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:

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?)

SAS ETL Enforcement

IRON LAW: NO SAS CODE WITHOUT PERFORMANCE VALIDATION FIRST

Before writing or executing ANY SAS code on WRDS, you MUST validate performance patterns. This is not negotiable.

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:

Excuse	Reality	Do Instead
"Sort-merge is simpler to write"	Hash is 10x faster for lookup joins and requires no sorting	Write the hash — it's 5 extra lines
"year(date) is readable"	Readable but prevents index usage — full table scan on millions of rows	Use BETWEEN with date literals
"I'll parallelize later"	Later never comes and the job runs 18x slower sequentially	Write the SGE array job NOW
"Single quotes work fine in hash"	Single quotes block macro resolution — your output dataset name is wrong	ALWAYS double quotes in h.output()
"PROC SQL is easier than hash"	PROC SQL still sorts for joins — hash avoids all sorting	Hash for lookups, SQL only for complex aggregations
"The job only takes a few minutes per year"	18 years × 3 minutes = 54 minutes sequential vs 3 minutes parallel	SGE array for ANY multi-year job
"%sysget works for getting the year"	Unreliable in SGE context — may return blank silently	Use -sysparm + &sysparm.

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

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
Form D / Reg D	wrdssec	wrds_vc_formd (parsed, 2000–2020); index: wrdssec_all.forms (all CIKs) or wrds_forms (filer only) — default to forms, see references/wrds-forms-tables.md
SEC EDGAR	wrdssec_all	forms (raw index, all CIKs per filing — default), wrds_forms (filer-only view), 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
FJC (Federal Judicial Center)	fjc	civil, criminal, bankruptcy, appeals
FJC Linking	fjc_linking	wrds_civil_link, wrds_criminal_link
SDC New Issues (IPO/SEO/Debt)	tr_sdc_ni	wrds_ni_details — equity + debt offerings
SDC Mergers & Acquisitions	tr_sdc_ma	wrds_ma_details — M&A transactions
TAQ Legacy	taq	mast_YYYY, wrds_iid_YYYY — second-level (1993–2006)
TAQ Millisecond	taqmsec	mastm_YYYY, wrds_iid_YYYY, ctm_YYYYMM, complete_nbbo_YYYYMMDD
Thomson S12 (Mutual Fund Holdings)	tfn (SAS) / tr_mutualfunds (PG)	s12 — 13F/N-CSR fund holdings
Thomson S34 (13-F Institutional)	tfn (SAS) / tr_13f (PG)	s34 — 13-F institutional holdings
FISD / Mergent (Bonds)	fisd_fisd	fisd_mergedissue, fisd_mergedissuer
PitchBook	pitchbk_companies_deals, pitchbk_investors_funds_lps, pitchbk_fund_returns	deal, company, fund, wrds_fund_returns — dealsize in USD millions

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/formd.md - Form D / Reg D: denormalization gotcha, dedup pattern, exemption codes, post-2020 gap, SEC TSV download
• references/edgar.md - SEC EDGAR filings, URL construction, DCN vs accession numbers
• references/connection.md - Connection pooling, caching, error handling
• references/taq.md - TAQ: master files, IID, raw tick processing (NBBO, VWAP, closing auctions), CRSP–TAQ merge, era transition (legacy vs millisecond)
• references/sas-etl.md - SAS hash objects, index-friendly WHERE, SGE array jobs, PROC SQL optimization
• references/postgres-vs-sas.md - Decision guide: when to use PostgreSQL vs SAS for WRDS ETL (benchmarks, constraints, hybrid pattern)
• references/fjc.md - FJC Integrated Database: civil/criminal case data, NOS codes, securities litigation queries, firm linking
• references/sdc-issuances.md - SDC New Issues: IPOs, SEOs, 144A equity, debt offerings — schema discovery, cleaning filters, CRSP/Compustat linking
• references/fisd-bonds.md - FISD/Mergent: corporate bond issuances, IG vs HY, 144A vs registered, rating classification, TRACE linking
• references/sdc-ma.md - SDC M&A: deal counts, PE/LBO vs strategic buyer, deal status codes, public vs private target
• references/fund-formation.md - Fund formation: Form D (pooled investment funds), EDGAR N-2 (closed-end fund IPOs), Form ADV (RIA registrations)
• references/pitchbook.md - PitchBook: schema architecture, dealsize/fundsize in USD millions, dealdate outliers, CIK crosswalk, fund performance (wrds_fund_returns), PE/VC/fund formation patterns

Example Files

Working code from real projects:

• examples/form4_disposals.py - Insider trading analysis (from SVB project)
• examples/wrds_connector.py - Connection pooling pattern
• examples/formd_regd.ipynb - Form D / Reg D: dedup validation, SEC TSV download, exemption trend charts
• examples/sdc_issuances_eda.ipynb - SDC New Issues: annual IPO/SEO/debt counts, 144A share, IG vs HY breakdown
• examples/sdc_ma_eda.ipynb - SDC M&A: annual deal counts, PE/LBO vs strategic, public vs private target trends
• examples/fund_formation_eda.ipynb - Fund formation: Form D 3C.1/3C.7 counts, EDGAR N-2 closed-end fund IPOs, Form ADV RIA registrations
• examples/pitchbook_eda.ipynb - PitchBook: PE deal activity, VC rounds by stage, fund formation by vintage, IRR/TVPI by strategy
• examples/voting_ownership_pipeline/ - Self-contained hybrid SAS+Python pipeline: ISS votes, 13-F inst. ownership, MF holdings via MFLINKS, merged panel. Canonical example of PostgreSQL vs SAS decision-making on WRDS. See README.md for architecture and usage.

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.