Metadata-Version: 2.1
Name: marketanalyst
Version: 0.2.3
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:
        
        getallsecurities:
        df = client.getallsecurities("nasdaq","stock")
        or 
        df = client.getallsecurities(lookup="aapl")
        or
        df = client.getallsecurities(master_id="67702,48525")
        This will return a dataframe like this:
             exchange_code  exchange_id symbol security_type  security_type_id  master_id  company_id                           name news_function  keyword_id currency country_code
        0           NASDAQ            1    AAL         STOCK                 4      45402       45402    American Airlines Group Inc    NASDAQ:AAL         5.0      USD           US
        1           NASDAQ            1   AAME         STOCK                 4      45403       45403  Atlantic American Corporation   NASDAQ:AAME         7.0      USD           US
        2           NASDAQ            1   AAOI         STOCK                 4      45404       45404    Applied Optoelectronics Inc   NASDAQ:AAOI         9.0      USD           US
        3           NASDAQ            1   AAON         STOCK                 4      45405       45405                       AAON Inc   NASDAQ:AAON        10.0      USD           US
        4           NASDAQ            1   AAPL         STOCK                 4      45406       45406                      Apple Inc   NASDAQ:AAPL        12.0      USD           US
        ...            ...          ...    ...           ...               ...        ...         ...                            ...           ...         ...      ...          ...
        2274        NASDAQ            1   ZSAN         STOCK                 4      49553       49553      Zosano Pharma Corporation   NASDAQ:ZSAN     15827.0      USD           US
        2275        NASDAQ            1   ZUMZ         STOCK                 4      48186       48186                     Zumiez Inc   NASDAQ:ZUMZ      4297.0      USD           US
        2276        NASDAQ            1    ZVO         STOCK                 4      43124       43124                      Zovio Inc    NASDAQ:ZVO         NaN      USD           US
        2277        NASDAQ            1   ZYNE         STOCK                 4      68720       68720    Zynerba Pharmaceuticals Inc   NASDAQ:ZYNE      4298.0      USD           US
        2278        NASDAQ            1   ZYXI         STOCK                 4      71587       71587                      ZYNEX INC   NASDAQ:ZYXI     21454.0      USD           US
        
        [2279 rows x 12 columns]
        
        
        
        getallindicator:
        df = client.getallindicator(lookup="eod")
        or
        df = client.getallindicator(indicator_category="4")
        or
        df = client.getallindicator(indicator="1,3")
        Return:
        
           indicator_id         indicator  indicator_category_id indicator_category       title                      definition    data_type  data_type_id
        0           371  D_EODCLOSE_EXT_1                      1              Price   EOD Close     Close Value of the security  TYPE_NUMBER             0
        1           372  D_EODCLOSE_EXT_2                      1              Price   EOD Close     Close Value of the security  TYPE_NUMBER             0
        2           373   D_EODHIGH_EXT_1                      1              Price    EOD High      High Value of the security  TYPE_NUMBER             0
        3           374   D_EODHIGH_EXT_2                      1              Price    EOD High      High Value of the security  TYPE_NUMBER             0
        4           375    D_EODLOW_EXT_1                      1              Price     EOD Low       Low Value of the security  TYPE_NUMBER             0
        5           376    D_EODLOW_EXT_2                      1              Price     EOD Low       Low Value of the security  TYPE_NUMBER             0
        6           377   D_EODOPEN_EXT_1                      1              Price    EOD Open      Open Value of the security  TYPE_NUMBER             0
        7           378   D_EODOPEN_EXT_2                      1              Price    EOD Open      Open Value of the security  TYPE_NUMBER             0
        8           379    D_EODVOL_EXT_1                      1              Price  EOD Volume  Volume traded for the security  TYPE_NUMBER             0
        9           380    D_EODVOL_EXT_2                      1              Price  EOD Volume  Volume traded for the security  TYPE_NUMBER             0
        
        getuserportfolio:
        df = client.getuserportfolio(11)
        
