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<p>Automation in securities trading offers advantages over human subjective trading, such as immunity to subjective emotional factors, high efficiency, and the ability to monitor multiple stocks simultaneously, making it a cutting-edge development path in the securities trading industry. In this paper, we first apply the concept of time-frequency decomposition, gradually moving from the first-order moments of securities prices to the higher-order moments. We then combine this with the EMD time-frequency decomposition method to analyze the securities price sequence and extract the characteristics of the securities price fluctuations. Finally, we use the differential long- and short-term memory network to construct an automatic optimization trading system. We compare the system’s performance with traditional technical analysis indexes, as well as the annualized returns of PPO and A2C models on various securities, to verify its performance under unilateral rising, oscillating rising, and plummeting quotes. Finally, we conducted a live test on 1000 GEM stocks. The system in this paper outperforms all traditional technical indicators, with an average annualized return of 71.85% at the lowest and 127.27% at the highest among 5 securities, demonstrating excellent performance. In the three quotes of Ningde Times, Aier Dental, and Goldfish that are rising one way, rising and falling over time, and rising again, the annualized returns of this paper’s system are 77.13%, 67.16%, and 12.66%, which are higher than those of the PPO and A2C models.</p>
</abstract>

Automation in securities trading offers advantages over human subjective trading, such as immunity to subjective emotional factors, high efficiency, and the ability to monitor multiple stocks simultaneously, making it a cutting-edge development path in the securities trading industry. In this paper, we first apply the concept of time-frequency decomposition, gradually moving from the first-order moments of securities prices to the higher-order moments. We then combine this with the EMD time-frequency decomposition method to analyze the securities price sequence and extract the characteristics of the securities price fluctuations. Finally, we use the differential long- and short-term memory network to construct an automatic optimization trading system. We compare the system’s performance with traditional technical analysis indexes, as well as the annualized returns of PPO and A2C models on various securities, to verify its performance under unilateral rising, oscillating rising, and plummeting quotes. Finally, we conducted a live test on 1000 GEM stocks. The system in this paper outperforms all traditional technical indicators, with an average annualized return of 71.85% at the lowest and 127.27% at the highest among 5 securities, demonstrating excellent performance. In the three quotes of Ningde Times, Aier Dental, and Goldfish that are rising one way, rising and falling over time, and rising again, the annualized returns of this paper’s system are 77.13%, 67.16%, and 12.66%, which are higher than those of the PPO and A2C models.

A study on automated improvement of securities trading strategies using machine learning optimization algorithms

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Automation in securities trading offers advantages over human subjective trading, such as immunity to subjective emotional factors, high efficiency, and the...

A study on automated improvement of securities trading strategies using machine learning optimization algorithms

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 10 abr 2024

Aceptado: 04 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2175

Palabras clave
Time-frequency information decomposition, LSTM, Differential optimization, Securities trading

© 2024 Yi Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study on automated improvement of securities trading strategies using machine learning optimization algorithms

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 10 abr 2024

Aceptado: 04 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2175

Palabras claveTime-frequency information decomposition, LSTM, Differential optimization, Securities trading

© 2024 Yi Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Time-frequency information decomposition, LSTM, Differential optimization, Securities trading