"Re-imagining automated stock trading"

“Algorithmic trading made easy for everyone”

Since the New York stock exchange was founded in 1792, Traders, big and small alike, have consistently worked to discover methods of trading the markets that would optimize profits and minimize losses. Sometime in the 1980s, traders figured out that with algorithms they could trade the markets faster and better than ever before. This new ability to control, plan and execute trades at unbelievable speeds, absolutely redefined the markets.

Quick note: I will be referring to algorithms as “algos” throughout this case study.

Now, while the advent of algorithms was a welcome development, regular traders began to point out some underlying issues restricting wide adoption.

Some of these roadblocks included
  • Inability to write code
  • The high cost of existing algorithms
  • Absence of financial resources needed to run algorithms around the clock
  • Difficulty in accessing already created algorithms
  • …and a lot more intricate roadblocks.

The pursuit of solutions to these issues is the entire mission of Algopear.  Algopear is a new startup that aims to eliminate the barriers of entry into algorithmic trading and further democratize the financial markets. We aim to achieve this by providing a platform that would connect investors and traders to a library of ready-to-go algorithms that they could use to automate their trading activities. The Algopear platform combines an open market-style approach to obtaining algorithms with an easy-to-use automated trading experience.

The process is, Algorithm developers, approach the company (Algopear) to list their algorithms on the Algopear market. The folks at Algopear then thoroughly test these algorithms to ensure they perform as promised/expected before they then list the algorithms on their marketplace. Meanwhile, On the user side of things, users would be able to register on the Algopear platform, purchase algorithms from the Algopear market and apply them to automate their trading activities.

When I joined the folks at Algopear for this project, the app wasn’t called Algopear then. It was called PushStash and they had an existing version of the app. It was not until some time afterward that the rebrand that renamed Pushstash to Algopear happened and we decided to revamp the app accordingly.

Some of the issues that prompted the redesign were:
  • Low user retention
  • Poor aesthetics
  • Inability to convince users to take action
  • Inability to meet the company’s financial goals
  • We also needed to efficiently incorporate the new features conceptualized to open new streams of revenue for the company

Below are some screens from the old version of the app:
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Problem statement

Besides Algopear there were other companies like C2 Collective, Robinhood, etc who were headed in similar directions and if Algopear was to dominate the industry and also stand the test of time as planned, then a solid design foundation was necessary.  The challenge was to come up with efficient scale-able solutions to the identified problems and then translate these solutions into exciting easy-to-use interfaces and experiences. I also needed to create a design system that was scale-able and would be capable of accommodating the new features we planned to add.

We were also sure the market opportunity existed because the challenges we were looking to solve were real challenges people desperately complained about.

My role

Solo product designer

The team

Me [Designer], Bennie [Head of marketing], Lakeisha [Lead dev who’s also the co-founder], and Ronnie [Founder]

The vision of success

At Algopear we aimed to catalyze a future where automated investing would be widely adopted. A future where everyone could easily harness the power of algorithmic trading. On the financial side of things, success for this project would mean a steady stream of income from all the laid down pipelines, along with hitting all the key performance indicators specified in the strategy phase.

This project is still ongoing but as is I have designed a thoroughly functional and easy-to-use app where people can quickly find algorithms that match their investment style, add them to their private list and activate whichever they liked to automate their investment process. All while being subtly guided every step of the way to ensure they don’t make any mistakes. Below I will be explaining my problem-solving process in detail.

Project begins 

For this project, I employed my three-step design process.

  • Understand
  • Design
  • Iterate 


“At the outset of the project, we didn’t have a clear mission or specific goals for the Algopear experience.”

In the old app, we simply sent users stock alerts on their preferred stocks and allowed them to monitor the performance of their favorite stocks. The old app had no hint of the automation that would eventually come to be the unique selling point of the redesigned app.

As we brainstormed what direction to go in, I kept mentally referring to one Steve Jobs quote to keep our decisions on track. The quote goes something like this::

“You’ve got to start with the customer experience and work backward to the Technology. You can’t start with the technology and then try to figure out how to sell it”

Instead of trying to figure out from the start what new features to add, I ensured we first answered questions like,

  • What we wanted users to achieve from interacting with our platform and
  • What emotions we wanted them to feel during and after the various experiences.

It took a lot of back and forth, a lot of changes to things already agreed on, and some minor confusion 😆, but we eventually perfected our plan to put the power of algorithmic trading in the hands of everyone. With a clear direction documented, it was time to proceed to hear from our users.

Hypothesis statements — First off, I needed to polish our strategies. I started by identifying all our hypothesis statements and assumptions. Some of them are listed below:

  • Users would be less hesitant to enter the world of algorithmic trading if we provide safeguards that protect them.
  • Users would be more willing to try things out if we tailored the app’s experience to their trading style and risk appetite.
  • Even in the midst of automation, users would still like to feel in control of the process.
  • Users would like to see necessary financial stats in their money’s performance, quickly and effortlessly.
  • Users would like their own privately curated algorithm lists separate from the marketplace lists
  • Users would appreciate recommendations on algorithms that have performed well and are likely to perform well

Demographics — I also outlined three major demographic categories our existing and potential user base would fall under. I named them P1, P2, and P3.

