Metadata-Version: 2.1
Name: marketanalyst
Version: 0.1.2
Summary: This is wrapper for marketanalyst api
Home-page: https://github.com/agrudgit/python-marketanalyst.git
Author: Sayanta Basu
Author-email: sayanta@agrud.com
License: UNKNOWN
Description: Requirement:
        
        This library requires greater than 3.6 version of python.
        
        Installment:
        First install marketanalyst package from pip so do
        
        pip install marketanalyst
        python -m pip install marketanalyst
        
        This will download the package itself and dependencies that is uses.
        
        How to use:
        
        Import the package.
        import marketanalyst
        Make a client which can be used to call all the other methods.
        	client = marketanalyst.client("your api key","your secret key")
        The client is ready to use, it can be used to call the below methods.
        
        Methods:
        
        All of these methods will return either a string with error message or a dataframe as a success
        Getallsecurities:
        df = client.Getallsecurities()
        This will return a dataframe like this:
                  id      title
        0      71877  AMEX:AAAU
        1      71878  AMEX:AADR
        2      67702  AMEX:AAMC
        3      48525   AMEX:AAU
        4      71880  AMEX:ACIM
        ...      ...        ...
        20631  56925    TSX:YRI
        20632  56932    TSX:ZAR
        20633  56933    TSX:ZAZ
        20634  56934    TSX:ZCL
        20635  56935    TSX:ZNC
        
        [20636 rows x 2 columns]
        
        Here title is the name of security and id represents the database id that was assigned to this security.
        
        getallcategory:
        df = client.getallcategory()
        return:
           id            title
        0   1      Commodities
        1   2       Currencies
        2   5   Global Indices
        3  27    Hong Kong ETF
        4  15  Indian Equities
        5  28    Singapore ETF
        6  29   Singapore REIT
        7   4      US Equities
        8  26         USA ETF 
        getallsubcategory:
        df = client.getallsubcategory("Commodities")
        Return:
            id  title
        0  464  COMEX
        1  463  NYMEX
        getallportfolio:
        df = client.getallportfolio()
        return:
              id                    title
        0   8003                   DOW 30
        1   8008                    FAANG
        2   8004               NASDAQ 100
        3   8010                    nmbjk
        4   8005             Russell 1000
        5   8006             Russell 2000
        6   8007             Russell 3000
        7   8002                  S&P 400
        8   8001                  S&P 500
        9   8009    Warren Buffett Stocks
        
        
        
        
        
        
        getallindicator:
        df = client.getallindicator()
        Return:
        
        
          id        title
        0  1        Price
        1  2    Technical
        2  3  Fundamental
        3  4   Financials
        
        getallsubindicator:
        df = client.getallsubindicator("Price")
        Return:
          id      title
        0  1        EOD
        1  2  Analytics
        
        getdata:
        df = client.getdata(["NASDAQ:AAPL"],"01/01/2012","01/01/2019","Price","EOD")
        Return:
                    e              s                      i                              v            d
        0      NASDAQ  AAPL  D_EODCLOSE_EXT_1     58.75  2012-01-03
        1      NASDAQ  AAPL  D_EODCLOSE_EXT_1     59.06  2012-01-04
        2      NASDAQ  AAPL  D_EODCLOSE_EXT_1     59.72  2012-01-05
        3      NASDAQ  AAPL  D_EODCLOSE_EXT_1     60.34  2012-01-06
        4      NASDAQ  AAPL  D_EODCLOSE_EXT_1     60.25  2012-01-09
        ...       ...   ...               ...       ...         ...
        17590  NASDAQ  MSFT    D_EODVOL_EXT_1  43935100  2018-12-24
        17591  NASDAQ  MSFT    D_EODVOL_EXT_1  51634700  2018-12-26
        17592  NASDAQ  MSFT    D_EODVOL_EXT_1  49498500  2018-12-27
        17593  NASDAQ  MSFT    D_EODVOL_EXT_1  38169300  2018-12-28
        17594  NASDAQ  MSFT    D_EODVOL_EXT_1  33173700  2018-12-31
        
        [17595 rows x 5 columns]
        
        
        
        getOHLCVData:
        df = client.getOHLCVData(["NASDAQ:AAPL","NASDAQ:MSFT"],"01/01/2012","01/01/2019")
        OR 
        df = client.getOHLCVData(["NASDAQ:AAPL","NASDAQ:MSFT"],"01/01/2012","01/01/2019","EOD")
        You can provide sub indicator type like this.
        Return:
                datetime      exchange  security    open     low     high     close     volume
        0     2012-01-03   NASDAQ     AAPL   58.49   58.43   58.93   58.75  75564699
        1     2012-01-04   NASDAQ     AAPL   58.57   58.47   59.24   59.06  65061108
        2     2012-01-05   NASDAQ     AAPL   59.28   58.95   59.79   59.72  67816805
        3     2012-01-06   NASDAQ     AAPL   59.97   59.89   60.39   60.34  79596412
        4     2012-01-09   NASDAQ     AAPL   60.79   60.19   61.11   60.25  98505792
        ...          ...      ...      ...     ...     ...     ...     ...       ...
        3514  2018-12-24   NASDAQ     MSFT   97.68   93.98   97.97   94.13  43935100
        3515  2018-12-26   NASDAQ     MSFT   95.14   93.96  100.69  100.56  51634700
        3516  2018-12-27   NASDAQ     MSFT    99.3    96.4  101.19  101.18  49498500
        3517  2018-12-28   NASDAQ     MSFT  102.09   99.52  102.41  100.39  38169300
        3518  2018-12-31   NASDAQ     MSFT  101.29  100.44   102.4  101.57  33173700
        
        [3519 rows x 8 columns]
        export_df:
        With this method you can export a dataframe to a csv or excel.
        client.export_df(df,'excel',r"D:\some_folder\filename")
        This example is for windows.
        
        
        
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
