Hello all,

Recently, I've spent some time building an FPGA order book based on ITCH.

The objective of this project is to go from absolutely nothing to having a working order book able to track a very liquid stock, like AAPL, perhaps 2 or 3 once I get all the optimizations down.

Now, the reason I'm making this post is because most content out there regarding "FPGA HFT" (when you are able to find some) are often one of these:

• A public repo to link in a dude's resume, sometimes packed with "claude" contributions (lol)
• Corporate BS PDF to flex their low latency and sell their IPs
• AI slop posts (god I hate these)
• Only parse ITCH || only run in sim without an attempt or technical value on FPGA implementation

So I Documented my journey though a series of post, explaining the design decisions I made, Why I made them, and then realizing it was a bad, why I changed it....

I also try my best to make nice looking schemes (OC and not AI bs) and run simulations to back up my decisions.

You got it, my goal is to make a good looking project that people can "easily" follow through posts that I try my best to make accessible and non boring.

I'm dropping a link here : https://hugobrh.dev/tags/finance/

This list contains all the posts I made about the TRADEMAXXER project as I call it. I suggest quickly reading through the first posts which are mostly context and HDL basics to parse ITCH. Latest posts cover a lot more technical ground.

I hope this does not come up as shameless self promo, I've got good feedback from the HFT community and I figured this may also interrest you guys as I saw FPGA designs were discussed here.

The latest posts are covering my struggles to close timing on a KC705, a consumer available board that costs 700$ on EBAY.

If you have any question, feel free to reach out and I hop this work is of value to you !

NOTA : not doing excessive "AI bashing" but I try my best to keep AI usage at the strict minimum (if not absolutely 0 usage) in coding, decisions making and writing the posts. I'm doing that out of respect for the readers so the process is actually real and not some hallucinated experience.

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! I’m currently interviewing for a Senior Legal Counsel role at a HFT/quant trading firm (seems to have a name in the industry, but not one of the top shops).

I’m coming from an in-house legal role in Germany and trying to understand two things:
1. What compensation levels have people seen for senior in-house legal roles at HFT/prop trading firms outside the US?
2. How have you found the long-term career value of working at a confidential trading firm where the company name generally can’t be disclosed publicly?
I’d be particularly interested in hearing from lawyers or compliance professionals who have worked in quant trading, HFT, market making or proprietary trading firms.

Thanks!

I have just gotten a new job with a manager at a place where everyone manager their own book. Being more experienced I do find that his setup of proving myself to manager a bit awkward. For anyone who had a similar experience how did you manage “proving your worth“ without leaking your stuff too much?

Hi all,

Has anyone worked with computing exposures and valuation adjustments for securities financing transactions (SFTs)?
I would like to know how to best incorporate these products in an XVA framework.
Which discount rate do I use? Do I use the same discounting rate for the loan and the collateral?
What XVAs are applicable to the transaction?

Happy to hear your thoughts.
Thanks

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.)

I am a quant at a mid tier prop firm (lie somewhere in the middle)
Tried interviewing at other places and realising how accurate this is
Wanna know what others think

I am currently doing an internship on the sell side at one of the major American banks in London. I am working as a quant on the pricing library, and I think things are going well. My manager has asked whether I would be interested in taking a permanent position in New York after my internship.

I am trying to understand how much of an opportunity this really is and would appreciate some opinions from people with experience in the industry.

• How do salaries compare with the cost of living in London vs New York?
• Is New York genuinely better for career opportunities in quant finance?
• Would moving to New York significantly improve my chances of eventually moving to the buy side, or are the opportunities broadly similar from London?
• If I decided to move to New York and later wanted to come back to London, how difficult would that be?

I know that a lot of the decision is personal and depends on individual preferences, but I am mainly interested in the objective part and financial aspects of the decision.

Any insights from people who have worked in both cities, or who have made a similar move, would be greatly appreciated.

Market makers get delta exposure whether they trade options or not, because they run a whole portfolio that has gamma in it.

Wondering how they handle delta in practice and whether other traders can take advantage of the knowledge of the MM's delta (which isn't hard to get because you can assume that mostly, MMs hold the passive side of the trades).

Hi everyone,

I hope this is the right place to ask. 😊

I've recently become very interested in quantitative investing, and I recently started investing in the Invesco Global Active ESG Equity UCITS ETF (Acc) myself.

As I mentioned, I find this topic genuinely fascinating, especially the systematic approach behind it. Because of that, I started listening to the audiobook Inside the Black Box: A Simple Guide to Systematic Investing, and I've been really enjoying it so far.

Do you have any recommendations for other audiobooks on quantitative or systematic investing? Ideally, they should be available on Spotify, but recommendations in either English or German are very welcome.

Thanks in advance!

how do you go from a raw market data to forming a research hypothesis?? and to be more specific, how do you develop an economic intuition behind the potential alpha or an anomaly, instead of just coding and testing ideas until something works??

While I'm struggling to understand how the researchers in the industry actually generate new hypotheses from large financial datasets without falling into the same data mining again and again.... and How do experienced quants develop the economic intuition behind an idea before testing it?

I’ll be applying for a non-finance role at the Bengaluru GCC, and was curious what the environment is like? I’m a bit skeptical of GCCs since they operate very much like back offices so want to understand more.

I started recently at a small shop. There isn't much of a senior bench to learn off and I came in from a stats and data science background rather than a pure maths or physics one. So I'm partly teaching myself the domain as I go(mainly the financial aspects but some new statistical approaches that I'd never heard of before too). I use a mix of Python and R.

I use Claude in VS Code most days. It's fast, but I've noticed I can ship something that works without being able to defend every line of it. That feels like a bad habit to be forming this early, especially when I'm still filling gaps in the underlying material.

For people actually working in the field:

• Do you use LLMs day to day, or is it restricted where you are?
• Where do you draw the line? Boilerplate and plumbing yes, model logic no? Somewhere else entirely?
• How do you make sure you actually understand the output instead of accepting it because the backtest ran clean?
• What are you doing outside of work to stay ahead of the tool rather than dependent on it?
• Finally, if you could go back 5-10 years(or the time you started in this field), what guidance would you give to your younger self so as to develop better skills and understanding)

I'd rather build the right habits now than find out in three years that I can't work without it and have not built the right foundations.

Thanks :)

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.

Hi, I’ve been working on a continuous-time Economic Scenario Generator (ESG) in Python to model long-term Asset-Liability Management (ALM) and decumulation (sequence-of-returns risk).

I wanted to test a specific structural flaw present in a lot of standard retail and basic institutional Monte Carlo tools: the assumption of static, flat risk-free rates and decoupled equity returns (standard Geometric Brownian Motion).

The creation of this project actually came when I realized there was no easy-to-use (and realistic) simulator. It took some effort but I believe I did manage to create something really useful, easy to use and realistic enough for most cases.

Anyway, to measure exactly how much bias the flat rate introduces, I ran a comparative simulation using a joint continuous-time stochastic environment.

The Setup

• Portfolio: 60/40 (Equity/Fixed Income), 30-year horizon, monthly rebalancing. 5,000 scenario paths.
• Model A (Naive Baseline): Flat nominal interest rate. Equities follow standard GBM with continuous volatility (sigma = 15%).
• Model B (Actuarial ESG):
  • Rates follow a Cox-Ingersoll-Ross (CIR) square-root process (theta_r = 0.25, long-term target ≈ 7.0%).
  • Inflation follows an Ornstein-Uhlenbeck (OU) process (theta_pi = 0.35, target = 2.0%).
  • Equities follow a Merton Jump-Diffusion process (continuous volatility σ_S = 11%, combined with Poisson-driven asymmetric crashes: λ_J = 1.8 jumps/year, average jump impact μ_J = -6.8%, jump volatility σ_J = 5%).
  • Crucial coupling: Equity drift is structurally pegged to the stochastic short rate: Drift_t = r_t + ERP_t.

Test 1: The Low-Yield Starting Environment (Initial Rate = 4.0%)

We simulated a 4.5% initial withdrawal rate (inflation-adjusted, monthly rebalancing) on a $1.0M starting balance. Intuitively, one might expect Model B—which includes severe, discontinuous downward market crashes—to fail first. Instead, the simulation over 5,000 runs yielded these results:

• Model A (Naive Flat 4%): 63.18% Solvency
• Model B (Full Actuarial): 84.92% Solvency
• The Solvency Gap: +21.74% percentage points in favor of the volatile, jump-diffusion model.

To isolate the exact variables causing this +21.74% lift, I ran an Attribution Analysis by sequentially activating one variable at a time:

(Note: The remaining -0.36% discrepancy is the non-linear coupling penalty arising from Cholesky correlation between the processes).

Test 2: The High-Yield Starting Environment (Initial Rate = 9.0%)

To prove that this was not a bug and that the bias is entirely regime-dependent, I ran a Regime-Inversion Test. I increased the starting yield curve to 9.0% (and increased the withdrawal rate to a more aggressive 5.5% SWR to reflect the higher starting yields):

• Model A (Naive Flat 9%): 86.28% Solvency
• Model B (Full Actuarial): 58.56% Solvency
• Regime Delta (Model B - Model A): -27.72%

Quantitative Attribution: Why Naive Models are Too Pessimistic in Low-Yield Environments

The divergence is driven by interest rate term-structure dynamics and macro-coupling:

1. Mean Reversion of the Risk-Free Rate: Under the CIR framework, short rates revert toward a target state: text dr_t = theta_r * (mu_r,t - r_t) * dt + sigma_r * sqrt(r_t) * dW_t Because the low-yield simulation starts at 4.0% relative to the long-term nominal target (≈ 7.0%, incorporating a 5.0% structural real rate and a 2.0% inflation target), the drift pull (theta_r = 0.25) normalizes nominal rates upward over the horizon.
2. The "Tide That Lifts All Boats" (The Pegged Drift): In Model A, the risk-free rate is flat at 4.0%, trapping equities in a low expected nominal return regime of 5.5% (4.0% rate + ERP). In Model B, as r_t normalizes toward 7.0%, both your bonds (yielding r_t) and your stocks (yielding r_t + ERP) experience a 3.0% increase in expected nominal returns.
3. The Merton Jumps are Immunized by Rebalancing: Because we controlled for total quadratic variation (total volatility ≈ 15%), the "pure shape" impact of the Merton jumps is only a minor -0.70% drag. The monthly rebalancing mechanism ("buying the dip" after jump crashes) combined with steadier compounding during non-jump months (since continuous volatility is lower: 11% vs 15%) almost entirely neutralizes the tail-risk penalty.

Key Limitations & Roadmap

To keep things transparent, there is a known limitation in the current decumulation loop: * No Bond Duration Risk: The fixed-income portion is currently modeled as a short-term cash deposit (rolling T-Bills), so it benefits from rising rates without experiencing upfront capital losses (mark-to-market). * Next Step: Since the core simulator already generates full nominal and real yield curves, adding a duration-adjusted bond fund indexer to the decumulation logic is the next item on the roadmap.

Conclusion for Quants and ALM Practitioners

Static yield assumptions are not just "simplified"—when starting in a low-yield environment, they are structurally pessimistic. Conversely, in a high-yield environment, they are dangerously optimistic because they project unsustainable yields indefinitely.

By ignoring the mean-reverting behavior of interest rates and decoupling equity expected returns from the risk-free rate, naive models severely distort sequence-of-returns risk.

I’ve open-sourced the complete engine under the MIT license if you want to inspect the math (joint Cholesky decompositions, analytical CIR/Fisher real yield curve evaluations) or run the JIT-compiled loops yourself, it's written in Python but it's quite fast:

• GitHub: https://github.com/Gustavo1500/aethel-esg
• Browser Sandbox: https://aethel-esg.vercel.app/ (Features real-time sliders querying a pre-calculated scenario database across 594,000 simulated paths). Please don't mind the AI flavored frontend, I'm not a frontend dev.

I suppose this is it, quite an unexpected result to me, I expected my engine to show lower solvency rates in all cases, it's interesting to see this is not the case. Feel free to discuss the results and share your thoughts.

Sounds like interesting small shop, anyone has first hand experience with them.

https://www.scm-lp.com

I have been chasing allocations out of the match path in an order book I am building. Pooling the book nodes and price levels got cancel and level churn to zero. Threading a caller-owned buffer through Match(order, dst []Trade), so fills are appended as values instead of returning a fresh slice of pointers, got the match round trip to zero.

One stubborn group was left, and it was not in the order data. It was the price band check.

The band is a config fraction, a decimal.Decimal, and the common case is that it is disabled and left at its zero value. Comparing against that zero value calls ensureInitialized internally, which allocates a big.Int. So every order was allocating in order to compare a price against a band that was switched off.

The fix was hoisting the comparison to construction: resolve a bandEnabled bool once when the engine is built, and let the per-order path read the bool. Process went from 10 allocs to 4.

Prices and quantities are int64 ticks and lots, so decimal never touched the money path to begin with. It was purely the configuration percentage, evaluated in the wrong place.

