Developer Spotlight #2: TraderSquare [blog series]

Our mission at Intrinio is to power a generation of applications that will fundamentally change the way our broken financial system works. Intrinio data feeds form the basis of large enterprise business reporting applications, Fintech web-apps, simple mobile apps and even blogs. It's rewarding to see our product come to life at the hands of today's most innovative developers building powerful things.

We're lucky to be in a business where we grow together with our customers, and we're proud to show off their hard work. Each blog in this series will feature a developer or a startup that has leveraged our financial data feeds to build something incredible.

These are their stories.

tradersquare

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Find, Analyze, and Value Stocks Quickly With Intrinio

Intrinio provides many different data feeds and applications in its Fintech marketplace and this blog explains how to use three of those apps to analyze stocks using the US Fundamentals and Stock Prices. Intrinio makes it possible to screen for stocks based on set parameters, quickly run a DCF on those companies, and then dig into the details for those that look under valued- all for a portion of the price the other guys charge. This article will show you how.

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Modeling Financial Data in R with Intrinio

This is the third blog in a series showing how to use Intrinio financial data in R or R Studio to create quant models. The tools at Intrinio are built to make modeling financial data straightforward. The first blog shows the basics of making an API call for financial data in R. The second blog shows how to write two functions, one to pull in historical stock prices and another to pull in historical fundamentals data.

This blog takes both of those blogs a step further, creating a single function that will pull in historical stock prices as well as historical fundamentals for many companies and many metrics at once. The function code as well as an explanation of what is going on under the hood is included, enabling R developers to quickly create a data frame for analysis with exactly the data they want.

Update 05/22/16- Check out this blog as well showing how to create a for loop in R to get multiple pages of data via API. This example shows the best way (known to the author) to parse JSON from an API in R.

Update 11/30/2017- Feel free to skip ahead to this recently released package that does the hard work for you.

Creating a data frame with the desired tickers, date ranges, and financial data

Please take a look at this code. Its not long, the point is to see how easy it is to pull the data you need:

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Developer Spotlight #1: WakeBotApp [blog series]

Our mission at Intrinio is to power a generation of applications that will fundamentally change the way our broken financial system works. Intrinio data feeds form the basis of large enterprise business reporting applications, Fintech web-apps, simple mobile apps and even blogs. It's rewarding to see our product come to life at the hands of today's most innovative developers building powerful things.

We're lucky to be in a business where we grow together with our customers, and we're proud to show off their hard work. Each blog in this series will feature a developer or a startup that has leveraged our financial data feeds to build something incredible.

These are their stories.

wakebot-app

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Historical Financial Data in R for Stocks

This blog is a follow up to a blog explaining how to pull Intrinio financial data into R and R-Studio. In that blog I showed the basics of how to get the data flowing. In this blog I take it one step further and provide custom functions that will allow you to pull historical data into R very efficiently. I plan to build quant models, predicting historical prices based on historical metrics for a stock, and use a subset of the historical data to back test my models. This blog explains how to get the data for such an analysis.

Update 05/22/16- Check out this blog as well showing how to create a for loop in R to get multiple pages of data via API. This example shows the best way (known to the author) to parse JSON from an API in R.

Update 11/30/2017- Feel free to skip ahead to this recently released package that does the hard work for you.

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Intrinio Financial Data in R and RStudio

Data analysts everywhere know that most of their time is spent gathering, cleaning, and formatting data. The actual analysis and interpreting the results is fun, fast, and easy once the data is structured how you need it. This blog shows how easy Intrinio makes it to complete the nasty part of analysis by using the Intrinio API to pull financial data into R.

Intrinio provides many data feeds via API and if you learn to use the API in conjunction with R, you can spend a lot more of your time running the analysis and analyzing the results and a lot less time on data entry.

If you don't use R this might not be the blog for you, but if you do, this blog will show you in step by step fashion how to save yourself a lot of money, and make yourself a lot of time, by using the Intrinio API in the R terminal or RStudio.

Feel free to skip ahead to this recently released package that does the hard work for you.

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Getting Started With Intrinio

Intrinio is on a mission to provide financial data in a way that is flexible and affordable so that fintech developers can build the next generation of game changing applications for investors. Our ecosystem includes the APIs developers need to build those applications as well as the applications themselves, allowing developers and investors to come together in the same marketplace.

What follows is a quick introduction to the resources and steps both types of users will need to take to get started with Intrinio.

What is the Intrinio Fintech Marketplace?

Cutting through the marketing jargon here is what Intrinio provides. We provide a bunch of different data feeds that allow users to access many different kinds of financial data. The marketplace lets users sign up for the data they need and the pricing plan that fits their needs.

Once a user has access to data, they can analyze the data in an application like Excel or they can build their own application like Vantage for other users to use. These applications, which rely on data from the marketplace, are also available for users in the marketplace.

The idea is to provide the data users need as well as lots of new applications to make that data even easier to analyze. You can browse the apps and browse the data feeds Intrinio provides and see the prices, terms, and limits for those products in the Marketplace.

Signing Up

Signing up for Intrinio is free and easy- all we require is an email and a password, no credit card, no spam. You can signup at www.intrinio.com/login. This will give you access to your account page where you will see your subscriptions as well as tutorials for accessing Intrinio data through an Excel add-in, a Google Sheets add-on, or directly through an API.

The Account Page

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Introducing the Intrinio Fintech Marketplace

Intrinio launched a fintech marketplace this week and this blog explains why Intrinio is making this change, how the user experience will be the same, and how it will be different.

Why a Fintech Marketplace?cszbm2-wyaekx4q

Intrinio's mission is to help investors save money and make time so they can live more meaningful lives. Investors need innovative applications that make data entry and analysis easier so they can spend more time conducting research, generating insights, and questioning assumptions.

The challenge developers face in creating these applications is getting data- traditional data providers make financial data for app development expensive and hard to use.

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