📬 TO: Hugging Face Team / Space Developer
Subject: Space Update Request - Comprehensive API Implementation
Dear Hugging Face Team,
This is a SPACE UPDATE REQUEST for implementing a comprehensive cryptocurrency data API that will serve as the unified backend for the Dreammaker Crypto Trading Platform.
🎯 Request Summary
Type: Update Existing Space
Goal: Implement 30+ API endpoints to serve all data needs
Priority: HIGH
Timeline: As soon as possible
📋 What We Need
We need our HuggingFace Space to provide a complete REST API and WebSocket service with the following capabilities:
Core Endpoints (Must Have)
- Market data (list of cryptocurrencies with prices)
- OHLCV chart data (candlestick data for charts)
- Real-time price ticker
- Latest cryptocurrency news
- Market sentiment analysis (Fear & Greed Index)
- AI trading signals
- Price predictions
- WebSocket for real-time updates
Additional Endpoints (Should Have)
- Order book data
- Recent trades
- Blockchain transaction history
- Whale alerts (large transfers)
- Market statistics
- Historical data
📄 Complete Specifications
All detailed specifications are in:
HUGGINGFACE_SPACE_DEPLOYMENT_REQUEST.md(120+ pages)
Key sections include:
- ✅ Complete API endpoint specifications with request/response formats
- ✅ Query parameters for each endpoint
- ✅ JSON response examples
- ✅ Backend implementation code (Python/FastAPI)
- ✅ Data source integration examples
- ✅ Caching strategy
- ✅ Rate limiting implementation
- ✅ WebSocket implementation
- ✅ AI/ML model integration
- ✅ Deployment configuration (Dockerfile, requirements.txt)
- ✅ Environment variables
- ✅ Testing procedures
- ✅ Performance requirements
🔑 Key Requirements
1. Technology Stack
- Python 3.9+
- FastAPI framework
- Redis for caching
- WebSockets for real-time
- PyTorch + Transformers for AI
- aiohttp for async HTTP
2. Data Sources
- CoinGecko API (market data)
- Binance API (OHLCV, trades)
- NewsAPI / CryptoPanic (news)
- Alternative.me (sentiment)
- Custom AI models (predictions)
3. Performance
- Response times < 500ms for most endpoints
- Smart caching (5s - 10min TTL depending on data type)
- Support for 1000+ concurrent WebSocket connections
- Rate limiting per IP/API key
4. Features
- Automatic fallback between data sources
- Consistent error handling
- CORS enabled for all origins
- Interactive API documentation (/docs)
- Health check endpoint
📦 What We're Providing
- Complete API Specification - Every endpoint documented with examples
- Backend Implementation Code - Python/FastAPI code ready to deploy
- Docker Configuration - Dockerfile and requirements.txt
- Environment Setup - All environment variables listed
- Testing Procedures - How to verify each endpoint
- Deployment Guide - Step-by-step deployment instructions
✅ Success Criteria
This update will be successful when:
- ✅ All API endpoints return valid JSON responses
- ✅ WebSocket connections are stable
- ✅ Response times meet performance requirements
- ✅ Data from multiple sources is properly aggregated
- ✅ AI models generate accurate predictions
- ✅ Caching improves performance
- ✅ 99.9% uptime maintained
🚀 Example Endpoints
Here are a few examples of what we need:
GET /api/market?limit=100
{
"success": true,
"items": [
{
"symbol": "BTC",
"name": "Bitcoin",
"price": 42150.25,
"change_24h": 2.34,
"volume_24h": 28500000000,
"market_cap": 825000000000
}
]
}
GET /api/ohlcv?symbol=BTC/USDT&timeframe=1h&limit=100
{
"success": true,
"data": [
{
"t": 1733428800000,
"o": 42100.50,
"h": 42250.75,
"l": 42050.25,
"c": 42150.25,
"v": 125.45
}
]
}
GET /api/ai/signals
{
"success": true,
"signals": [
{
"symbol": "BTC/USDT",
"type": "buy",
"confidence": 0.85,
"entry_price": 42150.25,
"target_price": 43500.00,
"stop_loss": 41000.00
}
]
}
(See full documentation for all 30+ endpoints)
📞 Questions & Support
If you have any questions about:
- API specifications
- Technical implementation
- Data sources
- Performance requirements
- Testing procedures
Please refer to the complete documentation in HUGGINGFACE_SPACE_DEPLOYMENT_REQUEST.md or contact us.
🎯 Why This Matters
This update will:
- Centralize all data access through one endpoint
- Reduce complexity in our frontend (60+ files currently making API calls)
- Improve performance with smart caching
- Provide better reliability with fallback mechanisms
- Enable real-time features via WebSocket
- Add AI-powered trading signals
Current situation: Data scattered across multiple APIs, hard to maintain
After update: Single, unified, powerful API serving all needs
📋 Checklist for Implementation
- Review complete API specifications
- Set up FastAPI backend
- Integrate data sources (CoinGecko, Binance, etc.)
- Implement caching layer (Redis)
- Add AI/ML models
- Set up WebSocket server
- Configure CORS
- Add rate limiting
- Create health check endpoint
- Test all endpoints
- Deploy to HuggingFace Space
- Verify production deployment
📄 Files Included
- HUGGINGFACE_SPACE_DEPLOYMENT_REQUEST.md - Complete specifications (120+ pages)
- HF_DEPLOYMENT_SUMMARY.md - Quick summary
- DATA_ARCHITECTURE_ANALYSIS_REPORT.md - Architecture analysis
- ENGINEERING_GUIDE.md - Development standards
🙏 Thank You
Thank you for taking the time to review this update request. We understand this is a significant implementation, but we've provided everything needed:
✅ Complete specifications
✅ Implementation code
✅ Testing procedures
✅ Deployment configuration
We're ready to provide any additional information or clarification needed.
Status: 🟡 Awaiting Implementation
Request Date: December 5, 2025
Request Type: Space Update (Not New Deployment)
Priority: HIGH
Best regards,
Dreammaker Development Team
🔗 Quick Links
- Main Specification:
HUGGINGFACE_SPACE_DEPLOYMENT_REQUEST.md - Architecture Report:
DATA_ARCHITECTURE_ANALYSIS_REPORT.md - Engineering Guide:
ENGINEERING_GUIDE.md - Quick Summary:
HF_DEPLOYMENT_SUMMARY.md
P.S. This is an UPDATE to our existing Space, not a request for a new Space deployment. We want to enhance our current Space with these comprehensive APIs.