AI & Backtesting

This section groups the project documents that explain how AI, investment logic, and replay are integrated.

Core artifacts

• AI and backtesting integration analysis
• Improvement plan: 60 concrete areas
• UX and visualization improvements
• Investment usage playbook

Integrated flow

1. live feeds and structured services create a current snapshot
2. AI and graph layers build evidence-grounded context
3. investment logic maps themes to assets and creates idea candidates
4. replay and walk-forward backtesting evaluate those ideas over time
5. learned priors flow back into live decision support

Current limits

• some probabilistic layers remain practical approximations
• replay quality depends on point-in-time data completeness
• learned sizing still mixes adaptive priors with hard guardrails

Read next

• Algorithms
• Architecture
• Features / Investment & Replay