Agent-Skills.md

Agent Skills: LSEG Data Library

This skill should be used when the user asks to "access LSEG data", "query Refinitiv", "get market data from Refinitiv", "download fundamentals from LSEG", "access ESG scores", "convert RIC to ISIN", or needs the LSEG Data Library Python API.

market-dataapiRefinitivfinancial-dataESG
financeID: edwinhu/workflows/lseg-data

Repository

edwinhu/workflows
edwinhu

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pnpm dlx add-skill https://github.com/edwinhu/workflows/tree/HEAD/skills/lseg-data

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skills/lseg-data/SKILL.md

Skill Metadata

Name
lseg-data
Description
This skill should be used when the user asks to “access LSEG data”, “query Refinitiv”, “get market data from Refinitiv”, “download fundamentals from LSEG”, “access ESG scores”, “convert RIC to ISIN”, “get shareholder activism data”, “query poison pills”, “access corporate governance data”, “find activist campaigns”, “get syndicated loans data”, “query loan deals”, “get infrastructure projects”, “query project finance data”, “get private equity data”, “query VC investments”, “find PE-backed companies”, “get M&A data”, “query mergers and acquisitions”, “find acquisition deals”, “get IPO data”, “query equity offerings”, “find new issues”, “get joint venture data”, “query strategic alliances”, “get news headlines”, “query news data”, “fetch news articles”, or needs the LSEG Data Library Python API.

Contents

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. Claiming data retrieval without inspecting results is LYING to the user about data quality.

Rationalization Table - STOP If You Think:

| Excuse | Reality | Do Instead | |--------|---------|------------| | “The query returned data, so it worked” | Returned data ≠ correct data | INSPECT for NULLs, wrong dates, invalid values | | “User gave me the RIC” | Users often use wrong suffixes | VERIFY symbology against RIC Symbology section | | “I’ll let pandas handle missing data” | You’ll propagate bad data downstream | CHECK for NULLs BEFORE returning | | “Field names look right” | Typos are common (TR.EPS vs TR.Eps) | VALIDATE field names in documentation first | | “Just a quick test” | Test queries teach bad habits | Full validation even for tests | | “I can check the data later” | You won’t | Inspection is MANDATORY before claiming success | | “Rate limits don’t matter for small queries” | Small queries add up | CHECK rate limits section, use batching |

Red Flags - STOP Immediately If You Think:

  • “Let me run this and see what happens” → NO. Validate field names and RICs FIRST.
  • “The API will error if something is wrong” → NO. API returns empty results, not errors.
  • “I’ll just return the dataframe to the user” → NO. Inspect sample BEFORE returning.
  • “Market data is always up-to-date” → NO. Check Date Awareness section (T-1 lag).

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/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.