AI-Trader/ROADMAP.md

AI-Trader Roadmap

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

Release Planning

v0.5.0 - Performance Metrics & Status APIs (Planned)

Focus: Enhanced observability and performance tracking

Performance Metrics API

• Performance Summary Endpoint - Query model performance over date ranges
  • GET /metrics/performance - Aggregated performance metrics
    • Query parameters: model, start_date, end_date
    • Returns comprehensive performance summary:
      • Total return (dollar amount and percentage)
      • Number of trades executed (buy + sell)
      • Win rate (profitable trading days / total trading days)
      • Average daily P&L (profit and loss)
      • Best/worst trading day (highest/lowest daily P&L)
      • Final portfolio value (cash + holdings at market value)
      • Number of trading days in queried range
      • Starting vs. ending portfolio comparison
    • Use cases:
      • Compare model performance across different time periods
      • Evaluate strategy effectiveness
      • Identify top-performing models
    • Example: GET /metrics/performance?model=gpt-4&start_date=2025-01-01&end_date=2025-01-31
  • Filtering options:
    • Single model or all models
    • Custom date ranges
    • Exclude incomplete trading days
  • Response format: JSON with clear metric definitions

Status & Coverage Endpoint

• System Status Summary - Data availability and simulation progress
  • GET /status - Comprehensive system status
    • Price data coverage section:
      • Available symbols (NASDAQ 100 constituents)
      • Date range of downloaded price data per symbol
      • Total trading days with complete data
      • Missing data gaps (symbols without data, date gaps)
      • Last data refresh timestamp
    • Model simulation status section:
      • List of all configured models (enabled/disabled)
      • Date ranges simulated per model (first and last trading day)
      • Total trading days completed per model
      • Most recent simulation date per model
      • Completion percentage (simulated days / available data days)
    • System health section:
      • Database connectivity status
      • MCP services status (Math, Search, Trade, LocalPrices)
      • API version and deployment mode
      • Disk space usage (database size, log size)
    • Use cases:
      • Verify data availability before triggering simulations
      • Identify which models need updates to latest data
      • Monitor system health and readiness
      • Plan data downloads for missing date ranges
    • Example: GET /status (no parameters required)
  • Benefits:
    • Single endpoint for complete system overview
    • No need to query multiple endpoints for status
    • Clear visibility into data gaps
    • Track simulation progress across models

Implementation Details

• Database queries for efficient metric calculation
• Caching for frequently accessed metrics (optional)
• Response time target: <500ms for typical queries
• Comprehensive error handling for missing data

Benefits

• Better Observability - Clear view of system state and model performance
• Data-Driven Decisions - Quantitative metrics for model comparison
• Proactive Monitoring - Identify data gaps before simulations fail
• User Experience - Single endpoint to check "what's available and what's been done"

v1.0.0 - Production Stability & Validation (Planned)

Focus: Comprehensive testing, documentation, and production readiness

API Consolidation & Improvements

• Endpoint Refactoring - Simplify API surface before v1.0
  • Merge results and reasoning endpoints:
    • Current: /jobs/{job_id}/results and /jobs/{job_id}/reasoning/{model_name} are separate
    • Consolidated: Single endpoint with query parameters to control response
    • /jobs/{job_id}/results?include_reasoning=true&model=<model_name>
    • Benefits: Fewer endpoints, more consistent API design, easier to use
    • Maintains backward compatibility with legacy endpoints (deprecated but functional)

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:
    • Configurable Log Levels - Environment-based logging control
      • LOG_LEVEL environment variable (DEBUG, INFO, WARNING, ERROR, CRITICAL)
      • Per-component log level configuration (API, agents, MCP tools, database)
      • Default production level: INFO, development level: DEBUG
    • Structured Logging - Consistent, parseable log format
      • JSON-formatted logs option for production (machine-readable)
      • Human-readable format for development
      • Consistent fields: timestamp, level, component, message, context
      • Correlation IDs for request tracing across components
    • Log Clarity & Organization - Improve log readability
      • Clear log prefixes per component: [API], [AGENT], [MCP], [DB]
      • Reduce noise: consolidate repetitive messages, rate-limit verbose logs
      • Action-oriented messages: "Starting simulation job_id=123" vs "Job started"
      • Include relevant context: model name, date, symbols in trading logs
      • Progress indicators for long operations (e.g., "Processing date 15/30")
    • Log Rotation & Management - Prevent disk space issues
      • Automatic log rotation by size (default: 10MB per file)
      • Retention policy (default: 30 days)
      • Separate log files per component (api.log, agents.log, mcp.log)
      • Archive old logs with compression
    • Error Classification - Distinguish error types
      • User errors (invalid input, configuration issues): WARN level
      • System errors (API failures, database errors): ERROR level
      • Critical failures (MCP service down, data corruption): CRITICAL level
      • Include error codes for programmatic handling
    • Debug Mode - Enhanced diagnostics for troubleshooting
      • DEBUG=true environment variable
      • Detailed request/response logging (sanitize API keys)
      • MCP tool call/response logging with timing
      • Database query logging with execution time
      • Memory and resource usage tracking

