AI-Trader/ROADMAP.md

AI-Trader Roadmap

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

Release Planning

v0.4.0 - Enhanced Simulation Management (Planned)

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

Simulation Resume & Continuation

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

Position History & Analysis

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

Performance Metrics

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

Data Management

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

v0.5.0 - Real-Time Trading Support (Planned)

Focus: Live market data integration and real-time decision making

Real-Time Market Data

• Live Price Feeds - Integration with real-time market data providers
  • WebSocket connections for streaming prices
  • Support for multiple data providers (Alpha Vantage, IEX Cloud, Polygon.io)
  • Fallback mechanisms for data provider failures
  • Rate limiting and connection management

Live Trading Mode

• Real-Time Simulation - Run AI agents with live market data
  • Separate mode from historical backtesting
  • Configurable update intervals (1min, 5min, 15min, 1hour)
  • Paper trading support (simulated execution)
  • Real broker integration (planned for later)

Scheduling & Automation

• Scheduled Simulations - Cron-like scheduling for automated runs
  • Daily market close simulations
  • Intraday update schedules
  • Configurable time zones and trading hours
  • Integration with external schedulers (Airflow, Prefect, n8n)

v0.6.0 - Multi-Strategy & Portfolio Management (Planned)

Focus: Strategy composition and portfolio-level optimization

Strategy Composition

• Multi-Strategy Models - Support for combining multiple AI strategies
  • Portfolio allocation across different AI models
  • Risk parity and factor-based allocation
  • Dynamic rebalancing based on performance
  • Strategy correlation analysis

Risk Management

• Advanced Risk Controls - Position limits and risk constraints
  • Maximum position size per symbol
  • Sector exposure limits
  • Portfolio-level stop losses
  • Volatility-based position sizing

Model Ensembles

• Ensemble Methods - Combine predictions from multiple models
  • Voting mechanisms (majority, weighted, rank-based)
  • Confidence-weighted predictions
  • Model performance tracking for weight adjustment

v0.7.0 - Alternative Data & Advanced Features (Planned)

Focus: Enhanced data sources and sophisticated analysis

Alternative Data Sources

• News & Sentiment Analysis - Integrate news and social media data
  • News API integration (NewsAPI, Benzinga, Bloomberg)
  • Sentiment scoring for individual stocks
  • Event detection (earnings, M&A, regulatory)
  • Social media sentiment (Reddit, Twitter/X)

Market Regime Detection

• Adaptive Strategies - Detect market conditions and adapt
  • Bull/bear/sideways market classification
  • Volatility regime detection
  • Sector rotation analysis
  • Economic indicator integration (GDP, unemployment, inflation)

Custom Indicators

• User-Defined Indicators - Plugin system for custom technical indicators
  • Python API for indicator development
  • Automatic caching and calculation
  • Vectorized computation support
  • Backtesting with custom indicators

Future Enhancements

Infrastructure & DevOps

• Kubernetes Deployment - Production-ready orchestration

  • Helm charts for easy deployment
  • Horizontal scaling for parallel simulations
  • Service mesh integration (Istio, Linkerd)
  • Observability stack (Prometheus, Grafana, Jaeger)

• Cloud Provider Support - Managed deployments

  • AWS (ECS, EKS, Lambda)
  • Google Cloud (Cloud Run, GKE)
  • Azure (AKS, Container Instances)
  • Terraform modules for infrastructure as code

Data & Storage

• Alternative Databases - Support for different storage backends

  • PostgreSQL for production workloads
  • TimescaleDB for time-series optimization
  • Redis for caching and real-time data
  • Object storage (S3, GCS, Azure Blob) for large datasets

• Data Pipeline - Robust data ingestion and processing

  • Apache Airflow for workflow orchestration
  • Delta Lake for data versioning
  • Data quality checks and validation
  • Automated data backups and recovery

Web UI & Visualization

• Dashboard Interface - Web-based monitoring and control

  • React/Vue.js frontend
  • Real-time charts and graphs (Plotly, D3.js)
  • Job management and configuration
  • Performance comparison visualizations
  • Portfolio allocation charts

• Jupyter Integration - Notebook-based analysis

  • Pre-built analysis notebooks
  • Interactive strategy development
  • Backtest result exploration
  • Custom metric calculation

AI & ML Enhancements

• Model Training & Evaluation - Train custom AI models

  • Training data preparation from historical results
  • Hyperparameter optimization (Optuna, Ray Tune)
  • Cross-validation and backtesting
  • Model versioning and registry (MLflow)

• Reinforcement Learning - RL-based trading agents

  • Gym/Gymnasium environment for trading
  • PPO, SAC, TD3 agent implementations
  • Reward shaping and curriculum learning
  • Multi-agent competitive scenarios

Integration & Extensibility

• Webhook Support - Event-driven notifications

  • Job completion notifications
  • Trade execution alerts
  • Error and failure notifications
  • Custom webhook endpoints

• Plugin System - Extensible architecture

  • Custom data sources
  • Alternative AI models (local LLMs, custom APIs)
  • Trading signal generators
  • Risk management rules

Testing & Quality

• Performance Testing - Load and stress testing

  • Locust/JMeter test scenarios
  • Database performance benchmarks
  • API latency measurements
  • Scalability testing

• Chaos Engineering - Resilience testing

  • Network failure simulations
  • Database connection failures
  • API provider outages
  • Recovery time measurements

Contributing

We welcome contributions to any of these planned features! Please see CONTRIBUTING.md for guidelines.

To propose a new feature:

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

Version History

• v0.1.0 - Initial release with batch execution
• v0.2.0 - Docker deployment support
• v0.3.0 - REST API, on-demand downloads, database storage (current)
• v0.4.0 - Enhanced simulation management (planned)
• v0.5.0 - Real-time trading support (planned)
• v0.6.0 - Multi-strategy & portfolio management (planned)
• v0.7.0 - Alternative data & advanced features (planned)

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Last updated: 2025-10-31