Repository: qiskit-community/qiskit-finance (language: python)

Issue:
Quandl python client replaced by Nasdaq Data link
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### Summary
closes #192


### Details and comments



Quandl python client replaced by Nasdaq Data link
<!--
⚠️ If you do not respect this template, your pull request will be closed.
⚠️ Your pull request title should be short detailed and understandable for all.
⚠️ Also, please add it in the CHANGELOG file under Unreleased section.
⚠️ If your pull request fixes an open issue, please link to the issue.

✅ I have added the tests to cover my changes.
✅ I have updated the documentation accordingly.
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### Summary
closes #192


### Details and comments





Discussion / hints:
## Pull Request Test Coverage Report for [Build 2868841995](https://coveralls.io/builds/51700092)

* **25** of **42**   **(59.52%)**  changed or added relevant lines in **4** files are covered.
* **1** unchanged line in **1** file lost coverage.
* Overall coverage decreased (**-0.5%**) to **83.017%**

---

|  Changes Missing Coverage | Covered Lines | Changed/Added Lines | % |
| :-----|--------------|--------|---: |
| [qiskit_finance/data_providers/yahoo_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fyahoo_data_provider.py#L90) | 0 | 1 | 0.0%
| [qiskit_finance/data_providers/exchange_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fexchange_data_provider.py#L94) | 9 | 16 | 56.25%
| [qiskit_finance/data_providers/wikipedia_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fwikipedia_data_provider.py#L76) | 5 | 14 | 35.71%
<!-- | **Total:** | **25** | **42** | **59.52%** | -->

|  Files with Coverage Reduction | New Missed Lines | % |
| :-----|--------------|--: |
| [qiskit_finance/data_providers/yahoo_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fyahoo_data_provider.py#L91) | 1 | 83.72% |
<!-- | **Total:** | **1** |  | -->

|  Totals | [![Coverage Status](https://coveralls.io/builds/51700092/badge)](https://coveralls.io/builds/51700092) |
| :-- | --: |
| Change from base [Build 2842562564](https://coveralls.io/builds/51608247): |  -0.5% |
| Covered Lines: | 699 |
| Relevant Lines: | 842 |

---
##### 💛  - [Coveralls](https://coveralls.io)

## Pull Request Test Coverage Report for [Build 2868841995](https://coveralls.io/builds/51700092)

* **25** of **42**   **(59.52%)**  changed or added relevant lines in **4** files are covered.
* **1** unchanged line in **1** file lost coverage.
* Overall coverage decreased (**-0.5%**) to **83.017%**

---

|  Changes Missing Coverage | Covered Lines | Changed/Added Lines | % |
| :-----|--------------|--------|---: |
| [qiskit_finance/data_providers/yahoo_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fyahoo_data_provider.py#L90) | 0 | 1 | 0.0%
| [qiskit_finance/data_providers/exchange_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fexchange_data_provider.py#L94) | 9 | 16 | 56.25%
| [qiskit_finance/data_providers/wikipedia_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fwikipedia_data_provider.py#L76) | 5 | 14 | 35.71%
<!-- | **Total:** | **25** | **42** | **59.52%** | -->

|  Files with Coverage Reduction | New Missed Lines | % |
| :-----|--------------|--: |
| [qiskit_finance/data_providers/yahoo_data_provider.py](https://coveralls.io/builds/51700092/source?filename=qiskit_finance%2Fdata_providers%2Fyahoo_data_provider.py#L91) | 1 | 83.72% |
<!-- | **Total:** | **1** |  | -->

|  Totals | [![Coverage Status](https://coveralls.io/builds/51700092/badge)](https://coveralls.io/builds/51700092) |
| :-- | --: |
| Change from base [Build 2842562564](https://coveralls.io/builds/51608247): |  -0.5% |
| Covered Lines: | 699 |
| Relevant Lines: | 842 |

---
##### 💛  - [Coveralls](https://coveralls.io)


