Looking at the latest numbers and the divergence keeps getting bigger - QRT up 28%, Winton, CFM, Graham and DE Shaw Oculus and all the systematic macro funds (also CTAs, lesser degree stat arb) are crushing it this year relative to the discretionary hedge funds esp in macro space.

Seeing same at pod shops - guys like Citadel have strong systematic - they are now up more than 30% in systematic this year. While weak players like Balyasny struggling.

Really strong flows also into big systematic players.

https://rupakghose.substack.com/p/the-year-of-the-systematic-hedge?utm_source=app-post-stats-page&r=1qelrn&utm_medium=ios

I've been trying to find ways to gather data on loans that have defaulted or were prepaid during their life, but it seems impossible unless your willing to cough up. I have access to a Bloomberg, but I cant seem to find anything. Any advice from people experienced with this type of stuff?

Does anyone have experience with IMC London for QR roles?
Got the first round coming up, and was wondering what could I expect?
Also how’s the culture and comp progression over there?

YOE: 5/6
Current TC: 170k smaller trading firm

I've spent the last few days using the existing framework of Qlib and RD-Agent to integrate my own pipeline onto machinery that is already established, and I am loving it. The automation features, the ability to run training cycles with a local LLM, and the ability to work from a foundation that works has been an awesome experience. Today I'll be working on my automation expanding to include linking Arxyv to the software so that I can parse hypothesis from papers to test as strategies(with sources documented of course). Anyone else doing anything similar? I was surprised how little these tools are discussed. I'd love to hear about anyone doing work that is similar.

Edit: Did I miss something, or is this just the wrong space? I was under the impression quant work was as much about development as anything else.

I’m currently developing an experimental quantitative trading system focused on cryptocurrency Level 3 order book data and market microstructure.
In an earlier prototype, I’ve implemented and tested
several components:

- L3 order book reconstruction with sequence and checksum verification

- Order Flow Imbalance, Queue Imbalance and Microprice

- Momentum and Hawkes inspired market stress measurements

- Experimental TDA regime classification using H0/H1

- Paper trading, transaction costs and risk management

I’ve also developed a newer CMD based prototype with offline tested execution and risk logic, although its live market data integration still needs further verification.

My main research interest is whether persistent homology can extract additional predictive information from evolving order book structures beyond conventional microstructure features.

I’m particularly interested in the following:

1. How would you define an appropriate filtration on L3 order book states or event sequences without introducing artificial topological structures?

2. How should persistence diagrams be interpreted when the underlying data is highly dynamic, asynchronous and affected by order cancellations and queue modifications?

3. Would you recommend calculating topological features on rolling event windows, liquidity graphs or time dependent point clouds?

4. What would be a rigorous way to test incremental predictive value over OFI, Queue Imbalance and Microprice, accounting for latency, transaction costs and nonstationarity?

5. Are there computationally efficient approaches to updating persistent homology for high frequency order book data without recomputing the entire filtration after every event?

I’m not looking for a generic trading strategy. I’m interested in the mathematical validity, statistical robustness and practical limitations of integrating TDA into an event driven quantitative model.
Any research papers, empirical studies or implementation insights would be appreciated.

I wanted to put this question out there, because I have spoken to a few quant researchers and it has changed some of my initial expectations. For the record, I am a physicist, so my background is not finance (quant(um), not quant(itative)).

I have the textbook Arbitrage Theory in Continuous Time by Björk and its awesome. I got really excited thinking this is the sort of thing I could do for work. Some quant researchers I spoke to told me that book likely isn't worth reading and to focus on more basic statistical modeling, stuff more quickly tied to PnL. Maybe the stuff in the textbook is more relevant for people doing derivatives pricing, exotic options, or volatility trading?

My questions to actual quant researchers are:

How often are you working with the stochastic calculus, model building, and getting into the details of the math/the solver, and what quant domain you work in?

Where do you tend to see this level of math being the core function of the job, as opposed to a good to know but not pertinent to day to day work roles?

Basically title. I’m not a quant but have been trading for quite some time & am under the impression quite a few markets are on Polymarket/Kalshi are quite inefficient. Anyone care to share their thoughts or experiences?

This question concerns a rather specific situation, but I hope it isn't too far off-topic.

