ALPE, an online reinforcement-learning regressor, is reported to beat batch ML models for mid-price forecasting, but the evaluation likely leaks the target into the inputs.
Domain-adapted Learning and Interpretability: DRL for Gas Trading
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abstract
Deep Reinforcement Learning (Deep RL) has been explored for a number of applications in finance and stock trading. In this paper, we present a practical implementation of Deep RL for trading natural gas futures contracts. The Sharpe Ratio obtained exceeds benchmarks given by trend following and mean reversion strategies as well as results reported in literature. Moreover, we propose a simple but effective ensemble learning scheme for trading, which significantly improves performance through enhanced model stability and robustness as well as lower turnover and hence lower transaction cost. We discuss the resulting Deep RL strategy in terms of model explainability, trading frequency and risk measures.
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Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading
ALPE, an online reinforcement-learning regressor, is reported to beat batch ML models for mid-price forecasting, but the evaluation likely leaks the target into the inputs.