UI Design
UX Design
Social Trading meets Analysis
Design of various features for the neobroker Smartbroker
TIMELINE
October 2025 – February 2026
TEAM
Kim Tuyen and me
ROLE
Fokus on social Trading
CONTEXT
Course project at the University of Applied Sciences Potsdam in collaboration with Smartbroker+
Disclaimer: This work was created during the winter semester of 2025 as a student project at the Potsdam University of Applied Sciences in collaboration with Smartbroker AG. It is an academic project and not a commercial commission.
Introduction
The project was developed as part of a larger initiative in collaboration with Smartbroker+ and under the direction of Malte Völker, who leads the design team there.
Together with my partner Kim Tuyen, we designed two features that could further explore the areas of personalization and innovation.
Concept development
Brainstorming
We knew that Smartbroker+ was very interested in the heavy trader demographic, so we made them the focus of our research. We analyzed the competition and looked at what other companies, especially American ones, were using in their advertising. We also took a closer look at the use of AI, though we didn’t want to place too much emphasis on that. We then spent a lot of time researching data visualization in the social trading sector and exploring where Smartbroker+ could fit into all of this...
The concept
Main problem
Heavy traders must operate under tight time constraints and in a highly dynamic market environment, while news, trading signals, and opinions are abundant but lack strategic context, quality filters, or clear relevance to their personal trading strategy
Our Solutions
Structured Social Spaces
Trading Rooms → in-depth analysis & scenarios, market sentiment & topics
Private Desks → customized implementation
AI as a classification & organization tool
Summarize discussions
Logically structure scenarios
Make risks & uncertainties explicit
Visualize changes over time
Separation of thought & action
No direct copy impulses
Individual decisions remain central
AI supports, but does not decide
USP
Social trading without showing off
AI as a framework, not an oracle
Focus on decision quality, not performance
Modular: scalable from beginner to pro
Takes the German mindset into account: matter-of-fact, discreet, self-determined
What is being solved?
Information overload without context
Uncertainty during complex market phases
Lack of reasons to return to the app regularly
Target group
Primary target group: Heavy Trader
High trading frequency
Custom strategies
Need for depth, control, and speed
Skeptical of traditional social trading
Secondary target group: ambitious trader
Learn by observing others
Want to make better decisions
Not yet fully systematic
Us, presenting the features at the Smartbroker+ office in Berlin
The final features
Collaborative Trading Rooms
Thesis: Users want to discuss trades and understand strategies, but they don't want to blindly copy them
Collaborative Trading Rooms and Desks
Join rooms to share trades, stay ahead of market movements, and gain a clear understanding of strategies through detailed charts. With an AI assessment, you can quickly see how well a trade aligns with your strategy.
Analysis Tool
Copy analyses from others and incorporate them into your own. By overlaying different analyses, you can execute trades more thoughtfully.
Goal
Beginners receive high-quality information that is also verified by AI. Active traders have the opportunity to share their knowledge and gain access to an advanced analysis tool, which, in the best-case scenario, allows them to generate more profit from their trades.
Strategy-Simulator
Users want to be able to test and understand strategies simultaneously without exposing themselves to real risks.
The Strategy
The simulator is a strategy and simulation tool that allows users to analyse trading behaviour, run through alternative scenarios and test multiple strategies. It compares actual trading (Strategy A) with an ideal strategy (Strategy B) to highlight the performance gap. It does this by comparing the real portfolio with rule-based and AI-optimised scenarios. These help to identify causes of psychological errors at the asset and portfolio levels. As a result, users can make more disciplined, lower-risk and higher-return decisions.
AI-Summery
The AI Summary provides a clear overview of your trading behavior in the strategy simulator, showing you where you are making mistakes and what specific changes you should make to trade more successfully and with less risk.
Goal
The user develops a structured and thoughtful decision-making process by gaining a clear understanding of issues and ways to improve their trading behavior.
