Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading

Algorithmic trading is a continuous perception and decision making problem, where environment perception requires to learn feature representation from highly nonstationary and noisy financial time series, and decision making requires the algorithm to explore the environment and simultaneously make correct decisions in an online manner without any supervised information. To address these two problems, we propose a time-driven feature-aware...