General Code/Help

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  Custom Observer for debug arrows on graph, to be called in stratergy, error
  • HarryBurge  

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  @HarryBurge said in Custom Observer for debug arrows on graph, to be called in strategy, error: I am creating my calls to addpoint like this in my strategy. Few points: Having a strategy dependent on specific observer is probably not the best idea design-wise. Why the same logic can't be run right inside the observer? All datas are available for observers AFAIR.
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  IB - Switch between Multiple (Near) Online Datafeeds
  interactivebro datafeeds multiple online • • zkl  

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  @vladisld said in IB - Switch between Multiple (Near) Online Datafeeds: print(f'{self.datas[1].datetime.datetime(0).isoformat}:{self.datas[1].open[0] ....') Re: "what do you mean..?" I had the impression that cerebro (ES: brain) did not collect the data for the 2nd feed. Your answer and your short example-hint helped a lot. I was wrong. from __future__ import (absolute_import, division, print_function, unicode_literals) import backtrader as bt import datetime #import matplotlib import time import pytz class St(bt.Strategy): #pr_St__class = St() #NameError: name 'St' is not defined #print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ pr_St__class', pr_St__class) #here he defines the structure of one record D-OHLC-V def logdata(self): txt = [] txt.append('{}'.format(len(self))) #current object lenght of data, number of records for pricing txt.append(' data_s') #1st datafeed txt.append('{}'.format(self.data.datetime.datetime(0).isoformat())) txt.append('{:.2f}'.format(self.data.open[0])) txt.append('{:.2f}'.format(self.data.high[0])) txt.append('{:.2f}'.format(self.data.low[0])) txt.append('{:.2f}'.format(self.data.close[0])) txt.append('{:.2f}'.format(self.data.volume[0])) txt.append(' data_c') #2nd datafeed txt.append('{}'.format(self.datas[1].datetime.datetime(0).isoformat())) txt.append('{:.2f}'.format(self.datas[1].open[0])) txt.append('{:.2f}'.format(self.datas[1].high[0])) txt.append('{:.2f}'.format(self.datas[1].low[0])) txt.append('{:.2f}'.format(self.datas[1].close[0])) txt.append('{:.2f}'.format(self.datas[1].volume[0])) print(','.join(txt)) def next(self): self.logdata() def run(args=None): cerebro = bt.Cerebro(stdstats=False) #with this i get clean data, no history. A clean structure store = bt.stores.IBStore(port=7496, clientId=1001) #20200701 with TWS THIS finally worked well data_s = store.getdata(dataname='EUR.USD-CASH-IDEALPRO', timeframe=bt.TimeFrame.Ticks, stopafter=10, #stopafter backfill_start=False, backfill=False, latethrough=True, tz='CET' ) cerebro.resampledata(data_s, timeframe=bt.TimeFrame.Seconds, compression=1) data_c = store.getdata(dataname='CHF.USD-CASH-IDEALPRO', timeframe=bt.TimeFrame.Ticks, stopafter=10, #stopafter backfill_start=False, backfill=False, latethrough=True, tz='CET' ) cerebro.resampledata(data_c, timeframe=bt.TimeFrame.Seconds, compression=1) cerebro.addstrategy(St) cerebro.run() # cerebro.plot(plotter=None, iplot=True) #Unfortunately I always get a lot of unneeded nformatio like platform Darwin, V 3.1.3, loaded modules, fonts if __name__ == '__main__': run() Having changed the code above (based on your recommendation) now I see what data is there: Server Version: 76 TWS Time at connection:20200702 22:27:13 CET 1, data_s,2020-07-02T22:27:14,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:27:14,1.06,1.06,1.06,1.06,0.00 2, data_s,2020-07-02T22:27:14,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:27:22,1.06,1.06,1.06,1.06,0.00 3, data_s,2020-07-02T22:27:31,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:27:22,1.06,1.06,1.06,1.06,0.00 4, data_s,2020-07-02T22:27:31,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:28:57,1.06,1.06,1.06,1.06,0.00 5, data_s,2020-07-02T22:28:58,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:28:57,1.06,1.06,1.06,1.06,0.00 6, data_s,2020-07-02T22:29:01,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:01,1.06,1.06,1.06,1.06,0.00 7, data_s,2020-07-02T22:29:01,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:08,1.06,1.06,1.06,1.06,0.00 8, data_s,2020-07-02T22:29:01,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:10,1.06,1.06,1.06,1.06,0.00 9, data_s,2020-07-02T22:29:01,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:14,1.06,1.06,1.06,1.06,0.00 10, data_s,2020-07-02T22:29:21,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:14,1.06,1.06,1.06,1.06,0.00 11, data_s,2020-07-02T22:29:21,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:25,1.06,1.06,1.06,1.06,0.00 12, data_s,2020-07-02T22:29:29,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:25,1.06,1.06,1.06,1.06,0.00 13, data_s,2020-07-02T22:29:37,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:29:25,1.06,1.06,1.06,1.06,0.00 14, data_s,2020-07-02T22:30:02,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:30:02,1.06,1.06,1.06,1.06,0.00 15, data_s,2020-07-02T22:30:02,1.12,1.12,1.12,1.12,0.00, data_c,2020-07-02T22:30:07,1.06,1.06,1.06,1.06,0.00 data_s is the 1st feed EUR.USD data_c is the 2nd feed CHF.USD As an outlook this is getting more interesting when displaying a stock and the corresponding option(s). It needs to be improved as I am using for the resampling the compression=1. Not always there is data. Increasing the compression to 10 could be a workaround. Even better when i leav it empty For now I can go ahead. My question is answered and solved so that i can go ahead. Thank you very much vladisld.
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  TimeFrame.Days works but TimeFrame.Minutes doesn't
  • Wayne Filkins  

