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Why does Market Data Quality matter for your business?

Why does Market Data Quality matter for your business?

Is there a way to identify high quality market data providers? This article presents the results of the comparison of multiple providers of data, and reveals the few trends we can capture out of it.

13 min read
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Latest

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Impact of Trading Conditions

Ever wondered how tick data is structured? This article delves into the importance of detailed financial analysis, and the extent to which it is affected by various trading conditions.

19 min read
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How Systemathics can help you access financial data

In this post, we will focus on how Systemathics helps you access financial data.

1 min read
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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?

3 min read
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Algorithmic Trading Best Practices Stress-Testing

In the following article, we will focus on what event-driven stress-test. Why does stress-test matter? And how to run easily a stress-test ?

1 min read
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Market by Order (MBO) vs Market by Limit (MBL)

This article describes the two representations of the market book : Market by Order (MBO) / Market by Limit (MBL).

1 min read
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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?

4 min read
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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.

2 min read
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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.

2 min read
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DAX expansion

In this article, we will get an insight on how an index impacts a stock liquidity. We will look back on how DAX index expansion from 30 to 40 stocks impacted liquidity for these new stocks leveraging several APIs we provide.

3 min read
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facebook

Facebook down

In this article, we will get insight on Facebook outage impact on its stock price.

2 min read
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CAC 40 Correlation

In this article, we will get an insight on correlation analysis for an index, in this case we chose CAC 40 (French index 🇫🇷 on Euronext) use-case leveraging several APIs we provide.

3 min read
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strategy

Future roll

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.

3 min read
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Pair trading strategy

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.

3 min read
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Best execution

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.

3 min read
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analytics

Tick bars

In this article we are going through a tick bars calculation and adjustment with corporate actions data using our API within our web cloud-based JupyterLab environment.

2 min read
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Symbol changes management

Symbol or ticker changes can be handled as a corporate actions event or a static data update. In this article we are dealing with symbol changes automated management as a corporate actions market event.

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Cloud based financial data

Our fully managed solutions are cloud-based, subscribe and start immediately retrieving on-demand data. We made technological choices enabling a cloud-agnostic and open architecture approach. Your cloud choice can be cost-effective 💰 and green 🌱

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etf

ETFs integration

ETFs data is now live among 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 and cloud-based JupterLab environment.

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Product features

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.

3 min read
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academia

Academia program

We are delighted to promote applied research in Finance and connect professionals and academics. We offer free access to a representative financial data coverage from trusted sources within Ganymede, our web and cloud JupyterLab environment.

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On-demand financial data and analytics

We are pleased to launch on-demand data solution to support and streamline your investment and risk management workflow. We are looking forward to getting your thoughts and suggestions.

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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.

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