HedgeMind

An intelligent, real-time trading and portfolio management system powered by agentic AI and streaming data pipelines.

🌐 Live Demo: http://13.50.225.240:80

---

Table of Contents

• Overview
• Repository Structure
• Folder Descriptions
• Documentation
• References

---

Overview

HedgeMind is a comprehensive trading intelligence platform that integrates real-time market data processing, machine learning-based forecasting, and agentic decision-making systems. The repository is organized into distinct modules, each serving a specific purpose in the overall architecture.

Each folder contains its own detailed README with implementation specifics, setup instructions, and usage guidelines.

---

Repository Structure

HedgeMind/
│
├── LIVE/                              # Production environment - Live market data
│   ├── Agentic_System/
│   │   ├── chat/
│   │   ├── clarification/
│   │   ├── config.sh
│   │   ├── debezium/
│   │   ├── docker-compose.yml
│   │   ├── macro/
│   │   ├── market_analyzer/
│   │   ├── orch/
│   │   ├── portfolio/
│   │   ├── sql/
│   │   ├── strategy/
│   │   └── utils/
│   ├── ga-strat-app/
│   │   └── ga-strat-app/
│   ├── kubernetes_sarimax/
│   │   ├── README.md
│   │   └── technical_analysis/
│   └── trading-system-main/
│       ├── ALL_DEPLOYMENTS/
│       ├── README.md
│       ├── deploy-kafka.sh
│       └── deploy.sh
│
├── STATIC/                            # Development environment - Simulated streams
│   ├── aws_macro_deployment/
│   │   ├── Dockerfile.api
│   │   ├── Dockerfile.consumer
│   │   ├── Dockerfile.producer
│   │   ├── README.md
│   │   ├── api_server.py
│   │   ├── data/
│   │   ├── deployment.sh
│   │   ├── docker-compose.yaml
│   │   ├── docker-compose.yml
│   │   ├── metrics.py
│   │   ├── models/
│   │   ├── pathway_consumer_training.py
│   │   ├── pathway_fred_producer.py
│   │   └── requirements.txt
│   ├── chronos_static/
│   │   ├── README.md
│   │   ├── chronosConsumer/
│   │   ├── docker-compose.yml
│   │   ├── ohlcProducer/
│   │   ├── output/
│   │   ├── redditProducer/
│   │   ├── sarimaxConsumer/
│   │   ├── selection/
│   │   ├── spike_detector/
│   │   └── tweetProducer/
│   └── portfolio_static/
│       ├── README.md
│       ├── backend/
│       ├── data/
│       ├── docker-compose.yml
│       ├── evaluation/
│       ├── output/
│       └── processing/
│
│
├── MCP/                               # Model Context Protocol implementation
│   ├── mcp_deploy/
│   │   ├── __MACOSX/
│   │   └── mcp_deploy/
│   └── pathway_mcp/
│       ├── __MACOSX/
│       └── pathway_mcp/
│
├── WEBAPP/                            # Web application (Frontend & Backend)
│   ├── Makefile
│   ├── README.md
│   ├── backend/
│   │   ├── Dockerfile
│   │   ├── api/
│   │   ├── config/
│   │   ├── db.sqlite3
│   │   ├── kafka_consumer/
│   │   ├── logs/
│   │   ├── manage.py
│   │   ├── requirements.txt
│   │   ├── schema.yml
│   │   ├── setup_chat.sh
│   │   ├── templates/
│   │   ├── test_email_notifications.py
│   │   ├── test_kafka_connection.py
│   │   ├── test_notifications.py
│   │   ├── test_pnl_websocket.py
│   │   ├── test_portfolio_api.py
│   │   └── users/
│   ├── deploy-commands.txt
│   ├── deploy-kafka-config.sh
│   ├── docker-compose.override.yml.example
│   ├── docker-compose.yml
│   ├── docker-manager.sh
│   ├── frontend/
│   │   ├── Dockerfile
│   │   ├── README.md
│   │   ├── app/
│   │   ├── components/
│   │   ├── contexts/
│   │   ├── eslint.config.mjs
│   │   ├── hooks/
│   │   ├── lib/
│   │   ├── next.config.ts
│   │   ├── package.json
│   │   ├── pnpm-lock.yaml
│   │   ├── postcss.config.mjs
│   │   ├── public/
│   │   ├── theme-preview.html
│   │   ├── tsconfig.json
│   │   ├── types/
│   │   └── utils/
│   ├── health-check.sh
│   ├── models/
│   │   ├── macro/
│   │   └── technical_analysis/
│   ├── nginx.conf
│   ├── setup-kafka-topics.sh
│   ├── test.html
│   ├── test_news_simulator.py
│   ├── websocket-realtime-test.html
│   └── websocket-test.html
│
├── Videos/                        │   └── demo.mp4 
│   └── summary_video.mp4
│
├── Appendix.pdf                          # Supplementary materials & references
│
└── Report.pdf

---

Folder Descriptions

LIVE

Contains the production-ready codebase that operates on live market data. This includes the final agent implementation, orchestration logic, market analysis modules, portfolio management, and trading strategy execution. All services are containerized and deployed via Docker and Kubernetes.

STATIC

A development and testing environment that mirrors the LIVE folder structure. Instead of connecting to live market feeds, it utilizes Pathway to simulate data streams. This allows for safe testing, debugging, and experimentation without impacting real trading operations.

AGENTIC SYSTEM

Houses the Pathway-based Agentic System derived from the X Pack framework. This module is responsible for autonomous decision-making, reasoning, and task execution within the HedgeMind pipeline.

MCP

Contains the custom Model Context Protocol (MCP) implementation. This module handles context management and communication between various AI models and system components, enabling seamless integration of multiple intelligent agents.

WEBAPP

The user-facing application layer comprising:

• Backend: Django-based REST API with Kafka consumers, WebSocket support, and user management
• Frontend: Next.js application with real-time data visualization and interactive dashboards

Videos

Contains demo videos and walkthroughs showcasing the functionality, features, and usage of the HedgeMind platform.

Report

The formal project report documenting the system design, methodology, implementation details, and evaluation results.

Appendix

Supplementary materials including additional documentation, configurations, and supporting resources. All references cited in the Report are documented here.

---

Documentation

Each major folder contains its own README.md with:

• Setup and installation instructions
• Configuration guidelines
• API documentation (where applicable)
• Deployment procedures

---