        {
            "global_portfolio": {
                "portfolio": {
                    "AMEX:ADR": "2",
                    "AMEX:ETF": "4",
                    "AMEX:STOCK": "5",
                    "AS:STOCK": "38",
                    "AUPVT:STOCK": "42",
                    "BATS:ETF": "6",
                    "BSE:ETF": "7",
                    "BSE:STOCK": "8",
                    "CAPVT:STOCK": "43",
                    "CHPVT:STOCK": "44",
                    "CO:STOCK": "36",
                    "COMEX:SPOT": "9",
                    "DEPVT:STOCK": "45",
                    "FOREX:CROSS": "10",
                    "FOREX:SPOT": "11",
                    "FRPVT:STOCK": "46",
                    "GBPVT:STOCK": "47",
                    "HKEX:ETF": "12",
                    "HKEX:HSHARES": "29",
                    "HKEX:STOCK": "28",
                    "INDEX:INDEX": "13",
                    "INDMF:MF": "14",
                    "KO:STOCK": "31",
                    "LSE:STOCK": "40",
                    "NASDAQ100": "63",
                    "NASDAQ:ADR": "15",
                    "NASDAQ:ETF": "16",
                    "NASDAQ:STOCK": "17",
                    "NSE:ETF": "18",
                    "NSE:REIT": "26",
                    "NSE:STOCK": "19",
                    "NYMEX:SPOT": "20",
                    "NYSE:ADR": "21",
                    "NYSE:STOCK": "22",
                    "PA:STOCK": "34",
                    "PORTFOLIO:INDEX": "41",
                    "RUSSELL2000": "69",
                    "SGX:ETF": "23",
                    "SGX:REIT": "24",
                    "SGX:STOCK": "27",
                    "SHG:STOCK": "33",
                    "SP500": "67",
                    "SW:STOCK": "30",
                    "TO:STOCK": "39",
                    "TSE:STOCK": "35",
                    "TW:STOCK": "32",
                    "USPVT:STOCK": "48",
                    "XETRA:STOCK": "37",
                    "ZAPVT:STOCK": "49"
                },
                "user_id": "2"
            },
            "user_portfolio": {
                "portfolio": {
                    "KRISTAL-GLOBAL-INDICES": "58",
                    "KRISTAL-GLOBAL-STOCKS": "57",
                    "KRISTAL-INDICES": "59"
                },
                "user_id": "11"
            }
        }
        
        getportfoliodetails:
        df = client.getportfoliodetails(11,58)
        Return:
        
          master_id                   name exchange_id exchange_code symbol security_type_id holdings_type holdings
        0     61821       NASDAQ Composite           4         INDEX   CCMP               23             0     None
        1     61869  DJ Industrial Average           4         INDEX   INDU               23             0     None
        2     62384         NYSE Composite           4         INDEX    NYA               23             0     None
        3     62870          S&P 500 Index           4         INDEX    SPX               23             0     None
        
        getportfoliodata:
        df = client.getportfoliodata(11,58,"371,373")
        Return:
        
           master_id indicator_id      value data_type     ts_date   ts_hour
        0      61821          371   11939.67         0  2020-09-01  00:00:00
        1      61821          373   11945.72         0  2020-09-01  00:00:00
        2      61821          375   11794.78         0  2020-09-01  00:00:00
        3      61821          377   11844.13         0  2020-09-01  00:00:00
        4      61821          379          0         0  2020-09-01  00:00:00
        5      61869          371   28645.66         0  2020-09-01  00:00:00
        6      61869          373   28659.26         0  2020-09-01  00:00:00
        7      61869          375   28290.91         0  2020-09-01  00:00:00
        8      61869          377   28439.61         0  2020-09-01  00:00:00
        9      61869          379  428663800         0  2020-09-01  00:00:00
        10     62384          371   13113.74         0  2020-09-01  00:00:00
        11     62384          373   13113.93         0  2020-09-01  00:00:00
        12     62384          375   13004.17         0  2020-09-01  00:00:00
        13     62384          377   13032.04         0  2020-09-01  00:00:00
        14     62384          379          0         0  2020-09-01  00:00:00
        15     62870          371    3526.65         0  2020-09-01  00:00:00
        16     62870          373    3528.03         0  2020-09-01  00:00:00
        17     62870          375     3494.6         0  2020-09-01  00:00:00
        18     62870          377    3507.44         0  2020-09-01  00:00:00
        19     62870          379          0         0  2020-09-01  00:00:00
        
        getdata:
        df = client.getdata(["aapl","msft"],"price","2020-01-01,07:00:00","2020-01-05,12:00:00")
        Return:
        
