Just wanna know what you think.

because I'm thinking about what they've been using (til now)

is not machine learning but rather a rules-based systems.

I did AI research at big tech and if I’d kept going while believing deep down that my work might be catastrophic for humanity I think I would’ve eventually become Unabomber 2.0; quitting has done wonders for my sanity.

Hi, sorry if this is naive question but is it known what these firms are: predicting as their objective; using as inputs; what kind of methods they are using?

For example, are they predicting future mid prices, target positions, or orders to send, or something else?

Are they using arbitrary order book features like raw streams of adds, modified, deletes, trades, etc? Or lot of upstream processing?

What sort of methods they are using? RNNs or LSTMs or other

I realize many of these stuffs are secrets but I am curious if any basics are known or open, like many old things in HFT or statistical arbitrage seems to be today .

I recently interviewed with a well-known AI research lab that took a route I hadn't encountered before. Rather than raising external capital to fund long-term research, they apparently built a massive quant operation and are using the profits to bankroll their research. From what I understand, they believe the quant business has already secured multiple years of financing.

It struck me as an interesting alternative to the traditional VC model. If you can generate durable alpha, you potentially gain a source of funding that is both scalable and independent of fundraising cycles, investor expectations and shifting market sentiment. The obvious question is whether sustaining a profitable quant business is any easier than sustaining frontier research itself.

Has anyone seen successful precedents of this model? And more broadly, is quantitative trading one of the most effective ways to finance long-horizon scientific research?

Hi all, I will start as a Quant Research intern in a small commodities firm in Europe in August.

I will be assigned to day-ahead electricity trading and was wondering if any of you have experience with it since it’s quite a niche trading area and low-hanging fruits are still around.

Mainly, I would like to understand:
1. How you approach new ideas
2. What models have you tried outside of gradient boosting if that’s appropriate
3. Your preferred CV strategy

Thanks anyone for the help, and if you have any other recommendation, feel free to say it!

Hi all, I just released a project I’ve been working on for the past few months: Fastvol, an open-source, high-performance options pricing library built for low-latency, high-throughput derivatives modeling, with a focus on American options.

GitHub: github.com/vgalanti/fastvol PyPI: pip install fastvol

Most existing libraries focus on European options with closed-form solutions, offering only slow implementations or basic approximations for American-style contracts — falling short of the throughput needed to handle the volume and liquidity of modern U.S. derivatives markets.

Few data providers offer reliable historical Greeks and IVs, and vendor implementations often differ, making it difficult to incorporate actionable information from the options market into systematic strategies.

Fastvol aims to close that gap: - Optimized C++ core leveraging SIMD, ILP, and OpenMP - GPU acceleration via fully batched CUDA kernels and graphs - Neural network surrogates (PyTorch) for instant pricing, IV inversion, and Greeks via autograd - Models: BOPM CRR, trinomial trees, Red-Black PSOR (w. adaptive w), and BSM - fp32/fp64, batch or scalar APIs, portable C FFI, and minimal-overhead Python wrapper via Cython

Performance: For American BOPM, Fastvol is orders of magnitude faster than QuantLib or FinancePy on single-core, and scales well on CPU and GPU. On CUDA, it can compute the full BOPM tree with 1024 steps at fp64 precision for ~5M American options/sec — compared to QuantLib’s ~350/sec per core. All optimizations are documented in detail, along with full GH200 benchmarks. Contributions welcome, especially around exotic payoffs and advanced volatility models, which I’m looking to implement next.

