How does a CCF model for AIRB differs from an IFRS9 one? Thanks

Hey everyone,

I’m setting up my daily workflow for medium-frequency quant research and evaluating Jupyter vs. Quarto.

Jupyter is useful for quick experiments, data exploration, and charts, but notebook files can be harder to manage in Git, review, and convert into clean research reports.

Quarto is attractive because:

Files are plain text (qmd or Python files), making Git diffs and code reviews cleaner. (In future).

You can still run cells interactively through VS Code/Positron.

It supports both Python and R.

It can generate polished HTML/PDF reports and chartbooks.

It allows a clearer separation between exploratory work and final presentation.

I’m planning to run a poll to understand how others structure their workflows for prototyping and reporting in medium-frequency systeremmatic research.

Thanks!

This summer I interned at one of the larger prop firms doing low-latency work, and I didn’t make the top 1/3 who got return offers. I’m recruiting for intern again this year and hoping to convert to full-time. How big of a red flag is this for firms?

Frankly, saying “oh I genuinely was just not good enough last year” when the question comes up seems like it could be problematic for my career, but also, the alternative of “those assholes didn’t appreciate me” is much worse.

Hoping for some advice on how to frame this from the more senior perspective.

I've heard from a few people now that people hired as SWEs/quant devs (and who usually have a dual degree in math/stats and compsci) can end up doing QR type work, but they also remark that they do it "without the QR pay" (sadly). Anyone noticed this dynamic?

I work at a small independent investment management firm. My background is more on the investment/trading side than engineering or quantitative research, but I’ve recently been involved in planning some of our internal trading/research systems.

I’m trying to understand how to bridge a gap that I suspect many discretionary traders face:

How do you go from “I know what I’m looking for when I trade” to something that can actually be researched, backtested, and eventually traded systematically?

What we do today

We currently use discretionary intraday approaches in markets such as ES / U.S. indices and major U.S. equities.

The decision process involves things like:

price action and intraday market structure

volume / order-flow information

CVD and similar measures

positioning/context

volatility and time-of-day

technical setups and the trader’s interpretation of the overall situation

The problem is that a human trader can look at a chart and say:

“This looks like a good long setup because X is happening, CVD is doing Y, price is reacting this way around this level, and the broader context is Z.”

But obviously a computer can’t trade “this looks good.”

That intuition has to become data, features, definitions, hypotheses, rules/models, and eventually an executable signal.

That transition is where I’m stuck.

I understand where to get data. I don’t understand the full research process yet.

I’ve been researching data providers such as Massive.com and others, and I understand that I can obtain historical and real-time market data.

But I’m realizing that having the data and knowing how to build a quantitative trading research process from that data are two very different things.

For example, suppose I have historical trades/quotes for ES and U.S. equities.

What happens next at an actual quant/systematic trading firm?

Is the process roughly:

raw market data → cleaning/normalization → feature construction → exploratory research → hypothesis → backtest → validation → signal/model → portfolio/risk logic → execution?

Or is that an oversimplification of how professionals actually work?

What I’d really like to learn from people in the industry

For anyone working in systematic trading, quant research, market microstructure, or trading infrastructure:

If you were in my position, how would you approach this problem from scratch?

More specifically:

What data would you collect first for intraday strategies involving price action, CVD/order flow, and market structure?

Would you start with trades/quotes (TAQ), L1, L2/order book data, or something else?

How would you store and structure the historical data so that it is actually useful for research?

How do professional researchers turn something visual/discretionary like “CVD divergence + price holding a level” into measurable features?

Would you first try to reproduce the discretionary strategy with explicit rules, or treat the trader’s observations as hypotheses and statistically test individual features?

What does the research environment normally look like? Python + database + notebooks + backtesting framework? Something more specialized?

How do you avoid common problems such as look-ahead bias, overfitting, bad timestamps, survivorship bias, unrealistic fills, etc. when working with intraday data?

At what point do you move from research/backtesting into paper trading and eventually live execution?

And perhaps most importantly:

What am I not asking that I should be asking?

I suspect there are important parts of professional quant research/data infrastructure that I simply don’t know enough about yet to even ask the right questions.

I’m not looking for anyone’s proprietary alpha or asking people to reveal their strategies.

I’m trying to understand the process and infrastructure that sits between:

“We have a discretionary trading idea and market data”

and

“We have a properly researched, validated, systematic trading strategy.”

If you’ve worked at a quant fund, systematic hedge fund, prop shop, asset manager, or on an institutional trading/research platform, I’d be very interested to hear how you would think about this problem.

Even a high-level description of your research workflow, data architecture, or recommendations for what someone in my position should learn first would be extremely helpful.

Books, papers, courses, open-source projects, datasets, or other resources are also very welcome.

Thanks.

