Reinforcement learning is explored as a candidate machine learning technique
to enhance existing analytical solutions for optimal trade execution with
elements from the market microstructure. Given a volume-to-trade, fixed time
horizon and discrete trading periods, the aim is to adapt a given volume
trajectory such that it is dynamic with respect to favourable/unfavourable
conditions during realtime execution, thereby improving overall cost of
trading. We consider the standard Almgren-Chriss model with linear price impact
as a candidate base model. This model is popular amongst sell-side institutions
as a basis for arrival price benchmark execution algorithms. By training a
learning agent to modify a volume trajectory based on the market's prevailing
spread and volume dynamics, we are able to improve post-trade implementation
shortfall by up to 10.3% on average compared to the base model, based on a
sample of stocks and trade sizes in the South African equity market.

探索强化学习作为一种候选机器学习技术，结合市场微观结构元素，以增强现有的分析解决方案，实现优化交易执行。利用标准 Almgren-Chriss 模型作为基本模型，在实时执行过程中，通过训练学习代理人根据市场的传播和量动态来修改交易量轨迹，从而提高交易的成本效益， 相比基本模型，我们在南非股票市场的样本中，平均实现剩余成本比提高了 10.3%。