Metadata-Version: 2.1
Name: quiverquant
Version: 0.1.53
Summary: Quiver Quantitative Alternative Data
Home-page: https://github.com/Quiver-Quantitative/python-api
Author: Chris Kardatzke
Author-email: chris@quiverquant.com
License: UNKNOWN
Description: # Quiver Quantitative Alternative Data
        This package allows you to access several alternative data sources which are updated daily and mapped to tickers. These include:
        - Trading by US congressmen
        - Corporate Lobbying
        - Government Contracts
        - Patents
        - Off-exchange short volume
        - Companies' Wikipedia page views
        - Discussion on Reddit's r/wallstreetbets
        - Discussion on Reddit's r/SPACs
        - Companies' Twitter followings
        - Flights by corporate private jets
        - Political Beta
        
        This data can be used for backtesting and implementing trading strategies.
        
        ### Receiving API Token
        You can sign up for a Quiver API token [here](https://api.quiverquant.com). 
        
        The pricing starts at $10/month, please [e-mail me](mailto:chris@quiverquant.com) if that is an issue and I may be able to help cover.
        
        ## Getting Started
        #### Prerequisites
        - Python version 3 installed locally
        - Pip installed locally
        
        #### Installation
        The package can easily be installed in your terminal by entering
        ```python
        pip install quiverquant
        ```
        
        ### Usage
        ```python
        #Import the package
        import quiverquant
        
        #Connect to the API using your token
        #Replace <TOKEN> with your token
        quiver = quiverquant.quiver("<TOKEN>")
        
        #Get data on WallStreetBets discussion
        dfWSB = quiver.wallstreetbets()
        
        #Get data on WallStreetBets discussion of GameStop
        dfWSB_GameStop = quiver.wallstreetbets("GME")
        
        #Get recent trades by members of U.S. Congress
        dfCongress = quiver.congress_trading()
        
        #Get trades of a Tesla by members of congress
        dfCongress_Tesla = quiver.congress_trading("TSLA")
        
        #Get trades made by U.S. Senator Richard Burr
        dfCongress_Burr = quiver.congress_trading("Richard Burr", politician=True)
        
        #Get recent corporate lobbying
        dfLobbying = quiver.lobbying()
        
        #Get corporate lobbying by Apple
        dfLobbying_Apple = quiver.lobbying("AAPL")
        
        #Get data on government contracts
        dfContracts = quiver.gov_contracts()
        
        #Get data on government contracts to Lockheed Martin
        dfContracts_Lockheed = quiver.gov_contracts("LMT")
        
        #Get data on off-exchange short volume
        dfOTC = quiver.offexchange()
        
        #Get data on off-exchange short volume for AMC
        dfOTC_AMC = quiver.offexchange("AMC")
        
        #Get data on Wikipedia page views
        dfWiki = quiver.wikipedia()
        
        #Get data on Wikipedia page views of Microsoft
        dfWiki_Microsoft = quiver.wikipedia("MSFT")
        
        #Get data on companies' Twitter following
        dfTwitter = quiver.twitter()
        
        #Get data on GE's Twitter following
        dfTwitter_GE = quiver.twitter("GE")
        
        #Get data on r/SPACs discussion
        dfSPACs = quiver.spacs()
        
        #Get data on r/SPACs discussion of CCIV
        dfSPACs_CCIV = quiver.spacs("CCIV")
        
        #Get data on recent corporate private jet flights
        dfFlights = quiver.flights()
        
        #Get data on private jet flights by Target
        dfFlights_Target = quiver.flights("TGT")
        
        #Get data on patents by Apple
        dfPatents_Apple = quiver.patents("AAPL")
        ```
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
