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Quantitative Trading Optimization Model Based on Moving Average and Risk Prediction

Lanjing Qi, Ailing Dai, Hongxuan Shi

Abstract


Quantitative investment can bring very large returns to investors and is increasingly popular among investors. Based on this, a quantitative trading model of averaging strategy is constructed for both gold and bitcoin products, and then the market risk model and trading frequency model based on multi-prediction model is constructed based on the optimization of the lagging loss that exists in the averaging strategy when the market is in a period of oscillation. The market risk and trading frequency are taken into account in the averaging model, and the trading ratio is dynamically changed to adapt to different market patterns on the basis of constant trading dates to achieve the optimization of the averaging strategy. The model integrates the impact of historical prices on the trading strategy. Here, the daily trading prices of gold and bitcoin from September 2016 to October 2021 are used as experimental data, and the experimental results show the effectiveness of the model.

Keywords


The Averaging Model; Coefficient of Variation; Trading Strategy Optimization

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References


Wang, JJ., Zhuang, ZZ., Feng, L., Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy[J]. Mathematics, 2022, 10(4).

Qin, QX., Zhou, GJ., Lin, WZ., Futures Quantitative Trading Strategies Based on Market Capital Flows[J]. Applied Economics and Finance, 2018, 5(2).

Li, ZY., Zhang, YB., Zhong, JY., Yan, XX., Lv, XG., Research on Quantitative Trading Strategy Based on Neural Network Algorithm and Fisher Linear Discriminant[J]. International Journal of Economics and Finance, 2017, 9(2).

Zhang, H., Research on optimization of quantitative forex trading strategy based on genetic algorithm[J]. Journal of Tianjin Business Vocational College,2020, 8 (06): 14-20.




DOI: http://dx.doi.org/10.18686/fm.v7i2.4271

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