- Remove unused files
- README update, Docstring corrections, documentation corrections
- Update travis settings
Available via pip
Because the analyzer has not been able to calculate any
If you only have 1 year of data and taking into account that the default timeframe for the calculation is
years, no calculation can take place. Sharpe needs at least 2 samples for the given timeframe, to calculate the variance.
Rather than using
AnnualReturn, consider using
TimeReturn and specify the actual target timeframe.
Let's try to summarize:
Non-Live Data feeds have well defined interfaces and it is documented
You only have to override
_load(self), which will be in charge of loading the values into the lines of the data series (in most cases these will be:
volume and for futures
True if it has been able to fill the next set of values or
False to indicate the end of the data stream.
What's left for the implementer: the decision as to how
_load(self) receives the values from the feed. Examples:
From a file: for each call to
_load you can simply read the next line, process the data, fill the lines and return, until
EOF is met.
There is even documentation of how to do it for a binary file Docs - Binary Datafeed Development
This has been for example generalized for
CSV-based sources by adding a
_loadline method, which receives the line broken down into tokens. In this case only overriding of
_loadline is needed. See Docs - CSV Data Feed Development
Live Data Feeds could have a couple of things added
tzoffset, the chosen pattern for data reception and the extra return value for
_load which is
None to indicate that the data feed has currently nothing to deliver but could have it later (the data stream is active but has not come to an end)
The problem with the rest ... it is provider dependent. And the hackhaton (aka BacktraderCon 2017) last weekend has proven it. The initial implementation the guys had followed the guidelines from the Oanda implementation, but because the provider (Kraken) only offers polling and has low limits for its Rate Limiting policy, everything is a historical download at the end of the day. Suddenly instead of 2 queues, both queues are the same but the usage is different.
Broker have a well defined interface
Here it is really a matter of work in the store, which offers a private interface to the broker. Through this interface for example the store will convert a order from backtrader to an order which is palatable by the real broker. On the way back, broker notifications have to be adapted, to change the status of orders appropriately.
A kind of paper to give guidelines can be considered (will be done so to say), but at the end of the day and the guys from the BacktraderCon could tell a lot about it, it's about the very small details from each broker.
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
tzbounded like the data feeds
pytzdoesn'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)
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
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
numpy arrays, those indicators should make it into the main distribution
Reverting Resampling/Replaying to the original model [Discarded]
Once the above items are done ... v2 can be kickstarted to try to
Using: [bt.utils.date.num2date(date) for date in self.datas.datetime.get(size=150)]
In place of: self.datas.datetime.get(size=150)
This is partially correct. If you are working with timezones, that will only give you
UTC time. The correct form would be:
current_datetime = self.data.num2date()
those_150_datetimes = [self.data.num2date(x) for x in self.data.datetime.get(size=150)]
The event is over ...
and we even ...
A repository with the snippets:
There you will also find a PDF with the notes that were taking during the event (including the pictures)
And of course ... the best picture ever
The x-axis is there (luckily, because if not everything would be displayed on a singularity rim and we should fear a potential earth implosion into such an event) but the x-ticks (i.e: timestamps in this case) are not displayed in the latest latest version of
The previous stable version
3.0.3 and the last LTS version
2.2.4 do display the x-ticks.
pip install --force matplotlib==YOUR-PREFERRED-WORKING-VERSION
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
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
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
I have just read this tread and you statement
Since no one I guess has a full understanding of the platform yet, we probably should limit ourselves to bug fixing only, at least initially - and see how it goes.
This is probably spot on and something I have failed to realize. Since your fork doesn't yet contain any changes, I am adding a few bug fixes to the main repository and release a new version with them.
Note: 6219.15 is the close price of the 1h data at 2020-03-20 01:00:00, while 6219.15 is also the open price of the 1min data
No. There is no such thing as the closing price of the
1-hour data, because that data doesn't exist. Naming things properly does help.
Additionally the information you provide is wrong, which is confirmed by looking at your data.
Note for the other readers: for whatever the reason, this data defies all established standards and has the following format:
Your data indicates that
6219.15is the closing price of the
00:59:00, hence the last bar to go into the resampling for
00:59:00(60 bars -if all are present-) which is first delivered to you as a resampled bar at
6219.15is the opening price of the
01:00:00 you have two bits of information available:
1-hourresampled data for the period
Given how you pass it ... you could probably try this better
def notify_timer(self, timer, when, timername): if timername=='buytimer': self.buy_stocks() elif timername=='selltimer': self.sell_stocks()
given that you have no additional
**kwargs. But a more general approach I would suggest this
def notify_timer(self, timer, when, **kwargs): timername = kwargs.get('timername', None) if timername=='buytimer': self.buy_stocks() elif timername=='selltimer': self.sell_stocks()
where you can also decide if a default logic for
None is needed.
Note: notice the usage of
def next(self): self.date = self.data.datetime date = bt.num2date(self.date).date() prev_sentiment = self.sentiment if date in date_sentiment: self.sentiment = date_sentiment[date] self.lines.sentiment = self.sentiment
In any case that's probably where the error happens. There is no definition of
self.sentiment and the indicator understands you are looking for the line named
sentiment. The problems
Any input would be greatly appreciated
But you provide no input with regards to the error. Only
TypeError: must be real number, not LineBuffer
Python exceptions provided a stacktrace which points to the different line numbers in the stack ... allowing to trace the error to the origin point (and showing any potential intermediate conflict)
The error is only telling us that you are passing an object (a
LineBuffer) there where a
float should have been passed by you. But not where you are doing it ... which is in the stacktrace ...
You have to use this:
The built-in module is only compatible with the old
oandatest.py works with the old built-in module.
Timers go to
notify_timer which receives the
when it is happening and any extra
**kwargs you may created the timer with. You can use any of the latter to differentiate timers.
Or you can simply use the
timer id to associate different logic to each timer.