Mock Market 📈

A Full-Stack Investment Simulation Platform featuring Hybrid AI Analysis

Mock Market is a trading simulation application that bridges the gap between traditional quantitative finance and modern Generative AI. It utilizes a high-performance C++ engine for technical indicator calculations and Google Gemini for qualitative market sentiment analysis, providing users with a comprehensive "Buy/Sell/Hold" decision for any given stock.

🚀 Key Features

• Risk-Free Paper Trading Users sign in securely via Google and instantly receive a $100,000 virtual portfolio to practice trading strategies without financial risk.

• Real-Time Market Data Fetches live pricing and historical data using Yahoo Finance and Finnhub APIs.

• Hybrid Intelligence Engine

  • Quantitative: A dedicated C++ microservice calculates complex technical indicators (RSI, SMA, Bollinger Bands) in real-time.
  • Qualitative: Google Gemini (GenAI) acts as a "Portfolio Manager," synthesizing news headlines with the C++ hard math to make human-like trading decisions.

• AI-Powered Insights Returns clear textual analysis with a structured Buy / Sell / Hold signal and reasoning.

• Microservices Architecture Fully containerized using Docker and Docker Compose.

🛠 Architecture Overview

The application is split into three distinct services communicating via HTTP and TCP sockets:

1. Frontend (React) – User interaction, portfolio management, secure authentication via Firebase (Google Sign-In).
2. Backend (Node.js/Express) – Orchestrator: fetches data, talks to C++ engine, feeds results + news into Gemini.
3. CPP-Algos (C++) – High-performance TCP server for technical indicator calculations.

📂 Project Structure

mock-market/
├── backend/          # Node.js Express Server (API & Orchestration)
├── cpp-algos/        # C++ Quantitative Analysis Engine
├── frontend/         # React Client Application
├── docker-compose.yml
└── README.md

⚙️ Prerequisites

• Node.js (v16 or higher)
• C++ Compiler (MinGW for Windows or GCC/Clang for Linux/macOS)
• Docker Desktop (recommended for easy deployment)
• API Keys:
  • Finnhub API Key → finnhub.io
  • Google Gemini API Key → Google AI Studio

🔧 Installation & Setup

Method 1: Docker (Recommended)

Note: The current C++ code uses winsock2.h. For Docker (Linux containers), refactor socket code to standard BSD sockets (<sys/socket.h>, <netinet/in.h>).

1. Clone the repository:

   git clone [https://github.com/yourusername/mock-market.git](https://github.com/yourusername/mock-market.git)
   cd mock-market

2. Create .env in the backend/ directory:

   PORT=5000
   FINNHUB_API_KEY=your_finnhub_key_here
   GEMINI_API_KEY=your_gemini_key_here

3. Build and run:

   docker-compose up --build

   • Frontend → http://localhost:80
   • Backend → http://localhost:5000

Method 2: Manual Setup (Local Development – Windows)

1. Start the C++ Engine

   cd cpp-algos
   g++ src/main.cpp -o algo_service.exe -lws2_32 -static
   ./algo_service.exe

   → Listens on port 8080

2. Start the Backend

   cd backend
   npm install
   # Make sure .env exists with your keys
   node server.js

   → Listens on port 5000

3. Start the Frontend

   cd frontend
   npm install
   npm start

   → Opens at http://localhost:3000

💡 How It Works – The "Signal" Endpoint

When a user searches for a stock (e.g., AAPL):

1. Data Fetch – Backend pulls last 60 days of price data (Yahoo Finance) + recent news (Finnhub).
2. Quant Analysis – Price array sent to C++ engine (TCP port 8080).
3. Math Calculation – C++ returns RSI, SMA, Bollinger Bands, etc.
4. AI Synthesis – Backend sends technical string + news headlines to Google Gemini.
5. Decision – Gemini returns structured JSON:

   { 
     "signal": "BUY", 
     "confidence": 0.82, 
     "reasoning": "Strong upward momentum..." 
   }

🚧 Cross-Platform Note

Current cpp-algos/src/main.cpp uses <winsock2.h> → Windows-only.

• To run inside Docker (Linux): replace with standard POSIX sockets.
• Local Windows development works out-of-the-box.

🤝 Contributing

Contributions are welcome! Feel free to open issues or submit Pull Requests.

📄 License

This project is licensed under the ISC License – see the LICENSE file for details.