Market Signal Processing Pipeline
Built pipelines that extract structured signals from public market data to rank opportunities.
Overview
I needed a system to cut through noise in public work marketplaces—endless postings with opaque pricing. I built a scraper that structures unstructured listings, extracts rate indicators, and ranks leads by expected hourly value. A daily digest surfaces the top opportunities without manual review. Separately, I deployed a read-only analytics dashboard for prediction markets, visualizing liquidity patterns to support disciplined decision-making. Both systems enforce execution discipline: data flows in, signals generate, but human judgment remains the final gate.
Highlights
- 01
Daily digest ranks leads by computed hourly rate
- 02
Read-only dashboard visualizes prediction market liquidity
- 03
Pipeline runs on cron with zero manual intervention
System Architecture
Data flows from public sources through extraction, ranking, and read-only visualization layers.
Questions people ask
- How does the pipeline rank opportunities?
- It scrapes unstructured listings to extract rate indicators and ranks leads by computed expected hourly value.
- What technologies power the data infrastructure?
- The system uses Python for scraping, PostgreSQL and Redis for storage, and cron for automated daily execution.
- Does the system support prediction market analysis?
- Yes, it includes a read-only dashboard visualizing liquidity patterns to support disciplined decision-making.