## 6 Results
### Result Analysis  
Results demonstrate the ElliottAgents platform's ability to not only identify Elliott wave patterns but also to translate these patterns into practical trading strategies with specific price targets and risk management guidelines. Our agents can communicate using natural language, and Figures 19 and 20 demonstrate a single message returned by an agent, used for communication in order to perform analysis. The initial phase of experimentation demonstrated the system's capability to detect and interpret various Elliott wave formations with high accuracy. The analyses generated by the platform proved accessible to end-users without requiring advanced financial knowledge, suggesting potential applications for both individual investors and professional traders.  
Cross-validation testing revealed insights into the system's pattern recognition capabilities. The analysis of 1000 samples across multiple securities demonstrated that complete impulsive wave patterns (1-2-3-4-5) consistently yielded higher prediction accuracy compared to incomplete patterns (1-2-3-4). The integration of deep reinforcement learning through backtesting substantially enhanced pattern recognition accuracy, with improvements of up to 16% observed in daily interval analysis. This improvement was consistent across both traditional equity markets and cryptocurrency trading pairs, suggesting robust adaptability to different market conditions.  
The platform demonstrated particular strength in daily interval analysis compared to hourly intervals, indicating enhanced effectiveness in identifying and analyzing longer-term market movements. This finding aligns with the Elliott wave principle's emphasis on fractal pattern recognition across different time scales. The system's performance in cryptocurrency markets proved especially noteworthy, maintaining consistent accuracy despite the characteristically higher volatility of these markets.  
The small number of detected patterns in certain test phases can be attributed to the strict non-overlapping criteria implemented in the pattern recognition algorithm, particularly relevant in daily interval analysis where wave formations often extend over multiple trading sessions. This methodological constraint, while potentially limiting the quantity of identified patterns, contributed to the high quality and reliability of the pattern recognition results.  
These experimental outcomes validate the efficacy of combining traditional Elliott wave analysis with modern artificial intelligence techniques through a multi-agent architecture. The system's demonstrated ability to maintain consistent performance across varying market conditions, timeframes, and asset classes suggests its potential utility as a practical tool for market analysis and trading decision support. The integration of backtesting capabilities through DRL proved particularly valuable in enhancing the system's predictive accuracy, indicating the potential for continued improvement through learning from the past.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Interval**} & \multirow{2}{*}{**Cryptocurrency**} & \multicolumn{4}{c}{**1-2-3-4 Patterns**} & \multicolumn{4}{c}{**1-2-3-4-5 Patterns**} \\ \cline{3-8}  & & **N** & **Without** & **With** & **N** & **Without** & **With** \\  & & **Backtesting** & **Backtesting** & & **Backtesting** & **Backtesting** \\ \hline \multirow{3}{*}{Hourly Interval} & BTC/USD & 8 & 62.50\% & 62.50\% & 6 & 50.00\% & 66.67\% \\  & ETH/USD & 9 & 44.44\% & 66.67\% & 6 & 66.67\% & 66.67\% \\ \cline{1-1}  & SOL/USD & 12 & 66.67\% & 83.34\% & 9 & 66.67\% & 77.78\% \\ \hline \hline \end{tabular} N: number of patterns found.  
\end{table}
Table 4: _Cont._