Published by Jessica Weisman-Pitts
Posted on March 11, 2024
6 min readLast updated: January 30, 2026
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I’m Zak Westphal, CEO and co-founder of StocksToTrade. I started this whole journey as a trader, trying to make my own way in the market. But let me tell you, it was not easy being a small retail investor up against those big institutional players. I felt like I was bringing a knife to a gunfight mo...
I’m Zak Westphal, CEO and co-founder of StocksToTrade. I started this whole journey as a trader, trying to make my own way in the market. But let me tell you, it was not easy being a small retail investor up against those big institutional players. I felt like I was bringing a knife to a gunfight most days. The lack of access to all the fancy tools and breaking information those hedge funds had was beyond frustrating.
So I decided to do something about it. I teamed up with my co-founder to create StocksToTrade, a platform built specifically to level the playing field for retail traders. Our mission is to bring sophisticated, algorithmic trading tools and educational resources to everyday people investing their hard-earned money. The goal is to democratize access so all traders, big and small, have a fair shot at success.
As CEO, I get to drive that mission forward while we continually innovate new features to meet traders’ evolving needs. It’s been an amazing journey helping the retail trading community access institutional-grade technologies. And we’re just getting started in making high-caliber trading more inclusive for all.
In short, we’ve harnessed AI’s data crunching abilities to level the playing field. For example, take our IRIS Analytics tool. IRIS uses artificial intelligence to gobble up large amounts of real-time market data that would take a human weeks to get through. Then it delivers super-detailed yet unbiased stock ratings and reports tailored specifically for retail traders. It’s bringing hedge-fund-level analysis straight to your laptop.
Similarly, our News Sentiment Analysis tool leverages natural language processing and machine learning capabilities to scan news articles and determine how events could impact specific stocks. So you get a quick readout of whether the latest headlines are positive or negative for a company.
By integrating these AI capabilities into our easy-to-use platform, we’re delivering institutional-grade insights to retail traders on a silver platter. No special access required. Our mission is to get potentially market-moving intelligence into the hands of traders when it matters most. And AI helps us achieve that at scale while keeping the playing field level.
Integrating robust AI capabilities presented some meaningful challenges that our team worked diligently to address. Ensuring accuracy and reliability of the models was paramount, given the complexity and dynamic nature of financial markets. Traders rely on our insights to inform impactful decisions, so subpar algorithms could carry real consequences. To achieve trustworthy outputs, we invested substantially in data quality and testing processes, combined with oversight procedures.
We also focused hard on giving users some customization control. Trading styles and risk tolerance vary widely person-to-person. So we allowed for tweaking of parameters to align insights to individual needs. We can’t have a one-size-fits-all AI for such a personal game.
Oh, and we faced a classic tech challenge – how do you take these advanced AI systems and actually make them usable and helpful to traders instead of confusing and overwhelming? We took great pains to smooth the user experience – clear visualizations, educational resources alongside the tools, intuitive design, the whole package.
It took effort, but I’m proud to say we’ve made our AI offerings truly accessible and valuable for retail traders at different experience levels. And we’re constantly working to improve the tech even more.
We take the responsible use of AI incredibly seriously around here. We get that just unleashing algorithms without proper precautions could spell disaster for traders and the market. We’ve seen what can happen when risky models go unchecked.
So we put extensive backstops in place, like automatic price guardrails and stop losses in volatile conditions. And we invest heavily in educational resources – I mean pages and pages of content – explaining exactly what our AI can and cannot do. We want traders to fully understand these are insightful tools, not a crystal ball.
At the end of the day, our algorithms can’t replace human judgment and experience. Our mission isn’t to automate the trader away through AI, but to provide individual investors with way more informational firepower. Think of it as equipping people to make better decisions, not removing the human element entirely. There’s just no substitute for personal responsibility and discretion in trading.
We’ve only begun to tap AI’s vast potential in fintech. As data and computing scale exponentially, transformative personalized insights and automation will democratize sophisticated trading for retail investors. Leveling the playing field is just the start. However, with great power comes accountability.
Achieving transparency, fairness and strong governance must be priorities amidst this AI-fueled revolution. If developed responsibly, AI can uplift average investors through unprecedented access to institutional tools. But user education and ethical frameworks will be essential to ensure empowerment beats exploitation. The future looks bright if we approach AI thoughtfully.
AI isn’t a magic “fix all” button. Start by truly understanding specific problems your users face. How could tailored AI solutions improve their experience and empower their goals? Engage deeply with your community along the process. What are their needs and concerns? If the tech doesn’t help them, what’s the point?
Ensure AI augments human intelligence rather than replaces it. Allow for oversight and explanation of model workings. Set guardrails. And maybe most overlooked – invest in top-notch data. Bad inputs equal bad outputs, no matter how slick the algorithms.
In the end, AI should simplify lives – not complicate them. If you build it thoughtfully for humans from the get-go, you have a bigger chance at unlocking significant value for your customers.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn. It encompasses various technologies, including machine learning and natural language processing.
Machine Learning is a subset of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed. It is widely used in various applications, including financial analysis.
Algorithmic trading is the use of computer algorithms to automate trading decisions in financial markets. It allows for faster execution of trades and can analyze large datasets to identify trading opportunities.
Retail investing refers to individual investors buying and selling securities for their personal accounts, as opposed to institutional investors who manage funds on behalf of others.
Democratization in finance refers to making financial services and investment opportunities accessible to a broader audience, particularly retail investors, often through technology and innovative platforms.
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