Financial data

Financial data

Quadratic includes a built-in financial data API for Python — no API keys or external accounts required. Use q.financial to pull stock prices, company fundamentals, financial statements, news, and technical indicators straight into your sheet.

Quick start

All methods are async — use await:

# Daily stock prices as a DataFrame
prices = await q.financial.stock_prices('AAPL', start='2025-01-01')
prices

# Latest real-time quote
quote = await q.financial.realtime_price('NVDA')
quote['last_price']

Available methods

Method	Returns
stock_prices(identifier, start, end, frequency)	Historical prices (DataFrame)
realtime_price(identifier)	Real-time quote (dict)
intraday_prices(identifier)	Intraday bars
company(identifier)	Company profile (dict)
company_news(identifier, start_date)	Recent news
financial_statements(identifier, statement, fiscal_year, fiscal_period)	Income statement, balance sheet, or cash flow (DataFrame)
dividends(identifier)	Dividend history
splits(identifier)	Split history
data_point(identifier, tag)	Single numeric metric
data_point_text(identifier, tag)	Single text metric
historical_data(identifier, tag)	Metric time series
technical_indicator(identifier, indicator, ...)	Technical indicators (DataFrame): sma, rsi, macd, bollinger_bands, vwap, atr, adx, obv, stochastic, cci

Prefer adjusted prices for historical analysis — they account for splits and dividends.

Prefer formulas?

The Excel-compatible STOCKHISTORY function retrieves historical prices without writing Python:

=STOCKHISTORY("MSFT", "2025-01-01", "2025-12-31")

Rate limits

Financial data requests are limited to 500 requests per team per minute. If your team hits the limit, requests briefly pause and a notification appears in the app.