See our policy page for more information. You can learn more about the MLQ app here or sign up for a free account here. The platform combines fundamentals, alternative data, and ML-based insights. If you're interested in learning more about data science and machine learning for trading and investing, check out our AI investment research platform: the MLQ app. This guide is certainly not comprehensive with regard to each data visualization library, instead it aims to provide an overview of the most important capabilities. This article is based on notes from this course on Python for Financial Analysis and Algorithmic Trading. There are many tools at our disposal for data visualization and the topics we will cover in this guide include:
MATLAB includes an interface for importing historical and real-time market data from free and paid sources including Bloomberg, Refinitiv, FactSet, FRED, and Twitter.IT groups can deploy IP protected models directly to desktop and web applications such as Excel, Tableau, Java, C++, and Python.Analysts use prebuilt apps and tools to visualize intermediate results and debug models.
MATLAB automatically generates documentation for model review and regulatory approval.MATLAB is fast: Run risk and portfolio analytics prototypes up to 120x faster than in R, 100x faster than in Excel/VBA, and up to 64x faster than Python.Leading institutions use MATLAB to determine interest rates, perform stress tests, manage multi-billion dollar portfolios, and trade complex instruments in less than a second. In just a few lines of MATLAB ® code, you can prototype and validate computational finance models, accelerate those models using parallel processing, and put them directly into production.
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