@toinou222 instead of modifying the commission scheme you can simply increment the value of the account with every funding.

dt = self.datetime.datetime() if not self.position.size: # Entry logic ... else: # Collect funding rate = self.data._dataname.loc[dt].funding if not np.isnan(rate): funding = -self.position.size * rate self.broker.add_cash(funding) # Exit logic ...

Here I am assuming that you are using Pandas DataFeed and there exists a funding column in it with NaNs everywhere except at 0-8-16h. You can pull the historical data using Binance's API endpoint.

@hoflz5 after some more testing I can conclude that this works great. When using 1m data it is very close to the binance live trading stream (but not completely, I guess I would have to use tick data for that).

@ringtail One thing you can try is to check the length once you run the backtest and log it to terminal. This way you only fail once and can run the next test with the appropriate adjustements to start date.

Another thing I do to assist with this problem is to have a start date for data and a start date for trading. I make the room between these two more than enough for whatever strategy I'm running.

Start date for data is the one you are already using. Start date for trading you just put a check at the beginning of next and return if the start date is not met.

I think everything is much easier with travel companies. I have recently booked a trip via http://youtravel.me/. I think you can also check it out.

@csibi-levente14 AFAIU close method will just issue a sell/buy order to nullify (close) whatever position you have - so in case of long position the sell order will be issued, vice versa for short one.

BTW, the issued order is returned by the 'close' method - so you may examine what order was issued.

@tradersnorth said in Forex custom commission and multiplier:

cerebro.broker.setcommission(leverage=50, stocklike=False, commission=comm, mult=100)

AFAIU setcommision method just creates a generic CommInfoBase object using passed parameters to this method.

It means the commission parameter is expected to be (quoting from CommInfoBase docs):

``commission`` (def: ``0.0``): base commission value in percentage or monetary units

In your case, using cerebro.addcommissioninfo method is probably more appropriate. It allows passing a custom CommInfoBase inherited commission object.

I have resolved the issue sorry for the trouble !!.

I do this as follows:
class MyStrategy(bt.Strategy):
params = (('short', 5), ('long', 10))
def init(self):
self.inds = dict()
for i, d in enumerate(self.datas):
self.inds[d] = dict()
self.inds[d]['sma_s'] = bt.indicators.MovingAverageSimple(d.close, period=self.params.short)
self.inds[d]['sma_l'] = bt.indicators.MovingAverageSimple(d.close, period=self.params.long)
self.inds[d]['start_time'] = d.datetime.date(self.params.long)
self.inds[d]['start_flag'] = 0

def prenext(self):
for i in range(3):
if self.datas[i].datetime.date() == self.inds[self.datas[i]]['start_time']:
self.inds[self.datas[i]]['start_flag'] = 1
log_str = log_str + 'i={}, date={}, sma_s={}, sma_l={}\n'.format(i, self.datas[i].datetime.date(),
self.inds[self.datas[i]]['sma_s'][0],
self.inds[self.datas[i]]['sma_l'][0])
elif self.inds[self.datas[i]]['start_flag'] == 1:
log_str = log_str + 'i={}, date={}, sma_s={}, sma_l={}\n'.format(i, self.datas[i].datetime.date(),
self.inds[self.datas[i]]['sma_s'][0],
self.inds[self.datas[i]]['sma_l'][0])

prenext() process data when time is not the same. If the time is the same, then go to next().

@dasch
I logged the data and the first 3 trades don't happen when 5-min indicator is used and that's the difference between results.
Thanks for your help.

@sky_aka41 I think you are pretty close. I tried this all in a strategy (not a custom indicator) and it seems to be working.

Note I changed the prob_change and probability expressions slightly.

class St(bt.Strategy): params = (("momLength", 34), ("maLength", 55), ("pLength", 13)) def __init__(self) : self.momentum = bt.ind.Momentum(self.datas[0].close, period=self.p.momLength) self.accelerate = bt.ind.Momentum(self.momentum, period=self.p.momLength) self.prob_change = bt.ind.Momentum(self.datas[0].close, period=1) > 0 self.probability = bt.ind.SumN(self.prob_change) / self.p.pLength self.adjusted_close = self.datas[0].close + (self.momentum + self.accelerate*0.5 ) * self.probability self.lines.MaMA = bt.ind.SMA(self.adjusted_close, period=self.p.maLength)

@albertoburchi said in Store the results of a cerebro loop on a list of stock symbols:

print the analyzers printSQN(backtest_results.analyzers.sqn.get_analysis()) printTradeAnalysis(backtest_results.analyzers.ta.get_analysis())

At this point you are in more of a python situation than a backtrader situation. You will get dictionaries as results of the backtests and you need to decide how you want to save this information. I personally save the results to tabs for a spreadsheet for each analyzer, or also use a database like postgres. But really, the choice is yours.

@run-out said in Implementing Bracket Orders for binance API:

if order.status == order.Partial:
return

Should read:

if order.status == order.Partial: do stuff here

@davidavr Thank you !

@hanaan easier, maybe but yes it provides more or less the same information. Analysis of the resulting data and plotting (I use btplotting) may help to visualize the sometimes subtle differences.

@davidavr
Thanks for your response! I know better now!

@fanpeix Perhaps PctRank will get you close to the mark? If gives you the percent position but with bucket sizes based on the perdiod, I think.