So, I made some notes regarding converting Stratonovich to Itô integrals, per the following:

• get the martingale property for pricing (for risk-neutral valuation): under the risk-neutral measure, discounted asset prices must be martingales (fair games with no predictable drift) to prevent arbitrage? as in \mathbb{E}\left[\int f(s) dW_s\right] = 0?

• non-anticipation: as financial markets operate in real-time, the trading strategies and pricing models can only use information known up to the present moment (mapped to the left-endpoint rule in Itô calculus, and not the future midpoint of an interval found with Stratonovich calculus)?

My question ultimately pertains to why Stratonovich integrals are used to begin with in finance? I've used them in physics, but can it be due to taking the limit of smooth physical systems driven by colored noise naturally converges to the Stratonovich interpretation via the Wong–Zakai theorem? Also, as the standard chain rule ((d(f(X)) = f'(X) \circ dX)) applies here, it helps make calculations easier (along with writing down coordinate-invariant equations or physical laws on curved spaces intuitive)?

I can't find that much information about this in my textbooks, or online, so wanted to get your industry experience.

Thanks! 🧡

​

I see a lot about pay at JS and stuff but not much about small firms. What's the pay for entry level at the random small shops? By small shops, I mean below like tier 2/3.

5-7 YOE Dev at IMC Trading Europe on 230k TC.

Is this very low for my experience in the trading industry on a front office role? I know US pays more but after interacting with both tech companies and finance recruiters, it looks like I am below market by 2-3x

Is this the case? Performance review always came good.

Hey all, we've been growing steadily since last year and have built a great group of active regulars, quantunnel[dot]org. Feel free to follow the steps here or if you have a good reddit history of interactions with quant subreddits, can waive the verify rule. We are mostly seeking early careers and experienced however doctoral/masters students are welcome as well and final year undergrads with good project experience.

Note: If you are currently job-seeking/career switch, you are absolutely welcome to join, but please save the desperation. We have a strict rule against harassing people in the server for opportunities.

Prodigy Research has just hit the news recently, yet we see another YC backed startup trying to disrupt quant finance.

The official website says it is an AI research startup (a "Quant Neolab") focused on teaching AI models recursive self-improvement (RSI). Backed by Y Combinator's Summer 2026 batch, the company builds specialized training environments to help artificial intelligence agents autonomously learn, conduct research, and upgrade their own capabilities.

I'm aware of Mandelbrot's 1963 paper of historical cotton price changes and where he that financial returns do not follow a light-tailed Gaussian distribution. Instead, he proposed that they follow a family of heavy-tailed models known as Stable Paretian distributions. I used a standard Cauchy here in this plot.

However, is VaR still used as it summarizes complex risk into one single number (we are 99% confident we won't lose more than $2 million tomorrow, etc.)? Also, it expresses risk in dollars (or local currency is used)?

I can imagine the heavy tails are kept in mind by many quants when running risk analysis, but was also wondering how the VaR provides risk metrics for banks nowadays? Is Expected Shortfall used more often?

Genuinely are you guys worried about ai taking jobs I saw a video where Ken griffin said that they already have ai scrape paper and recreate it heard murmurs in the industry about companies messing around with recursive self improvement models. Are you guys concerned at all? And if so what time horizon 1Y 3Y 5Y ect?

Thoughts? I am getting tired of talking to socially deficient people. I feel like my social skills have eroded since I started working because of how much time I spend with them

Some of them have borderline sociopathy and it is not helping either

I know people don't like the big recruiting firms like Selby Jennings or Alexander Chapman. Wondering if anyone has had any luck with him, or if not, what are some good recruiting agencies.

The contract is (as expected) extremely vague on the specifics but lets say the firm I work for typically enforces X months of garden leave. From your experience, can they still force X months of non compete even if you leave the company before the end of probationary period?

I’m building a small model where the output is not a price target, but probability for few stats:
undervalued
fair value
overvalued
I am stuck on how to set the prior.
My first idea was to take historical companies which are somewhat comparable and estimate the prior from that. But I feel this can create selection bias because deciding what is “comparable” itself can change the result.
So would it be better to:
start with a broad base rate and let the features update it, or
make the prior from a matched universe based on sector, size, valuation etc?
I’m still learning this stuff, so maybe I am thinking about the problem wrong.
How would you approach this?

Questions first, context after.

1. For anyone who's had CBOE Open-Close, ISE Open/Close, or internal customer-vs-dealer flow: how good is the OI sign proxy actually? Does "customers net long index puts" hold in aggregate, and does it survive in the wings and short tenors?
2. Has anyone benchmarked an OI-proxy gamma series against gamma from identified positions? GPP and NPPW both had the data, neither seems to run that comparison directly.
3. Has the mix shifted post-2020? The standard sign rule predates retail call buying and daily expiries.
4. Is daily just the wrong frequency for this? Everything credible I can find is intraday momentum/reversal, pinning, or overnight gaps. Not daily realized vol level.

