The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy. This is quite effective when the predictions have sufficient signal, markets are liquid, and transaction costs are low. However, those conditions often do not hold in thinly traded financial markets and markets for differentiated assets such as real estate or vehicles. In these markets, the trading strategy must consider the long-term effects of taking positions that are relatively more difficult to change. In this work, we propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model and addresses these challenges. We test our algorithm on 20+ years of equity data from Bursa Malaysia.

翻译：机器学习在算法交易系统中的应用日益普遍。在典型设置中，监督学习用于预测资产的未来价格，这些预测驱动简单的交易与执行策略。当预测信号充分、市场流动性高且交易成本较低时，这种方法相当有效。然而，在交易稀薄的金融市场以及房地产、车辆等差异化资产市场中，这些条件往往不成立。在此类市场中，交易策略必须考虑建仓后难以调整头寸的长期影响。本文提出一种基于所学预测模型信号的强化学习算法，以应对上述挑战。我们使用马来西亚证券交易所20多年的股票数据对该算法进行了测试。