AI Hedge Fund Crypto

](README.md) [

A next-generation algorithmic trading framework that leverages graph-based workflow architecture, ensemble technical analysis, and AI language models to make data-driven cryptocurrency trading decisions. This system employs a directed acyclic graph (DAG) of specialized nodes for multi-timeframe analysis, enabling sophisticated signal generation through weighted combinations of diverse trading strategies.

At its core, the system builds upon LangGraph's computational graph architecture to process market data through a pipeline of technical analysis nodes. Each strategy implements a BaseNode interface that processes multi-interval data for multiple assets simultaneously. The framework then aggregates these signals using adaptive weighting mechanisms, evaluates risk parameters, and formulates position management decisions through large language model (LLM) analysis.

The system stands out through its:

• AI-Enhanced Decision Making: Integration of large language models (LLMs) for portfolio management decisions, combining technical signals with sophisticated reasoning
• Compositional Architecture: Distinct nodes for data fetching, strategy execution, risk management, and portfolio management
• Signal Ensemble Approach: Weighted aggregation of multiple technical strategies (trend following, mean reversion, momentum, volatility, and statistical arbitrage)
• Multi-Timeframe Analysis: Simultaneous processing across various time intervals for more robust signal generation
• Dynamic Strategy Visualization: Automatic generation of computational graph visualizations to better understand the decision flow
• Comprehensive Backtesting: Robust historical performance evaluation with detailed metrics and visualizations

Backtest Results

Sample backtesting results showcasing how well-designed trading strategies can generate high-quality signals when implemented in this framework. The system's performance is driven by the quality of your strategies, with the framework enabling efficient implementation, LLM-based decision refinement, and multi-timeframe analysis:

These results demonstrate the system's ability to:

• Generate profitable trading signals across different market conditions
• Manage risk effectively with position sizing rules
• Adapt to changing market dynamics with multi-timeframe analysis
• Provide detailed performance metrics for strategy evaluation

Architecture

The system is built around a highly configurable directed acyclic graph (DAG) architecture, where data flows through various processing nodes. This architecture provides exceptional flexibility, allowing users to customize both the available strategies and the timeframes for analysis without modifying the core code.

Node-Based Workflow System

At its heart, the system uses LangGraph to create a dynamic computational graph where: