Academic Journal of Computing & Information Science, 2022, 5(7); doi: 10.25236/AJCIS.2022.050713.
Ronghuan Li, Weijie Chen, Wenhao Xu, Chen Li
School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, China
In this paper, we aimed to build a quantitative investment trading model based on a combination of a multivariate cycle ARMA model and Apriori. We first note that in order to have a sound investment strategy, a forecast for the next trading day needs to be made. To do this, a basic time series forecasting model was first built to predict the value of gold and bitcoin for the next day based on the market volatility of the previous 40 days. The next step is developing a trading strategy model with a stable rate of return and some risk tolerance. At the same time, we developed a fixed stop-loss strategy to protect the strategy's stability and improve the risk resistance performance. Ultimately, using this model, we calculated that on 10 September 2021, we will have a return of $4816941 in Bitcoin and $1129.0503 in gold.
Quantitative Trading; Trading Strategies; Apriori; ARMA model; time series forecasting model
Ronghuan Li, Weijie Chen, Wenhao Xu, Chen Li. Prediction on the Value Trends of Bitcoin and Gold-on Account of ARMA Time Series Forecasting Model. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 7: 79-84. https://doi.org/10.25236/AJCIS.2022.050713.
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