@backtrader Actually I have a problem running this, following the solution in the blog post that you wrote. I am not sure if I am missing something but it seems that the indicator is computed all at once during strategy initialization. This is a problem since the indicator should be influenced by trades, which are computed later. This is my indicator code:

class DynamicHighest(bt.Indicator): """indicates the highest high since a trade was open.""" lines = ('dyn_highest',) def __init__(self): self._tradeopen = False self.addminperdiod = 2 def tradeopen(self, isopen): self._tradeopen = isopen print("tradeopen {isopen}".format(isopen=isopen)) def next(self): if self._tradeopen: print("trade is open") self.lines.dyn_highest[0] = max(self.data[0].high, self.dyn_highest[-1]) else: print("trade is close") self.lines.dyn_highest[0] = 0

now when I run cerebro I get this output:

trade is close trade is close trade is close . . . trade is close trade is close trade is close trade is close trade is close dyn_highest: 0.00 | stoplimit: -0.03 | close: 0.13 BUY tradeopen True dyn_highest: 0.00 | stoplimit: -0.03 | close: 0.15 dyn_highest: 0.00 | stoplimit: -0.03 | close: 0.19 dyn_highest: 0.00 | stoplimit: -0.03 | close: 0.17 dyn_highest: 0.00 | stoplimit: -0.03 | close: 0.19 dyn_highest: 0.00 | stoplimit: -0.03 | close: 0.20 dyn_highest: 0.00 | stoplimit: -0.04 | close: 0.19 dyn_highest: 0.00 | stoplimit: -0.04 | close: 0.20 dyn_highest: 0.00 | stoplimit: -0.04 | close: 0.19 dyn_highest: 0.00 | stoplimit: -0.04 | close: 0.19 dyn_highest: 0.00 | stoplimit: -0.04 | close: 0.23 . . .

So, as we see dyn_highest stays 0 the whole time.
Is there a way to prevent precalculation of the indicator?

Momentarily confused when trying out Kaber's code -- for those who care my code is python 2, and his code appears to be python 3. Changing super() to super(classname, self) successfully executes under python 2.

Hi @backtrader

Thanks for taking such a close look at it! Awesome...

I will download the latest commit and take a look later this week.

There are several libraries to track RSS feeds: https://wiki.python.org/moin/RssLibraries

But the key issue here is how you implement the model in a trading strategy:

Pull

Using some other trigger event (like a 1-minute data feed) you pull to see if a new event is available and act accordingly. This doesn't preclude background processing, in which case the pulled info will not be directly the feed but whatever you want to make out of it

Push

Whenever your listening thread and processing engine detect something, you do immediately act. In this case and within the context of backtrader and using the 1-minute data feed from above, it could mean that when you act with the RSS event, your price information is, for example, 45 seconds too old. Where you are the one defining too old. It might be that you need to react with the latest info and latest price quotes to make your strategy work or that 45 seconds is an acceptable delay.

You can of course reduce the timeframe resolution and work with 10-seconds bars to reduce the lag.

@backtrader Wow, thank you very much!

OCO orders are all active at the same time.

Bracket orders (the ones you submit with parent) have a different lifecycle:

parent is 1st alive. children are inactive If the parent is cancelled the children are simply discarded (they weren't active) If the parent is executed, the children are activated Both children behave now like OCO orders

@duality_ said in Use indicator without cerebro or strategies?:

Is it possible to use the indicators in Backtrader without Cerebro or strategies?

No. The goal was never to replicate ta-lib

Solution: using analyzer.
Maybe will be usefull for somebody: next analyzer calculate and save realized pnl and opened positions for each strategy date to date:

import backtrader as bt from collections import OrderedDict class TotalReturns(bt.Analyzer): '''This analyzer reports the total pnl for strategy Params: - timeframe (default: ``None``) If ``None`` then the timeframe of the 1st data of the system will be used - compression (default: ``None``) Only used for sub-day timeframes to for example work on an hourly timeframe by specifying "TimeFrame.Minutes" and 60 as compression If ``None`` then the compression of the 1st data of the system will be used Methods: - get_analysis Returns a dictionary with returns as values and the datetime points for each return as keys ''' def __init__(self): self.total_pnl = 0 self.unrealized = 0 # Unrealized pnl for all positions all strategies self.positions = OrderedDict.fromkeys([d._name or 'Data%d' % i for i, d in enumerate(self.datas)], 0) # Current strategy positions def start(self): tf = min(d._timeframe for d in self.datas) self._usedate = tf >= bt.TimeFrame.Days def notify_order(self, order): if order.status in [order.Completed, order.Partial]: self.total_pnl += order.executed.pnl - order.executed.comm self.positions[order.data._name] += order.executed.size def next(self): if self._usedate: self.rets[self.strategy.datetime.date()] = self.total_pnl else: self.rets[self.strategy.datetime.datetime()] = self.total_pnl def stop(self): for dname in self.positions: self.unrealized += (self.strategy.dnames[dname].close[0] - self.strategy.positionsbyname[dname].price) * \ self.strategy.positionsbyname[dname].size

@backtrader Just what I needed, didn't realize this existed. Thanks again! Will be sure to scour the documentation more before any other questions I may have.

Thank you that seemed to have worked!

Haha - it was hiding in plain sight! or I was just being blind! Thanks a lot guys!

