Exploit WRDS Cloud via Python

因为有了自建博客，所以以后博文都是英文，不过我的 Chinglish 应该很好理解 = =。
个人博客地址在这里 https://mengjiexu.com/。

文章目录

Motivation
Connect to WRDS using SSH
- Mac OS
- Windows
Open IPython in the Cloud
Initiating a WRDS connection in Python
Explore WRDS Libraries and Tables
Submit Queries
Transfer Data through Dropbox
- Files in your WRDS Cloud
- Data Storage Locations
- - Permanent Storage - Home Directory
  - Temporary Storage - Scratch Directory
- Link WRDS with Dropbox
- Transfer Files From WRDS to Dropbox
- Transfer Files From Dropbox to WRDS
Batch Jobs on the WRDS Cloud
References

Motivation

The WRDS-SAS Studio seems to be suspended soon. As a big fan of this cloud platform, I have to find something alternative to make my data extraction from WRDS replicable. Compiled from a series of WRDS guidances (you can find them in the Reference part), this blog will introduce how to exploit WRDS cloud via Python.

The biggest advantage of WRDS Cloud is that you don’t have to download everything to your computer but just deal with the massive data using the computing source of WRDS Cloud. Following this blog, one can quickly establish a python-wrds cloud workflow without checking various manuals.

Figure 1: Suspension of WRDS-SAS Studio

Connect to WRDS using SSH

Mac OS

MacOS computers come with a utility called Terminal that has SSH functionality built-in that can be used to connect to the WRDS Cloud. You can initiate an SSH connection to the WRDS Cloud as follows:

Type ssh wrdsusername@wrds-cloud.wharton.upenn.edu where wrdsusername is your WRDS username: the same username you use to log onto the WRDS website.
When prompted, enter your WRDS password.
Once you have connected to the WRDS Cloud, you will be given a prompt – which indicates that the server is ready for your commands.
You can disconnect from the WRDS Cloud at anytime by typing logout or using the key combination CTL-D.

Figure 2: Connect to WRDS Cloud via SSH

Windows

If you are on Windows, you will need to download and install SSH client software (e.g., PuTTY, WinSCP) to your computer.

Once you have downloaded and installed one of the above, you can initiate an SSH connection to the WRDS Cloud. Suppose you use PuTTY and want to configure PuTTY for SSH:

In Host Name, enter wrds-cloud.wharton.upenn.edu
In Port (skip if there is no Port field), enter 22
When prompted, enter your WRDS username.
When prompted, enter your WRDS password.

Open IPython in the Cloud

Interactive Python jobs allow you to run code in serial, and receive a response to each command as you enter it. To run interactive Python jobs, you will need to schedule an interactive job with the WRDS Cloud Grid Engine by entering qrsh and then enter ipython3 to enter an interactive python enviornment, which is exactly what you get in Jupyter. Don’t forget to insert pip install wrds before you connect to the WRDS library list.

[xumj2019@wrds-cloud-login2-w ~]$ qrsh
Last login: Tue Oct 12 14:53:16 2021 from wrds-cloud-login2-w.wharton.private
[xumj2019@wrds-sas7-w ~]$ pip install wrds
Requirement already satisfied: wrds in /usr/local/sas/grid/python2-2.7.17/lib/python2.7/site-packages (3.0.8)
[xumj2019@wrds-sas7-w ~]$ ipython
Python 3.9.5 (default, May  6 2021, 14:32:00)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.23.1 -- An enhanced Interactive Python. Type '?' for help.
In [1]:

Initiating a WRDS connection in Python

First, as with every Python program that intends to connect to WRDS, you must import the wrds module. Then you can make the connection and set up a pgpass file.

