Metadata-Version: 2.1
Name: gs-quant
Version: 0.5.6
Summary: Goldman Sachs Quant
Home-page: https://marquee.gs.com
Author: Goldman Sachs
Author-email: developer@gs.com
License: http://www.apache.org/licenses/LICENSE-2.0
Description: # GS Quant
        
        GS Quant is a python toolkit for quantitative finance, which provides access to an extensive set of derivatives pricing data through the Goldman Sachs Marquee developer APIs. Libraries are provided for timeseries analytics, portfolio manipulation, risk and scenario analytics and backtesting. Can be used to interact with the Marquee platform programmatically, or as a standalone software package for quantitiative analytics.
        Created and maintained by quantitative developers (quants) at Goldman Sachs to enable development of trading strategies and analysis of derivative products. Can be used to facilitate derivative structuring and trading, or as statistical packages for a variety of timeseries analytics applications.
        See also Getting Started notebook in the gs_quant folder or package.
        
        ## Installation
        pip install gs_quant
        
        ## Dependencies
        Python 3.6 or 3.7  
        Package dependencies can be installed by pip.
        
        ## Example
        ```python
        import datetime
        import numpy as np
        import pandas as pd
        
        from gs_quant.session import Environment, GsSession
        
        # N.b., GsSession.use(Environment.PROD, <client_id>, <client_secret>, scopes=('read_product_data','run_analytics')) will set the default session
         
        with GsSession.get(Environment.PROD, <client_id>, <client_secret>, scopes=('read_product_data','run_analytics')):
            # get coverage for a dataset; run a query
        	from gs_quant.api.dataset import Dataset
            weather = Dataset('WEATHER')
            coverage = weather.get_coverage()
            df = weather.get_data(datetime.date(2016, 1, 15), datetime.date(2016, 1, 16), city=['Boston', 'Austin'])
        
            # calculate vol for a time series
        	from gs_quant.timeseries import realized_volatility
            range = pd.date_range('1/1/2005', periods=3650, freq='D')
            curve = pd.Series(np.random.rand(len(range)), index=range)  # randomly generated
            vol = realized_volatility(curve, 252)
            vol.plot()  # requires matplotlib
            
            # price an interest rates swap and compute its bucketed delta
        	from gs_quant.api.instrument import IRSwap
        	from gs_quant.api.common import Currency, PayReceive
        	import gs_quant.api.risk as risk
            irs = IRSwap(PayReceive.Pay, "5y", Currency.USD, fixedRate=0.035)
            pv = irs.price()
            irDelta = irs.calc(risk.IRDelta)
        ```
        
        ## Help
        Questions? Comments? Write to data-services@gs.com
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Description-Content-Type: text/markdown
Provides-Extra: notebook
Provides-Extra: test
Provides-Extra: develop
Provides-Extra: kerb
