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docs: restructure roadmap with v1.0 stability milestone and v1.x features
Major changes: - Simplified v0.4.0 to focus on smart date-based simulation API with automatic resume - Added v1.0.0 milestone for production stability, testing, and validation - Reorganized post-1.0 features into manageable v1.x releases: - v1.1.0: Position history & analytics - v1.2.0: Performance metrics & analytics - v1.3.0: Data management API - v1.4.0: Web dashboard UI - v1.5.0: Advanced configuration & customization - Moved quantitative modeling to v2.0.0 (major version bump) Key improvements: - v0.4.0 now has single /simulate/to-date endpoint with idempotent behavior - Explicit force_resimulate flag prevents accidental re-simulation - v1.0.0 includes comprehensive quality gates and production readiness checklist - Each v1.x release focuses on specific domain for easier implementation
This commit is contained in:
504
ROADMAP.md
504
ROADMAP.md
@@ -4,81 +4,441 @@ This document outlines planned features and improvements for the AI-Trader proje
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## Release Planning
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## Release Planning
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### v0.4.0 - Enhanced Simulation Management (Planned)
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### v0.4.0 - Simplified Simulation Control (Planned)
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**Focus:** Improved simulation control, resume capabilities, and performance analysis
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**Focus:** Streamlined date-based simulation API with automatic resume from last completed date
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#### Simulation Resume & Continuation
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#### Core Simulation API
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- **Resume from Last Completed Date** - API to continue simulations without re-running completed dates
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- **Smart Date-Based Simulation** - Simple API for running simulations to a target date
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- `POST /simulate/resume` - Resume last incomplete job or start from last completed date
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- `POST /simulate/to-date` - Run simulation up to specified date
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- `POST /simulate/continue` - Extend existing simulation with new date range
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- Request: `{"target_date": "2025-01-31", "models": ["model1", "model2"]}`
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- Query parameters to specify which model(s) to continue
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- Automatically starts from last completed date in position.jsonl
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- Automatic detection of last completed date per model
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- Skips already-simulated dates by default (idempotent)
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- Validation to prevent overlapping simulations
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- Optional `force_resimulate: true` flag to re-run completed dates
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- Support for extending date ranges forward in time
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- Returns: job_id, date range to be simulated, models included
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- Use cases:
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- `GET /simulate/status/{model_name}` - Get last completed date and available date ranges
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- Daily simulation updates (add today's date to existing run)
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- Returns: last_simulated_date, next_available_date, data_coverage
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- Recovering from failed jobs (resume from interruption point)
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- Behavior:
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- Incremental backtesting (extend historical analysis)
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- If no position.jsonl exists: starts from initial_date in config or first available data
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- If position.jsonl exists: continues from last completed date + 1 day
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- Validates target_date has available price data
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- Skips weekends automatically
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- Prevents accidental re-simulation without explicit flag
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#### Position History & Analysis
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#### Benefits
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- **Position History Tracking** - Track position changes over time
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- **Simplicity** - Single endpoint for "simulate to this date"
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- Query endpoint: `GET /positions/history?model=<name>&start_date=<date>&end_date=<date>`
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- **Idempotent** - Safe to call repeatedly, won't duplicate work
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- Timeline view of all trades and position changes
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- **Incremental Updates** - Easy daily simulation updates: `POST /simulate/to-date {"target_date": "today"}`
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- Calculate holding periods and turnover rates
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- **Explicit Re-simulation** - Require `force_resimulate` flag to prevent accidental data overwrites
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- Support for position snapshots at specific dates
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- **Automatic Resume** - Handles crash recovery transparently
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#### Performance Metrics
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#### Example Usage
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- **Advanced Performance Analytics** - Calculate standard trading metrics
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```bash
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- Sharpe ratio, Sortino ratio, maximum drawdown
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# Initial backtest (Jan 1 - Jan 31)
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- Win rate, average win/loss, profit factor
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curl -X POST http://localhost:5000/simulate/to-date \
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- Volatility and beta calculations
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-d '{"target_date": "2025-01-31", "models": ["gpt-4"]}'
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- Risk-adjusted returns
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- Comparison across models
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#### Data Management
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# Daily update (simulate new trading day)
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- **Price Data Management API** - Endpoints for price data operations
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curl -X POST http://localhost:5000/simulate/to-date \
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- `GET /data/coverage` - Check date ranges available per symbol
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-d '{"target_date": "2025-02-01", "models": ["gpt-4"]}'
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- `POST /data/download` - Trigger manual price data downloads
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- `GET /data/status` - Check download progress and rate limits
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- `DELETE /data/range` - Remove price data for specific date ranges
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#### Web UI
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# Check status
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- **Dashboard Interface** - Web-based monitoring and control interface