          master_id indicator_id         value data_type     ts_date   ts_hour
        0     45406          330  1.357336e+12         0  2020-01-02  00:00:00
        1     45406          330  1.344140e+12         0  2020-01-03  00:00:00
        2     45406          335  2.509190e+01         0  2020-01-02  00:00:00
        3     45406          335  2.484795e+01         0  2020-01-03  00:00:00
        4     45406          337  5.330931e+00         0  2020-01-02  00:00:00
        5     45406          337  5.279104e+00         0  2020-01-03  00:00:00
        6     45406          415  1.025470e-02         0  2020-01-02  00:00:00
        7     45406          415  1.035538e-02         0  2020-01-03  00:00:00
        8     45406          744  1.532789e+01         0  2020-01-02  00:00:00
        9     45406          744  1.517887e+01         0  2020-01-03  00:00:00
        0     47070          330  1.226399e+12         0  2020-01-02  00:00:00
        1     47070          330  1.211129e+12         0  2020-01-03  00:00:00
        2     47070          335  2.996642e+01         0  2020-01-02  00:00:00
        3     47070          335  2.959328e+01         0  2020-01-03  00:00:00
        4     47070          337  9.445457e+00         0  2020-01-02  00:00:00
        5     47070          337  9.327845e+00         0  2020-01-03  00:00:00
        6     47070          415  1.145561e-02         0  2020-01-02  00:00:00
        7     47070          415  1.160005e-02         0  2020-01-03  00:00:00
        8     47070          744  1.156122e+01         0  2020-01-02  00:00:00
        9     47070          744  1.141726e+01         0  2020-01-03  00:00:00
        
        getOHLCVData:
        df = client.getOHLCVData(["aapl","msft","AAAU"],"2020-01-01,07:00:00","2020-01-30,12:00:00")
        
        Return:
        
