General Discussion

• N

  Backtest vs live bars "off-by-1" discrepancy?
  • nooby_mcnoob

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  I bumped into similar situation. Looks like by default, DELAYED data is using "rightedge=False" convention, while LIVE data is using "rightedge=True", which causes the discrepancy.
• M

  Extend CSV feeder with string column
  • mttax

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• Z

  Impact of a data feed's boundary time convention (rightedge)
  • z_kvn

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• S

  notify_trade is not correctly recognize trade close when a trade is closed by stop loss or stop trail
  • sanmao

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• K

  Position analyzer with entry/exit details
  • kausality

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  Awesome work there! I don't need to add anything. I am currently doing some live bot like trading and therefore I though adding a continuous dump feature would be helpful. This little modification also allows one to dump the position details in a CSV file class trade_list(bt.Analyzer): params = ( ("continuous", False), ("fname", "position_{0}.csv".format(time.strftime("%Y%m%d-%H%M%S"))), ("dump", True) ) def __init__(self): self.trades = [] self.cumprofit = 0.0 def get_analysis(self): return self.trades def _write_to_file(self): if not self.p.dump: return df = pd.DataFrame(self.trades) df.to_csv(self.p.fname, index=False) def notify_trade(self, trade): if trade.isclosed: brokervalue = self.strategy.broker.getvalue() dir = "short" if trade.history[0].event.size > 0: dir = "long" pricein = trade.history[len(trade.history)-1].status.price priceout = trade.history[len(trade.history)-1].event.price datein = bt.num2date(trade.history[0].status.dt) dateout = bt.num2date(trade.history[len(trade.history)-1].status.dt) if trade.data._timeframe >= bt.TimeFrame.Days: datein = datein.date() dateout = dateout.date() pcntchange = 100 * priceout / pricein - 100 pnl = trade.history[len(trade.history)-1].status.pnlcomm pnlpcnt = 100 * pnl / brokervalue barlen = trade.history[len(trade.history)-1].status.barlen pbar = pnl / barlen self.cumprofit += pnl size = value = 0.0 for record in trade.history: if abs(size) < abs(record.status.size): size = record.status.size value = record.status.value highest_in_trade = max(trade.data.high.get(ago=0, size=barlen+1)) lowest_in_trade = min(trade.data.low.get(ago=0, size=barlen+1)) hp = 100 * (highest_in_trade - pricein) / pricein lp = 100 * (lowest_in_trade - pricein) / pricein if dir == "long": mfe = hp mae = lp else: mfe = -lp mae = -hp self.trades.append({"ref": trade.ref, "ticker": trade.data._name, "dir": dir, "datein": datein, "pricein": pricein, "dateout": dateout, "priceout": priceout, "chng%": round(pcntchange, 2), "pnl": pnl, "pnl%": round(pnlpcnt, 2), "size": size, "value": value, "cumpnl": self.cumprofit, "nbars": barlen, "pnl/bar": round(pbar, 2), "mfe%": round(mfe, 2), "mae%": round(mae, 2)}) if self.p.continuous: self._write_to_file() def stop(self): if not self.p.continuous: self._write_to_file()
• T

  Multiple Live Data Feeds
  • TOTORO

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  @totoro It depends on what you are doing inside the loop. If you are making some calculations like std devs, atr etc then I don't think that even with 100's of data feed your lag could reach 10s. If you are running some neural network based complex calculations then it can though. You can use something like the time command to get an idea about your execution time.
• C

  Anyone use backtrader to do live trading on Bitcoin exchange?
  crypto pairs trading • • CptanPanic

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  @søren-pallesen Hi, Did you get correct speed for backfilling few minutes bars when you use Bitmex feed AND Bitmex broker? It is seems that 1-minute bars are received in one block but a simple strategy with 1 indicator needs at least 10sec per bar to run/compute the "next" function. May be, it is the normal behaviour of Backtrader running live?
• K

