ML
MoneyLion Inc.
stock NYSE

Great list — it’s clear you put real thought into structuring it.
One practical note from an **algo trading (personal use) perspective**:
Most people don’t fail because they haven’t read enough books. They fail because they never move from theory to **building, testing, and managing risk**.
If your goal is *personal systems*, not a quant job, the highest-ROI focus is:
* **Market intuition** (Malkiel, Lefèvre, Bogle)
* **Time series + position sizing** (Tsay / Hamilton + Vince)
* **Implementation in Python** (Hilpisch, Clenow, Jansen)
* **System design & risk** (Chan, Carver, Kaufman)
Books like *Security Analysis*, *Stochastic Calculus*, or heavy portfolio theory are excellent intellectually, but low priority unless you already have profitable systems running.
Also worth highlighting:
Most durable retail strategies still come from **simple momentum, trend-following, and rules-based systems**, not complex ML. ML only helps once you already have clean data, solid signals, and strict risk control.
Overall, this is a strong map of the territory.
The real edge comes from reading **less**, but building and testing **more**.

This is so refreshing honestly. Everyone chasing the perfect indicator or ML model to “predict” the market when thats literally impossible.
You figured out what actually matters - execution discipline beats prediction every single time. No leverage, no futures, no martingale should be tatooed on every new traders forehead lol.
Props for open-sourcing instead of selling it as some $500/month “premium signal service” like everyone else does.​​​​​​​​​​​​​​​​

I have been investing in intel since 2021. Took significant losses but then increased my holdings about 20x when it dropped to 20. At this point it’s about 75% of my NW.
The main issue was that they didn’t start using the new ASMl machines and fell behind in technology, TSMC with govt subsidies captured all the market. The last ceo Pat G invested heavily into rebuilding the company and obviously the profits shrank as money went into investments.
The other issue was that GPUs started being used for compute first for bitcoin then for ML/AI. Even though intel was the largest gpu manufacturer because of integrated gpus they werent into high performance gpus so that is taking years to catch up. Their newest gpu and xe3 architecture in Panther lake looks really good.
Now Intel appears to have caught up with 18A node. Appears to have decent GPUs.
Their newest gpu high performance pcs and server products on their nee 18A node are coming out later this year. Jaguar shores their AI gpu build on 18A also coming out probably this year. There are rumors that their will be new high performance gpu too.
Also they invested heavy in ASML new machines and bought all supply in 2024. Which will be used for 14A node.
Things appear to moving in the right direction. Once there is confirmation by that time market cap will be 500billion +.

I can't speak to barclays. I can speak to having interviewed candidates at other banks for this type of positions
Generally in banks the interview is unstructured for superdays. They will probably look at your resume and ask questions based on that, so make sure you didn't fudge anything and can confidently talke about it.
I'd review stats. I would expect them to ask questions about regression, time series, logistic regression. You should know what assumptions are, what they are, and how you would handle violations of assumptions i.e. what do you do if you heteroskedasticity, what is non-stationary time series and how do you test for it handle it. Regression and not ML is the bread and butter tool in bank quant jobs and you need some depth with it. To the extent you are asked about ML, it will probably be more about decision trees than other areas of machine learning.
If the job involves pricing they may test optimization and simulation related questions, i.e monte-carlo, boot strapping.
Then the rest is really up to the interviewer. Some places have a list of questions they ight ask you to work through that the interviewer may or may not use. Some may ask you to white board a problem (though I think its rarer in super day). Some may ask brain teasers etc. The issue for bank quant interviews is sometimes its up to the luck of the draw as it depends on the interviewer. Banks aren't tech companies wher they have this very structured process.
If I was conducting the interview, I really would be focused a lot on seeing how well you understood statistics over other things. I would want to make sure your someone that understood math/stats over someone who just blindly types code into python. I am also would not soemone that would be trying to conduct IQ test. But other interviewers will have a different approach.
If you know the name of the interviewers, look them up. You can tell a lot about someone b ased on their education and what they work on.

