## 5 Experiment and Analysis  
Our experiment used the S&P 500 data from 2012 to 2014 as our training dataset. This selection was instrumental in training our models, providing a comprehensive representation of market activities over this period. To circumvent memory overflow issues commonly encountered in prolonged training sessions, we adopted a strategy of fine-tuning model parameters after every set of 200 episodes. The training process was executed in a phased manner, with each model undergoing training ten times, each consisting of 200 episodes, amounting to a total of 2000 training rounds. The learning rate was subsequently reduced to 90% of its value from the previous phase. This progressive reduction in the learning rate facilitated nuanced model tuning and optimisation. The optimisation was conducted using the Adam optimiser.  
In addition to the primary training process, we conducted comprehensive comparative experiments to determine the most effective hyperparameters for our model. These experiments focused on two key hyperparameters: the temperature coefficient and window length. The window length, a crucial hyperparameter in time-series analysis, was also rigorously examined. To ascertain the most effective window length for capturing market dynamics, we considered a range of durations, specifically 3, 5, 7, 10, 15, 20, 30, and 50 days. For the temperature coefficient, to control the portfolio diversity, we explored various settings, including 500, 1000, 3000, 5000, and 10000, to observe the impact on model performance. Each model, configured with a distinct set of these hyperparameters, was initially trained on our training dataset (S&P 500 data from 2012 to 2014). Following the training phase, we saved the model parameters and then validated their performance on a separate validation dataset from 2015. This approach allowed us to thoroughly assess the influence of each hyperparameter on the model's efficacy, enabling us to fine-tune our model for optimal performance.  
Upon identifying the most effective hyperparameters through our rigorous evaluation process, we applied these optimised settings to a backtest dataset from 2016. This phase of our research involved conducting comparative ablation studies using four distinct dataset compositions, each offering unique insights into the model's performance. The primary dataset encompassed a comprehensive blend of macroeconomic indicators, sentiment scores from news articles, and historical stock trading data, providing a holistic view of market influences. The second dataset was curated by excluding macroeconomic indicators while maintaining sentiment scores and historical trading data, allowing us to assess the impact of macroeconomic factors on model performance. In contrast, the third dataset excluded news sentiment scores but included macroeconomic indicators alongside historical data, enabling us to isolate and evaluate the influence of sentiment analysis. The fourth and final dataset was streamlined to include only historical stock trading data, offering a baseline to gauge the added value of macroeconomic and sentiment data.  
Moreover, to contextualise our model's performance within the broader landscape of financial analytics, we conducted comparative analyses against other leading deep learning models and established traditional financial models. This comprehensive approach allowed us to validate our model's efficacy and understand its standing relative to the current state-of-the-art methodologies in financial portfolio management.