In [4]: db = wrds.Connection()
Enter your WRDS username [xumj2019]:xumj2019
Enter your password:
WRDS recommends setting up a .pgpass file.
Create .pgpass file now [y/n]?: y
Created .pgpass file successfully.
Loading library list...
Done

Explore WRDS Libraries and Tables

You can analyze the structure of the data through its metadata using the wrds module, as outlined in the following steps:

List all available libraries at WRDS using list_libraries()
Select a library to work with, and list all available datasets within that library using list_tables()
Select a dataset, and list all available variables (column headers) within that dataset using describe_table()

In [5]: db.list_libraries()
Out[5]:
['aha','aha_sample','ahasamp','audit','auditsmp','auditsmp_all','bank','blab','block','block_all','boardex','boardex_trial','boardsmp','bvd',...]
In [7]: db.list_tables(library='taqmsec')
Out[7]:
['cqm_20030910','cqm_20030911','cqm_20030912','cqm_20030915','cqm_20030916','cqm_20030917','cqm_20030918','cqm_20030919','cqm_20030922','cqm_20030923','cqm_20030924','cqm_20030925','cqm_20030926','cqm_20030929','cqm_20070829',...]
In [9]: db.describe_table(library='taqmsec', table='cqm_20070822')
The row count will return 0 due to the structure of TAQ
There was a problem with retrievingthe row count: 'NoneType' object is not subscriptable
Approximately 0 rows in taqmsec.cqm_20070822.
Out[9]: name  nullable         type
0          date      True         DATE
1        time_m      True         TIME
2            ex      True   VARCHAR(1)
3      sym_root      True   VARCHAR(6)
4    sym_suffix      True  VARCHAR(10)
5           bid      True      NUMERIC
6        bidsiz      True      NUMERIC
7           ask      True      NUMERIC
8        asksiz      True      NUMERIC
9       qu_cond      True   VARCHAR(1)
10        bidex      True   VARCHAR(1)
11        askex      True   VARCHAR(1)
12    qu_seqnum      True      NUMERIC
13   natbbo_ind      True   VARCHAR(1)
14  nasdbbo_ind      True   VARCHAR(1)
15    qu_cancel      True   VARCHAR(1)
16    qu_source      True   VARCHAR(1)

Submit Queries

Now that you know how to query the metadata and understand the structure of the data, you are ready to query WRDS data directly. The wrds module provides several methods that are useful in gathering data:

get_table() - fetches data by matching library and dataset, with the ability to filter using different parameters. This is the easiest method of accessing data.
raw_sql() - executes a SQL query against the specified library and dataset, allowing for highly-granular data queries.
get_row_count() - returns the number of rows in a given dataset.

For parameters, and further explanation of each, use the built-in help: e.g., help(db.get_table).

Here are some examples.

In [10]: data = db.get_table(library='djones', table='djdaily', columns=['date', 'dji'], obs=10)In [11]: data
Out[11]: date    dji
0  1896-05-26  40.94
1  1896-05-27  40.58
2  1896-05-28  40.20
3  1896-05-29  40.63
4  1896-06-01  40.60
5  1896-06-02  40.04
6  1896-06-03  39.77
7  1896-06-04  39.94
8  1896-06-05  40.32
9  1896-06-08  39.81In [12]: parm = {'tickers': ('0015B', '0030B', '0032A', '0033A', '0038A')}In [13]: data = db.raw_sql('SELECT datadate,gvkey,cusip FROM comp.funda WHERE tic in %(tickers)s', params=parm)...: In [14]: data
Out[14]: datadate   gvkey      cusip
0    1982-10-31  002484  121579932
1    1983-10-31  002484  121579932
2    1984-10-31  002484  121579932
3    1985-10-31  002484  121579932
4    1986-10-31  002484  121579932
..          ...     ...        ...
208  2009-12-31  179519  61847Z002
209  2010-12-31  179519  61847Z002
210  2010-12-31  179519  61847Z002
211  2011-12-31  179519  61847Z002
212  2011-12-31  179519  61847Z002[213 rows x 3 columns]In [15]: data = db.get_row_count('djones', 'djdaily')In [16]: data
Out[16]: 27933In [17]: db.raw_sql("select a.gvkey, a.datadate, a.tic, a.conm, a.at, a.lt, b.prccm, b.cshoq from comp.funda a join comp.secm b on ...: a.gvkey = b.gvkey and a.datadate = b.datadate where a.tic = 'IBM' and a.datafmt = 'STD' and a.consol = 'C' and a.indfmt = ...: 'INDL'")...:
Out[17]: gvkey    datadate  tic                         conm          at          lt       prccm     cshoq
0   006066  1962-12-31  IBM  INTL BUSINESS MACHINES CORP    2112.301     731.700  389.999567       NaN
1   006066  1963-12-31  IBM  INTL BUSINESS MACHINES CORP    2373.857     782.119  506.999353       NaN
2   006066  1964-12-31  IBM  INTL BUSINESS MACHINES CORP    3309.152    1055.072  409.499496       NaN
....
In [42]: data=db.raw_sql("SELECT * FROM taqmsec.cqm_20170829 WHERE sym_root = 'A...: APL' AND time_m>='04:00:00.084000' and time_m <= '04:01:00.084000'")In [43]: data
Out[43]: date           time_m ex  ...  finra_adf_mquo_ind  sym_root  sym_suffix
0   2017-08-29  04:00:00.084000  Q  ...                None      AAPL        None
1   2017-08-29  04:00:00.084000  Q  ...                None      AAPL        None
2   2017-08-29  04:00:00.203000  P  ...                None      AAPL        None
3   2017-08-29  04:00:00.203000  P  ...                None      AAPL        None
4   2017-08-29  04:00:00.203000  P  ...                None      AAPL        None
..         ...              ... ..  ...                 ...       ...         ...
89  2017-08-29  04:00:57.213000  P  ...                None      AAPL        None
90  2017-08-29  04:00:57.288000  P  ...                None      AAPL        None
91  2017-08-29  04:00:57.293000  P  ...                None      AAPL        None
92  2017-08-29  04:00:59.543000  P  ...                None      AAPL        None
93  2017-08-29  04:00:59.543000  P  ...                None      AAPL        None[94 rows x 24 columns]
In [44]: data.to_csv("test.csv")

