Neural Networth

This is a trading simulation game built using React (Vite), a Go backend, MongoDB, and Gemini. A market generated by Gemini, where players can execute trades and react to market fluctuations.

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Inspiration

The inspiration for Neural Networth came from my fascination with the stock market and real-time trading. I wanted to create an interactive platform where users could experience the thrill of trading in a dynamic market environment, without the risks associated with real-world trading.

What It Does

Overview

I have developed a real-time trading simulation game built using React (Vite) for the frontend, a Go backend, and a MongoDB database.

The game simulates a dynamic trading environment where players can execute trades and react to market fluctuations.

  • Companies: Companies are listed on the market and have stocks that can be bought or sold. Each company has a ticker symbol, a name, and a value.
  • Market: Created by Gemini, the market is a dynamic environment that changes every round. The market is influenced by the actions of the players.
  • Portfolio: Players can view their portfolio, which shows their current holdings and their value.
  • Trades: Players can execute trades to buy or sell stocks. Trades are executed in real-time and affect the market.

How I Built It

Neural Networth is a real-time market simulation platform I built using state‑of‑the‑art web technologies. My backend is built with Go and Gin, enabling rapid HTTP development and robust WebSocket support for real-time communication. The frontend is developed in React with Vite for lightning‑fast builds and modern component architecture. MongoDB powers my database, providing scalability and flexible data models. I incorporated WebSockets to ensure that changes in market data and portfolio states are delivered instantly to users.

Tech Stack

  • Frontend: React (Vite)
    Leveraging component‑based architecture for modular and maintainable user interfaces.
  • Backend: Go (Gin)
    Offering high performance with efficient concurrency patterns and robust REST and WebSocket communication.
  • Database: MongoDB
    A NoSQL solution chosen for its flexibility and scalability in managing market data.
  • Real-time Communication: WebSockets
    Ensuring seamless, low‑latency updates between the server and client, critical for market simulations.

Challenges I Ran Into

  • CORS & WebSocket Integration:
    Integrating Cross-Origin Resource Sharing (CORS) with WebSockets under tight deadlines posed significant challenges. I addressed these by carefully configuring Gin and Gorilla WebSocket to allow all origins during development and later tightening security for production.

  • Concurrency & Thread-Safety:
    Managing real‑time rounds and portfolio updates required careful handling of race conditions and thread‑safety. I implemented robust locking mechanisms in my round management logic to ensure consistent state updates.

  • Scalability Under Time Constraints:
    Balancing rapid feature development with ensuring a maintainable codebase was challenging. I focused on auto‑scaling rounds and real‑time updates without compromising performance.

  • Deployment of Backend:
    I faced significant challenges in deploying the backend. Despite having a functional backend locally, setting up the deployment environment proved difficult. Time constraints and complexities with server configurations hindered my ability to properly deploy the Go-based backend. Issues with hosting services, containerization, and ensuring compatibility between the frontend and backend systems meant that I could not make the backend accessible over the web in time for the project deadline. This was a crucial part of the project, and not being able to deploy it limited the functionality that users could experience. Moving forward, I plan to delve deeper into backend deployment strategies, perhaps utilizing cloud platforms like AWS or Docker containers to streamline this process.

Accomplishments That I'm Proud Of

  • Real-Time AI Market Simulation:
    Successfully built an AI-driven market simulator that allows users to participate in dynamic rounds and interact in real time.

  • Robust Backend Architecture:
    My Go‑based backend efficiently manages WebSocket connections, ensuring that real‑time data flows seamlessly without interruption.

  • Streamlined Frontend Experience:
    Utilization of React with Vite has enabled me to create a responsive, modern user interface that feels both fast and intuitive.

What I Learned

  • Integration of Diverse Technologies:
    Working across Go, React, and MongoDB has given me a deeper understanding of how to integrate disparate systems into a cohesive platform.

  • Real-Time Communication Nuances:
    I learned the intricacies of WebSocket communication alongside dealing with browser-specific CORS policies.

  • Building Under Pressure:
    Tight deadlines pushed me to focus on essential functionality and make smart decisions about trade-offs between feature complexity and release schedules.

What's Next for Neural Networth

  • Enhanced AI Decision Making:
    I plan to improve the AI algorithms powering the market simulation, providing more dynamic and realistic market behaviors.

  • Expanded User Features:
    Upcoming features include leaderboards, detailed portfolio analyses, and social features that allow users to compete and collaborate in a simulated market environment.

  • Scalability & Security Enhancements:
    Improvements in the backend architecture and WebSocket management are planned to handle a growing user base while ensuring the platform remains secure.

Game Features

  • Rounds: The game is played in rounds, with each round representing a day in the market. At the end of each round, the market changes, and players can see how their trades have affected the market and their portfolio.

  • Advisors: Provide advice on when to buy or sell stocks, given by Gemini. Sometimes they are right, sometimes they are wrong.

  • News: News articles that affect the market. News articles are generated by Gemini and can be positive or negative.

Neural Networth is only just beginning its journey. With each iteration, I plan to introduce new features to enrich the simulation and make it a truly engaging experience.

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