A robust AI-based automated trading system

点击翻译按钮获取中文摘要

The use of AI platforms within the financial markets industry of trading is still new. Hence this industry has a lot of room to grow. Day trading strategies can easily be manipulated by a trader's emotions which leads to poor decisions on both beginners and professional traders. Therefore, there is a need for a platform that predicts the market patterns and suggests the entry and exit points based on multiple strategies, while helping traders to stay disciplined when the market is highly volatile. One requirement of such solution is to predict the market based on the users mood in online social networks (e.g., by sentiment analysis of twits). However, such AI-enabled automated trading systems must be very robust with respect to noises, malicious behavior, and attacks. Attacks can be done by individual adversaries as well as social bots, making them harder to track down. In addition to AI models robustness, the implementation of these systems also needs to be reliable with respect to any environmental failure, uncertainties, or stress (e.g., network glitches, power shut down, overload transactions, etc.). Therefore, in this project we propose an integrated automated robustness testing for AI-enabled automated trading systems. The solution covers both software-level robustness testing as well as robustness of the AI algorithms, especially when the inputs are sentiments learnt from online social networks such as Twitter.