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Some readers may have noticed the recent launch of propfunding.com.
This is a new prop firm that runs on a different model, whereby the trader only pays a fee if they pass to the funded account stage.
To me that up ends how you actually make money from the model, so we figured we’d talk to the company’s founder Shaun Opoku to find out more about it. Enjoy!
Can you give some background on yourself and how you ended up founding Prop Funding
I’ve always been fascinated by finance. My journey began in 2015 when a lifestyle FX influencer sent me a DM, promising I could make easy money by copying his trades. It didn’t end well, but it sparked a deep curiosity.
I dove into Telegram groups, online courses, and endless nights studying charts, eventually landing a role as an analyst at DT4X, a trading education company. That experience gave me a foundation in market structure and trader behavior.
By 2019, I found myself in the prop trading space, back when it was truly the wild west. Working on a risk desk at a firm providing liquidity to other props taught me how exposure is managed and what makes a funding model sustainable.
In 2020, I launched Prime Bridge (PB) for B2B services and Traders Central Fund (TCF) for retail traders. It was a slow start. Back then, prop trading wasn’t mainstream, and most banks or payment providers didn’t understand the business model. But persistence paid off.
When COVID hit, everything changed. Overnight, retail trading exploded. One month, we went from mid-five-figure revenue to mid-six-figure revenue at TCF, and PB’s B2B operations surged as we became a go-to liquidity and white-label provider for new firms entering the market.
By late 2022, I could tell the industry was peaking. Growth was normalizing, and frankly, I was burnt out. We had been sprinting nonstop. After some reflection with the team, we decided to sell TCF in early 2023 while retaining Prime Bridge, which was more stable and B2B-focused.
Still, I never lost sight of the retail side. Watching the prop industry evolve and seeing the same core misalignments persist made it clear we had unfinished business. That’s what led to PropFunding.com, launched in August 2025: a new model built on transparency, alignment, and data, not upfront fees and conflict.
Why did you decide to launch the company?
I launched PropFunding.com because I felt like there was unfinished business from our Traders Central Fund days. Back then, most prop models, including ours, relied heavily on challenge fees and profiling traders on the funded stage to partially A book them to manage payout risk. It worked for a while, but as the space got crowded with copycats, competition turned into a race to the bottom on both price and rules.
When the music’s playing, you dance; so we adapted to stay competitive. But eventually, it became clear that the model wasn’t sustainable. Fortunately, we had built a strong cash reserve and had quietly started working on something different: a data algorithm designed to generate alpha from trader behavior. The logic was straightforward: if most traders fail, their patterns hold valuable signals that can be monetized to sustainably fund payouts.
We knew it would take time. So rather than force it, we sold TCF, focused on scaling Prime Bridge, and continued refining the algorithm in the background, testing it with B2B data over several years. By 2025, the tech, data, and timing will finally be aligned.
That’s when we brought PropFunding.com to life, a model that flips the old system on its head. Traders pay nothing upfront; our profitability comes from data-driven monetization, and the early results have been impressive. We know it’s still early, but this feels like the future we were trying to build all along.
A key part of the model is that traders only pay a fee if they pass. How do you envisage this working, given that most props are making their revenues from challenge fees?
At PropFunding.com, we’ve developed a model that eliminates the need for upfront challenge fees altogether. Each month, traders can join a free challenge through a capped cohort, and the first 10 % to pass get funded. That structure allows us to control payout exposure from the start, as we decide how many accounts to activate rather than relying on upfront sales.
Unlike most firms, which earn from entry fees, our revenue comes from data monetization and strategy performance. The trading data generated across our ecosystem powers algorithms on our in-house accounts and, over time, will be licensed to larger trading firms on a profit-share basis. We’re also developing a subscription-based copy-trading platform that will allow retail traders to mirror these strategies and benefit from the same alpha generation without paying a profit split.
All of this creates a self-sustaining loop — traders enter the challenge for free → their anonymized data fuels monetization and strategy profits → that revenue funds payouts → and the cycle repeats. It’s a model where growth doesn’t rely on constant new sign-ups but on the strength and scalability of the data itself.
A big topic in the industry is managing risk and a-booking trades. Are you doing this? If so, can you say how or provide some insights into how its structured?
