A production-grade cryptocurrency analytics system that leverages LLM orchestration and specialized AI models to deliver actionable insights from multi-modal data sources.
The Crypto AI Analytics Platform combines advanced machine learning, natural language processing, and blockchain analytics into a unified system. Built on a monolithic architecture with Model Context Protocol (MCP) orchestration, the platform processes market data, social sentiment, and on-chain metrics to generate interpretable, citation-backed insights.
- Natural language interface powered by multi-LLM orchestration (Gemini, Groq, OpenRouter)
- Dynamic orchestration of specialized analytical modules via MCP
- Hybrid query classification with context-aware response generation
- Source attribution and citation trails
- Prophet-based price prediction with trend decomposition
- CNN-LSTM deep learning model for sequential pattern recognition
- Temporal Fusion Transformer (TFT) for multi-horizon forecasting
- SARIMAX for statistical time-series modeling
- SHAP explainability for model transparency
- Fine-tuned DistilRoBERTa on cryptocurrency-specific corpus
- VADER sentiment scoring for news and social content
- Retrieval-Augmented Generation (RAG) for grounded insights
- Vector-based semantic search with Qdrant
- Real-time whale transaction monitoring via Infura
- Exchange flow analysis (deposits vs. withdrawals)
- Technical analysis pattern detection (RSI, MACD, etc.)
- Network activity metrics from Ethereum
- Market data aggregation via CCXT (100+ exchanges)
- Social sentiment from Reddit (PRAW) and CryptoPanic
- Fear & Greed Index from Alternative.me
- Real-time trade polling and OHLCV backfill
The platform employs a three-tier architecture optimized for low-latency inter-module communication:
┌─────────────────────────────────────────────────────────────┐
│ User Interface Layer │
│ │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ React Frontend │ │ REST API (FastAPI) │ │
│ │ Vite + Tailwind│ │ │ │
│ └─────────────────┘ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────┐
│ Core Processing Layer │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ LLM Agent Orchestrator │ │
│ │ (Gemini / Groq / OpenRouter) │ │
│ │ Model Context Protocol │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │
│ │ Forecasting │ │ Sentiment │ │ On-Chain │ │
│ │ Module │ │ Module │ │ Module │ │
│ │ │ │ │ │ │ │
│ │ Prophet │ │ DistilRoBERTa │ │ Whale Alerts │ │
│ │ CNN-LSTM │ │ RAG Pipeline │ │ Exchange Flows│ │
│ │ TFT │ │ VADER │ │ TA Patterns │ │
│ │ SARIMAX │ │ │ │ │ │
│ └───────────────┘ └───────────────┘ └───────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────┐
│ Data & Integration Layer │
│ │
│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌─────────┐ │
│ │TimescaleDB│ │ Redis │ │ Qdrant │ │ MLflow │ │
│ │ (OHLCV) │ │ (Cache) │ │ (Vectors) │ │(Models) │ │
│ └───────────┘ └───────────┘ └───────────┘ └─────────┘ │
│ │
│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌─────────┐ │
│ │ CCXT │ │ Infura │ │ Reddit │ │CryptoPanic │
│ │(Exchanges)│ │(Ethereum) │ │ (PRAW) │ │ (News) │ │
│ └───────────┘ └───────────┘ └───────────┘ └─────────┘ │
└─────────────────────────────────────────────────────────────┘
- Query Reception: User submits natural language query through React frontend or API
- Intent Classification: Hybrid classifier analyzes query and determines required analytical modules
- Tool Orchestration: Agent invokes relevant MCP tools in parallel (forecasting, sentiment, on-chain)
- Data Processing: Each module executes specialized analysis on requested data
- Result Synthesis: Multi-LLM synthesis aggregates outputs and generates explainable response
- Response Delivery: Final answer with citations, visualizations, and trading signals returned to user
The monolithic architecture was chosen to optimize for:
| Aspect | Benefit |
|---|---|
| Performance | Internal function calls eliminate network overhead between modules |
| Maintainability | Unified deployment, logging, and debugging infrastructure |
| Data Sharing | Modules share database connections and memory without serialization costs |
| Modularity | MCP protocol provides clear service boundaries without distributed system complexity |
- SHAP values for forecast feature importance
- LIME explanations for model interpretability
- RAG citations linking to source documents
