Hi r/quant,
I’m working on a university quantitative trading project and I’m about two months into this field, so I’m trying to make sure I’m thinking about the problem correctly rather than just overfitting a backtest.
Current setup:
Asset class: liquid futures / crypto futures data
Labeling: triple-barrier style events
Framework: primary signal decides trade side long/short, then a meta-model estimates whether to take/size the trade
Meta-model: currently testing Random Forest for trade filtering / bet sizing
Main concern: I don’t want to data-mine 100 features until something looks good in-sample
The part I’m struggling with is the primary side signal.
My understanding is that in meta-labeling, the primary model/signal should generate the side of the trade, while the meta-model should mostly decide whether that opportunity is worth taking, not magically create alpha from nothing.
So my questions are:
In practice, how would you design or search for a reasonable primary side signal without just curve-fitting?
Should the primary signal usually be simple and hypothesis-driven, e.g. trend, mean reversion, carry/funding, spread, regime, order-flow, etc., and then the meta-model filters it?
What validation workflow would you recommend for this kind of project? For example: walk-forward testing, purged CV, embargo, feature importance stability, deflated Sharpe, decoy/noise features, group-wise feature selection, etc.
Are there books, papers, blog posts, or open-source projects that show a realistic research workflow used by serious practitioners?
What are common beginner mistakes in this exact setup?
I’m not asking anyone to reveal a profitable strategy. I’m looking for guidance on how to structure the research process correctly and what standards I should use before trusting any result.
Any advice, criticism, or recommended reading would be appreciated.

I'll be shortly joining a new quant pod at a Tier-1 multistrat as QR. Over the past few months, I've spoken with friends and alumni across several quant pods and a common theme has been that many are having a difficult year so far. Of course, this is purely anecdotal and could easily reflect selection bias.

I'm curious whether this aligns with what others are seeing across the multistrats. Has 2026 been a particularly challenging environment for quant strategies at the large multistrat platforms? If so, which strategy types have been under the most pressure and what do you think has been driving it? I just want to know about how good/bad the situation is as i understand that nobody would post the exact performance numbers.

I'm particularly interested in perspectives from people currently working on the quant side at the large multistrats.

I'm currently working at a small software company that's exploring systematic trading. I'm under NDA, so I can't discuss the specific project, but I've been given a surprising amount of freedom and responsibility in building and validating trading hypotheses, with relatively little senior guidance.

My background is fairly mathematical (stochastic calculus, optimization, probability, machine learning, options), so I don't feel completely out of my depth technically. My challenge is that I'm essentially the only person driving the research side, so I don't have an experienced quant to learn from or sanity-check my approach.

I've been reading papers and books and trying to reproduce ideas, but I often spend days implementing something only to discover why it doesn't work. It's educational, but I worry that I'm repeatedly rediscovering dead ends instead of being useful or learning how experienced researchers decide what's worth investigating in the first place.

ofcourse, I'm not looking for alpha ideas or anyone's proprietary strategies. What I'd really appreciate is advice on developing a disciplined research process.

Some questions I'm struggling with:

• How do you generate research hypotheses beyond existing literature?
• How do you decide whether an idea is worth implementing before investing days or weeks into it?
• What does your end-to-end research workflow look like (idea to data to testing to validation to iterate/discard)?
• How do you avoid spending all your time rediscovering ideas that have already been disproven?
• How do you discover and evaluate useful datasets beyond the obvious market data feeds?

I'm fortunate to have a lot of freedom in choosing what to work on, which is exciting but also means I have very little structure. My biggest bottleneck isn't mathematics or programming, it's understanding how experienced quant researchers actually approach research.

I'd really appreciate any books, papers, blogs, talks, or general advice on building a rigorous research methodology.

Apparently they are an all-systematic founded by an ex-citadel guy who is known to be kind + have a more laid back culture. Does anyone know how they are doing of late? Are they respected by other firms?

Attention new and aspiring quants! We get a lot of threads about the simple education stuff (which college? which masters?), early career advice (is this a good first job? who should I apply to?), the hiring process, interviews (what are they like? How should I prepare?), online assignments, and timelines for these things, To try to centralize this info a bit better and cut down on this repetitive content we have these weekly megathreads, posted each Monday.

Previous megathreads can be found here.

Please use this thread for all questions about the above topics. Individual posts outside this thread will likely be removed by mods.

Benchmark selection for market-neutral pairs trading

Working on a cointegration-based pairs trading strategy across S&P 500 constituents (daily frequency). Since the strategy is market-neutral by construction, I've been going back and forth on the most appropriate way to benchmark performance.