Right now, I have the opportunity to switch from a low-paying job with relatively short working hours—which is nonetheless very demanding—to a role in quantitative research. The issue is that I have a two-year-old child, and this move would require relocating my family.

Assuming—for the sake of argument—that I pass the selection process, I have four questions:

1. What is the actual work-life balance like in a research position? I'm assuming 40–50 hours a week, and I'm trying to figure out whether that would still leave enough meaningful time with my son and wife.
2. Is the money you make in 2–3 years of hard work enough to make a meaningful financial difference if you then return to a country with relatively low salaries, such as Italy?
3. After 2–3 years of experience, how realistic is it to transition to other roles that are less demanding in terms of working hours, or to remote work? Our long-term plan is to live in Italy.
4. Should I already be looking at adjacent roles—such as portfolio analytics, risk modelling, or asset management research—that might offer a better work-life balance? I'd appreciate suggestions.

I've been researching fundamental Alpha factors on WorldQuant BRAIN, focusing on a simple hypothesis:

Can improvements in a company's operating profitability help predict future stock returns?

Algorithm Overview

The strategy is based on three steps:

1. Measure operating profitability: Evaluate how efficiently a company generates operating income relative to its capital base.
2. Identify historical strength: Compare the company's current profitability with its own historical performance to identify relatively strong profitability conditions.
3. Sector-relative ranking: Compare companies within the same sector to reduce broad sector-level differences.

The goal is not simply to find highly profitable companies, but to identify those showing stronger profitability characteristics relative to their own history.

In-Sample (IS) Backtest Results

Annual IS Performance

Observations

• The Alpha has relatively low turnover, which may be consistent with the slower-moving nature of fundamental information.
• It generated positive returns in four out of five reported IS years.
• Performance was particularly strong in 2021 and 2022.
• The weak performance in 2020 raises questions about how the signal behaves under different market conditions.

These are in-sample results only, not evidence of out-of-sample predictive performance.

Questions for the Community

1. Do you think historical profitability strength has more predictive value than absolute profitability?
2. How would you distinguish genuine business improvement from accounting-driven changes in profitability ratios?
3. What robustness tests would you prioritize before evaluating this type of Alpha out of sample?

I'm trying to develop a more hypothesis-driven research process rather than repeatedly adjusting parameters to improve Sharpe.

I'd appreciate any feedback or suggestions from more experienced Alpha researchers!

I'm 19, studying math/stats, with around 3 years until graduation. I recently got really interested in power markets, especially congestion trading.

I'm thinking about going pretty deep into the field over the next few years, but is it worth specializing this early? Or would you stick to more general quant research?

I know the domain knowledge is pretty niche, so just wondering what the downsides are.

I've been trading discretionary for about 13 years (a lot different than the quant side), program, have a decent grasp of math and statistics, but no matter what I cannot conceptualize how to even start building a quantitative model. I should note that I'm intrinsically-motivated to learn rather than work for a firm or anything like that and I am not a quantitative analyst. I just like to learn.

What my first recent instinct was is to import the landscape of technical indicators, randomize parameters, and randomize entry conditions and exit conditions and then apply these test trades to monte carlo charts, equities, crypto, bonds, commodities, and metals and perform test trades hypothesis test, train sharpe, etc. Of course as one would imagine, not a single verdict was robust of thousands of test cases. All were overfit, likely overfit or few inconclusive.

I'm not asking for a strategy. I'm just trying to get the brains to conceptualize what would a strategy even entail?

Is this akin to forecasting the weather

or

x% of the time y occurs, then z% of the time when y occurs, a occurs b% and based on that conditional there is an edge

or

cross-validation linear regression models (I'd assume to figure qualitative impact)

or

linear regression comparison between two assets (suppose gold and fixed income)

Either way, if anyone has an resources that might allow me better grasp this concept, I'd appreciate it. One resource I've stumbled across is "Max Dama on Automated Trading".

Does anybody here have any idea of how North Moore is as a team, it's well known that it's not as good as Limestone but does anybody have an idea of the salary (base + expected bonus) for 2 YoE+ quants and the scale of PnL they make. Also how is the WLB in Tower?

And does North Moore trade India markets as well or primarily US equities + crypto like Limestone?

