Research Article
LSTMGA-QPSBG: An LSTM and Greedy Algorithm-based Quantitative Portfolio Strategy for Bitcoin and Gold
@INPROCEEDINGS{10.4108/eai.19-5-2023.2334307, author={Leyi Zhang}, title={LSTMGA-QPSBG: An LSTM and Greedy Algorithm-based Quantitative Portfolio Strategy for Bitcoin and Gold }, proceedings={Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19--21, 2023, Hangzhou, China}, publisher={EAI}, proceedings_a={ICBBEM}, year={2023}, month={7}, keywords={quantitative trading lstm greedy algorithm bitcoin gold}, doi={10.4108/eai.19-5-2023.2334307} }- Leyi Zhang
Year: 2023
LSTMGA-QPSBG: An LSTM and Greedy Algorithm-based Quantitative Portfolio Strategy for Bitcoin and Gold
ICBBEM
EAI
DOI: 10.4108/eai.19-5-2023.2334307
Abstract
Quantitative trading plays a pivotal role in financial markets. Over the past decade, quantitative trading has made remarkable improvements. Due to instability and nonlinearity in financial markets, it is still challenging to formulate high-return trading strategies to address the problem of long-term time series forecasting in financial markets. To tackle this issue, we propose an LSTM and Greedy Algorithm-based quantitative portfolio strategy in this work. First, an LSTM-based price prediction model is presented to forecast the closing price on the final trading day. Subsequently, a greedy algorithm is employed to identify the optimal daily trading strategy to pursue the overall optimal solution and achieve maximal profits. The experimental results show that the maximum value of the VaR percentage is about 9.1%, proving that the proposed strategy is feasible and effective.