support for news and sentiments

alpha_vantage/alphaintelligence.py

@@ -0,0 +1,52 @@
+from .alphavantage import AlphaVantage as av
+
+
+class AlphaIntelligence(av):
+
+
+    """This class implements all the api calls to alpha intelligence
+    """
+    @av._output_format
+    @av._call_api_on_func
+    def get_news_sentiment(self,tickers, topics=None, time_from=None, time_to=None,sort=None,limit=None):
+        """live and historical market news & sentiment data from a large & growing selection of premier news 
+        outlets around the world, covering stocks, cryptocurrencies, forex, and a wide range of topics such 
+        as fiscal policy, mergers & acquisitions, IPOs, etc
+        Visit alpha vantage documentation for details on keyboard arguments.
+        Keyword Arguments:
+            tickers: stock/crypto/forex symbols of your choice
+            topics: news topics of your choice 
+            time_from: time as UTC (ETC + 5 = UTC) as YYYYMMDDTHHMM
+            time_to: time as UTC (ETC + 5 = UTC) as YYYYMMDDTHHMM
+            sort: sort results based on EARLIEST, LATEST, RELEVANCE; default=Latest
+            limit: number of matching results, default=50
+        Returns: Pandas dataframe
+
+        """
+        _FUNCTION_KEY = "NEWS_SENTIMENT"
+        return _FUNCTION_KEY, 'feed', None
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_top_gainers(self):
+        """returns the top 20 gainers in the US market as pandas dataframe
+        """
+        _FUNCTION_KEY = "TOP_GAINERS_LOSERS"
+        return _FUNCTION_KEY, 'top_gainers', None
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_top_losers(self):
+        """returns the top 20 losers in the US market as pandas dataframe
+        """
+        _FUNCTION_KEY = "TOP_GAINERS_LOSERS"
+        return _FUNCTION_KEY, 'top_losers', None
+
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_most_actively_traded(self):
+        """returns the top 20 most active traded tickers in the US market as pandas dataframe
+        """
+        _FUNCTION_KEY = "TOP_GAINERS_LOSERS"
+        return _FUNCTION_KEY, 'most_actively_traded', None

alpha_vantage/alphavantage.py

@@ -26,7 +26,7 @@ class AlphaVantage(object):
     _RAPIDAPI_URL = "https://alpha-vantage.p.rapidapi.com/query?"
 
     def __init__(self, key=None, output_format='json',
-                 treat_info_as_error=True, indexing_type='date', proxy=None, rapidapi=False):
+                 treat_info_as_error=True, index=True, indexing_type='date', proxy=None, rapidapi=False):
         """ Initialize the class
 
         Keyword Arguments:
@@ -35,6 +35,7 @@ def __init__(self, key=None, output_format='json',
                 server not able to answer the call.
             treat_info_as_error: Treat information from the api as errors
             output_format:  Either 'json', 'pandas' os 'csv'
+            index: Yes or No; Giving option to override indexing if not interested
             indexing_type: Either 'date' to use the default date string given
             by the alpha vantage api call or 'integer' if you just want an
             integer indexing on your dataframe. Only valid, when the
@@ -70,6 +71,7 @@ def __init__(self, key=None, output_format='json',
         # Not all the calls accept a data type appended at the end, this
         # variable will be overridden by those functions not needing it.
         self._append_type = True
+        self.index = index
         self.indexing_type = indexing_type
         self.proxy = proxy or {}
 
@@ -282,17 +284,19 @@ def _format_wrapper(self, *args, **kwargs):
                                                                      dtype='object')
                             return data_pandas, meta_data
 
-                    if 'integer' in self.indexing_type:
-                        # Set Date as an actual column so a new numerical index
-                        # will be created, but only when specified by the user.
-                        data_pandas.reset_index(level=0, inplace=True)
-                        data_pandas.index.name = 'index'
+                    if self.index:
+                        if 'integer' in self.indexing_type:
+                            # Set Date as an actual column so a new numerical index
+                            # will be created, but only when specified by the user.
+                            data_pandas.reset_index(level=0, inplace=True)
+                            data_pandas.index.name = 'index'
+                        else:
+                            data_pandas.index.name = 'date'
+                            # convert to pandas._libs.tslibs.timestamps.Timestamp
+                            data_pandas.index = pandas.to_datetime(data_pandas.index)
+                        return data_pandas, meta_data
                     else:
-                        data_pandas.index.name = 'date'
-                        # convert to pandas._libs.tslibs.timestamps.Timestamp
-                        data_pandas.index = pandas.to_datetime(
-                            data_pandas.index)
-                    return data_pandas, meta_data
+                        return data_pandas, meta_data
             elif 'csv' in self.output_format.lower():
                 return call_response, None
             else:

test_alpha_vantage/test_integration_alphavantage.py

@@ -232,3 +232,59 @@ def test_get_digital_currency_weekly(self):
                                       output_format='pandas',
                                       symbol='BTC',
                                       market='CNY')
+
+
+
+    def test_news_sentiment(self):
+
+        ## Ouput will be a pandas dataframe
+#         # Test dictionary as output
+#         ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST)
+#         self._assert_result_is_format(ns.get_news_sentiment,
+#                                       tickers=TestAlphaVantage._API_EQ_NAME_TEST)
+        # Test panda as output
+        ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST,
+                            output_format='pandas')
+        self._assert_result_is_format(ns.get_news_sentiment, output_format='pandas',
+                                      tickers=TestAlphaVantage._API_EQ_NAME_TEST)
+
+
+
+    def test_top_gainers(self):
+
+        ## Ouput will be a pandas dataframe
+        # Test dictionary as output
+#         ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST)
+#         self._assert_result_is_format(ns.get_top_gainers)
+
+        # Test panda as output
+        ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST,
+                            output_format='pandas')
+        self._assert_result_is_format(ns.get_top_gainers, output_format='pandas')
+
+
+    def test_top_losers(self):
+
+        ## Ouput will be a pandas dataframe
+        # Test dictionary as output
+#         ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST)
+#         self._assert_result_is_format(ns.get_top_gainers)
+
+        # Test panda as output
+        ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST,
+                            output_format='pandas')
+        self._assert_result_is_format(ns.get_top_losers, output_format='pandas')
+
+
+    def test_top_active(self):
+
+        ## Ouput will be a pandas dataframe
+        # Test dictionary as output
+#         ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST)
+#         self._assert_result_is_format(ns.get_top_gainers)
+
+        # Test panda as output
+        ns = AlphaIntelligence(key=TestAlphaVantage._API_KEY_TEST,
+                            output_format='pandas')
+        self._assert_result_is_format(ns.get_most_actively_traded, output_format='pandas')
+