General Discussion

• 

  Getting Started with backtrader (blog post)
  • Curtis Miller

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  R

  Nice blog post @Curtis-Miller

  Just getting back to working on backtrader after a 6 month hiatus. Lots of great progress in the platform and the community. Great work @backtrader

• D

  How to use user defined Commission
  • Devinderjeet Singh

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  B

  The commission info schemes are not exposed to the variation of the value of your account, because they are matched against the assets which have no relationship to the status of your account.

  It is currently not possible.

• D

  how to use generic csv file
  • Devinderjeet Singh

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  B

  Pictures are nice, but if you use the facilities of a forum (like pasting text and quoting it to highlight it, as pointed out above) it could help more.

  It will also be of no help to future users, when you remove the picture from your google drive share. Your post has been edited to add the figure for future reference.

  In any case it seems you are pretty new to Python. Your error has nothing to do with backtrader. See

  Python - Docs - 8.1. datetime — Basic date and time types
• 

  What is the direction of Backtrader
  • Richard O'Regan

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  There have been suggestions on how to monetize backtrader, for example:

  Professional services Access to specific feature packages for example on a subscription services On-line backtesting service (ala Quantopian for example) but targeted towards businesses which want to run it in-house

  But at the end of day it would seem the market for such things is rather limited

• T

  Multi-asset ranking and rebalancing
  • timzhang

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  2947
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  J

  @timzhang Could you share some coding of your stock ranking? I am trying to do stock ranking as well, but still have some issues get it done. Thank you.

• C

  Oandatest no attribute 'contractdetails'
  • cortex

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  C

  @backtrader
  I have asked for a legacy account . at the end of the year legacy account wont access the api service anymore.
  I try with the legacy account (2121212 )

  here the code :
  from future import (absolute_import, division, print_function,
  unicode_literals)

  import argparse
  import datetime

  The above could be sent to an independent module

  import backtrader as bt
  from backtrader.utils import flushfile # win32 quick stdout flushing

  StoreCls = bt.stores.OandaStore
  DataCls = bt.feeds.OandaData
  #BrokerCls = bt.brokers.OandaBroker

  class TestStrategy(bt.Strategy):
  params = dict(
  smaperiod=5,
  trade=False,
  stake=10,
  exectype=bt.Order.Market,
  stopafter=0,
  valid=None,
  cancel=0,
  donotcounter=False,
  sell=False,
  usebracket=False,
  )