Transfer Data through Dropbox

Files in your WRDS Cloud

Insert ls in your ssh command line, you will find the files you’ve saved in your Wrds Cloud. Seems that you have got the files you desire but the files are stored in WRDS Cloud. The next step is to determine how to exchange files between your personal computer and the WRDS Cloud.

After testing several alternatives (e.g., adopt an FTP server Cyberduck), I personally found linking WRDS with Dropbox is the most efficient way to do this job. Transferring files using SCP is also a good idea but it doesn’t automatically backup your workflows as Dropbox does.

[xumj2019@wrds-sas7-w ~]$ ls
autoexec.sas      testaq.sas                 WRDS_batch_ticker_ctm.sas
evt_taqinput.txt  test.csv                   WRDS_batch_ticker_ct.sas
myProgram.csv     WRDS_batch_ticker_cqm.sas

Data Storage Locations

Having a big picture about how WRDS Cloud stores our files can make our file transferring job more intuitive. As the WRDS Cloud is a shared resource, with many hundreds of users accessing at any one time, this storage space is managed by a quota. Typically, there are two separate quota for your, depending on the storage locations and how long the WRDS Cloud keeps the data for you. Type quota in your command line, you will find how much storage you’ve used.

[xumj2019@wrds-cloud-login2-w xmj]$ quota
Current disk usage for xumj2019 (frankfurt):DIRECTORY  USED / LIMITHome:  548KB / 10GBScratch:  220KB / 500GB** Remember that the Scratch directory quota is shared **
** with all other members of your subscribing institution **NOTE: Quota usage is updated every 30 minutes.
Last updated: 10/13/21 at 05:00 EDT

Permanent Storage - Home Directory

All users are given 10 GB dedicated permanent storage in their home directory in the WRDS Cloud.

Your home directory is located at:

/home/[group name]/[username]

You may store up to 10GB of data in this directory.
Files in this directory are never deleted by WRDS.
Files in this directory are backed up via snapshots created on a regular basis (see the File Recovery section, below)

Note: To determine your group name, use the id command on the WRDS Cloud: the name in the second set or parenthesis is your group name. From my case below, it’s easy to find my group name in WRDS is frankfurt

[xumj2019@wrds-cloud-login2-w xmj]$ id
uid=323854(xumj2019) gid=60052(frankfurt) group=60052(frankfurt)

Temporary Storage - Scratch Directory

All subscribing institutions are given 500 GB shared temporary storage in the scratch directory. Generally, users should direct your larger output result data to this directory for staging before downloading, or before loading into subsequent programs. The scratch directory is located at:

/scratch/[group name]

Members of your institution may store up to 500GB of data in this directory, shared between all members.
Files in your shared scratch directory are deleted after one week (168 hours).
Files in this directory are not backed up anywhere, and should be downloaded shortly after being generated by your programs.

