Wrds

Contents

• Query Enforcement
• SAS ETL Enforcement
• Quick Reference: Table Names
• Connection
• Critical Filters
• Parameterized Queries
• Additional Resources

WRDS Data Access

WRDS (Wharton Research Data Services) provides academic research data via PostgreSQL at wrds-pgdata.wharton.upenn.edu:9737.

Query Enforcement

IRON LAW: NO QUERY WITHOUT FILTER VALIDATION FIRST

Before executing ANY WRDS query, you MUST:

1. IDENTIFY what filters are required for this dataset
2. VALIDATE the query includes those filters
3. VERIFY parameterized queries (never string formatting)
4. EXECUTE the query
5. INSPECT a sample of results before claiming success

This is not negotiable. Skipping sample inspection is NOT HELPFUL — the user builds analysis on data with undetected quality problems.

Rationalization Table - STOP If You Think:

Red Flags - STOP Immediately If You Think:

• "Let me run this query quickly to see what's there" → NO. Check Critical Filters section first.
• "I'll just pull everything and filter later" → NO. Database-level filtering is mandatory.
• "The table name is obvious from the request" → NO. Check Quick Reference section for exact names.
• "I can inspect the data after the user sees it" → NO. Sample inspection BEFORE claiming success.

Query Validation Checklist

Before EVERY query execution:

For Compustat queries (comp.funda, comp.fundq):

• Includes indfmt = 'INDL'
• Includes datafmt = 'STD'
• Includes popsrc = 'D'
• Includes consol = 'C'
• Uses parameterized queries for variables
• Date range is explicitly specified

For CRSP v2 queries (crsp.dsf_v2, crsp.msf_v2):

• Post-query filter: sharetype == 'NS'
• Post-query filter: securitytype == 'EQTY'
• Post-query filter: securitysubtype == 'COM'
• Post-query filter: usincflg == 'Y'
• Post-query filter: issuertype.isin(['ACOR', 'CORP'])
• Uses parameterized queries

For Form 4 queries (tr_insiders.table1):

• Transaction type filter specified (acqdisp)
• Transaction codes specified (trancode)
• Date range is explicitly specified
• Uses parameterized queries

For ALL queries:

• Sample inspection with .head() or .sample() BEFORE claiming success
• Row count verification (is result size reasonable?)
• NULL value check on critical columns
• Date range validation (does min/max match expectations?)

SAS ETL Enforcement

IRON LAW: NO SAS CODE WITHOUT PERFORMANCE VALIDATION FIRST

1. MERGE STRATEGY — Is hash or sort-merge appropriate? Justify the choice.
2. WHERE CLAUSES — Are all date/string filters index-friendly? No functions on indexed columns.
3. PARALLELISM — Can this job run as an SGE array? Year-by-year is always parallelizable.
4. SQL OPTIMIZATION — For PROC SQL: pass-through opportunity? Indexed join columns?

Writing SAS code that forces full table scans when indexes exist is NOT HELPFUL — the user's job runs 100x slower than necessary and may timeout.

SAS Code Validation Checklist

Before EVERY SAS program execution:

For merges/joins:

• Small lookup + large fact table → hash object (not PROC SORT + DATA merge)
• Hash uses defineKey/defineData/defineDone pattern correctly
• h.output() uses double quotes for macro resolution (not single quotes)
• call missing() initializes hash data variables for non-matches
• Both tables >50M rows → sort-merge is justified (document why)

For WHERE clauses (CRITICAL):

• NO year(date), month(date), datepart(dt) wrapping indexed columns
• Date filters use BETWEEN "01jan&year."d AND "31dec&year."d range pattern
• String filters avoid upcase(), substr() on indexed columns
• Compound date filters collapsed to single range (not year() = X AND quarter() = Y)

For batch processing:

• Multi-year jobs use SGE array (#$ -t start-end) not sequential loop
• Year passed via -sysparm (not -set or %sysget)
• Per-year log files (not single shared log)
• Memory allocation appropriate for workload (#$ -l m_mem_free=4G minimum)
• Single-year benchmark run completed before full array submission

For PROC SQL:

• Join columns are not wrapped in functions
• calculated keyword used for computed column references in HAVING
• Pass-through SQL considered for direct WRDS PostgreSQL queries
• No redundant subqueries that could be hash lookups

For macros:

• Macro variables terminated with period (&year. not &year)
• Double quotes used where macro resolution is needed
• options mprint mlogic symbolgen used during development

SAS Rationalization Table - STOP If You Think:

SAS Red Flags - STOP Immediately If You're About To:

• Write where year(date) = anything → STOP. Use BETWEEN with date literals.
• Write proc sort; data; merge for a lookup join → STOP. Use hash object.
• Write a %do year = start %to end loop → STOP. Use SGE array job.
• Use single quotes in h.output(dataset: '...') → STOP. Use double quotes.
• Submit a full array job without testing one year first → STOP. Benchmark first.
• Use -set or %sysget for SGE task parameters → STOP. Use -sysparm.

