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  4. Vol 7, Issue 7, 2023

Analysis of the best trading strategies based on neural networks

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DOI: 10.23977/acss.2023.070701 | Downloads: 14 | Views: 419

Author(s)

Shurong Dong 1

Affiliation(s)

1 College of Textile and Clothing Engineering, Soochow University, Suzhou, Jiangsu, 215006, China

Corresponding Author

Shurong Dong

ABSTRACT

This article build a prediction model based on the historical trading volume of gold and Bitcoin, then establish an investment return maximization model to maximize the return amount, and finally propose a feasible trading strategy to traders based on the model results. First, this article will process the data visualization after missing values to analyze the trend of bitcoin and gold trading volume, yield, risk and volatility respectively. Second, This article combine the ARIMA model and the LSTM model to establish an ensemble learning model, and use the critic method to combine the two series of predictions with appropriate weights to predict the asset price of gold and bitcoin for each trading day during the period from 2016 to 2021, again, According to the ensemble learning prediction results, the optimal average cost method (DCA) (DCA) is used to solve the prediction curve and verify the optimality, and it is concluded that there is a maximum benefit when k=19% and p=54%. Finally, based on the above predictive analysis results, this article explain to traders the model building process and the results it presents, and propose feasible investment decisions about gold and bitcoin from the aspects of trading behavior and trading psychology.

KEYWORDS

Integrate learning, trading strategies, maximize returns

CITE THIS PAPER

Shurong Dong, Analysis of the best trading strategies based on neural networks. Advances in Computer, Signals and Systems (2023) Vol. 7: 1-7. DOI: http://dx.doi.org/10.23977/acss.2023.070701.

REFERENCES

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[3] JIANG Haitao, HE Jinkai. Risk analysis of Bitcoin[J]. Wuhan Finance, 2014(7):45-46.
[4] LIU Lizhen,WANG Qinglong. Analysis of Bitcoin Herd Effect Based on Imitation Contagion Model [J]. Journal of Beijing University of Posts and Telecommunications (Social Science Edition), 2015(2):27-33.
[5] LI Ying, GAO Yan, FAN L-ya, et al. Short-term gold price prediction based on support vector regression machine[J]. Practice and understanding of mathematics, 2011, 41(24):1-6.
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