Fix patch:
diff --git a/.pylintdict b/.pylintdict
index 4819ffc6..30070f8c 100644
--- a/.pylintdict
+++ b/.pylintdict
@@ -132,6 +132,8 @@ tutuncu
 unittest
 univariate
 uno
+url
+userid
 variational
 vec
 visualisation
diff --git a/docs/tutorials/11_time_series.ipynb b/docs/tutorials/11_time_series.ipynb
index 384d2eea..7626c9a0 100644
--- a/docs/tutorials/11_time_series.ipynb
+++ b/docs/tutorials/11_time_series.ipynb
@@ -88,7 +88,7 @@
     },
     {
      "data": {
-      "image/png": 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45mVSQuWQqnq02/4RcMgM+y1NsjnJpiRnjaa0OZvu1oTDZtqnqnYBTwEHj6S6+ZvNcQG8q3uLcF2Sw6eZn0SzPfZJ9MYkdye5IclrZrNglPf+7FGSm4FDp5m6uP+kqirJTJ+DH1FV25McDdyaZEtVPdS6Vs3bN4Crq2pnkj9gcDb29jHXpJnN6/aaBRMqVXXqTHNJfpxkRVU92p1WPjbDa2zvHrcluQ04gcH7/IVkNrcm7N7nkST7Ai9m8A3khWyvx1VV/WO4gsG1ssVgUd5uUlU/7W1fn+QzSZZX1R5voJyUtz8bgfXd9nrg61N3SLIsyZJuezlwEnDfyCqcvdncmtA/3rOBW6u7craA7fW4plxnWAt8f4T1PZ82Au/pPgV6A/BU7+36xNrD7TV7Nu4r0LO8Sn0wcAvwIHAzcFA3vgq4ott+E7CFwacOW4Dzxl33Ho7nDOABBmdRF3djlwJru+2lwFeArcCdwNHjrrnRcf05cG/3b/RN4FXjrnmWx3U18CjwCwbXS84D3gu8t5sPgz829lD3396qcdfc6Lgu7P17bQLeNJvX9Wv6kpqalLc/kiaEoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ19X8xELbFA//eUQAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -109,7 +109,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -164,7 +164,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -220,7 +220,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -253,15 +253,15 @@
     "### Access to closing-price time-series\n",
     "\n",
     "While the access to real-time data usually requires a payment, it is possible \n",
-    "to access historical (adjusted) closing prices via Wikipedia and Quandl\n",
+    "to access historical (adjusted) closing prices via Wikipedia and Nasdaq Data Link\n",
     "free of charge, following registration at:\n",
-    "https://www.quandl.com/?modal=register\n",
+    "https://data.nasdaq.com/sign-up\n",
     "In the code below, one needs to specify actual tickers of actual NASDAQ\n",
-    "issues and the access token you obtain from Quandl; by running the code below, you agree to the Quandl terms and \n",
+    "issues and the access token you obtain from Nasdaq Data Link; by running the code below, you agree to the Nasdaq Data Link terms and \n",
     "conditions, including a liability waiver.\n",
     "Notice that at least two tickers are required for the computation\n",
     "of covariance and time-series matrices, but hundreds of tickers may go \n",
-    "beyond the fair usage limits of Quandl."
+    "beyond the fair usage limits of Nasdaq Data Link."
    ]
   },
   {
@@ -425,7 +425,7 @@
     "for an overview of the coverage.\n",
     "\n",
     "The access again requires a valid access token to replace REPLACE-ME below. The token can be obtained on a trial or paid-for basis at:\n",
-    "https://www.quandl.com/ "
+    "https://data.nasdaq.com/sign-up "
    ]
   },
   {
@@ -474,7 +474,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -523,8 +523,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.18.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.9.0</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0.dev0+9201ed8</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.14.0.dev0+436e42e</td></tr><tr><td><code>qiskit-nature</code></td><td>0.1.4</td></tr><tr><td><code>qiskit-finance</code></td><td>0.3.0</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.2.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.2.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.8.8 (default, Apr 13 2021, 12:59:45) \n",
-       "[Clang 10.0.0 ]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>12.0</td></tr><tr><td colspan='2'>Thu Jul 15 20:38:23 2021 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0.dev0+db61292</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0.dev0+f3ffe8d</td></tr><tr><td><code>qiskit-nature</code></td><td>0.4.4</td></tr><tr><td><code>qiskit-finance</code></td><td>0.4.0</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.8.13</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>default, Mar 28 2022 06:16:26</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>12.0</td></tr><tr><td colspan='2'>Thu Aug 11 16:53:42 2022 EDT</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -536,7 +535,7 @@
