Quick fix, though in no way tested:

in stores/ibstore.py, lines 725, 751 and 836, add 'CMDTY', etc. if contract.m_secType in ['CASH', 'CFD', 'CMDTY']: .. when defining the data feed, use full kwargs, etc. data = ibstore.getdata( dataname = 'XAUUSD', sectype = 'CMDTY', exchange = "SMART", currency = "USD", )

See this update for: Blog - Strategy Selection Revisited

Thanks @backtrader and @ab_trader for the suggestion.

There are no restrictions as to what you can test with backtrader. And the conditions and requirements of (almost?) any financial instrument can be simulated.

Account Leverage Asset margin requirements Asset multiplier Nominal value Shorting with interest Others

In most cases choosing a predefined commission scheme and configuring it is enough:

Docs - Commissions: Stocks vs Futures Docs - Extending Commissions Docs - User Defined Commissions Docs - Commissions: Credit

What backtrader for sure isn't is a data provider. You say

The thing I noted though is that most libraries/platform that make use of Python are focused on equities and US equities in particular

You may be talking about things like Quantopian (which now offers not only US equities but Futures) in which the online platform automatically provides you with data for US Equities.

In backtrader you need to provide the data yourself. There are many ways os instantiating a data source, including online sources, or you can provide yours. See

Docs - Data Feeds Reference Docs - CSV Data Feed Development Docs - Binary Datafeed Development Docs - Extending a Datafeed Docs - Pandas DataFeed Example

@backtrader That was it. Thank you.

No.

See here: https://github.com/blampe/IbPy/issues/32

@backtrader Thank you, fixed.

@etcetc said in Oanda live data - open, close, high, low are the same:

Every printed line is still hourly, even though I would expect these lines to print out 60 times for each "hour".

60 minutes is 1 hour and that's the backfilling before the data goes into live.

In any case your strategy will not be called 60 times every hour

It will be called as many times as ticks are received by the system. Depending on how active the market is ... that could well be some thousand times ... until the hour is complete and the next bar starts.

Thanks @backtrader !!

Those projects and the blog post are describing the behavior of Limit Order Books. Of course when you have real-time you can monitor each and every change of those and see how things are executed (and even estimate how things are going to be executed)

Not bearing any relationship to timeframes (minutes, hours, ...), because you see every change (ticks) is the problem.

Let's imagine the scenario:

You pass 1-minute data to backtrader A set of indicators gives you the signal to enter the market long (buy) You fire the order in backtrader

And this is what happens:

backtrader gets the next 1-minute bar And it tries to see, depending on the execution type, if the order can be matched against the range of prices from that bar

With, for example, Limit orders:

The matching price can be at many points in the bar

Now the question:

Are you going to provide a Limit Order Book for each an every possible price in the 1-minute bar? (Luckily it's not an infinite number, but depending on the range it can be a huge amount of entries in the Limit Order Book)

From a practical point of view the cost of the transaction and simulating the ask-bid spread effect can be achieved by including slippage (which is already supported by the broker)

You are being fooled by two factors:

Yourself (it happens during the development of backtrader all the time and even more often than that)

and

Data Preloading

1. Why Yourself?

prenext is where you can do dirty things, because it happens before the next phase is in effect. Data feeds and indicators may not be delivering values (or meaningful ones) because the corresponding minimum periods to deliver have not yet been met.

That means that accessing values should be done carefully and with several safeguarding measures in place. The most usual measure is to check if the object which is going to be checked has a positive length. As in:

def prenext(self): if len(self.data1): # is not zero print('it is then guaranteed that it has something like:', self.data.close[0])

Because the data feeds have different starting points they will start delivering at different times.

2. Why Data Preloading?

Because no safeguards are in place and the default (to speed up things) is to preload the data before doing anything, the python arithmetic for array indexing is taking you somewhere ... which happens to be the last value of the array.

The lifecycle of a Strategy is documented: Docs - Strategy

The SharpeRatio analyzer doesn't do rolling calculations, it does the calculation when everything is over.

