Helping retail investors assess all of their individual portfolios under one roof.

Helping retail investors assess all of their individual portfolios under one roof.

🗓 Completed in Q1 2023




Trade Blotter is an early stage Fintech startup.

The company aims to help individual investors who have multiple broker accounts, and therefore have their portfolio data scattered across products.

By importing data from broker accounts into Trade Blotter, investors are able to assess their portfolios as a whole, and view data sets they deem critical. These data sets include: Portfolio allocation, Profit and loss, Value at risk, Transaction history and much more.

Trade Blotter’s goal is to consolidate data and make it accessible to investors - that way they could view a complete story of their portfolio and make smarter bets.


The project revolved around designing an MVP that would deliver the core value the product has to offer.

To achieve this, the project spanned across customer research and need finding, defining features and multiple iterations of design.


⛳️ Research and understand target audience.

⛳️ Brainstorm opportunities to help target audience.

⛳️ Define MVP feature set.

⛳️ Design product.

⛳️ Build design system.

⛳️ Launch and measure MVP.


Product owner.

Product designer (myself 🖐).

x2 Front-end developers.

x2 Back-end developers.


Conducting research.

Exploring product opportunities.

Defining features for MVP.

Low fidelity product design.

Building design system.

High fidelity product design.

Aligning distributed team.


Online survey for quantitative input

An online survey was designed and shared with people from the investing community. The survey was a quantitative research methodology that was used to uncover user mental models and behaviours - this would help the product team empathise with the target audience and make better design decisions.

Research findings from survey.
Research findings from survey.

Key findings

88% of respondents review their overall performance across broker accounts.

84% of respondents check their portfolio data more than once a week.

76% of respondents consider how much of their money is invested in a particular security before making a new trade.

72% of respondents have an investment strategy or system that they follow.

72% of respondents rebalance their portfolio on a consistent basis.

72% of respondents export data (CSV. PDF.) from their broker accounts.

72% of respondents refer to data charts to review and understand their portfolio.

64% of respondents actively learn about investing.

Focus group for qualitative input

A focus group was organised and run with people from the investing community. The study was a qualitative research methodology that was used to uncover user needs, pain points as well as user ideas which would help the product team brainstorm solutions.

Synthesised research findings from focus group.
Synthesised research findings from focus group.

Key findings (pain points)

🚩 Participants are currently lacking a well balanced portfolio.

🚩 Participants are currently lacking systems to improve risk management.

🚩 Participants are currently lacking a data analysis infrastructure.

🚩 Participants are currently lacking access to portfolio volatility data.

🚩 Participants are currently lacking stats which are easy to understand for portfolio optimisation.

🚩 Participants are currently lacking diverse strategies for different market environments.

🚩 Participants are currently lacking portfolio back testing capabilities.

🚩 Participants are currently lacking long-term consistency in strategy.

🚩 Participants are currently lacking confidence around when to buy and sell securities.

🚩 Participants are currently lacking a streamlined decision process to enable replication later.


👉 Take a video tour of the design process



👉 Take a video tour of the design system




👉 Take a video tour of the highlights dashboard

👀 View the Figma file



👉 Take a video tour of the allocation strategy flow

👀 View the Figma file



Focus on nailing the core of the product first, test it and expand as necessary. The truth is that it’s impossible to guess what will be well received, what won’t and what your best future bets will be when you’ve barely left the camp.

Design systems should remain lean and scale along the way as the product grows. It’s a myth that teams need full design systems in place in order to design a product.

Take the time to learn about your audience to reduce risky assumptions. They’re not necessarily thinking what you’re thinking, they’re not necessarily worried about the things you are, there are also personas you probably don’t know about yet.

Design like a software engineer to ensure great collaboration and product releases. Design systems which scale, annotate the rules and logic, provide a solution for all states, walk through the details with the team.

Think through your UX copy thoroughly to guide the end user. People don’t always get industry jargon and may not care to do so. Nobody wants to read screens. People want to get their needs met.

learn to cut corners selectively. If it doesn’t provide value for the end user, the business, or the team, think twice about doing it. You may have more important things to focus on next.


(+356) 7920 0774