Why I Believe in Algorithmic Investing: A Story of Brothers, Data, and Discovery
- By Mark Lyman -
I didn’t set out to become an investment advisor. I’m an engineer by trade—a network engineer who needs to know exactly why something works or doesn’t. My entry into the investing world wasn’t strategic, it was more of a side interest, something my brother Brett got me interested in when he introduced me to Penny Stocks.
His pitch was simple. Buy at $0.0001. Sell at $0.0002. Double your money.
It wasn’t perfect, but it worked… mostly. I made some good money. I helped design a website that highlighted the penny stocks being targeted. But that idea quickly hit a wall: it wasn’t scalable. The opportunities were too niche, too fleeting, and too risky to build a long-term strategy around.
That’s when things took a hard pivot—into Forex.
Brett and another brother Ben started building more advanced strategies together to trade Foreign Exchange.
Ben & Brett would brainstorm and Brett would code the ideas. They had some success and learned some hard lessons. Eventually they realized that the volatility and risk in that space was simply too high. The strategies were interesting—but difficult to explain, inconsistent, and even harder to productize.
Eventually, they stepped away from Forex and came back to the US markets, but this time with a some new ideas and some hard earned experience.
The Breakthrough: When Data Starts to Speak
I still remember the day Brett showed me what he and Ben had discovered and coded.
It was a 3D scatter plot—a graph with a heat map overlay that revealed something almost… elegant.
They had mapped out thousands of parameter combinations for a single strategy. What I saw was stunning: clusters of profitability. Not just random spikes, but islands of consistently high-performing strategies surrounded by less effective ones.
As an engineer, that moment hit me hard.
It wasn’t just cool data, it was proof of stability. It was a system that could adjust with the market—without falling apart when conditions changed slightly. It was a model with integrity. Something that could be understood, tested, validated, and improved.
That’s when I knew I had to come back. And I’ve been working with Brett and Ben ever since.
What We’re Building Now
Since 2021, we’ve been refining our strategy—not just the math behind it, but also how it runs, how it scales, and how it can be explained. We named it Quantelligent. It’s an intelligent strategy based on quantitative analysis… Quantelligent.
We run the entire infrastructure in Amazon’s Cloud
—from market data ingestion to automated trade execution. It’s robust. It’s elegant. And it’s fast.
Lately I’ve been trying to translate the strategy - finding ways to explain the genius of what we’ve built to people who aren’t engineers.
It has been much harder than I had anticipated.
Because even when the data is there—even when the system is right in front of you—there’s still a trust barrier. Most people have been burned by hype, by “too good to be true” pitches, or by black-box tools they don’t understand.
Why This Method Matters to Me
I’ve used AI. I’ve worked with robo-advisors. I even have a retirement account managed by one. But the more I dug into them, the less I liked them.
AI can’t be adequatly debugged. It can’t even be meaningfully explained. It’s a black box you can only re-prompt, not re-engineer.
Robo-advisors are just glorified allocation tools. They automate basic principles, but there’s no strategy. No edge. No adaptability.
What we’ve built is something else entirely.
It’s transparent. It’s explainable. And more importantly—it’s engineerable.
We know exactly why it works, when it works, and what its limits are. And we can adjust it without guessing.
Final Thoughts
I’m not sharing this to pitch a product or promise anyone returns. That’s not what this is.
This is just my story. A journey that started with penny stocks and grew into something that I now trust with complete conviction—not because it’s magic, but because it makes sense.
In a world full of noise and hype, that clarity is rare. And that’s why I believe in algorithmic investing—not just as a strategy, but as a discipline built on data, logic, and real discovery.
