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:

Excuse	Reality	Do Instead
"I'll add filters later"	You'll forget and pull bad data	Add filters NOW, before execution
"User didn't specify filters"	Standard filters are ALWAYS required	Apply Critical Filters section defaults
"Just a quick test query"	Test queries with bad filters teach bad patterns	Use production filters even for tests
"I'll let the user filter in pandas"	Pulling millions of unnecessary rows wastes time/memory	Filter at database level FIRST
"The query worked, so it's correct"	Query success ≠ data quality	INSPECT sample for invalid records
"I can use f-strings for simple queries"	SQL injection risk + wrong type handling	ALWAYS use parameterized queries

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

Dataset	Schema	Key Tables
Compustat	comp	company, funda, fundq, secd
ExecuComp	comp_execucomp	anncomp
CRSP	crsp	dsf, msf, stocknames, ccmxpf_linkhist
CRSP v2	crsp	dsf_v2, msf_v2, stocknames_v2
Form 4 Insiders	tr_insiders	table1, header, company
ISS Incentive Lab	iss_incentive_lab	comppeer, sumcomp, participantfy
Capital IQ	ciq	wrds_compensation
IBES	tr_ibes	det_epsus, statsum_epsus
SEC EDGAR	wrdssec	wrds_forms, wciklink_cusip
SEC Search	wrds_sec_search	filing_view, registrant
EDGAR	edgar	filings, filing_docs
Fama-French	ff	factors_monthly, factors_daily
LSEG/Datastream	tr_ds	ds2constmth, ds2indexlist

Connection

import psycopg2

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

Auth: ~/.pgpass with chmod 600:

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

SSH: ssh wrds (uses ~/.ssh/wrds_rsa)

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):

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

# For lists, use ANY()
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

Pattern from oh-my-opencode: When querying historical data, be aware of current date context.

Current date is automatically available via datetime.now(). Use this for:

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

Example:

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))

Force current year awareness in date-dependent queries.