@backtrader Thanks! Yes I am exploring this route. I may come up with a few questions later.
Cheers

Release 1.9.63.122

Updated it to this:

''' //PRC_Reversal point indicator | indicator //09.04.2017 //Nicolas @ www.prorealcode.com //Sharing ProRealTime knowledge // https://www.prorealcode.com/topic/need-help-coding-the-following-reversal-indicator/ // https://www.prorealcode.com/prorealtime-indicators/reversal-point-indicator/ // LONG //Red/Green candle (reversal) //Bullish candle close above the open of the previous red candle //Space to the left (the low of the last 3 candles lower than the low of the last 50 candles) //default stochastic (8,3,3) was in the oversold area within the last 3 candles // SHORT //Green/Red candle (reversal) //Bearish candle close below the close of the previous green candle //Space to the left (the high of the last 3 candles higher than the high of the last 50 candles) //default stochastic (8,3,3) was in the overbought area within the last 3 candles. ''' from __future__ import (absolute_import, division, print_function, unicode_literals) # Import the backtrader platform import backtrader as bt import backtrader.indicators as btind class ReversalPoint(bt.Indicator): lines = ('revpoint',) plotinfo = dict( # Add extra margins above and below the 1s and -1s plotymargin=0.15, # Plot a reference horizontal line at 1.0 and -1.0 plothlines=[1.0, -1.0], # Simplify the y scale to 1.0 and -1.0 plotyticks=[1.0, -1.0]) # Plot the line "overunder" (the only one) with dash style # ls stands for linestyle and is directly passed to matplotlib plotlines = dict(revpoint=dict(ls='--')) params = dict( sto_period=8, sto_period_dfast=3, sto_oversold=20, sto_overbought=80, lperiod=50, speriod=3, ) def __init__(self): self.sto = btind.Stochastic(period=self.p.sto_period, period_dfast=self.p.sto_period_dfast) self.addminperiod(self.p.lperiod + self.p.speriod) self.c1 = bt.And(self.data.close(-1) < self.data.open(-1), self.data.close(0) > self.data.open(0)) self.c2 = self.data.close(0) > self.data.open(-1) self.c5 = bt.And(self.data.close(-1) > self.data.open(-1), self.data.close(0) < self.data.open(0)) self.c6 = self.data.close(0) < self.data.open(-1) def next(self): sList = self.sto.get(size=self.p.speriod) sLow = min(self.data.low.get(size=self.p.speriod)) lLow1 = min(self.data.low.get(ago=-1, size=self.p.lperiod)) lLow2 = min(self.data.low.get(ago=-2, size=self.p.lperiod)) lLow3 = min(self.data.low.get(ago=-3, size=self.p.lperiod)) sHigh = max(self.data.high.get(size=self.p.speriod)) lHigh1 = max(self.data.high.get(ago=-1, size=self.p.lperiod)) lHigh2 = max(self.data.high.get(ago=-2, size=self.p.lperiod)) lHigh3 = max(self.data.high.get(ago=-3, size=self.p.lperiod)) c1 = self.c1 c2 = self.c2 c3 = sLow < lLow1 or sLow < lLow2 or sLow < lLow3 c4 = len(list(filter(lambda x: (x < self.p.sto_oversold), sList))) > 0 tLong = c1 and c2 and c3 and c4 c5 = self.c5 c6 = self.c6 c7 = sHigh > lHigh1 or sHigh > lHigh2 or sHigh > lHigh3 c8 = len(list(filter(lambda x: (x > self.p.sto_overbought), sList))) > 0 tShort = c5 and c6 and c7 and c8 if tLong: self.revpoint[0] = 1.0 elif tShort: self.revpoint[0] = -1.0 else: self.revpoint[0] = 0.0

i also changed this calculation:

c8 = False for i in self.sto.get(size=3): if i > 80: c8 = True break

to this

c8 = len(list(filter(lambda x: (x > self.p.sto_overbought), sList))) > 0

@richard-o-regan Good afternoon. I just joined BackTrader. Long-time futures trader; technical. Years ago with aid of programmers created own linux-based backtesting and then real time trading. Now looking for back testing platform. Found QuantConnect on IB site, but others recommended Backtrader. I will be working with programmer, but based on number of years experience backtesting tens of thousands of iterations, what we did was to rather than merely take output, and rearrange, place all output into a large spd sheet. That included say all 500 iterations for a test, along with all the indiv vars, and columns such as you suggest, % pft tdes, etc. However, with spd sheet, could easily sort on columns, as well as sub-sorts. Regards,

@j45p41 said in Calculating RSI mismatch with Ninjatrader/Excel:

In that case is there a way of specifying how backtrder calculates the moving average?

Docs - Indicator Reference - Look for SmoothedMovingAverage

The calculation method is not a secret, it is in Welles Wilder book and even in Wikipedia and in the Reddit post linked above.

http://en.wikipedia.org/wiki/Moving_average#Modified_moving_average

@chat123 I believe the best way is the clear way. If you know how to implement @dasch 's approach, than use it.

Extend the PandasData feed instead.

See here for example: https://community.backtrader.com/topic/158/how-to-feed-backtrader-alternative-data/5

@pari-lingampally said in Plot on different axis:

I have an indicator that plots multiple lines, is it possible to plot one of the lines on a different axis? I found the sameaxis param but did not seem to work.

No. You'll have to put that line in yet another indicator.

@backtrader Thank you for your answer and I am sure a library will be coming together soon. I will certainly contribute as I get further into my backtrader journey :)