Research on Trading Strategy Based on Machine Learning
DOI:
https://doi.org/10.54097/hset.v4i.841Keywords:
Financial time series, long short-term memory network (LSTM), XGBoostAbstract
In this paper, we choose the best trading strategy by building a suitable forecasting model. Specifically, we choose XGBoost, ELM, and long short-term memory network (LSTM) as our baseline models in constructing forecasting models. Furthermore, based on further predictions given by our model, we construct a set of ground rules. On this basis, two correction methods, disturbance correction and multi-step correction are proposed for the dilemma. Then we studied the investment period of this strategy and found that the optimal investment period is 1 day. Finally, a sensitivity analysis of the commission rates for bitcoin and gold is performed.
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References
C. Y. Fu, Prediction and anomaly detection of high-frequency financial time series based on LSTM and self-encoder, Sichuan University, 2021.
L. C. Ning, A pplication of deep learning in financial time series classification, Zhejiang University, 2021.
T. Cong, High-frequency financial time series prediction based on improved EMD-LSTM, Jiangxi University of Finance and Economics, 2021.
G. J. Qi, J. Luo, Small data challenges in big data era: A survey of recent progress on unsupervised and semi-supervised methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
L. Z. Yi, Research on price prediction and comparison of stock index futures based on machine learning algorithm, Zhejiang University, 2020.
W. Gang, Time series prediction based on limit learning machine, Shenyang University of technology, 2019.
C. C. Zhi, Research on financial time series prediction based on deep learning, University of Electronic Science and Technology of China, 2021.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
