Data Tools

Blockchain technology is swiftly growing and interfering with several areas of the finance world, specifically the financial services sector. This technology is a secure, decentralized database that keeps track of every single digital transaction that takes place in the network, without the need of a third-party source monitoring it. The way information is recorded on the ledger system makes it extremely difficult for hackers to alter or corrupt.

In this article, I will show you how to set up a basic Extract, Transform and Load (ETL) process to pull end-of-day stock market exchange data into the Relational Database Service (RDS platform) provided by Amazon Web Services (AWS) using the Python scripting language and the MySQL database language.

You’ve done it! You’ve created the one trading strategy that will rule them all. You’ve backtested it on your laptop against Snowflake or a flat file, and it’s now ready for some real, streamed data. You hook it up to the options or multi-exchange websocket client in firehose mode (you know, so you get all the things), hit start, and are excited to see some #winning results. Instead, you hear the aircraft engine-like noise of your laptop fans speeding up to maximum and you singe some leg hair as the bottom of the laptop turns into lava.

Every business under the sun exists to make a profit and gradually expand their profit margin, but the only way to really know whether that’s happening is through good accounting practices. One of the many ways to evaluate the financial health of a company is to look at net income, the true “bottom line” of a firm that indicates income after accounting for all costs, expenditures, depreciation, amortization, interest, and taxes. While net income is an important measure, many companies also look to EBIT for a more accurate snapshot of their finances.

This week's Customer Spotlight is dedicated to TickerStats, an interactive, user-friendly platform that provides new and seasoned investors with financial information such as stock per share data, fundamentals, and options.  TickerStats makes investing fun by offering free tools and games through its engaging platform.  We spoke to Head Engineer, Roman Bansal, on the inception of TickerStats, the Company’s biggest successes, and how they’ve set themselves apart from their competitors.

Unusual options activity can be useful as a predictive tool to determine upcoming stock price moves. Here's an overview of what unusual options activity means and how Intrinio is offering this data to investors.

How has Intrinio automated the data supply chain to provide higher-quality financial data, faster, at a more competitive price? The secret is machine learning. In this blog series, we will be offering a high-level overview of what machine learning is and how it can be used to analyze and interpret financial information.

Intrinio’s wealth of data, innovative technology, and friendly and helpful staff draws companies in all areas of the fintech industry to partner with Intrinio. Not sure if it’s the right fit for your company? Check out this blog post to get started!

Intrinio offers multiple access methods to help you easily retrieve real-time, delayed, end of day, or historical financial data. Here's a breakdown of the four main access methods that Intrinio offers so you can pick the perfect access tool for your use case.

Can you sort through billions of data points to find exactly the data you’re looking for? Can you connect multiple datasets and run complex queries…all within a few seconds? You can now. Intrinio is excited to announce direct database access to our financial data through Snowflake’s platform.