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

One of the latest additions lets you do direct time comparisons directly to lines to generate signals. Probably nobody has used it yet. An example:

class MySignal(bt.Indicator): lines = ('mysignal',) def __init__(self): self.lines.mysignal = self.data.datetime > datetime.time(10, 30)

This generates an indicator which can be used as a signal to only execute orders when the daily time goes over 10:30.

But since you are probably looking for day, you'll need to work it out in the next method comparing the self.data.datetime.date() (maybe even getting the weekday) against something of your choosing

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

@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?

@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.

@amit Have a look here for highest and lowest.

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

@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.

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.

@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..

@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.

@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.

@Giovanni-Caridi said in No trades happened:

I didn't think bad formatting could lead to coding errors

Python is highly space/formatting dependent. Try downloading black formatter. It automatically formats your code and will try to help you if you are off.

@lifeng This is not actually the same problem, but nevertheless it was answered in your other post:
https://community.backtrader.com/topic/3411/data-feed-format/6

Note: Please refrain from asking the same question in different posts next time.

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'])

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 ?

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

Sure, send me an email. Mine email is in my profile.

@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!

@run-out alright... I think I got it!

class Win_Buy(bt.Strategy): params = (("period", 5),) 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 __init__(self): self.risk = 0.01 self.diff = self.data - self.data(-1) self.win_record = self.data(0) > self.data(-1) self.prob_win = bt.ind.SumN(self.win_record, period=self.p.period) self.prob_loss = self.p.period - self.prob_win self.wins = bt.ind.SumN( bt.If(self.diff >= 0, self.diff, 0), period=self.p.period) self.losses = bt.ind.SumN( bt.If(self.diff <0, -self.diff,0), period=self.p.period) self.avg_win = bt.DivByZero(self.wins, self.prob_win, 0) self.avg_loss = bt.DivByZero(self.losses, self.prob_loss, 0) self.avg_risk = bt.ind.Average(self.data.close*self.risk, period=self.p.period) def next(self): self.dsharpe = self.avg_win[0] / (self.avg_risk[0]) if self.dsharpe > 1: # previous close less than the previous close # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.data[0]) self.buy() else: pass

With Prints...

2018-01-09, c 24.37 diff 0.02, winr 1.00, pwin 3.00, ploss 2.00, wins 0.25, plosses 0.14, avg_win 0.08, avg_loss 0.07, avg_risk 0.24, DSHAREP 0.34 2018-01-10, c 24.25 diff -0.12, winr 0.00, pwin 2.00, ploss 3.00, wins 0.12, plosses 0.26, avg_win 0.06, avg_loss 0.09, avg_risk 0.24, DSHAREP 0.25 2018-01-11, c 24.32 diff 0.07, winr 1.00, pwin 2.00, ploss 3.00, wins 0.09, plosses 0.26, avg_win 0.05, avg_loss 0.09, avg_risk 0.24, DSHAREP 0.18 2018-01-12, c 24.34 diff 0.02, winr 1.00, pwin 3.00, ploss 2.00, wins 0.11, plosses 0.18, avg_win 0.04, avg_loss 0.09, avg_risk 0.24, DSHAREP 0.15 2018-01-15, c 24.40 diff 0.06, winr 1.00, pwin 4.00, ploss 1.00, wins 0.17, plosses 0.12, avg_win 0.04, avg_loss 0.12, avg_risk 0.24, DSHAREP 0.17 2018-01-16, c 24.31 diff -0.09, winr 0.00, pwin 3.00, ploss 2.00, wins 0.15, plosses 0.21, avg_win 0.05, avg_loss 0.11, avg_risk 0.24, DSHAREP 0.21 2018-01-17, c 24.35 diff 0.04, winr 1.00, pwin 4.00, ploss 1.00, wins 0.19, plosses 0.09, avg_win 0.05, avg_loss 0.09, avg_risk 0.24, DSHAREP 0.20

Does this seem right?
Also if I just wanted to use DSharpe as a bt.Indicator would i just change the class from Strategy to Indicator? and the purchase metrics?