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The Art of Investing: The Secrets of Gold and Bitcoin Prices

Zhuofan Zhong, Shijie Gao, Jiahui Huang, Haoyu Zhou, Siyu Xu, Yang Lin

Abstract


Market traders frequently buy and sell volatile assets with the goal of maximizing total returns. In the complex market, how to predict the general trend of the market more accurately, how to determine the buying point or selling point, to maximize the target income, is the primary consideration of investors more scientifically and reasonably. In this paper, based on the closing price data of bitcoin and gold on Nasdaq from September 11, 2016 to September 10, 2021,[1] we first build a LSTM neural network prediction model for sliding sequence prediction, on this basis, we build a trading strategy selection model based on nonlinear programming, and introduce Monte Carlo algorithm to optimize the solution.


Keywords


LSTM Model;Sharp Ratio;Nonlinear Programming;Monte Carlo Algorithm

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References


Sun, RQ. A study of LSTM neural network-based price trend prediction model for U.S. stock indexes. Capital University of Economics and Business, 2017.

Mao, JH. Study on the factors influencing the accuracy of stock market time series prediction based on LSTM deep neural network. Jinan University, 2017.

Chen, L. Research on financial time series forecasting algorithm based on LSTM model. Harbin Institute of Technology, 2019.

Li, Y, Design and implementation of an intelligent stock prediction system based on deep neural networks. Northwestern University, 2019.

Liu, DX, Xin, M. Research on wavelet neural network prediction model for time series, 1999.




DOI: http://dx.doi.org/10.18282/ff.v11i2.3170

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