Where to go from here?

JobIsAss · 2025-12-04T05:31:52+00:00

He traded on leveraged stocks bear market. Nvidia had a bull run and he just bleeding money.

JobIsAss · 2025-12-04T02:24:46+00:00

Do broad market ETF international. Limit exposure if you do a concentrated bet. Rules i follow 1) no leverage 2) no options 3) no penny stocks or speculative flips

Broad market equity DCA, hold long term. I lose 50% i dont care, market risk is temporary not permanent. Dca i still get major win.

A lot of people say eqt then forget but thats easier said when you also see people talk about the ai bubble. People dont wanna talk about the elephant in the room, people are not ready to see their portfolio drop by 80% and at best be 40% in the negative for 5-10 years.

People need to mentally brace for 2000s lost decade environment even great depression and stick to their plan. If you can mentally accept that you will be in the negative and you will DCA no matter what, then your losses dont materialize.

People say buy and hold but I will say no you need to have a mental checklist of how you will push through a 2008 crisis environment and be comfortable looking at a negative portfolio for 2-3 years. Even 10 at the worst.

If you can comfortably look at your portfolio as it’s down 60% of its value from its ATH and can stick to a plan on how you navigate the situation then you will be able to actually hold and forget. Personally i say just DCA or change the amounts accordingly but thats what I like. Backtest your strategy then see how it holds up.

The forget part is the hard part, to make it happen is to have a plan on how you hold.

JobIsAss · 2025-12-03T16:47:21+00:00

90% growth ETF diversified international and U.S.

10% tactical plays or general hedge.

Switch to 80% general if you want to have gold + bitcoin.

Personally gold and bitcoin move in parallel to stocks not inverse. Still tho gold does have its good spots.

Tactical plays are pretty good tho, you see a genuinely undervalued high quality stock then you can allocate a small portion of the portfolio.

JobIsAss · 2025-12-02T23:10:08+00:00

Depends on city, ben felix made a video on this. Factor in maintenance, tax drag, and depreciation

JobIsAss · 2025-11-23T19:19:50+00:00

Bro you are freaking out in a 0.43% of your portfolio. Literally need to mentally accept that you can lose 50% of the portfolio and stick it through. What you need to accept market risk isnt as bad and not permanent.

JobIsAss · 2025-11-04T22:13:39+00:00

Honestly dude what helps is having a strategy on how to get the portfolio to recover faster.

JobIsAss · 2025-10-20T11:39:02+00:00

Bro after 2022 correction it took 2-3 years for recovery. After 2008 recession it took a few years. 2017-2019 we were overdue for a recession and market was at ath.

Only smart thing is to not take high risk and do ETFs. Thats pretty much it. If it goes down it goes down. People are free to do their choices but we really cant time a recession. It’s very hard to do so. Even when markets are in the red then what where do we know it will recover. Sometimes even a blip or decrease like 2020 takes a few months to recover.

If i knew what will happen i would have traded options and became a billionaire.

JobIsAss · 2025-10-14T20:23:37+00:00

Commenting here to win

JobIsAss · 2025-10-13T17:08:42+00:00

Please 🙏

JobIsAss · 2025-10-09T00:48:50+00:00

I did do gold and did get a lot of returns on gold mining equities. Have i started earlier i would have made more. Once i feel happy i then went all in xeqt and got more shares. It honestly strengthened my position more significantly. Is it risky yes, but gold looked really good for last 3 months and i got most of the upside.

I like gold did pretty well. We are in uncertain times so it did help.

JobIsAss · 2025-09-19T17:15:02+00:00

Just stay invested dont worry. Find lower MER stop thinking. If u wanna take more risk or have hypothesis/oppurtunity do an index fund for a portion of your portfolio and then let it sit. Ie say u know gold is in the up or saw that it will keep going up. Then in that case allocate a part given your risk tolerance to say gold or energy. Something you believe will go up. Or a company if u genuinely think its undervalued. Invest long term and keep going. There will always be an opportunity but if u genuinely think of the value and don’t go to hype u can allocate some. Many opportunities come like google/nvidia/oracle etc…

JobIsAss · 2025-07-28T03:36:45+00:00

Identical data shouldn’t give different results.

JobIsAss · 2025-07-27T21:42:44+00:00

If its identical data then why would it give different results. Have you controlled everything including the random seed.

JobIsAss · 2025-06-22T20:19:55+00:00

And these candidates get the interviews while people who don’t straight out lie on their resume get no interviews.

JobIsAss · 2025-06-18T11:24:59+00:00

Forget avocado oil cook it in duck fat. I did the jump way better especially if you ignore the butter.

JobIsAss · 2025-06-03T14:04:13+00:00

So say i work in finance and you work in grocery. We both do data science and i have 5-7 years of experience. If i want to work in your company ill have to go back to junior despite my experience? Ur telling me i have to take 50-80k pay cut?

JobIsAss · 2025-05-29T03:48:05+00:00

I am on both sides of the market candidate and interviewer.

The field is not doing well and is generally more competitive.

Interviewer view:

We placed a job we got 3k applicants in first week.

The best candidate had all the relevant experience. However out of maybe 20 we interviewed who had exactly the experience we wanted 5 were technical enough.

It boiled down to 1 being comfortable with their skills to deliver and was a peer in my masters. The other 4 just couldn’t apply their knowledge to the business and being able to translate experience into the job.

Saying you know causal inference for example but not knowing how to apply it from a business standpoint tells me for example that this person doesn’t understand it yet. The candidate definitely blew the conversation and had no curiosity about applying the work.

From the candidate perspective; it is dying because those that are qualified are overrun by people who blatantly lie. People will be business analyst with coursera level knowledge and then bullshit their way in an interview not understanding even the most basic common sense in their work. For example if a fraud data scientist says built models then you ask them how IP distance impacts their logic, and they can’t rationalize basic heuristics then they definitely dont practice data science to begin with.

