@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?

The problem is that the bricks need to be placed at a timestamp. At each timestamp you only have one output value for the lines of the data feed (in general these will be OHLC) In summary: you cannot therefore generate multiple bricks. Generating bricks of dissimilar sizes, what you apparently have already achieved, is your only possibility.

I ran some tests and for this case, we should use ago parameters as the starting point in "future" to look backwards, for example : print(f"{list(self.Ichimoku.senkou_span_a.get(ago= 3, size=4))}") which will yield the same result as : print(f"{[self.Ichimoku.senkou_span_a[i] for i in range(4)]}") However, If we pass ago=0 it will get the last 3 values, [0],[-1] ,[-2], which for this particular case is not in our interest. I checked the documentation in the Getting slice part and it makes sense. Thanks for the help.

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 :)

@dowdtw I figured it out. No Dramas.:)

@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.

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.

@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.

I am using this approach since a while now and I have to say that I am quite happy with it: https://github.com/verybadsoldier/backtrader/commit/f03a0ed115338ed8f074a942f6520b31c630bcfb Makes it possible for me to evaluate OptReturn objects without the need of any further input (like a cerebro object).

@ed-bartosh ccxt had some unique properties that made it possible to implement the broker straight from BrokerBase and give access to more then 1 exchange simultaneously in a way that feel very integrated and easy to use. remember that ccxt project is all about unifying the variety of exchanges under one api. and a key part of my development was that the feed had some exchange info inside it. but to make a more generic broker means to really create a broker that hold a collection of brokers inside (that are not really unified) and switching between them like you proposed before. The biggest problem this way, is that there are still methods that will get called by Backtrader in the background on the 'active' broker without the intention of the user. A better solution that may work more like what i wrote is if every feed will hold some broker information in a well known way and then you don't need to guess on what broker.

@ab_trader I see. I'll figure out what's best for me. Thanks!

@backtrader Yes,you are right!