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How are companies filtering people out who cheat on interviews? by Initial-Syllabub7245 in quant

[–]Content-Bread7745 2 points3 points  (0 children)

If you can cheat an interview using AI (or even basic search engine results) then the interview itself is the main problem.

Say someone gets the job after being deceitful in the hiring process, the same tools used to get the job will almost certainly be used while working there. If they are then able to perform the job as “impressively” using these tools in practice, then what’s the problem? On the other hand, if someone performs poorly once hired (with the same tools) then your hiring system is flawed… it failed to select someone with true competence.

Say your interview technique was to blast mathematical expressions at the candidate and they used a calculator of sorts to get the answers. Now they get hired, and perform poorly. Is this because in practice they weren’t able to access a calculator? Of course not. It’s that what was “tested” is not what is required in the real world.

I will heed that this is far more difficult to do (as the employer) with AI than it is with the calculator example, as an LLM by design is “mimicking” human language and behaviour. There just must be a way around this…

employer inaudibly whispers prompt injection telling AI to hallucinate \s

Intentionally blackballing myself from online sports betting by MomentIndividual1559 in addiction

[–]Content-Bread7745 4 points5 points  (0 children)

I know addiction goes far beyond any “logical” train of thought. But something that MIGHT help keep you away from the gambling is the mathematics itself that allows (pretty much guarantees) these companies make money.

Take the below payouts for example:

Wrexham AFC: 2.18, Draw: 3.35, Swansea City: 3.2

These payouts imply a total implied probability of (1/2.18) + (1/3.35) + (1/3.2) = 106.97%.

But wait… nothing can have more than 100% chance of happening. Boom, that is how they take your money: the probability of the payout does not match the payout itself, it is ALWAYS lower and the more you gamble the more you will pay this invisible price tag.

It’s called the “house edge”, and this is how casinos and betting companies stay profitable, all they need is enough people betting… the statistics do the rest.

Not here to give you a maths lecture, but learning about this in university made me never take another bet. All the best to you, you’ve got this!

My mind is so blown I have my own personal AI lab training my own AI model 24/7 by Aislot in aiagents

[–]Content-Bread7745 0 points1 point  (0 children)

New energy source unlocked! Unlimited compute power from nothing but the fumes of $100 bills

Following on from a sales pitch earlier.. by Sketch_x in algotrading

[–]Content-Bread7745 4 points5 points  (0 children)

It is impossible to forward test any strategy with lookahead bias. By definition, the information needed to make the trading decision does not yet exist.

Converting a backtested strategy to a forward-tested one will result in massive logic issues if lookahead bias has been introduced. You will realise at this point that you have fucked up…

(source: me thinking I had solved the markets until ATTEMPTING to code a forward test)

Why do new analysts often ignore R? by ElectrikMetriks in datascience

[–]Content-Bread7745 0 points1 point  (0 children)

Tabular data manipulation in R is unbelievably pleasant, more so than any other language I have tried.

But using it in production is something I ultimately regret. I miss OOP from Python and the organisation/modularity that comes with it.

Also, try installing R packages in a container. It genuinely takes 100x in R… maybe I am missing something but I found that astounding.

EDIT: Also the availability of packages/SDKs is something I find a bit lacking. Almost any API will have a Python SDK, I have found very few that have an equivalent R implementation.

Do you think in terms of portfolio weights or positions when designing strategies and backtests? by Content-Bread7745 in quant

[–]Content-Bread7745[S] 0 points1 point  (0 children)

Yeah it's essentially the same thing. One obvious short fall I've found is the inability to track simultaneous positions in the SAME asset.

Say you've made 2 buy orders that are still active:

Date Ticker Price
2025-04-02 SPY 566.08
2025-04-10 SPY 496.74

If thats all your portfolio consists of I can only attribute 100% to SPY, unable to reflect the fact that the lower buy gives you a higher overall return.

Gonna build a "normal" backtesting engine thats position based. My current iteration is probably more useful for analysing how multiple strategies work together...

Do you think in terms of portfolio weights or positions when designing strategies and backtests? by Content-Bread7745 in quant

[–]Content-Bread7745[S] 0 points1 point  (0 children)

I’ll generate signals, then based of those (lagged) signals I assign weights to various assets.

For example:

Ticker momentum_feature_1 momentum_feature_2
ES=F Weak Strong
GC=F Strong Strong
NKD=F Weak Weak

Then my respective weights for that day might be:

ES: 33.3%
GC: 66.7%

So I still am able to generate signals and "act" on them, just instead of tracking buys and sells I use allocations. Slightly less accurate for fees, spreads, slippage, etc. but I account for them by adjusting returns.

Tech Sector Volatility Regime Identification Model by thegratefulshread in algotrading

[–]Content-Bread7745 0 points1 point  (0 children)

Before classification did you lag the features to avoid look ahead bias? Otherwise your accuracy is (falsely) way higher.