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Deep Reinforcement Learning for Online Optimal Execution Strategies
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Deep Reinforcement Learning for Online Optimal Execution Strategies
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This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) to address this issue, with a focus on transient price impact modeled by a general decay kernel. Through numerical experiments with various decay kernels, we show that our algorithm successfully approximates the optimal execution strategy. Additionally, the proposed algorithm demonstrates adaptability to evolving market conditions, where parameters fluctuate over time. Our findings also show that modern reinforcement learning algorithms can provide a solution that reduces the need for frequent and inefficient human intervention in optimal execution tasks.
Forward citations
Cited by 3 Pith papers
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Can Reinforcement Learning Efficiently Discover Price Manipulation?
Under intermediate volatility and limited samples, model-free DDPG finds dynamic-arbitrage strategies more reliably than SLSQP run on noisily estimated Almgren-Chriss impact parameters, even though the latter knows th...
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Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution
In a two-agent Almgren-Chriss liquidation game, deep RL agents given intra-episode history of prices and own actions achieve supra-competitive outcomes more frequently and persistently than agents without such memory.
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TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution
TT-DAC-PS, an enhanced version of TD3, achieves lower mean implementation shortfall than PPO, SAC, A2C, TWAP, VWAP, and AC on LOB data from ten U.S. stocks.
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