u/Ok_Philosophy_4031

Curious how different firms actually run their quant research and trading infrastructure today.

For people at funds / prop shops / systematic teams: how much of your stack is internally built versus using vendor or open-source tools? What is your tech stack?

I’m especially interested in the boring but important parts: data ingestion, feature stores, backtesting, experiment tracking, alpha library management, portfolio construction, risk checks, deployment, monitoring, and post-trade attribution. Everybody talk strategies and research, but logistics are just as important if not more.

Do most serious teams still end up building almost everything themselves because the workflow is too specific, or are there parts of the stack where off-the-shelf tools have genuinely become good enough?

Would also be interested in how this differs between single-PM pods, central quant platforms, and smaller emerging managers.

I refrained from using the term "vibe coded" here because we do have in place proper PRDs and system designs and implementation plans that we feed to the agent.

I'm an MLE and SWE, so it's not like I can't read code or don't know any proper practices. What is different though are that

1. Since we are not spending 6-8 hours a day working with the code base unlike pre-coding agents, we lack the indepth knowledge and intuition that helps us narrow down root cause when bugs arise, or know how to best extend the current code base to support a new feature

2. Because of the productivity from coding agents, the size of the code base and sophistication of the app relative to number of developers has also increased. Each developer now needs to maintain context and understanding over a greater surface area and number of micro services. Over time this makes reviewing extension to the app and code base increasingly more difficult cognitively.

Anyone else dealing with this too? Is it just a problem with our engineering processes? What works for you?

YC has funded startups in institutional investment tech. But so far, none have really made it.

That is the tension behind why we wrote this. Being in SF, part of an accelerator and talking to founders constantly, YC comes up almost every week as the default “serious founder” path. But for high-finance investment tech, I think YC’s blind spot is mistaking speed for truth.

Every founder in SF talks about applying to YC. Being here, and constantly speaking with other founders, I hear it all the time. YC has become the default answer to “what’s next?” for ambitious early-stage startups.

But I think that default deserves more scrutiny, especially for founders building institutional investment technology.

YC is world-class when the bottleneck is speed: ship faster, talk to users, compress learning cycles, grow visibly, raise capital, repeat. That works incredibly well when the user is reachable, the pain point is legible, and adoption can compound quickly. But high-finance investment tech is not classic SaaS. The bottleneck is often not speed. It is trust.

A hedge fund, asset manager, allocator, PM, analyst, risk team, or compliance function does not adopt software just because the demo is impressive. They adopt when the product earns the right to sit inside a real investment workflow, where capital, careers, reputations, and regulatory obligations are on the line.

That means the real validation loop is not “can we launch this in three weeks?” It is: Can the output be trusted? Can it be audited? Can it be cited in an investment memo? Can it survive scrutiny from a PM? Can it integrate with messy proprietary workflows? Can the institution defend using it when things go wrong?

This is why I suspect YC has produced many great fintech companies, but no obvious category-defining institutional investment-tech company. Payments, banking, cards, crypto exchanges, and expense management are hard, but they are cleaner software problems. High-finance investment tech is software plus judgment, provenance, workflow embedding, data rights, institutional credibility, and long-cycle trust formation.

So my controversial take: for some verticals, YC may create the wrong kind of pressure. It can make founders over-index on visible momentum when the real company is being built in invisible trust layers.

For investment tech, the best “accelerator” may not be a three-month batch. It may be a year spent embedded with serious finance users, learning what they actually trust, what they reject, what they pay for, and what they are willing to move into production.

P.S Got removed by the mods in r/ycombinator. Probably because we hit a sore spot. :P

Edit: Apologies for the misleading use of "we". Had to remove the link to our site to be compliant with guidelines in this sub. For clarity we are a startup building in the investment tech space.

YC has funded startups in institutional investment tech. But so far, none have really made it.

That is the tension behind why we wrote this. Being in SF, part of F.Inc, and talking to founders constantly, YC comes up almost every week as the default “serious founder” path. But for high-finance investment tech, I think YC’s blind spot is mistaking speed for truth.

Every founder in SF talks about applying to YC. Being here, part of F.Inc, and constantly speaking with other founders, I hear it all the time. YC has become the default answer to “what’s next?” for ambitious early-stage startups.

But I think that default deserves more scrutiny, especially for founders building institutional investment technology.

