[1] Deep reinforcement learning with positional context for intraday trading针对盘中交易的位置上下文的深度强化学习来源:ARXIV_20240613[2] HARd to BeatHARd要击败来源:ARXIV_20240613
[1] Deep reinforcement learning with positional context for intraday trading
标题:针对盘中交易的位置上下文的深度强化学习作者:Sven Goluža, Tomislav Kovačević, Tessa Bauman, Zvonko Kostanjčar来源:ARXIV_20240613Abstract : Deep reinforcement learning (DRL) is a well suited approach to financial decision making, where an agent makes decisions based on its trading strategy developed from market observations. Existing DRL intraday trading strategies mainly use price based features to construct the state space. They neglect the contextual information related to the position of the strategy, which is an important aspect given the......(摘要翻译及全文见知识星球)Keywords :
[2] HARd to Beat
标题:HARd要击败作者:Francesco Audrino, Jonathan Chassot来源:ARXIV_20240613Abstract : We investigate the predictive abilities of the heterogeneous autoregressive (HAR) model compared to machine learning (ML) techniques across an unprecedented dataset of 1,455 stocks. Our analysis focuses on the role of fitting schemes, particularly the training window and re estimation frequency, in determining the HAR model s performance. Despite extensive hyperparameter tuning, ML models fail to surpass the linear benchmark set......(摘要翻译及全文见知识星球)Keywords :
