Reinforcement learning for stock prediction and high-frequency trading with t+1 rules

HIGHLIGHTS

• who: WEIPENG ZHANG et al. from the Shanghai Jiao Tong University, Shanghai, China have published the paper: Reinforcement Learning for Stock Prediction and High-Frequency Trading with T+1 Rules, in the Journal: (JOURNAL)
• what: To address this problem the authors propose a novel dynamic parameter optimization algorithm based on reinforcement learning for stock prediction and trading and to generate an adaptive trading framework. Owing to the successful integration of the machine_learning model, the new generation HFT framework consists of two modules at the production level, 1) a machine_learning model, aims to determine the order . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.