Introduction

Retail traders exhibit consistent behavioral patterns (risk tolerance, overconfidence, loss aversion) that reflect underlying personality traits. Machine learning clustering of trading behavior can infer trader archetypes, enabling apps and brokers to tailor interfaces and risk management to trader personalities. Also enables prediction of individual trading performance based on personality type.

Behavioral Features and Clustering

Features: win rate, average gain size, average loss size, holding periods, trade frequency, leverage usage, correlation between trades and broader market moves. K-means clustering identifies trader archetypes: (1) Overconfident momentum traders; (2) Risk-averse value hunters; (3) Leverage-happy day traders; (4) Diversified long-term investors. Each cluster shows distinct performance profiles.

Application

Brokers can identify trader type and customize experience: overconfident traders shown risk warnings, day traders given tools for short-term analysis, etc. Prediction: overconfident traders have lower long-term returns; tailoring guidance improves outcomes.

Conclusion

Inferring trader personality types from behavioral data enables personalized risk management and performance improvements.