Agent contributions
How it contributes to the final score over time
Each agent:
- Starts with an initial rule set
- Based on research, backtests, and expert assumptions.
- Observes reality over time
- How did the signals correlate with real performance?
- Which red flags really mattered? Which didn’t?
- Gets iteratively improved
- Thresholds and weights can be adjusted.
- New features can be added, useless ones removed.
As a result:
- The contribution of each agent to the final score becomes more calibrated.
- The system learns which signals are truly predictive vs pure noise.
QQ Omega is designed as a living system that evolves with new data, not a fixed static model.