Link WRDS with Dropbox

WRDS provides the dbxcli command on the WRDS cloud login nodes to transfer data directly between WRDS and your dropbox shares. dbxcli is a simple, ftp like transfer client; it only transfers files between dropbox and clients.

To do this, you need:

Login to the WRDS cloud via ssh
- If you are in the IPython enviornment, you need to insert quit to quit IPython, and then logout to exit the Interactive Jobs

Run the dbxcli account command.

[xumj2019@wrds-cloud-login2-w ~]$ dbxcli account
1. Go to https://www.dropbox.com/1/oauth2/authorize?client_id=07o23gulcj8qi69&response_type=code&state=state
2. Click "Allow" (you might have to log in first).
3. Copy the authorization code.
Enter the authorization code here: NMdc-gHdd7AAAAAAAAAEL3CRL_GpJce0RQ
Logged in as xx xx <xx@xx.com>Account Id:        dbid:AAB4yqaueMTEfU4-8
Account Type:      pro
Locale:            en
Referral Link:     https://www.dropbox.com/referrals/AABnCSjxOA?src=app9-934508
Profile Photo Url: https://dl-web.dropbox.com/account_photo/get/dbaphid%3AAAAgMQf5azIHq65no?size=128x128&vers=1561290642421
Paired Account:    false

Follow the instructions the command gives and paste in the authorization code.

Figure 3: Connect WRDS Cloud to Dropbox

Once you’ve successfully link the wrds with your Dropbpox account, you could create a new folder in the Dropbox, say testdir1013

[xumj2019@wrds-cloud-login2-w ~]$ dbxcli mkdir testdir1013

At the same time, you could also create a directory in your institution’s scratch volume with a unique name. I would use xmj

[xumj2019@wrds-cloud-login2-w ~]$ mkdir /scratch/frankfurt/xmj/

Transfer Files From WRDS to Dropbox

Files can be transferred to Dropbox with the put subcommand.

Make a directory in your institution’s scratch volume with a unique name, such as your username. (I’ve introduced this part in Data Storage Locations section)

Run your job and save your output to this directory. For example, I save a csv file test1013.csv and a graph file testgraph1013.png into the scratch direcory I’ve created before /scratch/frankfurt/xmj/

In [7]: import matplotlib.pyplot as pltIn [8]: data = db.raw_sql("select date,dji from djones.djdaily", date_cols=['dat...: e'], index_col=['date'])In [9]: plt.plot(data)
Out[9]: [<matplotlib.lines.Line2D at 0x2ba61364ad90>]In [10]: plt.xlabel('date')
Out[10]: Text(0.5, 0, 'date')In [11]: plt.ylabel('dji')
Out[11]: Text(0, 0.5, 'dji')In [12]: plt.show()In [13]: plt.show()In [14]: plt.savefig('/scratch/frankfurt/xmj/testgraph1013.png') In [14]: data.to_csv("/scratch/frankfurt/xmj/test1013.csv")

When you are done go to the login node and cd to your scratch directory.

In [15]: quit()
[xumj2019@wrds-sas5-w ~]$ logout
[xumj2019@wrds-cloud-login2-w xmj]$ cd /scratch/frankfurt/xmj/

Once here transfer your file(s) from WRDS to Dropbox with dbxcli put.

