Exception has occurred: ImportError
cannot import name 'feeds'
Hi I am getting this issue with backtrader. Its running on a server and works fine in jupyter but wehn i try to debug in vscode i get this error at line
import backtrader as bt
It should be pointed to the correct python library since everything else works fine!
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-414-3bfb3089a414> in <module>
11 #cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='myTradesStats')
12 #cerebro.broker.setcommission(commission=0.001)
---> 13 results = cerebro.run()
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/cerebro.py in run(self, **kwargs)
1125 # let's skip process "spawning"
1126 for iterstrat in iterstrats:
-> 1127 runstrat = self.runstrategies(iterstrat)
1128 self.runstrats.append(runstrat)
1129 if self._dooptimize:
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/cerebro.py in runstrategies(self, iterstrat, predata)
1291 self._runonce_old(runstrats)
1292 else:
-> 1293 self._runonce(runstrats)
1294 else:
1295 if self.p.oldsync:
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/cerebro.py in _runonce(self, runstrats)
1686 return
1687
-> 1688 self._brokernotify()
1689 if self._event_stop: # stop if requested
1690 return
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/cerebro.py in _brokernotify(self)
1358 notification to the strategy
1359 '''
-> 1360 self._broker.next()
1361 while True:
1362 order = self._broker.get_notification()
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/brokers/bbroker.py in next(self)
1219
1220 else:
-> 1221 self._try_exec(order)
1222 if order.alive():
1223 self.pending.append(order)
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/brokers/bbroker.py in _try_exec(self, order)
1065
1066 if order.exectype == Order.Market:
-> 1067 self._try_exec_market(order, popen, phigh, plow)
1068
1069 elif order.exectype == Order.Close:
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/brokers/bbroker.py in _try_exec_market(self, order, popen, phigh, plow)
875 p = self._slip_down(plow, exprice, doslip=self.p.slip_open)
876
--> 877 self._execute(order, ago=0, price=p, dtcoc=dtcoc)
878
879 def _try_exec_close(self, order, pclose):
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/brokers/bbroker.py in _execute(self, order, ago, price, cash, position, dtcoc)
749 # Adjust to returned value for closed items & acquired opened items
750 if self.p.shortcash:
--> 751 closedvalue = comminfo.getvaluesize(-closed, pprice_orig)
752 else:
753 closedvalue = comminfo.getoperationcost(closed, pprice_orig)
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/comminfo.py in getvaluesize(self, size, price)
210 return abs(size) * self.get_margin(price)
211
--> 212 return size * price
213
214 def getvalue(self, position, price):
ValueError: cannot copy sequence with size 1047 to array axis with dimension 8954
Can anyone help with this?
Making my account extremely large did solve the issue.
Alternatively I can divide close by 10**10
Thanks
hi,
I receive this log resopnse:
2019-06-25, Close, 5821658823529.41
2019-06-25, Close, 5827325581395.35
2019-06-25, Order Canceled/Margin/Rejected
2019-06-25, Close, 5831271259418.73
2019-06-25, Order Canceled/Margin/Rejected
class CustomDataLoader(btfeeds.PandasData):
lines = ('g_p',)
params = (
#('open', 'Open'),
#('high', 'High'),
#('low', 'Low'),
#('close', 'Close'),
#('volume', None),
#('openinterest', None),
('g_prediction', -1),
)
class TestStrategy(bt.Strategy):
def __init__(self):
self.close = self.datas[0].close
self.gp = self.datas[0].g_p
self.order = None
self.buyprice = None
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 next(self):
self.log('Close, %.2f' % self.close[0])
if self.order:
return
if self.gp == 0:
if self.position:
self.order = self.sell()
if self.gp == 1:
#self.buy(stake = 1, exectype=Order.Market)
#print('%03d %f' % (len(self), self.data.l.close[0],))
self.order = self.buy()
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log('BUY EXECUTED, Price: %.2f' %(order.executed.price))
self.buyprice = order.executed.price
#self.buycomm = order.executed.comm
else: # Sell
self.log('SELL EXECUTED, Price: %.2f' %(order.executed.price))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
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))
cerebro = bt.Cerebro()
data = CustomDataLoader(dataname= df5, plot=True)
cerebro.addstrategy(TestStrategy)
cerebro.adddata(data)
cerebro.broker.setcash(100000.0)
cerebro.addsizer(bt.sizers.FixedSize, stake=10)
cerebro.broker.setcommission(commission=0.001)
cerebro.run()
cerebro.broker.getvalue()
running on jupyter.