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curl http://localhost:5000/simulate/status/gpt-4
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- Job management dashboard
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- View active, pending, and completed jobs
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- Start new simulations with form-based configuration
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- Monitor job progress in real-time
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- Cancel running jobs
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- Results visualization
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- Performance charts (P&L over time, cumulative returns)
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- Position history timeline
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- Model comparison views
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- Trade log explorer with filtering
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- Configuration management
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- Model configuration editor
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- Date range selection with calendar picker
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- Price data coverage visualization
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- Technical implementation
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- Modern frontend framework (React, Vue.js, or Svelte)
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- Real-time updates via WebSocket or SSE
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- Responsive design for mobile access
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- Chart library (Plotly.js, Chart.js, or Recharts)
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- Served alongside API (single container deployment)
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#### Development Infrastructure
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# Force re-simulation (e.g., after config change)
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- **Migration to uv Package Manager** - Modern Python package management
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curl -X POST http://localhost:5000/simulate/to-date \
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- Replace pip with uv for dependency management
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-d '{"target_date": "2025-01-31", "models": ["gpt-4"], "force_resimulate": true}'
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- Create pyproject.toml with project metadata and dependencies
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```
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- Update Dockerfile to use uv for faster, more reliable builds
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- Update development documentation and workflows
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- Benefits:
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- 10-100x faster dependency resolution and installation
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- Better dependency locking and reproducibility
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- Unified tool for virtual environments and package management
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- Drop-in pip replacement with improved UX
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### v0.5.0 - Advanced Quantitative Modeling (Planned)
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#### Technical Implementation
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- Modify `main.py` and `api/app.py` to support target date parameter
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- Update `BaseAgent.get_trading_dates()` to detect last completed date from position.jsonl
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- Add validation: target_date must have price data available
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- Add `force_resimulate` flag handling: clear position.jsonl range if enabled
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- Preserve existing `/simulate` endpoint for backward compatibility
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### v1.0.0 - Production Stability & Validation (Planned)
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**Focus:** Comprehensive testing, documentation, and production readiness
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#### Testing & Validation
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- **Comprehensive Test Suite** - Full coverage of core functionality
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- Unit tests for all agent components
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- BaseAgent methods (initialize, run_trading_session, get_trading_dates)
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- Position management and tracking
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- Date range handling and validation
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- MCP tool integration
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- Integration tests for API endpoints
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- All /simulate endpoints with various configurations
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- /jobs endpoints (status, cancel, results)
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- /models endpoint for listing available models
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- Error handling and validation
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- End-to-end simulation tests
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- Multi-day trading simulations with mock data
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- Multiple concurrent model execution
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- Resume functionality after interruption
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- Force re-simulation scenarios
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- Anti-look-ahead validation tests
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- Verify price data temporal boundaries
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- Verify search results date filtering
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- Confirm no future data leakage in system prompts
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- Test coverage target: >80% code coverage
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- Continuous Integration: GitHub Actions workflow for automated testing
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#### Stability & Error Handling
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- **Robust Error Recovery** - Handle failures gracefully
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- Retry logic for transient API failures (already implemented, validate)
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- Graceful degradation when MCP services are unavailable
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- Database connection pooling and error handling
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- File system error handling (disk full, permission errors)
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- Comprehensive error messages with troubleshooting guidance
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- Logging improvements:
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- Structured logging with consistent format
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- Log rotation and size management
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- Error classification (user error vs. system error)
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- Debug mode for detailed diagnostics
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#### Performance & Scalability
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- **Performance Optimization** - Ensure efficient resource usage
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- Database query optimization and indexing
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- Price data caching and efficient lookups
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- Concurrent simulation handling validation
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- Memory usage profiling and optimization
