Merge pull request #255 from renatomello/fundamental_data

Fundamental data

alpha_vantage/alphavantage.py

@@ -219,7 +219,11 @@ def _format_wrapper(self, *args, **kwargs):
                 self, *args, **kwargs)
             if 'json' in self.output_format.lower() or 'pandas' \
                     in self.output_format.lower():
-                data = call_response[data_key]
+                if data_key is not None:
+                    data = call_response[data_key]
+                else:
+                    data = call_response
+
 
                 if meta_data_key is not None:
                     meta_data = call_response[meta_data_key]
@@ -232,7 +236,22 @@ def _format_wrapper(self, *args, **kwargs):
                     output_format = override.lower()
                 # Choose output format
                 if output_format == 'json':
-                    return data, meta_data
+                    if isinstance(data, list):
+                        # If the call returns a list, then we will append them
+                        # in the resulting data frame. If in the future
+                        # alphavantage decides to do more with returning arrays
+                        # this might become buggy. For now will do the trick.
+                        if not data:
+                            data_pandas = pandas.DataFrame()
+                        else:
+                            data_array = []
+                            for val in data:
+                                data_array.append([v for _, v in val.items()])
+                            data_pandas = pandas.DataFrame(data_array, columns=[
+                                k for k, _ in data[0].items()])
+                        return data_pandas, meta_data
+                    else:
+                        return data, meta_data
                 elif output_format == 'pandas':
                     if isinstance(data, list):
                         # If the call returns a list, then we will append them

alpha_vantage/fundamentaldata.py

@@ -0,0 +1,117 @@
+from .alphavantage import AlphaVantage as av
+
+from datetime import datetime
+search_date = datetime.now().date().strftime('%Y-%m-%d')
+
+class FundamentalData(av):
+
+    """This class implements all the api calls to fundamental data
+    """
+    def __init__(self, *args, **kwargs):
+        """
+        Inherit AlphaVantage base class with its default arguments.
+        """
+        super(FundamentalData, self).__init__(*args, **kwargs)
+        self._append_type = False
+        if self.output_format.lower() == 'csv':
+            raise ValueError("Output format {} is not compatible with the FundamentalData class".format(
+                self.output_format.lower()))
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_company_overview(self, symbol):
+        """
+        Returns the company information, financial ratios, 
+        and other key metrics for the equity specified. 
+        Data is generally refreshed on the same day a company reports its latest 
+        earnings and financials.
+
+        Keyword Arguments:
+            symbol:  the symbol for the equity we want to get its data
+        """
+        _FUNCTION_KEY = 'OVERVIEW'
+        return _FUNCTION_KEY, None, None
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_income_statement_annual(self, symbol):
+        """
+        Returns the annual and quarterly income statements for the company of interest. 
+        Data is generally refreshed on the same day a company reports its latest 
+        earnings and financials.
+
+        Keyword Arguments:
+            symbol:  the symbol for the equity we want to get its data
+        """
+        _FUNCTION_KEY = 'INCOME_STATEMENT'
+        return _FUNCTION_KEY, 'annualReports', 'symbol'
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_income_statement_quarterly(self, symbol):
+        """
+        Returns the annual and quarterly income statements for the company of interest. 
+        Data is generally refreshed on the same day a company reports its latest 
+        earnings and financials.
+
+        Keyword Arguments:
+            symbol:  the symbol for the equity we want to get its data
+        """
+        _FUNCTION_KEY = 'INCOME_STATEMENT'
+        return _FUNCTION_KEY, 'quarterlyReports', 'symbol'
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_balance_sheet_annual(self, symbol):
+        """
+        Returns the annual and quarterly balance sheets for the company of interest.
+        Data is generally refreshed on the same day a company reports its latest 
+        earnings and financials.
+
+        Keyword Arguments:
+            symbol:  the symbol for the equity we want to get its data
+        """
+        _FUNCTION_KEY = 'BALANCE_SHEET'
+        return _FUNCTION_KEY, 'annualReports', 'symbol'
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_balance_sheet_quarterly(self, symbol):
+        """
+        Returns the annual and quarterly balance sheets for the company of interest.
+        Data is generally refreshed on the same day a company reports its latest 
+        earnings and financials.
+
+        Keyword Arguments:
+            symbol:  the symbol for the equity we want to get its data
+        """
+        _FUNCTION_KEY = 'BALANCE_SHEET'
+        return _FUNCTION_KEY, 'quarterlyReports', 'symbol'
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_cash_flow_annual(self, symbol):
+        """
+        Returns the annual and quarterly cash flows for the company of interest.
+        Data is generally refreshed on the same day a company reports its latest 
+        earnings and financials.
+
+        Keyword Arguments:
+            symbol:  the symbol for the equity we want to get its data
+        """
+        _FUNCTION_KEY = 'CASH_FLOW'
+        return _FUNCTION_KEY, 'annualReports', 'symbol'
+
+    @av._output_format
+    @av._call_api_on_func
+    def get_cash_flow_quarterly(self, symbol):
+        """
+        Returns the annual and quarterly cash flows for the company of interest.
+        Data is generally refreshed on the same day a company reports its latest 
+        earnings and financials.
+
+        Keyword Arguments:
+            symbol:  the symbol for the equity we want to get its data
+        """
+        _FUNCTION_KEY = 'CASH_FLOW'
+        return _FUNCTION_KEY, 'quarterlyReports', 'symbol'