     {
      "data": {
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-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -564,7 +563,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -578,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.13"
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diff --git a/qiskit_finance/data_providers/data_on_demand_provider.py b/qiskit_finance/data_providers/data_on_demand_provider.py
index 19ea9e9b..6003ab97 100644
--- a/qiskit_finance/data_providers/data_on_demand_provider.py
+++ b/qiskit_finance/data_providers/data_on_demand_provider.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2021.
+# (C) Copyright IBM 2019, 2022.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -78,16 +78,17 @@ def run(self) -> None:
         http = urllib3.PoolManager(cert_reqs="CERT_REQUIRED", ca_certs=certifi.where())
         url = "https://dataondemand.nasdaq.com/api/v1/quotes?"
         self._data = []
-        for ticker in self._tickers:
-            values = {
-                "_Token": self._token,
-                "symbols": [ticker],
-                "start": self._start.strftime("%Y-%m-%d'T'%H:%M:%S.%f'Z'"),
-                "end": self._end.strftime("%Y-%m-%d'T'%H:%M:%S.%f'Z'"),
-                "next_cursor": 0,
-            }
-            encoded = url + urlencode(values)
-            try:
+        stocks_error = []
+        try:
+            for ticker in self._tickers:
+                values = {
+                    "_Token": self._token,
+                    "symbols": [ticker],
+                    "start": self._start.strftime("%Y-%m-%d'T'%H:%M:%S.%f'Z'"),
+                    "end": self._end.strftime("%Y-%m-%d'T'%H:%M:%S.%f'Z'"),
+                    "next_cursor": 0,
+                }
+                encoded = url + urlencode(values)
                 if self._verify is None:
                     response = http.request(
                         "POST", encoded
@@ -97,15 +98,21 @@ def run(self) -> None:
                     # or forces the certificate path (str)
                     response = http.request("POST", encoded, verify=self._verify)
                 if response.status != 200:
-                    msg = f"Accessing NASDAQ Data on Demand with parameters {values} encoded into "
-                    msg += encoded
-                    msg += " failed. Hint: Check the _Token. Check the spelling of tickers."
-                    raise QiskitFinanceError(msg)
+                    logger.debug(response.data.decode("utf-8"))
+                    stocks_error.append(ticker)
+                    continue
                 quotes = json.loads(response.data.decode("utf-8"))["quotes"]
                 price_evolution = []
                 for q in quotes:
                     price_evolution.append(q["ask_price"])
                 self._data.append(price_evolution)
-            except Exception as ex:  # pylint: disable=broad-except
-                raise QiskitFinanceError("Accessing NASDAQ Data on Demand failed.") from ex
+        finally:
             http.clear()
+
+        if stocks_error:
+            err_msg = (
+                f"Accessing NASDAQ Data on Demand with symbols: {stocks_error}, "
+                f"start: {self._start}, end: {self._end} failed. "
+                "Hint: Check the token. Check the spelling of ticker."
+            )
+            raise QiskitFinanceError(err_msg)
diff --git a/qiskit_finance/data_providers/exchange_data_provider.py b/qiskit_finance/data_providers/exchange_data_provider.py
index 99c5b777..740119eb 100644
--- a/qiskit_finance/data_providers/exchange_data_provider.py
+++ b/qiskit_finance/data_providers/exchange_data_provider.py
@@ -13,20 +13,13 @@
 """ Exchange data provider. """
 