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- Long-running simulation stability testing (30+ day ranges)
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- Load testing: multiple concurrent API requests
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- Resource limits and rate limiting considerations
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#### Documentation & Examples
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- **Production-Ready Documentation** - Complete user and developer guides
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- API documentation improvements:
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- OpenAPI/Swagger specification
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- Interactive API documentation (Swagger UI)
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- Example requests/responses for all endpoints
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- Error response documentation
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- User guides:
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- Quickstart guide refinement
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- Common workflows and recipes
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- Troubleshooting guide expansion
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- Best practices for model configuration
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- Developer documentation:
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- Architecture deep-dive
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- Contributing guidelines
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- Custom agent development guide
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- MCP tool development guide
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- Example configurations:
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- Various model providers (OpenAI, Anthropic, local models)
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- Different trading strategies
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- Development vs. production setups
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#### Security & Best Practices
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- **Security Hardening** - Production security review
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- API authentication/authorization review (if applicable)
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- API key management best practices documentation
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- Input validation and sanitization review
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- SQL injection prevention validation
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- Rate limiting for public deployments
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- Security considerations documentation
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- Dependency vulnerability scanning
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- Docker image security scanning
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#### Release Readiness
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- **Production Deployment Support** - Everything needed for production use
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- Production deployment checklist
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- Health check endpoints improvements
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- Monitoring and observability guidance
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- Key metrics to track (job success rate, execution time, error rates)
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- Integration with monitoring systems (Prometheus, Grafana)
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- Alerting recommendations
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- Backup and disaster recovery guidance
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- Database migration strategy
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- Upgrade path documentation (v0.x to v1.0)
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- Version compatibility guarantees going forward
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#### Quality Gates for v1.0.0 Release
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All of the following must be met before v1.0.0 release:
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- [ ] Test suite passes with >80% code coverage
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- [ ] All critical and high-priority bugs resolved
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- [ ] API documentation complete (OpenAPI spec)
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- [ ] Production deployment guide complete
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- [ ] Security review completed
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- [ ] Performance benchmarks established
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- [ ] Docker image published and tested
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- [ ] Migration guide from v0.3.0 available
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- [ ] At least 2 weeks of community testing (beta period)
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- [ ] Zero known data integrity issues
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### v1.1.0 - Position History & Analytics (Planned)
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**Focus:** Track and analyze trading behavior over time
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#### Position History API
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- **Position Tracking Endpoints** - Query historical position changes
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- `GET /positions/history` - Get position timeline for model(s)
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- Query parameters: `model`, `start_date`, `end_date`, `symbol`
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- Returns: chronological list of all position changes
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- Pagination support for long histories
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- `GET /positions/snapshot` - Get positions at specific date
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- Query parameters: `model`, `date`
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- Returns: portfolio state at end of trading day
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- `GET /positions/summary` - Get position statistics
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- Holdings duration (average, min, max)
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- Turnover rate (daily, weekly, monthly)
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- Most/least traded symbols
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- Trading frequency patterns
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#### Trade Analysis
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- **Trade-Level Insights** - Analyze individual trades
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- `GET /trades` - List all trades with filtering
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- Filter by: model, date range, symbol, action (buy/sell)
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- Sort by: date, profit/loss, volume
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- `GET /trades/{trade_id}` - Get trade details
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- Entry/exit prices and dates
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- Holding period
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- Realized profit/loss
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- Context (what else was traded that day)
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- Trade classification:
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- Round trips (buy + sell of same stock)
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- Partial positions (multiple entries/exits)
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- Long-term holds vs. day trades
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#### Benefits
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- Understand agent trading patterns and behavior
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- Identify strategy characteristics (momentum, mean reversion, etc.)
