I Am My Hedge Fund
Historical investing account — October 11, 2026: This is my February 28 quant postmortem and personal investing thesis. The reported roughly $300,000 was spent over about eighteen months, not established as an annual operating cost or a pure trading loss. Personal conviction is not evidence of investment outperformance. The former investing-page promises do not establish an active complete portfolio ledger; the current resource library links related decision guides, while this investing account remains historical.
February 2026
TL;DR:
- I spent $300K and hired 5 full-time people to build a quant trading operation in 2020-2021.
- We had researchers, strategists, developers. We still lost.
- In February I described managing my own money alone and intending to publish my thesis. The original unverified destination was founderreality.com/investing.
- No fund. No fees. No hiding. Here's why I think a written personal thesis is the approach I want to test, not proof that conviction beats a quantitative team — and why the era of the solo capital allocator is just beginning.
I need to tell you about the most expensive lesson I've ever learned.
In 2020, I was 21 years old and I decided I was going to build a quantitative trading operation. Not dabble in it. Build it. Properly.
I hired five people full-time. Researchers to find signals. Strategists to design systems. Back-end developers to build the infrastructure. Front-end developers to build the dashboards. A real operation. A real team. Real salaries.
Over the next year and a half, we burned through roughly $300,000. Almost all of it operational — salaries, infrastructure, data feeds, compute, tools.
Here's the part that still stings: we burned so much money building the operation that we barely had enough runway to actually run the strategies with meaningful capital behind them.
We spent $300K constructing a machine to trade, and ran out of gas before the machine could even do its job properly.
We failed. And not gracefully.
Not "we learned a lot and pivoted" failed. We lost money. The strategies didn't work at the scale we needed. The edge we thought we had wasn't an edge — it was noise that looked like signal in backtests.
We were competing against firms with billions in capital, decades of data science, and infrastructure that made ours look like a science fair project.
I walked away from the wreckage. Took the loss. Moved on.
That was the most expensive education of my life. And I think about it almost every day. Not with regret. With clarity. Because what I learned in that failure is the foundation of everything I do with money now.
What Actually Went Wrong
Let me be specific, because "we failed" is vague and vague isn't useful.
We were competing in layers where humans lose.
Quant trading is a speed and data processing game. You're trying to find patterns faster than the next algorithm, execute faster, process more data. That's a machine game. We were bringing knives to a gunfight — and the other side had nuclear weapons.
The big quant shops — Citadel, Renaissance, Two Sigma, DE Shaw — they spend hundreds of millions per year on talent and infrastructure. They hire the best mathematicians and physicists on earth.
They have data advantages we couldn't touch. We were a 21-year-old and five people in Toronto trying to out-compute firms that spend more on a single data feed than our entire annual budget.
That's not a brave bet. That's arrogance disguised as ambition.
The strategies worked in backtests and died in production.
We were trading equities — mostly automated strategies built around supertrend indicators and countertrend setups. The kind of momentum and mean-reversion plays that look beautiful in historical data. We backtested them extensively. Great returns. Low drawdowns. Sharpe ratios that make you feel like a genius.
We also tried automating options strategies. Couldn't do it. Options were too complex — too many variables, too many edge cases, too much nuance in execution. That should have been a signal. If the thing you're trying to automate is too complex for your own automation, maybe the whole approach has a problem.
But we pressed on with equities. And then we ran the strategies live and everything fell apart. Slippage. Execution costs. Market conditions that shifted since the backtest period. Signals that were real in 2018 but arbitraged away by 2021. The supertrend setups that looked so clean in backtests got chopped up in real markets. The countertrend plays got run over when trends extended further than any historical pattern suggested they would.
The market is an adversarial system — it adapts. The patterns you found yesterday are the patterns everyone else found today.
Then our investor pulled out.
That was the end. When you're burning $20,000+ a month in fixed costs and your capital source walks, there's no graceful pivot. No "let's iterate." It just collapses. One day you have an operation, the next day you have a Slack workspace full of people you can't pay and strategies you'll never get to run properly.
Looking back, the investor pulling out was the best thing that could have happened. If they'd stayed, we would have burned another $300K learning the same lesson slower. The market was teaching us something and we weren't listening: you don't beat machines by building a worse machine.
I was operating in layers AI dominates. This is the part I only understood later. I was asking humans to do what algorithms do better: process data, find statistical patterns, execute at speed. Every single thing our team did was something a better-funded team could do faster with more data and better hardware.
I was competing on compute. On speed. On data processing. Those are machine layers. I had no business being there.
What Changed
Fast forward to now. February 2026.