SAS Reference

See references/sas-etl.md for complete patterns:

• Hash object merge (basic, multidata, accumulator)
• Index-friendly WHERE clause quick reference table
• SGE array job templates with memory and logging
• PROC SQL pass-through and optimization
• Macro quoting and debugging

Quick Reference: Table Names

Connection

Initialize PostgreSQL connection to WRDS:

import psycopg2

conn = psycopg2.connect(
    host='wrds-pgdata.wharton.upenn.edu',
    port=9737,
    database='wrds',
    sslmode='require'
    # Credentials from ~/.pgpass
)

Configure authentication via ~/.pgpass with chmod 600:

wrds-pgdata.wharton.upenn.edu:9737:wrds:USERNAME:PASSWORD

Connect via SSH tunnel:

ssh wrds

This uses ~/.ssh/wrds_rsa for authentication.

Critical Filters

Compustat Standard Filters

Always include for clean fundamental data:

WHERE indfmt = 'INDL'
  AND datafmt = 'STD'
  AND popsrc = 'D'
  AND consol = 'C'

CRSP v2 Common Stock Filter

Equivalent to legacy shrcd IN (10, 11):

df = df.loc[
    (df.sharetype == 'NS') &
    (df.securitytype == 'EQTY') &
    (df.securitysubtype == 'COM') &
    (df.usincflg == 'Y') &
    (df.issuertype.isin(['ACOR', 'CORP']))
]

Form 4 Transaction Types

WHERE acqdisp = 'D'  -- Dispositions
  AND trancode IN ('S', 'D', 'G', 'F')  -- Sales, Dispositions, Gifts, Tax

Parameterized Queries

Always use parameterized queries (never string formatting):

Use scalar parameter binding for single values:

cursor.execute("""
    SELECT gvkey, conm FROM comp.company WHERE gvkey = %s
""", (gvkey,))

Use ANY() for list parameters:

cursor.execute("""
    SELECT * FROM comp.funda WHERE gvkey = ANY(%s)
""", (gvkey_list,))

Additional Resources

Reference Files

Detailed query patterns and table documentation:

• references/compustat.md - Compustat tables, ExecuComp, financial variables
• references/crsp.md - CRSP stock data, CCM linking, v2 format
• references/insider-form4.md - Thomson Reuters Form 4, rolecodes, insider types
• references/iss-compensation.md - ISS Incentive Lab, peer companies, compensation
• references/edgar.md - SEC EDGAR filings, URL construction, DCN vs accession numbers
• references/connection.md - Connection pooling, caching, error handling
• references/sas-etl.md - SAS hash objects, index-friendly WHERE, SGE array jobs, PROC SQL optimization

Example Files

Working code from real projects:

• examples/form4_disposals.py - Insider trading analysis (from SVB project)
• examples/wrds_connector.py - Connection pooling pattern

Scripts

• scripts/test_connection.py - Validate WRDS connectivity

Local Sample Notebooks

WRDS-provided samples at ~/resources/wrds-code-samples/:

• ResearchApps/CCM2025.ipynb - Modern CRSP-Compustat merge
• ResearchApps/ff3_crspCIZ.ipynb - Fama-French factor construction
• comp/sas/execcomp_ceo_screen.sas - ExecuComp patterns

Date Awareness

When querying historical data, leverage current date context for dynamic range calculations.

Current date is automatically available via datetime.now(). Apply this to:

• Data range validation (e.g., "get data for last 5 years")
• Fiscal year calculations
• Event study windows

Implement dynamic date ranges in queries:

from datetime import datetime, timedelta

# Query last 5 years of data
end_date = datetime.now()
start_date = end_date - timedelta(days=5*365)

query = """
SELECT * FROM comp.funda
WHERE datadate BETWEEN %s AND %s
"""
df = pd.read_sql(query, conn, params=(start_date, end_date))

Always incorporate current date awareness in date-dependent queries to ensure results remain fresh across time.