marketanalyst 0.2.3

This is wrapper for marketanalyst api

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.