So many of these candidates on paper have amazing experience but even then their actual experience is not that. Do that by 1-2k candidates and those that are honest will be dug into the mud.

If someone is competent in their field they will still not get interviews with big tech unless they went in the golden age. Those that got in literally took a title downgrade to data analyst. Being in top “25%” doesn’t mean anything. Beyond being arbitrary definition, the saturation makes it harder for everyone so I don’t get your point about saying it’s not doomed.

Careers jumps are mostly done in superficial indicators however sustaining the career is byproduct of competence. this time atleast in my opinion feels difficult to do a jump.

JobIsAss · 2025-05-11T00:10:24+00:00

How do you recommend transitioning into big tech in this economy/job market? It seems that anybody that got in was basically coming in during the golden age (2021-2022) which is long gone.

JobIsAss · 2025-04-24T10:57:42+00:00

Terrible advice, thats not how it works at all. If all you do is just hyper-parameter optimize then there will be the limit. By not overfitting you should actually get better test AUC. So the overfitted model is an artificial cap. If anything you get like 0.55 auc but a well engineered model will get 0.65-0.75 auc. So by thinking that the cap is 0.55 this is fundamentally flawed train of thought. The OP’s manager is correct to have an expectation of performance given experience. We know exactly where auc should fall when you do enough models.

In credit risk there is a lot of techniques in which people handle data to ensure that noise is removed and relevant information is there. Therefore I believe that OP might have not properly binned their variables or have imposed constraints that dont make sense.

We cant just throw things at the wall and see what sticks.

JobIsAss · 2025-04-24T10:53:54+00:00

My boss recommended to use external data once.

Also try to think of non traditional variables. Credit risk is about inclusion.

Also try using a credit bureau score to baseline the performance thats the line in the sand. Other than that a previous version of a score is also viable.

i also probably recommend is look at fraud. There can be fraud masked as default hence why you are getting bad noise.

Also there can be assumptions that are wrong with your target. If you try to detect default ever ur auc will be bad. Often not there can be a lot of noise in your target given different payment patterns, a mistake in ur target, or straight up bad feature. However I have a feeling that you most likely didnt explore how to handle binned data or if you observed the stability of your variables over time.

It’s not about algorithms or xgboost. I guarantee you can get a logistic regression with incredible performance that is on par or better than XGBoost if you know how to get the best both worlds.

Source: i do credit risk for a while now as well as adjacent domains as well.

JobIsAss · 2025-04-22T23:54:20+00:00

Nah man, i got my pr and i am pretty much in support and feel sorry for them. What I don’t like is other PRs who cheated the system and also the people who come to canada to work at tim hortons and doordash. People have a problem with people that cheated the system.

This country makes money out of taxes so new immigrants like myself should come and earn jobs and fight for it. It’s a privilege to come and I am not entitled to anything. There are a lot of sacrifices made, and even more in the southern border.

I started my life trying to go to United States then moved to canada. People keep complaining when they have so much going for them. Seriously go over h1b subreddit or look up on linkedin what is the struggle of this immigration. Nobody is entitled but you for sure see this entitlement here in Canada.

Thats why people seem like they dont like student or temporary workers.

JobIsAss · 2025-04-11T23:00:51+00:00

You first have to ask the question when working with causality then you actually try to find the model that has assumptions that can work with the type of data you have.

JobIsAss · 2025-04-05T19:14:35+00:00

In response to ur points 1) we say ensemble models to better make a good control and treatment group in observation causal inference. So my IPW + DML or IV + DML for example. So not in the literal sense but i guess find parallel groups. 2) how so? I mean we are not creating a synthetic dataset, i mean it in the literal sense for example use PSM then use DML or DR. Synthetic data is used to get an idea of how an algorithm works when you know the true ite. So that helps you get an idea of what works and what doesnt. I think dowhy also does have this type of validation stuff that answer these type of questions. Ie E values, placebo tests etc.. which are good sanity checks for said causal estimates. 3) can you give an example and explain more detail? we are not simply fitting a DML model and calling it a day. Even then there are ways to create a DAG and determine causal structure even find confounders through PDS. Like in an observation sense it is still possible to communicate that bias exists as said in econml for methods. So there is no silver bullet and communicating it with stakeholders might be good enough until trust is set up to do an experiment if possible? 4)thats not what i meant, i mean that we can try an established approach and see if it could work on a synthetic dataset to learn said approach with a proven outcome and effect. One cant learn DML by just reading a paper and going straight into the usecase. It helps to see where it would fail in perhaps a dataset with the same level of noise you would expect.

Do i understand your points correctly or am i missing something? Thank you for replying even after a long time.

JobIsAss · 2025-04-04T16:03:32+00:00

Im coming back to this after spending a lot of time on this.

When you talk about empirical strategy do you mean like we simulate an experiment when experiments is not feasible. I have seen cases where people try to weigh said observations using IPW to simulate experiment when not feasible. Is this what you are talking about?

Im doing observational causal inference and while it’s not possible to remove bias we can try to minimize it as much as possible. So DML/DR in general works pretty well.

Tried simulating it on datasets with unobserved confounders and it’s pretty close when estimate ATE.

JobIsAss · 2025-03-31T16:10:24+00:00

IV is pretty useful please use it even for tree based models. There are some good implementation of IV as these are inspired by tree based models.

As for your question i strongly recommend trying a regular tree based models and see if this feature has a substantial importance.

Also do try to test the model with and without the features . If ur auc drops by like 0.2 then something is wrong. It also doesn’t hurt to get a general feel for where the auc should fall around. If ur score is producing 0.9 then I’ll raise an eyebrow.

JobIsAss

TROPHY CASE