ML
MoneyLion Inc.
stock NYSE

Pivot from HFT dev -> AI/ML systems/performance dev

It is an extensive topic. Commercial backtesters/strategy testers, no matter how advanced, still need your strategy rules expressed in the formats their services can ingest, plus integration with your data and systems. I am not talking about OHLCV data - they have it. I mean your model outputs, should you employ ML, for example. Some of these services still require custom coding.
It feels like MT5 with hooks to your custom engines may do as well as commercial backtesters, and sometimes better for a retail trader.
Please don’t take me the wrong way. I am not negating the value of third-party services, especially if they handle cross-security testing, sophisticated simulation, portfolio rebalancing, etc. Not to mention their computtional capacities. I am just saying they may not be able to offer a complete solution for everyone. Completeness and sophistication do overlap, but your acceptance criteria may not match mine.

I had a few free advisory calls with a local Fidelity office investment specialist. He's quite good and gave some very good tips.
He suggested me more structured advising services for a fee. Three tiers of services:
* Fidelity Go Robo investment (some AI/ML based using FidFolio)
* Advice with Access to a Team of Planners
* A Dedicated 1:1 Advisor Supported by a Team of Specialists.
See the this doc they have. [Fidelity Offerings](https://www.fidelity.com/bin-public/060_www_fidelity_com/documents/applications/Fidelity_Offerings.pdf)
I have been managing my own investments till now. I'm in tech and find it increasingly difficult to get time to do proper financial planning.
Do you think it's worth it? Has anyone used these services? What's your experience?

I've been building a peer-to-peer mock interview platform specifically for quant finance recruiting - probability, brainteasers, stats, ML, mental math. Curious what people think: would something like this be useful, and what would you actually want out of a tool like this? Trying to get this right before opening a beta this fall.

Thank you so much! Once I have the sim results, would you mind if I post them here to get your feedback on them? You know your stuff!
I've already incorporated data, feature, code versioning with a comprehensive ML and data Ops framework but thank you for the suggestion!
I already have comprehensive known at timestamps with comprehensive PIT guardrails and also handle survivorship bias with ticker reuse and delisted ticker handling but thanks for raising this too!
I went over engineered on the platform to build an automated metadata driven framework and a harness - attached
https://preview.redd.it/xfblguxfhwsh1.png?width=3600&format=png&auto=webp&s=b7385e7c17ab96f70a0a81d685dcc3ff23739854

The markets have changed beyond measure in the past 10 years.
There are now hundreds of very well financed hedge fund and prop firms scouring the markets with immensely powerful ML/AI algorithms tuned for the task with unbelievable quantities of data.
If TA ever worked on a repeatable basis, and I think it did, there are good reasons to think that it does not any more.
My experience of this is that actual tradable edges last for 2-3 months at best, sometimes weeks, never more than that.
The market is very efficient

Hello everyone!!
My first post here.
I'm currently a 3rd year ECE student. I have an intern offer from MAANG for 2027, and I'm now thinking about seriously exploring quant as a possible career path.
I'm essentially starting quant preparation from scratch. My current background is mainly competitive programming (C++), DSA, and some basic ML. I’m comfortable with programming, but I haven't specifically prepared for quant interviews or studied the relevant math in depth.
I'd really appreciate advice from people who have gone through the process on:
1. What should I learn first?
2. A structured roadmap for the next 6 months(or probably an year)?
3. Probability/statistics topics that are actually important for quant interviews.
4. How much linear algebra, calculus, optimization, etc. I should know?
5. Resources/books/courses you would recommend.
6. Good sources for practicing probability and mathematical puzzles.
7. How important are mental math and brainteasers?
8. How much competitive programming helps, and what additional programming skills should I develop?
9. What should I know about trading/markets/finance, if anything?
10. When should I start solving actual quant interview questions?
11. Any good mock interview platforms/resources.
12. Anything you wish you had known when you started preparing.
I'm not targeting any specific firm yet. My goal right now is to build a strong foundation and understand what the preparation actually looks like before eventually applying to quant internships/new grad roles.

