Optimal Strategy: A Comprehensive Model for Predicting Price Trend and Algorithm Optimization

Chenfeng Xie, Xiaoya Wu, Xue Bai
{"title":"Optimal Strategy: A Comprehensive Model for Predicting Price Trend and Algorithm Optimization","authors":"Chenfeng Xie, Xiaoya Wu, Xue Bai","doi":"10.1145/3558819.3565125","DOIUrl":null,"url":null,"abstract":"In this paper, we study the price changes of bitcoin and gold over time by building an LSTM-based price prediction model and a genetic algorithm-based yield optimization model. Specifically, we build a two-layer LSTM-based time series forecasting model. In order to study how to make decisions on capital allocation and profit maximization, a single-objective optimization model based on a genetic algorithm is established. Furthermore, to prove that our decision model is optimal, we use the error evaluation metrics MSE, MAE, and R2 to perform statistical analysis on the predicted asset value and the actual asset value, where R2 reaches 0.8857. Finally, we perform a sensitivity analysis on transaction commissions.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3558819.3565125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we study the price changes of bitcoin and gold over time by building an LSTM-based price prediction model and a genetic algorithm-based yield optimization model. Specifically, we build a two-layer LSTM-based time series forecasting model. In order to study how to make decisions on capital allocation and profit maximization, a single-objective optimization model based on a genetic algorithm is established. Furthermore, to prove that our decision model is optimal, we use the error evaluation metrics MSE, MAE, and R2 to perform statistical analysis on the predicted asset value and the actual asset value, where R2 reaches 0.8857. Finally, we perform a sensitivity analysis on transaction commissions.
最优策略:价格趋势综合预测模型及算法优化
本文通过建立基于lstm的价格预测模型和基于遗传算法的收益优化模型,研究了比特币和黄金的价格随时间的变化。具体来说,我们建立了一个基于两层lstm的时间序列预测模型。为了研究如何进行资本配置和利润最大化决策,建立了基于遗传算法的单目标优化模型。此外,为了证明我们的决策模型是最优的,我们使用误差评估指标MSE、MAE和R2对预测的资产价值和实际的资产价值进行统计分析,其中R2达到0.8857。最后,我们对交易佣金进行了敏感性分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信