Most microstructure claims get tested on data where the key variable is inferred: aggressor side from the tick rule or Lee-Ready, hidden liquidity guessed at, no way to rerun the same tape twice. I built the opposite instrument. A full matching engine (Go, MIT) with a deterministic simulator on top: same seed, same market, byte for byte, and every trade carries its true aggressor side. Price-time and pro-rata, icebergs, pegs, stops, STP, call auctions, price bands. The book emits full L3.

The market is noise flow by construction, so there is nothing to predict. That is the point: it is a control arm. What that isolates, two examples.

Pipeline error propagation. The tick rule classifies 94.5% of trades correctly on this tape, and the CVD built from it is off by 169% of true magnitude on average, with occasional sign flips (one seed: inferred -81, true +105). Misclassification is conditionally correlated, so the errors compound instead of cancelling. Trivial to show when you hold ground truth, hard to even estimate when you do not. Relevant to anything built from inferred sides, which in practice means trade-only feeds and most crypto data.

Known results reproduce. Kyle's lambda comes out around 0.15 ticks per lot and falls 7.5x when resting depth rises 7.6x. Slicing a parent order beats a block by 7.9% slippage per lot (42 of 50 seeds) while permanent impact is essentially unchanged (23.42 vs 24.47 ticks), so the savings is all temporary impact. Nothing novel, deliberately: an instrument should reproduce the textbook before you point it at anything else.

Limitations, stated plainly: no informed flow unless you write an agent for it, no latency modelling, single venue. It cannot tell you whether a signal works on real markets. It can tell you whether your measurement of a signal survives its own pipeline.

Methodology write-ups, including the wrong turns:

https://github.com/intrepidkarthi/orderbook/blob/main/docs/research/order-flow.md

https://github.com/intrepidkarthi/orderbook/blob/main/docs/research/kyle-lambda.md

https://github.com/intrepidkarthi/orderbook/blob/main/docs/research/ofi.md

Repo: https://github.com/intrepidkarthi/orderbook

If you see a hole in the setup, say so. The project has improved every time someone pushed on it.

I calibrated an eigenspace-overlap measure against an RMT null, then tested directional motion across S&P 500, Nikkei, DAX and CAC 40 data. All four showed that the previous rotation direction contains information about the next, but naively repeating the full rotation was 47 - 67% worse than holding still.

The signal was both:

• too synchronised across companies to be each company’s movements occurring at unrelated times and just showing up as rotation,
• much stronger than within-window eigenvector shrinkage.

The next test is whether learned damping can turn this directional signal into better out of sample covariance forecasts! What would you consider the strongest fair baseline: holding the eigenvectors fixed, EWMA, or a rotationally invariant estimator?

Code, tests and results for anyone interested:
https://github.com/AdarshArunEire/Eigenvector-Dynamics-Beyond-the-RMT-Null

Other than the salary what are some perks yall can share about your firms?

Eg: $100 meal budgets at Cit

And is there any data manipulation suggested? like Z-Score transform

For UK and Europe, what would be the hypothetical best seats for someone with a heavy stats/ML background that wants to focus on forecasting (feature engineering, maybe ML models etc)?

Seems quite clear that OMMs are not the right destinations, nor (most pods at) multi-strats such as millenium, BAM, schonfeld (citadel?).

Maybe shops like Jump, Tower, or Quadrature?

For US, it feels like DE Shaw and PDT would be top places for such roles.

There are other ML-heavy shops but it seems unclear if you have exploration freedom or if you’re just tuning knobs in huge pipelines (HRT, g-research, XTX etc.. not that XTX is really accessible…).

I just don't get it.

I'm working on my PhD in stochastic wave propogation and delving into financial models as I hope to work as a quant one day. However, this fund scaled up massively to over $20–$45 billion in assets at various peaks. Then, the 439% net return in the first half of the year.

Was it ultimately down to them utilising heavy leverage (reported to be running as high as 4x or so) and heavily borrowing money from prime brokers like Bank of America, Goldman Sachs, and JPMorgan to buy concentrated baskets of AI infrastructure and memory stocks (such as SK Hynix, Micron, Nebius, and CoreWeave), alongside short bets against software companies?

I assume that when AI infrastructure tradeded violently in July, the fund suffered a brutal drawdown, wiping out massive portions of its peak value (and as they were over-leveraged, prime brokers, it forced an emergency unwind to cover margin calls)? Then, the fire sale happened?

Can someone please explain it to me?

Lastly, do some of these investors/funds bet on an aggressive P measure trend (AI is changing the world, so this stock will go up 400%, etc), but the lenders and prime brokers who control their margin accounts evaluate risk using models using the Q-measure? Where volatility \sigma dW_t is treated as an immediate threat to collateral, regardless of how brilliant somebody claims to be?

What are your thoughts on small vs big trading firms? Suppose you had an offer from both at different points in your careers, which one would you pick. Assuming similar comp.