P1: Demographics likely to adopt product fastest. – Investment savvy people, high-risk appetite, very financially capable and experienced folks. High risk – High rewards kind of traders. Risk value of 300k USD and above.

P2: Demographics likely to adopt the product moderately. -Average experience with high financial capability. Averagely experienced with average financial capability. Mid risk – Mid rewards kind of trader. Average risk appetite too. Risk value of 50-300k USD

P3: Demographics likely to adopt product slowest. – Low-risk appetite, little to no experience. Very unwilling to lose money. Not as financially capable as the other categories. Risk value of 20k USD and below.

Business goals — From the meetings and discussions we had, I was also able to deduce some business goals.


  • Secure seed funding
  • Build an app that would give a solid experience all-round
  • Grow to first one thousand users
  • Begin the path to becoming a household name in the investment space
  • Communicate credibility and expertise in every interaction


  • Induce profits from algorithm sales and subscriptions


  • Dominate the retail algorithm industry
  • Educate more people on trading and the markets and basically demystify the financial markets to more people

KPIs — The major KPI we were looking to measure was an increase in user base on both the algo maker and investor side.

User research recruitment — For interviews, I was looking for people who fit our three different demographics. I sent out initial questions via a questionnaire and then I reached out to users with interesting responses to gain further insight. I also worked to prove or disprove my hypotheses by kickstarting conversations with only open-ended questions. 

User research findings — After hearing from and discussing with potential users, Some common themes began to emerge. While some of my hypotheses were confirmed, I also discovered many things my hypotheses were missing.

  • New or Low experienced users agreed that they actually would like to start in a way that posed the least risk and work their way up from there.
  • Even though algorithms were automated and could pause transactions by themselves, users still wanted to be able to shut down things ASAP whenever they felt like. — * Now even though this isn’t an action we wanted users to always take, we decided we definitely needed to give them the feature. But to also satisfy what we feel was the right thing to do, I decided to provide, infoboxes where we could advise users on whether or not to take certain actions, but ultimately the decision still rested in their hands.
  • Users agreed they would like to be able to compare and contrast the stats of different algorithms to decide which was best for them.
  • Experienced users also pointed out that they wanted to be able to analyze things quickly hence needed any stats page to be properly laid out.

My research also birthed some “How to Do We” statements like

  • How do we eliminate the resistance people have to anything involving investment and trading
  • How do we make the experience engaging and pleasurable
  • How do we demonstrate our unique value proposition best
  • How do we ensure the entire experience is free of difficulty
  • How do we make the app’s experience easy for inexperienced traders
  • How do we bring users up-to-speed on difficult terms

User scenario — I also crafted a thorough user scenario that neatly weaved the stories of our three user bases into one story. Unfortunately, this is not something we want to share with the public. But below I will share some take-aways.

I consciously wrote our user scenario to intersect with most of our user demographics outlined above. The character in the story earns a relatively good amount of money, so this puts him among the richer part of our user base, but he also isn’t willing to risk much in the beginning, this intersects with our less rich user base. He’s also intentionally and consistently looking for where to put his money, this is also common with our richer user base, but he’s also inexperienced and would like proper and solid guidance every step of the way and this attribute cuts through all the sections of our user demographics.

Also, somewhere along the line, he begins to share more attributes with our experienced userbase by him becoming willing to risk more money as he makes some profit/loss and gains experience. As for how he’s likely to find us, we outlined possible means, like from friends, from searching online, on reputable sites, from ads he’s shown, from stumbling on it online in conversations, from physical adverts, banners, Tv ads, etc

Figuring out how he could find us allowed us to demonstrate our value proposition early on in the user’s encounter with the app. Some other major takeaways that were also influenced by my research results included:

