# AI-Trader Roadmap

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

## Release Planning

### v0.4.0 - Simplified Simulation Control (Planned)

**Focus:** Streamlined date-based simulation API with automatic resume from last completed date

#### Core Simulation API
- **Smart Date-Based Simulation** - Simple API for running simulations to a target date
  - `POST /simulate/to-date` - Run simulation up to specified date
    - Request: `{"target_date": "2025-01-31", "models": ["model1", "model2"]}`
    - Automatically starts from last completed date in position.jsonl
    - Skips already-simulated dates by default (idempotent)
    - Optional `force_resimulate: true` flag to re-run completed dates
    - Returns: job_id, date range to be simulated, models included
  - `GET /simulate/status/{model_name}` - Get last completed date and available date ranges
    - Returns: last_simulated_date, next_available_date, data_coverage
  - Behavior:
    - If no position.jsonl exists: starts from initial_date in config or first available data
    - If position.jsonl exists: continues from last completed date + 1 day
    - Validates target_date has available price data
    - Skips weekends automatically
    - Prevents accidental re-simulation without explicit flag

#### Benefits
- **Simplicity** - Single endpoint for "simulate to this date"
- **Idempotent** - Safe to call repeatedly, won't duplicate work
- **Incremental Updates** - Easy daily simulation updates: `POST /simulate/to-date {"target_date": "today"}`
- **Explicit Re-simulation** - Require `force_resimulate` flag to prevent accidental data overwrites
- **Automatic Resume** - Handles crash recovery transparently

#### Example Usage
```bash
# Initial backtest (Jan 1 - Jan 31)
curl -X POST http://localhost:5000/simulate/to-date \
  -d '{"target_date": "2025-01-31", "models": ["gpt-4"]}'

# Daily update (simulate new trading day)
curl -X POST http://localhost:5000/simulate/to-date \
  -d '{"target_date": "2025-02-01", "models": ["gpt-4"]}'

# Check status
curl http://localhost:5000/simulate/status/gpt-4

# Force re-simulation (e.g., after config change)
curl -X POST http://localhost:5000/simulate/to-date \
  -d '{"target_date": "2025-01-31", "models": ["gpt-4"], "force_resimulate": true}'
```

#### Technical Implementation
- Modify `main.py` and `api/app.py` to support target date parameter
- Update `BaseAgent.get_trading_dates()` to detect last completed date from position.jsonl
- Add validation: target_date must have price data available
- Add `force_resimulate` flag handling: clear position.jsonl range if enabled
- Preserve existing `/simulate` endpoint for backward compatibility

### v1.0.0 - Production Stability & Validation (Planned)

**Focus:** Comprehensive testing, documentation, and production readiness

#### Testing & Validation
- **Comprehensive Test Suite** - Full coverage of core functionality
  - Unit tests for all agent components
    - BaseAgent methods (initialize, run_trading_session, get_trading_dates)
    - Position management and tracking
    - Date range handling and validation
    - MCP tool integration
  - Integration tests for API endpoints
    - All /simulate endpoints with various configurations
    - /jobs endpoints (status, cancel, results)
    - /models endpoint for listing available models
    - Error handling and validation
  - End-to-end simulation tests
    - Multi-day trading simulations with mock data
    - Multiple concurrent model execution
    - Resume functionality after interruption
    - Force re-simulation scenarios
  - Anti-look-ahead validation tests
    - Verify price data temporal boundaries
    - Verify search results date filtering
    - Confirm no future data leakage in system prompts
  - Test coverage target: >80% code coverage
  - Continuous Integration: GitHub Actions workflow for automated testing

#### Stability & Error Handling
- **Robust Error Recovery** - Handle failures gracefully
  - Retry logic for transient API failures (already implemented, validate)
  - Graceful degradation when MCP services are unavailable
  - Database connection pooling and error handling
  - File system error handling (disk full, permission errors)
  - Comprehensive error messages with troubleshooting guidance
  - Logging improvements:
    - Structured logging with consistent format
    - Log rotation and size management
    - Error classification (user error vs. system error)
    - Debug mode for detailed diagnostics