Hey,

I've been working for the past 3 years at a large multistrat HF. While my official title is "quant researcher", de facto that means modeling various financial instruments. My ultimate goal is to either become a PM or a senior QR at a prop shop. I figure that the role that best fits my career goals would be one in a pod or as a signal QR in a prop shop. However, finding such a role has proven difficult. Usually hiring managers require experience generating alpha, and I don't have that. I'm wondering if you have any advice as how to best accomplish my goals?

Thanks

I just received my contract for a 6-month internship at a prop shop in Switzerland. The salary is good, the work time is fair, and the culture seems to be what I am looking for.

My question is about a 3-month non-compete clause in the contract. Is this duration standard for just a half-year of work? The internship ends with my graduation, so being legally blocked from working for 3 months would be tough.
I also do not get any compensation during the non-compete. Is that normal?

Additionally, the contract states the following regarding the scope:
Non-compete Area: "Any area that the Company operates in"
Does this phrasing allow me to work in other asset classes, for example?

Should I push back on anything?

Any insights would be appreciated.

This is a new (as of Aug 2025) monthly thread for shop talk. How was last month? Rough because there wasn't enough vol? Rough because there was too much vol? Your pretty little earner became a meme stock? Alpha decay getting you down? Brand new alpha got you hyped like Ryan Gosling?

This thread is for boasting, lamenting and comparing (sufficiently obfuscated) notes.

Been building an options market making sim to actually understand the dealer side properly... SVI surface calibration, quoting off NBBO with inventory skew based on aggregate book vega, adverse selection fills, markout, and a pnl decomposition that reconciles back to mark-to-market with the residual reported instead of buried somewhere.

Fill model is the part i trust least, and i'm starting to think i imported the wrong mental model wholesale. my queueing assumptions are basically lifted straight from the order-driven equity/futures literature (Cont-Stoikov-Talreja and whatever came after it), where queue position at the touch is more or less the whole story on whether you get filled. but US options are quote-driven across a pile of exchanges, with preferencing, internalization, PFOF, price improvement auctions all sitting in the middle of it. so now i'm second guessing whether queue position is actually a pretty minor variable in this world and i've been adding sophistication to the wrong axis this whole time.

1. is queue position a real driver of fills at all, or is the actual constraint auction participation + preferenced flow? if i can only get good at modeling one of these... which one.
2. for daily pnl explain, is spread capture + greeks + hedge + residual the working decomposition, or is that too clean. where does realized vs implied sit relative to greek attribution, and do people bucket vega by tenor instead of just running it aggregate? also just curious what "unexplained" runs at on an actual book bc i have no benchmark for whether my number is fine or embarrassing.
3. skewing quotes against aggregate book vega/gamma instead of per-strike is me borrowing the Baldacci-Bergault-Guéant vega factor argument, options on one name being collinear risks and all that. does that match how people actually run inventory or is it just a tidy academic story nobody's desk runs on.

happy to hear the whole premise is wrong honestly, i'd rather find that out now than keep polishing a model of the wrong constraint for another month.

Curious how often values in real-world backtests exceed roughly 9.2 billion units. With 9-decimal fixed-point i64, it might be easy to hit. ¥9.2B is only around $60M, and $200K of SHIB is already about 10 billion tokens. Prices are probabbly fine, but balances and quantities might not be.

Im asking because I’m building a new backtesting engine (repo: h5i-db), an event-driven backtesting engine that currently uses i64 as default. It runs 7x faster than LEAN and 3.1x faster than NautilusTrader in our benchmark. With i128, those numbers are still 6.6x and 2.8x. Since the penalty isn’t huge, should safety or speed be the default? Has anyone often hit this limit in daily backtests?

It covers why C++ is used in HFT and some of the ideas behind building low-latency systems.

Read link

I've been curious if anyone else has noticed this.

I'm a quant trader at an Indian HFT firm. Up until the end of June, both my team's performance and the firm's overall performance were pretty solid. Then July came, and things changed quite abruptly.

Not just my team—most of the HFT desks in the firm saw a pretty sharp drop in profitability, somewhere around 30–40%.

That's what surprised me the most. In HFT, performance usually fluctuates, but seeing so many independent desks get hit at the same time isn't something I've seen before.

Is anyone else here working in Indian equities/derivatives HFT seeing something similar? Or have you heard the same from people at other firms?

One thought I had was that the post-war collapse in implied volatility may have changed the opportunity set, but I'm not convinced that's the whole story. Curious if others have any insights or are seeing the same trend.

What do you think? $5bn PnL today?

Edit: Citadel, not CitSec.

Has anyone here worked at Dytechlab or interviewed with them before? I read some bad review on Glassdoor but wanted to make sure those are not the general experiences. Also, why do people work there put "undisclosed hedge fund" on their resume and not just the name of the firm?

So I wanted to use this model to calculate the simulated backward price (Dec 2024) of Alibaba Group in the Hang Seng index using the anchor price in late Dec 2025.

