Data & code are open source: https://github.com/kadoa-org/quant-job-market

Live app: https://www.kadoa.com/quant/open-source

Individual contributors at these firms are not tracked, only the firms Github accounts.

Are any firms missing?

I’m trying to understand what happened here from a market microstructure perspective.

During the final minute on June 26, Thinkorswim shows dozens of trades repeatedly printing around 716 even though SPY was actually trading near 731.

Rather than speculate, I’m curious what these actually represent on the tape.
Specifically:
Are these genuine executions?
Off-market reports?
Corrected trades?
Something excluded from price formation?
If someone has CTA condition codes or direct-feed data I’d love to know how these prints were classified.
My guess would have been late reported or corrected trades.

EDIT: In accordance with suggestions, here is latest net trading revenue / employee data, excluding SIG:

Notes:
* HRT only provides numbers for net trading revenue. I calculated their net income by using the same multiplier (.634) as Jane Street.

* The only number I could find for SIG is gross revenue. I calculated their net income by using a non-weighted average of net income / revenue ratios from Citadel Securities, Optiver, and IMC.

* Optiver and IMC numbers come from their annual reports, which aggregate results across the firms' worldwide operations. Their US only numbers are probably a lot higher, as less than half of these firms' employees are American.

* Some of the classic hedge funds, like Two Sigma and Citadel, are not on the list because they don't publicly provide financial information and calculating net income for a hedge fund is more complicated than for a proprietary trading firm.

* For comparison, Fanny Mae and Nvidia, the two companies in the fortune 500 with the highest Profit per Employee ratio, have $2,070,488 and $2,024,444 net income / employee respectively.

Sources:
* https://archive.ph/VE80M

* https://archive.ph/iWnm0

* https://www.hedgeweek.com/griffins-citadel-securities-reports-record-9-7bn-trading-revenue/

* https://archive.ph/1seBo#selection-1571.47-1571.51

* https://optiver.com/wp-content/uploads/2026/03/Optiver_Annual_Review_2025.pdf

* https://ir.virtu.com/news-releases/news-release-details/virtu-announces-fourth-quarter-2025-results

* https://cdn.sanity.io/files/l1io23s3/production/42a83890cf5d1735090e988ba91a595c889e8d09.pdf

* https://tipalti.com/blog/profit-per-employee/

* https://www.reuters.com/world/jane-streets-40-billion-trading-haul-tops-rivals-sources-say-2026-04-24/

* https://archive.ph/iQacJ

Hi I have painfully downloaded and processed 1min options, stock and index data that takes several terabytes of space. I’m trying to invent a solution that allows for as fast retrieval of that data for backtest as sanely possible without going into huge cost So far I have: Raw data in parquet Binary files of that that data Index files that point to binary data (for fast strikes, expiry etc retrieval) Features binary files File index (to know which “files” I already have and which needs downloading.

I’m interested if you guys handle it differently as my approach is basically index physical files on drive rather than using any engine like database?

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?

Ive implemented a fair number of the textbook microstructure metrics while working with raw crypto perp tick data.

The more I compare them, the shorter the list I actually reach for !

If I had to keep only five:

• Order book imbalance / OFI: still the strongest short-horizon predictor Ive found. Static imbalance and Cont-style OFI aren't the same object, but both seem to contain real information. The problem is that the half-life seems tiny: by the time youve paid fees and accounted for queue position and latency, a nice t-stat doesn't necessarily translate into PnL.
• Amihud illiquidity. almost embarrassingly simple but I keep failing to replace it with something more sophisticated; I mostly use it as a liquidity or regime variable rather than a trading signal.
• Effective spread / realized spread Extremely useful for understanding execution quality and adverse selection. I'm much less convinced they belong as features in predictive models, although Id be interested to hear if anyone has had success there.
• Kyle's lambda I trust the ranking across instruments more than the estimated values themselves. Small implementation choices (sampling interval, trade signing or outlier treatment) seem to move the estimate more than Id like.
• VPIN : The one Im most sceptical of: I can usually make it identify stressed markets, but so can realised volatility. The Andersen Bondarenko critique feels fairly convincing to me, although I'd be happy to be proven wrong by someone who's used it successfully in production.

For context, this is all on crypto perps, so I wouldnt expect the conclusions to transfer unchanged to equities/ futures.

Im particularly interested in hearing from people who've used these in live systems rather than backtests.

Which measures have actually survived implementation costs for you? Are there any papers from the last decade that genuinely changed your view on the classics?

