## 3 Results
### Instrument Allocation  
The overall proportion of each instrument in a portfolio is calculated by aggregating the total currency allocated to that instrument throughout the entire testing period. This cumulative allocation is divided by the total cumulative portfolio value for the same period. This method provides a robust measure of average exposure to each financial instrument, accounting for market fluctuations and changes in portfolio weights over time. This allows us to assess the relative importance of each instrument in the portfolio and observe how consistently agents favored specific assets during the evaluation phase. The S&P 500 (\({}^{\prime}\)GSPC) and NASDAQ (\({}^{\prime}\)IXIC) indices are prominently favored across many agent configurations and feature extractors. DDPG agents with MLP feature extractors, for instance, frequently allocate a large portion of the portfolio to \({}^{\prime}\)GSPC, indicating its dominance. Similarly, \({}^{\prime}\)IXIC receives significant allocation in some setups, such as PPO with CNN extractors. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ann.** & \multicolumn{2}{c}{**Ann.**} & \multirow{2}{*}{**Calmar**} & \multirow{2}{*}{**Sharpe**} & \multirow{2}{*}{**Sortino**} & \multicolumn{2}{c}{**Max**} & \multirow{2}{*}{**Agent**} & \multirow{2}{*}{**Feat.Ext.**} & \multirow{2}{*}{**Lookback**} & **Reb.** & \multirow{2}{*}{**Indicators**} \\
**Return** & & & & & & & & & & & \\ \hline
0.14 & 0.19 & 0.57 & 0.80 & 1.12 & \(-\)0.25 & \({}^{\prime}\)GSPC & - & - & 1 & - \\
0.09 & 0.14 & 0.35 & 0.69 & 0.94 & \(-\)0.25 & EWP & - & - & 1 & - \\ \hline
0.04 & 0.31 & 0.09 & 0.29 & 0.40 & \(-\)0.50 & PPO & MLP & 16 & 10 & Yes \\
0.03 & 0.30 & 0.05 & 0.23 & 0.32 & \(-\)0.52 & DQN & CNN & 28 & 10 & No \\
0.02 & 0.31 & 0.02 & 0.21 & 0.28 & \(-\)0.69 & PPO & CNN & 16 & 1 & No \\
0.01 & 0.26 & 0.02 & 0.19 & 0.25 & \(-\)0.59 & DQN & MLP & 28 & 10 & Yes \\
0.01 & 0.03 & 0.27 & 0.36 & 0.63 & \(-\)0.04 & DQN & CNN & 28 & 10 & Yes \\
0.00 & 0.02 & 0.11 & 0.18 & 0.25 & \(-\)0.03 & DQN & CNN & 16 & 10 & Yes \\
0.00 & 0.25 & \(-\)0.01 & 0.12 & 0.14 & \(-\)0.62 & PPO & MLP & 28 & 1 & No \\ \(-\)0.01 & 0.39 & \(-\)0.02 & 0.17 & 0.24 & \(-\)0.51 & DQN & CNN & 16 & 1 & Yes \\ \(-\)0.01 & 0.24 & \(-\)0.02 & 0.07 & 0.09 & \(-\)0.51 & PPO & MLP & 16 & 1 & No \\ \(-\)0.04 & 0.28 & \(-\)0.06 & 0.00 & 0.00 & \(-\)0.66 & PPO & CNN & 28 & 10 & Yes \\ \hline \hline \end{tabular} Bottom-10 experimental results by annualized return compared to the benchmark portfolios (in grey).  
\end{table} 
Table 6: Performance metrics for bottom-10 RL agent configurations and baselines. In contrast, the Russell 2000 (\({}^{\star}\)RUT) and FTSE (\({}^{\star}\)FTSE) indices are either underrepresented or completely absent in several configurations, suggesting that the agents might find these indices less favorable for maximizing returns given the prevailing market conditions. Their lower inclusion could indicate that these indices are less predictable or offer lower risk-adjusted returns. CASH, representing the risk-free asset, holds a significant portion in some agent configurations, particularly with DQN agents using CNN feature extractors. This suggests a conservative, risk-averse strategy, where the agent opts to hedge against market volatility by allocating a larger portion of the portfolio to CASH. In certain DQN configurations, such as with CNN feature extraction, the allocation to CASH is notably high, indicating a preference for safety during periods of market instability. In general, DDPG agents (with either MLP or CNN extractors) show more diversified portfolios across various instruments, whereas DQN agents tend to focus on fewer instruments or exhibit extreme concentration in one or two indices.