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  @Wayne-Filkins perfect
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  Unconverted data remains at .000 tried everything please help
  • cept0r  

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  @cept0r It's never a waste to ask as it lead you to thinking through the solution. Nice!
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  Trading only on Candle Stick Pattern.
  • Nikhil Morye  

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  Got my way around this peculiar problem with bracket order, putting the code out if anyone needs it, the code needs to tweaked around its too big for no reason but hopefully it helps who faced a similar issue like mine. class TestStrategy(bt.Strategy): params = dict (buyperiod=1.15,sellperiod=0.95) 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.dataopen = self.datas[0].open self.datahigh = self.datas[0].high self.datalow = self.datas[0].low self.marubozu = bt.talib.CDLMARUBOZU(self.dataopen,self.datahigh,self.datalow,self.dataclose) self.order = None self.buyprice = None self.buycomm = None self.sellprice = None self.sellcomm = None def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: return 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 elif order.issell(): #self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %(order.executed.price,order.executed.value,order.executed.comm)) self.sellprice = order.executed.price self.sellcomm = order.executed.comm self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') pass 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): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[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.marubozu[0] == 100: self.log('BUY CREATE, limitprice %.2f, price %.2f, stopprice %.2f' %(self.dataopen*1.35,self.dataclose[0],self.datalow[0])) self.order = self.buy_bracket(limitprice=self.dataopen*self.p.buyperiod, price=self.dataclose, stopprice=self.datalow) elif self.marubozu[0] == -100: self.log('Sell CREATE, limitprice %.2f, price %.2f, stopprice %.2f' %(self.datalow*0.65,self.dataclose[0],self.datahigh[0])) self.order = self.sell_bracket(limitprice=self.datalow*self.p.sellperiod, price=self.dataclose, stopprice=self.datahigh) cerebro = bt.Cerebro() cerebro.addstrategy(TestStrategy) data = bt.feeds.YahooFinanceData(dataname='SBIN.BO',fromdate=datetime(2015, 1, 1),todate=datetime(2020, 6, 30),reversed=True) cerebro.adddata(data) cerebro.broker.setcash(1000.0) cerebro.broker.setcommission(0.001) print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) cerebro.run() print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) #cerebro.plot();
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  'frompackages' directive functionality seems to be broken when using inheritance
  • vladisld  