I have a nice positive-EV 1DTE RUT approach that unfortunately doesn't upscale well (for multiple various reasons), so have been looking at applying it to NDX where the contract size is much larger. Also in 2025 the RUT was big-picture rangebound (although potentially volatile on any given day) until late in the year, while NDX was the opposite (and appears to still be the opposite in early 2026).
Unfortunately, despite similar 1DTE IV it seems like the same approach applied to NDX could actually be negative EV. My general approach is to use custom ML model and experience/elementary TA to make a direction+magnitude pick late in the day, open late in the day a 1DTE broken wing iron fly (BWIF), and watch like a hawk premarket and 9:30-11am ET (maximum) and be out of the position, legging out as prudent, regardless of outcome NLT 11am ET.
To give an example, I priced things today (effectively Friday EOD pricing) for Monday EOD expiration for both RUT and NDX. The 1DTE IV is very similar between the securities (within one %-age point, and right now very low).
Using the minimum RUT spread width (5 pts) for "de-risking" direction and 2x that (10 pts) for "risk" direction, with the equivalent distance from the money and spreads for NDX (which is close to 10x RUT value), I find that for RUT the "max directional profit" (assuming it moves in the direction I want) is \~15-20% of the spread width for RUT, but zero for NDX. Meaning that if I did this for NDX I could only make money if I hold to the bitter end and get lucky with "pinning", whereas for RUT I could profitably exit early if it moves my way overnight/early-going.
There are two biases in play that I attempted to correct for--
Bias #1: It's EOD pricing, so potentially distorted in terms of bid-ask spread. However, adjusting for NDX absolute value for RUT absolute value, they are similar.
Bias #2: Currently the put-call IV skew is opposite for NDX than it is for RUT. I corrected for this by pricing both directions on both securities with the same BWIF width percentage differentials, and for both bearish bias and bullish bias the risk/reward is the same, playable for RUT but not for NDX.
So what could be causing this behavior, and can it be exploited in quasi-arbitrage fashion? (Not really arbitrage, as NDX and RUT can and do behave differently within the span of a single overnight/day.) Is there a "greek" that I am missing?

I can tell you haven't taken regression with linear algebra. What you wrote is correct in its intuition. Perfect multi-collinearity means your data matrix isn't of full column rank. If you've taken linear algebra this means for a data matrix X, (X'X) isn't of full rank and htere fore cannot be inverted. This means you cannot compute OLS. Now this can easily be handled by removing the perfectly co-linear variables.
Most statistical packages will automatically throw out one of the two the collinear variables instead of throwing an error. They will usually write a warning that they've automatically done this for you. But this is why this question can immediately inform someone of how well they actually know regression.
Traditional quant finance draws heavily from financial econometrics and in a first phd level econometrics course, roughly half the course will be spent on OLS assumptions.
This kind of thing doesn't seem that important, but what many people fail to understand including some people in quant finance is which assumptions are about incorrect inference, rather than estimation. What implications of things being unbiased are and are not. Furthermore, if you don't know these kinds of things an interview how can I trust you know anything about for example the implications of other assumptions. Such as non-stationary time series, which is actually important forecasting.
One of the other reasons important is more about pedagogy. When you study OLS from a ML perspective people are generally more interested in forecasting accuracy. Out of sample prediction is the name of the game in ML. This means that assumptions about error terms that determine wehether or not an estimator is unbiased are of lesser importance when someone is taught OLS. In econometrics, inference is actually a bigger goal. Not knowing these assumptions are akin to not knowing which ones are important for the specific problem you are focusing on.

study math for sure. if youre interested in just trading work its probably enough, but if youre interested in research you should also add cs. make sure you focus on the following aiming to take these by the time youre a junior
math: calc, linalg, probability, stats, preferably some more rigorous stuff in probability, stats, including grad sequences so that you can touch on things like stochastic processes.
cs: data structures, algorithms, programming, machine learning, and more stuff in this area.

these are the basics, and if you're well-versed in this area conceptually you're set to navigate interviews, but you can definitely learn more. Then there's specializing in areas:
If you're interested in dev work, make sure you become pretty good with C++. not personally a dev but I hear a lot of systems/C++ is super important for dev stuff. For research work you probably wanna hammer down on ML as that's (from what I hear) a lot of the processes, and look to take more advanced/rigorous ML classes. take theory where you can as from what I've heard firms prioritize conceptual understandings over just implementation ones. For trader work you can explore, but its definitely the least 'courseworky' track. Game Theory can be cool, but there's not really a way to prepare course wise for trading. Best bet is to just try your hand at problem solving stuff, espeically any ones focused on probability, as those are probably some help for interviews.
you don't really need to worry about studying finance at all. I don't think theres really a single major quant firm that cares about you having a finance background, and most think its a negative signal if thats the only area where you're quantitatively proficient.
hope this helps.
**also most importantly: enjoy your freshman year, it flies past.**

Aber sowas von! Mein Job macht mir ernsthaft viel Spass und ist voller spannender Herausforderungen die mich interessieren.
IT, aktuell ein wilder Mix aus IoT für Robotics, ML, Cloud und Software Engineering.

See slide# 10 onwards in https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=3257419. If you go to his SSRN profile and search for "lecture" - there are about 10 slide decks you can go through.

At its most basic, 1. create volume bars, 2. apply CUSUM detection on some chosen variable (eg: volume imbalance) - when the CUSUM detection fires you sample that bar and 3. apply triple barrier labelling (this is what gives you the asymmetry). Prado's hypothesis is that when a structural break occurs (where CUSUM is the proxy) such examples is what provides "good" data for the ML algorithm to learn better from. I'm currently working on implementing this approach for an intraday strategy.