                       datetime exchange symbol      open      high       low    close      volume
        0   2020-01-02 00:00:00   NASDAQ   AAPL  296.2400  300.6000  295.1900  300.350  33911864.0
        1   2020-01-03 00:00:00   NASDAQ   AAPL  297.1500  300.5800  296.5000  297.430  36633878.0
        2   2020-01-06 00:00:00   NASDAQ   AAPL  293.7900  299.9600  292.7500  299.800  29644644.0
        3   2020-01-07 00:00:00   NASDAQ   AAPL  299.8400  300.9000  297.4800  298.390  27877655.0
        4   2020-01-08 00:00:00   NASDAQ   AAPL  297.1600  304.4399  297.1560  303.190  33090946.0
        5   2020-01-09 00:00:00   NASDAQ   AAPL  307.2350  310.4300  306.2000  309.630  42621542.0
        6   2020-01-10 00:00:00   NASDAQ   AAPL  310.6000  312.6700  308.2500  310.330  35217272.0
        7   2020-01-13 00:00:00   NASDAQ   AAPL  311.6400  317.0700  311.1500  316.960  30521722.0
        8   2020-01-14 00:00:00   NASDAQ   AAPL  316.7000  317.5700  312.1700  312.680  40653457.0
        9   2020-01-15 00:00:00   NASDAQ   AAPL  311.8500  315.5000  309.5500  311.340  30480882.0
        10  2020-01-16 00:00:00   NASDAQ   AAPL  313.5900  315.7000  312.0900  315.240  27207254.0
        11  2020-01-17 00:00:00   NASDAQ   AAPL  316.2700  318.7400  315.0000  318.730  34454117.0
        12  2020-01-21 00:00:00   NASDAQ   AAPL  317.1900  319.0200  316.0000  316.570  27710814.0
        13  2020-01-22 00:00:00   NASDAQ   AAPL  318.5800  319.9900  317.3100  317.700  25458115.0
        14  2020-01-23 00:00:00   NASDAQ   AAPL  317.9200  319.5600  315.6500  319.230  26117993.0
        15  2020-01-24 00:00:00   NASDAQ   AAPL  320.2500  323.3300  317.5188  318.310  36634380.0
        16  2020-01-27 00:00:00   NASDAQ   AAPL  310.0600  311.7700  304.8800  308.950  40485005.0
        17  2020-01-28 00:00:00   NASDAQ   AAPL  312.6000  318.4000  312.1900  317.690  40558486.0
        18  2020-01-29 00:00:00   NASDAQ   AAPL  324.4500  327.8500  321.3800  324.340  54149928.0
        19  2020-01-30 00:00:00   NASDAQ   AAPL  320.5435  324.0900  318.7500  323.870  31685808.0
        20  2020-01-02 00:00:00   NASDAQ   MSFT  158.7800  160.7300  158.3300  160.620  22634546.0
        21  2020-01-03 00:00:00   NASDAQ   MSFT  158.3200  159.9450  158.0600  158.620  21121681.0
        22  2020-01-06 00:00:00   NASDAQ   MSFT  157.0800  159.1000  156.5100  159.030  20826702.0
        23  2020-01-07 00:00:00   NASDAQ   MSFT  159.3200  159.6700  157.3200  157.580  21881740.0
        24  2020-01-08 00:00:00   NASDAQ   MSFT  158.9300  160.8000  157.9491  160.090  27762026.0
        25  2020-01-09 00:00:00   NASDAQ   MSFT  161.8350  162.2150  161.0300  162.090  21399951.0
        26  2020-01-10 00:00:00   NASDAQ   MSFT  162.8235  163.2200  161.1800  161.340  20733946.0
        27  2020-01-13 00:00:00   NASDAQ   MSFT  161.7600  163.3100  161.2600  163.280  21637007.0
        28  2020-01-14 00:00:00   NASDAQ   MSFT  163.3900  163.6000  161.7200  162.130  23500783.0
        29  2020-01-15 00:00:00   NASDAQ   MSFT  162.6200  163.9400  162.5700  163.180  21417871.0
        30  2020-01-16 00:00:00   NASDAQ   MSFT  164.3500  166.2400  164.0300  166.170  23865360.0
        31  2020-01-17 00:00:00   NASDAQ   MSFT  167.4200  167.4675  165.4300  167.100  34371659.0
        32  2020-01-21 00:00:00   NASDAQ   MSFT  166.6800  168.1900  166.4300  166.500  29517191.0
        33  2020-01-22 00:00:00   NASDAQ   MSFT  167.4000  167.4900  165.6800  165.700  24138777.0
        34  2020-01-23 00:00:00   NASDAQ   MSFT  166.1900  166.8000  165.2700  166.720  19680766.0
        35  2020-01-24 00:00:00   NASDAQ   MSFT  167.5100  167.5300  164.4500  165.040  24918117.0
        36  2020-01-27 00:00:00   NASDAQ   MSFT  161.1500  163.3750  160.2000  162.280  32078067.0
        37  2020-01-28 00:00:00   NASDAQ   MSFT  163.7800  165.7550  163.0730  165.460  24899940.0
        38  2020-01-29 00:00:00   NASDAQ   MSFT  167.8400  168.7500  165.6900  168.040  35127771.0
        39  2020-01-30 00:00:00   NASDAQ   MSFT  174.0500  174.0500  170.7900  172.780  51597470.0
        40  2020-01-02 00:00:00     AMEX   AAAU   15.2400   15.2750   15.2000   15.250     43147.0
        41  2020-01-03 00:00:00     AMEX   AAAU   15.4500   15.4900   15.4179   15.450     53449.0
        42  2020-01-06 00:00:00     AMEX   AAAU   15.7600   15.7658   15.5900   15.620     84879.0
        43  2020-01-07 00:00:00     AMEX   AAAU   15.6400   15.6999   15.6399   15.680     37083.0
        44  2020-01-08 00:00:00     AMEX   AAAU   15.7500   15.7500   15.4865   15.560    136634.0
        45  2020-01-09 00:00:00     AMEX   AAAU   15.4800   15.5100   15.4100   15.465     24655.0
        46  2020-01-10 00:00:00     AMEX   AAAU   15.5000   15.5700   15.4955   15.570     99055.0
        47  2020-01-13 00:00:00     AMEX   AAAU   15.5000   15.5100   15.2600   15.450    170858.0
        48  2020-01-14 00:00:00     AMEX   AAAU   15.4000   15.4400   15.3750   15.435     43493.0
        49  2020-01-15 00:00:00     AMEX   AAAU   15.5000   15.5400   15.4549   15.520     41800.0
        50  2020-01-16 00:00:00     AMEX   AAAU   15.5000   15.5001   15.4500   15.490     78193.0
        51  2020-01-17 00:00:00     AMEX   AAAU   15.5300   15.5800   15.5100   15.530     91640.0
        52  2020-01-21 00:00:00     AMEX   AAAU   15.4500   15.6700   15.4400   15.550    164200.0
        53  2020-01-22 00:00:00     AMEX   AAAU   15.5400   15.5500   15.5100   15.550     28900.0
        54  2020-01-23 00:00:00     AMEX   AAAU   15.5400   15.6400   15.5400   15.590    125200.0
        55  2020-01-24 00:00:00     AMEX   AAAU   15.5600   15.7200   15.5600   15.680     68600.0
        56  2020-01-27 00:00:00     AMEX   AAAU   15.8000   15.8000   15.7400   15.790     56800.0
        57  2020-01-28 00:00:00     AMEX   AAAU   15.7200   15.7300   15.6500   15.650     37700.0
        58  2020-01-29 00:00:00     AMEX   AAAU   15.6500   15.7400   15.6400   15.730     29700.0
        59  2020-01-30 00:00:00     AMEX   AAAU   15.7600   15.8100   15.7000   15.740     87200.0
        
        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