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- Debug unexpected trading decisions
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- Compare trading styles across models
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### v1.2.0 - Performance Metrics & Analytics (Planned)
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**Focus:** Calculate standard financial performance metrics
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#### Risk-Adjusted Performance
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- **Performance Metrics API** - Calculate trading performance statistics
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- `GET /metrics/performance` - Overall performance metrics
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- Query parameters: `model`, `start_date`, `end_date`
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- Returns:
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- Total return, annualized return
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- Sharpe ratio (risk-adjusted return)
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- Sortino ratio (downside risk-adjusted)
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- Calmar ratio (return/max drawdown)
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- Information ratio
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- Alpha and beta (vs. NASDAQ 100 benchmark)
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- `GET /metrics/risk` - Risk metrics
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- Maximum drawdown (peak-to-trough decline)
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- Value at Risk (VaR) at 95% and 99% confidence
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- Conditional VaR (CVaR/Expected Shortfall)
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- Volatility (daily, annualized)
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- Downside deviation
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#### Win/Loss Analysis
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- **Trade Quality Metrics** - Analyze trade outcomes
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- `GET /metrics/trades` - Trade statistics
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- Win rate (% profitable trades)
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- Average win vs. average loss
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- Profit factor (gross profit / gross loss)
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- Largest win/loss
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- Win/loss streaks
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- Expectancy (average $ per trade)
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#### Comparison & Benchmarking
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- **Model Comparison** - Compare multiple models
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- `GET /metrics/compare` - Side-by-side comparison
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- Query parameters: `models[]`, `start_date`, `end_date`
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- Returns: all metrics for specified models
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- Ranking by various metrics
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- `GET /metrics/benchmark` - Compare to NASDAQ 100
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- Outperformance/underperformance
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- Correlation with market
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- Beta calculation
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#### Time Series Metrics
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- **Rolling Performance** - Metrics over time
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- `GET /metrics/timeseries` - Performance evolution
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- Query parameters: `model`, `metric`, `window` (days)
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- Returns: daily/weekly/monthly metric values
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- Examples: rolling Sharpe ratio, rolling volatility
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- Useful for detecting strategy degradation
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#### Benefits
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- Quantify agent performance objectively
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- Identify risk characteristics
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- Compare effectiveness of different AI models
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- Detect performance changes over time
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### v1.3.0 - Data Management API (Planned)
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**Focus:** Price data operations and coverage management
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#### Data Coverage Endpoints
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- **Price Data Management** - Control and monitor price data
