NeuroQuant: High-Frequency AI Sentiment Trading Engine

⚡ Executive Summary

NeuroQuant is an event-driven algorithmic trading platform designed to bridge the gap between unstructured financial news and quantitative trading signals. It leverages Natural Language Processing (FinBERT) to analyze financial news in real-time, generating trading signals that are executed against a simulated market environment with sub-second latency.

🏗 System Architecture

The platform is built as a containerized microservices architecture optimized for high throughput:

1. AI Engine (Python/PyTorch):
   • Ingests real-time news streams.
   • Uses a customized ProsusAI/finbert model for sentiment classification.
   • Publishes polarity scores to the Kafka event bus.
2. Messaging Backbone (Apache Kafka + Zookeeper):
   • Handles asynchronous communication between the AI and Execution layers.
   • Ensures zero-loss data streaming during high-volatility events.
3. Trading Backend (Node.js/TypeScript):
   • Consumes AI signals via Kafka consumers.
   • Executes Buy/Sell orders via TypeORM/PostgreSQL with ACID compliance.
   • Manages portfolio state and risk logic.
4. NeuroQuant Prime Terminal (React/Vite/Tailwind):
   • Professional-grade dashboard with Glassmorphism UI.
   • Features real-time "Sonar" signal detection, scrolling news tape, and interactive charts.

🚀 Quick Start

Prerequisites

• Docker & Docker Compose
• Git

Deployment

1. Clone the Repository:

   git clone [https://github.com/your-username/neuroquant.git](https://github.com/your-username/neuroquant.git)
   cd neuroquant

2. Launch the Stack:

   docker-compose -f docker-compose.prod.yml up -d --build

3. Verify Health: Access the Terminal at: http://localhost:5173

4. Simulate Market Data: The AI engine waits for news input. To trigger the simulation feed:

   docker exec -it trading_ai_prod python producer.py

🛠 Technology Stack

• Frontend: React 18, TypeScript, Tailwind CSS, Recharts
• Backend: Node.js, Express, TypeORM
• Database: PostgreSQL 15
• AI/ML: Python 3.9, Hugging Face Transformers, FinBERT, Kafka-Python
• Infrastructure: Docker Compose

---

👨‍💻 Engineer

Nayan Pandit InvestTech Equity Investment Banking Analyst & Jr. Quant Engineer

Disclaimer: This project is for educational and research purposes only. It simulates trading environments and does not execute real financial transactions.