Money is never destroyed. It only changes hands. They buy gpus, energy, pay ML engineers, data. The people working in this sector are getting paid, then they go to the strip club, and the young single mom also gets paid.
Monday changing hands is the lifeblood of the economy. What should they be doing it, hoarding it? putting it under their mattress? That's when the economy dies out.
Oh but you say, if they used that money more productively, they would have made more money! And where would that money have come from I ask you? Their clients - other companies that somebody would point the finger to and say "Look at those idiots burning money paying Google" like the posts calling people who buy every new iphone stupid.
is money. In the end the government prints more and whether google execs burn their money building datacenters, or sniffing cocaine of a down syndrome dwarf in Thailand - the outcome is always the same. Government is going to print that government is going to print, and that money will circulate in the economy. What changes is who gets to hold it.
That's all. Money can never be spend badly. Money is good only when it is being spent in any way possible.

Mean reversal:
Use ML or DL to predict will close price stay in +-Y% in next X candle, it will determine is future will flat? Plus your secret sauce…
For Trend following its easier to detect, predict 👍

You know good and well NOBODY besides ML/CS engineers refers to the hard code or math when using the term "AI" on a public stock forum.
This post being a joke is no reason to be so far off base.. 💀

Because people use the term AI synonymously with ML, making it lose a lot of meaning. Up till ChatGPT the majority of AI use is basically very simple pattern matching: recommendation engines, self driving, chess, etc. This is already very economically productive in extremely narrow scenarios. But LLMs and language enable the next step, generalization. This is the key to unlocking system 2 thinking and functioning in the real world out of a constrained sandbox. Now you don't need to perfectly pattern match, you can understand something the first time you see it by reasoning from first principles.
So the "L" is the most important part here.
If you watch the Jensen presentation he explains how it works. There's a legacy pattern-matcher dumb AI. In cars this will never be safe, since it is impossible to get literally everything into the training data. Then there's the VLM that basically replaces it, but can use the legacy AI as a tool. The second layer, applied to everything, will consume vastly more compute. The second layer can even help train the first layer though the way Jensen describes it, the VLM is in control and the legacy layer acts as a guardrail.

I use ML with time series momentum, EMAs of 20 and 200 day for price and volume, ATR, and RSI with good results. My system is available on [http://stocksignal.cc](http://stocksignal.cc) where you can put in TSLA for an analysis. Since 1/1/2020, the returns would be over 7000% versus 1551% with buy and hold. If you register an email and DM me with the email you used to register, I'll give you free access to check it out.

Not deep learning at all? What ML do quant shops use

This is the exact problem most ML engineers hit. You nailed the hard part (good forecasts), but that's only 20% of what makes a strategy work.
The gap between "price will go up" and "profitable trade" involves:
- Position sizing and risk management (Kelly criterion, portfolio heat)
- Trade timing—even perfect forecasts can blow up if entry/exit timing is off
- Slippage and transaction costs eating all your edge
- Regime changes—your model works in trending markets but dies in choppy ones
Two things I'd look at:
1. How much does your P&L improve if you add basic filters (volatility regimes, moving average confirmation)? This teaches you what actually moves P&L vs what academic papers focus on.
2. Read about strategy pipeline architecture—checkout papers on mean reversion detection and volatility filtering. The "bridge" is understanding when your ML model's confidence matters.
You're not about to get fired. This is exactly where every quant who started in ML ends up. The fact you're thinking about it means you're already ahead.

ML Engineers, what resources you used to learn Quant Trading?

If you're doing work at the cutting edge (ie, successful ML PhD), the firms will be interested in finding ways to monetize your knowledge & ideas. Rentec was basically built by the first team to do statistical NLP.

I run Sharp11 (sharp11.org), an AI research lab where we’ve been testing LLMs + more traditional ML (Transformers, Random Forest/GBM, even some RL) in live market conditions across crypto/FX/indices. The biggest lesson so far: **models are great at pattern recognition and regime context, but they’re also brittle when the market shifts**.
So we *don’t* let an ML model trade autonomously. Instead, we use AI as a **decision-support layer** (where/when conditions are favorable, filtering setups, contextualizing macro/news via RAG), and we keep **risk + execution controlled by explicit rules** (position sizing, volatility filters, max drawdown stops, no-trade zones, etc.). That human-in-the-loop constraint layer is what prevents the system from blowing up when the model “hallucinates confidence” or drifts.
**AI helps most when it narrows the search space and improves timing/selection — but you still need deterministic risk management and guardrails.** Fully hands-off AI trading is usually where people get hurt.

In low touch trading buying and selling is executed by systems that are recalibrated by ML methods overnight
(possibly, there are some rather MFT than HFT setups where it happens online)