You can subclass it, override __init__ and create the sub analyzer TimeReturn analyzer with your _doprenext of choice.

@gminorcoles I am just a user of the bt, not an owner. Sometimes I share pieces of the code. @backtrader is an owner.

if you want/can offer any enhancements, then just create pull request with that enhancement. @backtrader will check it and incorporate if it will be suitable from his point of view.

I only just got around to looking at this again. If anyone else, has the same requirement, the code I developed is as follows:

class stampDutyCommisionScheme(bt.CommInfoBase): ''' This commission scheme uses a fixed commission and stamp duty for share purchases. Share sales are subject only to the fixed commission. The scheme is intended for trading UK equities on the main market. ''' params = ( ('stamp_duty', 0.005), ('commission', 5), ('stocklike', True), ('commtype', bt.CommInfoBase.COMM_FIXED), ) def _getcommission(self, size, price, pseudoexec): ''' If size is greater than 0, this indicates a long / buying of shares. If size is less than 0, it idicates a short / selling of shares. ''' if size > 0: return self.p.commission + (size * price * self.p.stamp_duty) elif size < 0: return self.p.commission else: return 0 #just in case for some reason the size is 0.

Cheers

ok, thanks, i will wait impatiently this feature:

class St(bt.Strategy): def buildTimers(self): #Start trading timers={} timerStart=self.add_timer( when=self.p.start, offset=self.p.offset, repeat=self.p.repeat, weekdays=self.p.weekdays, ) timers['timerStart']=timerStart.p.tid #Timer Bin Size timerBinSize=self.add_timer( when=self.p.start, offset=self.p.offset, repeat=datetime.timedelta(seconds=self.bin_size), weekdays=self.p.weekdays, ) timers['timerBinSize']=timerBinSize.p.tid #Start trading timerEnd=self.add_timer( when=self.p.end, offset=self.p.offset, repeat=self.p.repeat, weekdays=self.p.weekdays, ) timers['timerEnd']=timerEnd.p.tid return timers def __init__(self): self.bin_size=self.p.bin_size self.limit_orders = [] self.order_book=None self.timers=self.buildTimers() def build_timer2(self): timerDimingFrequency=self.add_timer( when=self.p.start, offset=self.p.offset, repeat=datetime.timedelta(seconds=self.bin_size), weekdays=self.p.weekdays, ) self.timers['timerDimingFrequency']=timerDimingFrequency.p.tid def notify_timer(self, timer, when, *args, **kwargs): for key, value in self.timers.copy().items(): print(key,value) if value==timer.p.tid: if key=='timerStart': print('strategy starts '+str(when) ) elif key=='timerBinSize': print('bin starts ' +str(when) ) self.build_timer2() elif key=='timerEnd':

One use case:

You can implement a logic in your strategy which continuously checks the time of the day to see if only 5 minutes remain until the end of the session. This is the only moment in the session in which your fund executes the daily operations.

If this precise minute is missing (nothing was negotiated), your time comparison will fail. You can enhance the logic to not only check for == but also for >=, but you then need a bool to understand if you have already acted.

The timer will be called only once and 5 minutes before the session end without a constant logic check in next and with no bool involved, because it will only be called once.

The strategy contains the datetime information and things can be done manually. In general is a way to express things in a different way which may be more elegant.

Some people want to do portfolio rotation on the 15th of each month, but that day may be a weekend or a bank holiday. The timer will be called with the next possible day, allowing the rotation to always happen on the 15th or the 1st trading day thereafter (which matches the reality of life)

backtrader is not a data provider. The data feeds connect to providers. A provider can be:

A CSV file A database A Pandas DataFrame An online service over Direct Sockets (IB), HTTP (Oanda), COM (VisualChart)

If you have data from those sources which is from futures and with 1-minute resolution, it will be handled.

You can use pip provided with Anaconda to install packages not available through conda. That's what I did. Works fine for me.

Thank you. I'll create a store and post it for review if anyone finds it useful.

@Curtis-Miller Thanks!! I will check the PyFolio