I manage my own money. Just me. No team. No fund. No investors. No management fees. No performance fees. No quarterly letters. No LPs to answer to.
I intended to publish my positions and theses openly. This essay does not establish that a complete, continuously updated portfolio ledger was delivered.
I wanted public writing to make my reasoning easier to check. Readers can inspect the positions and arguments I actually disclose; those essays are not a complete return record. I believe in what I own and I'm willing to be wrong in public.
I wrote about this last week in "I Sold Everything in One Hour" — the specific positions and the thinking behind them. This piece is different. This is about why I can do this at all.
Why a single person with no team can allocate capital in a way that I genuinely believe will outperform most professionally managed money over the next 30 years.
And it comes back to the lesson from that $300K failure.
The Lesson: Compete Where Machines Can't
In 2020, I was trying to beat algorithms at being algorithms. Speed, data, pattern recognition, execution. All machine layers.
I lost because I was supposed to lose. The game was rigged — not by anyone cheating, but by physics. Machines are faster. They process more. They don't sleep. They don't get emotional. In a pure computation game, humans lose. Period.
But investing isn't a pure computation game. Not anymore. Not in 2026.
Here's what I mean.
The quant shops are incredible at what they do. They will beat you on any strategy that depends on speed, data volume, or statistical pattern recognition. Don't compete there. You'll lose. I proved that personally with $300K.
I now focus on a written investment thesis rather than competing on execution speed; AI can still help generate and challenge that thesis.
Thesis is a belief about how the world is changing. It's not a pattern in historical data. It's a judgment about the future based on understanding technology, policy, human behavior, and structural economic shifts.
It requires conviction. It requires living in the world and seeing things that data alone can't show you.
No algorithm in 2020 would have told you to load up on nuclear energy companies because AI would consume so much electricity that the energy grid becomes the bottleneck. That's not a statistical pattern. That's a reading of the world. A human reading of the world.
No backtest would have told you that the intelligence premium — the model of charging for human expertise — would start collapsing in 2025-2026 because AI made that expertise nearly free.
That's a thesis built on watching the technology, using it daily, and understanding what it means for every business model built on information asymmetry.
These aren't signals in a dataset. They're beliefs about where the world is going. And a generated argument is not evidence of a profitable position or a substitute for my responsibility for the decision.
AI can help process information and propose decisions. I remain responsible for selecting, funding and reviewing my own positions; conviction alone does not establish that I will eventually be right.
That's a human game. That's where I play now.
What My "Hedge Fund" Actually Looks Like
Let me describe the operation so you can see how absurd the contrast is.
2020-2021:
Five full-time employees. Researchers, strategists, developers. Roughly $300K in operating spending over about eighteen months, as reported above. Complex infrastructure. Multiple strategies running simultaneously. Dashboards, alerts, automated execution.
2026:
Me. A laptop. AI tools for research and analysis. My own brain for thesis and selection. Total operating cost: my AI subscriptions and a brokerage account.
That's it. That's the whole fund.
And here's what I actually do. I read. I think. I build analytical workflows using AI to process information faster — earnings reports, industry data, policy changes, competitive dynamics. AI handles the data processing. I handle the "so what."
I don't day-trade. I don't try to time the market. I don't run algorithms. I buy companies I believe in for the next 30 years and I hold them.
I'm buying businesses at the bottom of the economic stack — energy, compute infrastructure, application-layer monopolies — that I believe will compound as AI reshapes every industry above them.
My approach is thesis rather than speed or data volume. I have not established an investing edge through audited returns and an appropriate benchmark. It's the ability to look at the world, decide what's changing, and position ahead of it. Then have the patience to wait.
A team of five couldn't generate alpha through computation. One person with conviction and a clear thesis? We'll see. But I like my odds better now than I did then. Not because I'm smarter. Because I'm finally competing in the right layer.
Why Transparency Is the Point
Most hedge funds hide. That's the model. You raise money, charge 2 and 20, send quarterly letters that explain away bad quarters, and hope the LP base doesn't notice when you underperform the index for five years.
I'm not raising a fund. I'm not managing anyone else's money. I'm not charging fees. So I have a luxury that most fund managers don't: I can be completely honest in public, in real time, with my name on it.
At publication I intended to disclose positions, costs and changes. I have not established a complete public transaction ledger here. The dated investing essays show the reasoning and positions actually reported, not every holding, purchase or sale.
My intention was to write honestly when a thesis failed, explaining what I learned and what I changed. This was a commitment I wanted to keep, not evidence of a complete correction record.
I do this for a few reasons.