- Transparent model decision-making process
- Async data ingestion with Celery task queue
- Redis caching for low-latency query processing (sub-second for cached queries)
- Streaming responses for long-running analyses
- Strict schema enforcement with Pydantic models
- Input sanitization and type checking
- Comprehensive error handling with detailed logging
- Modular design allows easy addition of new analytical tools
- MCP protocol standardizes tool interfaces
- Support for custom models and data sources
- MLflow model registry for versioning and deployment
| Category | Technologies |
|---|---|
| Framework | FastAPI, Python 3.10+ |
| LLM Orchestration | Gemini, Groq, OpenRouter, Model Context Protocol |
| Forecasting | Prophet, TensorFlow/Keras (CNN-LSTM), PyTorch Forecasting (TFT), Statsmodels (SARIMAX) |
| NLP | DistilRoBERTa (Transformers), Sentence-Transformers, VADER Sentiment |
| Vector Store | Qdrant |
| Explainability | SHAP, LIME |
| Validation | Pydantic |
| MLOps | MLflow |
| Category | Technologies |
|---|---|
| Framework | React 18 |
| Build Tool | Vite |
| Styling | Tailwind CSS |
| Charts | Recharts |
| Icons | Lucide React |
| Category | Technologies |
|---|---|
| Time-Series DB | TimescaleDB (PostgreSQL) |
| Cache | Redis |
| Vector DB | Qdrant |
| Model Registry | MLflow |
| Blockchain | Web3.py, Infura |
| Containerization | Docker, Docker Compose |
| Category | Technologies |
|---|---|
| Market Data | CCXT |
| Social Media | Reddit (PRAW) |
| News | CryptoPanic API |
| Sentiment Index | Alternative.me (Fear & Greed) |
| On-Chain | Infura (Ethereum) |
crypto-analytics-platform/
├── src/
│ ├── core/ # Configuration, database, logging
│ ├── data/ # Data ingestion and storage
│ │ ├── ingestion/ # CCXT, Reddit, CryptoPanic, Ethereum clients
│ │ └── storage/ # CRUD operations and models
│ ├── modules/
│ │ ├── agent/ # LLM orchestrator and query classifier
│ │ ├── forecasting/ # Prophet, CNN-LSTM, TFT, SARIMAX models
│ │ ├── sentiment/ # DistilRoBERTa, RAG pipeline
│ │ ├── onchain/ # Whale alerts, exchange flows, TA patterns
│ │ └── dashboard/ # Frontend data serializers
│ ├── mcp_servers/ # MCP tool servers
│ ├── services/ # FastAPI routers
│ └── utils/ # Cache, GCS loader
├── frontend/ # React + Vite application
├── infrastructure/ # Dockerfiles, nginx, SQL scripts
├── tests/ # Test suite
└── notebook/ # Jupyter notebooks
- Trading desk decision support with explainable forecasts
- Risk assessment using multi-modal sentiment indicators
- Compliance-ready analytics with full citation trails
- Reproducible analysis with documented data sources
- Model comparison and benchmarking
- Behavioral pattern analysis across market conditions
- Natural language queries for complex market questions
- Automated whale activity alerts
- Sentiment-aware price predictions
- Technical analysis pattern detection
- Ensemble modeling combining multiple algorithms
- Automated hyperparameter optimization with Optuna
- Real-time model retraining pipeline
- Wallet clustering for behavioral segmentation
- Chart pattern recognition using computer vision
- Smart contract interaction analysis
- Selective microservice extraction for high-load modules
- Multi-region deployment with edge caching
- Enhanced monitoring and observability
This platform is built on research investigating the performance trade-offs between monolithic and microservice architectures in AI-driven financial systems. The implementation validates that monolithic designs with internal modularity can achieve superior latency and maintainability characteristics for certain use cases.
Key findings include:
- 50x reduction in inter-module communication latency
- 38% improvement in complex query orchestration time
- 28 percentage point increase in output faithfulness through RAG
For detailed methodology and experimental results, refer to the accompanying research paper:
"Crypto AI Analytics Platform: A Monolithic LLM-Orchestrated Architecture for Multi-Modal Cryptocurrency Analysis"
Developed by Muhammed Sarfras P C as a demonstration of advanced AI system architecture, LLM orchestration, and explainable machine learning in financial applications.
⚠️ This platform is designed for educational and research purposes. The analytics and predictions provided should not be construed as financial advice. Cryptocurrency investments carry significant risk.