 from typing import Union, List
-import datetime
 import logging
+import datetime
+import nasdaqdatalink
 
-from qiskit.exceptions import MissingOptionalLibraryError
 from ._base_data_provider import BaseDataProvider, StockMarket
 from ..exceptions import QiskitFinanceError
 
-try:
-    import quandl
-
-    _HAS_QUANDL = True
-except ImportError:
-    _HAS_QUANDL = False
-
 logger = logging.getLogger(__name__)
 
 
@@ -35,7 +28,7 @@ class ExchangeDataProvider(BaseDataProvider):
 
     Please see:
     https://github.com/Qiskit/qiskit-finance/blob/main/docs/tutorials/11_time_series.ipynb
-    for instructions on use, which involve obtaining a Quandl access token.
+    for instructions on use, which involve obtaining a Nasdaq Data Link access token.
     """
 
     def __init__(
@@ -48,23 +41,16 @@ def __init__(
     ) -> None:
         """
         Args:
-            token: Quandl access token
+            token: Nasdaq Data Link access token
             tickers: tickers
             stockmarket: LONDON, EURONEXT, or SINGAPORE
             start: first data point
             end: last data point precedes this date
 
         Raises:
-            MissingOptionalLibraryError: Quandl not installed
             QiskitFinanceError: provider doesn't support given stock market
         """
         super().__init__()
-        if not _HAS_QUANDL:
-            raise MissingOptionalLibraryError(
-                libname="Quandl",
-                name="ExchangeDataProvider",
-                pip_install="pip install quandl",
-            )
         self._tickers = []  # type: Union[str, List[str]]
         if isinstance(tickers, list):
             self._tickers = tickers
@@ -95,8 +81,7 @@ def run(self) -> None:
         Loads data, thus enabling get_similarity_matrix and get_covariance_matrix
         methods in the base class.
         """
-        quandl.ApiConfig.api_key = self._token
-        quandl.ApiConfig.api_version = "2015-04-09"
+        nasdaqdatalink.ApiConfig.api_key = self._token
         self._data = []
         stocks_notfound = []
         stocks_forbidden = []
@@ -104,21 +89,25 @@ def run(self) -> None:
             stock_data = None
             name = self._stockmarket + "/" + ticker_name
             try:
-                stock_data = quandl.get(name, start_date=self._start, end_date=self._end)
-            except quandl.AuthenticationError as ex:
-                raise QiskitFinanceError("Quandl invalid token.") from ex
-            except quandl.NotFoundError:
+                stock_data = nasdaqdatalink.get(name, start_date=self._start, end_date=self._end)
+            except nasdaqdatalink.AuthenticationError as ex:
+                logger.debug(ex, exc_info=True)
+                raise QiskitFinanceError("Nasdaq Data Link invalid token.") from ex
+            except nasdaqdatalink.NotFoundError as ex:
+                logger.debug(ex, exc_info=True)
                 stocks_notfound.append(name)
                 continue
-            except quandl.ForbiddenError:
+            except nasdaqdatalink.ForbiddenError as ex:
+                logger.debug(ex, exc_info=True)
                 stocks_forbidden.append(name)
                 continue
-            except quandl.QuandlError as ex:
-                raise QiskitFinanceError(f"Quandl Error for '{name}'.") from ex
+            except nasdaqdatalink.DataLinkError as ex:
+                logger.debug(ex, exc_info=True)
+                raise QiskitFinanceError(f"Nasdaq Data Link Error for '{name}'.") from ex
             try:
                 self._data.append(stock_data["Close"])
             except KeyError as ex:
-                raise QiskitFinanceError(f"Cannot parse Quandl '{name}' output.") from ex
+                raise QiskitFinanceError(f"Cannot parse Nasdaq Data Link '{name}' output.") from ex
 
         if stocks_notfound or stocks_forbidden:
             err_msg = f"Stocks not found: {stocks_notfound}. " if stocks_notfound else ""
diff --git a/qiskit_finance/data_providers/wikipedia_data_provider.py b/qiskit_finance/data_providers/wikipedia_data_provider.py
index 35ef4ec4..a2a7a6a3 100644
--- a/qiskit_finance/data_providers/wikipedia_data_provider.py
+++ b/qiskit_finance/data_providers/wikipedia_data_provider.py
@@ -13,20 +13,13 @@
 """ Wikipedia data provider. """
 
 from typing import Optional, Union, List
-import datetime
 import logging
+import datetime
+import nasdaqdatalink
 
-from qiskit.exceptions import MissingOptionalLibraryError
 from ._base_data_provider import BaseDataProvider
 from ..exceptions import QiskitFinanceError
 
-try:
-    import quandl
-
-    _HAS_QUANDL = True
-except ImportError:
-    _HAS_QUANDL = False
-
 logger = logging.getLogger(__name__)
 
