## 6 Results
### Quantity Tests for Pattern Recognition  
The final phase of experimentation focused on quantitative validation of pattern detection accuracy and the impact of DRL on prediction reliability. This phase implemented a rigorous cross-validation methodology analyzing 1000 temporal data points (candlesticks)  
Figure 17: All waves (marked with blue lines) detected by ElliottAgents for Alphabet stock over a 2-year period applied in one chart. Figure 18: All waves (marked with blue lines) detected by ElliottAgents for BTC-USD over a 2-year period applied in one chart. at daily intervals across previously examined equity instruments and the BTC-USD cryptocurrency pair. The experiments examined two distinct pattern categories: incomplete impulsive waves (1-2-3-4) and complete impulsive waves (1-2-3-4-5), and in each case waves could not overlap. The methodological approach incorporated comparative analysis between standard pattern recognition and DRL-enhanced prediction capabilities. Pattern validation was validated on price direction prediction (upward or downward price movement). The validation window, denoted as \(n\) periods, was determined through theoretical relationships established in the EWP: for incomplete patterns (1-2-3-4), \(n\) was calculated as 1.62\(\times\) the first wave length, corresponding to the theoretical extent of wave five. For complete patterns (1-2-3-4-5), validation examined the relationship between wave 5 and the subsequent corrective wave A, requiring that waves have the same length. The experimental results, presented in Table 3, demonstrate several findings from the cross-validation analysis conducted across 1000 data samples in dual time intervals. Analysis shows that complete impulsive wave patterns consistently achieved higher predictive accuracy compared to their incomplete counterparts. This differential in predictive capability suggests that fully formed wave structures provide more reliable indicators for future price movements, aligning with theoretical expectations from the EWP. Analysis conducted at hourly intervals reveals a reduction in pattern identification frequency. This can be attributed to the increased granularity of price movements at shorter time scales, where market noise may obscure underlying wave formations. The integration of DRL demonstrated substantial impact on prediction accuracy across all tested scenarios. This enhancement suggests successful implementation of the learning process. Table 4 presents the results of the cross-validation experiments for 1000 data samples in two time intervals on Bitcoin cryptocurrency. Again, the identification of a complete impulsive wave pattern contributes to better predictions of subsequent price movements than an incomplete impulse wave pattern even in intensive cryptocurrency markets. The use of DRL resulted in a improvement in prediction, similar to the one that we display in Table 3, showing that agents are able to use the learning process with historical data in better interpretation of patterns. \begin{table}
\begin{tabular}{c 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}  & & \multirow{2}{*}{**N**} & **Without** & **With** & \multirow{2}{*}{**N**} & **Without** & **With** \\  & & & **Backtesting** & **Backtesting** & & **Backtesting** & **Backtesting** \\ \hline \multirow{3}{*}{Daily Interval} & BTC/USD & 12 & 58.34\% & 66.67\% & 9 & 66.67\% & 88.89\% \\  & ETH/USD & 13 & 53.84\% & 61.54\% & 9 & 77.78\% & 77.78\% \\ \cline{1-1}  & SOL/USD & 10 & 50.00\% & 70.00\% & 8 & 62.50\% & 75.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of pattern recognition with and without backtesting for cryptocurrency market.