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
  • ⚠️ SECURITY WARNING: v1.0.0 does not include API authentication. The server should only be deployed in trusted environments (local development, private networks). Documentation must clearly warn users that the API is insecure and accessible to anyone with network access. API authentication is planned for v1.1.0.
  • 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:
    • Automated schema migration system for production databases
    • Support for ALTER TABLE and table recreation when needed
    • Migration version tracking and rollback capabilities
    • Zero-downtime migration procedures for production
    • Data integrity validation before and after migrations
    • Migration script testing framework
    • Note: Currently migrations are minimal (pre-production state)
    • Pre-production recommendation: Delete and recreate databases for schema updates
  • 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 - API Authentication & Security (Planned)

Focus: Secure the API with authentication and authorization

Authentication System

• API Key Authentication - Token-based access control
  • API key generation and management:
    • POST /auth/keys - Generate new API key (admin only)
    • GET /auth/keys - List API keys with metadata (admin only)
    • DELETE /auth/keys/{key_id} - Revoke API key (admin only)
  • Key features:
    • Cryptographically secure random key generation
    • Hashed storage (never store plaintext keys)
    • Key expiration dates (optional)
    • Key scoping (read-only vs. full access)
    • Usage tracking per key
  • Authentication header: Authorization: Bearer <api_key>
  • Backward compatibility: Optional authentication mode for migration

Authorization & Permissions

• Role-Based Access Control - Different permission levels
  • Permission levels:
    • Admin - Full access (create/delete keys, all operations)
    • Read-Write - Start simulations, modify data
    • Read-Only - View results and status only
  • Per-endpoint authorization checks
  • API key metadata includes role/permissions
  • Admin bootstrap process (initial setup)

Security Features

• Enhanced Security Measures - Defense in depth
  • Rate limiting per API key:
    • Configurable requests per minute/hour
    • Different limits per permission level
    • 429 Too Many Requests responses
  • Request logging and audit trail:
    • Log all API requests with key ID
    • Track failed authentication attempts
    • Alert on suspicious patterns
  • CORS configuration:
    • Configurable allowed origins
    • Secure defaults for production
  • HTTPS enforcement options:
    • Redirect HTTP to HTTPS
    • HSTS headers
  • API key rotation:
    • Support for multiple active keys
    • Graceful key migration

Configuration

• Security Settings - Environment-based configuration
  • Environment variables:
    • AUTH_ENABLED - Enable/disable authentication (default: false for v1.0.0 compatibility)
    • ADMIN_API_KEY - Bootstrap admin key (first-time setup)
    • KEY_EXPIRATION_DAYS - Default key expiration
    • RATE_LIMIT_PER_MINUTE - Default rate limit
    • REQUIRE_HTTPS - Force HTTPS in production
  • Migration path:
    • v1.0 users can upgrade with AUTH_ENABLED=false
    • Enable authentication when ready
    • Clear migration documentation

Documentation Updates

• Security Documentation - Comprehensive security guidance
  • Authentication setup guide:
    • Initial admin key setup
    • Creating API keys for clients
    • Key rotation procedures
  • Security best practices:
    • Network security considerations
    • HTTPS deployment requirements
    • Firewall rules recommendations
  • API documentation updates:
    • Authentication examples for all endpoints
    • Error responses (401, 403, 429)
    • Rate limit headers documentation

Benefits

• Secure Public Deployment - Safe to expose over internet
• Multi-User Support - Different users/applications with separate keys
• Usage Tracking - Monitor API usage per key
• Compliance - Meet security requirements for production deployments
• Accountability - Audit trail of who did what

Technical Implementation

• Authentication middleware for Flask
• Database schema for API keys:
  • api_keys table (id, key_hash, name, role, created_at, expires_at, last_used)
  • api_requests table (id, key_id, endpoint, timestamp, status_code)
• Secure key generation using secrets module
• Password hashing with bcrypt/argon2
• JWT tokens as alternative to static API keys (future consideration)

v1.2.0 - 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

v1.3.0 - 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

v1.4.0 - 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

v1.5.0 - 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

v1.6.0 - 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 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
• v0.4.0 - Daily P&L calculation, day-centric results API, reasoning summaries (current)
• v0.5.0 - Performance metrics & status APIs (planned)
• v1.0.0 - Production stability & validation (planned)
• v1.1.0 - API authentication & security (planned)
• v1.2.0 - Position history & analytics (planned)
• v1.3.0 - Advanced performance metrics & analytics (planned)
• v1.4.0 - Data management API (planned)
• v1.5.0 - Web dashboard UI (planned)
• v1.6.0 - Advanced configuration & customization (planned)
• v2.0.0 - Advanced quantitative modeling (planned)

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Last updated: 2025-11-06