Market Signal Engines

The problem

Discretionary trading decisions are hard to audit. I wanted the signal generation written down as code — reproducible, testable, and comparable across venues.

How it's built

• Ingestion of price, social, and news data into a Postgres store
• Sentiment scoring pipelines in Python with per-source weighting
• Arbitrage scanner comparing funding and spreads across venues
• Prediction-market probability feeds used as an independent signal against price
• PineScript indicators for the discretionary layer on top

Outcome

Signals live in code instead of in my head, which makes them reviewable — and it turned into the data backbone that Frameworx was built on.