Add Claude's notes from the last revision.

NOTES.md

@@ -144,6 +144,61 @@ After running comprehensive backtests across multiple periods from 2013-2024, we
    - Impact of training window length on model stability
    - Methods for early detection of transition into stress periods
 
+### Uncertainty Estimation Improvements
+
+Attempted several approaches to improve uncertainty estimation in the model:
+
+1. Initial market-aware uncertainty scaling
+   - Added dynamic scaling based on market conditions
+   - Incorporated trend strength, drawdowns, and volatility regimes
+   - Improved CI coverage consistency between stress/normal periods
+   - Results showed more balanced coverage (88% for 95% CI, ~60-65% for 68% CI)
+
+2. Volatility projection system
+   - Attempted to model how volatility evolves through cycles
+   - Added cycle-aware volatility forecasting
+   - Included volatility clustering effects
+   - Implementation challenges with numerical stability
+   - Led to issues with extreme value generation
+
+3. Lessons Learned
+   - Direct volatility modeling proved more complex than anticipated
+   - Simple bounds and scaling may be more robust than sophisticated projections
+   - Need to balance sophistication with stability
+   - Market awareness improved results but requires careful calibration
+   - Systematic backtesting revealed weaknesses not apparent in cherry-picked tests
+
+#### Uncertainty Estimation Improvements
+
+Initial changes focused on improving the model's uncertainty estimates:
+- Enhanced CI calculation with more consistent coverage
+- Better balanced coverage between stress and normal periods
+- Results showed more reliable performance (88% for 95% CI, ~60-65% for 68% CI)
+- Maintained good performance across different market conditions
+
+#### Attempted Market Regime Analysis (Reverted)
+
+Subsequently attempted to add market regime awareness:
+- Added volatility regime detection and adaptation
+- Incorporated trend strength and market condition analysis
+- While theoretically promising, this change was ultimately reverted because:
+  - Added complexity without clear performance benefits
+  - Introduced additional failure modes in backtests
+  - Benefits didn't justify the increased complexity
+  - Original uncertainty improvements were already handling different market conditions well
+
+#### Key Learning
+Sometimes simpler is better: The initial uncertainty improvements provided robust performance without needing explicit regime detection. This experience reinforces that additional complexity should only be retained when it provides clear, measurable benefits.
+
+#### Future Considerations
+
+1. Consider returning to simpler uncertainty estimation with targeted improvements
+2. Focus on numerical stability while maintaining market awareness
+3. Need better validation of extreme scenario handling
+4. Consider alternative approaches to long-term uncertainty growth
+
+Key insight: While more sophisticated volatility modeling seemed promising, the added complexity introduced more failure modes. Future work should focus on making the existing system more robust rather than adding new layers of complexity.
+
 ## Current Implementation
 The model uses four main functions:
 1. `analyze_trends()`: Calculates cycle-position-specific log returns