#### Performance & Scalability
- **Performance Optimization** - Ensure efficient resource usage
  - Database query optimization and indexing
  - Price data caching and efficient lookups
  - Concurrent simulation handling validation
  - Memory usage profiling and optimization
  - Long-running simulation stability testing (30+ day ranges)
  - Load testing: multiple concurrent API requests
  - Resource limits and rate limiting considerations

#### Documentation & Examples
- **Production-Ready Documentation** - Complete user and developer guides
  - API documentation improvements:
    - OpenAPI/Swagger specification
    - Interactive API documentation (Swagger UI)
    - Example requests/responses for all endpoints
    - Error response documentation
  - User guides:
    - Quickstart guide refinement
    - Common workflows and recipes
    - Troubleshooting guide expansion
    - Best practices for model configuration
  - Developer documentation:
    - Architecture deep-dive
    - Contributing guidelines
    - Custom agent development guide
    - MCP tool development guide
  - Example configurations:
    - Various model providers (OpenAI, Anthropic, local models)
    - Different trading strategies
    - Development vs. production setups

#### Security & Best Practices
- **Security Hardening** - Production security review
  - API authentication/authorization review (if applicable)
  - API key management best practices documentation
  - Input validation and sanitization review
  - SQL injection prevention validation
  - Rate limiting for public deployments
  - Security considerations documentation
  - Dependency vulnerability scanning
  - Docker image security scanning

#### Release Readiness
- **Production Deployment Support** - Everything needed for production use
  - Production deployment checklist
  - Health check endpoints improvements
  - Monitoring and observability guidance
    - Key metrics to track (job success rate, execution time, error rates)
    - Integration with monitoring systems (Prometheus, Grafana)
    - Alerting recommendations
  - Backup and disaster recovery guidance
  - Database migration strategy
  - Upgrade path documentation (v0.x to v1.0)
  - Version compatibility guarantees going forward

#### Quality Gates for v1.0.0 Release
All of the following must be met before v1.0.0 release:
- [ ] Test suite passes with >80% code coverage
- [ ] All critical and high-priority bugs resolved
- [ ] API documentation complete (OpenAPI spec)
- [ ] Production deployment guide complete
- [ ] Security review completed
- [ ] Performance benchmarks established
- [ ] Docker image published and tested
- [ ] Migration guide from v0.3.0 available
- [ ] At least 2 weeks of community testing (beta period)
- [ ] Zero known data integrity issues

### v1.1.0 - Position History & Analytics (Planned)

**Focus:** Track and analyze trading behavior over time

#### Position History API
- **Position Tracking Endpoints** - Query historical position changes
  - `GET /positions/history` - Get position timeline for model(s)
    - Query parameters: `model`, `start_date`, `end_date`, `symbol`
    - Returns: chronological list of all position changes
    - Pagination support for long histories
  - `GET /positions/snapshot` - Get positions at specific date
    - Query parameters: `model`, `date`
    - Returns: portfolio state at end of trading day
  - `GET /positions/summary` - Get position statistics
    - Holdings duration (average, min, max)
    - Turnover rate (daily, weekly, monthly)
    - Most/least traded symbols
    - Trading frequency patterns

#### Trade Analysis
- **Trade-Level Insights** - Analyze individual trades
  - `GET /trades` - List all trades with filtering
    - Filter by: model, date range, symbol, action (buy/sell)
    - Sort by: date, profit/loss, volume
  - `GET /trades/{trade_id}` - Get trade details
    - Entry/exit prices and dates
    - Holding period
    - Realized profit/loss
    - Context (what else was traded that day)
  - Trade classification:
    - Round trips (buy + sell of same stock)
    - Partial positions (multiple entries/exits)
    - Long-term holds vs. day trades

#### Benefits
- Understand agent trading patterns and behavior
- Identify strategy characteristics (momentum, mean reversion, etc.)
- Debug unexpected trading decisions
- Compare trading styles across models

