@ab_trader true. anyway it's not what I'm looking for as commission is not included into calculation.

@Harel-Rozental said in Smart optimizations and Backtrader:

I managed to get my optimizations to run as quick as optstrategy by subclassing Cerebro and pulling out some stuff out of run() and addstrategy() into different functions (one to initialize data loading, and another which gets re-executed from outside the class for optimization).
I also manage multiprocessing from the outside.

Do you mind to share some code? Or kind of a layout of what and where was implemented in more details?

You can use it without being merged. Again, see: Community - EoDevelopment 1.x

@ab_trader - I am doing my best. I have already submitted pull requests for new Fisher Transform indicator and Simulated Order. And I am sharing all my code snippets here along with explanations whenever relevant.

When I say the framework should have this or that, I dont mean that someone should implement them but that we shall implement it for everybody's benefit.

@ab_trader DRo = Daniel Rodriguez for those that didn't immediately get, it took me a few secs :)

Hi,

i do like this, as suggested by @backtrader: I preprocess all my securities data including as an extra column their membership status. I do this while creating the survivorship bias free dataset.

In 'next' I look for it as it is another indicator. If True I trigger a EW target buy order otherwise a 0% target sell order.

@Laurent-Michelizza Thank you very much! when I log the position,I also find this question and use almost your way to solve it.
But in the future,I guess,we do a auto-close function when the future expires, it is more easy to use. After this four times reading the source code,I maybe try this function.

@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.

@vladisld said in Rollover multiple contracts [help needed]:

@chewbacca said in Rollover multiple contracts [help needed]:

File "/home/user/PycharmProjects/backtrader/Strategies/VixLive.py", line 615, in notify_timer
self.initial_position = self.position.size
File "/home/user/PycharmProjects/backtrader/venv/lib/python3.8/site-packages/backtrader/lineseries.py", line 461, in _ _ getattr _ _
return getattr(self.lines, name)
AttributeError: 'Lines_LineSeries_LineIterator_DataAccessor_Strateg' object has no attribute 'position'

The error backtrace above seems strange. Accessing the position property in the strategy should call the bt.Strategy's getposition method:

class Strategy: ... def getposition(self, data=None, broker=None): data = data if data is not None else self.datas[0] broker = broker or self.broker return broker.getposition(data) position = property(getposition)

This should be reflected in the error backtrace. It looks like the self.position was called from the indicator and not from the strategy.

It could be useful to if you could share the whole VixLive.py code?

By default data0 is used as the "primary", correct?
vx2_1_min = cerebro.rolloverdata(*rollfeeds_vx2_minute, **rollkwargs_vx2_minute) -- is data0 since it's called & added first.

Correct me if I'm wrong..

@run-out

Thanks again for the long reply!
I just tried cheat on open, but the result is the same.
I thought about including a buy margin and it seems this is the way to go... for that example...
Hence, i assume the orders are not done by theirs reference numbers otherwise order 75 would not be canceled?

Best

@amit Have a look here for highest and lowest.

@ssbilis No because were are dealing with the results of get which is a list. You want the last item in the list, which is -1

Hello @samk

This strategy might have some flaws but it shows quite well the intention to buy when the price is in bullish trend...

How would you change it to fit more your ideas ?

perfect, so this is it:

class CustomDataLoader(btfeeds.PandasData): lines = ('TOTAL_SCORE','Beta',) params = ( ('openinterest',None), #None= column not present ('TOTAL_SCORE',-1), ('Beta',-1) ) datafields = btfeeds.PandasData.datafields + (['TOTAL_SCORE','Beta'])

@RandyT said in Pyramiding support:
Thanks Randy for getting back.

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().

@steffen

Thanks for the link. I was thinking to create simple strategy in bt which will go thru all of the contracts and return me continuous contract data feed built by my rules.

Since I am using daily bars I am not so concerned about live trading right now. I is beneficial, but not mandatory.