  def __init__(self): # To control operation entries self.orderid = list() self.order = None self.counttostop = 0 self.datastatus = 0 # Create SMA on 2nd data self.sma = bt.indicators.MovAv.SMA(self.data, period=self.p.smaperiod) print('--------------------------------------------------') print('Strategy Created') print('--------------------------------------------------') def notify_data(self, data, status, *args, **kwargs): print('*' * 5, 'DATA NOTIF:', data._getstatusname(status), *args) if status == data.LIVE: self.counttostop = self.p.stopafter self.datastatus = 1 def notify_store(self, msg, *args, **kwargs): print('*' * 5, 'STORE NOTIF:', msg) def notify_order(self, order): if order.status in [order.Completed, order.Cancelled, order.Rejected]: self.order = None print('-' * 50, 'ORDER BEGIN', datetime.datetime.now()) print(order) print('-' * 50, 'ORDER END') def notify_trade(self, trade): print('-' * 50, 'TRADE BEGIN', datetime.datetime.now()) print(trade) print('-' * 50, 'TRADE END') def prenext(self): self.next(frompre=True) def next(self, frompre=False): txt = list() txt.append('Data0') txt.append('%04d' % len(self.data0)) dtfmt = '%Y-%m-%dT%H:%M:%S.%f' txt.append('{:f}'.format(self.data.datetime[0])) txt.append('%s' % self.data.datetime.datetime(0).strftime(dtfmt)) txt.append('{:f}'.format(self.data.open[0])) txt.append('{:f}'.format(self.data.high[0])) txt.append('{:f}'.format(self.data.low[0])) txt.append('{:f}'.format(self.data.close[0])) txt.append('{:6d}'.format(int(self.data.volume[0]))) txt.append('{:d}'.format(int(self.data.openinterest[0]))) txt.append('{:f}'.format(self.sma[0])) print(', '.join(txt)) if len(self.datas) > 1 and len(self.data1): txt = list() txt.append('Data1') txt.append('%04d' % len(self.data1)) dtfmt = '%Y-%m-%dT%H:%M:%S.%f' txt.append('{}'.format(self.data1.datetime[0])) txt.append('%s' % self.data1.datetime.datetime(0).strftime(dtfmt)) txt.append('{}'.format(self.data1.open[0])) txt.append('{}'.format(self.data1.high[0])) txt.append('{}'.format(self.data1.low[0])) txt.append('{}'.format(self.data1.close[0])) txt.append('{}'.format(self.data1.volume[0])) txt.append('{}'.format(self.data1.openinterest[0])) txt.append('{}'.format(float('NaN'))) print(', '.join(txt)) if self.counttostop: # stop after x live lines self.counttostop -= 1 if not self.counttostop: self.env.runstop() return if not self.p.trade: return if self.datastatus and not self.position and len(self.orderid) < 1: if not self.p.usebracket: if not self.p.sell: # price = round(self.data0.close[0] * 0.90, 2) price = self.data0.close[0] - 0.005 self.order = self.buy(size=self.p.stake, exectype=self.p.exectype, price=price, valid=self.p.valid) else: # price = round(self.data0.close[0] * 1.10, 4) price = self.data0.close[0] - 0.05 self.order = self.sell(size=self.p.stake, exectype=self.p.exectype, price=price, valid=self.p.valid) else: print('USING BRACKET') price = self.data0.close[0] - 0.05 self.order, _, _ = self.buy_bracket(size=self.p.stake, exectype=bt.Order.Market, price=price, stopprice=price - 0.10, limitprice=price + 0.10, valid=self.p.valid) self.orderid.append(self.order) elif self.position and not self.p.donotcounter: if self.order is None: if not self.p.sell: self.order = self.sell(size=self.p.stake // 2, exectype=bt.Order.Market, price=self.data0.close[0]) else: self.order = self.buy(size=self.p.stake // 2, exectype=bt.Order.Market, price=self.data0.close[0]) self.orderid.append(self.order) elif self.order is not None and self.p.cancel: if self.datastatus > self.p.cancel: self.cancel(self.order) if self.datastatus: self.datastatus += 1 def start(self): if self.data0.contractdetails is not None: print('-- Contract Details:') print(self.data0.contractdetails) header = ['Datetime', 'Open', 'High', 'Low', 'Close', 'Volume', 'OpenInterest', 'SMA'] print(', '.join(header)) self.done = False

  def runstrategy():
  args = parse_args()