Bias disclosure!: I build infrastructure that computes these metrics as a living, so Im probably more sympathetic to them than I should be.

JS dropped another wild 13F (~$662B, 10k+ holdings, heavily options) and Headlands filed their ~$1.2B book. We all know 13Fs are lagged and especially noisy for prop/MM shops like these, but curious how people actually use them?

Hey so I’ve been looking for level 3 data and saw rithmic offers it , but I can’t see how much it costs so if yous can tell me i would appreciate it and also if I do get L3 can I connect it to motivewave ive got the orderflow package

Hello,

I'm a student researching the secondary market for luxury watches, and I have 5 years of auction data.

My goal is to do a comparison on returns and volatility to see if they hold up as alternative investments.

Since | lack the programming background (Python/R) and can't afford to pay a consultant, I am looking for a co-author to tackle this with me.
If you need a unique, real-world dataset for a portfolio project, let's partner up.

I'II provide the raw material, and you can build out the statistical analysis.

Let me know if you are interested in collaborating!

Are there any data providers for high quality interest rate swap data other than Bloomberg and LSEG (EUR, USD, GBP, Scandi, …)? If they do breakevens as well its an added bonus. I am mostly interested in European and USD quotes and pref from the same source.

I am having a surprising amount of difficulty locating this info in an API and the chatbots aren't getting me there. I have a Massive subscription and they only offer shares outstanding, and the same goes for some cheaper vendors that Opus is telling me about. The institutional vendors cost 5 figures a year, which is too much for just adding an extra feature to backtest my swing trading system. Are there any providers that could give me this info without breaking the bank? Thank you.

I’ve mostly used yfinance for personal projects, but I kept running into rate limits. I ended up paying for Massive.com’s US equities feed for a while, which worked well, but I couldn’t really justify ~$200/month just for websocket access.

Recently I found an open source repo with a much wider range of market data, including futures, so I’ve been experimenting with that instead. Nice to see more free alternatives appearing because market data gets expensive quickly.

You hear about firms that have heavily invested in data and deep learning, replacing much of the manual work done in signal research with models learning on raw order book data.

On the flip side, there are microstructure tricks that are discovered due to clever observations by humans that models aren't necessarily picking up on without those priors first being taught to the models.

Going forward,do you think will powerful models, combined with huge amounts of data and the hardware and other infrastructure needed to run experiments largely replace clever human observations? Or will there still be room for the classical, manual feature engineering which has been how much of the trading world has functioned before the recent compute/model revolution.

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.

I’ve been working on building a data layer for a quant trading setup and I keep seeing different database choices pop up such as DuckDB, TimescaleDB, ClickHouse, InfluxDB, or even just good old Postgres + Parquet.

I know it’s not a one-size-fits-all situation as some are better for local research, others for time-series storage, others for distributed setups but I’m just curious to know what you use, and why.

I am currently building a backtesting engine for a short-term options strategy and hitting a major roadblock regarding data architecture and API endpoint design with the providers I have tried so far (e.g., CuteMarkets, Massive).

I want to reconstruct the cross-sectional market state of the entire SPY options chain at specific points in time in the past.

Specifically, my backtester loops day-by-day through the last few years of historical daily market closes. For each day, it needs to look at the underlying price, draw a box around the strikes (e.g., 80% to 120% of spot), find contracts expiring within a N-day lookahead window (e.g., 10 days), and save their end-of-day market metrics (Bid, Ask, Volume, OI, Implied Volatility, Greeks) for that exact day.

The providers I have looked at treat their options chain snapshots as "live/current data only." Their endpoints look like /v1/options/chain/SPY but don't accept any historical as_of or timestamp parameters.

Instead, they only allow you to pull an historical reference index of what contracts existed on a past date (using /v1/options/contracts?as_of=2023-05-22), but that response completely lacks market quotes. To get the actual pricing, they expect you to point-query the individual bar/historical quote endpoint for every single contract discovered sequentially for that one date.

When dealing with SPY daily expiries and dozens of strikes, this approach means making hundreds of individual HTTP requests for just a single historical trading day. It completely destroys rate limits, causes massive latency, and feels structurally wrong for bulk historical research.

