Applying Genetic Algorithms with Speciation for Optimization of Grid Template Pattern Detection in Financial Markets Abstract   This paper presents a new computational finance approach. It combines a grid pattern recognition technique allied to an evolutionary computation optimization kernel based on Genetic Algorithms, creating a dynamic way to attribute a score to the signal that takes volatility into consideration and normalizing the pattern detection by fixing the grid size with the ultimate goal of reduce risk and increase profits. For pattern matching, a template based approach using a fixed size grid of weights is adopted to describe the desired trading patterns, taking not only the closing price into consideration, but also the variation of price in each considered time interval of the time series. The scores assigned to the grid of weights will be optimized by the Genetic Algorithm and, at the same time, the genetic diversity of possible solutions will be preserved using a speciation technique, giving time for individuals to be optimized within their own niche. The adoption of this approach has the goal of reducing the investment risk and check if it outperforms similar approaches. This system was tested against stateoftheart solutions, namely the existing adaptable grid of weights and a non speciated approach, considering real data from the stock market. The developed approach using the grid of weights had 21.3% of average return over the testing period against 10.9% of the existing approach and the use of speciation improved some of the training results as genetic diversity was taken into consideration.

LargeScale Regularized Portfolio Selection Via Convex Optimization During the past years, statistical regularization methods have shown their advantages for portfolio selection in various aspects like stabilizing the portfolio weights, selecting sparse portfolios, and reducing the portfolio turnovers. In this paper, a regularized portfolio selection model is proposed based on the sorted (or ordered weighted) l 1  norm. The proposed model is general to incorporate both the Markowitzxe2x80x99s meanvariance portfolio problem and the index tracking portfolio problem. To be capable of handling largescale portfolio selection (i.e., the asset universe is large), an efficient algorithm based on the alternating direction method of multipliers is developed. Numerical simulations validate the benefits of the proposed model and the high efficiency of the algorithm.