Memecoin Tracker: Project Overview
Memecoin Tracker is a comprehensive API and web application designed to monitor real-time data for trending memecoins like Dogecoin and Shiba Inu. Key features include:
- Live price tracking
- Market capitalization analysis
- Weekly price change visualization
Future updates will introduce an AI-driven investment recommendation system, leveraging predictive analytics to enhance decision-making.
Core Components
Backend (FastAPI)
- Python-based server fetching and processing memecoin data from CoinGecko API.
Database (PostgreSQL)
- Stores historical and real-time memecoin metrics.
Frontend (Next.js)
- Interactive dashboards displaying price trends and market insights.
Local Setup Guide
Prerequisites
Step-by-Step Instructions
Clone the Repository
git clone https://github.com/EvanBrightside/memecoin-tracker.git cd memecoin-trackerConfigure Environment Variables
Create
.envfiles with these settings:Root
.envPOSTGRES_USER=memcoadmin POSTGRES_PASSWORD=memcopassword POSTGRES_DB=memcodatabasefrontend/.envNEXT_PUBLIC_API_URL=http://localhost:8000app/.envDATABASE_URL=postgresql://memcoadmin:memcopassword@db/memcodatabase FRONTEND_URL=http://localhost:3000
Launch Services
docker-compose up --build- Backend:
http://localhost:8000 - Frontend:
http://localhost:3000
- Backend:
Stop Containers
docker-compose down
Future Roadmap
Upcoming Features
✅ Machine Learning Integration
- Predictive algorithms for price forecasting.
✅ Smart Recommendations
- Personalized investment strategies based on market trends.
✅ Expanded Coin Coverage
- Support for additional memecoins like PepeCoin and Floki.
✅ Multi-API Integration
- Aggregate data from diverse sources for richer insights.
👉 Explore advanced crypto tools to complement this project.
FAQ
1. What data does Memecoin Tracker provide?
- Real-time prices, market cap, and 7-day price charts for memecoins.
2. How often is the data updated?
- Prices refresh every 5 minutes via CoinGecko API.
3. Can I contribute to this project?
- Yes! Fork the repo and submit pull requests.
4. Will there be mobile support?
- A responsive web version is prioritized; native apps may follow.
5. Is historical data available?
- PostgreSQL stores 30 days of historical metrics (expandable).
6. How accurate are the predictions?
- Future ML models will undergo rigorous backtesting before release.