  # Create a cerebro cerebro = bt.Cerebro() storekwargs = dict( token=args.token, account=args.account, practice=not args.live ) if not args.no_store: store = StoreCls(**storekwargs) if args.broker: if args.no_store: broker = BrokerCls(**storekwargs) else: broker = store.getbroker() cerebro.setbroker(broker) timeframe = bt.TimeFrame.TFrame(args.timeframe) # Manage data1 parameters tf1 = args.timeframe1 tf1 = bt.TimeFrame.TFrame(tf1) if tf1 is not None else timeframe cp1 = args.compression1 cp1 = cp1 if cp1 is not None else args.compression if args.resample or args.replay: datatf = datatf1 = bt.TimeFrame.Ticks datacomp = datacomp1 = 1 else: datatf = timeframe datacomp = args.compression datatf1 = tf1 datacomp1 = cp1 fromdate = None if args.fromdate: dtformat = '%Y-%m-%d' + ('T%H:%M:%S' * ('T' in args.fromdate)) fromdate = datetime.datetime.strptime(args.fromdate, dtformat) DataFactory = DataCls if args.no_store else store.getdata datakwargs = dict( timeframe=datatf, compression=datacomp, qcheck=args.qcheck, historical=args.historical, fromdate=fromdate, bidask=args.bidask, useask=args.useask, backfill_start=not args.no_backfill_start, backfill=not args.no_backfill, tz=args.timezone ) if args.no_store and not args.broker: # neither store nor broker datakwargs.update(storekwargs) # pass the store args over the data data0 = DataFactory(dataname=args.data0, **datakwargs) data1 = None if args.data1 is not None: if args.data1 != args.data0: datakwargs['timeframe'] = datatf1 datakwargs['compression'] = datacomp1 data1 = DataFactory(dataname=args.data1, **datakwargs) else: data1 = data0 rekwargs = dict( timeframe=timeframe, compression=args.compression, bar2edge=not args.no_bar2edge, adjbartime=not args.no_adjbartime, rightedge=not args.no_rightedge, takelate=not args.no_takelate, ) if args.replay: cerebro.replaydata(data0, **rekwargs) if data1 is not None: rekwargs['timeframe'] = tf1 rekwargs['compression'] = cp1 cerebro.replaydata(data1, **rekwargs) elif args.resample: cerebro.resampledata(data0, **rekwargs) if data1 is not None: rekwargs['timeframe'] = tf1 rekwargs['compression'] = cp1 cerebro.resampledata(data1, **rekwargs) else: cerebro.adddata(data0) if data1 is not None: cerebro.adddata(data1) if args.valid is None: valid = None else: valid = datetime.timedelta(seconds=args.valid) # Add the strategy cerebro.addstrategy(TestStrategy, smaperiod=args.smaperiod, trade=args.trade, exectype=bt.Order.ExecType(args.exectype), stake=args.stake, stopafter=args.stopafter, valid=valid, cancel=args.cancel, donotcounter=args.donotcounter, sell=args.sell, usebracket=args.usebracket) # Live data ... avoid long data accumulation by switching to "exactbars" cerebro.run(exactbars=args.exactbars) if args.exactbars < 1: # plotting is possible if args.plot: pkwargs = dict(style='line') if args.plot is not True: # evals to True but is not True npkwargs = eval('dict(' + args.plot + ')') # args were passed pkwargs.update(npkwargs) cerebro.plot(**pkwargs)

  def parse_args(pargs=None):
  parser = argparse.ArgumentParser(
  formatter_class=argparse.ArgumentDefaultsHelpFormatter,
  description='Test Oanda integration')

