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 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()
This will return a dataframe like this:
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")
This will return a dataframe like this:
id title
0 464 COMEX
1 463 NYMEX
- getallportfolio:
df = client.getallportfolio()
This will return a dataframe like this:
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()
This will return a dataframe like this:
id title
0 1 Price
1 2 Technical
2 3 Fundamental
3 4 Financials
- getallsubindicator:
df = client.getallsubindicator("Price")
This will return a dataframe like this:
id title
0 1 EOD
1 2 Analytics
- getdata:
df = client.getdata(["NASDAQ:AAPL"],"01/01/2012","01/01/2019","Price","EOD")
This will return a dataframe like this:
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. This will return a dataframe like this:
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.
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