Indicators/Strategies/Analyzers

• 

  Building SMA on top of RSI
  • Vasu Dev

  2
  0
  Votes
  2
  Posts
  163
  Views

  B

  That sounds almost like sacrilege. Who would dare doing such a thing ... Fo sure not those who dare reading the Docs - Quickstart Guide
• M

  indicator with dynamically changing data
  • momentum

  5
  0
  Votes
  5
  Posts
  692
  Views

  M

  I am trying to generate a dynamically changing data feed based on the RollOver class but it does not work always correctly when trying to update the history at the same time (self.params.update_history_upon_roll set to True). For example when moving to a data feed with longer history it gives wrong results. Any ideas? Thanks def _roll(self): if self._ds: clock1 = self.lines[0].lencount self._dexp = self._d self._d = self._ds.pop(0) self._dts.pop(0) clock3 = self._d.lines[0].lencount if self.params.update_history_upon_roll: if clock3 < clock1: self._d.lines.rewind() for i in range(len(self.lines.lines)): self.lines[i].array = self._d.lines[i].array[:] self.lines[i].lencount = self._d.lines[i].lencount self.lines[i].idx = self._d.lines[i].idx if clock3 < clock1: self.lines.advance() return True else: return False def _load(self): while self._d is not None: if self._doroll: self._roll() self._doroll = False _next = self._d.next() if _next is None: # no values yet, more will come continue if _next is False: # no values from current data src if self._ds: self._d = self._ds.pop(0) self._dts.pop(0) else: self._d = None continue dt0 = self._d.datetime.datetime() # current dt for active data # Synchronize other datas using dt0 for i, d_dt in enumerate(zip(self._ds, self._dts)): d, dt = d_dt while dt < dt0: if d.next() is None: continue self._dts[i] = dt = d.datetime.datetime() # Move expired future as much as needed while self._dexp is not None: if not self._dexp.next(): self._dexp = None break if self._dexp.datetime.datetime() < dt0: continue if self._dexp is None and self._checkdate(dt0, self._d): # rule has been met ... check other factors only if 2 datas # still there if self._ds and self._checkcondition(self._d, self._ds[0]): if self.params.upon_condition: self._roll() else: self._doroll = True # Fill the line and tell we die for i in range(len(self.lines.lines)): self.lines[i][0] = self._d.lines[i][0] return True # Out of the loop -> self._d is None, no data feed to return from return False
• S

  Multiple Data Feeds (Instruments) on a single strategy
  • satheesh7134

  5
  0
  Votes
  5
  Posts
  1331
  Views

  B

  @satheesh7134 said in Multiple Data Feeds (Instruments) on a single strategy: print('BUY Signal:, %.2f' % self.getdatabyname(datafeed).close[0]) self.order = self.buy() He wasn't telling the system what to buy (or sell). See Docs - Strategy and the reference for buy / sell or as pointed out by @Divyanshu-Bagga @divyanshu-bagga said in Multiple Data Feeds (Instruments) on a single strategy: @satheesh7134 Go through the multi data feeds example. @param-jeet said in Multiple Data Feeds (Instruments) on a single strategy: In that case, wouldn't cerebro.run( ) execute twice? If I run twice then I would get two different datasets. What if, I want to execute this strategy on both data sets and generate single report as one single portfolio. You enumerate within the strategy.
• 

  Stochastic RSI indicator.
  • Nikolai

  10
  2
  Votes
  10
  Posts
  1104
  Views

  

  @Tomas Hi. I wrote an example strategy for you. You can access the indicator lines through the name in __init__ method. class CryptoStochRSI(bt.Strategy): params = ( ('stoch_k_period', 3), ('stoch_d_period', 3), ('stoch_rsi_period', 14), ('stoch_period', 14), ('stoch_upperband', 80.0), ('stoch_lowerband', 20.0), ('take_profit', 0.04), ('stop_loss', 0.01), ('size', 20), ('debug', False), ) def __init__(self): self.stoch = StochasticRSI(k_period=self.p.stoch_k_period, d_period=self.p.stoch_d_period, rsi_period=self.p.stoch_rsi_period, stoch_period=self.p.stoch_period, upperband=self.p.stoch_upperband, lowerband=self.p.stoch_lowerband) def next(self): if not self.position: close = self.data.close[0] price1 = close price2 = price1 - self.p.stop_loss * close price3 = price1 + self.p.take_profit * close if self.stoch.l.fastk[0] < 20: self.buy_bracket(price=price1, stopprice=price2, limitprice=price3, exectype=bt.Order.Limit) if self.stoch.l.fastk[0] > 80: self.sell_bracket(price=price1, stopprice=price3, limitprice=price2, exectype=bt.Order.Limit)
• R

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

  8
  0
  Votes
  8
  Posts
  271
  Views

  R

  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

  5
  0
  Votes
  5
  Posts
  258
  Views

  B

  @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

  1
  0
  Votes
  1
  Posts
  172
  Views

  No one has replied

• 

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

  2
  1
  Votes
  2
  Posts
  350
  Views

  B

  Thanks for the contribution and nice post.
• A

  compound hourly return?
  • Alain

  4
  0
  Votes
  4
  Posts
  230
  Views

  A

  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

  5
  0
  Votes
  5
  Posts
  298
  Views

  B

  See: Community - Release 1.9.72.122
• A

  Question regarding Smoothed Moving Average
  • Alain

  6
  0
  Votes
  6
  Posts
  395
  Views

  B

  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

  1
  0
  Votes
  1
  Posts
  315
  Views

  No one has replied

• M

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

  3
  0
  Votes
  3
  Posts
  321
  Views

  M

  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

  Does Backtrader support Parallel Strategies?
  • Jacob

  10
  0
  Votes
  10
  Posts
  1652
  Views

  B

  @shubham-sharma said in Does Backtrader support Parallel Strategies?: Can you please explain this? Yes. 1 strategy ... 1 x time. 2 strategies... 2 x time. 3 strategies ... 3 x time ... @shubham-sharma said in Does Backtrader support Parallel Strategies?: and Is there any parameter to pass or something else to run them parallelly? No. As quoted above, you also don't know what the GIL is. Please read about it.
• J

  How to get data stream notifier from inside indicator
  • jwsmith

  5
  0
  Votes
  5
  Posts
  386
  Views

  J

  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

  4
  0
  Votes
  4
  Posts
  251
  Views

  B

  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

  5
  0
  Votes
  5
  Posts
  305
  Views

  

  @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

  2
  0
  Votes
  2
  Posts
  207
  Views

  

  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

  5
  0
  Votes
  5
  Posts
  372
  Views

  B

  @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.
• 

  Closed trade list (including MFE/MAE) analyzer
  analyzer trades mfe mae • • ab_trader

  10
  6
  Votes
  10
  Posts
  1746
  Views

  S

  @scottz1 resolved by changing... import TradeList to from TradeList import TradeList