After testing the code above and plotting the divergences that it found, I think that the issue is with this line: self.hfi = bt.ind.FindFirstIndexHighest(self.data.high, period=self.p.hl_period) It looks for the highest point (same for the lowest) within the hl_period (100 bars) Then in the following line it looks again backwards from that point to find the second high h_iterable = self.data.get(size=self.p.hl_period, ago=-int(self.hfi[0]) - self.p.hl_min) So basically the code returns signals for very old divergences. If you are on daily chart, you will try to trade on a divergence that is 100 days old. So the first day after the high point it signals a sell, then for the next 100 days it still signals a sell ! I have amended the first line to look for a high/low point for the last day only. In this way if the divergence is growing then it's fine you still get a sell signal, but if the divergence is gone then you don't try to keep selling for 100 days.

@sujay-bawaskar said in Plotting specific data from Multidata: Is there any way we can plot Yes, the way of the documentation: Docs - Plotting Use plotinfo and plot

Agree with you @backtrader... If it ain't broke, don't fix it

Hey Nikolai! This is fantastic news, thank you so much! :)

@ciceron hi again. I was wrong. Oanda broker has StopTrail implementation. if order.exectype == bt.Order.StopTrail: okwargs['distance'] = order.trailamount https://github.com/ftomassetti/backtrader-oandav20/blob/master/btoandav20/stores/oandav20store.py I didn't have any practice with Oanda broker, so i don't know how stoptrail works on Oanda side. But your understanding is correct for backtesting.

I apologize if what I am going to ask now seems plain but I am trying to familiarize myself with the sketch of creation of the backtest strategy, but this line of code is not obvious to me and I would like someone to clarify it for me : **eval('dict(' + args.strat + ')')) from what I know, this is a function eval () whose parameter is the sequence runs python code but this record absolutely puzzled me because it seems to me that it is from packing of a dictionary items .- **

@andrewsky said in Multiple time period on a single stock: self.data.get(size=self.p.period) If you ask for size=self.p.period you are always going to get period data points. If you need the array to grow with each iteration use the len of the data which keeps on growing with each iteration Docs - Platform Concepts - Lines len @andrewsky said in Multiple time period on a single stock: instead of self.data.get(), can I use self.data.close.get() and self.data.datetime.get() ? self.data.get is giving you the default for a multi-line object, which in most cases will be self.data.close.get. Working with the individual lines is obviously possible.

@mango-loco said in Multi-Data Indicator implementation help.: Is the above code the most efficient/cleanest/best way to go about handling the operations I'm doing? The fact is that backtrader allows you to do it in a declarative way or in an iterative way (and to mix both). There seems to a fixation out thee in many cases in using numpy (imported as np) for whatever reason which I fail to understand, even if it is meant to only calculate a single value. But if it suits you, there is no reason to do it otherwise. @mango-loco said in Multi-Data Indicator implementation help.: Is there a way/hack to eliminate the "instantiation lag" that occurs with indicators in BT? Is it possible to pass in data from before the start of the backtest so that any indicators are already fully instantiated at t=0? There is NO lag. Your reasoning and understanding is wrong. If you want a 20-period moving average you need 20 data points to calculate it. Full stop. If you want your indicators at YOUR DEFINED t=0 to be available, work with a data set which has an earlier start datetime.

Hi if the question is still open: you need to provide an account number, the value "Bearer" for account is wrong. You can find the account at oanda. Cheers

Thanks @backtrader for your reply. Today I am working on this part but I am confused and I am not sure doing the right thing. I try to explain the goal, maybe you can give me your opinion and put me on right road. I have a simple channel breakout strategy made by 2 indicators (entry channel breakout, exit channel breakout). The strategy backtested uses the entry channel breakout to enter the market long or short and then uses the "exit channel breakout" to close the position. Now I will run the strategy (the original strategy or the SignalStrategy) every day on latest XX bar and I want to send an email when the entry signal is fired and another when it is time to exit. Actually I was thinking to use original strategy and use order generated as "signals", what do you think? And if it looks like acceptable how can I access the current order situation and type (long/short)?

Yass. Thank you @Developing-Coder this solves my issue. To get it working properly I had to add transmit=False to market order, after that it worked as desired. Thank you!

Soloved. Mistake on my end.