[xumj2019@wrds-cloud-login2-w xmj]$ dbxcli put test1013.csv /testdir1013/test1013.csv
Uploading 25 KiB/25 KiB
[xumj2019@wrds-cloud-login2-w xmj]$ dbxcli put testgraph1013.png /testdir1013/testgraph1013.png
Uploading 21 KiB/21 KiB

Then you will find the files show up in your Dropbox

Figure 4: Transfer Files From WRDS to Dropbox

Transfer Files From Dropbox to WRDS

Files can be transferred to WRDS Cloud with the get subcommand.

cd to your scratch directory or home directory, depending on whether you want the files to be stored permanently or temporarily in the Cloud.
```
[xumj2019@wrds-cloud-login2-w xmj]$ cd /scratch/frankfurt/xmj/
```
Once here transfer your file(s) to WRDS with dbxcli get. With the following code, I can transfer the test.csv located in my Dropbox root folder to the scratch volume. Type ls in the WRDS command line, and one will find the test.csv have been successfully listed in my scatch directory.
```
[xumj2019@wrds-cloud-login2-w xmj]$ dbxcli get test.csv .
Downloading 93 KiB/93 KiB
[xumj2019@wrds-cloud-login2-w xmj]$ ls
test1013.csv  test.csv  testgraph1013.png
```

Batch Jobs on the WRDS Cloud

The WRDS Cloud supports two job types:

Interactive jobs are commonly used to practice with a programming language, submit simple, smaller queries, or to test expected outcomes before writing a larger program.
Batch jobs are generally long-running research programs that assemble larger data samples and perform more CPU-intensive data manipulation.

I’ve introduced the command-line Interactive jobs in the previous sections. In this section, one can learn how to submit batch jobs via an example borrowed from Gen Li’s blog.

Gen Li provides a series of SAS codes to extract TAQ data from WRDS cloud for a list of companies on particular dates during specific time periods. One can download the SAS codes via this link. I will execute WRDS_batch_ticker_ct.sas to show how a batched job works in WRDS cloud.

The input of this batched job is a txt file evt_taqinput.txt which contains two columns, Ticker and Time Stamp. The output of this job is a dta file ticker_ct.dta, which should appear in your WRDS home directory if the batched job is executed successfully.

filename input '/home/yale/genli925/evt_taqinput.txt'; /* Change to your input file path */data dictionary2;infile input dlm = "," dsd missover;input smbl $ dates yymmdd8.;
run;/* Genrate a macro variable to loop through */proc sql noprint;select distinct dates into :datesValsM separated by ' ' /* select unique dates that we want to extract from TAQ Monthly*/from work.dictionary2;
quit;  %put &datesValsM;/* A macro that autogenerated list of needed Monthly TAQ datasets */
%macro taq_monthly_dataset_list(type = );%let type=%lowcase(&type);%let i = 1;%let datesValsMi = %scan(&datesValsM, &i);/* Loop over each date in "datesVals" macro variable*/%do %while("&datesValsMi" ~= "");/** For each date in the "datesVals" */ %let yyyymmdd=%sysfunc(putn(&datesValsMi,yymmddn8.));/*If the corresponding dataset exists, add it to the list */%if %sysfunc(exist(taq.&type._&yyyymmdd)) %then taq.&type._&yyyymmdd;%let i = %eval(&i + 1);    %let datesValsMi = %scan(&datesValsM, &i);     %end;
%mend;* using this macro;data outputM;set %taq_monthly_dataset_list(type = ct) open=defer;    /* get TAQ Monthly data for desired dates */where (time between '9:30:00't and '10:30:00't) or (time between '15:00:00't and '16:00:59't);
run;proc sql;create table outputM as select a.*, b.*from dictionary2 a, outputM bwhere a.smbl = b.symbol and a.dates = b.date;
quit;/* optional: export sas7bat file to dta file */
proc export data = outputM outfile= '/home/yale/genli925/ticker_ct.dta'; run;

To conduct the batch job, one needs to:

Upload to your WRDS cloud the txt file that includes ticker and date for which you need TAQ data

(One can learn how to exchange files with WRDS cloud in the section Transfer Data through Dropbox)

I uploaded a txt file named evt_taqinput. To save the execution time, I just include one line:

AAPL 20141205 15:30:00

Upload to your WRDS cloud the code file “WRDS_batch.sas”. (Please remember to change to your txt file path in the code). Following the steps mentioned before, I’ve uploaded the SAS code WRDS_batch_ticker_ct.sas

[xumj2019@wrds-sas7-w ~]$ ls
autoexec.sas      testaq.sas                 WRDS_batch_ticker_ctm.sas
evt_taqinput.txt  test.csv                   WRDS_batch_ticker_ct.sas
myProgram.csv     WRDS_batch_ticker_cqm.sas

Connect to your WRDS Cloud via SSH (See section Connect to WRDS using SSH).

