Nocturne .ai

Nova insights
Nova insights
aggregation pipeline
predictive ml model
predictive ml model
landing page

Inspiration

Financial markets today are heavily influenced by both quantitative data and retail investor sentiment. Platforms like Reddit have become powerful drivers of market conversations, yet most investors and analysts still rely on fragmented tools to track sentiment, analyze trends, and generate insights.

The idea behind Nocturne.ai was to create a unified AI-powered quant research terminal that combines market analytics, social sentiment signals, and AI-generated insights in a single interface. Inspired by professional financial terminals used by quantitative analysts, the goal was to design a platform that allows users to quickly understand market trends, sentiment momentum, and potential price movements.

By integrating AI models with sentiment data and visualization tools, Nocturne.ai aims to simplify how users interpret complex financial signals.

What it does

Nocturne.ai is an AI-powered fintech platform designed to function like a modern quantitative research terminal.

The platform enables users to:

Analyze stocks through a quant-style dashboard
Generate AI-powered market insights using Amazon Nova
Track retail investor sentiment from Reddit communities such as r/wallstreetbets, r/stocks, and r/investing
Visualize data through interactive charts such as sentiment trends, mention velocity, engagement scores, and prediction graphs
Access personalized signals through secure authentication

By combining sentiment analysis, machine learning predictions, and generative AI explanations, Nocturne.ai helps users gain a more comprehensive view of market behavior.

How we built it

Nocturne.ai was built using a full-stack architecture designed for performance and modularity.

The frontend was developed using React.js with Vite, allowing for a fast and responsive interface. Tailwind CSS was used for styling and layout, while Framer Motion added smooth animations to enhance the user experience. Data visualizations were implemented using Recharts to display insights such as sentiment trends and price predictions.

The backend was built with Node.js and Express.js, providing API endpoints for AI insights, sentiment data, and authentication. MongoDB with Mongoose was used to manage user accounts and store structured data.

To enable AI-powered analysis, we integrated Amazon Nova to generate contextual insights about stock tickers. Additionally, Google Generative AI (Gemini) was used for text analysis and reasoning tasks.

A custom web scraping pipeline was created to collect sentiment data from Reddit communities. The collected discussions are processed and exported into structured datasets which are then used to compute sentiment and engagement metrics.

Challenges we ran into

One of the main challenges was integrating multiple data sources and services into a cohesive system. The platform needed to combine scraped sentiment data, AI-generated insights, and live visualizations while maintaining responsive performance.

Designing a terminal-style interface that could display multiple types of analytics without overwhelming the user was another challenge. Achieving a balance between usability and information density required several UI design iterations.

Additionally, ensuring that AI-generated insights were structured and meaningful required experimentation with prompt design and response formatting.

Accomplishments that we're proud of

One of our biggest accomplishments was successfully building a fully functional quant-style dashboard that integrates multiple advanced technologies into a single platform.

We are particularly proud of:

Implementing an automated Reddit sentiment scraper
Integrating AI-generated financial insights into the dashboard
Building an interactive data visualization system for market signals
Creating a clean and professional terminal-like user interface
Developing a complete full-stack architecture within the hackathon timeframe

The result is a working prototype that demonstrates how AI and social sentiment analysis can be combined to improve financial research workflows.

What we learned

Through building Nocturne.ai, we gained valuable experience in:

Designing full-stack applications with modern JavaScript frameworks
Integrating generative AI models into real-world applications
Building data pipelines for web scraping and sentiment analysis
Creating interactive data dashboards and visual analytics
Structuring backend services to support AI-driven features

This project also helped us understand how combining quantitative market data with social sentiment signals can provide deeper insights into financial trends.

What's next for Nocturne.ai

Future improvements for Nocturne.ai include expanding the platform into a more advanced AI-powered research environment.

Planned enhancements include:

Real-time sentiment monitoring from multiple social platforms
Integration with additional financial market data APIs
More advanced machine learning models for price prediction
Portfolio tracking and custom alert systems
Expanded analytics tools for traders and analysts

Our long-term vision is to develop Nocturne.ai into a comprehensive AI-powered financial research terminal that helps users interpret complex market signals with greater clarity.