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Graph Crypto Analysis

This example implements a complete cryptocurrency research and analysis pipeline using the declarative graph building system, demonstrating end-to-end LLM-driven decision making for market analysis and investment recommendations.

📊 Workflow Diagram​

🎯 Core Features​

Intelligent Market Analysis:

  • LLM-driven token selection based on real-time market conditions
  • Multi-timeframe analysis (1h, 4h) for comprehensive market view
  • Dynamic decision flow guided by LLM analysis at each step

Advanced Technical Analysis:

  • Real-time indicator calculation (RSI, MACD, EMA) using PowerData toolkit
  • Market sentiment analysis and momentum evaluation
  • Risk assessment and volatility metrics for each token

LLM-Powered Synthesis:

  • Intelligent summarization of complex market data
  • Data-driven investment recommendations with reasoning
  • Short-term and macro-level market outlook generation

🚀 Key Capabilities​

  • Declarative Graph Building - GraphTemplate, NodeSpec, EdgeSpec for modular workflows
  • High-Level API Integration - HighLevelGraphAPI for automatic parameter inference
  • Complete Workflow - End-to-end from data ingestion to final recommendations
  • Real API Integration - Live Binance and cryptocurrency data via PowerData toolkit
  • LLM Decision Making - Every major decision guided by LLM analysis
  • Advanced State Management - Complex analysis state throughout the process
  • Error Recovery - Robust error handling and fallback mechanisms

📋 Prerequisites​

# Required environment variables
export OPENAI_API_KEY="your-openai-api-key"          # Primary LLM
export ANTHROPIC_API_KEY="your-anthropic-api-key"   # Alternative LLM
export TAVILY_API_KEY="your-tavily-api-key"       # Search engine

🏃 Quick Start​

# Navigate to examples directory
cd spoon-cookbook/example

# Install dependencies
pip install -r requirements.txt

# Run the declarative crypto analysis
python graph_crypto_analysis.py

🔍 What to Observe​

Architecture:

  • How GraphTemplate and NodeSpec simplify workflow construction
  • HighLevelGraphAPI automatically inferring parameters from queries
  • Modular node implementations with better separation of concerns

Data Flow:

  • Real market data fetching from Binance API and PowerData toolkit
  • LLM analysis of raw data for intelligent decision making
  • Step-by-step process from data collection to final recommendations

Technical Analysis:

  • Real-time indicator calculation using PowerData toolkit
  • Correlation of different data sources
  • Market sentiment analysis and quantification

LLM Decision Process:

  • Token evaluation and selection for analysis
  • Synthesis combining technical and fundamental analysis
  • Investment recommendations with detailed reasoning

📊 Sample Output​

🔍 MARKET ANALYSIS REPORT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

📈 SELECTED TOKENS FOR ANALYSIS: BTC, ETH, SOL, ADA

📊 TECHNICAL ANALYSIS:
• BTC/USDT: Bullish momentum, RSI: 68, MACD positive crossover
• ETH/USDT: Consolidation phase, approaching key resistance
• SOL/USDT: Strong uptrend, breaking previous highs
• ADA/USDT: Recovery phase, positive volume momentum

🎯 INVESTMENT RECOMMENDATIONS:
• SHORT-TERM: Consider BTC and SOL for momentum plays
• MEDIUM-TERM: Hold ETH through current consolidation
• RISK ASSESSMENT: Moderate volatility expected in next 24-48 hours

💡 MARKET OUTLOOK:
The current market shows strong bullish momentum with BTC leading...

📁 Source Code​

  • Main Example: graph_crypto_analysis.py
  • Supporting Modules:
    • spoon_ai/graph/builder.py - Declarative templates and high-level API
    • spoon_ai/tools/crypto_tools.py - PowerData integration helpers
    • spoon_ai/graph/ - Core engine and monitoring utilities
    • Tool System Docs

🎓 Learning Outcomes​

  • Using declarative graph building (GraphTemplate, NodeSpec, EdgeSpec)
  • Leveraging HighLevelGraphAPI for automatic parameter inference
  • Implementing modular, maintainable node functions
  • Building complete end-to-end analysis systems with LLM integration
  • Advanced cryptocurrency market analysis techniques
  • Real-time data processing and technical indicator calculation
  • LLM-driven decision making in complex workflows
  • Error handling and data validation in financial applications

💡 Best Practices​

  • Declarative architecture for improved modularity
  • High-level API usage for automatic parameter inference
  • Data validation and comprehensive error handling
  • Performance optimization and efficient data processing
  • Security considerations for API keys and financial data
  • Modular architecture with clean separation of concerns