Ideal portfolio creation has been the focus of considerable machine learning research in the domain of finance. In this paper, the development of a two-stage platform for generating stable stock-based portfolios is explored. The first stage involves clustering of stocks based on time-weighted correlations, using a modified version of the K-Means++ algorithm. This clustering helps in the quantification of portfolio diversification at a later stage. In the second step, a genetic paradigm is employed to optimize the returns of the portfolio in such a way as to ensure its diversification at the same time. This leads to the formation of a portfolio that shows a high and stable Markowitz ratio of returns/risk. The experimental results support the central hypothesis, and hint at possible commercial applications.
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