Choosing the right tech stack is critical for investors and fintech app developers, and Python has been increasing in popularity for a decade - for good reason. It’s simple syntax means quicker deployment and less code, and the open source libraries are a lifesaver. Plus - it’s the language of choice for most quants and financial institutions.
The next step after choosing your programming language is to find access to a great financial data API. It’s likely that you’ll need to integrate things like stock prices or financial metrics into your algorithms and/or platform, but finding an affordable data provider can be challenging. This is why most fintech developers are on the hunt for a free stock market API in Python.
If you’re just getting started, it’s likely that you don’t have a big budget. You may be playing around with your code, developing an MVP, or learning how to build your own investment models. It can be tempting to try to scrape together the data you need for free, but there are downsides to taking the free route.
The bad news: there is no such thing as a “Free” stock data API in Python. Sourcing the stock data, cleaning the data, storing the data, building the API, documenting the API, maintaining the API, and marketing the API all costs money. As you can see, there’s no way that sustainable companies can offer financial data APIs for free.
If you are willing to do some data transformation work on your own end, and can do it without a ready-made API, one option is to try to scrape the financial data you need from the internet. This requires some basic progr
amming knowledge, but it can be done quite easily. Unfortunately, a system like this is not scalable, reliable, or in many cases - legal. Most websites list “terms of service” that prevent you from scraping and using their data, and almost always from displaying or using it in a commercial manner. Websites change over time, so the scraped data you may be used to pulling in might break and take down your entire operation.
Other websites like Yahoo Finance offer a plethora of data libraries, but there is no official API and very strict terms of service. If you rely on data from a website like this, chances are you are violating the terms of service. This is not the way to build a reliable business or investment strategy.
An alternative would be to access the data via new up-start websites like Alpha Vantage or 12Data. These companies don’t offer financial data APIs in Python for free, but it’s extremely cheap.
If you are serious about investing or building finch apps, you should be wary of “free” stock APIs for Python. Scraping the data for free is unreliable, not scalable, comes with no API or support, and is a lot to maintain. Accessing the data from a “super cheap” upstart data website is extremely risky as well: you’ll spend time and resources on the integration and inevitably realize you get what you pay for - major issues with data quality, minimal support, and an unreliable integration.
Some of these options might work for you if you are in the very early stages of proving our your model or your app. However, once you are ready to seriously invest or develop an app that users will interface with, you will need to step beyond the “free” access to stock data APIs.
If you are at that stage, and either making investment decisions based on financial data, or displaying stock data in front of your end users, you can get reliable, affordable stock market API for Python via Intrinio. Intrinio offers a super affordable Starter Plan with stock data for just $100/month. It comes with full access to their API, documentation, SDKs, tutorials and support. Using the Intrinio API and Python SDK, you can copy and paste just 17 lines of code to get an entire year’s worth of daily stock prices for a given ticker.
Intrinio stock data packages are built to scale with you as you grow, so you have plenty of options to access more data over time. The founders of Intrinio are former fintech engineers themselves, so you can bet that the developer experience is smooth, the pricing affordable, and the team super nice and technical.
To get started, check out their Python SDK, GIthub, and Documentation. When you are ready to subscribe, or access a free trial, you can chat with the team at any time or sign up yourself on the website.