I worked 2017-2024 at Two Sigma1, a systematic hedge fund. I loved working there, and was sad to leave.
2020-2024 my team and I founded and scaled the industry’s first (afaik) “systematic buy-side”2 alpha capture business.
I’m now on non-compete leave through December 2025, blessed with time and hindsight. Here are some lessons I plan to take with me.
Default tech mindset: “How do we ship and scale this?”
Default quant mindset: “Why does this almost certainly fail?”
This is healthy paranoia. Many strategies plausibly generate value. Meanwhile, the market turns every alpha into beta into noise.
A checklist I learned the hard way:
“Think systematically” really means “list every option”.
For example, consider whether to de-mean a feature:
How many of these variations should I try? How do I pick the winner? Should I pick the winner the same way next time?
Every decision is like this, with layers and layers of choices. Dimensionalization helps.
When the markets are up (true most of the time), quants miss out. Market neutrality underperforms. Tech Equity > Quant Bonuses over the last 2 decades. And it’s more fun to root for everyone to win.
When the markets are down, everyone is worried, quants not excepted. But they are still making money.
10 orthogonal alpha strategies ≈ long 10 cheap call options3.
Buying 10 random call options does not recreate 10 orthogonal strategies.
Diversification comes from true differentiation in the source of return. Buying 10 tickets to the same lottery is not diversifying. Source independence is the scarce commodity.
The past is data. The present is noise. The future is revenue.
Decay starts immediately after release. Keep building; rest is expensive.
Very few things unexpectedly go right: surprise usually hurts. And usually you can’t retire after one lucky call.
But any one thing that goes wrong, even (especially) unrelated to investments and ideas, can lead to ruin.
There is not much confusion about what motivates people in quant finance. This is helpful for modeling, but brutal for trust.
Helpful, because if I know an agent acts in their own interest, their actions are a strong signal of what their interests are.
Brutal, because if I know anyone will take advantage of my weakness, I need to be on guard at all times.
Legalese is the duct tape over that bounded distrust.
There will probably not be a DeepSeek moment in quant finance. No hedge fund is open-sourcing their alphas. Information is too valuable:
If nothing can be trusted without a track record, and everything is a secret, how does anything get started?
Reputation is how. Reputation allows your model to have a lower discount rate, more optimistic scenario analyses, and stronger priors.
Not to mention that reputation helps you execute too.
One of the best things about a high Sharpe strategy is that I know quickly if it stops working. Sharpe 5 means I cut after a few losing days in a row. At Sharpe 1, I might wait out years of losses.
But I can’t scale Sharpe 5 as big as Sharpe 1. Often the Sharpe‑1 behemoth out‑earns the Sharpe‑5 jewel in dollar terms. And I eat $ PNL, not σ‑adjusted virtue.
Every green‑lit project blocks a better unseen one and mortgages bandwidth to execute. Budget attention, not just capital.
The important thing is the buy/sell decision, not the accuracy of the input:
Accuracy is helpful, but the decision pays the bills4.
Allies are aligned: they pursue shared interests or goals. When context changes, alliances shift.
Friends stick around. But it’s hard to make friends when everyone (rationally) fears vulnerability.
This contrasts with the lesson imparted by many strategy games, where Ally is the highest relationship tier, while Friend happens early.
Your time, tools, and ideas are worth exactly what you can generate with them. If someone will pay more than that, consider it a blessing. And then price your optionality.
What heuristics keep you solvent? Leave a comment; I read everything.
Opinions solely mine; errors solely mine. Nothing here is investment advice.
“systematic buy-side” ≠ “systematic sell-side” ≠ “discretionary buy-side”
A good payoff profile for an alpha strategy is call-option-like: bounded downside, positive mean, fat right tail.
Of course, over many decisions, being more accurate should help. But accuracy only compounds if you survive.