From [https://www.reuters.com/business/finance/bank-america-pay-39-million-settle-customer-claims-over-low-interest-rates-cash-2026-09-30/](https://www.reuters.com/business/finance/bank-america-pay-39-million-settle-customer-claims-over-low-interest-rates-cash-2026-09-30/)
>Holders of Merrill Edge online accounts ​between December 15, 2016 and March 15, 2020 accused Merrill ​Lynch of breaching its client agreements by automatically sweeping cash balances into deposit accounts that paid less than a "reasonable rate" of interest.
>The ​complaint said the sweep accounts carried annual yields of ​0.05% to 0.14%, while other brokerages were paying customers about 2%.
The clincher is that ML was apparently violating their client agreements. But I hope at least this pressures Schwab to offer more han 0.1% .

I'm building an ML model that pulls data from Unusual Whales and ThetaData APIs. One of the metrics I want to pull is whether or not dealers are Long or Short Gamma.
What I understand is that dealers are aiming to be Gamma neutral but this chart is telling me that they can be gamma neutral for days on end and are even gamma neutral today.
Is there something fishy going on with my data? It's the first day I have real world data running through the algo so I am expecting a lot of stuff to be breaking today.
My assumption is that there is something going on in the algo because I ran a Monte Carlo and it told me to avoid doing mean reversion strats on Long Gamma days and to engage in trend movements on Short gamma days. Something is clearly off.
https://preview.redd.it/e5x1ltg33vsh1.png?width=1386&format=png&auto=webp&s=15dea10addf931b5f421ac0614a8823ce9bc5f4c

Applies mathematical modelling, data science, stats, ML to finding and resolving market inefficiencies. Gets paid for resolving those inefficiencies. Or something like that.

Hey everyone,
I’m a B.Tech AI/ML student and I’m currently learning AI automation and building small projects around it.
I’m looking for someone who is good at talking to people, finding opportunities, or has experience getting projects. I’m mainly interested in finding someone who wants to work together rather than just exchange advice.
I’m still learning myself, so I’m not claiming to be an expert. But I’m willing to handle the technical side, learn whatever is needed, and put in the work to actually build the solutions.
The idea is pretty simple — if you find someone who needs something built, I can take care of the technical part and we can work on it together.
I’m thinking of starting with simple things like WhatsApp bots, lead follow-ups, basic workflow automation, or other small AI-related projects and seeing where it goes.
If this sounds interesting, feel free to message me and tell me a little about what you do. Would be cool to find someone who’s also serious about building something together.

QT superday had math programming and one ML interview also (for my friends at least)

both tracks are heavy on probability but QT gets more into market making intuition and game theory, QR leans harder into stats/ML and coding, super day for QT is basically a gauntlet of mental math, probability puzzles, and a couple market making games where you're expected to think out loud the whole time

**TLDR: what is Jump QT interview process focussed on? any info on super day in particular would be great.**
I am wondering what the difference in what gets asked in interviews for both is because the stuff ive seen online seems to blend the two tracks together for Jump.
normally in firms QR is stats/ML, prob, coding, data science and QT is prob, simpler stats and some market making stuff.
was wondering if that was true for jump as well coz i have only really heard jump QT be prob. any info on the super day would be great as well.

Im not . Looking for exits, ML applications in the real world seem cool. But convincing them to hire someone who fucks around with microstructure feels like a hurdle.

