Agent Skills: LSEG Data Library

Contents

• Query Enforcement
• Quick Start
• Authentication
• Core APIs
• Key Field Prefixes
• RIC Symbology
• Rate Limits
• Additional Resources

LSEG Data Library

Access financial data from LSEG (London Stock Exchange Group), formerly Refinitiv, via the lseg.data Python library.

Query Enforcement

IRON LAW: NO DATA CLAIM WITHOUT SAMPLE INSPECTION

Before claiming ANY LSEG query succeeded, follow these steps:

1. VALIDATE field names exist (check prefixes: TR., CF_)
2. VALIDATE RIC symbology is correct (.O, .N, .L, .T)
3. EXECUTE the query
4. INSPECT sample rows with .head() or .sample()
5. VERIFY critical columns are not NULL
6. VERIFY date range matches expectations
7. CLAIM success only after all checks pass

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

LSEG API Facts

• The API does not raise errors for invalid field names or wrong RICs — it returns empty results or NULL columns. Treating returned rows as correct data is an unverified claim presented as fact: inspect for NULLs, wrong dates, and invalid values before returning anything.
• Field-name typos are common and fail silently (TR.EPS vs TR.Eps). Validate field names against the documentation before executing.
• User-supplied RICs often carry the wrong exchange suffix. Verify against the RIC Symbology section (.O, .N, .L, .T) before querying.
• Market data has T-1 availability — today's data arrives tomorrow. Querying through today produces silent gaps; see the Date Awareness section.
• Rate limits bind per session (500 requests/minute) and per request (get_data() 10,000 data points, get_history() 3,000 rows) — many small queries still hit the session cap. Batch instead of looping.

Red Flags — STOP If About To:

• Execute a query without validating field names and RIC suffixes first → STOP. The API will not error for you.
• Return a dataframe without .head() or .sample() inspection → STOP. Handing over uninspected data gives the user undetected quality problems — unhelpful on its own terms.

Data Validation Checklist

Before EVERY data retrieval claim, verify the following:

For ld.get_data() (fundamentals/ESG):

• [ ] Field names use correct prefix (TR. for Refinitiv)
• [ ] RIC symbology verified (correct exchange suffix)
• [ ] Result inspection: .head() or .sample() executed
• [ ] NULL check on critical fields (e.g., revenue, EPS)
• [ ] Row count verification (is result size reasonable?)
• [ ] Date context verified (fiscal periods, as-of dates)

For ld.get_history() (time series):

• [ ] Field names are valid (OPEN, HIGH, LOW, CLOSE, VOLUME, or CF_ prefixes)
• [ ] Start/end dates specified explicitly
• [ ] Date range adjusted for T-1 availability (market data lag)
• [ ] Result inspection: check first and last rows
• [ ] NULL check on OHLCV fields
• [ ] Date continuity check (gaps in trading days expected, but not in date sequence)

For symbol_conversion.Definition() (mapping):

• [ ] Input identifier type specified correctly
• [ ] Result inspection: verify mapped values exist
• [ ] NULL check (some securities may not have all identifiers)

For ALL queries:

• [ ] Rate limits considered (batch if >10k data points)
• [ ] Session management: open_session() at start, close_session() at end
• [ ] Error handling: try/except for network failures
• [ ] Sample inspection BEFORE claiming data is ready

Quick Start

To get started with LSEG Data Library, initialize a session and execute queries:

import lseg.data as ld

# Initialize session
ld.open_session()

# Get fundamentals
df = ld.get_data(
    universe=[‘AAPL.O’, ‘MSFT.O’],
    fields=[‘TR.CompanyName’, ‘TR.Revenue’, ‘TR.EPS’]
)
print(df.head())  # Inspect sample data

# Get historical prices
prices = ld.get_history(
    universe=’AAPL.O’,
    fields=[‘OPEN’, ‘HIGH’, ‘LOW’, ‘CLOSE’, ‘VOLUME’],
    start=‘2023-01-01’,
    end=‘2023-12-31’
)
print(prices.head())  # Inspect sample data

# Close session
ld.close_session()

Authentication

Configure LSEG authentication using either a config file or environment variables.

Config File Method

Create lseg-data.config.json:

{
  “sessions”: {
    “default”: “platform.ldp”,
    “platform”: {
      “ldp”: {
        “app-key”: “YOUR_APP_KEY”,
        “username”: “YOUR_MACHINE_ID”,
        “password”: “YOUR_PASSWORD”
      }
    }
  }
}

Environment Variables Method

Set the following environment variables for LSEG authentication:

# Configure LSEG credentials via environment variables
export RDP_USERNAME=”YOUR_MACHINE_ID”
export RDP_PASSWORD=”YOUR_PASSWORD”
export RDP_APP_KEY=”YOUR_APP_KEY”

Core APIs

| API | Use Case | Example | |-----|----------|---------| | ld.get_data() | Point-in-time data | Fundamentals, ESG scores | | ld.get_history() | Time series | Historical prices, OHLCV | | ld.news.get_headlines() | News headlines | Company news, topic filtering | | symbol_conversion.Definition() | ID mapping | RIC ↔ ISIN ↔ CUSIP |

