Building an AI Trading Signal Engine
Fusing prices, macro data, news and EDGAR fundamentals into an ensemble pipeline — with backtesting discipline at the center.
A trading signal engine is easy to prototype and hard to trust. The difference is discipline — persistence, backtesting and feedback loops, not just a model.
Multi-source ingestion
- Prices via yfinance.
- Macro series via FRED.
- News sentiment scored per ticker.
- Fundamentals via EDGAR XBRL — 10 years of statements.
From prediction to decision
The /predict pipeline engineers features, runs an ensemble model, and hands the probabilities to a decision engine that emits CALLS / PUTS / HOLD with an assistive advisor.
The part that matters most
Backtesting. Walk-forward, out-of-sample evaluation is what separates a real signal from an overfit curve. Every signal is persisted, outcomes are tracked, and models retrain on a schedule — so the system actually improves.
Lesson
In quantitative ML, rigor beats cleverness. The backtesting harness is the product.
This is a placeholder draft — the full article is coming soon.