Customer Trading Analysis

In this project, I was tasked with analyzing hundreds of thousands of trading data entries spanning three months—February, March, and April. The objective was to uncover patterns and insights that could enhance trading strategies and client engagement.

I organized and presented the data across multiple dimensions:

• Tickets Per Month: analyzing the total trades executed, categorized by each trader type and by earnings

• Logins Per Month: analyzing the total users executing trades, categorized by each trader type and by earnings

• Trading Types vs Levels: comparing the trading styles (scalpers, day traders, swing traders, position traders) against their experience levels (beginner, intermediate, advanced)

This analysis and the accompanying visualizations helped identify which trader types were most active, how user engagement levels correlated with trading activity, and how various trading styles influenced profitability and loss. This is important for the company to create personalized and tailored trading advice to increase retention.

This project showcased my ability to manage and analyze large datasets effectively, as well as translate complex data into clear, strategic insights, presented through a slide deck. This is essential for driving informed business decisions.This project, translating them into understandable and strategic insights in the form of a slide deck that are critical for driving business decisions.