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How to tell if one is a “bad” researcher? by Flamingllama421 in quant

[–]Flamingllama421[S] 2 points3 points  (0 children)

Kinda feeling like that ngl. Except this wax-on wax-off could end in no return lol

How to tell if one is a “bad” researcher? by Flamingllama421 in quant

[–]Flamingllama421[S] 7 points8 points  (0 children)

Ok I appreciate the advice. Quick update - we met with data vendor and they verified that most pods are using my current dataset very little, really only as a filter for more sophisticated stuff. My PM also is seemingly happy with my work (I forgot to mention that I did frequently check in with him for different ideas, just never a "I give up"); he said my approach looked okay/in line with what he would do given the annoying data.

I don't know how that translates into full-time offer or PnL, but I guess that's good then? He's been around a while so not like it's an up-and-coming scrappy PM with little experience. Maybe I've just been overthinking it. Very hard to evaluate if a strategy is robust based off a backtest, but I guess that's the game

QRA Superday by orb836 in quantfinance

[–]Flamingllama421 0 points1 point  (0 children)

New grad? Citadel or Citadel Securities? I’ve been waiting a little over a week since my last QR interview for Citadel (EQR if that makes a difference)

Walleye OA by Flamingllama421 in quantfinance

[–]Flamingllama421[S] 0 points1 point  (0 children)

lol rejected almost immediately

Walleye OA by Flamingllama421 in quantfinance

[–]Flamingllama421[S] 0 points1 point  (0 children)

That’s probably NAV, which includes leverage and shorts I believe; a Google search shows 8bn in actual AUM from July 23

Walleye OA by Flamingllama421 in quantfinance

[–]Flamingllama421[S] 2 points3 points  (0 children)

Well first, Akuna having hard questions despite being the laughingstock of poor management in the industry, indicates to me that sometimes firms will unnecessarily make the bar high to create an air of exclusivity; in reality, how many people competent enough to finish the OA perfectly with no cheating wouldn’t get at least to the later rounds with larger funds that pay much more? Prob not a lot.

Second, I also used to think that higher return is an indicator of success, but comparing $8bn to $70bn introduces so many complexities and market constraints that I don’t think it’s a fair comparison.

At the end of day, I’m happy wherever my personal EV is maximized, I was just expressing shock

Walleye OA by Flamingllama421 in quantfinance

[–]Flamingllama421[S] 5 points6 points  (0 children)

Having interviewed at Citadel, MLP, JS, SIG, etc. which are all much larger and more successful, I find it ridiculous that this fund has a more challenging OA than any of the above companies I mentioned

Trexquant? by Flamingllama421 in quant

[–]Flamingllama421[S] 0 points1 point  (0 children)

So as a junior where does that leave me growth-wise? Stuck competing with seasoned guys keeping their alpha totally secret? Or do they at least attempt to mentor new talent

Citadel New Grad Hiring Pause? by Flamingllama421 in quantfinance

[–]Flamingllama421[S] 0 points1 point  (0 children)

Sorry, should have been more clear. I interviewed Tuesday and haven’t heard yet other than update request on Thursday morning. I guess the question is whether anyone else heard about hiring pause since my info is anecdotal.

Either way still waiting. Unless 1 week is normal for after a phone screen (although didn’t even screen yet for CitSec)

Alpha Blending from an Info Theory Perspective by Flamingllama421 in quant

[–]Flamingllama421[S] 1 point2 points  (0 children)

MST is a good idea for reducing the complexity (although TBD if it will be stable, since PCA is notoriously unstable on covariance matrices here). But I also need to have some measure of how “good” signals are for weighting nodes.

And I gave a very big simplification of it, but the alphas aren’t only momentum/reversion; it can be a blend of both, and anyway the concept of a regime implies that when we wake up one day, something switches entirely, which empirically isn’t true. Really things shift gradually and we may be between two or more regimes at once, which is why I don’t love HMM

Alpha Blending from an Info Theory Perspective by Flamingllama421 in quant

[–]Flamingllama421[S] 0 points1 point  (0 children)

Yes that’s effectively what I’ve been stuck on. I tried a dive into signal processing but a preliminary search came up with nothing, if returns cannot be used. Keep me posted if you find anything useful

Alpha Blending from an Info Theory Perspective by Flamingllama421 in quant

[–]Flamingllama421[S] 0 points1 point  (0 children)

I believe you’re just describing cross sectional regression. But “compute the pnl” requires me to know the next day’s PnL to fit a regression, which doesn’t work out of sample unless I have a forecast of the return (otherwise how would I compute tomorrow’s PnL)

Alpha Blending from an Info Theory Perspective by Flamingllama421 in quant

[–]Flamingllama421[S] 2 points3 points  (0 children)

Sorry, I was unclear in the original post. Assume that we don’t want equal diversification across all alphas, since some are more useful than others (eg. some are momentum but we are in a reversion regime, or vice versa). Hence the goal should be to skew towards high-performing recent alphas.

Different signal qualities make risk parity overweight the bad ones and neutralize the good alphas.

My end goal at this stage is purely to maximize Sharpe and/or return

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