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Portfolio management via reinforcement learning is at the forefront offintech research, which explores how to optimally reallocate a fund intodifferent financial assets over the long term by trial-and-error. Existingmethods are impractical since they usually assume each reallocation can befinished immediately and thus ignoring the price slippage as part of thetrading cost. To address these issues, we propose a hierarchical reinforcedstock trading system for portfolio management (HRPM). Concretely, we decomposethe trading process into a hierarchy of portfolio management over tradeexecution and train the corresponding policies. The high-level policy givesportfolio weights at a lower frequency to maximize the long term profit andinvokes the low-level policy to sell or buy the corresponding shares within ashort time window at a higher frequency to minimize the trading cost. We traintwo levels of policies via pre-training scheme and iterative training schemefor data efficiency. Extensive experimental results in the U.S. market and theChina market demonstrate that HRPM achieves significant improvement againstmany state-of-the-art approaches.

Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution