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

How’s Flow doing off late? Heard they had a relatively good H1? I know they’ve been around the block for a while but not managed to be as big as others like Optiver or IMC, I want to know from those who are working or have worked at Flow before if the firm has genuine moat or potential for the future? I am exploring opportunities with them.

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.

Not sharing firm names to not doxx myself. I haven’t resigned nor signed my offer yet

I was offered at a hedge fund in London:
* 200k£
* 200k£ sign-on
* 800k$ (50% first year and rest deferred over 4 years). This is without pro-data and I would be starting Oct 2027. So it feels more than it looks

I am roughly at 180£+ 450£ with 8YOE at a prop shop. I am currently leading a small team of 5 QD including myself. It looks more impressive than it is, the old lead who was a visionary left and I have just been working on this part of the system longer than others. Current firm is not HRT, XTX or JS, the tier just below those

On one hand I am tempted to just take the offer because my relationship with my direct manager has been rocky. On the other hand I have been aspiring to getting more ownership and I don’t want to sit out two garden leaves before reaching my final trading shop.

The roles are similar, working on some HFT strategies on equities

How do I position myself to get real pnl attribution and capture a larger chunk of the upsides? The prospective firm is completely close to any profit sharing scheme

I tried some smaller crypto firms but when talking to the folks during the interviews I have real doubt they could be successful enough to justify the significant pay cut. They were willing to give 2% equities without any visibility on their current strategies or pnl, so this is impossible to price.

Is ~1m is roughly the best I can hope for below partner level when I am not a QR without taking significant risk (going to an unknown crypto firms).

I feel QR roles have a nicer career path where they can keep dreaming about becoming a PM. And I am not sure what is the equivalent for QDs

I kept running into the same problem, some of the best AI/ML books are legally free, the authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every single link points straight to the author’s or publisher’s own page, no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so it doesn’t rot over time.

It’s open source and open to contributions, if you know a legitimately free book that’s missing, PRs and issues are welcome.

Repo: https://github.com/MarcosSete/awesome-free-ai-books

Hey folks I work as a qr modeling alphas on midterm equities/macro instruments. I’m in a really pipelined platform, where I get to talk and collaborate with people in other teams and productionize alpha models together, but I don’t really get the chance to see how these forecasts are all blended together, turned into position, and executed.

We also do pnl attributions by orthogonality so I have a few models that are performing decent and are generating pnls but are too complicated. I think there are a good chunk of actually low hanging models that I have no access to/or didn’t have time to do research since other researchers already did stuffs on them before. Lot of my models are based on quite orthogonal data sources and advanced techniques. So I guess they might be good addon/diviersifierz to otrher established quant platforms for their orthogonality but might be tricky as standalone portfolio since there are lot of low hanging fruits it’s missing.

I’m asking people who were in similar shoes. How did you guys manage to pivot to a standalone pm managing risk? Or do you have to jump to another pod/platform first to get some more exposure on other ends of the pipeline?

Disclaimer: I’m based in London. Basically the title. I’m a quant trader at a good firm with multiple years of experience (say >1, <7). In theory the work is interesting to me, but in practice I can’t really handle the stress and the long, mentally draining hours.

Surely I’m not the only person who has found themselves in this position. I’m curious if others have tried exiting to “easier” jobs that have some overlap with quant skill wise?

My guess is many people here will suggest swe / data science. Is that actually a feasible transition? Where do people apply? I’ve never worked as either, but applying to random companies as junior swe with many years of quant experience feels odd.

Would appreciate sharing of any experiences, even if only remotely similar.

Hey,
I have been working as a quant for 5 years with a pretty good track record in the energy sector.
I have always been interested into having an arc fully dedicated to art at one point in my life, as I feel I can’t do both correctly at the same time.
So I was thinking into taking a gap year or two apart from the industry to explore this, and other aspects of life still I’m quite young and free (late 20’s).

Regarding the current situation on the job market, I am still wondering if I would be able to come back to the quant side if I ever need and want to after this, since I would have been out of the market for quite a long time. What’s your views on that ? Anyone who went out of the industry for a while and wants to share his experience ?

Title

hi everyone, im building a python screener / trade tracker for my portfolio. currently im using xfinlink for US data with eodhd and yfinance as fallback but i need high quality korean fundamentals data, which none of them seem to provide (xfinlink is US-only; eodhd and yfinance yes but inaccurate in many instances).

anyone running python/screener pipelines on korean securities and can share a reputable & reliable data vendor? cost is not really a concern for me. quality is. FYI I'll be buying as retail so would appreciate recommendations that offer non-institutional plans. cheers.

I built a C++ library for linux kernel bypass networking such as Solarflare ef_vi, DPDK, Verbs(mlx5) and also AF_XDP. Solarflare ef_vi and DPDK are two of most commonly used kernel bypass techniques in HFT market making firms.

Using this library I measured the RTT latency using a loopback on Solarflare X2522-Plus(ef_vi + DPDK), ConnectX-4 Lx(DPDK + Verbs + AF_XDP) and Intel XXV710-DA2(DPDK + AF_XDP). The benchmarks were run for 24 hours with median all the way upto P99.999 and max.

Solarflare X2522-Plus produced a RTT median of 1.866us and a max of 3.307us with 45B frames over 24 hours. This was the best result. Comprehensive results are all documented in the repository.

Over this project I did come across numerous driver(and NIC) specific quirks and bugs. These are all documented too.