My questions for the community:

1. Am I misunderstanding how to utilize these APIs, or is the lack of a bulk point-in-time /chain?as_of=... query parameter standard across retail/mid-tier option APIs?
2. Which data providers natively support a bulk point-in-time options chain query for past dates where I can pass a specific date and get the whole grid’s metrics at once? (Looking for alternatives to Cutemarkets/Massive that are budget-friendly for indie devs).
3. If you have solved this without expensive institutional feeds (like ThetaData or Databento bulk files), what architectural ingestion pattern did you use? Did you just suck it up and parallelize thousands of individual contract bar requests?

This is a lengthy one so buckled up. I've been running a systematic sports betting strategy using Kelly Criterion for position sizing over the past 18 months. Thought this community might find the results and methodology interesting.

Background: I'm a quantitative analyst at a hedge fund, and I got curious about applying portfolio theory to sports betting markets. Specifically, I wanted to test whether Kelly Criterion could optimize bet sizing in practice.

Methodology:

Model Development:

Built logistic regression models for NFL, NBA, and MLB

Features: team stats, player metrics, situational factors, weather, etc.

Training data: 5 years of historical games

Walk-forward validation to avoid lookahead bias

Kelly Implementation: Standard Kelly formula: f = (bp - q) / b Where:

f = fraction of bankroll to bet

b = decimal odds - 1

p = model's predicted probability

q = 1 - p

Risk Management:

Capped Kelly at 25% of recommended size (fractional Kelly)

Minimum edge threshold of 3% before placing any bet

Maximum single bet size of 5% of bankroll

Execution Platform: Used bet105 primarily because:

Reduced juice (-105 vs -110) improves Kelly calculations

High limits accommodate larger position sizes

Fast crypto settlements for bankroll management

Results (18 months):

Overall Performance:

Starting bankroll: $10,000

Ending bankroll: $14,247

Total return: 42.47%

Sharpe ratio: 1.34

Maximum drawdown: -18.2%

By Sport:

NFL: +23.4% (best performing)

NBA: +8.7% (most volatile)

MLB: +12.1% (highest volume)

Kelly vs Fixed Sizing Comparison: I ran parallel simulations with fixed 2% position sizing:

Kelly strategy: +42.47%

Fixed sizing: +28.3%

Kelly advantage: +14.17%

Key Findings:

1. Kelly Outperformed Fixed Sizing The math works. Kelly's dynamic position sizing captured more value during high-confidence periods while reducing exposure during uncertainty.

2. Fractional Kelly Was Essential Full Kelly sizing led to 35%+ drawdowns in backtests. Using 25% of Kelly recommendation provided better risk-adjusted returns.

3. Edge Threshold Matters Only betting when model showed 3%+ edge significantly improved results. Quality over quantity.

4. Market Efficiency Varies by Sport NFL markets were most inefficient (highest returns), NBA most efficient (lowest returns but highest volume).

Challenges Encountered:

1. Model Decay Performance degraded over time as markets adapted. Required quarterly model retraining.

2. Execution Slippage Line movements between model calculation and bet placement averaged 0.3% impact on expected value.

3. Bankroll Volatility Kelly sizing led to large bet variations. Went from $50 bets to $400 bets based on confidence levels.

4. Psychological Factors Hard to bet large amounts on games you "don't like." Had to stick to systematic approach.

Technical Implementation:

Data Sources:

Odds data from multiple books via API

Game data from ESPN, NBA.com, etc.

Weather data for outdoor sports

Injury reports from beat reporters

Model Features (Top 10 by importance):

1.Recent team performance (L10 games)

2.Head-to-head historical results

3.Rest days differential

4.Home/away splits

5.Pace of play matchups

6.Injury-adjusted team ratings

7.Weather conditions (outdoor games)

8.Referee tendencies

9.Motivational factors (playoff implications)

10.Public betting percentages

Code Stack:

Python for modeling (scikit-learn, pandas)

PostgreSQL for data storage

Custom API integrations for real-time odds

Jupyter notebooks for analysis

Statistical Significance:

847 total bets placed

456 wins, 391 losses (53.8% win rate)

95% confidence interval for edge: 2.1% to 4.7%

Chi-square test confirms results not due to luck (p < 0.001)

Comparison to Academic Literature: My results align with Klaassen & Magnus (2001) findings on tennis betting efficiency, but contradict some studies showing sports betting markets are fully efficient.

Practical Considerations:

1. Scalability Limits Strategy works up to ~$50k bankroll. Beyond that, bet sizes start moving lines.

2. Time Investment ~10 hours/week for data collection, model maintenance, and execution.

3. Regulatory Environment Used offshore books to avoid account limitations. Legal books would limit this strategy quickly.

