## 4 ElliottAgents System Architecture
### Continous Learning  
The ElliottAgents platform implements a continuous learning process [13; 14] that enables agents to adapt and refine their knowledge over time [51]. This process is designed to enhance the system's predictive capabilities without relying on traditional fine-tuning methods. Instead, agents learn organically through their interactions and observations of the cryptocurrency market environment.  
At the core of this process is the Backtester agent, which plays a crucial role in accumulating and leveraging historical knowledge. As illustrated in Figure 14, the Backtester's workflow begins with a query to determine if relevant results are already available in the backtesting knowledge base. If not, the agent initiates an analysis by fetching the necessary data, performing EWP analysis, and interpreting the results. These findings are then stored in the Neo4j v5.20 graph database for future reference.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Role** & **Responsibilities** \\ \hline \multirow{3}{*}{Coordinator} & Serves as the central orchestrator of the system, managing workflow coordination and inter-agent communication. The agent interfaces between users and analytical components, distributes user inputs across the agent network, and ensures assembly of final outputs. \\ \hline \multirow{3}{*}{Data Engineer} & Functions as the data preparation specialist, responsible for processing raw market data based on user-specified parameters. The agent fetches market historical prices through external yfinance API and prepares structured datasets for analysis. \\ \hline \multirow{3}{*}{Elliott Wave Analysts} & Specializes in technical pattern recognition through identification of Elliott wave formations in historical data. This agent implements wave pattern detection algorithms and generates annotated price charts with wave overlays. \\ \hline \multirow{3}{*}{Backtester} & Employs deep reinforcement learning to validate Elliott wave pattern predictions. The agent maintains a historical pattern performance database and optimizes pattern recognition accuracy through iterative learning processes. \\ \hline \multirow{3}{*}{Technical Analysis Expert} & Provides advanced pattern interpretation by synthesizing backtesting results with wave patterns. The agent accesses RAG-enhanced Elliot wave theory knowledge base and determines highest probability market scenarios. \\ \hline \multirow{3}{*}{Investment Advisor} & Develops actionable trading strategies through integration of technical analysis insights. The agent performs RAG-assisted market context evaluation and determines comprehensive investment strategies. \\ \hline \multirow{3}{*}{Report Writer} & Produces comprehensive analysis documentation by synthesizing multi-agent analytical outputs and creating user-friendly market analysis reports. \\ \hline \hline \end{tabular}
\end{table}
Table 1: System agent roles and their responsibilities.  
This iterative process allows the system to build a repository of analyzed patterns and outcomes over time. This approach ensures that the system's predictions are grounded in a historical data and previously observed market behaviors.  
The continuous learning process is further enhanced by the agents' ability to freely learn from their environment [6]. Rather than undergoing periodic fine-tuning, which can be resource-intensive and potentially disruptive, the agents incrementally update their understanding based on the outcomes of their predictions and the evolving market conditions. This adaptive learning approach allows for real-time improvements in prediction accuracy and adaptability to changing market dynamics.