Python in Finance: Real Time Data Streaming within Jupyter Notebook

By neub9
3 Min Read

In this blog post, we’ll show you how to visualize live data streams in real-time directly within Jupyter Notebook. Normally, dynamic charts need manual updates, such as reloading the page to fetch new data. However, this can be impractical for fast-paced industries like finance, where missing out on crucial information due to not refreshing the page can be detrimental.

That’s why we’ll teach you how to move from manual updates to a real-time streaming method in Jupyter Notebook, making your projects more efficient and responsive. We will cover the following topics:

1. Real-Time Visualization: Learn how to watch data evolve second by second, right before your eyes.
2. Jupyter Notebook Mastery: Harness the full power of Jupyter Notebook for dynamic, streaming data.
3. Python in Quant Finance Use Case: Implement a financial application with real-world data.
4. Stream Data Processing: Understand how to process data in real-time, a skill becoming increasingly crucial in today’s fast-paced data world.

By the end of this post, you’ll know how to build real-time visualizations like the one below within your Jupyter Notebook, enabling you to keep up with the rapidly changing data.

At the core of our project is the concept of stream processing, which handles and analyzes data in real-time as it’s generated. This can be likened to live traffic updates on Google Maps, allowing for immediate and efficient decision-making in response to changing conditions.

In the finance sector, stream data processing has become essential, as every second counts, whether for fraud detection or trading. An example we’ll focus on is Bollinger Bands, a tool that is commonly used in financial trading. We’ll be making Bollinger Bands with a twist by integrating trading volumes to provide a more comprehensive and accurate perspective.

The technical implementation will involve various steps, including fetching sample data, setting up the data source for computation, calculating the stats essential for Bollinger Bands, and building an interactive dashboard using Bokeh and Panel. For each step, we will explain the code and provide insights into the process.

This tutorial makes use of several frameworks such as Pathway for data processing and Bokeh for creating dynamic visualizations. Moreover, we’ll be using Panel to enhance our project with real-time dashboard capabilities, enabling us to keep our visualizations up-to-date as new data streams come in.

In the end, we’ll create a compelling visualization using a combination of Bokeh plots and Panel tables, bringing our analysis to life and enabling us to make informed decisions based on real-time data. This tutorial will offer hands-on experience in working with real-time data within Jupyter Notebook, providing practical knowledge that you can apply to real-world projects.

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