal(bt.SignalStrategy): 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): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close self.signal = self.datas[0].signal self.order = None 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, 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') 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]) #Check if we are in the market if not self.position : if self.signal[0] == 1: self.log('BUY CREATE, %.2f' % self.dataclose[0]) #Keep track of the created order to avoid a 2nd order self.order = self.buy() else: #Already in the market...we might sell if self.signal[0] == 0: self.log('SELL CREATE, %.2f' % self.dataclose[0]) self.order = self.sell() cerebro = bt.Cerebro() # Set our desired cash start cerebro.broker.setcash(4000.0) cerebro.adddata(data) cerebro.addstrategy(MLSignal) cerebro.run() print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) print('Final value is %.2f times the initial investment'%(cerebro.broker.getvalue())) cerebro.plot() I wish you a very nice day, Best, Boris
• J

  How to get data stream notifier from inside indicator
  • jwsmith  

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  Aha, yes I see your point. Well, for now this will work :) Thanks for pointing it out! Josh
• J

  Strategy.__init__() slows down the whole backtest after it’s first execution?
  • jburb  

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  What you probably miss is that @jburb said in Strategy.__init__() slows down the whole backtest after it’s first execution?: bt.indicators.SimpleMovingAverage(self.datas[0], period=self.params.maperiod) This is only a declaration. No actual calculation is taking place there. If it actually did, live data feeds would be impossible. As stated in other posts: backtrader is not pandas. Docs - Platform Concepts and see Stage 1
• S

  How to write algorithm for both long and short selling?
  • Sasha  

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  @sasha said in How to write algorithm for both long and short selling?: I just wanted to know how to write the code for the short and long positions. if not self.position: # open long if <long entry signal>: self.buy() # open short elif <short entry signal>: self.sell() else: # close long or short if <long exit signal> or <short exit signal>: self.close()
• A

  How to plot close price +/- ATR
  • alexgiul  

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  In order to have something automatically plotted in bt it should be data feed, indicator or observer. So you may want to develop an indicator or observer. Docs - Indicator Development DOcs - Observers and Statistics On the other side, formula you are using will not give you a picture shown, it will be only a channel moving together with the close price. It will never cross the price. Hope you understand it and this formula is only first step. Some time ago in order to develop similar picture I developed stop loss/take profit observer. Here is the link, hope it might help Stop loss/Take profit observer
• 

  Analyzers stats when datas have only trading days
  • exu  

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  @exu said in Analyzers stats when datas have only trading days: just checking on how the built-in analyzers deal with that... I fail to understand what the built-in analyzers have to deal with. They have de-facto default values (the ones mostly used by the industry/people/call-it-X) which you can customize to your liking.
• T

  Set minimum trade time / don't sell until certain amount of time has elapsed
  • tw00000  

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  If you issue it several times in a row before the broker has had a chance to close a position, of course.
• M

  Best practice for date range for Emini Backtest
  • msfinedu  

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  Updating the thread for people facing the same problem. This is directly related to future contracts. IBPy looks for m_includedExpired to be true to dish out expire futures minutes data. A possible solution to circumvent this is provided by backtrader here https://community.backtrader.com/topic/1533/extract-historical-expired-futures-contract-data-from-ib/2
• A

  sharpe ratio wrong?
  • Alain  

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  Sorry but you have, even if your intent was other. There is a huge difference between a thread title like this: Question with regards to the SharpeRatio calculation and this: sharpe ratio wrong? By writing your own calculation to compare results you had already concluded that the results provided by backtrader weren't right, but not that you were not using the analyzer properly. It's not that I believe your post is ill-intentioned, but you had already concluded.
• 

  Zigzag Indicator return "maximum recursion depth exceeded while calling a Python object"
  • yogi wahyu  

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  Hi. Zigzag is the indicator, not a strategy. You should inherit from bt.Strategy and pass this class to cerebro. import backtrader as bt from custom.zigzag import ZigZag class Futures(bt.Strategy): params = ( ('period', 20), ) def __init__(self): self.zigzag = ZigZag() cerebro = bt.Cerebro() data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1), todate=datetime(2012, 12, 31)) cerebro.adddata(data) cerebro.addstrategy(Futures) cerebro.run() cerebro.plot()
• 

  Hidden Markov Model - Create Indicator & use in strategy
  • Amw_775  

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  You should take 2 (or 3) steps back and develop a simple indicator. From the documentation - Docs - Indicator Development (you should read the entire doc) def next(self): self.lines.dummyline[0] = max(0.0, self.params.value) Now look for that in your code.
• C

  More Backtrader way to calculate covariance matrix
  • cnimativ  

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  @cnimativ said in More Backtrader way to calculate covariance matrix: My current implementation uses a counter to wait out the lookback period and then runs all calculations in the def next() method. You should be using the addminperiod API or subclassing from the indicator PeriodN. @cnimativ said in More Backtrader way to calculate covariance matrix: While it 'works', what is the more backtrader way of doing the same thing? You calculate something in next using numpy. What would you be looking for? @cnimativ said in More Backtrader way to calculate covariance matrix: But how do I access the value of N periods of indicators, run calculations, and then construct cross-asset indicators? You are already using get @cnimativ said in More Backtrader way to calculate covariance matrix: And would it be possible to have, say, an indicator that contains multidimensional matrices? Not a clue what you mean. Indicator have lines. You may of course store anything in a member attribute.