@backtrader I swap the ordering of stop and profit. Now the stoploss got placed but profit was missing. IB gateway: v974.4g http://cdn.quantconnect.com/interactive/ibgateway-latest-standalone-linux-x64-v974.4g.sh I moved the stop from 2834.25 to 2842.25, but profit order didn't get placed at all.. def moveBracketStopLoss(self, bracket, bt): # cancel stop order automatically cancels the profit order of the bracket too self.cancel(bracket[1]) self.cancel(bracket[2]) before = bracket[1].created.price stopOrder = profitOrder = None # need to replace both stopOrder and profitOrder if bracket[0].ordtype == 0: # buy profitOrder = self.sell( price=bracket[2].created.price, size=bracket[0].size, exectype=bt.Order.Limit, transmit=False) stopOrder = self.sell( price=bracket[0].executed.price, size=bracket[0].size, exectype=bt.Order.Stop, transmit=True, oco=profitOrder) if bracket[0].ordtype == 1: # sell profitOrder = self.buy( price=bracket[2].created.price, size=bracket[0].size, exectype=bt.Order.Limit, transmit=False) stopOrder = self.buy( price=bracket[0].executed.price, size=bracket[0].size, exectype=bt.Order.Stop, transmit=True, oco=profitOrder) bracket[1] = stopOrder bracket[2] = profitOrder Helper.log(self, 'Move stoploss to breakeven for ', 'long' if bracket[0].ordtype == 0 else 'short', 'position from ', before, 'to', bracket[1].created.price)

atom.io , with some plugins for python installed, linter, linter-python, autocomplete-python, bracket-colorizer , file-icons, minimap, python-autopep8

@backtrader @ab_trader I finally implement the Indicator as below code, it seems work. but two small questions, when the ATR is coupled? and how do I set the minperiod when data with different time frames, I worked around by using len(self.l.art)>0, else it would raise errors. import numpy as np import backtrader as bt from ext.ult.ult import * from ext.Ind.OCHLInd import OCHLInd # https://www.tradingview.com/script/mtWhbCRf-RS-MTF-Volatility-Stop-V0/ class MtfVSInd(bt.Indicator): """ """ lines = ('trend', 'vs', 'max_close', 'min_close', 'atr') params = ( ('period', 5), ('factor', 1.618), ('mode', 0) # debug modes ) plotinfo = dict(subplot=False) plotlines = dict(vs=dict(_plotskip=False), trend=dict(_plotskip=True, _method='bar', alpha=0.50, width=1.0), max_close=dict(_plotskip=True, color='black'), min_close=dict(_plotskip=True, color='red'), atr=dict(_plotskip=True), ) def _plotinit(self): if self.p.mode == 1: self.plotinfo.subplot = True self.plotlines.vs._plotskip = True self.plotlines.trend._plotskip = True self.plotlines.max_close._plotskip = True self.plotlines.min_close._plotskip = True self.plotlines.atr._plotskip = False elif self.p.mode == 2: self.plotinfo.subplot = True self.plotlines.vs._plotskip = True self.plotlines.trend._plotskip = False self.plotlines.max_close._plotskip = True self.plotlines.min_close._plotskip = True self.plotlines.atr._plotskip = True elif self.p.mode == 3: self.plotinfo.subplot = True self.plotlines.vs._plotskip = True self.plotlines.trend._plotskip = True self.plotlines.max_close._plotskip = False self.plotlines.min_close._plotskip = False self.plotlines.atr._plotskip = True def __init__(self): # the ATR is Day time frame self.l.atr = bt.ind.ATR(self.data1, period=self.p.period) # get the avg(high+low) self.hl = OCHLInd(self.data0, type='hl') super(MtfVSInd, self).__init__() def next(self): if len(self.l.atr) > 0: if np.isnan(self.l.trend[-1]): self.l.trend[0] = 1 elif self.data0.l.close[0] > self.l.vs[-1]: self.l.trend[0] = 1 elif self.data0.l.close[0] < self.l.vs[-1]: self.l.trend[0] = -1 else: self.l.trend[0] = self.l.trend[-1] chg_trend = self.l.trend[0] - self.l.trend[-1] if chg_trend != 0: self.l.max_close[0] = self.data0.l.close[0] elif self.data0.l.close[0] >= self.l.max_close[-1]: self.l.max_close[0] = self.data0.l.close[0] else: self.l.max_close[0] = self.l.max_close[-1] if chg_trend != 0: self.l.min_close[0] = self.data0.l.close[0] elif self.data0.l.close[0] <= self.l.min_close[-1]: self.l.min_close[0] = self.data0.l.close[0] else: self.l.min_close[0] = self.l.min_close[-1] if np.isnan(self.l.vs[-1]): self.l.vs[0] = self.hl[0] elif chg_trend > 0: self.l.vs[0] = self.data0.l.close[0] - self.p.factor * self.l.atr[0] elif self.l.trend[0] > 0: self.l.vs[0] = max(self.l.vs[-1], self.l.max_close[0] - self.p.factor * self.l.atr[0]) elif chg_trend < 0: self.l.vs[0] = self.data0.l.close[0] + self.p.factor * self.l.atr[0] elif self.l.trend[0] < 0: self.l.vs[0] = min(self.l.vs[-1], self.l.min_close[0] + self.p.factor * self.l.atr[0]) else: self.l.vs[0] = self.l.vs[-1]