In the cryptic world of blockchain, keeping track of wallet transactions can be akin to being a detective in a vast city of interconnected alleys. Introducing Orbit, a tool designed to navigate these alleys and uncover the relationships among blockchain wallets by crawling through their transaction history.

Introduction to Orbit

Orbit allows users to explore the intricate web of blockchain wallet interactions by recursively analyzing transactions. The output is visually rendered as a graph, highlighting major sources, sinks, and any suspicious connections. It is important to note that Orbit functions on Python 3.2 and above.

Getting Started with Usage

Let’s dive into the practical application of Orbit, step by step:

• Crawling a single wallet’s transaction history:

python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F

• Crawling multiple wallets:

python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F,1ETBbsHPvbydW7hGWXXKXZ3pxVh3VFoMaX

• Adjusting the number of transactions fetched: By default, Orbit fetches the last 50 transactions per wallet. You can change this with the -l option:

python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -l 100

• Modifying crawling depth: Orbit defaults to a crawling depth of 3. Adjust this with the -d option:

python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -d 2

• Targeting important wallets: Use the -t option to specify how many top wallets to crawl at each level:

python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -t 20

• Exporting data: View the data collected in a graph viewer of your choice:

python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -o output.graphml

Support Formats

Orbit supports various output formats:

• GraphML (compatible with most graph viewers)
• JSON (for raw processing)

Visualization

Once the crawling process is completed, the graph will open in your default browser. If this doesn’t occur, you can manually access quark.html.

Don’t fret if the graph seems chaotic at first—it’s merely indicative of the complex web of transactions! To make sense of the chaos, follow these steps:

• Select the **Make Clusters** option to organize the graph using a community detection algorithm.
• Utilize **Color Clusters** to distinguish different communities with varied colors.
• Apply the **Spacify** option to resolve any overlapping nodes and edges for a clearer view.

The thickness of edges in the graph illustrates the frequency of transactions between wallets, while the size of a node is determined by transaction frequency and its connections.

Understanding the Code Functionality

The usage of Orbit is similar to a detective journeying through a network of suspects—a wallet represents a suspect, and their transactions are clues to follow. Each line of code leads to deeper investigation, just like interviews unveil layers of a case. You can navigate through these layers using different parameters to gather insights or dig as deep as you want!

Troubleshooting

If you encounter any issues while using Orbit, here are some troubleshooting ideas:

• Ensure you are running Python 3.2 or above.
• If the crawling doesn’t yield expected results, double-check the wallet addresses for accuracy.
• For issues with visualization, ensure you have Quark properly installed as per its documentation.
• Make sure that your terminal displays no error messages. If it does, troubleshoot those messages for guidance.

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Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.