Would be something like this

class PandasDirectData_NumPyLines(feed.DataBase): params = ( ('datetime', 0), ('open', 1), ('high', 2), ('low', 3), ('close', 4), ('volume', 5), ('openinterest', 6), ) datafields = [ 'datetime', 'open', 'high', 'low', 'close', 'volume', 'openinterest' ] def start(self): super(PandasDirectData_NumPyLines, self).start() self._df = self.p.dataname def preload(self): # Set the standard datafields - except for datetime for datafield in self.datafields[1:]: # get the column index colidx = getattr(self.params, datafield) if colidx < 0: # column not present -- skip continue l = getattr(self.lines, datafield) l.array = self._df.iloc[:, colidx] field0 = self.datafields[0] dts = pd.to_datetime(self.index) getattr(self.l, field0).array = dts.apply(date2num) self._last() self.home()

Where datetime is directly taken from the index. The default column offset for the other fields is probably 1-off because of it, but it can luckily be configured

The Pull Requests for InfluxDB have been integrated in the main release 1.9.30.111 (the dependencies have been rewritten as frompackages directives)

Nevermind, fixed the error by adding 'dataname=' in front of the name of the pandas df.
data = bt.feeds.PandasData(dataname=spx)

Thank you. Good to know.

Thanks Dro, I will give this a try today.

You can directly upload files into the thread. Not much input would be needed.

An alternative is to enclose the data (which is apparently csv) as a code block (with no python specifyer) and it will allow simple copy & paste.

The idea can in theory work, but needs a complete re-implementation of how the indicators work.

https://community.backtrader.com/topic/159/tws-api-python

Alright,
I got you.

Thanks for the prompt response.

BR,
dpetrov

Hello,

I have the following scenario in mind:

Pass the system the following inputs:

historical data in period duration (5Y) out period duration (1Y) parameters and ranges

Let's say we have 10 years worth of data, then the ideal processing would be as follows:

Break the data in multiple chunks according to the periods defined above In Period 1: 2000-2005 Out Period 1: 2005-2006 In Period 2: 2001:2006 Out Period 2: 2006-2007 In Period 3: 2002-2007 Out Period 3: 2007-2008

Than start executiing the execution.

Optimize parameters for first In Period (2000-2005) - cerebro.optstrategy() Get best parameters from previous run and test on Out Period 1 - cerebro.addstrategy() Optimize again on the next In Period Test with the parameters from previous step on the next out period Optimize again ...... Test again on the next Out Period .....

At the end plot the combined strategy during all out periods from 2005 to 2010 by taking into consideration that parameters are being changed every year with the optimized values from last 5 years worth of data.

I am open for any ideas for implementation.

So far I am thinking to read the data in pandas, break it and loop through it in chunks and feed it to cerebro consecuently.

I have no idea how to combine the outputs from each cerebro run and how to plot all individual strategy executions into a single figure.

BR,
dpetrov

@backtrader Why wouldn't you close the position with close?

already did that. I used order_target as my zipline code that I'm porting to backtrader used order_target* APIs.

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

figured that out. the stop price was actually triggered. The high for the bar is higher than the stop price. As always, the explanation is simple, but it takes time to find it out :)

The idea is always to have a generic model, like it is the case with the timezones, in which you may pass pytz instances or something that, more or less, complies to the interface of pytz.

A similar approach will be taken.

Should be (meaning it will be there as soon as possible, because it should be there in __init__ and not after it)

Note: with CFD products you should consider taking the interest into account for both long and short positions.

Note2: that's the problem with snippet typing (as opposed to testing it), they usually contain typos. And the same applied partially to the documentation. Both posts have been corrected to avoid future confusion and the docs updated.

checksubmit controls if a submitted order can be accepted by the broker by simulating execution and with it checking if the current cash reserves are enough. It actually pseudo-executes all pending orders, because an order could actually be 1st opening a short and increasing the pseudo-cash reserves.

But this is only pseudo-execution. When the actual execution takes place, the actual cash level is checked again and execution will be rejected if not possible.

There isn't at the moment

@backtrader if you don't mind I would correct your summary slightly:

@ab_trader is looking to use the signals and position sizes provided by bt, and mirror them to broker account by hands. bt will keep position sizes, cash, value and commissions.

In that scenario there is no need to load the positions, cause they are mirrored by trader and are the same in bt and at broker account. In case of the discrepancies bt strategy can be easily adjusted.

The broker is being used for signal and position sizes providing as well as for tracking of the cash and value (no need to support the real broker).

I believe this summary reflects my proposals better. :)