Class _Array

backtesting/_util.py:96–149

ndarray extended to supply .name and other arbitrary properties in ._opts dict.

class _Array(np.ndarray):
    """
    ndarray extended to supply .name and other arbitrary properties
    in ._opts dict.
    """
    def __new__(cls, array, *, name=None, **kwargs):
        obj = np.asarray(array).view(cls)
        obj.name = name or array.name
        obj._opts = kwargs
        return obj

    def __array_finalize__(self, obj):
        if obj is not None:
            self.name = getattr(obj, 'name', '')
            self._opts = getattr(obj, '_opts', {})

    # Make sure properties name and _opts are carried over
    # when (un-)pickling.
    def __reduce__(self):
        value = super().__reduce__()
        return value[:2] + (value[2] + (self.__dict__,),)

    def __setstate__(self, state):
        self.__dict__.update(state[-1])
        super().__setstate__(state[:-1])

    def __bool__(self):
        try:
            return bool(self[-1])
        except IndexError:
            return super().__bool__()

    def __float__(self):
        try:
            return float(self[-1])
        except IndexError:
            return super().__float__()

    def to_series(self):
        warnings.warn("`.to_series()` is deprecated. For pd.Series conversion, use accessor `.s`")
        return self.s

    @property
    def s(self) -> pd.Series:
        values = np.atleast_2d(self)
        index = self._opts['index'][:values.shape[1]]
        return pd.Series(values[0], index=index, name=self.name)

    @property
    def df(self) -> pd.DataFrame:
        values = np.atleast_2d(np.asarray(self))
        index = self._opts['index'][:values.shape[1]]
        df = pd.DataFrame(values.T, index=index, columns=[self.name] * len(values))
        return df


class _Indicator(_Array):