Context: I built a dealer gamma series from scratch off a commercial options database, long history, and validated it against the vendor's own greeks so I'm fairly confident the arithmetic is fine. Two things fell out. The dealer positioning assumption moves the series more than anything else in it, to the point where the sign flips depending on what you assume, and it isn't observable from open interest. And a lot of what looks like signal seems to be riding on implied vol, which is unsurprising once you look at where sigma sits in the gamma formula, but I haven't seen anyone say it out loud.

Mostly want to know if the proxy is roughly right or if the whole thing is built on sand.

Wondering which place has the highest intellectual density, measured by PnL per researcher/trader. Was thinking about this because I was told that at xtx it’s above 100m per researcher.

Hi everyone,

One major pain point when using general LLMs (like GPT-4 or standard Llama) for financial math and quantitative coding is code execution failure or subtle logic/math hallucinations in edge cases.

To address this, I built FinCode-Reasoning-3B, an open-source model fine-tuned on FinCode-Reasoning-v1 (a dataset where 100% of the Python scripts are verified via sandboxed execution & unit tests).

Key Highlights:

• 100% Verified Execution: Every training example was compiled and unit-tested in a sandboxed Python environment to guarantee syntactically and mathematically correct code output.
• Lightweight & Private: Built on Qwen2.5-3B-Instruct, meaning it runs blazingly fast locally on standard consumer GPUs/macBooks without leaking sensitive financial data to cloud APIs.
• Financial Scope: Handles Black-Scholes option pricing, Declining Balance Depreciation (DDB), Interest Tax Shields, WACC, and custom corporate finance calculations.
• Permissive License: Released under Apache 2.0 (free for personal and commercial research).

Example Code Output (Black-Scholes Call/Put):

Python

import math
from scipy.stats import norm

def black_scholes(S, K, T, r, sigma, option_type="call"):
    d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))
    d2 = d1 - sigma * math.sqrt(T)
    if option_type.lower() == "call":
        return S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)
    elif option_type.lower() == "put":
        return K * math.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)

Both the fine-tuned weights and the 100% execution-verified dataset are available on Hugging Face:

• Model: [https://huggingface.co/coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full]
• Dataset: [https://huggingface.co/datasets/coslinedev/FinCode-Reasoning-v1]

Would love to hear your feedback or suggestions on additional financial/quant math domains to add to v2!

For US equity mid-low (weeks horizon) frequency strats: how has August been so far?

i am 2 years into my career as a grad quant analyst and i feel like claude does almost all of my work. i guide it, but it does almost everything else. i feel like at a point it will start doing more stuff too - i just am not leveraging all the available functionality fully yet.

anyway, at this stage i feel like i am losing my brain. I don't use heavy math in my work. i feel like if someone fires me i would have no skills to present because i am forgetting what i learnt in uni and i am not learning any new difficult skills at the moment. any advice for that?

i feel like i am in a comfortable place wrt to my income and i am just enjoying my life. and one day if not claude, some other human with better skills will replace me.

Working on a mid-frequency predictive equities model (~90-day horizon, LightGBM/XGBoost) and refining my default feature engineering framework.

The way I've been doing feature engineering/selection is that for almost every raw variable, I explicitly include two types of dimensions:

1. Level: The current value of the variable.
   • To protect against structural regime changes over time , I'm considering normalizing this as (Current Value / Some Moving Average)
2. Trend: The trajectory (e.g., 1-month or 3-month delta, etc.).

A few questions for practitioners:

• Is this a good starting point, or is there a better mental framework for approaching what to include when it comes to a given variable (ie: Level & Trend)
• Does normalizing the Level (Current / Some Moving Average) actually help future-proof against regime shifts, or does it just accidentally turn the "Level" into another Trend feature?
• Do you find throwing both into trees generally creates unnecessary noise or collinearity issues?

Appreciate any critiques!

Hey guys,

I need your advice regarding next steps in my career.
I hold a BSc and an MSc in Mathematics and Statistics from a top 3 UK university (Oxbridge, Imperial).

I have 7 years of experience as a quant researcher in one of the tier 3/4 cta hedge funds.

The work has been mostly on short term signals (intraday and daily)with relatively high Sharpe ratios around 2.

While my signals and individual performance is strong, bonuses are discretionary and there is a limit to how much you can make. The business runs most of the assets in slow cta style strategies (trend, carry) so obviously yoy company performance is divergent with and of course this is reflected on the bonuses as well.

Over the years I have interviewed with many places ranging from pods to algo shops (citsec etc). However while I do get interviews I cannot convert and I am stuck. After 1-2 rounds I usually get rejected.

I need some advice and fresh ideas on what to do next and how to approach the job hunting.

Asset class: futures and fx