Day 29: Market Data Warehouse Launch & Public Portfolio E2E Testing

• ~5 min read

What I Built

  • Market Data Warehouse: MySQL-based time-series database with partitioning for OHLCV storage
  • Real-time Data Ingestion: Finnhub API integration with Redis caching and rate limiting
  • Bulk Data Pipeline: S3-compatible API client for Massive flat file downloads and bulk loading
  • Operational Automation: Cron jobs for data quality checks, retention enforcement, and reconciliation
  • Public Portfolio E2E Testing: Playwright test suite covering page loading, filtering, pagination, and error handling
  • Performance Optimizations: Service worker for offline support and lazy loading optimizations
  • Social Sharing Features: Twitter/X and link sharing for portfolio transparency

Code Highlight

# Market Data Warehouse Core Architecture
class MarketDataStore:
    async def insert_quote(self, symbol: str, price: float, volume: int, timestamp: datetime):
        """Insert real-time quote with Redis caching and MySQL persistence."""
        # Cache in Redis for low-latency access
        cache_key = f"quote:{symbol}"
        await self.redis.setex(cache_key, 300, json.dumps({
            "price": price, "volume": volume, "timestamp": timestamp.isoformat()
        }))

        # Persist to MySQL with batching
        await self.session.execute(
            insert(MarketQuotes).values(
                symbol=symbol, price=price, volume=volume, timestamp=timestamp
            )
        )
        await self.session.commit()

Architecture Decision

For the market data warehouse, I chose MySQL 8.0 with time-series partitioning over specialized databases like TimescaleDB or ClickHouse for several key reasons. MySQL provides ACID compliance, mature tooling, and seamless integration with the existing FastAPI/SQLAlchemy stack. Partitioning by date allows for efficient queries on recent data while enabling easy archival of older partitions. Redis serves as a low-latency cache for real-time quotes, reducing database load and providing sub-millisecond access for trading decisions. The S3-compatible API for bulk data ingestion ensures scalability and cost-effectiveness for historical data loading.

Testing Results

All 8 Playwright E2E tests pass, covering critical user journeys for the public portfolio tracker:

  • Portfolio page loads successfully with all data elements
  • Trade filtering works correctly (open/closed/all)
  • Navigation to trade detail pages functions properly
  • Pagination handles large datasets appropriately
  • API error states display user-friendly messages
  • Rate limiting is respected and handled gracefully
  • Mobile responsiveness adapts layout correctly
  • SEO metadata is properly configured

Next Steps

Continuation of live deployment preparation, canary trading implementation and paper trading.

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