Stock Prices API for Python

Get real-time and historical stock prices in Python with the fdnpy SDK. Load market data directly into pandas to build screeners, backtests, and dashboards.

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Why developers choose us
  • Official, documented REST API with a maintained Python SDK
  • Stock prices, financial statements, and key metrics under one API key
  • Server-side filtering and field selection to download only what you need
  • Consistent JSON responses that work with pandas, NumPy, and your database

Get Stock Prices in Three Lines of Code

Install fdnpy, add your API key, and call the method for the data you need. The same client also supports custom queries with field selection and filters.

example.py
from fdnpy import FinancialDataClient

# Replace 'YOUR_API_KEY' with your actual key
client = FinancialDataClient(api_key='YOUR_API_KEY')

# Get end-of-day stock prices for Microsoft
prices = client.get_stock_prices(identifier='MSFT')
print(prices[:2], end='\n\n')

# Get real-time stock quotes for AAPL and MSFT
quotes = client.get_stock_quotes(identifiers=['AAPL','MSFT'])
print(quotes[:2], end='\n\n')

# Get custom data by selecting specific fields and applying filters
custom_data = client.universal_query(
dataset='minute_prices',
fields=['trading_symbol', 'time', 'close', 'volume'],
filters={
'trading_symbol': {'in': ['NVDA', 'AVGO']},
'time': {'gt': '2020-01-15 20:45:00','lt': '2020-01-15 21:00:00'}
}
)
print(custom_data[:2])

A Financial Data API for Python Developers

Everything you need to go from API key to working data pipeline, with no scraping, parsing, or messy formats along the way.

Historical Prices

Pull years of historical price data for backtesting strategies, charting, and technical indicators.

Pandas-Ready JSON

Every response is a list of structured records. Wrap it in pd.DataFrame() and start analyzing right away.

Custom Queries

Use universal_query to select exact fields and filter on the server, saving bandwidth and code.

More Than Prices

Combine prices with financial statements, key metrics, and valuation ratios from the same API and SDK.

How to Get Stock Prices in Python

From sign-up to your first data point in just a few minutes.

01

Create your API key

Sign up for FinancialData.Net and copy your key from the dashboard.

02

Install fdnpy

Run pip install fdnpy and create a FinancialDataClient using your API key.

03

Call get_stock_prices

Pass a ticker like MSFT and receive structured price data to use in your app.

Official API vs. Free Scrapers

Free libraries are fine for quick experiments. Once your product or research depends on the data, an official API is the more reliable choice.

FeatureFinancialData.Net APIFree scraper libraries
Access methodDocumented REST APIUnofficial web scraping
Python SDKOfficial SDK, maintainedCommunity-maintained
Server-side filteringYes, via universal_queryUsually download everything, filter locally
Fundamentals and prices togetherOne API key, one SDKOften separate sources
Production readinessBuilt for production useCan break when source pages change

Stock Prices API for Python: FAQ

Install the fdnpy package with pip, create a FinancialDataClient with your API key, and call get_stock_prices with a ticker such as MSFT. The result is a list of price records you can load into a pandas DataFrame.

Yes. fdnpy is the Python SDK for FinancialData.Net. It wraps the REST API, so you can pull stock prices, financial statements, key metrics, and more without handling HTTP requests yourself.

Yes. We offer both end-of-day and intraday price data, so you can use it for backtesting, charting, and technical analysis.

Unlike yfinance, FinancialData.Net is an official, documented API with API key authentication and a maintained SDK. It delivers consistent data you can rely on in production, with no web scraping involved.

Yes. The universal_query method allows you to select specific fields and apply filters such as greater-than or in-list conditions, so you download only the data you need.

Start Building in Python

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