Not everything that counts can be counted.

I began my professional life in Las Vegas where I learned marketing working with some of the largest names in entertainment and hospitality. Thirsting for a more refined experience, I later escaped to the North Bay, where the roads curve through vineyards and the air at dawn smells of fermenting grapes. Clients online imagined I worked from a glass tower in San Francisco; in truth the nearest skyline was a windbreak of redwoods, and my commute took fourteen steps across a porch slick with coastal fog. By daylight I built marketing‑technology pipelines for brands I seldom met in person. On rare occasions I surfaced in the cities. Mostly I stayed put, billing hours through fiber cables while ravens circled the vines.

At four‑forty‑five each morning I slipped into a second, more secret occupation. The monitors ignited, the data feeds began their intravenous drip, and I became a hobbyist quant, parsing tick data the way a cryptographer pores over intercepted chatter. The promise was intoxicating: uncover a repeatable edge, scale it, and never again write another quarterly marketing roadmap. I had no appetite for clients, staff meetings, or the PowerPoint pageantry that passes for urgency in enterprise sales. I wanted an income as solitary as my landscape.

For almost a decade I pursued that mirage. The scripts grew intricate, the back‑tests persuasive. Equity curves arced heavenward in smooth trajectories, and more than one friend urged me to make the conventional leap: raise capital, hang a hedge‑fund shingle, earn two‑and‑twenty while junior quants kept the engines purring. The suggestion missed the point. Launching a fund would have yoked me once again to things I despised—investor updates, compliance rituals, brand management—precisely the obligations I had fled to the country to escape. My models did reach what most would consider “fund‑worthy” scale, but I never had the bankroll required to run them entirely for myself, nor the will to run them for other people. And so the dream stalled—elegant, back‑tested, insufficient.

In a fit of entrepreneurial optimism I placed the algorithms on ice and threw myself into a startup, planning to sell the company and use the proceeds to stake my trading. The exit fizzled in a blaze of bad actors and legalese — and I emerged singed, capital‑light, and newly wary of the corporate hero’s journey. Consulting felt like surrender. The only path left, paradoxically, was the one I’d sought to shortcut: trading itself, stark and ungarnished.

So what was I to do? I had plenty of insights and theories that I had never fully integrated into my models, yet I no longer could take the chance of wasting more time doing system development in hopes of creating an edge strong enough to let me leapfrog into greatness.

Building these systems takes time and money, neither of which I had a surplus of. Weighing my options, the only path left, is the one that I previously refused to acknowledge for more than ten years. The path of the discretionary trader.

Sure, I knew that the highest risk‑adjusted returns still belonged to discretionary traders. I also knew that pursuing their path meant tip‑toeing through a minefield of cognitive biases, emotional whiplash, and psychological traps that can take even seasoned professionals years—sometimes decades—to master.

Yet as I reflected further, I saw there was no other option. There was far too much nuance in the markets that I had failed to train into my systems, and so if I wanted to capitalize on the insights I had generated through my research, I had to start manually trading. 

In many ways, that is when my true education of what it means to be a trader began. Historically as a quant, when my trader buddies would talk about trader psychology, it was interesting but did not have a tremendous amount of weight in my day to day practice. In an instance it became almost the only thing that mattered.

I would now need to learn what it would mean to master myself, in ways that I never imagined.