## 8 Challenges and Future Research Direction  
The current work on DRL-based QT consists of applying several traditional DRL algorithms to various QT circumstances. Exploring the efficacy of more sophisticated DRL approaches on financial data is a research path that is expected to become more  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Portfolio Evaluation Metrics** & **Description** & **Article** \\ \hline \multirow{4}{*}{Accumulated Return} & Accumulated Return refers to the & \\  & percentage increase or decrease in value & \\  & over the life of an investment. The & \\  & Accumulated Return value ought to be & \\  & positive and as high as feasible. & \\ \hline \multirow{4}{*}{Average daily return} & The term “average daily return” refers to & \\  & the mathematical mean of a set of returns & [40, 58, 86] \\  & made over time & \\ \hline \multirow{4}{*}{Maximum Drawdown} & Maximum drawdown is a risk indicator for & \\  & a portfolio chosen based on a certain & \\  & strategy. It calculates the largest single & \\  & decline in a portfolio’s value from its peak & \\  & to its bottom. & \\ \hline \multirow{4}{*}{Skewness} & Skewness is a measure of the distribution’s & \\  & symmetry or asymmetry. Skewness is 0 in & \\  & the perfectly symmetric distribution. If & \\  & Skewness is less than or more than one, the & \\  & data are skewed. & \\ \hline \multirow{4}{*}{Kurtosis} & Kurtosis is a measure of how much a & \\  & variable’s value fluctuates below or above & \\  & the mean. A Kurtosis score greater than 3 & \\  & implies a wide variance around the mean. & \\ \hline \multirow{4}{*}{Standard Deviation} & Investors use standard deviation to & \\  & calculate the volatility of a stock’s & \\  & performance. The greater the number & \\  & represented by the standard deviation, the & \\  & more volatile the stock. & \\ \hline \multirow{4}{*}{Sharpe ratio} & Portfolio risk-return analysis uses a Sharpe & \\  & ratio. The strategy with the highest Sharpe & [9, 40, 58, 86] \\  & ratio has the lowest risk. & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Portfolio evaluation metrics, description, and article.  
prominent in the near future. The lack of available data is the first significant obstacle in the process of developing lucrative DRL-based QT algorithms. Model-based DRL has the potential to solve this problem by acquiring knowledge about a financial market model to accelerate the training process. It is possible to employ the worst-case scenario, such as a financial catastrophe, as a regulator while simultaneously maximizing the cumulative gain. Second, the primary objective of the various QT duties is to strike a healthy balance between increasing profits and reducing losses as much as possible [1].  
Doing more comprehensive tests on live-trading platforms as opposed to backtesting or very limited real-time trading. This is one of the probable future areas that research might go in. Additional investigation on how DRL agents behave in volatile market settings (stock market). There are direct comparisons between the trading strategies used by DRL agents and those used by human traders. As an example, in a controller experiment, a professional day trader may be pitted against an algorithmic DRL agent while trading in the same market for the same amount of time to see which of the two would generate superior results [6].