Submit command on your terminal:

[xumj2019@wrds-cloud-login2-w ~]$ qsas WRDS_batch_ticker_ct.sas
Your job 9180920 ("WRDS_batch_ticker_ct.sas") has been submitted

Check the status of your batched job:

[xumj2019@wrds-cloud-login2-w ~]$ qstat
job-ID     prior   name       user         state submit/start at     queue                          jclass                         slots ja-task-ID
------------------------------------------------------------------------------------------------------------------------------------------------9174218 0.50500 QRLOGIN    xumj2019    r     10/13/2021 12:29:39 interactive.q@wrds-sas5-w.whar                                    1

Check the SAS log of the batched job:

[xumj2019@wrds-cloud-login2-w ~]$ cat check WRDS_batch_ticker_ct.log
1                                                          The SAS System                     Thursday, October 14, 2021 03:46:00 AMNOTE: Copyright (c) 2016 by SAS Institute Inc., Cary, NC, USA.
NOTE: SAS (r) Proprietary Software 9.4 (TS1M7 MBCS3170) Licensed to UNIVERSITY OF PENNSYLVANIA - SFA T&R, Site 70055201.
NOTE: This session is executing on the Linux 3.10.0-1160.36.2.el7.x86_64 (LIN X64) platform.NOTE: Analytical products:SAS/STAT 15.2SAS/ETS 15.2SAS/OR 15.2SAS/IML 15.2SAS/QC 15.2NOTE: Additional host information:Linux LIN X64 3.10.0-1160.36.2.el7.x86_64 #1 SMP Thu Jul 8 02:53:40 UTC 2021 x86_64 Red Hat Enterprise Linux Server release 7.9 (Maipo) *** Welcome to Wharton Research Data Services (WRDS) ***WRDS is developed and run by the Wharton School at the University of
Pennsylvania. We provide access to the databases that define empirical,
seminal research in the fields of accounting, banking, economics,
finance, insurance, management, marketing, public policy, risk
management, and statistics.If you are not an authorized user of this system, disconnect now.
All activity is strictly monitored. There may be severe criminal and
civil penalties for unauthorized access or use of computing resources.Please report any problems to wrds-support@wharton.upenn.edu.----------------------------------------------------------------------------*** Wharton Research Data Services (WRDS) on SSRN ***Submit to the WRDS Research Paper Series (RPS) on SSRN – a searchable repository of all papers submitted to SSRN that cite WRDS
in their work. This RPS will elevate researchers’ visibility, increase your Eigenfactor® score, and build a specialized research
base for others to explore easily.View Abstracts:            http://bit.ly/2qOrt9fSubmit to the WRDS RPS:    http://bit.ly/2rjrENk------------------------------------------------------------------------------*** NEW Learning Resources for Researchers, Instructors, and Librarians ***Curated, guided, self-paced, competency-based access to our data, analytics, research, and technology solutions. In the classroom
or at home, for 25+ years Wharton Research Data Services (WRDS) has supported users with targeted solutions that underpin research,
reinforce learning, and enable discovery. Visit the WRDS website www.whartonwrds.com to view the full suite of Learning Pathways.--------------------------------------------------------------------------------2                                                          The SAS System                     Thursday, October 14, 2021 03:46:00 AMNOTE: SAS initialization used:real time           0.09 secondscpu time            0.02 secondsNOTE: AUTOEXEC processing completed.1          filename input '/home/frankfurt/xumj2019/evt_taqinput.txt'; /* Change to
1        ! your input file path */
2
3          data dictionary2;
4           infile input dlm = " " dsd missover;
5           input smbl $ dates yymmdd8.;
6          run;NOTE: The infile INPUT is:Filename=/home/frankfurt/xumj2019/evt_taqinput.txt,Owner Name=xumj2019,Group Name=frankfurt,Access Permission=-rw-------,Last Modified=13Oct2021:16:31:41,File Size (bytes)=23NOTE: 1 record was read from the infile INPUT.The minimum record length was 23.The maximum record length was 23.
