📈 MarketIntel Engine: Real-Time Market Analysis

Description

Analyzing real-time market opportunities often requires expensive, rate-limited APIs or manual data aggregation across fragmented financial sources. To bridge the gap between unstructured web data and actionable market intelligence, I developed a robust FastAPI service capable of autonomously scraping, processing, and analyzing region-specific sector data. By implementing a clean, three-layer architecture, the service structurally maps raw DuckDuckGo Search results into an AI analysis pipeline, utilizing the latest google-genai Python SDK to generate structured, markdown-formatted reports with high precision.

Because exposing data collection and AI inference endpoints carries significant operational and financial risk, I engineered a custom security layer featuring simple header-based authentication and an in-memory, IP-based rate limiter to restrict users to 5 requests per 60 seconds. Once authenticated, the API utilizes asynchronous routing to fetch real-time market data without requiring paid search API keys, directly feeding the gemini-2.5-flash model to provide immediate, synthesized market insights safely and efficiently.

🚀 Deployment Status & What's Next: The core backend engine is fully containerized using Docker and is currently deployed live on an AWS EC2 instance to ensure reliable, scalable compute.

• 🔮 Coming Soon: We are putting the finishing touches on a dedicated frontend! A public link featuring a complete, interactive User Interface is coming very soon.

Motivation

This project was built to demonstrate a modern, production-ready backend architecture using Python 3.12 and FastAPI. It highlights a clean separation of concerns across routing, data acquisition, and LLM integration. By leveraging uv for blazing-fast dependency management and engineering a custom scraping layer to bypass the need for paid search APIs, the project serves as a showcase of resourceful system design and AI integration.

Quick Start

The project utilizes uv for extremely fast dependency management and environment setup.

1. Clone the repository:

git clone https://github.com/Shreyansh15624/trade-api-project
cd trade-api-project

2. Install dependencies using uv:

uv sync

(Alternatively, use pip install -r requirements.txt)

3. Configure Environment Variables: Create a .env file in the root directory and add your Google Gemini API key:

GEMINI_API_KEY=your_actual_api_key_here

4. Start the Application:

uv run main.py

5. Docker Setup (Alternatively): If you prefer to run this application via Docker, ensure docker is installed on your machine:

docker build -t marketintel-engine .
docker run -d -p 8000:8000 --env-file .env marketintel-engine

Usage

Once the server is running, FastAPI automatically provides interactive documentation. You can view the endpoints and test the API directly via:

• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc: http://127.0.0.1:8000/redoc

Example API Request: Use curl to test the endpoint. The custom X-API-Key header is required to pass the security middleware.

curl -H "X-API-Key: guest-token" http://127.0.0.1:8000/analyze/pharmaceuticals

The API will return a purely formatted Markdown string containing the market analysis, execution time, and insights.

Running the Automated Test Suite: This project includes automated tests for the security middleware and the core endpoint using pytest.

uv run pytest

Contributing

Contributions are welcome! If you'd like to improve the analysis pipeline or add new data sources:

1. Fork the repository.
2. Create a new branch for your feature (git checkout -b feature/NewMarketSource).
3. Commit your changes (git commit -m 'Add new DuckDuckGo scraping parameters').
4. Push to the branch (git push origin feature/NewMarketSource).
5. Open a Pull Request.

Please ensure all new features pass the existing pytest suite before submitting a PR.