AI-Trader/ROADMAP.md

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

# 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 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