first commit

README.md

@@ -0,0 +1,58 @@
+# PyIRess #
+
+PyIress is a Python interface onto the Iress web services API. See the [Website] (https://www.iress.com/za/company/products/iress-pro-market-data-desktop/).
+
+### Main Features ###
+
+* The code allows for easy extraction of the following types of data
+	Time series information
+	Dividend information
+	Index information
+* Version 0.1
+* This is the first version and the intention is to add to the list of available functions.
+* The following are currenly available:
+	- time_series : an items times series
+	- dividends : dividends for an item
+	- get_many : many of the above
+	- MarketCapitalizationHistorical : Index constituent information
+
+### Installation ###
+
+* Dependencies
+	zeep
+	For the [Zeep documentation] (https://python-zeep.readthedocs.io/en/master/).
+	pandas
+	For the dependencies of `pandas` please refer to the [pandas documentation](http://pandas.pydata.org/pandas-docs/stable/install.html).
+
+* To install the latest version
+	pip install pyiress
+
+
+### Some Notes on Use ###
+
+	Pandas is great for data analysis 
+
+
+		from pyiress import Iress
+		import pandas as pd
+		companyname = "<>"
+		username = "<>"
+		password = "<>" 
+		ApplicationID='app'
+
+		iress = Iress(companyname=companyname,username=username,password=password,show_request=False)
+		tickers=['AGL','BIL']
+		exchange = 'JSE'
+		start_date, end_date = pd.datetime(2012,9,30),pd.datetime(2018,8,30)
+		data=iress.get_many('time_series',tickers,exchange,start_date,end_date)
+		data=data[['ClosePrice']]
+		data.unstack(1).plot()
+		
+### Resources ###
+
+* Use the help in Iress Pro. Go to Help/API Documentation and select the pertinant resource.
+
+
+### Acknowledgements ###
+
+* Thanks to Vladimir Filimonov @vfilimonov whe wrote PyDatastream on which this project is based