test_alpha_vantage/test_alphavantage.py

@@ -4,6 +4,7 @@
 from ..alpha_vantage.techindicators import TechIndicators
 from ..alpha_vantage.sectorperformance import SectorPerformances
 from ..alpha_vantage.foreignexchange import ForeignExchange
+from ..alpha_vantage.fundamentaldata import FundamentalData
 
 from pandas import DataFrame as df, Timestamp
 
@@ -200,3 +201,27 @@ def test_foreign_exchange(self, mock_request):
                 from_currency='BTC', to_currency='CNY')
             self.assertIsInstance(
                 data, dict, 'Result Data must be a dictionary')
+
+    @requests_mock.Mocker()
+    def test_fundamental_data(self, mock_request):
+        """Test that api call returns a json file as requested
+        """
+        fd = FundamentalData(key=TestAlphaVantage._API_KEY_TEST)
+        url = 'https://www.alphavantage.co/query?function=INCOME_STATEMENT&symbol=IBM&apikey=test'
+        path_file = self.get_file_from_url("mock_fundamental_data")
+        with open(path_file) as f:
+            mock_request.get(url, text=f.read())
+            data, _ = fd.get_income_statement_annual(symbol='IBM')
+            self.assertIsInstance(data, df, 'Result Data must be a pandas data frame')
+
+    @requests_mock.Mocker()
+    def test_company_overview(self, mock_request):
+        """Test that api call returns a json file as requested
+        """
+        fd = FundamentalData(key=TestAlphaVantage._API_KEY_TEST)
+        url = "https://www.alphavantage.co/query?function=OVERVIEW&symbol=IBM&apikey=test"
+        path_file = self.get_file_from_url("mock_company_overview")
+        with open(path_file) as f:
+            mock_request.get(url, text=f.read())
+            data, _ = fd.get_company_overview(symbol='IBM')
+            self.assertIsInstance(data, dict, 'Result Data must be a dictionary')