  • Most people didn’t want the investment opportunity to require too much of a time investment.
  • Most people were also willing to learn in-depth but would rather learn as they go, rather than devote huge chunks of time into become professionals before starting.
  • People also didn’t want an app that was overly complicated or overly filled with technical jargon
  • Inexperienced traders were also hoping the app would somehow cater for their inexperience with the financial markets
  • Also, a relatively moderate portion of the interviewees leaned more towards playing it safe with their initial capital but were willing to put most of any profits made back into the markets.
Competitor analysis — I performed some analysis on the companies doing things closest to what we planned to achieve. The aim of this is to get a deeper understanding of what’s already out there and who and what we’ll need to outdo. It also helped me detect issues and mistakes potential competitors have made that we wouldn’t want to repeat.
*This is just a snippet of the entire table because I’m not allowed to share it in its entirety.
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Landscape documentation — I also performed a landscape analysis. This is the part where I look at apps and websites that have design patterns I think may be useful, or handle certain design problems that we’ll also face well, or exude a vibe that is similar to what I intend for our redesigned app to exude. The aim of this was to gather stuff that could serve as inspiration for both visual and experience design decisions.
*This is just a snippet of the entire table because I’m not allowed to share it in its entirety.
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This stage is where I began to convert all the synthesized information into actionable tasks. I employed two methods namely the Moscow Framework and Feature prioritization matrix to spell out and prioritize all that we needed to build so that in the next stage I could swiftly ideate on how to build them. I also crafted user personas based on all the things I had learned from previous research to assist in ensuring our prioritization decisions were on the mark. I also documented and analyzed our proposed User Journey to detect any undiscovered flaws in our plans, pain points that could arise, and opportunities for perfection we could act upon.
User Personas
Here I made sure to only add information that would be directly useful in the project. I also stayed generalist for information where an in-depth answer wasn’t necessary. I put together this persona from all what we had learnt to help me always keep in mind the goals that mattered to our users.
*This is just a snippet of the entire table because I’m not allowed to share it in its entirety.
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Features matrix 
This info was gotten from conversing with clients and potential users to figure out what both parties deemed important. After spelling out all that needed to exist in a features doc, the next step was prioritization. Getting a feel of the intersection of what was important to both parties allowed me prioritize design tasks.
*This is just a snippet of the entire table because I’m not allowed to share it in its entirety.
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MOSCOW Framework
This is also another tool I used to assist me in prioritizing things for both the version we were working on and for the long run.
*This is just a snippet of the entire table because I’m not allowed to share it in its entirety.
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Proposed User journey
*This is just a snippet of the entire table because I’m not allowed to share it in its entirety.
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User flow
This is a diagrammatic representation I made to depict how all the different parts of the app we’re connected and show at what points in the grand scheme of things they come up.
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In this stage, I began to translate the features we had agreed on into functional interfaces.
I started by curating an inspiration board that included the websites and apps I analyzed in the landscape doc along with many other designs and images I felt would aid my creative process.
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These are a few of the many rough initial wireframes I made that we had to go over  and iterate on till we were satisfied. 
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High-FI Designs

Splash screen sequence
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Registration screens 
These screens collect necessary information and allow users to choose which user category they fall into. Users not in the countries we operate in also get a notification and aren’t allowed into the app.
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Login and recovery screens
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Profile questionnaire screens
These screens ask users questions which their answers help us place different users in appropriate investment categories. The three categories are low risk-low rewards, mid-risk mid-rewards, and high-risk high rewards.
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Algo trading process screens
Arrows represent the process of activating algo.
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Home and to-dos
  1. User profile
  2. Timeframe switcher
  3. User’s balance
  4. Graphical representation of money stats
  5. Performance info
  6. Recent algo activity
  7. Stats minimizer
  8. To-dos info
  9. Recent Algo lists
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Algo store screens
  1. Algo type switcher
  2. Single algorithm
  3. Featured algorithm
  4. Quick add to list button
  5. Important algo stats
  6. Timeframe switcher
  7. Performance stats graph
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Settings screens
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Testing results 

I took two angles to my testing process, testing while still designing to influence certain decisions and testing after designing to make iterations. For example, some users found the initial version of the homepage to be very long and due to this complaint, I modified the layout to make some parts of the home collapsible and expandable, such that the most important things were given priority and additional info could be easily accessed if needed all while keeping things compact.
Testing was also how I learned that users really wanted the ability to quickly gather algorithms they liked somewhere and then look over them to make a decision. This was what influenced the addition of the “Quick add to list” button that’s visible by sliding an algo to the left. These and many more testing feedback influenced the multiple iterations of various screens I had to make.


Over 80 meticulously designed screens, a solid design language and system, and an efficiently organized Figma component library of the icons, assets, and color and typography styles are but a few of the deliverables and documentation that have been a result of this project. I also want to point out that this project is still ongoing as it is currently being developed. I worked to organize all the components and design-spec in a manner that would let me get as close as I could to eliminating any friction during the design<>dev hand-off process.  

Results & Impact

Since the app is still in development, I had to craft a fully functioning interactive prototype from the designs I made so I could test the impact of the redesign [If you want to see the prototype you can mail me to request it as I’m not allowed to put it on here].
From my tests with the prototype, About 92% of participants were able to effortlessly go from onboarding to activating an algo, which is the major action flow in the app. I also tested different flows in both the previous app and the redesigned prototype one after the other to get a feel of how much easier the redesign made things. Almost all the participants were impressed by the vast difference in things both visually and user experience-wise.
When the app is fully developed, I will return to this post with usage stats from the general public and I’m very confident they will be highly positive.
Although still in development, my designs for the Algopear app have also drawn attention as I got invited to nominate it for the A’ Design awards. These interfaces along with the excellent presentation skills of the founder and co-founder have also allowed us to raise funding from investors and win start-up competitions. We aim to have the Algopear app live and functional in the coming months and I can’t wait for it to get into the hands of the public…