MarketWatch Aggregator
A multi-source data scraping platform aggregating financial market data from 15+ sources into unified dashboards with real-time updates, historical analysis, and trend predictions.
Client
MarketWatch Analytics
Role
Backend
Timeline
3 months
Team
3 developers
Overview
MarketWatch Analytics' analysts spent 8+ hours daily gathering data from financial websites, consolidating into spreadsheets, and analyzing trends. Aggregator pulls data from 15+ sources in real-time into unified dashboards, reducing research time by 70%.
Process
Built scalable scraping architecture using Playwright for reliable browser automation. Created data pipeline with validation, aggregation, and storage. Built dashboard for visualization and analysis.
Key Features
Challenges & Solutions
Built modular scrapers with multiple selector strategies (CSS, XPath, text matching), added automated failure detection with alerts, created fallback scrapers for critical data. Now recovers in <2 hours. Reliability improved to 96%.
Implemented data validation rules, created normalization pipeline, added source comparison logic, and documented source precision. Created data quality scores. Inconsistencies reduced to <0.1%.
Implemented time-series database (InfluxDB) for efficient historical data, added data aggregation at hourly/daily levels, created materialized views for common queries. Query speed improved from 8s → 200ms.
Implemented WebSocket for live updates, added Redis cache layer for frequently accessed data, optimized scraping to run every 30 seconds for critical data. Update lag reduced to <2 seconds.
Results
Research Time
70% reduction
Data Reliability
collection
Update Latency
real-time
Query Speed
performance
Data Consistency
across sources
Daily Throughput
99.9% uptime
Goals
- •Consolidate market data from multiple sources
- •Reduce analyst research time
- •Provide real-time data for decision-making
- •Enable trend analysis and forecasting
Tech Stack
- •Node.js
- •Playwright
- •PostgreSQL
- •Redis
Target Users
- •Market analysts
- •Portfolio managers
- •Traders
- •Research teams
Key Learnings
- •Web scraping requires resilience to site changes—modular design is essential
- •Data normalization is as important as collection
- •Time-series databases are better for financial data than relational DBs
- •Real-time systems require WebSocket + caching, not just optimized queries
Future Plans
- •Add machine learning models for price prediction
- •Expand to cryptocurrency and forex markets
- •Implement sentiment analysis from social media
- •Build mobile app for on-the-go analysis