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This is wrapper for marketanalyst api

Project 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 getexchanges: df = client.getexchanges("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"],"2012-01-01","2019-01-01","Price","EOD") Return: exchange symbol indicator value date 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"],"2012-01-01","2019-01-01") OR df = client.getOHLCVData(["NASDAQ:AAPL","NASDAQ:MSFT"],"2012-01-01","2019-01-01","EOD") You can provide sub indicator type like this. Return: datetime exchange symbol 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.

websocket def receive_data(data): print(data)

client.onDataEvent("nasdaq-aapl",receive_data) receive_data will recieve the data from socket and it will be a string

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