test_alpha_vantage/test_data/mock_company_overview

@@ -0,0 +1,61 @@
+{
+    "Symbol": "IBM",
+    "AssetType": "Common Stock",
+    "Name": "International Business Machines Corporation",
+    "Description": "International Business Machines Corporation operates as an integrated solutions and services company worldwide. Its Cloud & Cognitive Software segment offers software for vertical and domain-specific solutions in health, financial services, and Internet of Things (IoT), weather, and security software and services application areas; and customer information control system and storage, and analytics and integration software solutions to support client mission critical on-premise workloads in banking, airline, and retail industries. It also offers middleware and data platform software, including Red Hat, which enables the operation of clients' hybrid multi-cloud environments; and Cloud Paks, WebSphere distributed, and analytics platform software, such as DB2 distributed, information integration, and enterprise content management, as well as IoT, Blockchain and AI/Watson platforms. The company's Global Business Services segment offers business consulting services; system integration, application management, maintenance, and support services for packaged software; finance, procurement, talent and engagement, and industry-specific business process outsourcing services; and IT infrastructure and platform services. Its Global Technology Services segment provides project, managed, outsourcing, and cloud-delivered services for enterprise IT infrastructure environments; and IT infrastructure support services. The company's Systems segment offers servers for businesses, cloud service providers, and scientific computing organizations; data storage products and solutions; and z/OS, an enterprise operating system, as well as Linux. Its Global Financing segment provides lease, installment payment, loan financing, short-term working capital financing, and remanufacturing and remarketing services. The company was formerly known as Computing-Tabulating-Recording Co. and changed its name to International Business Machines Corporation in 1924. The company was founded in 1911 and is headquartered in Armonk, New York.",
+    "Exchange": "NYSE",
+    "Currency": "USD",
+    "Country": "USA",
+    "Sector": "Technology",
+    "Industry": "Information Technology Services",
+    "Address": "One New Orchard Road, Armonk, NY, United States, 10504",
+    "FullTimeEmployees": "352600",
+    "FiscalYearEnd": "December",
+    "LatestQuarter": "2020-06-30",
+    "MarketCapitalization": "114234572800",
+    "EBITDA": "15576999936",
+    "PERatio": "14.1327",
+    "PEGRatio": "8.7525",
+    "BookValue": "23.076",
+    "DividendPerShare": "6.52",
+    "DividendYield": "0.0529",
+    "EPS": "8.811",
+    "RevenuePerShareTTM": "85.058",
+    "ProfitMargin": "0.1043",
+    "OperatingMarginTTM": "0.1185",
+    "ReturnOnAssetsTTM": "0.0362",
+    "ReturnOnEquityTTM": "0.4097",
+    "RevenueTTM": "75499003904",
+    "GrossProfitTTM": "36489000000",
+    "DilutedEPSTTM": "8.811",
+    "QuarterlyEarningsGrowthYOY": "-0.458",
+    "QuarterlyRevenueGrowthYOY": "-0.054",
+    "AnalystTargetPrice": "135.19",
+    "TrailingPE": "14.1327",
+    "ForwardPE": "11.2867",
+    "PriceToSalesRatioTTM": "1.4762",
+    "PriceToBookRatio": "5.4017",
+    "EVToRevenue": "2.2076",
+    "EVToEBITDA": "11.0349",
+    "Beta": "1.2071",
+    "52WeekHigh": "158.75",
+    "52WeekLow": "90.56",
+    "50DayMovingAverage": "124.9097",
+    "200DayMovingAverage": "122.2713",
+    "SharesOutstanding": "890579008",
+    "SharesFloat": "889189445",
+    "SharesShort": "21600483",
+    "SharesShortPriorMonth": "23242369",
+    "ShortRatio": "4.51",
+    "ShortPercentOutstanding": "0.02",
+    "ShortPercentFloat": "0.0243",
+    "PercentInsiders": "0.108",
+    "PercentInstitutions": "58.555",
+    "ForwardAnnualDividendRate": "6.52",
+    "ForwardAnnualDividendYield": "0.0529",
+    "PayoutRatio": "0.7358",
+    "DividendDate": "2020-09-10",
+    "ExDividendDate": "2020-08-07",
+    "LastSplitFactor": "2:1",
+    "LastSplitDate": "1999-05-27"
+}