Deep reinforcement learning for the optimal placement of cryptocurrency limit orders

DOI10.1016/j.ejor.2021.04.050zbMath1490.91251OpenAlexW3159769297MaRDI QIDQ2242354

Matthias Schnaubelt

Publication date: 9 November 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10419/216206

zbMATH Keywords

financemachine learningoptimal executiondeep reinforcement learninglimit order books

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Financial applications of other theories (91G80)

Related Items (1)

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Uses Software

• Adam
• GitHub
• AlphaZero
• Stable Baselines

Cites Work

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• Bitcoin price forecasting with neuro-fuzzy techniques
• Deep learning with long short-term memory networks for financial market predictions
• Simple statistical gradient-following algorithms for connectionist reinforcement learning
• More statistical properties of order books and price impact
• Algorithmic trading for online portfolio selection under limited market liquidity
• Separating the signal from the noise -- financial machine learning for Twitter
• Limit order books
• LIQUIDATION IN LIMIT ORDER BOOKS WITH CONTROLLED INTENSITY
• Aggressive Orders and the Resiliency of a Limit Order Market*
• Optimal order placement in limit order markets
• Optimal execution with limit and market orders
• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
• Deep learning for limit order books

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