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Why do data scientists waste up to 70% of their time and money collecting and cleaning data?
The following article will focus on understanding, highlighting, and sharing some insights on the real reasoning behind why do data scientists waste valuable time collecting and cleaning their data. The article will also briefly delve into the real “cost” of managing high quality data.
Multiple assets tick requests
This article is a quick note on how the user can handle tick data from multiple assets at the same time. We will show the few steps to retrieve tick data from multiple assets in a single request thanks to our web cloud-based JupterLab environment and our API.
In this article, we will outline the maturity date approch to future rolling and to building a continuous stream using our API within our web cloud-based JupyterLab environment. A step-by-step explanation of tick data requests, technical indicators and continuous price stream creation.
In this article we are going through a best execution sample using our API within our web cloud-based JupyterLab environment. A step-by-step explanation of commonly used best execution approaches illustrated using tick data and execution performance measurements.
Strategic technological choices and workflow decisions need to be made all along the financial data lifecycle. In this article, we are delivering our feedbacks and sharing our experience on dealing with different financial data types and coming from multiple sources.
Leading the way in smarter data consumption
We are here to make quality financial data available for everyone. Our solutions aim to help our clients reduce technology costs and get actionable data. Please reach out for business opportunities.