qiskit.finance.data_providers.DataOnDemandProvider¶

class DataOnDemandProvider(token, tickers, start=datetime.datetime(2016, 1, 1, 0, 0), end=datetime.datetime(2016, 1, 30, 0, 0), verify=None)[source]¶

NASDAQ Data on Demand data provider.

Please see: https://github.com/Qiskit/qiskit-tutorials/blob/master/legacy_tutorials/aqua/finance/data_providers/time_series.ipynb for instructions on use, which involve obtaining a NASDAQ DOD access token.

Parameters

token (str) – data on demand access token
tickers (Union[str, List[str]]) – tickers
start (datetime) – first data point
end (datetime) – last data point precedes this date
verify (Union[str, bool, None]) – if verify is None, certify certificates will be used (default); if this is False, no certificates will be checked; if this is a string, it should be pointing to a certificate for the HTTPS connection to NASDAQ (dataondemand.nasdaq.com), either in the form of a CA_BUNDLE file or a directory wherein to look.

__init__(token, tickers, start=datetime.datetime(2016, 1, 1, 0, 0), end=datetime.datetime(2016, 1, 30, 0, 0), verify=None)[source]¶

Parameters

token (str) – data on demand access token
tickers (Union[str, List[str]]) – tickers
start (datetime) – first data point
end (datetime) – last data point precedes this date
verify (Union[str, bool, None]) – if verify is None, certify certificates will be used (default); if this is False, no certificates will be checked; if this is a string, it should be pointing to a certificate for the HTTPS connection to NASDAQ (dataondemand.nasdaq.com), either in the form of a CA_BUNDLE file or a directory wherein to look.

Methods

`__init__`(token, tickers[, start, end, verify])	type token `str`
`get_coordinates`()	Returns random coordinates for visualisation purposes.
`get_covariance_matrix`()	Returns the covariance matrix.
`get_mean_vector`()	Returns a vector containing the mean value of each asset.
`get_period_return_covariance_matrix`()	Returns a vector containing the mean value of each asset.
`get_period_return_mean_vector`()	Returns a vector containing the mean value of each asset.
`get_similarity_matrix`()	Returns time-series similarity matrix computed using dynamic time warping.
`run`()	Loads data, thus enabling get_similarity_matrix and get_covariance_matrix methods in the base class.

get_coordinates()¶

Returns random coordinates for visualisation purposes.

Return type: Tuple[float, float]

get_covariance_matrix()¶

Returns the covariance matrix.

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

get_mean_vector()¶

Returns a vector containing the mean value of each asset.

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

get_period_return_covariance_matrix()¶

Returns a vector containing the mean value of each asset.

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

get_period_return_mean_vector()¶

Returns a vector containing the mean value of each asset.

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

get_similarity_matrix()¶

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

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

run()[source]¶

Loads data, thus enabling get_similarity_matrix and get_covariance_matrix methods in the base class.

Return type: None