Using Nasdaq Data Link and Quandl API for Financial Data
Summary
This article introduces Nasdaq Data Link as a source of traditional financial, ESG, and alternative datasets, then explains how to retrieve data through the Quandl API in Python. It describes dataset categories and subscription access, and outlines the workflow: obtain an API key, choose a dataset, follow its Python usage instructions, and request data for a date range or with a frequency collapse. Examples cover historical stock prices, precious metals data, and company fundamentals.
The piece is a practical data-access overview rather than a trading strategy or empirical study. It explains API endpoints and keys by analogy, and notes that free and paid plans have different dataset availability and request limits. Data offerings and platform details can change over time, and access to some alternative datasets is restricted to institutional clients. The article does not assess data quality, compare vendors, or show how retrieved data affects strategy performance.
Key ideas
- Nasdaq Data Link aggregates traditional financial, ESG, and alternative datasets from multiple publishers.
- The Quandl API can retrieve datasets in Python after an account and API key are configured.
- Dataset requests can specify instruments and date ranges, and some series can be collapsed to a coarser frequency.
- Free and paid subscriptions differ in dataset access and API request limits.
- The article explains data retrieval but does not evaluate the accuracy or trading value of the datasets.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.