Module indicators

Classes

class Indicator (out_feat: List[str], numba: bool = False, name: Optional[str] = None, **expected_params: type)

This class is a decorator designed to make an Indicator out of a function.

Example

>>> @Indicator(out_feat=["SMA"], period=int)
... def SMA(data: np.ndarray, index: List[str], features: List[str],
...     previous_data: Optional[npt.NDArray[np.float32]], period: int = 10) -> np.ndarray:
...
...     out = np.zeros(len(data), dtype=np.float32)
...     for i in range(len(data) - period + 1):
...         out[i] = data[i: i+period, 0].sum() / period
...     return out[:, np.newaxis]
>>> # Usage
>>> series = pd.DataFrame(np.random.rand(1000), index=pd.date_range(start="2021-01-01", periods=1000, freq="min"), columns=["Close"])
>>> indicator_10d = SMA()
>>> indicator_20d = SMA(period=20)
>>> out_10d = indicator_10d.get(series)
>>> out_20d = indicator_20d.get(series)

The indicator function receives the data as a 2D numpy array, the index as a list of datetimes (or string if jit compiled), the features as a list of strings, and any parameters set by the user. The function must return a 2D numpy array with the same length as the input data.

Args:

out_feat: The features name that this indicator will return. (Will be the column name in resulting df)
numba: Whether the function uses numba JIT compilation or not. If True, the index passed to the indicator logic will be a list of strings, otherwise a list of datetime objects.
name: The name of the indicator. If not set, the name of the function will be used.
expected_params: The expected parameters of the indicator. Used to validate the parameters passed to the indicator.

Static methods

def run(data: numpy.ndarray[typing.Any, numpy.dtype[numpy.float32]], index: Union[List[datetime.datetime], List[str]], features: List[str], previous_data: Optional[numpy.ndarray[typing.Any, numpy.dtype[numpy.float32]]] = None, **params) ‑> numpy.ndarray[typing.Any, numpy.dtype[numpy.float32]]

This method can be overridden to implement the indicator logic. This method is called when the callback method is not set.

Args:

data: The input data as a 2d numpy array.
index: The index of the input data. (datetime if not compiled with numba or string if compiled
features: The features of the input data
previous_data: An array of the previous indicator results. The recent points in the array will be nan. These are the values to fill. This parameter is useful when streaming the indicator (more compute efficient, but not all indicators support it.)
params: Any parameters set by the user

Returns:

A 2D array with the indicator results. Must be the same length as the input data.

Instance variables

var name : str

Returns:

A unique name for the indicator. (The name of the function and its identifier if different from 0)

var out : List[str]

Returns:

The output features of the indicator

var type_name : str

Returns:

The name of the indicator type

Methods

def get(self, data: pandas.core.frame.DataFrame, previous_values: Optional[pandas.core.frame.DataFrame]) ‑> pandas.core.frame.DataFrame

Run the indicator logic on the data and returns the result as a DataFrame.

Args:

data: The input dataframe with a datetime index. (Usually the chart)
previous_values: The previous values of the indicator. (Used when streaming the indicator)

Returns:

The indicator results as a dataframe

def get_args(self) ‑> str

Returns:

The arguments of the indicator as a string

def get_name_no_id(self) ‑> str

Returns:

The name of the indicator without the identifier

def set_callback(self, cb: Callable[[numpy.ndarray[Any, numpy.dtype[numpy.float32]], Union[List[datetime.datetime], List[str]], List[str], Optional[numpy.ndarray[Any, numpy.dtype[numpy.float32]]], ...], numpy.ndarray[Any, numpy.dtype[numpy.float32]]]) ‑> None

Set the callback method to be used when the indicator is called.

Args:

cb: The indicator logic

Returns:

None

def set_id(self, new: int)

Set the identifier of the indicator. Used to identify the indicator in the indicator set when there are multiple indicators with the same name.

Args:

new: The new id

Returns:

None

def set_params(self, **params: Any) ‑> None

Set the parameters of the indicator.

Args:

params: The parameters set by the user

Returns:

None