Trading Engine
From tick-by-tick backtest to live trading
Smart Orders
Execution algorithms for tick-by-tick market data
Tick capture
Keep your eyes and ears on the market
Expertise
Leverage our expertise with consulting.
Strategy Validator
Build trust and discover new trading strategies!
Ganymede
Discover our financial data store
Blog
Discover our insights
Press Room
Get our latest press release
Events
Be part of our upcoming events
About Us
Learn more about us and discover our vision
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Explore our latest updates and browse our research articles
Systemathics Tutorial - Ganymede API and Python Pandas DataFrame
How to use Python dataframe with Ganymede API ?
Market impact
Truly efficient live trading is the result of #quality #backtesting tools.
The role of data in the accuracy of AI models
Just how important is data in the world of AI?
Tick Data
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.
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.
How Systemathics can help you access financial data
In this post, we will focus on how Systemathics helps you access financial data.
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? related_url: /blog/impact-of-trading-conditions.html
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 ?
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).
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.
Ganymede - ICE DataVault persbericht
Systemathics Ganymedes Cloud Data Platform-as-a-service integreert met ICE DataVault
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.
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.
Facebook down
In this article, we will get insight on Facebook outage impact on its stock price.
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.
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.
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.
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.
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.
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 🌱
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.
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.
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.
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.
