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 marketanalystThis will download the package itself and dependencies that is uses.
How to use:
import marketanalystMake a client which can be used to call all the other methods.
client = marketanalyst.client()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(exchange="nasdaq",security_type="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]Here title is the name of security and id represents the database id that was assigned to this security.
- getallcategory:
df = client.getallindicator(lookup="eod")OR
df = client.getallindicator(indicator_category="4")OR
df = client.getallindicator(indicator="1,3")This will return a dataframe like this:
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(user=11)This will return a dataframe like this:
{
"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(user=11,portfolio=58)This will return a dataframe like this:
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(user=2,portfolio=67,indicators="371,373")This will return a dataframe like this:
master_id indicator_id value data_type ts_date ts_hour
0 43365 371 75.51 0 2020-09-04 00:00:00
1 43365 373 76.97 0 2020-09-04 00:00:00
2 43633 371 37.09 0 2020-09-04 00:00:00
3 43633 373 37.7 0 2020-09-04 00:00:00
4 44178 371 53.29 0 2020-09-04 00:00:00
.. ... ... ... ... ... ...
991 82803 373 62.6 0 2020-09-04 00:00:00
992 82804 371 117.77 0 2020-09-04 00:00:00
993 82804 373 119.7999 0 2020-09-04 00:00:00
994 82805 371 61.17 0 2020-09-04 00:00:00
995 82805 373 62.2066 0 2020-09-04 00:00:00
[996 rows x 6 columns]- getdata:
df = client.getdata(exchange=1,security_type=4,indicator_category=1,date_start="2020-01-01,07:00:00",date_end="2020-01-05,12:00:00",master_id="45406,45549",indicator_id="377,379")This will return a dataframe like this:
master_id indicator_id value data_type ts_date ts_hour
0 45406 377 7.406000e+01 0 2020-01-02 00:00:00
1 45406 377 7.429000e+01 0 2020-01-03 00:00:00
2 45406 379 1.354804e+08 0 2020-01-02 00:00:00
3 45406 379 1.463228e+08 0 2020-01-03 00:00:00
4 45549 377 1.875000e+03 0 2020-01-02 00:00:00
5 45549 377 1.864500e+03 0 2020-01-03 00:00:00
6 45549 379 4.035910e+06 0 2020-01-02 00:00:00
7 45549 379 3.766604e+06 0 2020-01-03 00:00:00- getOHLCVData:
df = client.getOHLCVData(exchange=1,security_type=4,date_start="2020-01-01,07:00:00",date_end="2020-01-31,12:00:00",master_id="45406,45549")This will return a dataframe like this:
datetime exchange_id master_id open high low close volume
0 2020-01-02 00:00:00 1 45406 74.06 75.1500 73.8000 75.09 135480400.0
1 2020-01-03 00:00:00 1 45406 74.29 75.1400 74.1300 74.36 146322800.0
2 2020-01-06 00:00:00 1 45406 73.45 74.9900 73.1900 74.95 118387200.0
3 2020-01-07 00:00:00 1 45406 74.96 75.2200 74.3700 74.60 108872000.0
4 2020-01-08 00:00:00 1 45406 74.29 76.1100 74.2900 75.80 132079200.0
5 2020-01-09 00:00:00 1 45406 76.81 77.6100 76.5500 77.41 170108400.0
6 2020-01-10 00:00:00 1 45406 77.65 78.1700 77.0600 77.58 140644800.0
