docs: add implementation plan and update roadmap

ROADMAP.md

@@ -78,6 +78,77 @@ This document outlines planned features and improvements for the AI-Trader proje
     - Unified tool for virtual environments and package management
     - Drop-in pip replacement with improved UX
 
+### v0.5.0 - Advanced Quantitative Modeling (Planned)
+
+**Focus:** Enable AI agents to create, test, and deploy custom quantitative models
+
+#### Model Development Framework
+- **Quantitative Model Creation** - AI agents build custom trading models
+  - New MCP tool: `tool_model_builder.py` for model development operations
+  - Support for common model types:
+    - Statistical arbitrage models (mean reversion, cointegration)
+    - Machine learning models (regression, classification, ensemble)
+    - Technical indicator combinations (momentum, volatility, trend)
+    - Factor models (multi-factor risk models, alpha signals)
+  - Model specification via structured prompts/JSON
+  - Integration with pandas, numpy, scikit-learn, statsmodels
+  - Time series cross-validation for backtesting
+  - Model versioning and persistence per agent signature
+
+#### Model Testing & Validation
+- **Backtesting Engine** - Rigorous model validation before deployment
+  - Walk-forward analysis with rolling windows
+  - Out-of-sample performance metrics
+  - Statistical significance testing (t-tests, Sharpe ratio confidence intervals)
+  - Overfitting detection (train/test performance divergence)
+  - Transaction cost simulation (slippage, commissions)
+  - Risk metrics (VaR, CVaR, maximum drawdown)
+  - Anti-look-ahead validation (strict temporal boundaries)
+
+#### Model Deployment & Execution
+- **Production Model Integration** - Deploy validated models into trading decisions
+  - Model registry per agent (`agent_data/[signature]/models/`)
+  - Real-time model inference during trading sessions
+  - Feature computation from historical price data
+  - Model ensemble capabilities (combine multiple models)
+  - Confidence scoring for predictions
+  - Model performance monitoring (track live vs. backtest accuracy)
+  - Automatic model retraining triggers (performance degradation detection)
+
+#### Data & Features
+- **Feature Engineering Toolkit** - Rich data transformations for model inputs
+  - Technical indicators library (RSI, MACD, Bollinger Bands, ATR, etc.)
+  - Price transformations (returns, log returns, volatility)
+  - Market regime detection (trending, ranging, high/low volatility)
+  - Cross-sectional features (relative strength, sector momentum)
+  - Alternative data integration hooks (sentiment, news signals)
+  - Feature caching and incremental computation
+  - Feature importance analysis
+
+#### API Endpoints
+- **Model Management API** - Control and monitor quantitative models
+  - `POST /models/create` - Create new model specification
+  - `POST /models/train` - Train model on historical data
+  - `POST /models/backtest` - Run backtest with specific parameters
+  - `GET /models/{model_id}` - Retrieve model metadata and performance
+  - `GET /models/{model_id}/predictions` - Get historical predictions
+  - `POST /models/{model_id}/deploy` - Deploy model to production
+  - `DELETE /models/{model_id}` - Archive or delete model
+
+#### Benefits
+- **Enhanced Trading Strategies** - Move beyond simple heuristics to data-driven decisions
+- **Reproducibility** - Systematic model development and validation process
+- **Risk Management** - Quantify model uncertainty and risk exposure
+- **Learning System** - Agents improve trading performance through model iteration
+- **Research Platform** - Compare effectiveness of different quantitative approaches
+
+#### Technical Considerations
+- Anti-look-ahead enforcement in model training (only use data before training date)
+- Computational resource limits per model (prevent excessive training time)
+- Model explainability requirements (agents must justify model choices)
+- Integration with existing MCP architecture (models as tools)
+- Storage considerations for model artifacts and training data
+
 ## Contributing
 
 We welcome contributions to any of these planned features! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
@@ -94,7 +165,8 @@ To propose a new feature:
 - **v0.2.0** - Docker deployment support
 - **v0.3.0** - REST API, on-demand downloads, database storage (current)
 - **v0.4.0** - Enhanced simulation management (planned)
+- **v0.5.0** - Advanced quantitative modeling (planned)
 
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-Last updated: 2025-10-31
+Last updated: 2025-11-01