Project Name - Vedic Financial Compute Engine
Project Statement - Fast interest and calculator using Nikhilam Sutra
Track - FinTech & Smart Computing
Team Members -
- Sahil Gazi (1230439255)
- Nishtha Sahani (1230439201)
- Priyanshu Kumar (1230439224)
- Jayati Mahato (1230439154)
Traditional financial calculators rely on conventional arithmetic methods that can be computationally heavy at scale. In high-frequency financial systems (loan portals, banking apps, micro-finance tools), efficiency and precision matter.
We propose a Vedic Financial Compute Engine that applies principles inspired by the Nikhilam Sutra from Vedic Mathematics to optimize financial computations such as:
- EMI calculation
- Simple Interest
- Compound Interest
The system provides:
- 🧮 EMI Calculator
- 📈 Simple Interest Calculator
- 📊 Compound Interest Calculator
- ⚡ Optimized computational logic
- 🏦 Bank-style interactive UI using Streamlit
- 📦 Deployment-ready structure
The goal is to demonstrate:
- Faster arithmetic operations
- Modular compute architecture
- Clean fintech-style frontend
- Modular Python package structure
- Clean separation of UI and computation logic
- Efficient EMI formula implementation
- Scalable design
- Ready for deployment (Render/Heroku compatible)
[ EMI = [ P × r × (1 + r)^n ] / [ (1 + r)^n − 1 ] ]
Where:
- P = Principal
- r = Monthly Interest Rate
- n = Number of Months
[ SI = ( P × R × T ) / 100 ]
[ CI = P × ( 1 + R / (100 × n) )^(n × T) − P ]
- Python 3.x
- Streamlit
- Modular Package Design
- Custom CSS Styling
- Reduced redundant recalculations
- Optimized exponentiation logic
- Minimal UI overhead
- Clean compute layer isolation
Future Scope:
- Benchmark vs traditional implementation
- Amortization schedule generator
- PDF report export
- REST API microservice version
- Integration with fintech backends
git clone <repo-url>
cd vedic-financial-engine
pip install -r requirements.txt
streamlit run app.py
streamlit run app.py --server.port=$PORT --server.address=0.0.0.0