YC is world-class when the bottleneck is speed: ship faster, talk to users, compress learning cycles, grow visibly, raise capital, repeat. That works incredibly well when the user is reachable, the pain point is legible, and adoption can compound quickly. But high-finance investment tech is not classic SaaS. The bottleneck is often not speed. It is trust.

A hedge fund, asset manager, allocator, PM, analyst, risk team, or compliance function does not adopt software just because the demo is impressive. They adopt when the product earns the right to sit inside a real investment workflow, where capital, careers, reputations, and regulatory obligations are on the line.

That means the real validation loop is not “can we launch this in three weeks?” It is: Can the output be trusted? Can it be audited? Can it be cited in an investment memo? Can it survive scrutiny from a PM? Can it integrate with messy proprietary workflows? Can the institution defend using it when things go wrong?

This is why I suspect YC has produced many great fintech companies, but no obvious category-defining institutional investment-tech company. Payments, banking, cards, crypto exchanges, and expense management are hard, but they are cleaner software problems. High-finance investment tech is software plus judgment, provenance, workflow embedding, data rights, institutional credibility, and long-cycle trust formation.

So my controversial take: for some verticals, YC may create the wrong kind of pressure. It can make founders over-index on visible momentum when the real company is being built in invisible trust layers.

For investment tech, the best “accelerator” may not be a three-month batch. It may be a year spent embedded with serious finance users, learning what they actually trust, what they reject, what they pay for, and what they are willing to move into production.

P.S Mod in r/ycombinator removed our post, probably because we hit a sore spot. So we are reposting here :P

YC has funded startups in institutional investment tech. But so far, none have really made it.

That is the tension behind why we wrote this. Being in SF, part of F.Inc, and talking to founders constantly, YC comes up almost every week as the default “serious founder” path. But for high-finance investment tech, I think YC’s blind spot is mistaking speed for truth.

Every founder in SF talks about applying to YC. Being here, part of F.Inc, and constantly speaking with other founders, I hear it all the time. YC has become the default answer to “what’s next?” for ambitious early-stage startups.

But I think that default deserves more scrutiny, especially for founders building institutional investment technology.

YC is world-class when the bottleneck is speed: ship faster, talk to users, compress learning cycles, grow visibly, raise capital, repeat. That works incredibly well when the user is reachable, the pain point is legible, and adoption can compound quickly. But high-finance investment tech is not classic SaaS. The bottleneck is often not speed. It is trust.

A hedge fund, asset manager, allocator, PM, analyst, risk team, or compliance function does not adopt software just because the demo is impressive. They adopt when the product earns the right to sit inside a real investment workflow, where capital, careers, reputations, and regulatory obligations are on the line.

That means the real validation loop is not “can we launch this in three weeks?” It is: Can the output be trusted? Can it be audited? Can it be cited in an investment memo? Can it survive scrutiny from a PM? Can it integrate with messy proprietary workflows? Can the institution defend using it when things go wrong?

This is why I suspect YC has produced many great fintech companies, but no obvious category-defining institutional investment-tech company. Payments, banking, cards, crypto exchanges, and expense management are hard, but they are cleaner software problems. High-finance investment tech is software plus judgment, provenance, workflow embedding, data rights, institutional credibility, and long-cycle trust formation.

So my controversial take: for some verticals, YC may create the wrong kind of pressure. It can make founders over-index on visible momentum when the real company is being built in invisible trust layers.

For investment tech, the best “accelerator” may not be a three-month batch. It may be a year spent embedded with serious finance users, learning what they actually trust, what they reject, what they pay for, and what they are willing to move into production.

https://podium-finance.com/blog/yc-and-investment-tech

Spent enough years in this business to know that the mythology of the lone PM was always only half true. Alpha has always mattered, but the real differentiator over time was operationalizing that alpha into something allocators, regulators, and counterparties could actually trust at scale. The rise of Millennium, Citadel, and now the struggles even elite launches like Jain Global face all reinforce the same lesson: modern hedge funds are increasingly machines for operational alpha as much as investment insight.

What resonates with me here is the idea that the future may not belong solely to better traders or quants, but to whoever best builds the infrastructure layer that forms, validates, and scales PMs before they ever become institutional products. In many ways, the next great evolution in hedge funds may be less about finding the next genius—and more about redesigning the system that produces them.

“Amateurs talk tactics, professionals study logistics” -General Omar Bradley.

https://podium-finance.com/blog/ai-native-hedge-funds-and-yc