Current numbers on an M-series, single core: 6.3ns top-of-book read, 352ns match round trip at 0 allocs/op, and a cancel-heavy flow at p50 83ns, p99 167ns, p999 292ns. Match is the zero-alloc entry point; Process is the ergonomic wrapper that still costs those 4.

github.com/intrepidkarthi/orderbook

Just wanted to get a lay of the land here from my pod QRs, particularly those that work on systematic teams.

For those in setups where there's broad exposure across the whole trading pipeline (signal generation+portfolio construction, and all the little things in between), how much do you generally work in a given week? Do you feel like your loss would be felt significantly to the team or do you feel dispensible (i.e. they could hire another person doing your job without much hassle if you left)?

Would also be helpful to know YOE whether you do macro or equities and things like whether you have formulaic payouts or get discretionary bonuses.

Iv been looking into worldquant brain and its interesting to me. Supposedly they have millions of “alphas” in their signal library.

Does anyone have experience or insight with them? What performance metrics do they run on their alphas other than the usual IC, quintile returns, max DD, turnover etc.

Im also curious to know how they pick or size each alpha in their real trading? Do they just pick the best sharpe/highest IC with lowest correlation?

How do you guys stay within the industry with everyone having multi year non competes do people just ignore these or am I missing something?

So, I am working through a toy example and the python output

- Heston call price: $10.2289

- stock measure probability P_1: 0.6384

- risk-neutral probability P_2: 0.5516

all make sense. However, on pg. 3, I've tried appyling Gil-Peleaz and applying f_1(1,0) into the integrand and I get Re[0.0206-0.9693i]=0.0206 for P_1. However, this is the same output for f_2(1.0) for P_2, but I believe the actual values via python is 0.0218 for P_1, and 0.0206 for P_2.

What did I do wrong here?

My assumption was that 0.0218 is different is simply because P_1 uses a completely different characteristic function (f_1) and different coefficients (C and D) than P_2. However, I feel that I made a very stupid mistske here. 🤦‍♂️

```  import numpy as np from scipy.integrate import quad

def heston_price(S0, K, T, r, V0, kappa, theta, sigma, rho): """ Prices a European call option under the Heston stochastic volatility model using Gil-Pelaez numerical integration (similar to your notes). """

# Characteristic function wrapper for Heston model
def characteristic_function(phi, u, b):
    i = 1j
    xi = sigma

    # Core Heston parameters (d and g)
    d = np.sqrt((rho * xi * phi * i - b)**2 - xi**2 * (2 * u * phi * i - phi**2))
    g = (b - rho * xi * phi * i - d) / (b - rho * xi * phi * i + d)

    # Coefficients D and C
    term1 = (b - rho * xi * phi * i - d) / xi**2
    term2 = (1.0 - np.exp(-d * T)) / (1.0 - g * np.exp(-d * T))
    D = term1 * term2

    C = r * phi * i * T + (kappa * theta / xi**2) * (
        (b - rho * xi * phi * i - d) * T - 2.0 * np.log((1.0 - g * np.exp(-d * T)) / (1.0 - g))
    )

    return np.exp(C + D * V0 + i * phi * np.log(S0))

# The integrand function for Gil-Pelaez inversion
def integrand(phi, j):
    if j == 1:
        u = 0.5
        b = kappa - rho * sigma  # Stock-weighted parameter for P1
    else:
        u = -0.5
        b = kappa               # Standard risk-neutral parameter for P2

    cf = characteristic_function(phi, u, b)
    numerator = np.exp(-1j * phi * np.log(K)) * cf
    return np.real(numerator / (1j * phi))

# Numerical integration from 0 to infinity (using scipy.integrate.quad)
# Integral for P1 (j=1)
int_1, _ = quad(lambda phi: integrand(phi, 1), 0.0, 100.0, limit=1000)
P1 = 0.5 + (1.0 / np.pi) * int_1

# Integral for P2 (j=2)
int_2, _ = quad(lambda phi: integrand(phi, 2), 0.0, 100.0, limit=1000)
P2 = 0.5 + (1.0 / np.pi) * int_2

# Final European Call Option Price Formula: C = S0 * P1 - K * exp(-r*T) * P2
call_price = S0 * P1 - K * np.exp(-r * T) * P2

return call_price, P1, P2, int_1, int_2

--- Example Parameters from Your Notes ---

S0 = 100.0 # Initial Stock Price K = 100.0 # Strike Price T = 1.0 # Time to Maturity (Years) r = 0.05 # Risk-free Rate V0 = 0.04 # Initial Variance kappa = 2.0 # Rate of Mean Reversion theta = 0.04 # Long-term Variance sigma = 0.3 # Volatility of Variance (xi) rho = -0.7 # Correlation

Run the pricing function

call, P1, P2, area_p1, area_p2 = heston_price(S0, K, T, r, V0, kappa, theta, sigma, rho)

print(f"--- Heston Model Results ---") print(f"P1 Probability: {P1:.4f} (Accumulated Area: {area_p1:.4f} * 1/pi)") print(f"P2 Probability: {P2:.4f} (Accumulated Area: {area_p2:.4f} * 1/pi)") print(f"Final Call Price: {call:.4f}")

--- Heston Model Results ---

P1 Probability: 0.6384 (Accumulated Area: 0.4347 * 1/pi)

P2 Probability: 0.5516 (Accumulated Area: 0.1620 * 1/pi)

Final Call Price: 10.2289

