Contents

• •Query 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. Claiming query success without sample inspection is LYING to the user about data quality.

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?)

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

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.