### v1.2.0 - Performance Metrics & Analytics (Planned)

**Focus:** Calculate standard financial performance metrics

#### Risk-Adjusted Performance
- **Performance Metrics API** - Calculate trading performance statistics
  - `GET /metrics/performance` - Overall performance metrics
    - Query parameters: `model`, `start_date`, `end_date`
    - Returns:
      - Total return, annualized return
      - Sharpe ratio (risk-adjusted return)
      - Sortino ratio (downside risk-adjusted)
      - Calmar ratio (return/max drawdown)
      - Information ratio
      - Alpha and beta (vs. NASDAQ 100 benchmark)
  - `GET /metrics/risk` - Risk metrics
    - Maximum drawdown (peak-to-trough decline)
    - Value at Risk (VaR) at 95% and 99% confidence
    - Conditional VaR (CVaR/Expected Shortfall)
    - Volatility (daily, annualized)
    - Downside deviation

#### Win/Loss Analysis
- **Trade Quality Metrics** - Analyze trade outcomes
  - `GET /metrics/trades` - Trade statistics
    - Win rate (% profitable trades)
    - Average win vs. average loss
    - Profit factor (gross profit / gross loss)
    - Largest win/loss
    - Win/loss streaks
    - Expectancy (average $ per trade)

#### Comparison & Benchmarking
- **Model Comparison** - Compare multiple models
  - `GET /metrics/compare` - Side-by-side comparison
    - Query parameters: `models[]`, `start_date`, `end_date`
    - Returns: all metrics for specified models
    - Ranking by various metrics
  - `GET /metrics/benchmark` - Compare to NASDAQ 100
    - Outperformance/underperformance
    - Correlation with market
    - Beta calculation

#### Time Series Metrics
- **Rolling Performance** - Metrics over time
  - `GET /metrics/timeseries` - Performance evolution
    - Query parameters: `model`, `metric`, `window` (days)
    - Returns: daily/weekly/monthly metric values
    - Examples: rolling Sharpe ratio, rolling volatility
    - Useful for detecting strategy degradation

#### Benefits
- Quantify agent performance objectively
- Identify risk characteristics
- Compare effectiveness of different AI models
- Detect performance changes over time

### v1.3.0 - Data Management API (Planned)

**Focus:** Price data operations and coverage management

#### Data Coverage Endpoints
- **Price Data Management** - Control and monitor price data
  - `GET /data/coverage` - Check available data
    - Query parameters: `symbol`, `start_date`, `end_date`
    - Returns: date ranges with data per symbol
    - Identify gaps in historical data
    - Show last refresh date per symbol
  - `GET /data/symbols` - List all available symbols
    - NASDAQ 100 constituents
    - Data availability per symbol
    - Metadata (company name, sector)

#### Data Operations
- **Download & Refresh** - Manage price data updates
  - `POST /data/download` - Trigger data download
    - Query parameters: `symbol`, `start_date`, `end_date`
    - Async operation (returns job_id)
    - Respects Alpha Vantage rate limits
    - Updates existing data or fills gaps
  - `GET /data/download/status` - Check download progress
    - Query parameters: `job_id`
    - Returns: progress, completed symbols, errors
  - `POST /data/refresh` - Update to latest available
    - Automatically downloads new data for all symbols
    - Scheduled refresh capability

#### Data Cleanup
- **Data Management Operations** - Clean and maintain data
  - `DELETE /data/range` - Remove data for date range
    - Query parameters: `symbol`, `start_date`, `end_date`
    - Use case: remove corrupted data before re-download
    - Validation: prevent deletion of in-use data
  - `POST /data/validate` - Check data integrity
    - Verify no missing dates (weekday gaps)
    - Check for outliers/anomalies
    - Returns: validation report with issues

#### Rate Limit Management
- **API Quota Tracking** - Monitor external API usage
  - `GET /data/quota` - Check Alpha Vantage quota
    - Calls remaining today
    - Reset time
    - Historical usage pattern

#### Benefits
- 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

#### 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.

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** - Simplified simulation control (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)

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Last updated: 2025-11-01