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.
Hai, et al., Research on asset trading strategy based on forecasting model and decision- making trading model, Academic Journal of Computing & Information Science 5 (2022) 47–54
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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.