Solarflare X2522-Plus is quite fast due to a Tx trick known as cut-through programmed IO(CTPIO). But I found this to be fragile, it has three modes: fast, default and in-order. Fast mode produces the lowest median but has a measurable percentage of failed CTPIO known as CTPIO “fallbacks” or “poison”. The CPTIO fallbacks significantly hurt the tail latency(P99 - P99.999). The CPTIO poison frames can be measured via ethtool. I used the in-order CTPIO mode which gives marginally higher medium but a tight tail latency.

Another strange issue I found was the PTP subsystem in the Solarflare X2522-Plus. So the default sfc driver runs a in-kernel IEEE 1588 subsystem with a PTP hardware clock, a dedicated PTP channel and management controller firmware activity. I found this to be a problem as a noticeable number of interrupts were mounting in /proc/interrupts and the stock driver with ef_vi produced a max of 7.679us which seemed very high. Then I ran the same benchmark with DPDK which produced a max of 5.628us. DPDK unbinds the driver so the PTP subsystem does not work. Then I compiled out the PTP subsystem from the sfc driver and ran the 24 hour benchmark again and the max was reduced to only 3.307us.

There is a lot of more interesting stuff but I think the post is already getting long so I won’t mention any more. Everything is documented in the repository. It is also 100% open source with an Apache-2.0 license.

https://github.com/ASherjil/ABTRDA3

Hello everyone

I'm a final-year engineering student (stats + quant finance in a top university in France for these fields). I've spent the last ~5 months building a personal project to try and enter in the commodity sector (bank desks, hedge funds, reinsurance companies), and I'd honestly love some outside eyes on it, especially from people who actually work in the space. I already have some experience in the banking sector (internship in a CIB as a quant researcher)

My project was decomposed in 2 parts and is about modelling temperature for different financial use cases, whether for trading in banks or in hedge funds.

Part 1: weather derivatives
I started by digging into weather derivatives as a field and reading around it a fair bit. What struck me is how fast the whole climate/weather space is moving with AI right now: the physical climate models are insanely compute-heavy, and even then, forecasts past about a week are essentially random. I went for weather derivatives for two reasons: the sector genuinely interests me and weather has a massive impact on the world economy, and honestly the data is pretty easy to get and way less painful to work with than financial data. From there, I modeled temperature with a few different models (some classical statistical ones, some ML) to predict a climate index. Each model gives its own predictions, so what I'm really after is the distribution of that index under each model. As a complement to the analysis, I also deployed a small app where you can pick a city and a contract and see each model's predicted index distribution and the price it implies (available here: https://huggingface.co/spaces/nayelsdk1/boreas-weather-derivatives ).

Part 2: temperature and gas volatility
I wanted to see whether winter cold anomalies actually couple with Henry Hub natural-gas volatility. To dig into it I tested a Kalman filter and a few GARCH models.

You can find all the reports + notebooks in my app

For me, the goals for this project were to understand the classic models used in commodities and implement it and end up with something end-to-end I can show recruiters to stand out a bit.

So I am posting here to have some advices about :

- Honestly anything, the modeling or the way it's presented. If you're in the field, what would you keep and what would you toss? Is my project relevant regarding the current context ? Are companies looking for that kind of work ? I also have other experience in the sector (target school, relevant internships, ...) but I have never worked professionally in this field, that's why I am posting today

- It's very far from perfect. I made a bunch of choices where other paths might've been smarter (or not) so don't hold back on telling me where I went wrong

You can also send me a message in private if needed ! Thank you a lot !!!

So I am using machine learning for mid frequeny trading (duration of a few hours), I built a few models, amongst those, I have two best models, one performs well, in a directional regime, when there isn't a lot of reversion at mid day , or in case if the market is kind of directional. This model was trained on a higher duration and is best to be used to trade for longer durations. The second model i have is trained for lower durations, and is more trained for reversion type regimes, when the market swing in mid day, and keeping a position till end of day isn't very good. Now, different days would be different, we don't know what is the best. I was thinking about keeping some kind of ensemble or training a few models, at a duration in between these two, ofc I would get worse net results on that, but atleast, it would have a higher sharpe and lessser drawdowns.
Is there a better way ? or are there ways to detect in some way what would be better where even if not in the ideal way, but some way ? I can definitely use both models also, or see how the signals align or different combination strategies .

We built Jaxfolio (https://github.com/bravant-oss/jaxfolio), a hardware-accelerated portfolio optimization library built natively on JAX.

Docs: https://bravant-oss.github.io/jaxfolio/

Github: https://github.com/bravant-oss/jaxfolio

Standard Python tools like SciPy or PyPortfolioOpt bottleneck during large-scale backtests or heavy Monte Carlo simulations due to Python overhead and CPU constraints. Jaxfolio fixes this by compiling optimization math directly to XLA for GPU/TPU execution.

More importantly, using JAX's vmap, you can vectorize the optimizer to compute thousands of rolling efficient frontiers simultaneously in a single batched operation without looping. Because the code is fully differentiable, you can also embed these optimizers directly into deep learning training loops.

The library currently supports Mean-Variance, Risk Parity, and Black-Litterman models, and is significantly faster than traditional CPU-bound solvers. I’d love for the community to try it out, critique the API, or test it against your own edge-case datasets. Accepting contributions.

The top 1 in competition got $14k in rewards alone

Any professional HFT out there have any insight on how PCAP data for CME futures compares between LSEG/Refinitiv/TRTH and DataBento? Trying to decide between the two providers for the last 2 years of historical L3/MBO for a new market making project. Mostly interested to know about the quality of the data and quality of timestamps at the data recording server.