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- `GET /data/coverage` - Check available data
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- Query parameters: `symbol`, `start_date`, `end_date`
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- Returns: date ranges with data per symbol
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- Identify gaps in historical data
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- Show last refresh date per symbol
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- `GET /data/symbols` - List all available symbols
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- NASDAQ 100 constituents
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- Data availability per symbol
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- Metadata (company name, sector)
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#### Data Operations
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- **Download & Refresh** - Manage price data updates
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- `POST /data/download` - Trigger data download
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- Query parameters: `symbol`, `start_date`, `end_date`
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- Async operation (returns job_id)
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- Respects Alpha Vantage rate limits
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- Updates existing data or fills gaps
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- `GET /data/download/status` - Check download progress
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- Query parameters: `job_id`
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- Returns: progress, completed symbols, errors
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- `POST /data/refresh` - Update to latest available
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- Automatically downloads new data for all symbols
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- Scheduled refresh capability
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#### Data Cleanup
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- **Data Management Operations** - Clean and maintain data
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- `DELETE /data/range` - Remove data for date range
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- Query parameters: `symbol`, `start_date`, `end_date`
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- Use case: remove corrupted data before re-download
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- Validation: prevent deletion of in-use data
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- `POST /data/validate` - Check data integrity
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- Verify no missing dates (weekday gaps)
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- Check for outliers/anomalies
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- Returns: validation report with issues
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#### Rate Limit Management
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- **API Quota Tracking** - Monitor external API usage
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- `GET /data/quota` - Check Alpha Vantage quota
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- Calls remaining today
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- Reset time
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- Historical usage pattern
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||||||
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||||||
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#### Benefits
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- Visibility into data coverage
|
||||||
|
- Control over data refresh timing
|
||||||
|
- Ability to fill gaps in historical data
|
||||||
|
- Prevent simulations with incomplete data
|
||||||
|
|
||||||
|
### v1.4.0 - Web Dashboard UI (Planned)
|
||||||
|
|
||||||
|
**Focus:** Browser-based interface for monitoring and control
|
||||||
|
|
||||||
|
#### Core Dashboard
|
||||||
|
- **Web UI Foundation** - Modern web interface
|
||||||
|
- Technology stack:
|
||||||
|
- Frontend: React or Svelte (lightweight, modern)
|
||||||
|
- Charts: Recharts or Chart.js
|
||||||
|
- Real-time: Server-Sent Events (SSE) for updates
|
||||||
|
- Styling: Tailwind CSS for responsive design
|
||||||
|
- Deployment: Served alongside API (single container)
|
||||||
|
- URL structure: `/` (UI), `/api/` (API endpoints)
|
||||||
|
|
||||||
|
#### Job Management View
|
||||||
|
- **Simulation Control** - Monitor and start simulations
|
||||||
|
- Dashboard home page:
|
||||||
|
- Active jobs with real-time progress
|
||||||
|
- Recent completed jobs
|
||||||
|
- Failed jobs with error messages
|
||||||
|
- Start simulation form:
|
||||||
|
- Model selection (checkboxes)
|
||||||
|
- Date picker for target_date
|
||||||
|
- Force re-simulate toggle
|
||||||
|
- Submit button → launches job
|
||||||
|
- Job detail view:
|
||||||
|
- Live log streaming (SSE)