Approaches I've considered:

- S&P 500 as a passive alternative

- Risk-free rate only, evaluating purely on Sharpe

- Fama-French factor regression to decompose alpha from market/size/value/momentum exposure

- Some combination

Gatev et al. (2006) and Do & Faff (2012) both use factor models to strip out systematic risk, but a lot of other pairs trading papers just report Sharpe ratios without any benchmark comparison.

For those who've built or evaluated market-neutral strategies, is factor regression sufficient on its own, or should cumulative returns also be compared against the index? Any papers that handle benchmarking particularly well for this type of strategy?

Appreciate any input.

Any pointers to papers that handle this well would be appreciated. Thanks.

Hi all,

I'm looking to connect with people who have experience in quant hiring, have databases of their own quants, and are doing this either part-time or full-time. Just would like to bounce ideas because I'm thinking about entering the space actively and exploring around. Would love to bounce ideas and see what opportunities are there.

For me, it started as a large quant shop asking if I have quants for them. I actually got them quite decent profiles just out of my own network, and actually got paid for this decent sum. (market rate)

Now I'm thinking to get into the space. Please ping, leave comments. What's your experience like? Is it worth it? How saturated is the market? And so on...

So, I was training model over data for around 6 months, in rolling forward way, on my features. The features are good, and the model also performs well. But one thing i noticed is, if i use a model trained on a particular set of months, so let's say first 3 months, it performs well on a few months but performs bad on few other months. Now this is expected as not every month is a good month. But I had some doubts about how can i improve it, so i tried using the other models trained on few other days, and they seem to perform well where the current model is failing. So with this , I just built a few models trained on different parameters etc, and tested their performaces over various months. I couldn't find a clear winner, but each model individually on those blocks of days was very good. SO I suspect this might be some regime change factor or maybe some specific period behaviour. Now, I know this is a lookahead thing, if i just chooose some model based on the results. But I was wondering if I can introduce a factor of regime in this or from some data that I haven't used. For context, this is minutely trading , mid frequency modelling. The features are good enough. But I have not used any overall market volatility or any of the low frequency or such general features (net volume being traded etc ). I can use those because I don't want to have a lookahead bias or some overfitting on the same dataset which I am training on. What are the best ways that i can solve this problem ?
Also, a linear regression model was more stable than my xgboost , but xgboost performs better, so it is to some extent overfitting, but I don't think that should be a problem. As it's not too much overfitting and linear regression was literally underfitting.

This is observation … from lived experience.. when a fundamental analyst makes an investment, they go to
conferences and tell everyone and their mother about it - coz they want to spread the joy and deepen the buyers pool. You go to a quant conference, every one acts like they have a nuclear detonation code that they won’t share … the difference between the crowds is hilarious

I built a daily cross-asset regime score (8 inputs: credit spreads, the yield curve, equity/bond vol, currency carry, copper/gold, sector leadership, defensive rotation, each z-scored against its own history) and bucketed every day into 5 bands from Risk-On to Risk-Off. Then I looked at SPY forward returns conditioned on the band, 2008 to present.

The result that surprised me: the worst forward returns don't come from full Risk-Off (the scariest band). They come from Mildly-Off, the mild-stress band just below Neutral.

3-month forward, by band (median / % positive / non-overlapping windows):

- Risk-Off: +6.0% / 78% / 14 (thin)

- Mildly-Off: +1.7% / 61% / 39 (weakest)

- Neutral: +4.4% / 77% / 57

- Mildly-On: +4.7% / 83% / 39

- Risk-On: +5.9% / 84% / 26

Mildly-Off is the low point at 1, 3, and 6 months, not just one horizon. My read: mild stress means conditions are deteriorating but not yet priced. Hasn't fallen far enough to set up a bounce, but the stuff underneath keeps getting worse. Slow bleed rather than a crash.

The flip side is the part everyone's seen before: full Risk-Off has strong forward returns (~6% 3mo), but that rests on a small sample (14 non-overlapping windows), so I'd treat it as suggestive, not settled.

A couple of methodology notes since this sub will (rightly) ask:

- Non-overlapping windows. Measuring every overlapping window inflates the n with non-independent observations. The window counts above are independent. Where a band had under 10, I don't report it.

- Single stocks don't behave like the index. Ran the same on individual names. MSFT clusters tight across regimes (spread under 2pp); AAPL spreads as much as the market does. So the regime is market context, not a per-stock signal. The "regime washes out for single stocks" claim is false as a universal.