I've tried underlying volatility/change/volume over various time frames, adverse underlying move, option bid/ask, concession versus mid. Used linear models and gradient boosted decision trees. My best result is GBDT, spread in cents, spread relative to premium. R squared 0.2. Rmse 7.78 cents and mae 5.08 cents. These error levels are way too high to be useful. No level 2 or venue data available. Uses smart routing.

I wrote a paper on a simple execution algorithm and measured it on a full year of E-mini S&P 500 order-by-order data. Sharing it here for feedback.

Paper: https://arxiv.org/abs/2609.18019

The idea: when another participant adds a limit order near the best price, with a fixed probability place your own order at the same price, right behind theirs in the queue, and cancel when their order is cancelled. The algorithm makes no forecast; it inherits its prices from the order flow. An aggressive variant does the same on trades, crossing the spread at the traded price. Both run as percentage-of-volume mandates.

Setup: 246 regular sessions of 2025, ES futures, 100 contracts bought and 100 sold in each 10-minute window. Fills are simulated in a queue-exact replay of CME's market-by-order feed: the simulated order sits in the reconstructed FIFO queue and is filled only when recorded trades would have reached it.

Main results:

- Resting and crossing cost similar amounts. At similar participation (about 4.5% and 4.9% of volume), the passive version, which rests for 99% of its quantity, and the aggressive one, which crosses for about half, both cost about 0.095 ticks per contract against the arrival mid. The half spread a resting order earns is given back through adverse selection.

- Post-fill mark-outs are about 0.08 to 0.10 ticks against the fill at 1, 5 and 30 seconds and do not revert.

- Latency costs both, the aggressive version more. With the same delay applied to orders, cancels and market data, cost at 5 ms is about 0.15 ticks per contract for the passive version and about 0.30 for the aggressive one.

Limitations: the replay does not model market impact, so costs are before the market's reaction and participation is an upper bound. It is one instrument, which is one tick wide most of the session, and the parent order is fixed at 100 contracts.

The replay and trading system are open source (MIT): https://github.com/vincent212/kaspar-hft

Happy to answer questions, and interested in whether people have seen similar results for passive vs aggressive execution on large-tick contracts.

I am working on building a tool specifically for earnings calls. It flags hedging and deflections during meetings with timestamps + builds a knowledge graph of who is who when you start uploading multiple calls over time. I come from a ML background, but after talking to my quant friends I am questioning if this is valuable for you guys? From what I have seen there are tools that do this but they are crazy expensive. Let me know! Happy to explain more if needed.

The recent New York Times article analyzing which schools and which programs produce the highest earning graduates had some pretty high average incomes only four years after graduation. Is quant driving most of this?

Hi everyone,

I know the integration of LLMs into Quantitative Research roles has been discussed here before, but I haven't seen a concrete conclusion regarding its actual impact on alpha. Most posts just vaguely mention "interesting consequences for alpha" without elaborating further.

I'd love to start a focused discussion specifically on alpha generation. Are LLMs causing alpha to be arbitraged away quicker? Have you noticed that something is actually different in the prices?

What are your thoughts?

I'm an incoming graduate at a tier 2 HFT, and in my free time I made a completely free automatic job scraper which fetches 3k+ roles across 65 Quant firms + AI Labs in a single website. After making the website I feel like people here will genuinely find it useful, I've had 2k unique users for the past week.

I made this because last year when I was heavily interviewing, going through individual job boards every 5 days was a huge pain for me, so I made a list which can view all the open roles, standardised by category (trader/researcher/dev) and experience (internship/graduate/experienced)

Please have a go and let me know what you guys think! Any feedback will be greatly appreciated :DD

https://www.quant-scraper.com

Hey all, I know there was a similar post a few weeks back. But I wanted to re-frame the question. There's not really a quant finance industry in my country, so I would undoubtedly have to move to somewhere like NYC, London or Amsterdam. I'm curious about people's experiences and if they have any regrets. I'm worried about giving up time with family and friends I can't get back, and specifically about pet time. I have a family dog who's 6 years old, and sometimes I feel like no amount of money could ever be worth more than the limited amount of time I have with him. I know you can always travel back and the quant salary would probably help make that easier. But I'm curious how people dealt with these situations and if they had any regrets or not.

I'm currently a senior dev at a pod within a mid-sized trading firm, and I'm not particularly happy in my current role. I'm looking for a smaller firm where engineers have broad scope across the stack instead of being narrowly focused on one area, ideally one that isn't siloed like my current setup. Summit keeps coming up, and they have a Senior Low-Latency C++ opening that looks relevant.