pyiress/__init__.py

@@ -0,0 +1,2 @@
+### Dummy __init__ file
+from .pyiress import *

pyiress/pyiress.py

@@ -0,0 +1,265 @@
+from __future__ import print_function
+import zeep
+import numpy as np
+import pandas as pd
+import warnings
+
+_INFO = """PyIress documentation (GitHub):
+https://github.com/ceaza/pyiress"""
+
+WSDL_URL_GENERIC='http://127.0.0.1:51234/wsdl.aspx?un={username}&cp={companyname}&svc={service}&svr=&pw={password}'
+
+class PyIressException(Exception):
+    pass
+
+class Iress(object):
+    def __init__(self, companyname,username, password, service='IRESS',raise_on_error=True, show_request=False,
+                 proxy=None, **kwargs):
+        """Establish a connection to the IRESS Web Services with Version 4 desktop.
+
+           companyname / username / password - credentials for the Iress account.
+           service - only service for desktop version is IRESS.
+           raise_on_error - If True then error request will raise a "IressException",
+                            otherwise either empty dataframe or partially
+                            retrieved data will be returned
+           show_request - If True, then every time a request string will be printed
+
+           A custom WSDL url (if necessary for some reasons) could be provided
+           via "url" parameter.
+        """
+        import logging.config
+        self.services=[service]
+        self.show_request = show_request
+        if self.show_request:
+            logging.config.dictConfig({
+                                    'version': 1,
+                                    'formatters': {
+                                        'verbose': {
+                                            'format': '%(name)s: %(message)s'
+                                        }
+                                    },
+                                    'handlers': {
+                                        'console': {
+                                            'level': 'DEBUG',
+                                            'class': 'logging.StreamHandler',
+                                            'formatter': 'verbose',
+                                        },
+                                    },
+                                    'loggers': {
+                                        'zeep.transports': {
+                                            'level': 'DEBUG',
+                                            'propagate': True,
+                                            'handlers': ['console'],
+                                        },
+                                    }
+                                })
+        else:
+            logging.config.dictConfig({
+            'version': 1,
+            'disable_existing_loggers': True,
+            })
+        self.raise_on_error = raise_on_error
+        self.last_status = None     # Will contain status of last request
+        WSDL_URL = WSDL_URL_GENERIC.format(companyname=companyname,username=username,password=password,service=service)
+        self._url = kwargs.pop('url', WSDL_URL)
+        # Trying to connect
+        try:
+            self.client = zeep.Client(wsdl=WSDL_URL, strict=False)
+        except:
+            raise PyIressException('Cannot Connect')
+
+#       Create session
+        login_details={'UserName':username,
+                    'CompanyName':companyname,
+                    'Password':password,
+                    'ApplicationID':'app'}
+        IRESSSessionStartInputHeader = {"Parameters":login_details}
+        self.session=self.client.service.IRESSSessionStart(Input=IRESSSessionStartInputHeader)
+        self.IRESSSessionKey=self.session.Result.DataRows.DataRow[0].IRESSSessionKey
+        self.UserToken=self.session.Result.DataRows.DataRow[0].UserToken
+        self.last_response = None
+        self.header={'Header':{'SessionKey':self.IRESSSessionKey}}
+
+        # Check available data sources
+        if 'IRESS' not in self.services:
+            warnings.warn("'IRESS' source is not available for given subscription!")
+
+    @staticmethod
+    def info():
+        print(_INFO)
+
+    def sources(self):
+        """Return available sources of data.
+        Curretly only IRESS"""
+        return self.services
+
+    def version(self):
+        """Return version of Iress Client."""
+        res = self.client.namespaces
+        return res
+
+
+    def _time_series(self,ticker,exchange,start_date,end_date,freq='daily'):
+        '''
+        SecurityCode string Yes  No  The security code to filter by.  
+        Exchange string Yes  No  The exchange to filter by.  
+        DataSource string Yes  No  The data source to filter by.  
+        Frequency string No  No  The frequency type, one of 'daily', 'weekly', 'monthly', 'quarterly' or 'yearly'.  
+        TimeSeriesFromDate date No  No  The date to retrieve time series from.  
+        TimeSeriesToDate date No  No  The date to retrieve time series to. 
+
+        '''
+        parameters={'Parameters':  {'SecurityCode': ticker,
+                          'Exchange': exchange,
+                          'Frequency':freq,
+                          'TimeSeriesFromDate':start_date.strftime('%Y/%m/%d'),
+                          'TimeSeriesToDate':end_date.strftime('%Y/%m/%d')
+                          } } 
+
+        inputs={**self.header, **parameters}
+        res=self.client.service.TimeSeriesGet2(Input=inputs)
+        try:
+            data=zeep.helpers.serialize_object(res.Result.DataRows.DataRow)
+            df=pd.DataFrame(data)
+#            print(df.tail())
+            df['TimeSeriesDate']=pd.to_datetime(df.TimeSeriesDate)
+            df=df.set_index('TimeSeriesDate')
+#            print(df.columns)
+        except:
+            df=pd.DataFrame()
+
+        return df
+
+
+    def time_series(self,ticker,exchange,start_date,end_date,freq='daily',fields=[]):
+        '''
+        SecurityCode string Yes  No  The security code to filter by.  
+        Exchange string Yes  No  The exchange to filter by.  
+        DataSource string Yes  No  The data source to filter by.  
+        Frequency string No  No  The frequency type, one of 'daily', 'weekly', 'monthly', 'quarterly' or 'yearly'.  
+        TimeSeriesFromDate date No  No  The date to retrieve time series from.  
+        TimeSeriesToDate date No  No  The date to retrieve time series to. 
+
+        Available fields  - ['OpenPrice', 'HighPrice', 'LowPrice', 'ClosePrice', 'TotalVolume',
+                           'TotalValue', 'TradeCount', 'AdjustmentFactor', 'MarketVWAP',
+                           'ShortSold', 'ShortSoldPercent', 'ShortSellPosition',
+                           'ShortSellPositionPercent', '_value_1', 'exchange']
+        '''
+        part_date=start_date
+        data=pd.DataFrame()
+        while part_date < pd.Timestamp(end_date):
+            new_data=self._time_series(ticker,exchange,part_date,end_date,freq)
+            data=pd.concat([data,new_data])
+            part_date = data.index.max() + pd.DateOffset(1,'D')
+        return data
+
+    def dividends(self,ticker,exchange,start_date,end_date,freq=None,index_on='ExDividendDate'):
+        '''
+        SecurityCode string Yes  No  The security code to filter by.  
+        Exchange string Yes  No  The exchange to filter by.  
+        DataSource string Yes  No  The data source to filter by.  
+        Frequency string No  No  The frequency type, one of 'daily', 'weekly', 'monthly', 'quarterly' or 'yearly'.  
+        TimeSeriesFromDate date No  No  The date to retrieve time series from.  
+        TimeSeriesToDate date No  No  The date to retrieve time series to. 
+        
+        available fields = ['DividendAmount', 'AdjustedDividendAmount', 'FrankedPercent',
+                           'PayableDate', 'BooksClosingDate', 'DividendType', 'ShareRate',
+                           'DividendYield', 'DRPPrice', 'DividendDescription', 'DeclarationDate',
+                           'STCCreditsPerShare', '_value_1', 'exchange']
+        '''
+        parameters={'Parameters':  {'SecurityCode': ticker,
+                          'Exchange': exchange,
+                          'PayDateFrom':start_date.strftime('%Y/%m/%d'),
+                          'PayDateTo':end_date.strftime('%Y/%m/%d')
+                          } } 
+
+        inputs={**self.header, **parameters}
+        res=self.client.service.SecurityDividendGetBySecurity(Input=inputs)
+        data=zeep.helpers.serialize_object(res.Result.DataRows.DataRow)
+        df=pd.DataFrame(data)
+        df[index_on]=pd.to_datetime(df[index_on])
+        df=df.set_index(index_on)
+
+        return df
+
+    def MarketCapitalizationHistorical(self,indexcode,ticker,exchange,start_date,end_date):
+        '''
+        Input Parameters
+        For the items that are nullable input None
+        
+        Pos     Name        Type Nullable? DefaultValue Array? ArraySize Description Alias
+        1       IndexCode   string Yes  No  The index to filter by.  
+        2       SecurityCode string Yes  No  Security code.  
+        3       Exchange   string Yes  No  Exchange where the security is listed.  
+        4       MarketCapitalizationDateFrom date No  No  The date to retrieve market capitalization history from.  
+        5       MarketCapitalizationDateTo date No  No  The date to retrieve market capitalization history to. 
+
+        Output Columns
+        Pos Name Type Nullable? Default Value Description Alias
+        1 SecurityCode string No    Security code.  
+        2 Exchange string No    Exchange where the security is listed.  
+        3 GICSCode int32 Yes    Global Industry Classification Standard.  
+        4 MarketCapitalizationDate date No    Date at which market capitalization was calculated.  
+        5 IndexCode string No    Index code.  
+        6 IndexFactor double Yes    The index factor.  
+        7 IndexPoints double Yes    Index points.  
+        8 SharesOnIssue double Yes    Number of shares issued for the security at the time market capitalization was calculated.  
+        9 MarketCapitalizationStartOfDay double Yes    Market capitalization at the start of the day.  
+        10 MarketCapitalizationEndOfDay double Yes    Market capitalization at the end of the day.  
+        11 MarketWeightStartOfDay double Yes    Market weight at the start of the day.  
+        12 MarketWeightEndOfDay double Yes    Market weight at the end of the day.  
+        13 IndexPriceStartOfDay double Yes    Index price at the start of the day.  
+        14 IndexPriceEndOfDay double Yes    Index price at the end of the day. 
+        
+        
+        '''
+
+        parameters={'Parameters':  {'IndexCode':indexcode,
+                    'SecurityCode': ticker,
+                      'Exchange': exchange,
+                      'MarketCapitalizationDateFrom':start_date.strftime('%Y/%m/%d'),
+                      'MarketCapitalizationDateTo':end_date.strftime('%Y/%m/%d')
+                      } } 
+        remove_key=[]
+        for k,v in  parameters['Parameters'].items():
+            if v==None:
+                remove_key.append(k)
+        for k in remove_key:
+            del parameters['Parameters'][k]
+
+        inputs={**self.header, **parameters}
+        res = self.client.service.MarketCapitalizationHistoricalGet(Input=inputs)
+        data=zeep.helpers.serialize_object(res.Result.DataRows.DataRow)
+        df=pd.DataFrame(data)
+        return df
+
+
+    def get_many(self,data_type,tickers,exchange,start_date,end_date,freq='daily'):
+        '''
+        
+        '''
+        data_list=[]
+        date_field={'dividends':'ExDividendDate',
+        'time_series':'TimeSeriesDate'}
+        if data_type not in ['dividends','time_series']:
+            warnings.warn("Not available for this data type")
+            return
+        method_to_call = getattr(self, data_type)
+        for ticker in tickers:
+            data=method_to_call(ticker,exchange,start_date,end_date,freq)
+            data=data.reset_index()
+            data['ticker']=ticker
+            data['exchange']=exchange
+            data=data.set_index([date_field[data_type],'ticker'])
+            data_list.append(data)
+        df_data=pd.concat(data_list)
+        return df_data
+
+if __name__ == "__main__":
+    pass
+
+
+
+
+
+

