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- W3188770192 abstract "Quantitative trading can analyze financial data with its mathematical models and artificial intelligence models, and conduct transactions through computer programs to maximize trading returns. Therefore, it has attracted the attention of many investors. In this article, we explore the development potential of deep reinforcement learning in quantitative trading and propose an agent based on stock data indicators, market turbulence, and a combination of PPO, A2C, and DDPG trading strategies. Based on the time series in stock data, we use the Comprehensive strategy gradient training method, combined with current market conditions and historical data for automatic trading. This article conducted a single-share trading test and a multi-share mixed investment trading test. In single-share transactions, the highest stock yield from 2017 to 2020 reached 618.02%. In the multi-share mixed transaction, the 2017-2020 portfolio income reached 347.47%, and the 4-year average annual return was 86.87%." @default.
- W3188770192 created "2021-08-16" @default.
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- W3188770192 date "2021-06-18" @default.
- W3188770192 modified "2023-09-25" @default.
- W3188770192 title "Research on Stock Market Analysis Based on Deep Learning" @default.
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- W3188770192 doi "https://doi.org/10.1109/imcec51613.2021.9482065" @default.
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