Objective

This came out of my internship at Monex Investindo Futures. The goal was to increase the length of time users keep trading, and the approach was to sort clients into trading personas so the company could send each group messaging that fitted how they actually trade, rather than one message to everybody that everybody ignores. I did the analysis in Excel and presented it as a deck.

The personas

I defined four styles by how long a client holds a position. Scalpers trade within seconds to an hour and take small price movements. Day traders close within the same day. Swing traders hold for up to a week and work medium term price swings. Position traders hold for a week or longer and follow long term trends and macroeconomics.

How the volume splits

Across February, March, and April there were 2,199,091 tickets. Scalpers placed 1,364,791 of them, 62.06% of the total. Day traders placed 672,577, or 30.58%. Swing traders placed 133,067, or 6.05%. Position traders placed 28,656, or 1.30%.

The inversion

The pattern flips when you look at size instead of count. In February position traders averaged 0.12 lots per ticket against 0.08 for scalpers, so the clients placing the most trades are placing the smallest ones. Measured per session the gap runs the other way and much wider: scalpers averaged 5.30 lots per login against 1.21 for position traders, because they trade many times inside a single session.

Why that combination matters

A retention program built around the largest single trade targets position traders, who are 1.3% of activity. Built around session volume instead, it targets scalpers and day traders, who are nearly all of it. Those two readings of the same data point at completely different programs, which is the whole reason the segmentation was worth doing.

Trading Type against experience

I broke each persona out by profit, loss, and break even outcomes, so the company could see which trading styles were actually working for the clients using them. Then, I crossed trading style against experience level, which clients report as beginner, intermediate, or advanced when they sign up. If a particular style dominates among beginners, the company can tailor what it sends a new client from day one instead of waiting for enough activity to classify them. I ran the breakdown both ways. The share of each trading type within a level tells you what a beginner is likely to do. The share of each level within a trading type tells you who is actually behind the volume in a style like scalping, which matters because scalpers place most of the tickets.