StocksToTrade

As founder and CEO of StocksToTrade, Zak Westphal has made it his mission to place institutional-caliber trading platforms into the hands of ordinary investors. After years helping master the volatile domain of penny stocks, Westphal recognized major gaps in the retail trading landscape.

For too long, powerful tools were gatekept for wealthy firms while Main Street investors lacked access and education. So in 2012, Westphal set out to build an intuitive platform opening up algorithmic capabilities once siloed from public view. But he didn’t stop there.

Knowing technology alone only goes so far, Westphal embedded trader education into StocksToTrade’s DNA from day one. He understood that without context on executing prudent, researched trading strategies, even the most robust tools fall flat. His vision centered on informed usage – not just expanding access for its own sake.

In this interview, we pick the mind of this fintech pioneer on why other leaders overlook financial literacy gaps and how traders old and new alike can responsibly leverage cutting edge advancements through comprehensive skills building.

You place enormous emphasis on trader education – why is this something many fintech companies overlook?

In my experience, it’s too easy for fintech founders to get caught up developing the next killer trading app without considering if actual humans will benefit from it. In racing to one-up algorithms and automation capabilities, they breeze past crucial questions like “how accessible and understandable is this for everyday investors?”

At StocksToTrade, I knew putting trader education front-and-center from day one was non-negotiable. In my view, truly democratizing algorithmic trading isn’t just about building the highest tech platforms. It’s about translating complex methodologies into comprehensible modules people can customize based on individual risk-appetite.

We spend time consciously connecting dots between advanced stats-based signals and actionable real-world tips because tangible utility for retail traders has to be the real goal, not just institutional-grade capabilities. Otherwise, you end up with powerful but theoretical tools gathering virtual dust because they are too abstract for people to harness.

In your view, what are the limitations of pure automation? Where does human intuition still play a vital role?

There’s no question that algorithms can crunch data and spot patterns way faster than any human. But they have blindspots too. When weird outlier events happen or things shift in markets, preset programs often glitch up because they lack flexibility. They can’t course correct like human intuition does.

That’s why we see strategy meltdowns – the models get confused. I think people still need to oversee the automation to reality check things – does this signal align with logic or is it just a statistical hiccup? Human intuition balances out pure computational number-crunching, keeping it honest. The key is responsibly blending processing power with human oversight. Letting algorithms run too freely risks distortions sneaking in. Having people involved keeps strategies grounded.

What core principles guide your approach to introducing new technology to users? How do you avoid exclusion?

Fundamentally, I believe technology should empower people – not replace them. That ethos shapes everything we build. Our primary values focus on amplification over automation.

Before rolling out new tools, we ask “how can this expand possibilities for our users while avoiding potential exclusion?” That’s why we take time to carefully layer accessible educational resources into each launch.

Our goal is informed adoption – allowing traders at any skill level to leverage institutional-grade advancements through comprehensive training tailored to their aptitude. Because placing advanced algorithms into newcomers’ hands without support structures risks harming adoption. Progress demands responsibility.

What lessons have you learned from user feedback in terms of balancing tech capabilities with digestible education?

To be honest, I was shocked by how much our users valued the education we provided alongside our tools early on. Seeing people wrestle with even our most advanced platforms, I realized we had assumed wrong – more complexity doesn’t automatically mean better innovation if real traders can’t use it.

But the feedback underlined loud and clear just how crucial digestible training is for turning technology into actual understanding and skills. So we did a major rethink of our approach – pivoting hard into practical education versus chasing sophistication for its own sake. Now user input drives our mission to build bridges between human needs and complex advancements. Because empowering traders to apply new technologies matters most – not innovation simply measured by progress on paper.

What responsibilities come with innovating in areas like AI and algorithmic trading? How can leaders prevent misuse while expanding access?

Innovation broadly – but especially complex advancements like AI – require meticulous ethical guidance to prevent misuse alongside expanded access. I believe we technology leaders have profound responsibility to self-police the rollout of exponentially powerful systems balancing openness with prudence.

Across fintech specifically, this demands proactively modeling accountability and self-restraint while also further democratizing participation so more voices hold a seat at the table shaping progress directionally. If society loses input control of automated systems as they grow less understandable and predictable behind-the-scenes, nobody wins in the long run. True innovation puts people first – not capabilities detached from collective benefit.