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Quantitative trading strategy of gold and bitcoin based on RSI index

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DOI: 10.23977/ferm.2022.050207 | Downloads: 12 | Views: 586

Author(s)

Feixue Xing 1

Affiliation(s)

1 International College, Zhengzhou university, Zhengzhou, Henan, 450000, China

Corresponding Author

Feixue Xing

ABSTRACT

In order to quantify the trading strategy, this paper first uses the cyclic neural network and XGboost model to compare the trend of special currency and gold for reasonable prediction, and then applies RSI, BOL, CR index and mean variance theoretical model to the selection of trading strategies in the field of gold and bitcoin respectively, so that when investing in the face of the price changes of target investment assets in the complex market, It can capture the linear and nonlinear changes of gold and bitcoin price data, accurately and effectively predict the trend of asset price, so as to select the optimal trading strategy to maximize the total income. Therefore, the method proposed in this paper has strong research significance and practical value.

KEYWORDS

RSI, rading strategy selection, Cyclic neural network, XGboost

CITE THIS PAPER

Feixue Xing, Quantitative trading strategy of gold and bitcoin based on RSI index. Financial Engineering and Risk Management (2022) Vol. 5: 43-46. DOI: http://dx.doi.org/10.23977/ferm.2022.050207.

REFERENCES

[1] Zhang Yongjie, Zhang Wei, investment strategy and investment return: Research Based on computational experimental finance [a], Department of management and economics, Tianjin University, 2010.
[2] Qiao Wen, Wang Kun, comparative study of mean variance model and mean semi variance model, School of economics and management, Qilu Normal University, 2018.
[3] A Chunxiang, research on portfolio investment model and its algorithm, Xi'an University of Electronic Science and technology, 2005.
[4] Zola, research on portfolio strategy based on mean variance model, [a] University of Sydney business school, Sydney, 2006.
[5] Mcgen, research on Optimization of foreign exchange quantitative trading strategy based on technical analysis index combination, Guangxi University, 2019.

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