  parser.add_argument('--exactbars', default=1, type=int, required=False, action='store', help='exactbars level, use 0/-1/-2 to enable plotting') parser.add_argument('--stopafter', default=0, type=int, required=False, action='store', help='Stop after x lines of LIVE data') parser.add_argument('--no-store', required=False, action='store_true', help='Do not use the store pattern') parser.add_argument('--debug', required=False, action='store_true', help='Display all info received from source') parser.add_argument('--token', default=None, required=True, action='store', help='Access token to use') parser.add_argument('--account', default=None, required=True, action='store', help='Account identifier to use') parser.add_argument('--live', default=None, required=False, action='store', help='Go to live server rather than practice') parser.add_argument('--qcheck', default=0.5, type=float, required=False, action='store', help=('Timeout for periodic ' 'notification/resampling/replaying check')) parser.add_argument('--data0', default=None, required=True, action='store', help='data 0 into the system') parser.add_argument('--data1', default=None, required=False, action='store', help='data 1 into the system') parser.add_argument('--timezone', default=None, required=False, action='store', help='timezone to get time output into (pytz names)') parser.add_argument('--bidask', default=None, required=False, action='store_true', help='Use bidask ... if False use midpoint') parser.add_argument('--useask', default=None, required=False, action='store_true', help='Use the "ask" of bidask prices/streaming') parser.add_argument('--no-backfill_start', required=False, action='store_true', help='Disable backfilling at the start') parser.add_argument('--no-backfill', required=False, action='store_true', help='Disable backfilling after a disconnection') parser.add_argument('--historical', required=False, action='store_true', help='do only historical download') parser.add_argument('--fromdate', required=False, action='store', help=('Starting date for historical download ' 'with format: YYYY-MM-DD[THH:MM:SS]')) parser.add_argument('--smaperiod', default=5, type=int, required=False, action='store', help='Period to apply to the Simple Moving Average') pgroup = parser.add_mutually_exclusive_group(required=False) pgroup.add_argument('--replay', required=False, action='store_true', help='replay to chosen timeframe') pgroup.add_argument('--resample', required=False, action='store_true', help='resample to chosen timeframe') parser.add_argument('--timeframe', default=bt.TimeFrame.Names[1], choices=bt.TimeFrame.Names, required=False, action='store', help='TimeFrame for Resample/Replay') parser.add_argument('--compression', default=1, type=int, required=False, action='store', help='Compression for Resample/Replay') parser.add_argument('--timeframe1', default=None, choices=bt.TimeFrame.Names, required=False, action='store', help='TimeFrame for Resample/Replay - Data1') parser.add_argument('--compression1', default=None, type=int, required=False, action='store', help='Compression for Resample/Replay - Data1') parser.add_argument('--no-takelate', required=False, action='store_true', help=('resample/replay, do not accept late samples')) parser.add_argument('--no-bar2edge', required=False, action='store_true', help='no bar2edge for resample/replay') parser.add_argument('--no-adjbartime', required=False, action='store_true', help='no adjbartime for resample/replay') parser.add_argument('--no-rightedge', required=False, action='store_true', help='no rightedge for resample/replay') parser.add_argument('--broker', required=False, action='store_true', help='Use Oanda as broker') parser.add_argument('--trade', required=False, action='store_true', help='Do Sample Buy/Sell operations') parser.add_argument('--sell', required=False, action='store_true', help='Start by selling') parser.add_argument('--usebracket', required=False, action='store_true', help='Test buy_bracket') parser.add_argument('--donotcounter', required=False, action='store_true', help='Do not counter the 1st operation') parser.add_argument('--exectype', default=bt.Order.ExecTypes[0], choices=bt.Order.ExecTypes, required=False, action='store', help='Execution to Use when opening position') parser.add_argument('--stake', default=10, type=int, required=False, action='store', help='Stake to use in buy operations') parser.add_argument('--valid', default=None, type=float, required=False, action='store', help='Seconds to keep the order alive (0 means DAY)') parser.add_argument('--cancel', default=0, type=int, required=False, action='store', help=('Cancel a buy order after n bars in operation,' ' to be combined with orders like Limit')) # Plot options parser.add_argument('--plot', '-p', nargs='?', required=False, metavar='kwargs', const=True, help=('Plot the read data applying any kwargs passed\n' '\n' 'For example (escape the quotes if needed):\n' '\n' ' --plot style="candle" (to plot candles)\n')) if pargs is not None: return parser.parse_args(pargs) return parser.parse_args()

  if name == 'main':
  runstrategy()

• T

  Can i get btrun output into pandas dataframe
  • Tausif

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  T

  ok , Thanks

• C

  Contract Position
  • Cole Hersowitz

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  Views

  No one has replied

• 

  Implementation of OpenAI Gym environment for Backtrader
  • Андрей Музыкин

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  S

  This is great. Im using a GradientBoostingClassifier for long short signals. Is this a trainable agent? I know it already learns from past values when put online.

• C

  Job Position/Contract Work
  • Cole Hersowitz

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  No one has replied

• T

  how to store btrun output in a csv file
  • Tausif

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  Posts
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  Views

  B

  Use the redirect operator of the shell: command > myoutputfile.csv

  Or use --writer name=value and use out=xxx. The Writer options are documented here:

  Docs - Writer
• B

  YahooFinance Data Feeds
  • backtrader

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  11
  Posts
  850
  Views

  B

  Release 1.9.56.122 contains an exception for volume fields which are not convertible to float.

