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Hybrid genetic simulated annealing algorithm in the application portfolio

Author: DengBoQiao
Tutor: LiangYanChun
School: Jilin University
Course: Software Engineering
Keywords: Genetic Algorithms Simulated annealing algorithm Quadratic Programming Stock market
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 147
Quote: 0
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Abstract


Formed from the stock market that day, a reasonable investment of the profits arising from deeply attracted every investor, it has been unable to meet the money in the bank brought meager earnings, a lot of money into the stock market. But we also have to recognize that the cruelty of the stock market, if the trader made a mistake during the judgment will be facing painful blow, so for looking for a mature equity method has been tireless experts and scholars to explore the direction of the . However, many factors impact on the stock market, the stock system inside and outside the structure of the complex nature of the decision to complete such a task is quite difficult using traditional forecasting and statistical tools have been unable to adapt to this variability, new in this field The analysis model generation will be object of further studies. The traditional methods often combine genetic algorithms and neural networks applied to predict the stock market investment areas, this approach has achieved a certain degree of progress, it is mainly focused on a single stock or a few stocks such as small-scale predictions. However, the algorithm described in this paper is mainly securities from another angle to explore investment approach, in general, the number of shares purchased under the more the greater the amount of capital injection, the better the effect of profit. Therefore, this algorithm is mainly applicable to enterprise customers realize their investment in risk-free arbitrage. The algorithm uses a genetic algorithm, quadratic programming, and annealing algorithm and other traditional mathematical models, and also have done a corresponding improvement has been implemented to achieve better results. 1, the use of genetic algorithms from a large collection of stocks calculated a set of optimal portfolios, in order to avoid local convergence of genetic algorithms and other ills, we adjusted through the use of crossover and mutation operator, the fitness function and the improvement of the structure of the traditional algorithms, etc. measures, have been relatively satisfactory results. 2 For this portfolio, we use quadratic programming analog CSI 300 Index is calculated for each weight value stocks, reuse annealing algorithm by controlling the annealing temperature, adjust the termination condition, the weight of this group values fine-tuned to optimize the fitting effect. 3, the entire algorithm test data that we have adopted over the years the real stock market, adding annealing algorithm by comparing the results before and after the experiment as well as with other similar purpose algorithm comparison show that the method is feasible. This whole implementation process is completed in the company, as described in the algorithm is the company's products an important part. After a lot of improvement algorithm optimization, efficiency, accuracy has been significantly improved, with the advantages of similar products more prominent, proven hybrid genetic simulated annealing algorithm in the application portfolio with good viability.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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