RandomDataProvider
RandomDataProvider(tickers=None, stockmarket=StockMarket.RANDOM, start=datetime.datetime(2016, 1, 1, 0, 0), end=datetime.datetime(2016, 1, 30, 0, 0), seed=None)
Pseudo-randomly generated mock stock-market data provider.
Initializer :type tickers: Union[str, List[str], None] :param tickers: tickers :type stockmarket: StockMarket :param stockmarket: RANDOM :type start: <module ‘datetime’ from ‘/usr/lib/python3.7/datetime.py’> :param start: first data point :type end: <module ‘datetime’ from ‘/usr/lib/python3.7/datetime.py’> :param end: last data point precedes this date :type seed: Optional[int] :param seed: shall a seed be used?
Raises
QiskitFinanceError – provider doesn’t support stock market value
Methods
get_coordinates
RandomDataProvider.get_coordinates()
Returns random coordinates for visualisation purposes.
get_covariance_matrix
RandomDataProvider.get_covariance_matrix()
Returns the covariance matrix.
Returns
an asset-to-asset covariance matrix.
Return type
numpy.ndarray
Raises
QiskitFinanceError – no data loaded
get_mean_vector
RandomDataProvider.get_mean_vector()
Returns a vector containing the mean value of each asset.
Returns
a per-asset mean vector.
Return type
numpy.ndarray
Raises
QiskitFinanceError – no data loaded
get_period_return_covariance_matrix
RandomDataProvider.get_period_return_covariance_matrix()
Returns a vector containing the mean value of each asset.
Returns
a per-asset mean vector.
Return type
numpy.ndarray
Raises
QiskitFinanceError – no data loaded
get_period_return_mean_vector
RandomDataProvider.get_period_return_mean_vector()
Returns a vector containing the mean value of each asset.
Returns
a per-asset mean vector.
Return type
numpy.ndarray
Raises
QiskitFinanceError – no data loaded
get_similarity_matrix
RandomDataProvider.get_similarity_matrix()
Returns time-series similarity matrix computed using dynamic time warping.
Returns
an asset-to-asset similarity matrix.
Return type
numpy.ndarray
Raises
QiskitFinanceError – no data loaded
run
RandomDataProvider.run()
Generates data pseudo-randomly, thus enabling get_similarity_matrix and get_covariance_matrix methods in the base class.