The Short Version

This is a quantitative trading research platform. I look for strategies that have actual research behind them, test them against years of real data, and don't put money in until they pass walk-forward validation and Monte Carlo simulation. If a strategy can't prove itself on unseen data, it doesn't trade.

I am a researcher at heart and a serial optimizer. Beyond the trading, I run independent empirical studies on the standard models that the industry relies on. How often do limit orders actually fill? How far off is Black-Scholes from what the market actually trades at? These are questions most platforms don't ask because the answers make their backtests look worse. I collect the data, fit the models, and publish the results as they come in. The fill model and pricing model pages are ongoing studies that update daily with live data.

The Founder

I have a B.S. in Physics, a B.A. in Natural & Applied Sciences, and a Master's in Physics. My research was at Jefferson Lab, working on deep inelastic scattering experiments funded by DOE.

In particle physics, your code is your eyes. You can't watch a parton get knocked out of a proton and recombine into a shower of particles in 10^-23 seconds. You write models and analysis code, and that's how you see what happened. I spent years getting good at pulling real signals out of noisy data and knowing how much to trust the answer.

He wasn't wrong. Simons came from theoretical physics and built Renaissance. Shaw came from computational science and built D.E. Shaw. Derman went from particle physics at Columbia to running quant strategies at Goldman. It's the same problem in a different domain: signal extraction from noise.

That's what this platform does. Same discipline, applied to market data instead of collision data. No gut feelings, no chart patterns. Just math.

The Process

I start with research. Academic papers, AQR white papers, documented strategies from professional traders. I'm not looking for tips. I want edges where someone has done the work and shown a structural reason the opportunity should persist.

Most institutional strategies need capital or infrastructure that retail traders don't have, so I filter for what actually works with a small account. What's left gets backtested across years of real data, parameter-swept, then validated with walk-forward testing and Monte Carlo. I'm not looking for the prettiest backtest. I want the one that holds up on data it's never seen.

If it passes, I build a paper bot and run it on live market data with real slippage and commissions. The ORB futures bots went through this whole pipeline before real money went in. Nothing skips paper trading.

The part that surprised me is how much this feels like being back in the lab. In physics I built theoretical models, collected real data, measured the residuals, and improved the model. Here the instruments are different but the method is the same. The fill probability model is my detector. The options chain snapshots are my beam time. I am testing how well the standard theoretical models hold up against millions of real observations and building corrections where they fall short. It is experimental physics applied to markets. And watching live data converge to a confident model is still the most satisfying thing I know how to do. I do not think math is something we invented. I think it is the language the universe already speaks and we are slowly learning to read it. That conviction is what got me into physics and it is what keeps me here.

How I Work

• Hypothesis first. I don't optimize to fit historical data and call it edge. The strategy needs a reason to work before I test it.
• Uncertainty matters. A 70% win rate doesn't mean much if the confidence interval is wide. I bootstrap thousands of resamples to check if the edge is real or noise. Same standard as publishing a physics result.
• Real costs included. Commissions, slippage, and fill assumptions are built into every backtest. Options use a 66% bid-ask spread slippage model. Futures model limit order fill probability from live data. Most backtest results you see online skip this.
• Everything is published. Every trade, every loss, every drawdown. Check the journal.