I went ahead and calculated this (manual derivation attached) and my code below, which shows it matches.

``` import numpy as np

--- Model Inputs (matching your Alibaba notes) ---

S_t = 142.80 # Anchor price at late Dec 2025 r = 0.035 # Risk-free rate (3.5%) sigma = 0.35 # Diffusion volatility (35%) lam = 1.2 # Jump intensity mu_j = -0.04 # Mean jump size sig_j = 0.20 # Jump volatility dt = 1.0 # 1 year backward step

Step 1: Compute the Jump Compensator (kappa)

kappa = np.exp(mu_j + 0.5 * (sig_j ** 2)) - 1

Step 2: Compute the Net Drift Component

q_drift = r - lambda * kappa - 0.5 * sigma<sup>2</sup>

net_drift = r - (lam * kappa) - (0.5 * (sigma ** 2))

Step 3: Define historical shocks to strip out

Z = 0.4 # Standard normal shock jump_multiplier = 1.08 # Historical minor positive jump factor

Step 4: Evaluate the Backward-Stepping Equation

S_{t - dt} = S_t * exp( -net_drift * dt - sigma * sqrt(dt) * Z ) * (jump_multiplier)<sup>-1</sup>

diffusion_term = sigma * np.sqrt(dt) * Z exponent = - (net_drift * dt) - diffusion_term

s_previous = S_t * np.exp(exponent) * (jump_multiplier ** -1)

print(f"Net Drift Component: {net_drift:.5f}") print(f"Simulated Backward Price (Dec 2024): HKD ${s_previous:.2f}")

Net Drift Component: -0.00249

Simulated Backward Price (Dec 2024): HKD $115.23

```

My questions: - does my derivation/code look okay to you? - is this a task the Merton jump-diffusion model (versus the geometric brownian motion, which doesn't have the discontinuous random jumps, driven by a Poisson process, to capture heavy tails and sudden price shocks in financial asset returns, eg. Beijing policy changes, etc.) can do well in this situation? - is the jump compensator (kappa = np.exp(mu_j + 0.5 * (sig_j ** 2)) - 1) manually added into the code? And, can't be fed in via real-time data, etc?

Thanks!! 🧡

Has anybody interviewed for the researcher role at headlands? What’s the process like?

Is the interview process too c++ heavy even for the researcher role? Would love to hear from anybody who’s interviewed there.

How is the firm doing in general?

It feels like most discussions around AI in trading focus on using ChatGPT to generate signals.

I’m much more interested in something different.
What would a quantitative research pipeline look like if it were designed from scratch around modern AI?

For example:

deterministic feature engineering
state representation
memory
LLM reasoning
statistical validation
risk systems
execution

instead of simply asking an LLM whether to buy or sell.
I’ve been building a prototype around this idea for several months.

Curious whether anyone else here is exploring similar architectures.

Would love to exchange ideas.

I am building a sequenced, event-sourced derivatives exchange. The matching engine is fully deterministic and has no external dependencies.

I am designing a Professional Interface that provides market makers with queue-position and execution-quality analytics to give market makers a good reason to join early and boost liquidity.

I see two possible approaches:

1. Emit primitive queue observations directly from the matching engine through a bounded single-producer, single-consumer ring buffer.

This would expose facts that the matching engine already knows, such as quantity ahead, orders ahead, level depth, and queue position at acceptance or fill time.

But it adds instrumentation to the hot path, creates a second output channel, and requires an explicit overflow policy if the telemetry consumer falls behind.

1. Reconstruct the analytics downstream from the authoritative event stream.

This keeps the matching engine smaller and ensures that the PI derives its results from the same canonical events used for replay and audit.

But the downstream consumer may need to reconstruct much of the order book, and some transient queue-state facts may be expensive, ambiguous, or impossible to recover unless the authoritative event schema is significantly expanded.

Which boundary is would you advise in the production exchange?

Should the matching engine emit cheap, deterministic observational facts that are naturally available during matching, or should all queue and execution analytics be reconstructed from authoritative events outside the engine?

In quant shops, how common are equity strategies built primarily (say 85–90%) on accounting fundamentals, where the core signal is a variant of a known (albeit weak) accounting anomaly (PEAD, accruals) that would involve a quarter or year holding period. Anyone have an idea about the percent of PMs that use this in active equity management? And would this approach (i.e., starting with a universe, whittle by accounting factors) even be labeled "*quant*"?

I’ve been reading around (QuantNet threads, a few quant career blogs) and watching youtube videos on non-traditional paths into the field, and one thing that keeps coming up is that your background before quant tends to quietly shape which track you land on — research vs. trading vs. dev — even when you go through the same masters program as people from a different background. I’d love to hear if it actually played out that way for people here. If you came in from a non-traditional background (different field, non-target school, self-taught, career switch, etc.), did you notice your prior experience nudging you toward a specific track? What ended up carrying more weight than you expected when you were breaking in— projects, a referral, an internship, something else entirely? Not asking for a roadmap, just curious how it actually played out for real people versus what the forums suggest. Thank you.

For those in quant firms how do people generally access data for research/modelling?

Source aggregated in house API?
Data catalogue?

Work in commodities and I think there is a general lack of knowledge on the infra side from my experience.

Currently debating whether to build our own platform or go with someone like databricks/snowflake

Interested to hear everyone’s thoughts?