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Algorithmic Trading: Consistent Profit Strategies For Taiwanese Traders

Algorithmic Trading: Consistent Profit Strategies For Taiwanese Traders

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By Chun-Hao Chen Chun-Hao Chen Scilit Google Scholar 1 , Wei-Hsun Lai Wei-Hsun Lai Scilit Google Scholar 2 , Shih-Ting Hung Shih-Ting Hung Scilit Google Scholar 1 and Tzung -Pei Hong Tzung-Pei Hong Scilit Google Scholar 2, 3, *

Received: 27 November 2021 / Revised: 13 January 2022 / Accepted: 17 January 2022 / Published: 20 January 2022

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In the financial market, commodity prices change over time, yielding profit opportunities. Various trading strategies have been suggested to achieve good earnings. Pairs trading is such a 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 in reverse. In the existing approach, the genetic Bollinger Bands and correlation coefficient-based pair trading strategy (GBCPT) optimization technology is used 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 consider volatility and more critical parameters that affect profitability. It encodes six critical parameters into a chromosome. To evaluate the fitness of a chromosome, the coded parameters are used to observe the trading pairs and their trading signals generated by Bollinger Bands. The fitness value is then calculated by averaging the 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.

In financial markets, investment assets include bonds, funds, stocks and other derivative financial products, for example futures and options. Investors know the basic principle of profitability: buy an asset at a low price and sell it at a higher price. The difficult part is that suitable trading signals are difficult to find because of the different assets and trends in real financial markets. Due to this phenomenon, it is difficult to make profit. Thus, many approaches have been proposed to find trading strategies that make the profit more stable [1, 2, 3, 4, 5, 6, 7].

Such trading strategies involve a wide variety of different approaches [8, 9, 10, 11, 12], including regression, fuzzy theory, genetic algorithms (GA), artificial neural networks (ANN), memetic algorithms (MA), and support vector. machine (SVM), etc. According to the application type, trading strategies in the literature can be divided into two categories: (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].

Algorithmic Trading: Consistent Profit Strategies For Taiwanese Traders

Of these, pair 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 presents an arbitrage opportunity [25]. In other words, making a profitable pairs trading strategy takes into account how to find a pair of highly correlated stocks and how to generate useful trading signals for buying and selling. Pair trading can also be applied more widely, eg to cryptocurrencies and prosumer markets [26, 27].

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The genetic Bollinger Bands and correlation coefficient based pair trading algorithm (GBCPT) was proposed by Huang [28]. It includes 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. Additionally, Bollinger Bands are used to determine the rising/falling degrees of the pair. If both conditions are met, the transaction is required for expected rise and shorted for falling stocks. The pair transaction is closed when the closing conditions of the Bollinger Bands are met. However, there are other parameters in pair trading that affect the profitability of the strategy; these should be considered when designing the fitness function.

To solve the above problems, we propose the advanced genetic Bollinger Bands and correlation coefficient based pair trading algorithm (AGBCPT) to achieve more robust and reliable trading performance. The algorithm encodes six critical parameters into a chromosome: the correlation coefficient threshold, the entry width of the Bollinger Bands, the width of the Bollinger Bands, the correlation coefficient calculation days, the moving average calculation days, and the forward observation days. When evaluating fitness with such a chromosome, the encoded parameters are used to observe the trading pairs and their trading signals generated from the Bollinger Bands, after which the fitness value is calculated by the average return and volatility of long and short trading pairs. The genetic process is repeated to find suitable parameters until the termination conditions are met. Experiments conducted on 44 stocks selected from the Taiwan 50 index show the merits and effectiveness of the proposed approach.

This paper is organized as follows. Related work is described in Section 2 and the details of the proposed AGBCPT method are given in Section 3. The experimental results are discussed in Section 4, and Section 5 concludes and outlines future work.

Pairs trading is a neutral trading strategy that investors use to profit from changing market situations [1, 20, 21, 23, 29]. Based on the historical performance of highly correlated commodities, a pair trading strategy focuses on how to observe the trading pair as a target and achieve profit from it [21]. When the correlation weakens, for example, one stock rises and the other falls. Such a temporary discrete situation can be caused by changes in supply and demand, a sudden large number of transactions by a securities company or big news. These factors cause stock fluctuations. A pairs trading strategy then shorts the rising stock and extends the falling one at the same time, as investors expect the price difference between the two to converge in the future [23, 29]. Krauss classified pairs trading strategies into distance methods, cointegration methods, time series methods, stochastic control methods and other methods [23]. In recent years, abundant related research has been produced [25, 30, 31, 32, 33, 34, 35, 36]. Below we present approaches related to pair trading.

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2006, Gatev et al. published a well-known pairs trading paper. Their proposed GGR (Gatv, Goetzmann and Rouwenhorst) pairs trading method [25] uses six month trading periods from 1962 to 1997 on a large sample of US stocks. After testing the profitability of various trading rules, they observed that their strategy yielded annual excess returns of up to eleven percent with low exposure to systematic sources of risk. Doe et al. indicated paired transactions, which still give stable profits, give market and trading costs [31, 32]. Their study expands the GGR method, compares the test data over different years and different industries and confirms that the profitability of decline in pairs trading is mainly due to an increasing proportion of non-converging pairs. An experimental result also shows that more industry-matched portfolios give more substantial profits than portfolios selected from the whole market. They therefore reduce the convergence error of the selected stock portfolio.

For different situations and purposes, pairs trading also works with other methods that improve the performance of the pairs trading strategy [37]. For example, Rende et al. Experiments with the persistence-based decomposition (PBD) model in a large high-frequency pairs trading application [38]. Their study provides empirical evidence to show that the model is well suited for noisy high-frequency data in terms of model fit and prediction. Stäbinger et al.

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