My name is Gunit Singh. Let me tell you who I am — not the polished version, the real one.
I spent 15 years playing cricket at BCCI and UPCA level. That means I know what it feels like to train harder than anyone around you and still lose because of a single decision you made in a split second. Cricket at that level teaches you something that no book ever will: the game is 90% mental and 10% physical. The moment you let emotion drive a decision, you lose.
Then I spent 7 years in retail sales and management. I learned how people think, what they need to hear, and what they actually need. I got good at reading situations, understanding incentives, and closing. I learned that the best sales don't feel like sales — they feel like solutions.
Then I became a self-trained Python developer. Not through a course. Not through a bootcamp. Through building things I cared about. I taught myself machine learning, data analysis, quantitative methods, blockchain analytics — all because the problem was worth solving and nobody was solving it the right way.
And somewhere in between all of that, I became an intraday retail trader. MNQ — Micro E-mini Nasdaq futures. Nifty options. Trading in the morning, managing positions through the day, staring at the same charts that institutional traders are staring at — except they have a dozen analysts, a quant team, and an algorithmic consensus engine. And I had a price chart and my gut.
The institutional trader doesn't know more than you. They just have better systems to process what they already know. That gap is not knowledge. It's infrastructure.
There is a specific day I remember. I was watching a major macro event — one of those moments where the market is clearly choosing a direction. I had my analysis. I had my thesis. I had a clean setup. And then I found out — afterwards, not before — that the institutional desks had already positioned for exactly what happened. Not through insider information. Through quantitative analysis of options flow, market regime detection, and session probability modeling.
They saw it coming because they had the systems to see it. I didn't.
And I thought: why the hell does a hedge fund have better information from public market data than I do?
The answer, it turned out, was simple: they didn't have better information. They had better systems to process the same public information. They had multiple models analyzing the same data simultaneously. They had a regime detection system that told them whether the market was trending or choppy. They had an options desk analyzing implied volatility. They had a risk officer enforcing discipline when humans wanted to override.
They had a quant system. A trading desk intelligence layer. And I had a price chart.
So I started looking at what retail traders had access to. Signal services. Tip groups. Technical analysis courses. Trading bots that did one thing. TradingView indicators. None of it was a complete institutional intelligence system. None of it was designed for the retail trader the way a Bloomberg terminal is designed for the institutional trader.
The gap wasn't about intelligence. The retail trader is not less smart than the institutional trader. The gap was infrastructure. The retail trader didn't have access to the same analytical systems — the same quant desk intelligence, the same alpha detection, the same self-improving AI.
And I thought: this is a solvable problem. I've built software before. I've trained machine learning models. I understand quantitative analysis. And I understand the retail trader because I am one.
So I built Traders Regiment.
Not a signal service. Not a bot. Not an indicator.
Traders Regiment is a complete institutional-grade quant trading intelligence system — built for the retail trader. Here's what it does:
The vision is simple: give every retail trader the intelligence of a trading desk — the same hedge fund-level alpha detection, the same institutional regime awareness, the same quant system precision — at a fraction of the cost and accessible from a browser.
It's for the retail trader who is done being disadvantaged.
For the trader who has the skill, the discipline, the screen time — but not the institutional infrastructure. The trader who knows what they're looking at on the chart but wants to see it confirmed by something smarter than gut feel. The trader who wants a personal quant partner — always on, never emotional, always running the numbers.
It's for the intraday trader who trades MNQ or Nifty options and wants to know what the institutional desks are seeing. Not a tip. Not a signal. The actual institutional quant analysis.
I grew up in cricket where the difference between winning and losing was preparation meeting opportunity. You train. You prepare. You read the game. And then in the moment that matters, you execute with clarity.
That's exactly what trading is. And that's exactly what most retail traders are missing — not the courage, not the capital, not the skill. Just the preparation infrastructure that institutional traders take for granted.
When I'm not building Traders Regiment, I'm cycling, watching birds in my garden, singing, researching anything on earth that interests me, or sitting idle — which is underrated. I believe deeply in the value of thinking deeply about one thing until you understand it completely.
That's the same way I built this. I went all the way down the rabbit hole of quantitative trading systems, machine learning model architectures, self-improving AI systems, options pricing and Greeks, regime detection algorithms — until I understood not just how institutional trading systems work, but how to rebuild them for one person instead of a hundred.
And I did it. It's live. It works. And it's getting smarter every day.
The question was never whether retail traders deserve institutional-grade edge. The question was why nobody built it for them. We did.
Traders Regiment is the World's Most Advanced Trading AI. Your personal quant team. Always on.
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