AI-Trader/ROADMAP.md

AI-Trader Roadmap

This document outlines planned features and improvements for the AI-Trader project.

Release Planning

v0.4.0 - Enhanced Simulation Management (Planned)

Focus: Improved simulation control, resume capabilities, and performance analysis

Simulation Resume & Continuation

• Resume from Last Completed Date - API to continue simulations without re-running completed dates
  • POST /simulate/resume - Resume last incomplete job or start from last completed date
  • POST /simulate/continue - Extend existing simulation with new date range
  • Query parameters to specify which model(s) to continue
  • Automatic detection of last completed date per model
  • Validation to prevent overlapping simulations
  • Support for extending date ranges forward in time
  • Use cases:
    • Daily simulation updates (add today's date to existing run)
    • Recovering from failed jobs (resume from interruption point)
    • Incremental backtesting (extend historical analysis)

Position History & Analysis

• Position History Tracking - Track position changes over time
  • Query endpoint: GET /positions/history?model=<name>&start_date=<date>&end_date=<date>
  • Timeline view of all trades and position changes
  • Calculate holding periods and turnover rates
  • Support for position snapshots at specific dates

Performance Metrics

• Advanced Performance Analytics - Calculate standard trading metrics
  • Sharpe ratio, Sortino ratio, maximum drawdown
  • Win rate, average win/loss, profit factor
  • Volatility and beta calculations
  • Risk-adjusted returns
  • Comparison across models

Data Management

• Price Data Management API - Endpoints for price data operations
  • GET /data/coverage - Check date ranges available per symbol
  • POST /data/download - Trigger manual price data downloads
  • GET /data/status - Check download progress and rate limits
  • DELETE /data/range - Remove price data for specific date ranges

Web UI

• Dashboard Interface - Web-based monitoring and control interface
  • Job management dashboard
    • View active, pending, and completed jobs
    • Start new simulations with form-based configuration
    • Monitor job progress in real-time
    • Cancel running jobs
  • Results visualization
    • Performance charts (P&L over time, cumulative returns)
    • Position history timeline
    • Model comparison views
    • Trade log explorer with filtering
  • Configuration management
    • Model configuration editor
    • Date range selection with calendar picker
    • Price data coverage visualization
  • Technical implementation
    • Modern frontend framework (React, Vue.js, or Svelte)
    • Real-time updates via WebSocket or SSE
    • Responsive design for mobile access
    • Chart library (Plotly.js, Chart.js, or Recharts)
    • Served alongside API (single container deployment)

Development Infrastructure

• Migration to uv Package Manager - Modern Python package management
  • Replace pip with uv for dependency management
  • Create pyproject.toml with project metadata and dependencies
  • Update Dockerfile to use uv for faster, more reliable builds
  • Update development documentation and workflows
  • Benefits:
    • 10-100x faster dependency resolution and installation
    • Better dependency locking and reproducibility
    • 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 for guidelines.

To propose a new feature:

1. Open an issue with the feature-request label
2. Describe the use case and expected behavior
3. Discuss implementation approach with maintainers
4. Submit a PR with tests and documentation

Version History

• v0.1.0 - Initial release with batch execution
• 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: 2025-11-01