  Closing trade on the same day
  • kausality

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  EDIT: Post created by mistake. As I cannot delete it, I have edited it out.
• B

  Issue with RollOver and live data
  • BobDenar

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  Hi, I'm glad you've fixed this issue in the latest release. Unfortunately, there is a typo in the check for values in the data source: def _load(self): while self._d is not None: if self._d.next() is not 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 ... It should have been: def _load(self): while self._d is not None: if self._d.next() is False: # no values from current data src ... not was falsely added. Regards
• A

  Order update still unavailable?
  • arrr

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  Not really.
• 

  initializing resampled/replayed data
  • bb2

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  @backtrader thank you for the suggestion. Though would that still work If I need to replay multiple timeframes? I was thinking of resampling my low tf data dataframe with pandas to higher timeframe and use it for backfilling source. (if I got you right above). If I have multiple replays, then I will do the pandas resampling multiple times, but I think concatenating and backfilling wouldn't be the right approach, because they have different compressions on bt PandasData level. What would you suggest for multiple replays?
• K

  speed market replay tool for stocks to acquire more experience?
  • katie matriana

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• V

  Running Multiple Strategies & Combining the Output
  • Vandan Chopra

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  @backtrader Thanks a ton for that. It does sort out quite a bit for me. Also gives me confidence in you saying that I wouldn't need spark for something like this and that python by itself should be able to handle the load. (Anyways, it's going to be a fairly small system to begin with). PS. Looks like i'm going to have to reach out to you again in the future. But SUPER appreciate your response (tipping my hat :)
• D

  Using multiple strategies runs only one?
  • duality_

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  @vandan-chopra Sorry, but seeing your other posts, this is not what you are looking for, because this is about selecting a strategy based on its results and not about coordinating strategies.
• A

  Small question on the Documentation for Indicator Reference
  • Amien Johaadien

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  Indeed. Will be corrected.
• A

  Benchmarking & Actual Cash/Interest Rates over longer timeperiods
  • Amien Johaadien

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  @amien-johaadien said in Benchmarking & Actual Cash/Interest Rates over longer timeperiods: My thought was to build a synthetic cash index and load it as a datafeed and use that as a "Benchmark" using the observer method. Would that work and is there a better way of doing it? The Observer is only for visual representation. The functionality is actually in the Analyzer. But yes, that would be the right approach (imho) @amien-johaadien said in Benchmarking & Actual Cash/Interest Rates over longer timeperiods: Also when using optstrategy - can I still use benchmarks? Yes (unless you happen to trigger one of the pickling problems)
• 

  Bokeh Integration - Interactive Webbrowser Plotting
  • vbs

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  @vbs Hi, thanks for the response. Good news: I upgraded Bokeh to 0.13 and tried your minimised example with some yahoo csv daily data, and it did work in a notebook environment! Bad news: my own strategy code still didn't work : ( There's some new error information from the notebook though, please refer to the bottom of below message. Any guess of the issue? Is there something wrong with 2 resampled data? I am using minute data (data0) and two resampled datas 15-minutes, 60 minutes. Could this have caused some mess? /Users/kevin/anaconda2/envs/py36/lib/python3.6/site-packages/bokeh/models/sources.py:110: BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('4440411776close', 8475), ('4440411776colors_bars', 8475), ('4440411776colors_outline', 8475), ('4440411776colors_wicks', 8475), ('4440411776high', 8475), ('4440411776low', 8475), ('4440411776open', 8475), ('4440411776volume', 8475), ('4440411776volume_colors', 8475), ('4440443984', 8474), ('datetime', 8475), ('index', 8475) /Users/kevin/anaconda2/envs/py36/lib/python3.6/site-packages/bokeh/models/sources.py:110: BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('4440411776close', 8475), ('4440411776colors_bars', 8475), ('4440411776colors_outline', 8475), ('4440411776colors_wicks', 8475), ('4440411776high', 8475), ('4440411776low', 8475), ('4440411776open', 8475), ('4440411776volume', 8475), ('4440411776volume_colors', 8475), ('4440443984', 8474), ('4440444096', 8474), ('datetime', 8475), ('index', 8475) Exception Traceback (most recent call last) <ipython-input-5-fb7d00ce15b9> in <module>() 21 args.plot=True 22 ---> 23 es.runstrat(args) ~/Dropbox/quantiacs/InteractiveBroker/PyBackTrader/ES_bracketXover.py in runstrat(args) 251 #cerebro.plot(style='candle',iplot=False,volume=False) 252 b = Bokeh(style='bar', plot_mode='single', )# scheme=Tradimo()) --> 253 cerebro.plot(b) 254 255 def analyseDefaultArguments(args): ~/anaconda2/envs/py36/lib/python3.6/site-packages/backtrader/cerebro.py in plot(self, plotter, numfigs, iplot, start, end, width, height, dpi, tight, use, **kwargs) 989 rfig = plotter.plot(strat, figid=si * 100, 990 numfigs=numfigs, iplot=iplot, --> 991 start=start, end=end, use=use) 992 # pfillers=pfillers2) 993 ~/anaconda2/envs/py36/lib/python3.6/site-packages/backtrader_plotting/bokeh/bokeh.py in plot(self, obj, figid, numfigs, iplot, start, end, use, **kwargs) 402 403 if isinstance(obj, bt.Strategy): --> 404 self._blueprint_strategy(obj, start, end, **kwargs) 405 elif isinstance(obj, bt.OptReturn): 406 if not hasattr(obj, 'strategycls'): ~/anaconda2/envs/py36/lib/python3.6/site-packages/backtrader_plotting/bokeh/bokeh.py in _blueprint_strategy(self, strategy, start, end, **kwargs) 243 strat_figures.append(bf) 244 --> 245 bf.plot(master, strat_clk, None) 246 247 for s in slaves: ~/anaconda2/envs/py36/lib/python3.6/site-packages/backtrader_plotting/bokeh/figure.py in plot(self, obj, strat_clk, master) 164 height_set = self._scheme.plot_height_observer 165 else: --> 166 raise Exception(f"Unsupported plot object: {type(obj)}") 167 168 self.datas.append(obj) Exception: Unsupported plot object: <class 'backtrader.feed.DataClone'>
• K

  Feature Request: Modifying an open order
  • kausality

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• K

  How to determine if the data is being replayed in strategy?
  • kausality

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  @kausality said in How to determine if the data is being replayed in strategy?: From what I gathered from the docs, the len(self.data) will remain the same if the data is being sent to next() at the original(1-minute) timeframe. It will change when it receives the 5-minute(replayed) candle. Your 5-minutes replayed bar is constantly delivered. Only the 1st tick will contain an original 1-minute bar. The other 4 ticks will mix the incoming values to replicate (replay) how the final 5-minutes bar is constructed. @kausality said in How to determine if the data is being replayed in strategy?: So in this case, how will I determine within next() that the current data value(say: self.data.close[0]) refers to its original 1-minute value or the resampled 5-minute ohlc data? You cannot, because you cannot also guarantee that for each replayed 5-minutes bar you have 5 underlying 1-minute bars. If your dataset is perfect and the time boundaries too, you could count the 5 ticks that each replayed bar generates. @kausality said in How to determine if the data is being replayed in strategy?: How do I make this distinction in the code? You cannot also when trading live, because you only know that a bar is closed because you enter the next one. The only feasible approach: Add a 2nd resampled feed from the original data. This bar will be delivered only once when complete, before the 1st tick of the next replayed data comes in.
• 

  Purpose of adding multiple Strategies
  • vbs

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  @backtrader Got it. Thanks a ton for that. I didn't understand it all, but it gives me enough to start researching / learning/ building. :)