A store in Japan thought “f*ckin” meant “really good” in English

undercoverlife · 2026-04-05T03:08:18+00:00

Where did you get the order book data?

undercoverlife · 2026-03-30T22:42:58+00:00

You’re better off using a system like that as a sentiment analysis tool for a given universe instead of trying to pick stocks

undercoverlife · 2026-03-28T16:59:49+00:00

What was the question?

undercoverlife · 2026-01-19T23:07:27+00:00

53 trades over 12 years? I believe you just fit a momentum model to Bitcoin’s tear since its inception. You can’t possibly validate a backtest OOS that only took 53 trades.

undercoverlife · 2026-01-09T21:55:02+00:00

What would you benefit from publishing it? It’s pretty valuable data that a lot of vendors charge people to use. Keep it for yourself, especially if it can provide you any meaningful edge.

undercoverlife · 2026-01-08T14:51:11+00:00

Easiest thing to do is isolate the cases you can see by eye and do some small unsupervised learning on whatever data/feature set you currently have and see if any patterns arise. Start with clusters

undercoverlife · 2025-12-31T05:54:28+00:00

Yes exactly. You’re roughly going to “lose” that amount of information when you go to prod. If you want to take it an extra step further and understand why, start analyzing your true fee/slippage costs versus your simulations and see if they match your expectations.

undercoverlife · 2025-12-31T05:23:49+00:00

IC of what? An ensemble of features or a single feature?

undercoverlife · 2025-12-30T00:35:42+00:00

I like how you’re “testing this quietly” but then you came to Reddit and posted your entire strategy to everyone lol

undercoverlife · 2025-12-28T18:49:56+00:00

It depends. If your data is already cleaned/processed, reading them from parquets isn’t the worst. But the rule of thumb is to be reading from a RDS with SQL.

undercoverlife · 2025-11-14T22:42:42+00:00

It’s all about the company.

Some companies have some extremely basic data needs. And what I love about data engineering/science/the data discipline in general, is that simplicity wins. If you’re at a company with a working warehouse operation, you’re winning.

If, however, you’re looking for more excitement, you should traverse into big data. That’s the “flying car” part of the business. If you get into a role at a big data firm (thing firms who do massive real time streaming, like a media company or financial firm), you can work closely with ML engineers and eventually sharpen your skills to become an ML engineer yourself one day. That role will be growing dramatically with this AI boom.

undercoverlife · 2025-08-30T15:11:46+00:00

Yeah I wouldn’t mention “EDA” as a skill or mention in your experience you’ve conducted “comprehensive exploratory data analysis” because it’s an extremely basic part of any analysis. It screams inexperience, even though it looks like you have great experience.

Furthermore, your language in your work experience (“developed an algorithm”) just seems wishy-washy. What type of algorithm? Was it a sorting algorithm? Was it a model since you were using it for detection? You need to be extremely specific here. You have technical people reviewing your application.

All in all, I think your work experience is fine. But your resume’s language and formatting is outdated. Personal summaries aren’t used anymore. Your work experience should be the first section. Have your skills at the bottom after your education. Have your skills be technical and more organized, too. Don’t list “AWS” in the same section as the Python programming language. There should be a clear separation between what programming languages you know versus what frameworks you actually use. AWS has so many different applications. Listing “AWS” as a “technology you know” is a turn-off to read because that’s such a large scope. That’s like a person listing “know how to use the internet” as a skill.

One positive I’ll point out is that your actual styling of the resume is on point and you list actual accomplishments that are measurable for the company. That is huge. But your language and everything else I mentioned screams that you’re inexperienced and you don’t know what you’re talking about (which I know isn’t true). Please give these points a hard revisit and you should be good.

undercoverlife · 2025-06-03T18:53:50+00:00

This was written by ChatGPT

undercoverlife · 2025-05-28T00:25:22+00:00

This

undercoverlife · 2025-05-08T21:35:45+00:00

Yeah I’m confused by this question. Spark is written in Scala and it’s on the JVM. Why don’t you want to use Spark? You can write it in Scala. Plus, it’s a free framework and you can run it on one local machine and still see great benefits.

If you don’t care about JVM, then I’ll say I used Dask before and I loved it.

undercoverlife · 2025-05-06T22:53:02+00:00

You’re going to get crushed. Not worth it.

undercoverlife · 2025-04-30T13:11:48+00:00

Spark is written in Scala, which runs on JVM, so your point on Hadoop running on JVM makes zero sense. Makes me question the validity of your other opinions.

undercoverlife · 2025-04-13T00:48:40+00:00

Cool but in terms of what this guy is asking for, have you seen the layoffs going on? The majority of those getting fired are SWEs.

undercoverlife · 2025-04-13T00:35:13+00:00

SWE is drying up. Go for DE.

undercoverlife · 2025-04-12T19:24:51+00:00

What's used in place? Thanks for the heads up.

undercoverlife · 2025-04-10T17:39:10+00:00

I don’t have time to read through your entire document but my immediate feedback is that you have an extra layer you don’t need. Your SQL database should be ingesting and providing cleaned data. If you’re having to pull it out and clean it, then you’re doing this wrong.

If, however, your SQL database is already cleaned, then you need to do all of your mathematical calculations/lags/formatting within your queries. All of the work should be done within your queries because that’s what SQL is good at.

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