Comments (7)
Hi @none2003,
I may be able to add support for this by allowing multiple indicator names:
pybroker.indicator(name=['upperband', 'middleband', 'lowerband'], lambda data: talib.BBANDS(data.close))
I will look into the feasibility of this.
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Hi @none2003,
Yes, this is because indicators only support one output. You will need to create separate indicators for each of the upperband, middleband, and lowerband outputs.
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Hi @edtechre,
I have used backtesting.py, which also uses a decorator to encapsulate the results returned by the indicator:
self.upperband, self.middleband, self.lowerband = self.I(talib.BBANDS, self.data.Close)
These return values can then be used in the "next" method (similar to the "execute(ctx: ExecContext)" method in pyb):
...
if self.data.Close[-1] > self.upperband[-1]:
pass
...
This is much easier than defining a method for each return value of the indicator, and I really hope pyb supports such a feature.
Maybe you can take a look at their implementation of this "I" method:
https://github.com/kernc/backtesting.py/blob/master/backtesting/backtesting.py line 78
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Hi @none2003,
I may be able to add support for this by allowing multiple indicator names:
pybroker.indicator(name=['upperband', 'middleband', 'lowerband'], lambda data: talib.BBANDS(data.close))
I will look into the feasibility of this.
Hi @edtechre,
This looks good, have you had time to research on this?
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Hi @none2003,
I have decided not to make this change as it would not be compatible with how indicator calculations are parallelized across multiple CPUs. My recommendation is to create separate indicators for each output, which is only a minor inconvenience.
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Hi @edtechre
So what you suggest is like:
bbup = pybroker.indicator('bbup', lambda data: talib.BBANDS(data.close).upperband)
bbdn = pybroker.indicator('bbdn', lambda data: talib.BBANDS(data.close).lowerband)
bbmd = pybroker.indicator('bbmd', lambda data: talib.BBANDS(data.close).middleband)
Is that right? If so, will it repeat the bbands calculation 3 times?
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Hi @tsunamilx,
Yes, that is true. But the TA-Lib calculation is very fast, so you should see very little difference in performance.
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Related Issues (20)
- Multi-timeframe? HOT 2
- Indicator might be wrong when applying filter on backtesting data HOT 2
- Add MAE (Maximum Adverse Excursion) and MFE (Maximum Favorable Excursion) metrics. HOT 1
- Add regular defined contribution to portfolio HOT 1
- Add Ruff for Python linter and code formatter HOT 3
- Real-time question HOT 1
- The feature names should match those that were passed during fit HOT 6
- [Feature] Add support for Schwab HOT 2
- 设置了ctx.score的值后,买入的具体原则是怎样的,这个ctx.score具体作用和影响是什么呢 HOT 1
- How to set a different ctx.sell_fill_price HOT 1
- Walkforward questions HOT 1
- Question from the Rebalancing example HOT 1
- About update numpy to 2.0 HOT 4
- Can ctx.score work with ctx.sell_shares? HOT 1
- when use stop loss, how to get callback while trigger stop action HOT 1
- AlpacaCrypto Object is not callable HOT 1
- caching data bug in month or week frequence HOT 2
- AKShare got an unexpected keyword argument 'timeframe' HOT 3
- Rotational Trading not working when using start_of_month HOT 1
- [Question] symbol for indicator but not for trading HOT 1
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