Application of K-Means and Random Forest Algorithms for Trader Segmentation and Activity Status Prediction at GKINVEST
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Abstract
Trader activities at trading firms exhibit heterogeneous characteristics, necessitating an analytical approach to identify segmentation patterns and trader activity status. This study aims to analyze GKInvest trader segmentation using the K-Means algorithm and to build a Random Forest model to predict trader activity status based on historical activity characteristics. The dataset comprises trader data with key variables including 30-day transaction volume, 30-day login count, 30-day transaction count, days since last activity (recency), account tenure, number of active days, and the number of instruments traded. K-Means was employed as an unsupervised learning method to establish segments, while Random Forest was used independently as a supervised learning method to classify active versus inactive status. The K-Means analysis yielded three segments: Passive Traders (Churn Risk) comprising 1,135 traders (56.7%), Highly Active Traders (VIP) comprising 437 traders (21.9%), and Regular Active Traders comprising 428 traders (21.4%). The Random Forest model achieved an accuracy of 83%; performance for the "Active" class reached a precision of 84%, recall of 93%, and F1-score of 88% (with a weighted average F1-score of 82%). Feature importance analysis revealed that 30-day transaction volume made the largest relative contribution (~0.218), followed by 30-day login count (~0.184) and 30-day transaction count (~0.183). The findings demonstrate that K-Means and Random Forest can independently provide complementary perspectives for understanding the activity characteristics and status of GKInvest traders, serving as a foundation for developing data-driven trader activity monitoring systems.
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Copyright (c) 2026 Ramadhan Alvito Alfian, Safrizal
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