Key Field Prefixes

| Prefix | Type | Example | |--------|------|---------| | TR. | Refinitiv fields | TR.Revenue, TR.EPS | | TR.MnA | Mergers & Acquisitions | TR.MnAAcquirorName, TR.MnADealValue | | TR.NI | Equity/New Issues (IPOs) | TR.NIIssuer, TR.NIOfferPrice | | TR.JV | Joint Ventures/Alliances | TR.JVDealName, TR.JVStatus | | TR.SACT | Shareholder Activism | TR.SACTLeadDissident | | TR.PP | Poison Pills | TR.PPPillAdoptionDate | | TR.LN | Syndicated Loans | TR.LNTotalFacilityAmount | | TR.PJF | Infrastructure/Project Finance | TR.PJFProjectName | | TR.PEInvest | Private Equity/Venture Capital | TR.PEInvestRoundDate | | TR.Muni | Municipal Bonds | TR.MuniIssuerName | | CF_ | Composite (real-time) | CF_LAST, CF_BID |

RIC Symbology

| Suffix | Exchange | Example | |--------|----------|---------| | .O | NASDAQ | AAPL.O | | .N | NYSE | IBM.N | | .L | London | VOD.L | | .T | Tokyo | 7203.T |

Rate Limits

| Endpoint | Limit | |----------|-------| | get_data() | 10,000 data points/request | | get_history() | 3,000 rows/request | | Session | 500 requests/minute |

Additional Resources

Reference Files

• references/fundamentals.md - Financial statement fields, ratios, estimates
• references/esg.md - ESG scores, pillars, controversies
• references/symbology.md - RIC/ISIN/CUSIP conversion
• references/pricing.md - Historical prices, real-time data
• references/screening.md - Stock screening with Screener object
• references/fscreen.md - Fund screening (ETFs, mutual funds) with FSCREEN app
• references/fund-details.md - Fund details and characteristics
• references/news.md - News headlines, pagination, query syntax
• references/mna.md - Mergers & acquisitions deals (SDC Platinum, 2,683 fields)
• references/equity-new-issues.md - IPOs, follow-ons, equity offerings (SDC Platinum, 1,708 fields)
• references/joint-ventures.md - Joint ventures, strategic alliances (SDC Platinum, 301 fields)
• references/corporate-governance.md - Shareholder activism, poison pills (SDC Platinum)
• references/syndicated-loans.md - Syndicated loan deals (SDC Platinum)
• references/infrastructure.md - Infrastructure/project finance deals (SDC Platinum)
• references/private-equity.md - Private equity/venture capital investments (SDC Platinum)
• references/municipal-bonds.md - Municipal bond issuances (SDC Platinum)
• references/api-discovery.md - Reverse-engineering APIs via CDP network monitoring
• references/troubleshooting.md - Common issues and solutions
• references/wrds-comparison.md - LSEG vs WRDS data mapping

Example Files

• examples/historical_pricing.ipynb - Historical price retrieval
• examples/fundamentals_query.py - Fundamental data patterns
• examples/stock_screener.ipynb - Dynamic stock screening

Scripts

• scripts/test_connection.py - Validate LSEG connectivity

Local Sample Repositories

LSEG API samples at ~/resources/lseg-samples/:

• Example.RDPLibrary.Python/ - Core API examples
• Examples.DataLibrary.Python.AdvancedUsecases/ - Advanced patterns
• Article.DataLibrary.Python.Screener/ - Stock screening

Refinitiv Codebook

Interactive JupyterLab environment with pre-configured LSEG access:

• URL: https://workspace.refinitiv.com/codebook/
• Environment: JupyterHub with Python 3.8, pre-installed refinitiv.data library
• Session: Auto-authenticated via Workspace credentials ({name=’codebook’})

# In Codebook, session opens automatically with Workspace auth
import refinitiv.data as rd
rd.open_session()  # Returns session with name=’codebook’

# Query data immediately
df = rd.news.get_headlines(‘R:AAPL.O AND SUGGAC’, count=10)

Note: Codebook uses refinitiv.data (older name) rather than lseg.data. Both APIs are equivalent.

Date Awareness

When querying market data, account for current date context and market data lag.

Market Data Lag

Market data typically has T-1 availability, meaning today’s data becomes available tomorrow. Adjust date ranges accordingly.

Date Range Example

Use current date context when querying historical prices:

from datetime import datetime, timedelta

# Get recent market data
end_date = datetime.now()
start_date = end_date - timedelta(days=365)

# Adjust to exclude recent data (T-1 for market data availability)
end_date = end_date - timedelta(days=1)

df = ld.get_history(
    universe=”AAPL.O”,
    fields=[‘CLOSE’],
    start=start_date.strftime(‘%Y-%m-%d’),
    end=end_date.strftime(‘%Y-%m-%d’)
)

Remember: Always account for the T-1 lag in market data availability.