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Gold CatBoost Clusters Real EA operates on the CatBoost algorithm using clustering methods. A set of features is fed into the CatBoost model, constructed based on price data and moving averages (MA). These include distances between the price and MAs across different periods, as well as between the MAs themselves, which help capture deviation and trend strength. The features are normalized—by dividing by the price or MA—to make them scale-invariant. Additionally, rolling window
Majors CatBoost Clusters Real EA operates on the CatBoost algorithm using clustering methods. CatBoost is fed a set of features based on price and moving averages (MA) . What exactly: Distances Price - MA (for different periods) MA − MA → show the deviation and strength of the trend Normalization Division by price or MA → makes features scale-independent Window statistics (WINDOWS) Average, std, max, min → reflects volatility and behavior over a period Lags
