RandomDataProvider#

class RandomDataProvider(tickers=None, start=datetime.datetime(2016, 1, 1, 0, 0), end=datetime.datetime(2016, 1, 30, 0, 0), seed=None)[source]#

Bases: BaseDataProvider

Pseudo-randomly generated mock stock-market data provider.

Parameters:

tickers (str | List[str] | None) – tickers
start (datetime) – first data point
end (datetime) – last data point precedes this date
seed (int | None) – optional random seed

Methods

get_coordinates()#

Returns random coordinates for visualisation purposes.

Return type:: Tuple[ndarray, ndarray]

get_covariance_matrix()#

Returns the covariance matrix.

Returns:: an asset-to-asset covariance matrix.
Raises:: QiskitFinanceError – no data loaded
Return type:: ndarray

get_mean_vector()#

Returns a vector containing the mean value of each asset.

Returns:: a per-asset mean vector.
Raises:: QiskitFinanceError – no data loaded
Return type:: ndarray

get_period_return_covariance_matrix()#

Returns a vector containing the mean value of each asset.

Returns:: a per-asset mean vector.
Raises:: QiskitFinanceError – no data loaded
Return type:: ndarray

get_period_return_mean_vector()#

Returns a vector containing the mean value of each asset.

Returns:: a per-asset mean vector.
Raises:: QiskitFinanceError – no data loaded
Return type:: ndarray

get_similarity_matrix()#

Returns time-series similarity matrix computed using dynamic time warping.

Returns:: an asset-to-asset similarity matrix.
Raises:: QiskitFinanceError – no data loaded
Return type:: ndarray

run()[source]#

Generates data pseudo-randomly, thus enabling get_similarity_matrix and get_covariance_matrix methods in the base class.