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  PR submitted: https://github.com/mementum/backtrader/pull/411
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  SMA Crossever strategy not showing expected results
  • Sourav Basu Roy  

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  Do you have a backtrader strategy you would like help with?
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  How to write an indicator wich gives back values?
  • chhrissi2909  

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  Look in this article and read everything about adding lines. The lines you add will show up in your strategy and you can use them.
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  Resampling from my own data feed - strange behavior
  • leecallen  

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  Great, thank you for clearing that up for me.
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  custom indicator - error in strategy
  • Jens Halsberghe  

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  I meant of course 1 for uptrend, -1 for downtrend but I can't edit my post
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  Daily datafeed works BUT intraday doesn't
  • chhrissi2909  

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  For everybody in the Future :D (I fixed the problem to my self): import os, sys, argparse import pandas as pd import backtrader as bt from Strategien.GoldenCross import GoldenCross import datetime from tiingo import TiingoClient cerebro = bt.Cerebro() cerebro.broker.setcash(100000) symbol = "AAPL" path = "/Users/me/Desktop/allgemein/Visual Studio/Stock Data/S&P500 Aktien 1H/" + symbol + ".csv" pathIndex = "/Users/me/Desktop/allgemein/Visual Studio/Stock Data/Index EOD Data/^GSPC.csv" # read csv, parse date, use dates col as index stock_prices = pd.read_csv(path, index_col=1, parse_dates=True, infer_datetime_format=True) # create data feed pandas df stock_prices.index.name = "datetime" data = bt.feeds.PandasData(dataname=stock_prices) cerebro.adddata(data) cerebro.run() cerebro.plot(volume=False) cerebro.plot(style='candle') there are 2 key lines, the first is, that for intraday data, if there is no volume you have to tell that backtrader like this: cerebro.plot(volume=False) And the second key line was how to read the csv: stock_prices = pd.read_csv(path, index_col=1, parse_dates=True, infer_datetime_format=True) Thanks for all your help :)
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  Issue with the optimizing
  • FM2020  

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  The default timeframe for the SharpeRatio analyzer is TimeFrame.Years. So this could be a reason if you don't have enough data. Also, please see the following post: "Why do I get 'None' as value for SharpeRatio?"
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  Installing Ta-Lib on OSX
  • Ziyi Liew  

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  Custom Commission Scheme that take a timeseries for Interest
  • lampalork  

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  @André-Tavares said in Custom Commission Scheme that take a timeseries for Interest: interest_long to my knowledge interest_long is a boolean flag not a float
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  How to close trade at day end
  • 刘晓光  

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  Trade outside of Regular Trading Hour on IB
  • xingweicmu  

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  @vladisld Thank you man! outsideRth=True is what I am looking for!
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  Optimization Exceptions on Windows 10 and Anaconda 3
  • Praveen Baratam  

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  Include the code in TradeHistory Class: def __reduce__(self): return (self.__class__, (self.status.status, self.status.dt, self.status.barlen, self.status.size, self.status.price, self.status.value, self.status.pnl, self.status.pnlcomm, self.status.tz,))
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  optimization and sharperatio and periodstats analyzers
  • momentum  

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  Include the code in TradeHistory Class: def __reduce__(self): return (self.__class__, (self.status.status, self.status.dt, self.status.barlen, self.status.size, self.status.price, self.status.value, self.status.pnl, self.status.pnlcomm, self.status.tz,))
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  plotting pivot points or lines
  • Wayne Filkins  

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  @ab_trader If I can use the fractal indicator to reference the true/false for any candle, like fractal[-14] to find out if 14 candles back was a high or low, then it will work, because I have to be able to get the high/low value from the fractal, not just knowing whether or not it is. I'll mess around with it, definitely looks cleaner than my method lol.
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  Interactive Brokers Multiple reqMktData
  • esteve  

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  @vladisld thanks for your response! i will use @bigdavediode workaround for now until i will find a permanent solution / develop my own feed for different data provider.