Skills openbb

install

source · Clone the upstream repo

git clone https://github.com/TerminalSkills/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/TerminalSkills/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/openbb" ~/.claude/skills/terminalskills-skills-openbb && rm -rf "$T"

manifest: skills/openbb/SKILL.md

safety · automated scan (medium risk)

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pip install
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source content

OpenBB

Open Data Platform for financial data. Connect once, consume everywhere — Python for quants, REST API for apps, MCP server for AI agents. Access stocks, crypto, forex, macro indicators, and alternative data.

GitHub: OpenBB-finance/OpenBB

Overview

OpenBB is an open-source financial data platform that aggregates data from multiple providers (Yahoo Finance, FRED, SEC, FMP, Polygon, and more). It offers a Python SDK, REST API server, and MCP server for AI agents, covering equities, crypto, forex, macro economics, and news.

Instructions

Installation

# Core package
pip install openbb

# With all data providers
pip install "openbb[all]"

Quick Start

from openbb import obb

# Stock price history
output = obb.equity.price.historical("AAPL")
df = output.to_dataframe()
print(df.head())

Equity Data

# Historical prices
df = obb.equity.price.historical("AAPL", start_date="2025-01-01").to_dataframe()

# Real-time quote
quote = obb.equity.price.quote("AAPL").to_dataframe()

# Fundamental analysis
income = obb.equity.fundamental.income("AAPL", period="annual").to_dataframe()
balance = obb.equity.fundamental.balance("AAPL").to_dataframe()
metrics = obb.equity.fundamental.metrics("AAPL").to_dataframe()

# Technical indicators
df = obb.equity.price.historical("AAPL", start_date="2025-01-01").to_dataframe()
sma = obb.technical.sma(data=df, length=20)
rsi = obb.technical.rsi(data=df, length=14)
macd = obb.technical.macd(data=df)

Crypto, Forex, and Macro

# Crypto
btc = obb.crypto.price.historical("BTC-USD").to_dataframe()

# Forex
eurusd = obb.currency.price.historical("EUR/USD").to_dataframe()

# Macro economics
gdp = obb.economy.gdp.nominal(country="united_states").to_dataframe()
cpi = obb.economy.cpi(country="united_states").to_dataframe()
rates = obb.economy.fred_series("FEDFUNDS").to_dataframe()

AI Agent Integration

Run OpenBB as an API server:

openbb-api
# Launches FastAPI at http://127.0.0.1:6900

Query from any language:

curl http://127.0.0.1:6900/api/v1/equity/price/historical?symbol=AAPL

OpenBB also exposes an MCP server so AI agents can query financial data directly.

Data Providers

Provider	Data	Free Tier
Yahoo Finance	Prices, fundamentals	Yes
FRED	Macro economics	Yes
SEC (EDGAR)	Filings, insider trades	Yes
FMP	Fundamentals, estimates	Limited
Polygon	Real-time prices	Limited

# Use a specific provider
obb.equity.price.historical("AAPL", provider="yfinance")

# Set API keys for premium providers
obb.user.credentials.fmp_api_key = "your_key"

Examples

Example 1: Full Stock Analysis Pipeline

from openbb import obb

def analyze_stock(ticker: str) -> dict:
    """Full analysis for AI agent consumption."""
    price = obb.equity.price.historical(ticker, start_date="2025-01-01").to_dataframe()
    fundamentals = obb.equity.fundamental.metrics(ticker).to_dataframe()
    news = obb.news.company(ticker, limit=5).to_dataframe()

    return {
        "ticker": ticker,
        "current_price": price["close"].iloc[-1],
        "52w_high": price["high"].max(),
        "52w_low": price["low"].min(),
        "pe_ratio": fundamentals["pe_ratio"].iloc[0] if len(fundamentals) > 0 else None,
        "market_cap": fundamentals["market_cap"].iloc[0] if len(fundamentals) > 0 else None,
        "recent_news": news["title"].tolist() if len(news) > 0 else [],
    }

analysis = analyze_stock("AAPL")

Example 2: Screening and Discovery

# Stock screener — find undervalued dividend stocks
screener = obb.equity.screener(
    market_cap_min=1e9,
    pe_ratio_max=20,
    dividend_yield_min=2.0
).to_dataframe()

# Top gainers/losers
gainers = obb.equity.discovery.gainers().to_dataframe()
losers = obb.equity.discovery.losers().to_dataframe()

# Company news
news = obb.news.company("AAPL", limit=20).to_dataframe()

Guidelines

Start with
```
pip install openbb
```
(core) — add
```
[all]
```
only if you need every provider
Use
```
.to_dataframe()
```
on all outputs for pandas integration
Free data from Yahoo Finance and FRED covers most research needs
Run
```
openbb-api
```
to expose data to non-Python applications
The MCP server lets AI agents query financial data autonomously
Check docs.openbb.co/python/reference for all available endpoints