# Proposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System

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3 In this paper, we verified the performance of the proposed system by two kinds of experiments. Experiment_1, shows the back test result using an optimum trading strategy searched by the GA(GA strategy). Table 2 shows all experimental conditions, term of data used in the GA and back test. Table 2, Conditions of the first experiment Figure 3, structure of a chromosome Chromosomes of the first generation are made from random gene array combinations. In the uniform crossover process, genes are crossed with set of buying or selling conditions and their criterion values. And in the mutation process, one set of conditions and its criterion values gene is selected randomly from selected chromosomes, after that new gene is selected from 15 buying conditions if the selected condition gene is a buying condition and its criterion is decided randomly in a given range. In a case where the selected gene shows a selling condition, its criterion is decided randomly a plus or minus value in a given range according to the type of the condition: loss cut or profit taking. Figure 4 shows a flow chart of chromosome operation in the mutation process. In Experiment_1, also 5% strategy is used in the back test to compare with the GA strategy, the 5% strategy on all ways takes long position, loss cut and profit taking when the ±5% loss or profit is occurred. Experiment_2 shows results of back test using three kinds of a chromosome evaluation function under the condition 2 shown in Table 2. Back tests are repeated three times in each test and their results show the average of all tests. Table 3 shows all the chromosome evaluation functions used in the GA. Table 3, Chromosome evaluation functions 7 stocks are used in the two kinds of experiments. All of these companies have enough aggregated market value and have not done stock split within three years. Table 4 shows all the stocks used in the back test. Table 4, Back test adopted brands Figure 4, Flow of the mutation method 4. Experiment of back test results using optimum trading strategies.

4 5. Experimental result Tables 5~9 show the results of the experiments. Table 9, Experiment 2 with Condition 2 Table 5, Experiment 1 with Condition 1 Table 6, Experiment 1 with Condition 2 Table 7, Experiment 1 with Condition 3 Table 8, Experiment 1 with Condition 4 6. Discussion According to the results of Experiment 1, the number of trades and the percentage of wins of GA strategy are smaller than that of 5% strategy in all experimental conditions. The reason of this result is because the product of percentage of wins and asset is used as an evaluation function. Therefore chromosomes with high percentage of wins and small number of trades are selected. Probably, the GA strategy has much more no holding days than 5% strategy considering the number of trades, max holding term and minimum holding term of GA strategy and 5% strategy. This result shows that the GA strategy missed the chance to takes a profit but it also can avert the risk of price falling down. In fact, the GA strategy decreased the asset smaller than the 5% strategy in the case where the price changing ratio takes a minus value. In addition, the max drawdown which shows the biggest loss ratio in the back test of the GA strategy is smaller than that of the 5% strategy. Therefore, the risk of the GA strategy is smaller than that of the 5% strategy. In addition, in the case of experiment 1 with Condition 2, the asset increase ratio of the GA strategy is much smaller than that of the 5% strategy. To analyze this reason, a result of the back test of two stocks under condition 2 in Table 10, stock chart of code 4502 is shown in Figure 5 and stock chart of code7203 is shown in Figure 6. The left part of the each stock chart shows the

6 more wide a searching range. References [1] Youngohc Yoon, Swales G, Predicting Stock Price Performance: A Neural Network Approach, System Sciences 1991 Twenty-Fourth Annual Hawaii International Conference. Vol.4, pp , 1991 [2] Matthias G. Schuster, A Multiobjective Genetic Programming Approach for Pricing and Hedging Derivative Securities, [3] Brent Doeksen, Ajith Abraham, Johnson Thomas and Marcin Paprzycki, Real Stock Trading Using Soft Computing Models, Information Technology: Coding and Computing, ITCC 2005, Vol.2, pp , 2005 [4] Yamashita T, Hirasawa K, Jinglu Hu, Application of multi-branch neural networks to stock market prediction, Neural Networks, 2005.IJCNN 05, Vol.4, pp , 2005 [5] W.B. Arthur, Asset Pricing Under Endogenous Expectations in an Artificial Stock Market SFI Studies in the Sciences of Complexity, Vol.27 in 1997.

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