It forces discipline.
A public decision record can make disclosed choices easier to revisit. It only covers what is actually documented, so a reader should not assume every trade is on the record. That accountability is what I hoped would make me think harder before acting.
It builds trust the right way.
I write about investing. I write about AI disruption. I write about the death of the intelligence premium. If I'm going to say these things publicly, my money should be where my mouth is. Publishing a position can show what I said I believed at a particular time; it does not independently verify all holdings or returns. It's the difference between commentary and commitment.
It's the opposite of the old model.
The traditional financial industry is built on opacity. Hidden fees. Complex structures. Information asymmetry between manager and investor.
I believe that model is dying — it's another form of the intelligence premium. "Pay me because I know things you don't." AI is making that value proposition increasingly questionable.
I'm not saying every fund manager is a fraud. Most are smart, hardworking people. But the structural advantage of professional money management is compressing.
The tools that used to require a Bloomberg terminal and a team of analysts are now available to anyone with an AI subscription and the willingness to do the work.
The democratization of financial analysis is real. And if it's real, then the question becomes: what's the actual edge? It's not access to information. It's not processing speed. It's thesis, conviction, and patience.
Those are things I can do alone. On the record. In public.
What I'm Not Doing
Let me be really clear about a few things.
This is not investment advice.
I'm sharing how I think about my own money. I'm a 27-year-old entrepreneur in Toronto. I'm not a financial advisor, not a licensed anything.
I have no idea what your financial situation is, what your risk tolerance is, or what you should do with your money. Don't take any of this as a recommendation.
I'm not running an actual fund.
There are no investors. No LPs. No fees. No structure. It refers to my personal brokerage account and my intention to share selected reasoning publicly. Calling it "my hedge fund" is a framing device, not a legal structure.
I could be completely wrong.
I burned $300K being wrong before. I could be wrong again. My thesis about energy, AI infrastructure, and the death of the intelligence premium could be premature, misguided, or flat-out incorrect. The market could prove me wrong for years. Maybe decades.
The difference between now and 2020 isn't that I'm more confident. It's that I'm competing in the right layer, I understand which approach I want to test, while recognizing that it may not produce an edge, and I'm willing to be publicly accountable for the outcome.
The Real Flex
Here's what I think the real flex is in 2026, and it's not what most people think.
It's not portfolio size. It's not returns. It's not a pitch deck with a track record that got you into some fund.
The real flex is this:
being able to manage your own capital, with your own thesis, on your own terms, with zero dependency on anyone else's permission or approval.
No LP telling you the thesis is too concentrated. No risk committee saying your energy allocation is too high. No investors panicking during a drawdown and forcing you to sell at the bottom. No management company taking 2% of assets whether you perform or not.
Just you, your thesis, and time.
I spent $300K learning that I shouldn't compete against machines at being a machine. That lesson costs most people their entire career in finance. I'm grateful I learned it at 21 with my own money instead of at 45 with other people's money.
Now I’m trying a different approach: thinking clearly about where the world is going, buying the companies that benefit, and waiting. No team. No overhead. No complexity.
The quant operation needed five people and burned $300K before it even got off the ground properly. My current operation needs me and an internet connection. And I genuinely believe the second version has a better chance of compounding wealth over 30 years than the first one ever did.
Not because I'm smarter than I was at 21. Because I finally figured out what game I'm actually playing.
The Invitation
I'm not asking you to follow my trades. I'm not asking you to agree with my thesis. I'm definitely not asking you to send me money.
I'm asking you to consider a question: what if you could do this yourself?
What if the edge in investing was never the algorithm, the team, the Bloomberg terminal, the MBA, or the fund structure? What if the edge was always thesis, conviction, and patience — and the rest was just overhead?
What if the $300K I burned wasn't a failure but the most efficient possible way to learn that the entire financial industry's value proposition is compressing toward zero?
I don't know if I'm right. Come back in five years and check.
But I'm on the record. With a date on it. And my money is exactly where my mouth is.
That's more than most people — and most hedge funds — can say.
At publication I intended to share investment reasoning through this original, now unverified destination: founderreality.com/investing. I write about building businesses, investing, and thinking clearly at founderreality.com. None of this is financial advice — it's one person's thinking, timestamped, for anyone who wants to revisit the reasoning actually disclosed.
Historical destination context — October 11, 2026: The two original investing-page links above are retained as publication-era destinations, not a current portfolio inventory or transaction service. For an actual dated account, read Why I’m Open-Sourcing My Portfolio, published April 20, 2026. That essay describes my reasoning and intention; it does not establish a complete live ledger.