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.

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/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.