 
@@ -47,21 +40,12 @@ def __init__(
     ) -> None:
         """
         Args:
-            token: quandl access token, which is not needed, strictly speaking
+            token: Nasdaq Data Link access token, which is not needed, strictly speaking
             tickers: tickers
             start: start time
             end: end time
-
-        Raises:
-            MissingOptionalLibraryError: Quandl not installed
         """
         super().__init__()
-        if not _HAS_QUANDL:
-            raise MissingOptionalLibraryError(
-                libname="Quandl",
-                name="WikipediaDataProvider",
-                pip_install="pip install quandl",
-            )
         self._tickers = None  # type: Optional[Union[str, List[str]]]
         tickers = tickers if tickers is not None else []
         if isinstance(tickers, list):
@@ -81,27 +65,29 @@ def run(self) -> None:
         Loads data, thus enabling get_similarity_matrix and
         get_covariance_matrix methods in the base class.
         """
-        quandl.ApiConfig.api_key = self._token
-        quandl.ApiConfig.api_version = "2015-04-09"
+        nasdaqdatalink.ApiConfig.api_key = self._token
         self._data = []
         stocks_notfound = []
         for _, ticker_name in enumerate(self._tickers):
             stock_data = None
             name = "WIKI" + "/" + ticker_name
             try:
-                stock_data = quandl.get(name, start_date=self._start, end_date=self._end)
-            except quandl.AuthenticationError as ex:
-                raise QiskitFinanceError("Quandl invalid token.") from ex
-            except quandl.NotFoundError:
+                stock_data = nasdaqdatalink.get(name, start_date=self._start, end_date=self._end)
+            except nasdaqdatalink.AuthenticationError as ex:
+                logger.debug(ex, exc_info=True)
+                raise QiskitFinanceError("Nasdaq Data Link invalid token.") from ex
+            except nasdaqdatalink.NotFoundError as ex:
+                logger.debug(ex, exc_info=True)
                 stocks_notfound.append(name)
                 continue
-            except quandl.QuandlError as ex:
-                raise QiskitFinanceError(f"Quandl Error for '{name}'.") from ex
+            except nasdaqdatalink.DataLinkError as ex:
+                logger.debug(ex, exc_info=True)
+                raise QiskitFinanceError(f"Nasdaq Data Link Error for '{name}'.") from ex
 
             try:
                 self._data.append(stock_data["Adj. Close"])
             except KeyError as ex:
-                raise QiskitFinanceError("Cannot parse quandl output.") from ex
+                raise QiskitFinanceError("Cannot parse Nasdaq Data Link output.") from ex
 
         if stocks_notfound:
             raise QiskitFinanceError(f"Stocks not found: {stocks_notfound}. ")
diff --git a/qiskit_finance/data_providers/yahoo_data_provider.py b/qiskit_finance/data_providers/yahoo_data_provider.py
index 08f2bd53..d4aef13f 100644
--- a/qiskit_finance/data_providers/yahoo_data_provider.py
+++ b/qiskit_finance/data_providers/yahoo_data_provider.py
@@ -103,6 +103,7 @@ def run(self) -> None:
                         stocks_notfound.append(ticker_name)
                     self._data.append(stock_value)
         except Exception as ex:  # pylint: disable=broad-except
+            logger.debug(ex, exc_info=True)
             raise QiskitFinanceError("Accessing Yahoo Data failed.") from ex
 
         if stocks_notfound:
diff --git a/releasenotes/notes/quandl-discontinued-92f3a1e020e6ff9c.yaml b/releasenotes/notes/quandl-discontinued-92f3a1e020e6ff9c.yaml
new file mode 100644
index 00000000..2608ac63
--- /dev/null
+++ b/releasenotes/notes/quandl-discontinued-92f3a1e020e6ff9c.yaml
@@ -0,0 +1,9 @@
+---
+other:
+  - |
+    Data providers' usage of `Quandl` has been switched to `Nasdaq Data Link` since the
+    `Quandl` python client has been discontinued and was replaced by `Nasdaq Data Link`.
+    The previous `Quandl` url redirects to the new one at https://data.nasdaq.com/ .
+    The userid/password and token there were preserved.
+    There is no need to register again.
+    More information at https://pypi.org/project/Nasdaq-Data-Link/
diff --git a/requirements.txt b/requirements.txt
index d31b33a1..a5384f67 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,5 +6,7 @@ psutil>=5
 fastdtw
 setuptools>=40.1.0
 pandas
-quandl
+nasdaq-data-link
 yfinance>=0.1.70
+certifi
+urllib3


Test patch:
diff --git a/test/data_providers/test_data_providers.py b/test/data_providers/test_data_providers.py
index 700643b4..e6163139 100644
--- a/test/data_providers/test_data_providers.py
+++ b/test/data_providers/test_data_providers.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2021.
+# (C) Copyright IBM 2019, 2022.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,6 +13,7 @@
 """ Test Data Providers """
 
 import unittest
+import warnings
 import os
 import datetime
 from test import QiskitFinanceTestCase, requires_extra_library
@@ -33,10 +34,21 @@
 class TestDataProviders(QiskitFinanceTestCase):
     """Tests data providers for the Portfolio Optimization and Diversification."""
 
