Research on the Three-Stage Trading Strategy of Gold and Bitcoin Using Technical Indicators and A LSTM Model

Abstract

In recent years, in pursuit of higher returns, people began to try the combination of bitcoin and gold. This study explored how to use the current and past prices of gold and bitcoin to get the best investment strategies for the next day to get the maximum return. The obtained historical price data of gold and bitcoin from 2016 to 2021 are divided into three stages according to the time axis, namely, stage Ⅰ (1st day-300th day), stage Ⅱ (300th day-400th day) and stage III (400th day-1825th day). Different methods are used for each stage to determine the trading strategy. In stage I, a comprehensive index is constructed by using four technical indexes closely related to the formulation of daily strategies in the financial market, such as moving average convergence and divergence, deviation rate, stochastic index and detrended price oscillator, and the trading strategy is determined by using this comprehensive index. In stage II, the four technical indicators and product prices in stage I are used as training sets, and the daily trading strategy is given by using the decision tree classification model. In the third stage, the long-term and short-term memory neural network is used to develop the future price prediction model of gold and bitcoin, and the trading strategies of gold, bitcoin and cash asset portfolios are formulated according to the dynamic optimization results of different types of traders' risk tolerance. The results show that during the five years from September 11, 2016 to September 10, 2021, the whole strategy created a net profit of $170,381 for aggressive traders and $7,694 for rational traders. After verification, our strategy is effective and robust, and its volatility is relatively small. The research provides theoretical guidance for investors on how to choose portfolios and trade to get the maximum return.