## 5 Experiment and Analysis
### Comparison with Other Deep Learning and Traditional Financial Methods  
In demonstrating the superior performance of the NSTD-10 model, we conducted extensive backtesting analysis over 2016 and compared it with deep learning architectures,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AR** & **MDD** & **Sharpe Ratio** & **Sortino Ratio** \\ \hline NSTD 10 & **3.353** & 0.277 & **1.859** & **282.140** \\ NSTD without News & 3.000 & 0.182 & 1.273 & 78.099 \\ NSTD without Macro & 2.311 & **0.158** & 1.668 & 0.000 \\ NSTD Pure & 1.801 & 0.237 & 0.443 & 3.101 \\ Transformer & 1.460 & 0.231 & 1.119 & 7.823 \\ RNN & 1.953 & 0.282 & 1.303 & 29.622 \\ LSTM & 1.921 & 0.248 & 1.824 & 0.000 \\ GRU & 1.622 & 0.251 & 1.772 & 158.717 \\ EI\({}^{3}\) & 2.196 & 0.291 & 1.446 & 27.188 \\ EIIE & 2.174 & 0.298 & 1.773 & 0.000 \\ OLMAR & 2.349 & 0.221 & 1.517 & 98.337 \\ UBAH & 2.349 & 0.221 & 1.517 & 98.337 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different metrics’ performance of models.  
Figure 4: Backtesting compared with ablation study in 2016.  
such as the transformer, RNN, LSTM, and GRU, as well as state-of-the-art EIIE and EI3, together with traditional financial algorithms like online moving average reversion (OLMAR) and the universal portfolio algorithm (UBAH). The evaluation hinged on critical financial metrics: annual return (AR), maximum drawdown (MDD), Sharpe ratio, and Sortino ratio. The analysis depicted in Figure 5, when considered alongside the quantitative metrics from Table 2, offers a nuanced assessment of various model performances. The NSTD-10 model emerges as the frontrunner, boasting an annual return (AR) of 3.353, indicative of its effective market trend utilisation for optimal capital increase. Its Sharpe ratio of 1.859 and a notably high Sortino ratio of 282.140 reflect its adeptness at securing returns per unit of risk, with a pronounced efficiency in mitigating downside risk. Delving into the NSTD model variations, the omission of news sentiment analysis (NSTD without News) results in a slight decrease in AR to 3.000 and a marked reduction in the Sortino ratio to 78.099, highlighting the significant contribution of sentiment data to forecasting precision and risk-adjusted returns. Excluding macroeconomic indicators (NSTD without Macro), we see a reduced AR of 2.311. However, it attains the lowest maximum drawdown (MDD) at 0.158, signifying macroeconomic insights' protective role against market downturns. The NSTD Pure variant, which solely incorporates historical trading data, reiterates the value of a diversified data approach, as evidenced by its lower metrics across the spectrum. Compared with established deep learning models such as the transformer, RNN, LSTM, and GRU, these models exhibit commendable results but do not match the NSTD-10's superior standards. Specifically, the LSTM model's high Sharpe ratio of 1.824 underscores its strength in handling time-series data. Nevertheless, this does not correlate with a high AR or Sortino ratio, pointing to potential limitations in market dynamics adaptation. The ensemble approaches EI3, and EIIE offers more consistent returns. However, their Sharpe and Sortino ratios fall short of those achieved by NSTD-10, suggesting that while reliable, they may not optimise the balance between risk and return. Traditional financial methods such as OLMAR and UBAH maintain stable risk management but lack NSTD-10's efficacy in capitalising on market conditions. Overall, the NSTD-10 model's unmatched performance in terms of overall returns and risk management sets a new standard in portfolio management. It underscores the strategic benefit of integrating comprehensive market data, from economic indicators to sentiment analysis, within a sophisticated algorithmic framework.