7 2020-01-13 00:00:00 1 45406 77.91 79.2700 77.7900 79.24 121532000.0
8 2020-01-14 00:00:00 1 45406 79.18 79.3900 78.0400 78.17 161954400.0
9 2020-01-15 00:00:00 1 45406 77.96 78.8800 77.3900 77.83 121923600.0
10 2020-01-16 00:00:00 1 45406 78.40 78.9300 78.0200 78.81 108829200.0
11 2020-01-17 00:00:00 1 45406 79.07 79.6800 78.7500 79.68 137816400.0
12 2020-01-21 00:00:00 1 45406 79.30 79.7500 79.0000 79.14 110843200.0
13 2020-01-22 00:00:00 1 45406 79.64 80.0000 79.3300 79.43 101832400.0
14 2020-01-23 00:00:00 1 45406 79.48 79.8900 78.9100 79.81 104472000.0
15 2020-01-24 00:00:00 1 45406 80.06 80.8300 79.3800 79.58 146537600.0
16 2020-01-27 00:00:00 1 45406 77.51 77.9400 76.2200 77.24 161940000.0
17 2020-01-28 00:00:00 1 45406 78.15 79.6000 78.0500 79.42 162234000.0
18 2020-01-29 00:00:00 1 45406 81.11 81.9600 80.3500 81.08 216229200.0
19 2020-01-30 00:00:00 1 45406 80.14 81.0200 79.6900 80.97 126743200.0
20 2020-01-31 00:00:00 1 45406 80.23 80.6700 77.0700 77.38 199588400.0
21 2020-01-02 00:00:00 1 45549 1875.00 1898.0100 1864.1500 1898.01 4035910.0
22 2020-01-03 00:00:00 1 45549 1864.50 1886.1966 1864.5000 1874.97 3766604.0
23 2020-01-06 00:00:00 1 45549 1860.00 1903.6900 1860.0000 1902.88 4065698.0
24 2020-01-07 00:00:00 1 45549 1904.50 1913.8900 1892.0434 1906.86 4134010.0
25 2020-01-08 00:00:00 1 45549 1898.04 1910.9998 1886.4448 1891.97 3511966.0
26 2020-01-09 00:00:00 1 45549 1909.89 1917.8200 1895.8038 1901.05 3174962.0
27 2020-01-10 00:00:00 1 45549 1905.37 1906.9400 1880.0000 1883.16 2856959.0
29 2020-01-14 00:00:00 1 45549 1885.88 1887.1100 1858.5500 1869.44 3446381.0
30 2020-01-15 00:00:00 1 45549 1872.25 1878.8600 1855.0900 1862.02 2896592.0
31 2020-01-16 00:00:00 1 45549 1882.99 1885.5900 1866.0200 1877.94 2659493.0
32 2020-01-17 00:00:00 1 45549 1885.89 1886.6400 1857.2500 1864.72 3997340.0
33 2020-01-21 00:00:00 1 45549 1865.00 1894.2700 1860.0000 1892.00 3707785.0
34 2020-01-22 00:00:00 1 45549 1896.09 1902.5000 1883.3400 1887.46 3216257.0
35 2020-01-23 00:00:00 1 45549 1885.11 1889.9750 1872.7600 1884.58 2484613.0
36 2020-01-24 00:00:00 1 45549 1891.37 1894.9900 1847.4400 1861.64 3766181.0
37 2020-01-27 00:00:00 1 45549 1820.00 1841.0000 1815.3400 1828.34 3528509.0
38 2020-01-28 00:00:00 1 45549 1840.50 1858.1100 1830.0200 1853.25 2808040.0
39 2020-01-29 00:00:00 1 45549 1864.00 1874.7500 1855.0200 1858.00 2101390.0
40 2020-01-30 00:00:00 1 45549 1858.00 1872.8700 1850.6100 1870.68 6327438.0
41 2020-01-31 00:00:00 1 45549 2051.47 2055.7200 2002.2700 2008.72 15567283.0- export_df: With this method you can export a dataframe to a csv or excel or json as well as json data to json file.
client.export_df(df,file_format='excel',path=r"D:\some_folder\filename")For JSON:
client.export_df(json_data,path=r"D:\some_folder\filename")OR
client.export_df(json_data,file_format='json',path=r"D:\some_folder\filename")This example is for windows. For now three formats are supported json,excel,csv.
