Best posts made by backtrader

1.9.60.122

• Remove unused files
• README update, Docstring corrections, documentation corrections
• Update travis settings

Available via pip

Thank you for the message. All options are being considered (messages from other threads were also conveyed)

1.9.66.122

• Fix regression introduced with 8f537a1c2c271eb5cfc592b373697732597d26d6
  which voids the count of lost trades
• Allow rollover to distinguish between no values temporarily (with None)
  and no values permanently (with False)
• Avoid math domain error for negative returns in logarithmic calculations
• Fix local variable declaration for compound returns
• Fix typo in date2num tz conversion which shows up in direct usage

A summary of what's on the table:

Time Scheduled Functions/Callbacks [Done]

• Functions will be called at specific times (periodical calling like ... "every 60 minutes starting at 08:30" may be considered)

• Timezone considerations

  • The strategy shows the time UTC-like format because it it not tz bounded like the data feeds
  • pytz doesn't have a notion of local timezone. This poses a small challenged when it comes to accept times which are referring to the actual local time (it may not seem so in real time, but it is in backtesting, in which the datetime reference is marked by the data feeds being backtested)
  • And even if local time is available, it may not be the wished reference, because the traded data feed can be in a different timezone (some of the use cases show 3 timezones in play: user, data 1 and data 2)

• The best possible place to add the scheduling seems to be in the strategy's __init__ method and as such should be a method of Strategy

• This doesn't preclude having a global scheduling which could be entered in cerebro to control any strategy.

Collection of numpy/pandas/statsmodel dependent indicators [Done]

• The discussions here have given birth to some.

• So far none has been added to the package for a good reason: backtrader was meant to be a pure Python package, in the sense that it wouldn't need any external dependencies beyond a regular Python distribution.

  (With the exception of matplotlib if plotting was wished)

• But at some point in time and even it not based on pandas or numpy arrays, those indicators should make it into the main distribution

Reverting Resampling/Replaying to the original model [Discarded]

• The original mode was to create another data feed which would then deliver own bars
• At some point in time both features were rewritten as filters manipulating the data stream, which has proven to be prone to quirks, problems and complicated code
• It seems sensible to rewrite the functionalities again as originally envisioned

Maybe

• Genetic Optimization

v2

• Once the above items are done ... v2 can be kickstarted to try to

  • Apply the same general ideas with regards to ease of use
  • Increase speed

The event is over ...

• We watched the Champions League Final
• We ate Bavarian food

and we even ...

• Did some backtrading

A repository with the snippets:

• GitHub - BacktraderCon

There you will also find a PDF with the notes that were taking during the event (including the pictures)

• GitHub - BacktraderCon 2017 Notes

And of course ... the best picture ever

Some of the contributions which were in the queue would for sure be nice additions for some users of `backtrader´. In order to allow for a seamless integration of those for as many people as possible, the following approach will be taken

• A side project named backtrader_contrib will be created

• This project will accept pull requests

• Elements submitted (indicators, analyzers, ...) will be injected into the main backtrader package

  • If a component generates an exception during importing it won't be injected

• The licensing is left open to the user with one exception: no GPLv2. Being the reason that some licenses like Apache License 2.0 is not compatible with GPLv2, but it is with GPLv3

  See: GNU - A Quick Guide to GPLv3 and GNU - Various Licenses and Comments about Them

  The repository will contain the license text for GPLv3, ASF 2.0, MIT, BSD

• The repository will be published as a pip package on a regular basis

The native Interactive Brokers TWS Python API uses type annotations which is not supported in Python 2.7.

Maybe something to consider for backtrader 2

A project is being considered now, that would imply adding support. To be decided in the next days

1.9.62.122

• Correct PSAR acceleration capping
• Enable PandasData line extension without the need to extend datafields

A couple of weeks later after we kick-started the idea of Algotopian and after having evaluated the interest from users, in the form of registrations, investment interest and team participation, we have made
a final decision with regards to the project.

Although the number of registered people exceeds our expectation, this number doesn't match the bare minimum needed with regards to investment.

It wasn't our intention to burn the names of agreed team members, advisors and an additional founder, because as we are doing, we wanted to be able to quietly cancel the project, without also having engaged into any kind
of funds collection.

We'll probably recycle the name Algotopian in connection to the further development of backtrader and potential services around it. But at the moment, the project is officially cancelled.

Thank you for your interest and best regards.

Use the length of the data feed: len(data1). It will only change when new data is available.

This is a very recent error introduced with this commit: https://github.com/backtrader/backtrader/commit/8f537a1c2c271eb5cfc592b373697732597d26d6

In attempting to fix the bool problem if you only had 1 trade the proper not to identify lost trades was lost.

