QTMRL applies A2C reinforcement learning to a dataset of 23 years of S&P 500 OHLCV data enriched with trend, volatility, and momentum indicators, claiming better profitability and risk control than nine baselines including ARIMA and LSTM.
Geibel, Reinforcement learning for mdps with constraints, in: Proceedings of the European Conference on Machine Learning, 2006, pp
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QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
QTMRL applies A2C reinforcement learning to a dataset of 23 years of S&P 500 OHLCV data enriched with trend, volatility, and momentum indicators, claiming better profitability and risk control than nine baselines including ARIMA and LSTM.