• X

  BracketOrder: stopamount and limitamount instead of stop|limitprice in the bracket order
  • xnox

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  No one has replied

• T

  How to prevent multiple submission of limit / bracket orders?
  • Taewoo Kim

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  Posts
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  X

  I do keep a simple set of my order levels, add working orders to the set, and remove them when they are fully closed or expire. That way in next I can check what I need to reopen.

  I have tried to write a custom fill order function which would reject duplicate orders, but it felt a lot more error prone than simply tracking state and not submitting duplicates.

• B

  Feature Request: OCO bracket orders?
  • beejaysea

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  Posts
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  E

  Thank you!! It worked out perfectly!
  Much appreciated!

• 

  Stock trading analytics and optimization with PyFolio and R's PerformanceAnalytics (blog post)
  • Curtis Miller

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  3
  Posts
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  Views

  

  @backtrader said in Stock trading analytics and optimization with PyFolio and R's PerformanceAnalytics (blog post):

  This analyzer should be removed. It was the 1st ever implementation and was never really reworked because TimeReturn (which allows customization of the timeframe independently of the data's actual timeframe) took over.

  Noted.

  Here the proposal would be to create an Indicator first and then use the indicator inside the analyzer. The advantage is that you can plug the indicator directly into the Strategy, in an Observer or in an Analyzer.

  I knew there had to be a better way than what I was doing. Thanks!

  Don't let yourself be bothered by someone who probably only speaks about one single thing at every social event and who only sees peers in those with a PhD an his level of focus in a single topic. This may be wrong and he may be the soul of every party, run marathons every weekend and attend charity events on Tuesdays. Even in that case, don't let yourself be bothered.

  Time is scarce everywhere. Shorter posts will free up some time and you'll probably keep on enjoying your own posting.

  Thanks for the words of encouragement! I'm going to try to keep up the blog but not make an effort to be regular about my posts. That is too much of a commitment. Anyway, I appreciate the thought.

• R

  idea
  • robomotic

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  B

  @robomotic said in idea:

  Hello, the commands will be pause/resume trading whereby the strategy will keep resampling the live data but if it receives a pause command it will not place any trading orders (to the broker), once it receives a resume command it will keep placing orders as before.

  Isn't that handled just by having a bool in your code which decides if an order will be sent to the broker or not?

  @robomotic said in idea:

  Hmm what if we have support for a message broker like RabbitMQ whose queue will act as an additional "command" data source and add a method to Strategy called notify_status()

  Same as above. Upon entering next you can pull a command, message, value (you name it) from anything and be the source for the bool above which decides if an order is a trading signal will send an order to the broker or not.

  A very generic approach is to pass a callable to the strategy, to fully isolate even your code from the actual details.

  class Strategy(bt.Strategy): params = ( ... # other params here ('msgpull', None), ) def next(self): cantrade = True if self.p.msgpull is not None: cantrade = self.p.msgpull() # or self.p.msgpull(self) to grant full strategy access to the callable .... # some other things if mybuysignal and cantrade: self.buy()
• R

  Interactive input strategy
  • robomotic

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  B

  No such thing is implemented. That's a huge use case. For starters some quick questions:

  Which commands? You are resampling live data to 15 seconds and you spend 31 seconds in the interactive mode, should the strategy skip the 2 15-seconds bars from the resampler? See them later and what happens if any of them generates a signal for trading?

  A possible approach:

  Create a thread during the initialization of your strategy and use a method of the strategy as the target This method, now running in parallel (as much as allowed by the GIL) can implement keyboard processing
• K

  Closing All Orders
  • KT

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  B

  See Docs - Strategy for the close method

  There aren't multiple ways.

• K

  Order
  • KT

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  K

  @Curtis-Miller Thanks!! I will check the PyFolio