This is now fixed in this push to the development branch: https://github.com/backtrader/backtrader/commit/cc2751a5f53166f68c5340eb876579f1a5590bf5

1.9.67.122

• Fix compression only scenarios when resampling and resampling after
  changes in 1.9.66.122
• Final correction for rollover fix introduced in 1.9.66.122
• Cover use case for mininum period calculation when all
  operations/indicators don't use the data feeds directly but lines of it

1.9.68.122

• [PR 376] Fix call to _nextday in TradingCalendar
• Clean up and rework of Yahoo Data. The data feeds seems to be reliable
  again
• IBStore Support for IND prices (simplfication of PR 373) with support to
  disable it

It would seem that downloading from Yahoo is not full of quirks and column swaps anymore. The feed has been cleaned up, removing traces of the original API and making it clearer and with more parameters where possible. The README in the repositovy has also been updated to reflect this fact.

You increase the number of objects you create.

Why should things run at the same speed regardless of the number of objects? It would be an incredible feat.

In your example you create one hundred thousand (100000) simple moving averages. Are you expecting that nothing happens with them during program execution?

The github tracker was becoming a type of bulletin board with questions and requests intermixed with any real issues.

And some users were even answering others' questions and even fulfilling some coding requests.

Hence the decision to try to unify everything under a community board which anyone can look into and where those questions, doubts and others can be better followed and managed.

It will hopefully be of help for those who like backtrader and find it useful

Hi,

• Using multiple strategies is posible
• Multiple assets and multiple timeframes too

A sizer can be used as a portfolio manager in the sense that it can:

• Modify and even invalidate (not authorize) an order by returning the size, which can be 0.
• The sizer has access to the strategy in which is running and the associated broker and with it to the universe of assets and for example net liquidation value

This means complex logic can be implemented to support the mentioned criteria: Margins, Var.

What a sizer cannot do:

• Order issuing. This logic is supposed to take place before the sizer is consulted
• Re-balancing of portfolios for example

A fully fledged portfolio manager would require development (not light for sure) and popular support (or other art of) for such a feature would be needed.

The solution above is for sure one that works. There are alternatives.

For example, Sizer` instances have a specific attribute which was thought for such thing.

• strategy (See the docs: Sizers - Smart Stakng )

Strategy and Cerebro instances share the same array of data feeds. With that in mind the code above could look different.

class VaRContracts(bt.Sizer):
    params = (('percent', 1),
              ('leverage', 40000),
              ('scaled', None),
              ('reverse', False),
              ('didx', 0),
    )

    def __init__(self):
        self._data = self.strategy.datas[self.p.didx]

    def _getsizing(self, comminfo, cash, data, isbuy):
        self.pchg = self.calc_pchg(self._data.close.get(size=51))

This uses an numeric index to address the array.

Using names could make even more sense. This has the advantage that you don't need to have created the data feed before adding the sizer.

cerebro.addsizer(lsizers.VaRContracts, percent=1, leverage=args.leverage,
                               scaled=args.scaled, dataname='SPY')

cerebro.adddata(data0, name='SPY')

class VaRContracts(bt.Sizer):
    params = (('percent', 1),
              ('leverage', 40000),
              ('scaled', None),
              ('reverse', False),
              ('dataname', None),
    )

    def __init__(self):
        self._data = self.strategy.getdatabyname(self.p.dataname)

    def _getsizing(self, comminfo, cash, data, isbuy):
        self.pchg = self.calc_pchg(self._data.close.get(size=51))

1.9.23.105

• Benchmark observer will observer after the strategy has reached its
  minimum period
• Refactoring of TimeFrameAnalyzerBase
• Ensure NoTimeFrame name is always returned rightly
• Documentation updates
• btrun will only load data feeds if they can be imported

One of the design goals of backtrader was to be able to quickly prototype things and that's why one can define and redefine what's in the data sources and things derived from the data sources.

If your data already contains the information in the csv format, you simply need to extend one of the CSV data feeds to load your extra column and it will be available to you.

This topic is therefore of course covered in the documentation: Extending a Data Feed

You still need to tell CSVGenericData where your fields are. Have a look for it at Data Feed Reference

There is a blog post documenting the birth of GenericCSVData: Blog Post

In addition and maybe to full simulate an indicator you can wrap the extra data value in an indicator:

class MyIndicator(bt.Indicator):
    lines = ('myline',)

    def __init__(self):
        self.lines.myline = self.data.myvaluename

Where myvaluename is the name you will assign to the extra data value present in your feed.