setup.cfg

@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md

setup.py

@@ -0,0 +1,35 @@
+from distutils.core import setup
+import sys
+
+if sys.version_info.major < 3:
+    break
+else:
+	install_requires_list = ['zeep', 'pandas']
+
+# Long description to be published in PyPi
+LONG_DESCRIPTION = """
+**PyIress** is a Python interface to the **Iress Pro Desktop Web Services
+(IPD)** SOAP client (non free), with some convenience functions for retrieving
+Iress data specifically. This package requires valid credentials for this
+API.
+
+For the documentation please refer to README.md inside the package or on the
+GitHub (https://github.com/ceaza/pyiress/blob/master/README.md).
+"""
+
+_URL = 'https://github.com/ceaza/pyiress'
+_VERSION = '0.0.1'
+
+setup(name='PyIress',
+      version=_VERSION,
+      description='Python interface to the **Iress Pro Desktop Web Services API',
+      long_description=LONG_DESCRIPTION,
+      url=_URL,
+      download_url=_URL + '/archive/v' + _VERSION + '.zip',
+      author='Charles Allderman',
+      author_email='[email protected]',
+      license='MIT License',
+      packages=['pyiress'],
+      install_requires=install_requires_list,
+      classifiers=['Programming Language :: Python :: 3', ]
+      )