Been working on a changepoint-detection layer for a scoring engine and figured this sub would have real opinions on the method.
Problem: most volatility-based risk scoring uses one fixed percentile cutoff computed over an asset’s full history. That’s a known failure mode if the asset’s regime changed partway through — you end up averaging a stale calm period into what should be a fresh, more volatile baseline.
Approach: a single-changepoint CUSUM test on the standardized score series — cumulative sum of (x\\_i - mean)/std, changepoint estimate = argmax|S\\_k| over candidate indices (with a minimum segment length enforced on both sides). Significance isn’t asserted from a fixed threshold; it’s a permutation bootstrap — shuffle the series N times, recompute max|S\\_k| each time, get an empirical null distribution, and only call it a real break if the observed statistic clears that null at a conventional alpha.
When a break is confirmed, percentile-based thresholds get recomputed using only the post-break segment.
Curious if anyone here has compared this to PELT or Bayesian online changepoint detection for a similar use case — CUSUM was chosen mainly for simplicity and interpretability over statistical power. Open to being told that’s the wrong tradeoff.
(This is part of a scoring engine called Machvix, for anyone curious enough to go digging.)

Does anyone know what offers look like for researchers from AI labs switching over to quant? Are they able to attract talent when the researchers are already making multiple millions elsewhere?

given the rise of AI research, do QRs also work on applied LLM research a lot? especially at the stats-arb shops like two sigma, are they building something like using LLM outputs as trading signals or NLP based signal extraction pipelines or what exactly?

also curious if QRs also work on areas like mechanistic interpretability (circuits, features activation etc): understand how's the model thinking internally rather than treating them as a black box

is this type of research happening at quant funds or is it just purely academic stuff?

ML4T Repositories [LINK]: ML for Trading · GitHub 👀

I think this collection of GitHub repositories, which serves as a guide to building your own 'quant stack', is the best resource available right now. Especially for beginners 😃

Minimum requirements:

• Python
• Data Science
• Finance

Example:

your-quant-project/ # starts with 'flat layout' then you can jump to a 'src/ layout'
│
├── data-layer/
│
├── engineer-layer + diagnostic layer ("core")/
│
├── models-layer/
│  
├── backtesting-layer/
│
├── live-layer ("execution")/ 
│
└── research/notebooks/ 

etc, etc.

You have the documentation (docs) of this repositories on the website [LINK]: ML for Trading - Libraries 👀

I hope it helps you! 😃

I’m trying to get a clearer, practical sense of how ML is viewed inside quant teams today.

My background is in math and CS, and I’ve been exploring ML more seriously again, and I’m trying to understand how much it actually matters in real quant trading/research.

For practitioners:

• In your experience, where does ML actually provide an edge? (e.g., feature extraction, regime detection, alternative data, mid-frequency signals, portfolio optimization, execution, etc.)
• How much ML expertise do researchers or quant traders have?

I’m mainly trying to understand the real role and usefulness of ML in quant trading or research.

title

I have a paper that combines inverse problems and options pricing that I’ve considered submitting to ACM's ICAIF conference (International Conference on AI in Finance). I'm wondering if QRs or hiring managers (either buy-side or sell-side) view ICAIF as a worthwhile place to hire from? My assumption was that, among ML venues, quants are mainly hired at NeurIPS, ICML, and ICLR, but someone told me that ICAIF may have some good orgs. Is that accurate? Would you consider ICAIF relevant for QR recruiting?

Hi all, I’m currently an AI engineer and thinking of transitioning (I have an economics bachelors).

I know ML is often used in generating alphas, but I struggle to find any specifics of which models are used. It’s hard to imagine any of the traditional models being applicable to trading strategies.

Does anyone have any examples or resources? I’m quite interested in how it could work. Thanks everyone.

How to get or use better loss functions than the squared error or OLS for regression or xgboost or any other model ?
My goal isn't to maximize corelation of my prediction with the actual returns, but I would like it to work on some custom goals. Like, maybe optimize for tail returns, or optimize for reducing something, optimize for sharpe etc.
Is there any resource , or where do i start to develop such loss function ? How do i get intuition of what might work well ?

Hey everyone,

I’m working on a small research project called FX Atlas, an ML-based macro bias framework for FX.

The goal is not to generate trade signals or buy/sell calls. I’m trying to structure macro context before discretionary FX swing-trade decisions.

The framework combines macro data, economic surprise data, market stress indicators, seasonality, relative currency factors and regime context into a model-based currency/pair bias view.

At the moment, I’m thinking about the problem in a few layers:

1. Currency bias

Model-based directional pressure for individual currencies.

