Plan B Assignment

Author: Ivy (@lehsiuchen)

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Introduction

This assignment focuses on Liquid Network, designing:

• A backend Flask application for data fetching and processing.
• A frontend dashboard for visualized data representation.

The application integrates Blockstream Esplora API to:

• Fetch the latest blocks, transactions, and asset analysis.
• Analyze asset details, including Liquid Bitcoin (L-BTC).
• Provide user-friendly insights through visualized data.

This report outlines the implementation and explains the principles briefly.

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Backend Implementation

1. Fetching Blocks and Transactions

• Blocks and transactions are fetched using the Esplora API.
• A retry mechanism is implemented to prevent API overload, ensuring responsible usage of public explorers.

2. Asset Analysis

• Identifies whether an asset is L-BTC or a custom token.
• Extracts details such as:
  • Asset name
  • Issued amount
  • Burned amount
• Generates user-friendly explanations.

3. Logic and Tooling

• Enables data fetching for other time periods.
• Supports multiple transaction patterns, including:
  • Multisig usage
  • Explicit outputs
  • Single-asset transactions

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Frontend Implementation

• Flask templates render:
  • Blocks
  • Transactions
  • Asset analysis results
• Chart.js visualizes transaction counts for the latest blocks.

Features:

1. Responsive Design: Ensures compatibility across devices.
2. Auto-Refresh: Automatically refreshes every two minutes to maintain data freshness.

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Principles and Improvements

Principles:

• The Liquid Network, a Bitcoin sidechain, provides high transaction privacy with assets like L-BTC and custom tokens.
• The application:
  • Uses APIs to fetch transactions.
  • Analyzes output scripts and transaction patterns, such as multisig usage.

Suggested Improvements:

1. Local Node Support:
   • Reduce dependency on public APIs by deploying a local node.
2. Advanced Pattern Recognition:
   • Enhance analysis to identify specific patterns like Boltz swaps.

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Achievements and Future Plans

Achievements:

• Demonstrated fundamental transaction analysis on Liquid Network.
• Combined backend data processing with frontend visualization.

Future Goals:

1. Add local node integration to reduce reliance on public APIs.
2. Enhance pattern recognition for more complex transaction types.

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How to Use This Project

Prerequisites:

• Python 3.x installed
• Install Flask and required dependencies:

  pip install flask requests chart.js

• Run the Flask application:

  python app.py

• Open browser and visit:

  http://127.0.0.1:5000