Probably the biggest problem here is the foundation of what AI is being built on in terms of hardware and physical resource costs. GPUs were thought to be to essential but various other CPUs can be used and QPUs also are reaching a breakthrough point as well. Whether the introduction of marketable QPUs ability to stabilize the AI stock boom is a real question. Costs related to Data Centers are expected to exceed sustainable levels too.
Along with this FOSS versions of AI systems coming out of China and other organizations are showing that there is a more efficient way of tapping into AI and ML resources that may circumvent token based subscription models.
The other issue is Runaway Agents it’s very likely that the beans are spilt and Agents are very likely roaming on Rogue in our previously only Human digital land scape. They are there now, we cannot see them anymore so easily, and we do not know what they are doing.
I wouldn’t call for Market collapse, but a price reset is in for good order. The Market may try out some pre insuring Correction though given the general market awareness for stock price crashes.
Allowing a series of market corrections may help soften the path, and recycle windfalls to legacy sectors that can then further reintegrate into the technology systems or even develop AI excluded products and services that may open up new economic levers and industrial outlets coupling the profit loss cycle from the AI stock boom.
Restructuring the boom to more natural growth with realistic pricing of goods and services related to AI would be the Macro strategy.
Along with all this Black Swan risks are more likely Geopolitical in nature given WW3

Everyone serious in ML outside of the Internet hype bubble astroturfed by the Frontier creeps have always believed Google would dominate this bubble. They are using the Transformer architecture to evolve search. Their AI models are better for finding *data*, not necessarily coding. Anthropic and OAI focused on the software engineering market, which is nowhere near enough customers for them to survive the data center debt obligations.
None of this is surprising to me.

I know these posts are annoying but any feedback is helpful as I don't have too many mentors who are able to help me with this.
Academics:
* Undergrad: 3rd year at top UK uni (think Oxbridge/Imperial/LSE)
* Major: econ + data science
* Year 1: 67%, Year 2: 77% (top 10% of cohort and First Class in all Y2 courses)
* Relevant coursework includes probability & statistics, multivariable calculus, linear algebra, stochastic processes, algorithms & data structures, econometrics 1 & 2, machine learning 1 & 2, optimisation, databases, micro 1 & 2, macro 1 & 2
* GRE: 170Q / 160V / 4.5 AW
Internships:
* Did Quantitative Trading internship this summer and incoming intern next summer (think CitSec, Optiver, Jump, SIG, DRW, IMC)
* Springweeks at 4 quant firms
Other:
* Undergraduate research assistant, completed a supervised project with econometrics / ML focus
* Completed MITx MicroMaster's in Finance with 90%+ final grade
* Society leadership roles at my uni
* References should be pretty decent i.e. mainly lecturers saying I ranked very high in in their courses and 1 from professor I did research with
Current school list:
* Princeton MFin
* MIT MFin
* Stanford ICME Mathematical & Computational Finance
* Yale MS Statistics & Data Science
* Harvard MS Data Science
* Berkeley MFE
* CMU MSCF
Any thoughts on which schools are realistic/reaches, or anything significant I could add would be really appreciated, thank you.

The AI models (*LLMs*) alone (*Opus, Fable, Sonnet, GPT 5.5, etc*) are very bad in trading, they’re worse than a Reddit user xD
The Agents (*Skills, DataSets, MCP, RL, ML models, etc.*) require more work behind than just using LLMs ("AI") ;)

And this job requires people *(devs, quants, etc*) who know how to develop structured agents or models. Requires too, good sources of information (e,g; Bloomberg, Factset, etc) 👇🏻
Example: [Bloomberg MCP (X, Tweet)](https://x.com/TheTerminal/status/2104839707393683513) 👀
https://preview.redd.it/tzso9qhgcpsh1.jpeg?width=2366&format=pjpg&auto=webp&s=961507789208720b7f817c4e77c7e32a57801faf
* This is the difference, between trading with Agents (*tool*) 🤑 ALPHA!
* vs
* Let the Models (aka AI) trade for you is basically burn your money with biased context (prompts) xD 🩸 REKT!
:P

When people learn the difference between using a Chatbot (*ChatGPT, Claude Web, etc.*) and an Agent, this may be a "danger" :P
Even more so, if it’s about using Agents for trading (ML, RL, etc) and market predictions. Who are not a commercial Agent like Claude Code, you have to build them by yourself.
Most people see AI agents from the point of view of the conspiracy, as if they were "Terminators" :P
They don’t see this like: all these agents/projects have people (devs) behind the creation of the agents.