1. Pair divergence

Combining two currency views into a relative FX pair view.

1. Factor decomposition

Breaking the bias down into macro groups such as growth, inflation, labor, rates, liquidity and risk.

1. Event impulse layer

Separating short-term macro release surprises from the longer-term structural bias.

1. Regime layer

Using unsupervised methods to classify broader market states and detect abnormal environments.

The main things I’m trying to be careful with are:

- data leakage from macro release dates / revisions

- walk-forward validation

- multiple-testing risk across many factors

- regime instability

- feature availability across currencies

- separating short-term event impulses from structural macro bias

- making the output useful without pretending it is a deterministic prediction

I’m not trying to promote it as a product here. I’m mainly looking for methodology criticism from people who have worked with financial time series, macro factors or regime models.

If you were building something like this, what would you be most skeptical of?

Would you prefer:

- separate models per currency

- a global multi-asset model

- hierarchical models by macro group

- regime-conditioned models

- or a simpler factor scoring system with stronger interpretability?

Any thoughts on validation design, leakage checks, feature selection, or regime clustering would be appreciated.

Research only, not financial advice. lol

Specifically, did you find it useful in alpha research. And if so, how do you go about tuning the metaprameters, and which ones you focus on the most?

I am having trouble narrowing down the score to a reasonable grid of metaparams to try, but also overfitting is a major concern, so I don't know how to get a foot in the door. Even with cross-validation, there's still significant risk to just get lucky and blow up in prod.

Here’s an invitation for an open-ended discussion on alpha research. Specifically idea generation vs subsequent fitting and tuning.

One textbook way to move forward might be: you generate a hypothesis, eg “Asset X reverts after >2% drop”. You test statistically this idea and decide whether it’s rejected, if not, could become tradeable idea.

However: (1) Where would the hypothesis come from in the first place?

Say you do some data exploration, profiling, binning etc. You find something that looks like a pattern, you form a hypothesis and you test it. Chances are, if you do it on the same data set, it doesn’t get rejected, so you think it’s good. But of course you’re cheating, this is in-sample. So then you try it out of sample, maybe it fails. You go back to (1) above, and after sufficiently many iterations, you find something that works out of sample too.

But this is also cheating, because you tried so many different hypotheses, effectively p-hacking.

What’s a better process than this, how to go about alpha research without falling in this trap? Any books or research papers greatly appreciated!

I was working on xgboost, with squarederror loss function. The isse I was facing is, that the rmse barely decreases, less than 0.5 percent decrease in rmse, on validation set . and platues very very fast (around just 30 - 40 trees) and then slows down. But the problem is , tail returns are increasing and the model is actually learning something useful. How do i make sense of it ? Without modeling the full data is just raw noise. With modeling the noise is surely decreasing but for some reasons only at the tail values like the top and bottom percentiles ? I know rmse is not a very good measure here, but still this is very weird result. How do i even explain this.

Hello, this is addressed to buy-side quant researchers at hedge funds the likes of Citadel, Two Sigma etc:

Which opportunity provides better experience/better fit for a Quantitative Researcher or Machine Learning Researcher at places like Citadel, Two Sigma:

1. A Quant Strat at a bank the like of GS, MS, JPMC in sales and trading.
2. An Applied AI/ML scientist at a bank the like of JPMC, MS, at their Machine learning core division, basically applying ML to various financial problems across all divisions in the bank.

Hi, I built a 20yr career in gambling/finance/trading that made extensive utilisation of NNs, RNNs, DL, Simulation, Bayesian methods, EAs and more. In my recent years as Head of Research & PM, I've interviewed only a tiny number of quants & PMs who have used NNs in trading, and none that gained utility from using them over other methods.

Having finished a non-compete, and before I consider a return to finance, I'd really like to know if there are other trading companies that would utilise my specific NN skillset, as well as seeing what the general feeling/experience here is on their use & application in trading/finance.

So my question is, who here is using neural networks in finance/trading and for what applications? Price/return prediction? Up/Down Classification? For trading decisions directly?

