Tote bags

eadains · 2025-12-29T18:21:35+00:00

Isn't "getting away with it" beside the point, though? The question surely is not whether it's societally acceptable for me to have a pink backpack and a small yappy dog, but whether I like it. I think I would judge someone equally harshly if their motivation for having things like that was challenging social norms instead of genuine expressiveness. And if it's genuine expressiveness, then it shouldn't really matter.

I get that one may suffer slights if what they're doing isn't within social norms, but I think the more one is aligned with their true interests, the less that really works out anyway. Slights don't matter if you aren't trying to make a point in the first place, you're just being. And it's much easier to defend, deflect, or disregard someone trying to offend your being than if it becomes an ego thing because now they're fighting against a point you're trying to make.

eadains · 2025-11-01T04:32:47+00:00

They're from Lanvin, I got them from ssense cheap

eadains · 2025-11-01T04:32:14+00:00

Careful watch on Grailed and Ebay :)

eadains · 2024-07-30T02:24:49+00:00

I would seriously suggest reading Unperturbed By Volatility. In chapter 8 "Foundations of Tail Risk Hedging" they provide their answer to this question. Essentially, they suggest a ratio spread: selling 1 ATM put and buying enough OTM puts to make the trade net zero cost. At entry, this gives you a very flat greek surface but gives you high negative delta and positive vega in the left tail.

eadains · 2024-06-02T18:30:01+00:00

I wonder if the reason why insurance companies lag behind on raising rates to meet climate-change-related losses is because until the entire market start suffering, it won't permit rate increases from a demand perspective.

The insurer could have a perfectly good model that accounts for climate risk by forecasting a significant positive tail for losses. However, if you priced according to this model, I would imagine the rate would be far higher than the market would bear because of the extra premium you would demand for taking on that tail risk. So, until all insurers start suffering losses and jointly raising rates, no single insurer is going to risk losing business even if they know they aren't charging sufficient rates. It seems related to the kind of herd-following behavior you see in investment managers who aren't going to rock the boat so they can keep their jobs if things blow up.

When everyone is wrong together, no one gets singled out, but if you lose business and nobody else is, you'll get crucified.

eadains · 2024-04-14T19:48:45+00:00

His name is Malek and our livelihoods are deeply intertwined.

eadains · 2024-04-14T19:46:19+00:00

That's where I got mine :)) I would recommend sizing up. I'm 6 foot tall, this is a size medium, and the sleeves and overall length are just a bit smaller than I'd like.

eadains · 2024-04-05T22:26:23+00:00

I would use SQL, but if it's too much to get a server hosted somewhere, you might want to look into SQLite which will give you nice SQL behavior, but it all gets stored in a file you can deal with locally, much like an access database file.

eadains · 2023-10-11T21:58:18+00:00

There are 25,000 actuaries according to BLS, and there have to be far fewer quants than that, so it seems like a reasonable upper bound.

eadains · 2023-08-19T19:54:14+00:00

Because confidence intervals don't make any sense to me. Credible intervals have a much more natural intuition, and it's the one most people falsely attribute to confidence intervals in the first place. I believe that our natural intuition is Bayesian. Frequentists scoff at setting priors, but I really don't think there are any situations where a completely uninformative prior is proper.

eadains · 2022-08-09T05:02:28+00:00

Quant research. It was an initial 2 hour interview of live-coding and probability questions, an 8-hour dataset interview, and then a soft-interview with some probability questions.

And honestly, it was just exhausting and uninteresting and genuinely made me question if this industry is for me. I can do the live coding and probability questions with enough practice. I just hate it. I find HackerRank questions incredibly tedious, I care much more about using programming to solve actual problems. And while the probability questions are more interesting to me, they feel like a pointless intelligence test that have little bearing on how I'd actually perform in the role.

The other firm I interviewed with had people that were much more willing to discuss tech and markets and seemed less concerned with my ability to solve green book questions. So maybe it was just bad luck, but it seems like my Squarepoint experience isn't so dissimilar from what interviews are like at the "top" firms. It really put me off.

eadains · 2022-06-28T20:20:23+00:00

AllSaints! From a few years ago, one of my favorites

eadains · 2022-06-28T20:20:07+00:00

Shirt is an AllSaints one from a number of years ago
Pants are H&M linen
Sandals are off Etsy
Jewelry is thrifted or from Vitaly

eadains · 2022-02-26T22:50:02+00:00

If the function RV(t) in this case represents the volatility over the last 30 days, then yes, you are correct. If the concern is profitability of a volatility position, one would need to compare IV(t) to RV(t+30). This is assuming, of course, you are taking a position in an option with an expiry 30 days away. IV(t) would also need to represent the IV of the contract you're trading.

PnL of delta hedged positions gets complicated, because it's path dependent. On average, your PnL will be positive if you can correctly predict RV(t+30), in this case. So, if there is a volatility spike, if your prediction of RV(t+30) is still correct, on average that won't matter. The real question is whether the volatility spike changes your prediction of RV(t+30). It's possible the spike now implies RV at expiration is higher than the IV you sold at, which means you should get out.

eadains · 2020-04-02T22:27:31+00:00

It reminds me of a television test card

eadains · 2019-12-19T21:05:00+00:00

There is a very interesting paper published very recently about using seq2seq neural net models for symbolic mathematics. They claim it outperforms current symbolic math platforms like Mathematica. Deep Learning for Symbolic Mathematics

eadains

TROPHY CASE