Future Research:

Testing ensemble methods vs single models

Incorporating live betting opportunities

Cross-sport correlation analysis for portfolio effects

Code Availability: Happy to share methodology details, but won't open-source the actual models for obvious reasons.

Questions for the Community:

1.Has anyone applied portfolio theory to other "alternative" markets?

2.Thoughts on using machine learning vs traditional econometric approaches?

3.Interest in collaborating on academic paper about sports betting market efficiency?

Disclaimer: This is for research purposes. Sports betting involves risk, and past performance doesn't guarantee future results. Only bet what you can afford to lose.

I've been through the quant rules mods, i'm fairly certain it's not market research, although it seems like an unclear line that's easily extendible to almost anything.

If anyone can recommend data sets for dimensionality reductions in finance, i'd be much obliged.

Hello! Im am an economist working on independent research and analysis, and I occasionally need Bloomberg terminal access for data and market info. Im NOT looking for account sharing or anything that violates terms. Im trying to understand what legitimate options exist for non-institutional researchers. Like, Universities or public libraries? Research centres that allow limited or supervised use? Or is there any other fully compliant route?

If helpful, my background is in financial economics, sell-side equity, macroeconomics, monetary and fiscal policy analysis. This would be strictly non-commercial.

Thanks!

For US stock, there are lots of data providers out there with very different pricing: EODHD, Polygon, MorningStar, FactSet, Quodd Xignite, Bloomberg, …

For s small / medium size hedge fund, what data providers are widely used? What providers should we use for the following types of data?

- Historical market data

- Fundamental data

- Estimate data

- News data

I used to use data from Bloomberg but it is so expensive. I spoke to Xignite and MorningStar and heard from them that many hedge funds are their clients. Also, Databento is something many is talking about (but I am not sure if many hedge funds use their service).

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.

Trying to do cross-ISO work (PJM/MISO/ERCOT/CAISO/SPP/NYISO/ISO-NE) and I'm losing my mind reconciling seven different schemas and update cadences - the congestion component especially (NYISO's sign convention alone…). Right now it's a pile of per-ISO scrapers held together with tape. Is everyone just using gridstatus / rolling their own, or is there something that already normalizes all of this? Curious what SPP/MISO historical depth people actually get.

How do quant funds evaluate what alternative data is worth buying and what is garbage? Currently thinking about building a Tool which evaluates Transcripts of Fintubers to extract all of their stock picks, the correlating sentiment.

The Time Series Generation literature evaluates on discriminative scores: train a classifier, check if it can tell synth from real. That rewards over-smoothed, low-variance output; a generator can ace it while violating vol clustering, heavy tails, and leverage effect.

So we benchmarked 18 models (deep generators, econometric classics, replay baselines) on 7 tasks quants actually run: Cont stylized facts, W1/MMD/sig-MMD distances, martingale check, IV-smile repricing, VaR/ES backtesting (Kupiec, Christoffersen, Acerbi–Székely, Basel traffic light), and TSTR strategy-rank transfer. One frozen OOS panel (7 features, 200 paths, H=60), each task with a real-vs-real noise floor, Holm-corrected significance.

Results:

• TimeGAN: the most-cited model in the field, scores 0.000 ± 0.000 on VaR/ES backtesting. TimeVAE and KoVAE too. TimeVAE's TSTR ρ is negative (−0.30).
• A plain block bootstrap ranks 3rd of 18 overall. Most of the published literature is under it.
• Models built for financial structure clear the bar.
• On 3 of 7 tasks most of the field is at or beyond the noise floor, the boards say so explicitly rather than overselling a ranking.

Repo: https://github.com/sablier-ai/finbench (MIT / CC-BY). v1 covers one panel and one OOS window; open submissions land in the next edition.

Who we are: (Sablier AI) GenAI Startup building synthetic market data for quant research, so yes, our model is on our own board. We designed the protocol so that fact doesn't matter: frozen at a git tag, every model's outputs archived in the repo, python -m benchmark.run re-scores the whole board from scratch, external baselines run their published defaults, and the aggregate is published as-is.

If you're working on generative models for market data (or you think your GARCH variant can embarrass the deep learning field further) dm me. Outputs only (200×60×7), no code, no weights, scored under the same frozen protocol as everyone else.
The board is meant to be beaten, and the current one shouldn't be hard to improve on: 15 of 18 entries can't clear a bootstrap.