Discussing the article: "Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (Final Part)"
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Check out the new article: Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (Final Part).
In the last two articles, we examined the FinAgent framework in detail. During this process, we implemented our own interpretation of the approaches proposed by its authors. We adapted the framework algorithms to meet our specific requirements. We have now reached another important stage: evaluating the effectiveness of the implemented solutions on real historical data.
Please note that during development, we introduced significant modifications to the core algorithms of the FinAgent framework. These changes affect key aspects of the model operation. Therefore, in this evaluation, we are assessing our adapted version, not the original framework.
The model was trained on historical data for the EURUSD currency pair for 2023 using the H1 timeframe. All indicator settings used by the model were left at their default values, allowing us to focus on evaluating the algorithm itself and its ability to work with raw data without additional tuning.
For the initial training stage, we used a dataset prepared in previous studies. We applied a training algorithm that generates "almost ideal" target actions for the Agent, allowing us to train the model without continuously updating the training dataset. However, while this approach worked effectively, we believe that regular updates to the training set would improve accuracy and broaden the coverage of different account states.
Author: Dmitriy Gizlyk