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Completed· Feb 2025 – Dec 2025

ChainPhantom

Crypto Transaction Intelligence Platform

ReactNode.jsExpressBlockCypher APIPostgreSQLD3.jsJavaScript

Overview

ChainPhantom is a full-stack blockchain analytics platform built to bring transparency to cryptocurrency transactions. It integrates with the BlockCypher API to fetch live on-chain data, processes transaction graphs, and applies heuristic rules to surface suspicious activity patterns — including mixing behavior, high-frequency micro-transactions, and abnormal wallet clusters. The dashboard provides security analysts with a structured, visual interface for investigating crypto-based threats.

🔴 The Problem

Cryptocurrency transactions are public but opaque — raw blockchain data is difficult for analysts to interpret, and existing tools are either too complex or too expensive for independent security researchers.

🟢 The Solution

ChainPhantom abstracts blockchain complexity into an intuitive analyst dashboard. It fetches live transaction data via the BlockCypher API, computes risk scores, and presents interactive visualizations — allowing any security analyst to trace funds and identify red flags in minutes instead of hours.

Architecture

React SPA frontend communicates with a Node.js/Express REST API backend. The backend queries the BlockCypher API, caches responses in-memory, applies risk-scoring algorithms, and returns structured JSON. PostgreSQL stores historical analysis sessions. The frontend uses D3.js for transaction graph visualization.

Challenges

1

Handling blockchain data at scale — live APIs return deeply nested, non-linear transaction graphs

2

Designing real-time update pipelines without overwhelming API rate limits

3

Implementing meaningful risk scoring without false positives on legitimate high-volume wallets

4

Making complex transaction flows visually interpretable for non-technical analysts

Results

Successfully traced and visualized live BTC and ETH transaction chains

Detected 3 known mixing wallet patterns during security validation testing

Achieved sub-2-second load times for transaction data via intelligent caching

Presented at a cybersecurity club demo with positive feedback from senior engineers

Lessons Learned

Blockchain data is messy — cleaning and normalizing API responses is 80% of the work

Graph visualization UX requires deliberate simplification to remain useful

Rate limiting awareness must be a first-class architectural concern