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Data Filtering : Daily data vs Tick data
In the following article, we will focus on high frequency data filtering. What is the main difference between daily data and tick data?
Algorithmic Trading - Best Practices: Stress-Testing
In the following article, we will focus on what is event-driven stress-test. Why does stress-test matter? And how to run easily a stress-test ?
Market by Order (MBO) / Market by Limit (MBL)
This article describes the two representations of the market book : Market by Order (MBO) / Market by Limit (MBL).
Why does Data Governance matter?
In the following article, we will focus on what data governance is. Why does data governance matter? And how does data governance improve the asset manager’s activity?
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.
Multi assets tick management
This quick how-to article explains how to handle multiple assets when querying the Tick data endpoint. This feature enables the user to retrieve data from a defined watchlist in a single request for an efficient and seemless workflow.
In this article, we will get an insight on how an index impacts a stock liquidity. We will look back on how DAX (German index 🇩🇪 on Deutsche Börse) expansion from 30 to 40 stocks impacted liquidity for these new stocks leveraging several APIs we provide.
The full sample and more data analytics samples and building blocks are available in our public Github repository.
In this article, we will outline the maturity date approach to future rolling and to building a continuous stream of 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 pair trading strategy sample using our API within our web cloud-based JupyterLab environment. A step-by-step explanation of tick data requests, technical indicators and trading signals generation. Please contact us to get your preview access and explore on-demand financial data and analytics.
In this step-by-step tutorial we are going through a tick bars calculation and adjustment with corporate actions data sample. We will be using our API within our web cloud-based JupyterLab environment. Please contact us to get your preview access and explore on-demand financial data and analytics.
Symbol changes management
Corporate actions pre-mapped, normalized and readily made available through our APIs include Symbol changes events. Our robust solutions help you spreading corporate actions events all along the investment, risk and data management workflow.
We are delighted to add ETFs to preview access data coverage, available until September 2021. You can now request on-demand fiancial data from trusted sources for equities, futures, indices and ETFs using our web cloud-based JupterLab environment and our API.
Our data solutions pillars
Our article explores how to deal with historical data. Strategic technological choices and workflow decisions need to be made all along the financial data lifecycle. We are delivering our feedbacks and sharing our experience on dealing with various data types from multiple sources.
We are delighted to promote applied research in Finance and connect professionals and academics. We offer free access to a representative data coverage from trusted sources within our web and cloud JupyterLab environment. Our offer is for students, interns and researchers...
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.