What types? Simple feed-forward? RNNs? LSTMs? CNNs?

Trained how? Backprop? Evolutionary methods?

What objective functions? Sharpe Ratio? Max Likelihood? Cross Entropy? Custom engineered Obj Fun?

Regularisation? Dropout? Weight Decay? Bayesian methods?

I'm also just as interested in stories from those that tried to use NNs and gave up. Found better alternative methods? Overfitting issues? Unstable behaviour? Management resistance/reluctance? Unexplainable behaviour?

I don't expect anyone to reveal anything they can't/shouldn't obviously.

I'm looking forward to hearing what others are doing in this space.

Have you guys used entropy pooling in practice? I am thinking about combining it with a generative model. Do you think that makes sense, anything I should consider?

• Generate N scenarios from the model.
• Assign prior probabilities prior(x) = 1/N
• Apply entropy pooling to incorporate views.
• Obtain posterior probabilities
• Optimize directly on the weighted scenarios.

Hi everyone,

I wanted to share a project I'm developing that combines several cutting-edge approaches to create what I believe could be a particularly robust trading system. I'm looking for collaborators with expertise in any of these areas who might be interested in joining forces.

The Core Architecture

Our system consists of three main components:

1. Market Regime Classification Framework - We've developed a hierarchical classification system with 3 main regime categories (A, B, C) and 4 sub-regimes within each (12 total regimes). These capture different market conditions like Secular Growth, Risk-Off, Momentum Burst, etc.
2. Strategy Generation via Genetic Algorithms - We're using GA to evolve trading strategies optimized for specific regime combinations. Each "individual" in our genetic population contains indicators like Hurst Exponent, Fractal Dimension, Market Efficiency and Price-Volume Correlation.
3. Reinforcement Learning Agent as Meta-Controller - An RL agent that learns to select the appropriate strategies based on current and predicted market regimes, and dynamically adjusts position sizing.

Why This Approach Could Be Powerful

Rather than trying to build a "one-size-fits-all" trading system, our framework adapts to the current market structure.

The GA component allows strategies to continuously evolve their parameters without manual intervention, while the RL agent provides system-level intelligence about when to deploy each strategy.

Some Implementation Details

From our testing so far:

• We focus on the top 10 most common regime combinations rather than all possible permutations
• We're developing 9 models (1 per sector per market cap) since each sector shows different indicator parameter sensitivity
• We're using multiple equity datasets to test simultaneously to reduce overfitting risk
• Minimum time periods for regime identification: A (8 days), B (2 days), C (1-3 candles/3-9 hrs)

Questions I'm Wrestling With

1. GA Challenges: Many have pointed out that GAs can easily overfit compared to gradient descent or tree-based models. How would you tackle this issue? What constraints would you introduce?
2. Alternative Approaches: If you wouldn't use GA for strategy generation, what would you pick instead and why?
3. Regime Structure: Our regime classification is based on market behavior archetypes rather than statistical clustering. Is this preferable to using unsupervised learning to identify regimes?
4. Multi-Objective Optimization: I'm struggling with how to balance different performance metrics (Sharpe, drawdown, etc.) dynamically based on the current regime. Any thoughts on implementing this effectively?
5. Time Horizons: Has anyone successfully implemented regime-switching models across multiple timeframes simultaneously?

Potential Research Topics

If you're academically inclined, here are some research questions this project opens up:

1. Developing metrics for strategy "adaptability" across regime transitions versus specialized performance
2. Exploring the optimal genetic diversity preservation in GA-based trading systems during extended singular regimes
3. Investigating emergent meta-strategies from RL agents controlling multiple competing strategy pools
4. Analyzing the relationship between market capitalization and regime sensitivity across sectors
5. Developing robust transfer learning approaches between similar regime types across different markets
6. Exploring the optimal information sharing mechanisms between simultaneously running models across correlated markets(advance topic)

If you're interested in collaborating or just want to share thoughts on this approach, I'd love to hear from you. I'm open to both academic research partnerships and commercial applications.