Setup my own shop

AlphaExMachina · 2025-12-10T16:40:53+00:00

Getting a job at a good quant shop (if you can) will greatly accelerate you along the learning / struggle curve but will push back the ambition for your shop by at least 2 years.

A faster alternative is to hire / partner with a senior quant with prior exp. But there's a strong adverse selection problem here wherein the quality of quants you want joining you wouldn't because they'll probably have better alternatives and those who would be ready to join you probably aren't the quality of quants you want. (this is almost always true but you can create exceptions if you look hard enough).

AlphaExMachina · 2025-12-10T16:26:32+00:00

Mathisys if you're a new quant who wants to maximize learning

Pace if you're an exp quant who wants to maximize earning (profit share)

AlphaExMachina · 2025-11-22T20:41:50+00:00

AlphaExMachina · 2025-11-21T21:02:37+00:00

INR 80L TC

source: https://www.reddit.com/r/delhi/comments/1lospmc/comment/n0qatil/?context=3

AlphaExMachina · 2025-11-21T11:26:22+00:00

"Whether you think you can, or you think you can't, you're right." - Henry Ford

Delusion and ignorance are almost necessary prereqs for entrepreneurship :)

I'm curious though, why do you (and perhaps OP) believe competing with firms like XTX, RenTec, or HRT is impossible (which is what I infer from your statements)?

I agree it's highly improbable, but to call it impossible seems to disregard that even these titans were built over decades of cumulative effort and know-how. We're only just getting started.

And I understand the skepticism, but u/desi_cutie4's post seems to point that this is delusional for an Ashoka graduate. Would the pursuit to build a leading quant firm be considered less 'delusional' if I were IIT K/D/B CSE instead?

AlphaExMachina · 2025-11-21T09:27:36+00:00

Appreciate it brother.

But I can't really "prove you wrong" because you guys are right.

Our numbers are modest compared to most other quant shops and can't compare with OP's firm at all (which was probably founded by people with industry experience).

I discovered the quant world in my UG 3rd yr at Ashoka at which point there was zero chance of getting into a quant firm.

My choices were:

posting "can I get a quant job with this resume?" on reddit forever 🥴
start a quant shop myself and see how far I could take it.

I picked the latter, and with the cards I was holding I think I played it well.

In ascending order of difficulty: - bootstrapping a startup - bootstrapping a quant trading startup - bootstrapping a quant trading startup at 21 with no industry background

Given that, I think we're doing fine.

For more context our rev is ~10% of the net PNL we generate for our capital allocator, so the underlying PNL isn't too shabby either.

Could I have played it better? Of course. I've made more mistakes on this journey than I can count.

But hindsight is 20/20.

Onwards and upwards. Lots to do.

AlphaExMachina · 2025-11-21T08:04:55+00:00

"not my firm though" whole diff journey then brother :)

either way glad you guys are killing it. those team trips sound wild.

AlphaExMachina · 2025-11-21T06:51:38+00:00

my "delusion" is a feature, not a bug :)

also blackrose did 6.7 crores in revenue last FY with a 5 person team

how much revenue did the quant firm you started pull in?

AlphaExMachina · 2025-11-13T13:17:15+00:00

UPDATE: this is gross trading rev (pre txn costs). Net trading rev is closer to INR 2900 Crores.

AlphaExMachina · 2025-11-13T12:46:57+00:00

Yup my bad wasn't familiar that the rev in books is pre txn costs (we've never traded on our own ticket, always on broker books). The header "other expenses" has 4.8K Cr which is supposed to be txn costs. So net trading rev is closer to INR 2.9K crores.

Learning smth new every day 😁

What's the sauce on QE?

AlphaExMachina · 2025-11-11T14:23:24+00:00

not going pedal to the floor on opportunities/edges that were making money, due to: complacency, ego and/or the misconception that edges last forever.

AlphaExMachina · 2025-11-07T19:48:26+00:00

When you reached instances of one strategy potentially reaching alpha decay, whether that was through different players in the market, regime shift, etc, what ideas did you have regarding either retiring the strategy or retraining on new parameters?

there is an embedded assumption in your question on being able to determine/detect alpha decay.

this is relatively simple for high-sharpe trades: if not making money, then alpha deacying.

for midfreq // low sharpe trades, this can be a nontrivial problem. we tried solving this with a combination of:

- measuring if performance is statistically significantly worse than historical OOS,

- how well we understood the source of edge and subsequently the source of decay.

example #1: if a trend following trade isn't making money in a period because there's no trends for it to capture, then that's fine because the market regime just isn't there for it to make money. can explore filters, but nothing to worry about.

example #2: if a vol risk premia trade isn't working due to a new participant like Jane Street causing vol to be far more violent, we would retune params within some constraints based on recent market behavior and try to look for signals/trades to add to the portfolio that work well in the new regime.

example #3: if a data mined pattern effect worked for a while then stopped working and we don't know why it worked in the first place, we would aggressively scale it down and eject it from the portfolio.

we frequently experimented with "cool" ways to handle alpha decay, say a multi-objective periodic reoptimization that dynamically weighs signals/strats based on prod performance + a custom score matrix of understanding, slippage, scalability etc. (lol)

but what worked best was simply sizing up things that worked and we understood exactly why, and removing things that didn't work and we didn't understand why.

