ol-sdk 0.2.2

Python client for Oxford Ledge Financial Terminal

Last Updated: 2026-04-04 09:40 ET

Oxford Ledge Python SDK

The official Python client for the Oxford Ledge Financial Terminal -- equities, credit, screening, AI analysis, and more from your Python scripts and Jupyter notebooks.

Installation

pip install oxford-ledge-sdk

For DataFrame support in Jupyter:

pip install oxford-ledge-sdk[pandas]

Quick Start

import oxford_ledge_sdk as ol

# Set your API key (get one at https://www.oxfordledge.com)
ol.set_api_key("ol_live_YOUR_KEY_HERE")  # Get your key at oxfordledge.com

# Company profile
company = ol.get_company("AAPL")
print(company.name, company.sector, company.market_cap)

# Fundamentals
fundamentals = ol.get_fundamentals("MSFT")
print(fundamentals.pe_ratio, fundamentals.roe)

# Historical prices (returns a ResultList)
prices = ol.get_prices("AAPL", start="2024-01-01")

# Screen for undervalued stocks
results = ol.screen(pe_max=15, roe_min=20, sector="Technology")

# AI-powered analysis
answer = ol.ask("Is AAPL undervalued based on its fundamentals?", ticker="AAPL")
print(answer)

Using the Client Directly

For more control (custom timeouts, multiple keys), use the client class:

from oxford_ledge_sdk import OxfordLedgeClient

client = OxfordLedgeClient(
    api_key="ol_live_YOUR_KEY_HERE",
    timeout=60,
    max_retries=5,
)

company = client.get_company("TSLA")
client.close()

# Or use as a context manager
with OxfordLedgeClient(api_key="ol_live_YOUR_KEY_HERE") as client:
    bonds = client.search_bonds("Apple")
    borrowers = client.search_borrowers("Apex Technology")

DataFrame Support (Jupyter)

Any method that returns a ResultList supports .to_dataframe():

import oxford_ledge_sdk as ol
ol.set_api_key("ol_live_YOUR_KEY_HERE")  # Get your key at oxfordledge.com

# Price history as a DataFrame
prices = ol.get_prices("AAPL", start="2024-01-01")
df = prices.to_dataframe()
df.plot(x="date", y="close", title="AAPL Price History")

# Screener results as a DataFrame
results = ol.screen(pe_max=20, dividend_yield_min=2)
df = results.to_dataframe()
df.sort_values("pe_ratio").head(10)

# Bond search
bonds = ol.search_bonds("Microsoft")
bonds.to_dataframe()[["issuer", "coupon", "maturity", "rating"]]

API Reference

Configuration

Function	Description
ol.set_api_key(key)	Set API key for module-level functions
ol.set_base_url(url)	Override base URL (for local dev)

Company Data

Function	Description
ol.get_company(ticker)	Company profile, quote, summary
ol.get_fundamentals(ticker, years=None)	Financial fundamentals
ol.get_fundamentals_pit(ticker, date)	Point-in-time fundamentals
ol.get_fundamentals_history(ticker, start, end)	Fundamental time series
ol.get_company_financials(ticker)	Full 3-statement financials
ol.get_peers(ticker)	Peer company tickers
ol.get_data_coverage(ticker)	Data source coverage map

Ownership & Insiders

Function	Description
ol.get_13f(ticker)	Institutional holders (13F)
ol.get_13d(ticker)	Activist / large-holder filings
ol.get_insiders(ticker)	Insider transactions
ol.get_insider_activity(ticker, days=90)	Insider buy/sell activity
ol.search_funds(query)	Search 13F institutional filers
ol.get_fund_holdings(cik)	Fund 13F holdings by CIK

Prices & Market

Function	Description
ol.get_prices(ticker, start, end, period)	Historical price data
ol.get_price_history(ticker, days=365)	Daily price history
ol.get_indicators()	Market indicators (indices, rates)
ol.get_global_markets()	International market overview
ol.get_sector_breakdown()	Sector performance
ol.get_calendar()	Economic/earnings calendar
ol.get_macro_events(days=7)	Upcoming macro events
ol.get_fred()	FRED macro data (GDP, CPI, etc.)
ol.get_yield_curve()	Treasury yield curve history
ol.get_index_movers()	Top movers in major indices
ol.get_market_earnings()	Upcoming earnings calendar
ol.get_random_ticker()	Random ticker for discovery

Screening

Function	Description
ol.screen(**filters)	Run stock screener with filters

Supported filters: pe_max, pe_min, roe_min, roe_max, sector, market_cap_min, market_cap_max, dividend_yield_min, and more.

