###### Abstract  
In the financial market, commodity prices change over time, yielding profit opportunities. Various trading strategies have been proposed to yield good earnings. Pairs trading is one such critical, widely-used strategy with good effect. Given two highly correlated paired target stocks, the strategy suggests buying one when its price falls behind, selling it when its stock price converges, and operating the other stock inversely. In the existing approach, the genetic Bollinger Bands and correlation-coefficient-based pairs trading strategy (GBCPT) utilizes optimization technology to determine the parameters for correlation-based candidate pairs and discover Bollinger Bands-based trading signals. The correlation coefficients are used to calculate the relationship between two stocks through their historical stock prices, and the Bollinger Bands are indicators composed of the moving averages and standard deviations of the stocks. In this paper, to achieve more robust and reliable trading performance, AGBCPT, an advanced GBCPT algorithm, is proposed to take into account volatility and more critical parameters that influence profitability. It encodes six critical parameters into a chromosome. To evaluate the fitness of a chromosome, the encoded parameters are utilized to observe the trading pairs and their trading signals generated from Bollinger Bands. The fitness value is then calculated by the average return and volatility of the long and short trading pairs. The genetic process is repeated to find suitable parameters until the termination condition is met. Experiments on 44 stocks selected from the Taiwan 50 Index are conducted, showing the merits and effectiveness of the proposed approach. Bollinger Bands; correlation coefficient; genetic algorithm; pairs trading strategy; trading strategy optimization Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] An Advanced Optimization Approach for Long-Short Pairs Trading Strategy Based on Correlation Coefficients and Bollinger Bands  
102231052231052310  
(1) prediction of financial time series [2; 4; 6; 13; 14; 15; 16]; and (2) stock selection, portfolio management, and optimization [7; 17; 18; 19]. Of these, pairs trading is a critical, widely used trading strategy [20; 21; 22; 23; 24] based on a central concept: for two highly-correlated assets, buy when one stock price falls behind and sell when the stock prices converge; this constitutes an arbitrage opportunity [25]. In other words, a profitable pairs trading strategy make take into account how to find a pair of highly correlated stocks and how to generate useful trading signals for buying and selling. Pairs trading can also be applied more widely, e.g., to cryptocurrency and prosumer markets [26; 27]. The genetic Bollinger Bands and correlation-coefficient based pairs trading algorithm (GBCPT) was proposed by [PERSON] [28]. It involves an optimization approach to determine parameters for correlation-based candidate pair generation and the Bollinger Bands-based trading signal discovery process. Stock pairs whose correlation coefficients meet the predefined threshold are expected to show more discrete trends in the future. In addition, Bollinger Bands are used to determine the rise/fall degrees of the pair. When both conditions are met, the transaction is longed for expected rises and shorted for declining stocks. The pair transaction is closed when the ending conditions of the Bollinger Bands are met. However, there are other parameters in pairs trading that affect the profitability of the strategy; these should be taken into consideration when designing the fitness function. To solve the above-mentioned problems, we propose the advanced genetic Bollinger Bands and correlation-coefficient based pairs trading algorithm (AGBCPT) to achieve more robust and reliable trading performance.