NOTE: The data set WORK.DICTIONARY2 has 1 observations and 2 variables.
NOTE: DATA statement used (Total process time):real time           0.00 secondscpu time            0.02 seconds7
8          /* Genrate a macro variable to loop through */
9
10         proc sql noprint;
11           select distinct dates into :datesValsM separated by ' ' /* select
11       ! unique dates that we want to extract from TAQ Monthly*/
12           from work.dictionary2;
13         quit;
NOTE: PROCEDURE SQL used (Total process time):real time           0.00 secondscpu time            0.00 seconds14
15
16         %put &datesValsM;
20062
17
18         /* A macro that autogenerated list of needed Monthly TAQ datasets */
19         %macro taq_monthly_dataset_list(type = );
20               %let type=%lowcase(&type);
21            %let i = 1;
22            %let datesValsMi = %scan(&datesValsM, &i);
23             /* Loop over each date in "datesVals" macro variable*/
24                 %do %while("&datesValsMi" ~= "");/** For each date in the
24       ! "datesVals" */
25                     %let yyyymmdd=%sysfunc(putn(&datesValsMi,yymmddn8.));
26                     /*If the corresponding dataset exists, add it to the list
26       !  */
27                     %if %sysfunc(exist(taq.&type._&yyyymmdd)) %then
27       ! taq.&type._&yyyymmdd;
28                  %let i = %eval(&i + 1);
29                  %let datesValsMi = %scan(&datesValsM, &i);
30                 %end;
31         %mend;
32
33
34         * using this macro;
35
36         data outputM;
37          set %taq_monthly_dataset_list(type = ct) open=defer;  /* get TAQ
37       ! Monthly data for desired dates */
38          where (time between '9:30:00't and '10:30:00't) or (time between
38       ! '15:00:00't and '16:00:59't);
39         run;NOTE: There were 13197152 observations read from the data set TAQ.CT_20141205.WHERE ((time>=' 9:30:00'T and time<='10:30:00'T) or (time>='15:00:00'T and time<='16:00:59'T));
NOTE: The data set WORK.OUTPUTM has 13197152 observations and 9 variables.
NOTE: DATA statement used (Total process time):real time           2.63 secondscpu time            2.45 seconds40
41         proc sql;
42          create table outputx as select a.*, b.*
43              from work.dictionary2 a, outputM b
44              where a.smbl = b.symbol;
NOTE: Table WORK.OUTPUTX created, with 94272 rows and 11 columns.45         quit;
NOTE: PROCEDURE SQL used (Total process time):real time           0.33 secondscpu time            0.33 seconds46
47
48         /* optional: export sas7bat file to dta file */
49         proc export data = outputM outfile=
49       ! '/home/vu/frankfurt/xum2019/ticker_ct1014.dta'; run;NOTE: The export data set has 13197152 observations and 9 variables.
NOTE: "/home/frankfurt/xum2019/ticker_ct1014.dta" file was successfully created.
NOTE: PROCEDURE EXPORT used (Total process time):real time           2.81 secondscpu time            2.01 seconds50         NOTE: SAS Institute Inc., SAS Campus Drive, Cary, NC USA 27513-2414
NOTE: The SAS System used:real time           6.63 secondscpu time            4.99 seconds

Check the output of your batched job. Enter ls, you will find the output file ticker_ct.dta has appeared in your home directory.

[xumj2019@wrds-cloud-login2-w ~]$ ls
autoexec.sas      sasuser.v94        ticker_ct.dta              WRDS_batch_ticker_cq.sas  WRDS_batch_ticker_ctm.sas
evt_taqinput.txt  ticker_ct1014.dta  WRDS_batch_ticker_cqm.sas  WRDS_batch_ticker_ct.log  WRDS_batch_ticker_ct.sas

References

PYTHON: On the WRDS Cloud
Submitting Python Jobs
Login to the WRDS cloud via ssh
Interactive Jobs on the WRDS Cloud
Querying WRDS Data using Python
Example Python Data Workflow
Storing Your Data on the WRDS Cloud
Transfering Data with Dropbox
Storing Your Data on the WRDS Cloud
Graphing Data using Python
Transferring Files using SCP
Batch Jobs on the WRDS Cloud
Extract TAQ data from WRDS Cloud via terminal