In the field of stock investment, decision-making depends on sophisticated information processing and data analysis. However, limitations of expertise, time, and resources make retail investors suffer from information overload and information imbalance. Recent improvement in computing power and the availability of high-quality data empower us to leverage Deep Learning techniques to bridge the gap. This project presents an integrated system that comprehensively monitors the risks of individual stocks and the overall market. The stock risk is estimated based on quantitative data of related stocks, where the relationship between business is measured by constructing an Enterprise Knowledge Graph using public knowledge. On the other hand, the market risk is estimated based on daily news. For each risk, a Temporal Convolutional Network is trained to output a continuous risk level that reveals both direction and amplitude of incoming changes. Eventually, key information and the predicted risk levels are organized into a condensed dashboard to interact with retail investors. Experiments on focal stocks in U.S. market suggest 67% and 56% accuracy in stock risk and market risk modeling respectively. Besides, visualizations on testing data show that our model has the potential to inform reverse changes of a stock movement ten days in advance.