From what I can tell, they're a relatively small market maker / prop shop. The size is in line with what I'm looking for, but it also means there's very little outside information on the firm. I'd like some outside perspective before spending any bandwidth on it.

For anyone familiar with the firm:

- How much access do devs have to the desks?
- How much influence do engineers have over architecture and technical direction?
- How does it compare with other small and mid-sized market makers, including on comp?

So pre-AI, running a solo quant fund was basically impossible. No one person can own the whole stack: data, research, backtesting, execution, risk. De Prado called this as the Sisyphus paradigm. Sisyphus is the Greek myth guy stuck pushing a boulder uphill forever, and de Prado uses it to dunk on how funds organize quants: lone researchers (or siloed pods) each expected to build a whole strategy end to end, and the boulder keeps rolling back down. His fix was the "meta-strategy paradigm," basically an assembly line where each specialist owns one station.

Now it's 2026 and AI has crossed a pretty wild line. Ken Griffin went from calling AI 'garbage' at Davos in January to saying he went home one Friday fairly depressed after seeing what an agentic system built inside Citadel could do, with PhD-level research that used to take months getting done in hours or days. When Ken Griffin is spooked, that's saying something lol

Skeptics will say AI isn't replacing quants at Citadel or Jane Street anytime soon. They've got crazy infra, exchange connectivity, proprietary data, decades of institutional knowledge. Fair enough, and hiring's still strong too, with hedge funds, prop shops and systematic managers doing most of it.

But I'm not talking about replacement. My take is trading neolabs: one person or a tiny team running a de Prado–style assembly line where most of the stations are AI agents. One cleans data, one does feature research, one tries to break your backtest, one watches risk. You stop being Sisyphus and become the foreman. You decide what to build, what to trust, and what to kill.

And no, you're not beating Citadel at latency or market making, forget that. If neolabs have an edge it's in stuff the big shops can't or won't bother with: capacity-constrained strats too small to move their needle, niche markets, weird alt data, holding periods that don't fit a pod's risk limits.

Now here's where I think people are getting it wrong. YC put AI-native hedge funds on its Spring 2026 wishlist and backed Standard Signal, a fund where AI researches and executes trades end-to-end. Cool, but I honestly don't think the VC route is the right model for neolabs. Trading IP is super sensitive, and the whole startup playbook is built around telling everyone what you're doing: demo days, pitch decks, investor updates. On top of that trading is a fixed pie. Every edge has limited capacity and it decays the moment it gets crowded. VCs need you to scale to something massive, but the edges a neolab can actually own are small by nature. Feels like a mismatch to me. The real neolabs are probably quiet, bootstrapped, trading their own money, and not posting about it.

Curious what you guys think. Anyone here running something like this with real capital? Does AI actually fix the Sisyphus problem? And is VC money ever the right move for this kind of thing, or does it kill the edge by design?

Just saw this on their website: https://www.citadel.com/what-we-do/global-quantitative-strategies/

Looks like they're opening an Irvine office?

Being a researcher is usually more tied to P&L so they get paid more as far as I know especially given their academic experience but given the over abundant supply of engineers, is it now harder to become an engineer at some of these top trading firms than to be a researcher given that there’s presumably less people competing for the latter?

I’ve also noticed some roles requiring a lot more software related work than before, and have noticed higher and higher compensation from companies like citadel where they seem to be offering people (especially newer grads) a lot more money to engineers. According to recent threads, some engineering new grads were getting 1M in total comp for their first year which as far as I’m aware is about the same for researchers. What makes this even crazier is that some of these are new grads who only have a bachelors, not a phd from a university which tends to be the case for a lot of researchers

Is this due to the higher competitiveness?

I'm sure this is true for others, but I've been hearing way more about EQR the last few months than I ever have before. Obviously, they've been hiring a lot, and I've hear that they've had a good year (so has everyone else though, so I'm unsure if the they've been an actual outlier). Plus, they've been building up their own alpha arm recently.

Beyond that though, I can't find anything except other people asking questions. That's probably an answer, but still, I'm curious: anyone have actual info on what's going on there? How's the performance or culture? Are their recent moves to grow already bearing fruit?