# A Stock Market Decision-Making Framework Based on CMR-DQN
## 4 Experiments and Results
### Experimental Results  
This section presents a comparison between the proposed CMR-DQN framework and five evaluation methods. The experimental results on six stock index datasets are shown in Table 1. Trading performance is assessed using four performance metrics: cumulative return (CR), Sharpe ratio (SR), annualized return (AR), and maximum drawdown (MDD). Table 1 highlights the performance of different trading methods across six assets. Among the listed metrics, the CMR-DQN strategy achieved the highest scores, indicating its superior performance. Compared to other strategies, CMR-DQN attained significantly higher cumulative returns and demonstrated the smallest maximum drawdown, underscoring its robust profitability and risk mitigation across the six datasets. For instance, on the IXIC dataset, the CMR-DQN strategy achieved a cumulative return of 49.15%, outperforming all other methods. Its Sharpe ratio was 0.99, reflecting excellent risk-adjusted returns. Additionally, the CMR-DQN strategy had the lowest maximum drawdown of 16.35% on the IXIC dataset, further highlighting its effectiveness. Similar observations can be made for the other assets listed in the table. In terms of cumulative returns, Sharpe ratio, and annualized returns, CMR-DQN consistently outperformed benchmark methods, demonstrating its superior performance. These results validate the proposed CMR-DQN framework's effectiveness in generating profitable trading strategies with low risk. Figure 6 displays the cumulative asset growth curves for various strategies, including B&H, DQN, Rainbow DQN, DQN-GLA, and the newly proposed CMR-DQN. It is evident from the graph that the performance of the CMR-DQN strategy surpasses that of the others. CMR-DQN not only significantly reduces loss risk but also achieves higher returns. Moreover, under the CMR-DQN strategy, total asset growth is smoother compared to the benchmark strategy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Asset** & **Metric** & **B\&H** & **DQN** & **Rainbow DQN** & **DQN-GLA** & **CMR-DQN** \\ \hline \multirow{5}{*}{PA} & CR[\%] & \(-\)19.07 & \(-\)24.46 & 8.16 & 14.62 & 28.22 \\  & SR & \(-\)0.22 & \(-\)0.28 & 0.11 & 0.16 & 0.29 \\  & AR[\%] & \(-\)33.24 & 43.29 & 13.76 & 25.15 & 50.48 \\  & MDD[\%] & 56.42 & 59.97 & 33.98 & 29.84 & 20.92 \\ \hline \multirow{5}{*}{ZG} & CR[\%] & \(-\)15.58 & 12.13 & 0.63 & 23.23 & 31.26 \\  & SR & \(-\)0.16 & 0.14 & 0.01 & 0.27 & 0.33 \\  & AR[\%] & \(-\)27.52 & 18.03 & 1.04 & 40.97 & 48.30 \\  & MDD[\%] & 52.35 & 31.97 & 39.70 & 23.86 & 18.73 \\ \hline \multirow{5}{*}{DJI} & CR[\%] & 29.03 & 5.54 & 45.05 & 45.09 & 52.32 \\  & SR & 0.30 & 0.09 & 0.48 & 0.49 & 0.69 \\  & AR[\%] & 51.98 & 9.38 & 84.28 & 84.39 & 99.73 \\  & MDD[\%] & 20.70 & 36.02 & 12.85 & 12.79 & 7.91 \\ \hline \multirow{5}{*}{FCHI} & CR[\%] & \(-\)1.54 & 45.03 & 42.70 & 53.16 & 55.23 \\  & SR & \(-\)0.02 & 0.48 & 0.46 & 0.72 & 0.73 \\  & AR[\%] & \(-\)2.03 & 84.29 & 79.42 & 101.5 & 106.04 \\  & MDD[\%] & 41.03 & 12.88 & 13.61 & 6.52 & 6.18 \\ \hline \multirow{5}{*}{IXIC} & CR[\%] & 4.82 & \(-\)0.62 & 17.54 & 8.81 & 20.37 \\  & SR & 0.03 & \(-\)0.01 & 0.21 & 0.11 & 0.24 \\ \cline{1-1}  & AR[\%] & 8.17 & \(-\)1.02 & 30.43 & 14.89 & 35.64 \\ \cline{1-1}  & MDD[\%] & 37.34 & 40.71 & 27.58 & 34.06 & 25.59 \\ \hline \multirow{5}{*}{N225} & CR[\%] & 11.52 & 35.86 & 36.32 & 37.02 & 41.96 \\ \cline{1-1}  & SR & 0.13 & 0.35 & 0.38 & 0.41 & 0.45 \\ \cline{1-1}  & AR[\%] & 19.64 & 65.50 & 66.42 & 67.83 & 77.89 \\ \cline{1-1}  & MDD[\%] & 32.05 & 17.42 & 16.33 & 15.78 & 14.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The trading performance of six assets across different models. The performance shown in Figure 6 aligns with the performance metrics in Table 1. Across different asset categories, CMR-DQN consistently outperforms other benchmark methods, demonstrating its effectiveness in generating profitable trading strategies. This strategy not only avoids substantial losses but also delivers additional returns compared to benchmark and alternative methods.