access self.data.i ?

[traderblakeq]

<Data i=86 (2017-08-18 01:30:00) Open=296.19, High=305.55, Low=296.19, Close=305.55, Volume=117.10346, Close_time=2017-08-18 01:45:00, Supertrend=False, Lowerband=277.04990005003623, Upperband=316.8900999499638>

How do I access i=86

have tried self.data.i --> does not exist & self.data.index is Timestamp.

2nd question:

I need to slice out from i=n to i=n

Any suggestion on how I could slice out from self.data - I guess it does not have the .loc

[kernc]

How do I access i=86

_Data __repr__ contains a hint on how "i" is made:

backtesting.py/backtesting/_util.py

Lines 138 to 142 in 65f54f6

	def __repr__(self):
	i = min(self.__i, len(self.__df) - 1)
	index = self.__arrays['__index'][i]
	items = ', '.join(f'{k}={v}' for k, v in self.__df.iloc[i].items())
	return f'<Data i={i} ({index}) {items}>'

Hint: As Strategy.data is lengthened incrementally, len(self.data) should always be the current "i" (index).

I need to slice out from i=n to i=n

Any suggestion on how I could slice out from self.data - I guess it does not have the .loc

Strategy.data doesn't have .loc, but individual arrays comprising it have __getitem__, so:

i = len(self.data)
cur_value = self.data.Close[i]
values_slice = self.data.Close[i - 5:i]
values_slice_series = self.data.Close.s.iloc[i - 5:i]

[traderblakeq]

Thank you!.

Im my usecase I would like to use the numpy array (self.data) for my method, as I guess it is alot faster than Pandas datastructure.

• is it possible to slice from self.data?.

[kernc]

If you know any Python, you can just look in the source:

backtesting.py/backtesting/_util.py

Lines 103 to 126 in 65f54f6

	class _Data:
	"""
	A data array accessor. Provides access to OHLCV "columns"
	as a standard `pd.DataFrame` would, except it's not a DataFrame
	and the returned "series" are _not_ `pd.Series` but `np.ndarray`
	for performance reasons.
	"""
	def __init__(self, df: pd.DataFrame):
	self.__df = df
	self.__i = len(df)
	self.__pip: Optional[float] = None
	self.__cache: Dict[str, _Array] = {}
	self.__arrays: Dict[str, _Array] = {}
	self._update()
	def __getitem__(self, item):
	return self.__get_array(item)
	def __getattr__(self, item):
	try:
	return self.__get_array(item)
	except KeyError:
	raise AttributeError(f"Column '{item}' not in data") from None

Currently, Strategy.data __getitem__ only supports querying for individual columns (i.e. self.data['Close'], self.data.Close) as ndarrays.

backtesting.py/backtesting/_util.py

Lines 43 to 47 in 65f54f6

	class _Array(np.ndarray):
	"""
	ndarray extended to supply .name and other arbitrary properties
	in ._opts dict.
	"""

This ought to be quite fast indeed!