QuantCore

🚀 Short Intro

QuantCore is a personal quant platform where I publish finance-focused projects in a clean and modular way. Right now, the active project is a fixed income analytics app that loads predefined bond portfolios from CSV files, computes core portfolio metrics, and exposes them through a FastAPI backend with a React dashboard.

🛠 Technologies Used

• Python
• FastAPI
• Pytest
• React
• Vite
• Tailwind CSS
• Recharts
• CSV-based local datasets

✨ Features

• Modular full-stack structure with separate backend, frontend, and tests
• Fixed income analytics for zero-coupon and fixed-rate bonds
• Portfolio market value, yield, duration, and convexity calculations
• Aggregated cashflow generation and visualization
• Preloaded portfolio profiles: conservative, mid, and aggressive
• REST API endpoints for health checks, bonds, positions, and portfolio summaries
• Dashboard with portfolio summary cards, cashflow chart, bonds table, and positions table
• Test coverage for models, loaders, engines, and API routes

👤 What Users Can Do

• Open the platform and navigate to the available quant projects
• Enter the Fixed Income module
• Review the bond universe loaded from local CSV data
• Inspect portfolio positions and total market exposure
• Visualize projected cashflows over time
• Analyze key fixed income risk and return metrics from the dashboard

🧠 The Process — How I Built It

This project is structured as a growing quant workspace rather than a single isolated app.

• Built a FastAPI backend around domain models such as Bond, Position, and Portfolio
• Added loaders to transform CSV data into validated objects
• Implemented bond and portfolio engines for analytics like YTM, duration, convexity, and cashflows
• Exposed the analytics through focused API routes
• Built a React frontend to consume the API and present the results in a simple dashboard
• Added automated tests to validate business logic and route behavior

📚 What I Learned

• How to structure a quant project with clear separation between data, models, services, schemas, and routes
• How to model fixed income instruments in code and derive portfolio-level metrics from them
• How to connect a Python API to a React dashboard in a clean way
• How to validate finance-related logic through focused unit and integration tests

🚧 Next Steps

QuantCore is evolving from a fixed-income analytics tool into a fully modular quantitative platform. Upcoming developments include:

• Integrate yield curve modeling and replace YTM-based valuation with interpolation and discount factor frameworks
• Transition from static CSV inputs to real-time data ingestion via external APIs
• Generate synthetic portfolios using Monte Carlo-style simulations for testing and analysis
• Enrich bond data with additional attributes (day count convention, currency, issuer, credit rating)
• Introduce portfolio optimization techniques to improve risk-return profiles and alpha generation
• Implement multi-user support and deploy the platform to AWS for scalability

▶️ How to Run the Project

Clone the repository:

git clone https://github.com/GeorgeCurioso/quant-core.git

Go into the project folder:

cd quant-core

Install backend dependencies:

pip install -r backend/requirements.txt

Run the backend:

uvicorn backend.app.main:app --reload

Install frontend dependencies:

cd frontend
npm install

Run the frontend:

npm run dev

Run the tests from the project root:

python -m pytest tests