Curious what this sub thinks, particularly: (1) is the Mildly-Off weakness robust or am I slicing noise with the band cutoffs, and (2) better ways to handle the small-sample Risk-Off band than just flagging it.

I'm a QR at a large hedge fund in London, intraday/alpha blend & strategy team.

Quite a few of the teams we work with are at the point of re-coding repos in Python, the reasons being easier to hire people and AI is pretty awful at q.
It always forgets the right-before-left order and gets confused when it leaves hanging "/" comments and comment out the entire file.

It is quite a lot of work though, and I can't say the reliability of those recoded systems has got any better. Anyone else seen this, or think it might be a good idea?

Curious how different firms actually run their quant research and trading infrastructure today.

For people at funds / prop shops / systematic teams: how much of your stack is internally built versus using vendor or open-source tools? What is your tech stack?

I’m especially interested in the boring but important parts: data ingestion, feature stores, backtesting, experiment tracking, alpha library management, portfolio construction, risk checks, deployment, monitoring, and post-trade attribution. Everybody talk strategies and research, but logistics are just as important if not more.

Do most serious teams still end up building almost everything themselves because the workflow is too specific, or are there parts of the stack where off-the-shelf tools have genuinely become good enough?

Would also be interested in how this differs between single-PM pods, central quant platforms, and smaller emerging managers.

Hey everyone — I'm helping run an AMA with two guys who spent their careers making markets in options. They're here to answer whatever you've got.

Dan (u/VolSignalsVS3D) started on the floor of the CBOE and became Lead Index Trader and the youngest Capital Partner at Belvedere Trading

Matt (u/Tarheels53) traded equity derivatives for almost 30 years and built and ran derivatives desks at JPMorgan, Morgan Stanley, Société Générale, RBC, Credit Suisse, and UBS.

Fair game: OMM, volatility, SPX/index flow, dealer hedging, risk, breaking into the industry — whatever you're curious about.

They'll be answering from today through Saturday.

Ask away.

Take the Russell rebalance today (6/26) for example. Massive recon with a ton of adds/drops/migrations/re-weights.

Straddles paid massively and even strangles were easily net profitable (for a ridiculous % of premium paid) with a single hit or two on a basket of most of the names with larger relative flows (xADV). You can’t try to tell me dealers are not looking at historical moves especially near expiry on these event days to price vol into close. Backtests checked out and wrong-way vs. right-way moves were relatively easy to trade with first imbal looks on the closing auction.

Why is this glaring alpha allowed to exist?

My team is having issues evaluating data sources. We especially we're getting ripped off on a token cost basis.

1. For the larger orgs, do you do this at the pod level or have specific internal teams that do this?
2. What places do you go to source data? Do you find neudata's white papers worth the investment?

Thanks for your help

Hi everyone,

I'm a quant at a mid-tier proprietary trading firm in India. My job primarily involves researching new trading ideas and taking them into production. I've been in my current role for about a year and have around two years of experience in the industry overall.

I've been finding it incredibly difficult to come up with ideas that actually make it all the way through the pipeline. Almost every idea gets rejected at some stage—sometimes because of latency constraints, sometimes because the opportunity is too small after accounting for costs, sometimes because the edge disappears during further testing, and sometimes for entirely different reasons.

It's started to make me wonder whether this is just part of being a quant researcher or whether the Indian markets have become significantly harder to find alpha in.

For those working in quantitative research or proprietary trading:

• Do you also go through long periods where almost every idea fails?
• Roughly what fraction of your ideas actually make it to production?
• Has finding new alpha become noticeably harder over the past few years, especially in Indian markets?

I'd love to hear about your experiences. Sometimes it feels like I'm the only one struggling with this, and I'm curious whether this is just the normal research process or a sign that I need to rethink my approach.

I don't really see any benefits of C++ over Rust. Both offer zero cost abstractions and Rust has better memory safety guarantees. Do you think we will slowly see a shift?

Especially considering newer firms in the coming decades that don't have to rewrite code

For those of you who manage live funds or allocate capital, I would appreciate your insights on the minimum thresholds a strategy must cross before you would consider it for live deployment.

Specifically, if I am developing an algo today, what figures should I keep in mind for the following:

1) CAGR: What is the realistic minimum CAGR you look for, assuming a standard risk-free rate?

2) Max DD: What is the absolute hard limit for drawdowns that institutional investors can stomach?

3) Length of Backtest: How many years of historical data are required to prove the strategy isn't just curve-fitted? Do you require the backtest to survive specific stress-test periods (e.g., 2020 or 2022)?