|
||||||
|
- Per-model progress
|
||||||
|
- Cancel job button
|
||||||
|
- Download logs
|
||||||
|
|
||||||
|
#### Results Visualization
|
||||||
|
- **Performance Charts** - Visual analysis of results
|
||||||
|
- Portfolio value over time (line chart)
|
||||||
|
- Multiple models on same chart
|
||||||
|
- Zoom/pan interactions
|
||||||
|
- Hover tooltips with daily values
|
||||||
|
- Cumulative returns comparison (line chart)
|
||||||
|
- Percentage-based for fair comparison
|
||||||
|
- Benchmark overlay (NASDAQ 100)
|
||||||
|
- Position timeline (stacked area chart)
|
||||||
|
- Show holdings composition over time
|
||||||
|
- Click to filter by symbol
|
||||||
|
- Trade log table:
|
||||||
|
- Sortable columns (date, symbol, action, amount)
|
||||||
|
- Filters (model, date range, symbol)
|
||||||
|
- Pagination for large histories
|
||||||
|
|
||||||
|
#### Configuration Management
|
||||||
|
- **Settings & Config** - Manage simulation settings
|
||||||
|
- Model configuration editor:
|
||||||
|
- Add/remove models
|
||||||
|
- Edit base URLs and API keys (masked)
|
||||||
|
- Enable/disable models
|
||||||
|
- Save to config file
|
||||||
|
- Data coverage visualization:
|
||||||
|
- Calendar heatmap showing data availability
|
||||||
|
- Identify gaps in price data
|
||||||
|
- Quick link to download missing dates
|
||||||
|
|
||||||
|
#### Real-Time Updates
|
||||||
|
- **Live Monitoring** - SSE-based updates
|
||||||
|
- Job status changes
|
||||||
|
- Progress percentage updates
|
||||||
|
- New trade notifications
|
||||||
|
- Error alerts
|
||||||
|
|
||||||
|
#### Benefits
|
||||||
|
- User-friendly interface (no curl commands needed)
|
||||||
|
- Visual feedback for long-running simulations
|
||||||
|
- Easy model comparison through charts
|
||||||
|
- Quick access to results without API queries
|
||||||
|
|
||||||
|
### v1.5.0 - Advanced Configuration & Customization (Planned)
|
||||||
|
|
||||||
|
**Focus:** Enhanced configuration options and extensibility
|
||||||
|
|
||||||
|
#### Agent Configuration
|
||||||
|
- **Advanced Agent Settings** - Fine-tune agent behavior
|
||||||
|
- Per-model configuration overrides:
|
||||||
|
- Custom system prompts
|
||||||
|
- Different max_steps per model
|
||||||
|
- Model-specific retry policies
|
||||||
|
- Temperature/top_p settings
|
||||||
|
- Trading constraints:
|
||||||
|
- Maximum position sizes per stock
|
||||||
|
- Sector exposure limits
|
||||||
|
- Cash reserve requirements
|
||||||
|
- Maximum trades per day
|
||||||
|
- Risk management rules:
|
||||||
|
- Stop-loss thresholds
|
||||||
|
- Take-profit targets
|
||||||
|
- Maximum portfolio concentration
|
||||||
|
|
||||||
|
#### Custom Trading Rules
|
||||||
|
- **Rule Engine** - Enforce trading constraints
|
||||||
|
- Pre-trade validation hooks:
|
||||||
|
- Check if trade violates constraints
|
||||||
|
- Reject or adjust trades automatically
|
||||||
|
- Post-trade validation:
|
||||||
|
- Ensure position limits respected
|
||||||
|
- Verify portfolio balance
|
||||||
|
- Configurable via JSON rules file
|
||||||
|
- API to query active rules
|
||||||
|
|
||||||
|
#### Multi-Strategy Support
|
||||||
|
- **Strategy Variants** - Run same model with different strategies
|
||||||
|
- Strategy configurations:
|
||||||
|
- Different initial cash amounts
|
||||||
|
- Different universes (e.g., tech stocks only)
|
||||||
|
- Different time periods for same model
|
||||||
|
- Compare strategy effectiveness
|
||||||
|
- A/B testing framework
|
||||||
|
|
||||||
|
#### Benefits
|
||||||
|
- Greater control over agent behavior
|
||||||
|
- Risk management beyond AI decision-making
|
||||||
|
- Strategy experimentation and optimization
|
||||||
|
- Support for diverse use cases
|
||||||
|
|
||||||
|
### v2.0.0 - Advanced Quantitative Modeling (Planned)
|
||||||
|
|
||||||
**Focus:** Enable AI agents to create, test, and deploy custom quantitative models
|
**Focus:** Enable AI agents to create, test, and deploy custom quantitative models
|
||||||
|
|
||||||
@@ -164,8 +524,14 @@ To propose a new feature:
|
|||||||
- **v0.1.0** - Initial release with batch execution
|
- **v0.1.0** - Initial release with batch execution
|
||||||
- **v0.2.0** - Docker deployment support
|
- **v0.2.0** - Docker deployment support
|
||||||
- **v0.3.0** - REST API, on-demand downloads, database storage (current)
|
- **v0.3.0** - REST API, on-demand downloads, database storage (current)
|
||||||
- **v0.4.0** - Enhanced simulation management (planned)
|
- **v0.4.0** - Simplified simulation control (planned)
|
||||||
- **v0.5.0** - Advanced quantitative modeling (planned)
|
- **v1.0.0** - Production stability & validation (planned)
|
||||||
|
- **v1.1.0** - Position history & analytics (planned)
|
||||||
|
- **v1.2.0** - Performance metrics & analytics (planned)
|
||||||
|
- **v1.3.0** - Data management API (planned)
|
||||||
|
- **v1.4.0** - Web dashboard UI (planned)
|
||||||
|
- **v1.5.0** - Advanced configuration & customization (planned)
|
||||||
|
- **v2.0.0** - Advanced quantitative modeling (planned)
|
||||||
|
|
||||||
---
|
---
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user