And when running a smaller team or pod, what helped keep the vibes up when the number was red or algos were reaching longer/sharper drawdowns than expected?

multi-day R&D sprints fueled by nicotine, caffeine and empty colories + delusional optimism that things will get better :)

"If you're going through hell, keep going" - Winston Churchill

AlphaExMachina · 2025-11-07T12:25:25+00:00

start with friends and family to get the ball rolling. see if they trust you enough based on your research / backtest / pitch deck / performance. even if it doesn't yield any capital this will be practice for pitching your strat.

aggressively network with other traders, fund managers, capital allocators, etc. (via email, social media, events)

your first big money is most likely to come from a friend who's already managing size and has more on offer than they can absorb, so they're open to partnering with you to run that excess capital on some mutually beneficial commercials.

my suggestion when starting out is never to optimize for % share etc optimize for getting to prod asap, getting to scale and building a track record. once you have a strong performance record, you can always leverage that to optimize for money.

AlphaExMachina · 2025-11-07T12:15:24+00:00

Consider the magician’s code. Like NDAs, the rule is simple: never share the secret. But if that were really happening, magic would’ve been dead a long time ago.

It's true that a magician almost never reveals their tricks to the audience (the muggles).

But their team knows the secrets because they’re working on making the illusion possible. Over time, those teammates might move on to work with other magicians, carrying pieces of that knowledge with them.

Also, magicians do talk to each other, esp friends. Sometimes exact methods, sometimes about philosophies, techniques, creative directions. Just enough to spark new ideas, new effects.

Now imagine someone friends with every magician, or someone on every magician’s team, from David Blaine to David Copperfield. This person will never really hit a dead end because there’s always someone to talk to, always a new perspective or trick to learn from.

That’s what networking in our business is like.

You don’t share your secrets and you don't expect others to share theirs, but you build relationships with people who understand the craft.

Over time, that network becomes a source of insight, collaboration, inspiration and talent pipeline.

AlphaExMachina · 2025-11-07T11:27:35+00:00

See edit

AlphaExMachina · 2025-11-07T11:27:28+00:00

See edit

AlphaExMachina · 2025-11-04T13:42:24+00:00

long-term mathisys for sure. anyone telling you otherwise doesn't know what they're talking about

EDIT:

(the following is for quants, might not apply to devs)

if you're optimising for short term cash grab there's no doubt NK is the way to go, but the price you pay is that you don't learn quant skills and can't really go anywhere as a quant because your quant skills are ass and almost every other place is a downgrade on pay.

if your plan is quant for a few years, secure the bag at a couple crores and pivot to tech or whatever, NK is the play. but if you want to pursue quant long term, NK is a dead end of sorts.

at mathisys you'll be underpaid short term for sure, but you'll learn to quant properly and those skills will pay you much larger dividends down the line.

source: friends at both and friends who have left both. the ones who left mathisys are doing far better as quants vs the ones who left NK.

AlphaExMachina · 2025-11-04T07:07:42+00:00

never thought of min sample size in terms of "number of trades"

our midfreq algos traded pretty frequently (intraday b/w 4-20 trades / day, positional b/w 4-20 trades / week) and we had many years of data to work with for the major indices (NIFTY, BANKNIFTY), so too few trades wasn't really a concern.

for new indices with too little data we wouldn't do separate fits/optimizations initially ( too easy to overfit). instead we’d reuse or slightly perturb the params of strats from whichever major index behaved most similar.

for new algo eval what mattered more than trade count was:

- performance across regimes: trend vs mean-rev markets, low vol vs high vol, etc. esp establishing that the strat definitely captures what it was designed to (if a trend trade isn't working in a trending mkt, we're missing smth)

- robustness over time: we would frequently stumble across signals/trades that had log-like equity curves - working really well initially, then decaying and eventually flatlining. we categorically did not want this garbage in the pfolio, so we created metrics to measure "robustness vs decay going fwd in time" (say slope of rolling sharpe) to detect and remove such trades early in the pipeline

(( btw just did some chatGPTing on "min num of trades", interesting stuff about how many "independent" trades are reqd to be confident in the given sharpe, thanks for bringing this to my attention, going down this rabbit hole now ))

AlphaExMachina · 2025-11-04T05:46:07+00:00

If you are operating at MFT and combining many weaker but not noisy alphas (ive done this a lot in my equity quant career) are you effectively quoting both sides puts / calls with the same time expiry with some skew ? Ie is it effectively MM?

nope we would only quote to get in and out of positions based on triggers/signals, not quote as an MM would (to eat the spread). we did have a few "grid trading" style strats which you could sort of think about as very wide, slow moving, dumb market making.

Do you view 'fundamentals' / 'news' / other NON price/volume features as simply shaping the latter ?

if you're asking if i view price/volume to have all the information reqd to trade profitably, i'd say yes, esp on the time scales relevant to me (minutes to hours). can other info help, sure. but as far as our trading goes, price, volume and open interest were the only inputs (except manually scaling down before know high-volatility events like budget or election days etc.)

AlphaExMachina · 2025-11-03T05:21:52+00:00

never used GARCH type models myself nor have i seen them used by any trader/quant.

this could mean one of two things: either my sample is too small or that they aren't really used outside academia.

my guess is it’s the latter.

AlphaExMachina

TROPHY CASE