<Riccardo Pirruccio/>
Building an Intelligent Crypto Trading System with Reinforcement Learning and LLMs
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Building an Intelligent Crypto Trading System with Reinforcement Learning and LLMs

machine-learning
reinforcement-learning
crypto
trading
LLM
LangGraph
PPO

Building an Intelligent Crypto Trading System with Reinforcement Learning and LLMs

In the rapidly evolving landscape of automated trading, I've been working on a novel approach that combines traditional reinforcement learning algorithms with the emerging capabilities of Large Language Models (LLMs). This project aims to create a robust crypto trading system that not only learns from market patterns but also benefits from high-level strategic guidance.

The Core Concept: PPO Meets SOAR

The heart of this system lies in the symbiotic relationship between two distinct AI paradigms:

  1. Proximal Policy Optimization (PPO) Model: A sophisticated reinforcement learning algorithm that makes the actual trading decisions (buy, sell, or hold with quantity specifications).

  2. SOAR-based Coach: A cognitive architecture implemented in LangGraph that analyzes the PPO model's actions and provides strategic guidance on risk adjustment and trading behavior.

What makes this approach unique is that instead of treating these components as separate systems, I've designed them to work in concert through a Redis-based communication bridge, creating a feedback loop that continually improves trading performance.

System Architecture

The system consists of four core components that work together to create an intelligent trading platform:

Revised Architecture (Current)

The Proximal Policy Optimization model serves as the primary decision-maker in our system. It's designed to:

  • Run independently on configurable intervals (default: 5 minutes)
  • Leverage GPU acceleration on an NVIDIA 4090
  • Make precise trading decisions based on market data and portfolio status
  • Communicate its state and actions to other system components

The PPO model is implemented using TensorTrade-NG, a powerful framework for building trading agents with reinforcement learning. Here's a glimpse of how the model architecture is structured:

ppo_agent.py
class PPOTradingAgent:
    def __init__(self, config):
        # Policy network: determines trading actions
        self.policy_net = nn.Sequential(
            nn.Linear(config['state_dim'], 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, config['action_dim'])
        )
        
        # Value network: estimates expected returns
        self.value_net = nn.Sequential(
            nn.Linear(config['state_dim'], 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 1)
        )
        
        # Initialize optimizer for both networks
        self.optimizer = optim.Adam([
            {'params': self.policy_net.parameters()},
            {'params': self.value_net.parameters()}
        ], lr=config['learning_rate'])

The model uses a hybrid action space that combines discrete actions (buy, sell, hold) with continuous trade sizing, allowing for fine-grained control over trading behavior.

SOAR Coach: The Strategic Advisor

The SOAR (State, Operator, And Result) cognitive architecture implemented in LangGraph provides high-level strategic guidance to the PPO model. Unlike traditional approaches that require retraining for behavior adjustment, the SOAR Coach:

  • Analyzes trading patterns and performance in real-time
  • Identifies potential improvements or risks
  • Generates structured guidance that the PPO model can immediately apply
  • Operates in inference-only mode for efficiency

The LangGraph implementation uses a series of specialized nodes to process the trading data:

Each node in this framework has a specific responsibility, from processing the incoming state data to formulating actionable guidance for the PPO model.

3. Redis Communication Bridge: The Nervous System

The Redis Communication Bridge serves as the central nervous system of our trading architecture, enabling efficient message passing between components:

  • Acts as the messaging system between PPO and Coach
  • Leverages existing Redis instance used by LangGraph
  • Uses dedicated channels for bidirectional communication
  • Passes JSON-structured data for efficient processing

This approach allows the PPO model and SOAR Coach to operate independently while maintaining a consistent feedback loop.

Frontend Interface: The Control Center

While the AI components handle the trading decisions, the frontend provides a comprehensive interface for monitoring and configuration:

  • Real-time visualization of portfolio performance and trading activity
  • Configuration controls for adjusting system parameters
  • Detailed analytics for performance evaluation
  • Risk monitoring dashboards

The frontend is built using React with TypeScript, Tailwind CSS for styling, and Recharts for data visualization, creating a responsive and intuitive user experience.

The Data Flow: A Continuous Feedback Loop

What makes this system particularly powerful is the continuous feedback loop between the PPO model and SOAR Coach:

This cyclical process allows the system to continuously improve without requiring explicit retraining of the PPO model, making it more adaptable to changing market conditions.

Technical Implementation Details

PPO Model Implementation

  • Framework: PyTorch for model development with TensorTrade-NG
  • State Features: Price data, volume, portfolio status
  • Actions: Buy, sell, hold with quantity specification
  • Risk Management: Configurable trade caps as percentage of portfolio
  • Interval Processing: Independent process running on timer

SOAR Coach Implementation

  • Framework: LangGraph for cognitive architecture
  • Analysis Focus:
    • Trading patterns recognition
    • Risk assessment
    • Timing optimization
  • Guidance Types:
    • Risk adjustments (increase/decrease risk tolerance)
    • Timing suggestions (wait for better opportunity)
    • Quantity recommendations (trade size optimization)

Redis Configuration

  • Channels:
    • ppo_state: For PPO to publish its state
    • coach_guidance: For coach to publish guidance
  • Data Structure: JSON format for all communications
  • Latency: Low-latency messaging to ensure timely guidance

Development Roadmap

The development follows a structured approach spanning 14 weeks from design to production:

Challenges and Considerations

Building this hybrid system presents several unique challenges:

  1. Integration Complexity: Ensuring seamless communication between different AI paradigms (reinforcement learning and LLMs)

  2. Performance Optimization: Balancing inference speed with decision quality, especially for the PPO model running on 5-minute intervals

  3. Risk Management: Implementing proper safeguards to prevent excessive losses during market volatility

  4. Guidance Effectiveness: Designing the SOAR Coach to provide actionable guidance that the PPO model can effectively utilize

  5. Evaluation Metrics: Determining appropriate metrics to evaluate the combined system performance

Future Enhancements

While the MVP focuses on a streamlined implementation, several enhancements are planned for future iterations:

  • Historical Database: Adding a SQLite database for tracking performance and enabling more sophisticated analysis
  • Enhanced Coaching Strategies: Expanding the range of guidance types the SOAR Coach can provide
  • Multi-Asset Trading: Extending the system to handle multiple cryptocurrencies simultaneously
  • Advanced Risk Management: Implementing more sophisticated risk control mechanisms
  • Backtesting Module: Adding comprehensive backtesting capabilities for strategy validation

Conclusion

By combining the precision of reinforcement learning with the strategic capabilities of LLM-powered cognitive architectures, this crypto trading system represents a novel approach to automated trading. The continuous feedback loop between the PPO model and SOAR Coach creates a system that can not only make effective trading decisions but also adapt its behavior based on high-level strategic guidance.

This project showcases how different AI paradigms can work together to create systems that are greater than the sum of their parts, potentially opening new avenues for intelligent trading solutions.

If you're interested in learning more about this project or discussing potential collaborations, feel free to reach out through my contact page.

Riccardo Pirruccio

Riccardo Pirruccio

Software Engineer

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