I assume I am missing out on setting something but I have used the generic strat in the quickstart guide as an example
Hi I am getting this backtrace when I try and plot withing jupyter.
Any ideas?
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-256-b2b5b6d55a52> in <module>
6 cerebro.run()
7 cerebro.broker.getvalue()
----> 8 cerebro.plot()
/data/NFS/akhil/python-env/lib64/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
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/plot/plot.py in plot(self, strategy, figid, numfigs, iplot, start, end, **kwargs)
218 downinds=self.dplotsdown[ind])
219
--> 220 self.plotdata(data, self.dplotsover[data])
221
222 for ind in self.dplotsdown[data]:
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/plot/plot.py in plotdata(self, data, indicators)
667 if self.pinf.sch.volume and voloverlay:
668 volplot = self.plotvolume(
--> 669 data, opens, highs, lows, closes, volumes, vollabel)
670 axvol = self.pinf.daxis[data.volume]
671 ax = axvol.twinx()
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/backtrader/plot/plot.py in plotvolume(self, data, opens, highs, lows, closes, volumes, label)
609
610 volylim /= self.pinf.sch.volscaling
--> 611 ax.set_ylim(0, volylim, auto=True)
612 else:
613 # plot a legend
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/matplotlib-3.2.1-py3.6-linux-x86_64.egg/matplotlib/axes/_base.py in set_ylim(self, bottom, top, emit, auto, ymin, ymax)
3573 self._process_unit_info(ydata=(bottom, top))
3574 bottom = self._validate_converted_limits(bottom, self.convert_yunits)
-> 3575 top = self._validate_converted_limits(top, self.convert_yunits)
3576
3577 if bottom is None or top is None:
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/matplotlib-3.2.1-py3.6-linux-x86_64.egg/matplotlib/axes/_base.py in _validate_converted_limits(self, limit, convert)
3099 if (isinstance(converted_limit, Real)
3100 and not np.isfinite(converted_limit)):
-> 3101 raise ValueError("Axis limits cannot be NaN or Inf")
3102 return converted_limit
3103
ValueError: Axis limits cannot be NaN or Inf
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
339 pass
340 else:
--> 341 return printer(obj)
342 # Finally look for special method names
343 method = get_real_method(obj, self.print_method)
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
246
247 if 'png' in formats:
--> 248 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
249 if 'retina' in formats or 'png2x' in formats:
250 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
130 FigureCanvasBase(fig)
131
--> 132 fig.canvas.print_figure(bytes_io, **kw)
133 data = bytes_io.getvalue()
134 if fmt == 'svg':
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/matplotlib-3.2.1-py3.6-linux-x86_64.egg/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)
2103 orientation=orientation,
2104 bbox_inches_restore=_bbox_inches_restore,
-> 2105 **kwargs)
2106 finally:
2107 if bbox_inches and restore_bbox:
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/matplotlib-3.2.1-py3.6-linux-x86_64.egg/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)
512 }
513
--> 514 FigureCanvasAgg.draw(self)
515 if pil_kwargs is not None:
516 from PIL import Image
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/matplotlib-3.2.1-py3.6-linux-x86_64.egg/matplotlib/backends/backend_agg.py in draw(self)
386 Draw the figure using the renderer.
387 """
--> 388 self.renderer = self.get_renderer(cleared=True)
389 # Acquire a lock on the shared font cache.
390 with RendererAgg.lock, \
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/matplotlib-3.2.1-py3.6-linux-x86_64.egg/matplotlib/backends/backend_webagg_core.py in get_renderer(self, cleared)
224 if need_new_renderer:
225 self._renderer = backend_agg.RendererAgg(
--> 226 w, h, self.figure.dpi)
227 self._last_renderer = backend_agg.RendererAgg(
228 w, h, self.figure.dpi)
/data/NFS/akhil/python-env/lib64/python3.6/site-packages/matplotlib-3.2.1-py3.6-linux-x86_64.egg/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi)
90 self.width = width
91 self.height = height
---> 92 self._renderer = _RendererAgg(int(width), int(height), dpi)
93 self._filter_renderers = []
94
ValueError: Image size of 191472350x251 pixels is too large. It must be less than 2^16 in each direction.
<Figure size 432x288 with 3 Axes>
Thanks guys that puts me on the right track.
Hi,
Looking to use use backtrader as a quick way of prototyping signals.
If I have a data frame with mid price of closes and some (one for simplicity) signals, how do I pass them to backtrader. I assume
I do not need to build a custom indicator and instantiate them to do this but in effect just pass the frame directly and iterate over it.