Tuning session; removed market maturity.

The market maturity score only complicated the model with no clear
benefit. Still working on getting the various backtests tuned.

NOTES.md

@@ -1,7 +1,7 @@
 # Bitcoin Price Model Description
 
 ## Overview
-This Bitcoin price prediction model uses a combination of log returns, cycle awareness, market maturity eras, and Monte Carlo simulation to generate price projections with confidence intervals. The model was developed through several iterations, with each refinement aimed at improving accuracy and reliability.
+This Bitcoin price prediction model uses a combination of log returns, cycle awareness, and Monte Carlo simulation to generate price projections with confidence intervals. The model was developed through several iterations, with each refinement aimed at improving accuracy and reliability.
 
 ## Core Components
 
@@ -45,12 +45,6 @@ Instead of working directly with prices or simple returns, the model uses log re
 - Maps historical returns to cycle positions
 - Allows the model to capture recurring patterns around halving events
 
-### Market Maturity
-- Recognizes distinct market eras with different characteristics
-- Adjusts projections based on market maturity level
-- Accounts for major market structure changes (e.g., futures introduction)
-- Provides era-specific calibration of uncertainty estimates
-
 ### Enhanced Monte Carlo
 - Base simulation using normal distribution
 - Includes era-specific adjustments for volatility and trends
@@ -74,20 +68,17 @@ When trained on 2016-2024 (two full cycles):
 ## Development History
 1. Started with direct cycle analysis of returns
 2. Added log-based analysis for better handling of exponential growth
-3. Incorporated market maturity through era-specific adjustments
-4. Refined volatility calculation using multiple timeframes
-5. Calibrated confidence intervals through era-aware scaling
+3. Refined volatility calculation using multiple timeframes
+4. Calibrated confidence intervals through era-aware scaling
 
 ## Current Implementation
 The model uses four main functions:
-1. `calculate_market_maturity_score()`: Evaluates market maturity indicators
-2. `analyze_trends()`: Calculates cycle-position-specific log returns
-3. `calculate_volatility()`: Computes era-adjusted volatility estimates
-4. `project_prices()`: Generates price projections using Monte Carlo simulation
+1. `analyze_trends()`: Calculates cycle-position-specific log returns
+2. `calculate_volatility()`: Computes era-adjusted volatility estimates
+3. `project_prices()`: Generates price projections using Monte Carlo simulation
 
 ## Strengths
 - Well-calibrated uncertainty estimates for post-2013 data
-- Captures both cycle effects and market maturity
 - Handles exponential price growth naturally
 - Balances complexity with interpretability
 - Adapts to different market eras
@@ -106,4 +97,60 @@ The model works best when:
 - Used for medium-term projections (months to years)
 - Interpreted probabilistically rather than as point forecasts
 
-The confidence intervals should be understood as ranges of likely outcomes based on historical patterns, not hard bounds on future prices.
+The confidence intervals should be understood as ranges of likely outcomes based on historical patterns, not hard bounds on future prices.
+
+# Bitcoin Price Model Development Log
+## Focus: Market Maturity Removal & CI Calibration
+
+### Initial State
+- Started with market maturity integrated model
+- CI coverage was inconsistent across periods
+- Complex behavior from maturity interactions
+
+### Key Changes Made
+1. Removed Market Maturity Component
+- Eliminated volume-based calculations
+- Removed efficiency metrics
+- Simplified volatility calculations
+
+2. Replaced with Direct Period Scaling
+- Introduced period-specific base adjustments
+- More granular era transitions
+- Progressive scaling for early period
+
+3. Simplified Volatility Calculation
+- Standard weights (20/50/30 split)
+- Direct period-based scaling factors
+- Removed complex regime detection
+
+4. Refined Uncertainty Growth
+- Reduced maximum growth caps
+- Simplified cycle position handling
+- More conservative projection adjustments
+
+### Final Performance
+Recent (2016-2024):
+- 68% CI: 69.0% (target achieved)
+- MAPE: 11.7% (excellent)
+
+Mid-Period (2015-2022):
+- 68% CI: 56.1% (below target)
+- Improved 95% CI performance
+
+Early Period (2013-2020):
+- Reduced excessive interval width
+- Maintained good MAPE (23.4%)
+
+### Key Learnings
+1. Market maturity added complexity without clear benefits
+2. Direct period scaling provides more controllable results
+3. Simpler adjustments lead to more consistent performance
+4. Period-specific calibration more effective than dynamic maturity
+
+### Next Steps
+1. Consider further tuning of mid-period coverage
+2. Potential refinement of transition period handling
+3. Explore alternative cycle position adjustments
+4. Review projection adjustment caps
+
+The model now provides more consistent and interpretable results while maintaining or improving key performance metrics.