John Barrios · Yale School of Management Claude Code for Accounting Research

Wrds

This skill should be used when the user asks to “query WRDS”, “access Compustat”, “get CRSP data”, “pull Form 4 insider data”, “query ISS compensation”, “download SEC EDGAR filings”, “get ExecuComp data”, “access Capital IQ”, “query TRACE bond data”, “clean TRACE”, “merge FISD”, “query TAQ”, “pull intraday trades or quotes”, “build NBBO”, “compute effective spread”, or needs WRDS PostgreSQL query patterns.

Download wrds.zip

Barrios Skills — John Barrios’s curated workflow for economists and accountants. Prioritize reproducible empirical work, clear identification language, and journal-ready output.

WRDS MCP (recommended for AI agents)

John Barrios’s wrds-mcp server exposes WRDS to Cursor, Claude, and other MCP clients.

Before querying WRDS, the user must:

1. Install the MCP from mcp/wrds-mcp/ — see install/wrds-mcp.md
2. Set their own credentials: WRDS_USERNAME and WRDS_PASSWORD (from their WRDS account)
3. Start the tunnel: bash mcp/wrds-mcp/tunnel/tunnel_up.sh and approve Duo MFA once per session
4. Register the MCP with WRDS_TUNNEL_PORT=49600 in Cursor or Claude config

When MCP tools are available (wrds_run_sql, wrds_get_compustat, etc.), prefer MCP over raw Python connections. Still apply all filter rules in this skill.

If the user has not configured MCP credentials, stop and walk them through install/wrds-mcp.md — do not guess usernames or passwords.

Contents

• WRDS MCP (recommended for AI agents)
• 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 TAQ queries (taqm_YYYY.ctm_* / cqm_* / complete_nbbo_*):** - [ ] Using taqm_YYYY (daily partitioned) for post-2003 work, not legacy taq - [ ] Trade filter: tr_corr = '00' (string, not integer) - [ ] Trade conditions: drop tr_scond containing Z, O, T, U, G, B (non-regular) - [ ] Regular trading hours: time_m BETWEEN '09:30:00' AND '16:00:00' - [ ] Price > 0 and size > 0 sanity checks - [ ] For NBBO: use complete_nbbo_* (not nbbom_* — incomplete by design) - [ ] If joining to CRSP: use wrdsapps_link_crsp_taqm with sdate/edate bounds - [ ] Consider wrds_iid_YYYY for pre-computed daily liquidity measures - [ ] Never SELECT * — specify columns (ctm has 20, cqm/nbbom are huge) - [ ] Sort explicitly by (time_m, time_m_nano, tr_seqnum) — insertion order not guaranteed

For TRACE queries (trace_enhanced.trace_enhanced): - [ ] Using trace_enhanced (uncapped) not trace_standard (capped) for research - [ ] Dick-Nielsen cleaning applied: drop trc_st IN ('X','C','W','Y') + matched originals - [ ] Pre-/post-2012-02-06 logic branch selected correctly - [ ] Agency double-count filter: NOT (cmsn_trd='Y' AND cntra_mp_id='D') - [ ] Commissioned trades handled (cmsn_trd='N' for price/yield work) - [ ] Special trades excluded: spcl_trd_fl != 'Y' (for price/liquidity analysis) - [ ] FISD merge for bond characteristics (coupon, maturity, rating, issuer) - [ ] Date range is explicitly specified on trd_exctn_dt - [ ] 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
TRACE Enhanced (bonds, uncapped)	trace_enhanced	trace_enhanced, trace_btds144a_enhanced, trace_agency_enhanced
TRACE Standard (bonds, capped)	trace_standard	trace, trade_summary
FISD bond characteristics (Mergent)	fisd	fisd_mergedissue, fisd_mergedissuer, fisd_issue, fisd_issuer
TAQ Daily MSEC (ticks, µs)	taqm_YYYY	ctm_YYYYMMDD, cqm_YYYYMMDD, complete_nbbo_YYYYMMDD, nbbom_YYYYMMDD, mastm_YYYY, wrds_iid_YYYY
TAQ Monthly (legacy, 1993–2014, sec)	taq	monthly consolidated trades/quotes
CRSP↔︎TAQ link	wrdsapps_link_crsp_taqm	link table

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

TAQ Standard Cleaning (Holden-Jacobsen 2014)

Minimum filter for academic intraday work:

-- Trades (ctm_YYYYMMDD)
WHERE tr_corr = '00'                               -- uncorrected
  AND price > 0 AND size > 0
  AND time_m BETWEEN '09:30:00'::time AND '16:00:00'::time
  AND (tr_scond IS NULL OR tr_scond = ''
       OR tr_scond !~ '[BGJKLOTUWZ4567]')          -- regular-way only

-- Quotes (cqm_YYYYMMDD)
WHERE bid > 0 AND ask > 0 AND ask > bid
  AND bidsiz > 0 AND asksiz > 0
  AND qu_cond IN ('A','B','H','O','R','W')
  AND time_m BETWEEN '09:30:00'::time AND '16:00:00'::time

Always prefer complete_nbbo_* over nbbom_* — the raw NBBO feed is incomplete when one exchange holds both sides of the top of book. If re-deriving, UNION nbbom with cqm records where natbbo_ind='1' OR nasdbbo_ind='4'. See references/taq.md.

TRACE Enhanced Cleaning (Dick-Nielsen 2014)

Apply BEFORE any price/volume analysis:

-- Post-2012-02-06: drop cancels, corrections, reversals + their matched originals.
-- Match originals on (cusip_id, entrd_vol_qt, rptd_pr, trd_exctn_dt,
-- trd_exctn_tm, rpt_side_cd, cntra_mp_id, msg_seq_nb).
WHERE trc_st = 'T'                              -- keep only clean originals
  AND NOT (cmsn_trd = 'Y' AND cntra_mp_id = 'D') -- drop agency double count
  AND spcl_trd_fl != 'Y'                         -- drop special-condition trades
  AND wis_fl != 'Y'                              -- drop when-issued
  -- For price/yield work, also:
  AND cmsn_trd = 'N'

Pre-2012-02-06 data uses asof_cd = 'R' for reversals instead of trc_st = 'Y'; use the pre-2012 branch. See references/trace.md.

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/trace.md - TRACE bond transactions, Dick-Nielsen cleaning, FISD merge, pre-/post-2012 logic
• references/taq.md - TAQ/TAQMSEC intraday trades, quotes, complete NBBO, Holden-Jacobsen filters, WRDS Intraday Indicators
• 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.