The Zero-to-Hero Guide to OpenBB: Open-Source Financial Data for Everyone

1. What Exactly Is OpenBB?

Imagine you want to:

• Download ten years of Apple stock prices with three lines of code
• Check today’s option chain for the S&P 500 without logging into a broker
• Combine U.S. GDP, EUR/USD quotes, and Bitcoin prices in one table

「OpenBB is an open-source platform that puts all of those data streams behind a single Python library and command-line tool.」
It does 「not」 give you trading advice; it simply hands you clean, ready-to-analyze data.

Quick Glossary

2. Installation in Five Minutes or Less

2.1 Checklist Before You Start

• 「Python」 3.9.21 – 3.12 (earlier or later versions are not supported)
• Internet connection (the library downloads data in real time)
• Optional but handy: VS Code with the Dev Container extension

2.2 One-Line Install

Open a terminal and run:

# Core library
pip install openbb

# Optional: command-line interface
pip install openbb-cli

2.3 Verify Everything Works

Type the following in Python:

from openbb import obb
data = obb.equity.price.historical("AAPL")
print(data.to_dataframe().head())

You should see a neat table with Apple’s daily prices. If that prints, the hard part is done.

3. Core Capabilities Walk-Through

OpenBB groups data into logical namespaces. Each namespace works the same way: you call a function, you get back an OBBject, and you turn that into a pandas DataFrame with .to_dataframe().

3.1 Stocks: History, Real-Time, and Fundamentals

# Ten years of daily prices
history = obb.equity.price.historical(
    symbol="AAPL",
    start_date="2015-01-01"
)

# Latest quote
quote = obb.equity.price.quote(["AAPL", "MSFT"])

# Income statement
income = obb.equity.fundamental.income("AAPL")

3.2 Options: Full Chains and Expirations

# List all expiration dates
expiries = obb.derivatives.options.expirations("SPY")

# Chain for one expiration
chain = obb.derivatives.options.chains(
    symbol="SPY",
    expiration="2025-09-19"
)

3.3 Foreign Exchange & Crypto

# Live EUR/USD
fx_live = obb.forex.price.quote("EURUSD")

# Bitcoin daily candles
btc = obb.crypto.price.historical("BTC-USD")

3.4 Macroeconomics

# U.S. GDP
gdp = obb.economy.gdp(countries=["us"])

# U.S. CPI
cpi = obb.economy.cpi(countries=["us"])

4. Where Does the Data Come From?

OpenBB itself does 「not」 store data. Instead, it routes your request to one of the integrated providers. The full, up-to-date list is published at:

https://docs.openbb.co/platform/reference

❝

「Tip:」 Some providers require an API key. After you obtain it, store it in an environment variable (e.g., export ALPHA_VANTAGE_API_KEY=your_key_here).

❞

5. OpenBB Workspace: Drag-and-Drop Visualization

If you prefer clicking to coding, OpenBB offers a web-based workspace. The workflow is simple:

1. Run the backend locally.
2. Point the web workspace to that backend.
3. Drag widgets to build charts and dashboards.

5.1 Launch the Local Backend

# Install everything at once
pip install "openbb[all]"

# Start the server
openbb-api

The terminal shows:
Uvicorn running on http://127.0.0.1:6900

Open http://127.0.0.1:6900 in your browser; you should see FastAPI’s interactive documentation.

5.2 Connect the Web Workspace

1. Visit https://pro.openbb.co and sign in.
2. In the left sidebar, click 「Apps → Connect backend」.
3. Fill in the form:

   • 「Name:」 OpenBB Platform
   • 「URL:」 http://127.0.0.1:6900
4. Click 「Test」. You should see “Test successful” along with the number of endpoints found.
5. Click 「Add」.

Once connected, the sidebar lists your local endpoints. Drag any data field onto the canvas to generate a live chart.

6. End-to-End Mini-Project: Apple vs. Fed Funds Rate

Let’s see whether Apple’s stock price moves opposite to U.S. interest rates.

import pandas as pd
import matplotlib.pyplot as plt
from openbb import obb

# 1. Download Apple daily closes
aapl = obb.equity.price.historical(
    symbol="AAPL",
    start_date="2020-01-01"
).to_dataframe()

# 2. Download the Fed Funds Effective Rate
fed = obb.economy.fred(
    series_id="DFF",
    start_date="2020-01-01"
).to_dataframe()

# 3. Merge on date
merged = pd.merge(
    aapl[["date", "close"]],
    fed.rename(columns={"value": "fed_rate"}),
    on="date"
)

# 4. Plot
fig, ax1 = plt.subplots(figsize=(10, 5))
ax1.set_ylabel("AAPL Close ($)", color="tab:blue")
ax1.plot(merged["date"], merged["close"], color="tab:blue")

ax2 = ax1.twinx()
ax2.set_ylabel("Fed Funds Rate (%)", color="tab:red")
ax2.plot(merged["date"], merged["fed_rate"], color="tab:red")

plt.title("Apple Stock vs. U.S. Fed Funds Rate")
plt.show()

The chart updates automatically whenever you rerun the script.

7. Frequently Asked Questions

8. Contributing: Three Easy Paths

1. 「Code」 – Read the Contributing Guide and open a pull request.
2. 「Issues」 – Use the templates for bug reports, enhancements, or new features.
3. 「Feedback」 – Drop into Discord for quick questions before writing code.

9. Licence and Risk Disclaimer

• 「Licence:」 GNU Affero General Public License v3.0. Full text at LICENSE
• 「Risk:」 Financial markets are volatile. Data may be delayed or contain errors. OpenBB and its data providers accept no liability for losses resulting from trading or reliance on the data.

10. Key Takeaway

Think of OpenBB as a 「Swiss-army knife for market data」:

• Developers get a clean Python API
• Analysts get drag-and-drop charts
• Researchers get extensible, auditable code

Install once—pip install openbb—and you hold the keys to global markets. Use it wisely, and always respect the risks.