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Suppose I have 3 stocks. Their historical returns and some variables like RSI, ATR, EMAs for all 3 of them. The goal is to compute the weights each stock should have in a portfolio. If I do something as simple as markowitz portfolio, it would mean I am only using the historical returns of the 3 stocks and ignoring other factors like RSI, ATR, EMAs.

My question is , how do I incorporate the historical stock returns and other factors and come up with a model that predicts the weights of the stocks in the portfolio?

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Markowitz portfolio optimization and variations of it usually boil down to maximizing expected returns while constraining the aggregate of your preferred measure for the portfolio.

The expected return does not have to be equal to the historical return in your model. You can feed in the factors of your choice within a model that outputs the expected return for each asset.

In literature, it is usually an alternative measure of variance that is used to feed into portfolio optimization.

This publicly available paper has a good overview of developments in this area in its introduction, if you want to read more: Porfolio Optimization with conditional VaR objective and constraints by Pavlo Krokhmal, Jonas Palmquist, and Stanislav Uryasev.

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