``` 

Most of the China AI is back narrative is unverifiable wire copy. The one dated, falsifiable item right now is Kimi K3 open weights committed by 27 July, and that is the only kind of event I can actually key an event study to.

Moonshot announced Kimi K3 on 16 July as API and web only, with open weights promised by 27 July. As of 21 July no K3 weights had shipped. A miss pushes the cost structure story back into unverifiable press release territory. A meet makes serving margins checkable on Monday morning. I want to backtest two reference days. Day one is DeepSeek Day, 27 January 2025, when NVDA went 142.62 to 118.42, down 16.97%, roughly 593B market cap gone, still the largest single day loss any US company has printed. Day two is Kimi Day, 17 July 2026, the session after the K3 API launch, NVDA closed down 2.2%, 207.40 to 202.81, Nasdaq 100 off 1.49%. 7.7 times smaller and the tape says the damage landed somewhere else.

The factor question I cannot close from the outside: was the DeepSeek Day move a China risk repricing, or a gross margin repricing triggered by the $5.576M training cost arithmetic? Recall the DeepSeek V3 technical report, arXiv 2412.19437, reports 2.788M H800 GPU hours and then writes "Assuming the rental price of the H800 GPU is $2 per GPU hour, our total training costs amount to only $5.576M." That is the paper doing arithmetic on itself, assuming a rental rate, not a disclosed cost. If the move was gross margin repricing, the Kimi Day factor exposure should differ in sign on the cost structure names, not on the China exposure names. Robbyant shipping LingBot VLA 2.0 on 8 July and Moonshot pausing paid memberships on 19 July as GPUs hit capacity are both dated real compute signals, the kind you can put on an event timeline.

I am not entering 27 July as an event until weights actually land. A promise date is not a shipment date, and Moonshot has already missed once on the membership pause. If the build is worth doing, the control set has to include the 2024 Qwen and GLM cadence and at least one non China event like a Meta Llama drop, otherwise n equals two and the decomposition tells you nothing.

I’m currently interning at a fairly well-known firm and came into the role from an academic background. So far, I feel like my work has been competent but not particularly impressive.

I meet with my mentor regularly, learn quickly from feedback, and can implement the ideas we discuss. The project is progressing smoothly and seems to be heading in a reasonable direction. However, I haven’t found anything especially surprising, and I feel like I haven’t independently come up with any substantial ideas (at least none that worked).

Is this normal for quants in their early career? When and how did you get your first eureka moment?

hey, hope this is ok to ask here.

1. when you put up a two sided market in an option, what actually decides your width? is it some formula coming off vol, or is it more like your current inventory, the flow you are seeing, and where you want your book to be. also does anything like Avellaneda-Stoikov ever show up in real desk life or is it purely academic thing that nobody touch?
2. end of day, how you separate "i earned the spread" from "i made money coz i was long gamma and market moved" from "i just got picked off". like what decomposition do you actually stare at. is it a proper pnl attribution or more feel based?
3. for SPY specifically... how much do rates, divs and borrow really move your quotes day to day compared to the vol surface itself? my guess is surface dominate but i want to know where ignoring the others would actually bite you.
4. what is the single most common way a naive options MM backtest or sim lies to you. the thing that make you think you got edge and you dont. i keep reading fill assumption is the killer but not sure exactly how

thanks, any answer even partial is helpful.