Making profits in stock market is a challenging task for both professional institutional investors and individual traders  . With the development combination of quantitative trading and reinforcement learning, more trading algorithms have achieved significant gains beyond the benchmark model Buy Hold (B H) . There is a certain gap between these algorithms and the real trading decision making scenarios. On the one hand, they only consider trading signals while ignoring the number of transactions. On the other hand, the information level considered by these algorithms is not rich enough, which limits the performance of these algorithms. Thus, we propose an algorithm called the Multi-frequency Continuous-share Trading algorithm with GARCH (MCTG) to solve the problems above, which consists of parallel network layers and deep reinforcement learning . The former is composed of three parallel network layers , respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks. The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
It is a difficult task for both professional investors and individual traders continuously making profit in stock market . With the development of computer science and deep reinforcement learning, Buy\& Hold (B \&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not enough, which limit the performance of reinforcement learning algorithms. Thus, we propose a parallel-network continuous quantitative trading model with GARCH and PPO to enrich the basical deep reinforcement learning model, where the deep learning parallel network layers deal with 3 different frequencies data (including GARCH information) and proximal policy optimization (PPO) algorithm interacts actions and rewards with stock trading environment . Experiments in 5 stocks from Chinese stock market show our method achieves more extra profit comparing with basical reinforcement learning methods and bench models .
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Making profits in stock market is a challenging
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institutional
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continuously making profit in stock market
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combination of quantitative trading and
of computer science and deep
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more trading algorithms have achieved significant gains beyond the benchmark model Buy
Buy\&
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H) . There is a certain gap between these algorithms and the real trading decision making scenarios. On the one hand, they only consider trading signals while ignoring the number of transactions. On the other hand, the information level considered by these algorithms is not rich
\&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not
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limits
limit
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these
reinforcement learning
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an algorithm called the Multi-frequency Continuous-share Trading algorithm with GARCH (MCTG) to solve the problems above, which consists of parallel network layers and
a parallel-network continuous quantitative trading model with GARCH and PPO to enrich the basical
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. The former is composed of three
model, where the deep learning
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, respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks. The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares
deal with 3 different frequencies data (including GARCH information) and proximal policy optimization (PPO) algorithm interacts actions and rewards with stock trading environment
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different industries of
5 stocks from
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basic DRL
basical reinforcement learning
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model
models