Here is the approach - compare length of the daily data feed on each next() call. This will help to avoid excessive next() calls due to resampling. You might be able to come up with the only next() call at the end of the week.

class MasterStrategy(bt.Strategy): def __init__(self): self.daily_length = 0 def next(self): if len(self.datas[0]) > self.daily_length: print('daily:{} len: {} | weekly:{} len: {} | monthly:{} len: {}'\ .format(bt.num2date(self.datas[0].datetime[0]), len(self.datas[0]),\ bt.num2date(self.datas[1].datetime[0]), len(self.datas[1]),\ bt.num2date(self.datas[2].datetime[0]), len(self.datas[2]))) self.daily_length = len(self.datas[0])

Output:

daily:2017-10-16 23:59:59.999989 len: 98 | weekly:2017-10-13 23:59:59.999989 len: 20 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-17 23:59:59.999989 len: 99 | weekly:2017-10-13 23:59:59.999989 len: 20 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-18 23:59:59.999989 len: 100 | weekly:2017-10-13 23:59:59.999989 len: 20 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-19 23:59:59.999989 len: 101 | weekly:2017-10-13 23:59:59.999989 len: 20 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-20 23:59:59.999989 len: 102 | weekly:2017-10-13 23:59:59.999989 len: 20 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-23 23:59:59.999989 len: 103 | weekly:2017-10-20 23:59:59.999989 len: 21 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-24 23:59:59.999989 len: 104 | weekly:2017-10-20 23:59:59.999989 len: 21 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-25 23:59:59.999989 len: 105 | weekly:2017-10-20 23:59:59.999989 len: 21 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-26 23:59:59.999989 len: 106 | weekly:2017-10-20 23:59:59.999989 len: 21 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-27 23:59:59.999989 len: 107 | weekly:2017-10-20 23:59:59.999989 len: 21 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-30 23:59:59.999989 len: 108 | weekly:2017-10-27 23:59:59.999989 len: 22 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-10-31 23:59:59.999989 len: 109 | weekly:2017-10-27 23:59:59.999989 len: 22 | monthly:2017-09-29 23:59:59.999989 len: 5 daily:2017-11-01 23:59:59.999989 len: 110 | weekly:2017-10-27 23:59:59.999989 len: 22 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-02 23:59:59.999989 len: 111 | weekly:2017-10-27 23:59:59.999989 len: 22 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-03 23:59:59.999989 len: 112 | weekly:2017-10-27 23:59:59.999989 len: 22 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-06 23:59:59.999989 len: 113 | weekly:2017-11-03 23:59:59.999989 len: 23 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-07 23:59:59.999989 len: 114 | weekly:2017-11-03 23:59:59.999989 len: 23 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-08 23:59:59.999989 len: 115 | weekly:2017-11-03 23:59:59.999989 len: 23 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-09 23:59:59.999989 len: 116 | weekly:2017-11-03 23:59:59.999989 len: 23 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-10 23:59:59.999989 len: 117 | weekly:2017-11-03 23:59:59.999989 len: 23 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-13 23:59:59.999989 len: 118 | weekly:2017-11-10 23:59:59.999989 len: 24 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-14 23:59:59.999989 len: 119 | weekly:2017-11-10 23:59:59.999989 len: 24 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-15 23:59:59.999989 len: 120 | weekly:2017-11-10 23:59:59.999989 len: 24 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-16 23:59:59.999989 len: 121 | weekly:2017-11-10 23:59:59.999989 len: 24 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-17 23:59:59.999989 len: 122 | weekly:2017-11-10 23:59:59.999989 len: 24 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-20 23:59:59.999989 len: 123 | weekly:2017-11-17 23:59:59.999989 len: 25 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-21 23:59:59.999989 len: 124 | weekly:2017-11-17 23:59:59.999989 len: 25 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-22 23:59:59.999989 len: 125 | weekly:2017-11-17 23:59:59.999989 len: 25 | monthly:2017-10-31 23:59:59.999989 len: 6 daily:2017-11-24 23:59:59.999989 len: 126 | weekly:2017-11-17 23:59:59.999989 len: 25 | monthly:2017-10-31 23:59:59.999989 len: 6

@USMacScientist HI! I am few days new to this.

I have your zig zag called in strategy class like any other indicator in quickstart guide. The chart produced is great! I appreciate you sharing your code. I produce the chart as well which is great.

self.zz = ZigZag(self.data, plotname='ZZ')

I like to humbly ask you how do I access your other lines in this study such as last_pivor_t etc?
I am trying to access the values stored in these lines. Thanks!

@sunnyalgotrader Hmm... I must be crazy because now I see only entry bars.