Bonds & Credit

Function	Description
ol.search_bonds(query)	Search corporate bonds
ol.search_borrowers(query)	Search BDC borrowers
ol.get_bdc_portfolio(ticker)	BDC portfolio holdings
ol.list_bdcs()	List all BDCs
ol.get_bdc_borrower(query)	Look up BDC borrower holders
ol.get_debt_maturities(ticker)	Debt maturity schedule
ol.get_capital_structure(ticker)	Capital structure breakdown
ol.get_credit_movers()	Bond price gainers/losers

Estimates & Valuation

Function	Description
ol.get_estimates(ticker)	Analyst estimates
ol.get_estimate_revisions(ticker)	Estimate revision momentum
ol.get_estimate_movers(days=30)	Biggest estimate movers
ol.get_rating_events(ticker)	Credit rating changes
ol.get_valuation_history(ticker)	Historical multiples
ol.get_capital_allocation(ticker)	Buybacks, dividends, capex
ol.get_sensitivity(ticker)	DCF sensitivity analysis
ol.get_implied_assumptions(ticker)	Market-implied assumptions
ol.get_portfolio_analytics(tickers)	Portfolio-level analytics

News

Function	Description
ol.get_headlines()	Latest market headlines
ol.search_news(query, ticker, days, limit)	Search news articles
ol.get_news_for_ticker(ticker)	News for a specific ticker

ETFs

Function	Description
ol.get_etf(ticker)	ETF data, holdings, sectors
ol.compare_etfs(tickers)	Compare 2-5 ETFs (overlap)
ol.list_etfs()	List all known ETFs

Dividends

Function	Description
ol.get_dividend_forecast(tickers)	Dividend forecast

Presentations & Lore

Function	Description
ol.search_presentations(query)	Investor presentations
ol.search_lore(query)	Financial lore and wisdom
ol.get_value_investing_random()	Random investing fact
ol.search_value_investing(query)	Search investing facts
ol.get_recommended_reading()	Curated reading list
ol.get_recommended_reading_random()	Random book recommendation

Options & Short Interest

Function	Description
ol.get_options(ticker)	Options chain
ol.get_short_interest(ticker)	Short interest data
ol.get_options_sentiment(type, days)	Put/call ratios (CBOE)

AI Analysis

Function	Description
ol.ask(question, ticker=None)	Ask the AI analyst

Error Handling

import oxford_ledge_sdk as ol
from oxford_ledge_sdk import AuthenticationError, RateLimitError, NotFoundError, QuotaExceededError

ol.set_api_key("ol_live_YOUR_KEY_HERE")  # Get your key at oxfordledge.com

try:
    company = ol.get_company("INVALID")
except NotFoundError:
    print("Ticker not found")
except AuthenticationError:
    print("Check your API key")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
except QuotaExceededError:
    print("Monthly budget exhausted -- check billing at oxfordledge.com")

Rate Limits

The SDK automatically:

• Tracks requests to stay within 60 RPM
• Retries on 429 (rate limit) and 5xx (server error) with exponential backoff
• Raises QuotaExceededError when monthly budget is exhausted (distinct from rate limiting)

Models

All structured responses use lightweight dataclasses. Access the raw API response via the .raw attribute:

company = ol.get_company("AAPL")
print(company.name)          # Typed field
print(company.raw["beta"])   # Full raw response dict

Available models: Company, Quote, Fundamentals, Bond, Borrower, NewsItem, Presentation.

Requirements

• Python 3.8+
• requests >= 2.28.0
• pandas >= 1.3.0 (optional, for DataFrame support)

Get Your API Key

Sign up at oxfordledge.com to get your API key. Keys start with ol_live_ (production) or ol_test_ (sandbox).

Disclaimer

This software is provided "as is", without warranty of any kind, under the MIT License (see LICENSE). It is a developer tool for accessing and parsing financial data. It is not investment, financial, legal, or tax advice, and nothing it returns is a recommendation to buy, sell, or hold any security. Data may be incomplete, delayed, or inaccurate. You are responsible for independently verifying anything you rely on and for your own investment decisions. Data returned by the Oxford Ledge API is governed by the terms at https://www.oxfordledge.com.

Contributions

By submitting a pull request or patch, you agree your contribution is licensed under the same MIT License as this project (inbound = outbound).