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Weak Factor Alpha Strategy 1

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The idea of this strategy comes from the weak learner in the classifier, which first generates technical indicators and then finds the best parameters through the algorithm and sees these technical indicators as weak factors. Finally uses these weak factors (features) as input for training CatBoost classifier with relative strength. By long the relatively strongest prediction combination, short the relatively weakest prediction combination, finally implement the alpha strategy.

In this article, we focusing on how we improve the method from past literature and use more reasonable approaches to construct the strategy. In the feature selection part, We use distance correlation as our target function. Compared to the traditional method of Pearson correlation, distance correlation can describe the non-linear relationship which supplies more information. Also, we use the k-means clustering model to avoid selecting the outlier as the fitted parameter.

Our data is SPY 500 symbols from 2015 to 2022/11 daily Open, High, Low, Close, Volume. We apply python package TuneTa to generate and select technical indicators. By this package, it encompass not only traditional technical indicators, but also unique indicators from TradingView.

Figure 1: Pearson’s and distance correlation difference

Figure 2: Technical Indicators with fitted parameter

Figure 1 shows the difference between Pearson’s correlation and distance correlation. If Pearson’s correlation is 0, there are not necessary independent(might be non-linear). However, if distance correlation is 0, we can say that they are independent.

Figure 2: shows the final outcome of the indicators, we can see that every technical indicator have an unique and fitted parameter.

In summary, we spend more process on generate diverse features rather than using unselected features. Also, by apply distance correlation as our target function and K-Means clustering to find suitable parameters for each indicator, making the feature selection more rational. In next report, we will describe how we use these features as an input to training ML classification model and construct the alpha strategy.

Contact us at for the full report.

David Wang(Tomo)

Quantitative Research Intern, Quantitative FinanceGamma Paradigm


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