HawkEye: A visual framework for agile cross-validation of deep learning approaches in financial forecasting

Financial forecasting represents a challenging task, mainly due to the irregularity of the market, high fluctuations and noise of the involved data, as well as collateral phenomena including investor mood and mass psychology. In recent years, many researchers focused their work on predicting the performance of the market by exploiting novel Machine Learning and Deep Learning tools and techniques. However, many of the approaches proposed in the literature do not take adequately into account some important specific domain issues in the analysis of the results. Among these, it is worth to mention the bias introduced by the choice of model weights initialization and the considered observation periods, as well as the narrow separation between significant results and noise, typical of the financial domain. A thorough analysis of these peculiar issues lead to a substantial increase of the experiments and results to analyze, making the discovery of meaningful hidden patterns very difficult and time consuming to perform. To cope with these concerns and accompanying the current Machine Learning Interpretability trend, in this paper we propose a visual framework for in-depth analysis of results obtained from Deep Learning approaches, tackling classification tasks within the financial domain and aiming at a better interpretation and explanation of the trained Deep Learning models. Our framework offers a modular view, both general and targeted, of results data, providing several financial specific metrics, including Sharpe and Sortino ratios, Equity curves and Maximum Drawdown, as well as custom period analysis and reports, experiment comparison tools, and evaluation features for different algorithms.