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        "author": "Goldman Sachs",
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            "License :: OSI Approved :: Apache Software License",
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        "description": "# GS Quant\n\n**GS Quant** is a Python toolkit for quantitative finance, which provides access to derivatives pricing and risk capabilities through the Goldman Sachs developer APIs, as well as standalone packages for financial analytics.\n\nIt is created and maintained by quantitative developers (quants) at Goldman Sachs to enable the development of trading strategies and analysis of derivative products. GS Quant can be used to facilitate derivative structuring, trading, and risk management, or as a set of statistical packages for data analytics applications.\n\nSee also Getting Started notebook in the gs_quant folder or package.\n\n## Requirements\n\n* Python 3.6 or greater\n* Access to PIP package manager\n\nYou can verity your Python version with the command python --version.\n\nAny Python-ready IDE will work. However, most of our team uses PyCharm.\n\n## Installation\n```\npip install gs-quant\n```\nGS users: \n```\npip install gs-quant[internal] --user\n```\n\n## Examples\n\nThe following example generates a random timeseries and computes 1-month (22 day) rolling realized volatility:\n\n```python\nimport gs_quant.timeseries as ts\n\nx = ts.generate_series(1000)           # Generate random timeseries with 1000 observations\nvol = ts.volatility(x, Window(22, 0))  # Compute realized volatility using a window of 22 and a ramp up value of 0\nvol.tail()                             # Show last few values\n```\n\nOut:\n```\nOut[1]:\n2021-12-20 12.898025\n2021-12-21 12.927230\n2021-12-22 12.929520\n2021-12-23 13.987033\n2021-12-24 14.048165\ndtype: float64\n```\n\nClients of Goldman Sachs have access to a wide array of data through our developer APIs (please contact your sales coverage for details):\n\n```python\nfrom gs_quant.session import Environment, GsSession\nfrom gs_quant.data import Dataset, Fields\nfrom datetime import date\n\nwith GsSession.get(Environment.PROD, <client_id>, <client_secret>, scopes=('read_product_data')):\n\n    basket_ds = Dataset(Dataset.GS.CB)\n    start_date = date(2007,1,1)\n\n    vip_px = basket_ds.get_data_series(Fields.CLOSE_PRICE, start=start_date, ticker='GSTHHVIP')\n    vip_px.tail()\n```\n\nEntitled users can also access pricing and risk engines programmatically:\n\n```python\nfrom gs_quant.instrument import IRSwap\nfrom gs_quant.common import Currency, PayReceive\nimport gs_quant.risk as risk\n\nwith GsSession.get(Environment.PROD, <client_id>, <client_secret>, scopes=('read_product_data','run_analytics')):\n\n    # price an interest rate swap and compute its bucketed delta\n    irs = IRSwap(PayReceive.Pay, \"5y\", Currency.USD, fixed_rate=0.0275)\n    pv = irs.price()\n    ir_delta = irs.calc(risk.IRDelta)\n```\n\n## Contributions\n\nContributions are encouraged! Please see CONTRIBUTING.MD for more details\n\n## Help\nIf you need any help or have feedback, please email us at: gs-quant@gs.com\n\n\n",
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