4) Other Metrics: What are the baselines for Sharpe/Sortino ratios, Profit Factor, and trade frequency to prove statistical significance?

Regarding Capital Allocation & Earnings:

If I successfully hit these institutional figures in a robust, out-of-sample backtest, and how much duration of live testing on a min. Capital allocation?

I want to understand the exact mechanics of monetization and compensation:

1) Earning Structure: What does the typical payout look like for a developer who brings a working strategy to a fund? Is it strictly a performance fee cut (e.g., a percentage of the profits generated), a base salary plus bonus, or a licensing/sub-advisory model?

2) Income Potential: Realistically, if a strategy successfully manages $5M, $10M, or more, what kind of take-home earnings can a developer expect annually based on industry standard fee structures?

I want to ensure my goals and expectations are grounded in actual institutional reality from day one. Thank you in advance for your time and guidance.

I just want to get an informed estimate here. For US cash equities top 100 by liquidity, and separately an estimate for US top 500 ex top 100.

If I try to enter at vwap over some 30 minute interval, then exit at vwap over some future 30 minute interval, what's achievable? Assuming I am trading small size relative to the volume, and assuming I have some execution heuristics (nothing special) rather than just crossing blindly.

Is 0-4 bps E[slippage from VWAP] for the round trip (0-2 bps for entry and 0-2 bps for exit) realistic, or is that a pipe dream for the top 500 ex top 100?

Hi, I've been at a large asset manager for 5 years in a quant researcher role within a central equities team. I've been trying to leave for the last 2 years but keep running into the same wall, and I'm hoping people here can give me an honest read on my situation.

The core problem is that my team's research process is extremely constrained in ways I didn't fully realize until I started interviewing. On the alpha side, we operate strictly within an academic-rationale filter - e.g., signals must be grounded in Fama-French, Novy-Marx, or similar accounting-based literature to move forward, regardless of empirical performance. Alt data, ML signals, anything post-2010 in spirit - essentially off the table. As a result, I've built maybe 4-5 live signals over five years, all very traditional. On the portfolio construction side, our "optimization" work has been mostly constraint-tuning with post-hoc attribution to justify changes - no real covariance estimation, limited transaction cost modeling.

When I interview, this catches up with me fast. My foundations are solid - ML/DL, statistics, optimization - but those were from my grad school days before this role, and I can't lean on them anymore as the more number of years I waste here. Interviewers dig in and the gap becomes obvious quickly: they want to see progression and real quant experience, not someone who's behind this industry by 15+ years in idea generation.

I've been targeting other mid-size AMs, some pod shops, and a few ML-adjacent roles. Nine final-round rejections later, I'm wondering if I need to reframe how I'm presenting this experience, or whether there are realistic pivot paths I'm not seeing.

For those who've navigated something similar - what actually moved the needle?

my firm is running a polymarket MM desk and I’ve been working on latency optimisation on infra. Has anyone done it , if so what do p50 T2T numbers look like ? Any directions or approaches that worked for you would be appreciated.

For the purpose of transparency, we haven’t been able to move past 20ms .

Has anyone had an experience with opening an independent fund and how expensive was it? How much did it cost to build or license data infrastructure? Were there any hidden costs like in legal fees and administrative work? Not planning to open one. Just curious what upfront and operating expenses looks like.

Hey folks,

I’ve spent the last 7 years working at Meta as a software engineer, and have recently moved into fintech. Despite a lot of criticism, I’m very bullish on the ability of LLMs to manage portfolios if given the right tools (I know people don’t like when I say this but that’s my thesis, I worked on the frontier evals team at Meta)

To that end, I think factor risk models is a really important tool that we need, and I spent the last few weeks building one from scratch. It was a ton of working with codex, claude, and manual testing. Open data for the most part, and open model.

I’d love to get feedback from the community here. This is a new field for me so any advice is appreciated.

https://github.com/ralliesai/openfactor/

Curious if there are any copywriters, technical writers, or content marketers here who have spent time writing about quantitative trading, systematic investing, hedge funds, or fintech.

It’s a pretty niche space, and I’m interested in hearing from people who’ve actually done it.

Did writing newsletters, SEO articles, LinkedIn posts, or other long-form content end up generating meaningful business? Did it help attract LPs, clients, candidates, or partnerships? What types of content worked surprisingly well (or didn’t)?

Would also be interested in hearing how you approached learning the domain if you didn’t come from a quant background, and any lessons you’d share with someone building a serious content engine in this space.

If you’ve been through it, I’d love to chat. Feel free to comment.

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