+    logger = None
+
     def setUp(self):
         super().setUp()
-        self._quandl_token = os.getenv("QUANDL_TOKEN") if os.getenv("QUANDL_TOKEN") else ""
+        self._nasdaq_data_link_api_key = (
+            os.getenv("NASDAQ_DATA_LINK_API_KEY") if os.getenv("NASDAQ_DATA_LINK_API_KEY") else ""
+        )
         self._on_demand_token = os.getenv("ON_DEMAND_TOKEN") if os.getenv("ON_DEMAND_TOKEN") else ""
+        warnings.filterwarnings(action="ignore", message="unclosed", category=ResourceWarning)
+        warnings.filterwarnings(action="ignore", module="urllib3", category=DeprecationWarning)
+
+    def tearDown(self):
+        super().tearDown()
+        warnings.filterwarnings(action="default", message="unclosed", category=ResourceWarning)
+        warnings.filterwarnings(action="default", module="urllib3", category=DeprecationWarning)
 
     @requires_extra_library
     def test_random_wrong_use(self):
@@ -50,7 +62,7 @@ def test_random_wrong_use(self):
         with self.subTest("test RandomDataProvider get_similarity_matrix"):
             self.assertRaises(QiskitFinanceError, rnd.get_similarity_matrix)
         wiki = WikipediaDataProvider(
-            token=self._quandl_token,
+            token=self._nasdaq_data_link_api_key,
             tickers=["GOOG", "AAPL"],
             start=datetime.datetime(2016, 1, 1),
             end=datetime.datetime(2016, 1, 30),
@@ -114,7 +126,7 @@ def test_wikipedia(self):
         """wikipedia test"""
         try:
             wiki = WikipediaDataProvider(
-                token=self._quandl_token,
+                token=self._nasdaq_data_link_api_key,
                 tickers=["GOOG", "AAPL"],
                 start=datetime.datetime(2016, 1, 1),
                 end=datetime.datetime(2016, 1, 30),
@@ -136,11 +148,7 @@ def test_wikipedia(self):
             self.skipTest(f"Test of WikipediaDataProvider skipped: {str(ex)}")
             # The trouble for automating testing is that after 50 tries
             # from one IP address within a day
-            # Quandl complains about the free usage tier limits:
-            # quandl.errors.quandl_error.LimitExceededError: (Status 429) (Quandl Error QELx01)
-            # You have exceeded the anonymous user limit of 50 calls per day. To make more calls
-            # today, please register for a free Quandl account and then include your API
-            # key with your requests.
+            # Nasdaq Data Link complains about the free usage tier limits.
             # This gets "dressed" as QiskitFinanceError.
             # This also introduces a couple of seconds of a delay.
 
@@ -162,7 +170,7 @@ def test_exchangedata(self):
         """exchange data test"""
         try:
             lse = ExchangeDataProvider(
-                token=self._quandl_token,
+                token=self._nasdaq_data_link_api_key,
                 tickers=["AEO", "ABBY"],
                 stockmarket=StockMarket.LONDON,
                 start=datetime.datetime(2018, 1, 1),
diff --git a/test/finance_test_case.py b/test/finance_test_case.py
index 395e4ffe..9d04d31d 100644
--- a/test/finance_test_case.py
+++ b/test/finance_test_case.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2018, 2021.
+# (C) Copyright IBM 2018, 2022.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -90,6 +90,9 @@ def setUpClass(cls) -> None:
             # to INFO if it is not a valid level.
             level = logging._nameToLevel.get(os.getenv("LOG_LEVEL"), logging.INFO)
             cls.log.setLevel(level)
+            logger = logging.getLogger("qiskit_finance")
+            logger.addHandler(file_handler)
+            logger.setLevel(level)
 
     def get_resource_path(self, filename: str, path: Optional[str] = None) -> str:
         """Get the absolute path to a resource.