Our approach to managing payout risk is straightforward — and transparent by design. Soon, we’ll be launching a payout pool dashboard on the PropFunding.com website where traders can see exactly how the pool is funded and sustained in real time.
The pool is supported by four primary inflows:
- Initial Seed: A base capital allocation used to bootstrap and stabilize the payout pool.
- PFP Bot: A percentage of profits from our in-house and partnered firm accounts that monetize trading data and return a share to the pool.
- LP Hedge: Profits generated by profiling funded traders and selectively A-booking portions of their live account flow where appropriate.
- Activation Fees: 100% of fees from traders who pass are allocated directly back into the pool.
Together, these streams create a sustainable payout loop that doesn’t rely on constant new challenge sales. It also acts as a safeguard; if one source underperforms, others can balance it out. Most importantly, the system’s transparency holds us accountable, ensuring that every qualifying trader receives their payout.
Given accounts are free, there is zero risk for the trader. Does this not encouraging more gambling? The trader has nothing to lose after all
That’s a fair question and one we considered carefully when building the model. If our setup were an open-ended, sign-up-anytime structure, I’d agree it could attract gamblers. But PropFunding.com operates very differently.
We use a monthly leaderboard format, where only a limited number of traders can join each cohort, and the first 10% to pass get funded. This creates structure and accountability; traders know they’re competing in real time, so strategy and patience matter more than luck.
We’ve also built safeguards into the system itself. The challenge uses 1:50 leverage, compared to the industry’s 1:100 or 1:200, has a daily profit cap limit, and we’ve removed volatile instruments like Gold and indices in Stage 1 of the evaluation to limit impulsive trading. These small design choices have already reduced daily liquidations across cohorts.
Our long-term vision is to push this alignment even further, a model where disciplined traders not only pass but eventually earn cashback as a share of profits generated from their monetized data. In short, it’s about rewarding consistency, not high risk-taking, a win-win for both sides of the model.
How do you differentiate between a gambler and a genuine trader?
It’s a tricky balance. I’ve always believed the traditional one- or two-step challenge isn’t the most effective way to profile traders to handle their payout risk when funded. But because it’s the model most traders are familiar with, we have to “play ball” for smoother user adoption.
What truly distinguishes a gambler from a genuine trader is data-driven behavior analysis. Over the years, we’ve developed internal tools that utilize the trading data we collect to predict a trader’s lifecycle upon reaching the funded stage. Things like position sizing, consistency in drawdown, reaction to losses, and trade timing tell you far more about a trader’s discipline than a pass rate ever could.
The reality is that roughly 64% of traders get liquidated within their first 90 days. Our focus is on managing firm exposure intelligently until those liquidations occur, using the insights gathered to better identify and support the 10% who genuinely trade with skill and patience.
You talk about using failed trader data to build strategies. How does that work?
I wouldn’t describe it as using “failed trader data”; at our core, PropFunding.com operates as a quantitative firm that studies trader behavior to generate alpha. As Jim Simons once said, “I don’t know why the planets orbit the sun, that doesn’t mean I can’t predict them.” That mindset perfectly sums up what we do.
When traders take our evaluations in unison, their collective behavior follows certain patterns once their accounts reach specific trigger levels, whether they’re in profit or drawdown. When those conditions occur, our algorithms activate.
For example, if our data shows that out of 1,000 traders, 64% of those who are up 1% during the London session tend to extend their gains to 2% without reverting to breakeven or drawdown, our system automatically triggers, mirroring those trades in the same direction and taking profit once the traders’ accounts reach that 2% mark.
We’re not guessing or relying on narratives; we’re observing repeated behaviors and using them to build predictive systems. We might not always know why the pattern exists, but if it’s consistent, we can monetize it, and that’s where the alpha comes from.
What’s your long-term vision for the company?
To be honest, I don’t set rigid long-term visions for any of my ventures, especially in finance, where everything moves fast. The key is to stay agile and build systems that can adapt as the consumer market evolves.
In the near term, our focus is clear: continue strengthening the performance track record of our data-monetization algorithm. That’s the foundation for scaling what comes next. Once we can demonstrate consistent results, we’ll begin licensing the system to larger firms, which will, in turn, allow us to expand monthly intake limits and introduce larger account sizes.
It’s less about chasing a grand vision and more about refining a model that works, one that gives more traders a fair shot at funding while proving that data-driven transparency can sustain an entire ecosystem.
