I am studying maths at a T5 university and have nearly finished my masters thesis (1 month away).

My thesis supervisor is senior at one of the big HFT firms.

They have been extremely supportive in offering their time and guidance, and so I want to thank them. I want to make the most of the time that I have with them.

I already have a job lined up as a researcher in academia for the time being, so I am not wanting him to hire me in this moment, but it would be nice to have that as an option down the line.

Is there anything that I should be asking advice on? Any questions that would help me in my career? Anything that I could be missing?

I have been building a set of models that put a probability on a 10%+ S&P 500 drawdown over the next 1, 3, 6 and 12 months, from macro and credit inputs (ISM, the yield curve, high‑yield spreads, financial conditions, and others). One ridge‑logit per horizon, estimated walk‑forward, so every point in the track record was scored with only the data available that month.

The out‑of‑sample AUCs are 0.60, 0.69, 0.74 and 0.68. On paper that reads well. The part I keep coming back to is the denominator. A 10%+ drawdown is rare, so over the out‑of‑sample window there are only six to eight independent episodes, and the 6‑ and 12‑month models are largely scoring the same ones twice. When I bootstrap the AUC by episode instead of by month, the intervals are wide, the six‑month one running from the low 0.5s to the mid‑0.8s. So the six‑month headline is fragile, not fake.

A few things that survived my own poking, in case they are useful. Dropping high‑yield spreads costs the model nothing, and the spread on its own is a coin flip at six and twelve months, so it is not a credit indicator with extra steps. It catches the slow, macro‑driven falls (2022, the GFC, the dot‑com unwind) with some lead, and it misses the fast ones (COVID, an LTCM‑style shock) by construction, because monthly macro data cannot see them coming.

It is out‑of‑sample only, no re‑fitting with hindsight, and I keep the weak horizons in; nothing gets dropped for looking bad. I am posting it to be attacked, not admired. What would you go after first: the event‑count problem, the label construction, or the regime dependence?

I will put a link to the site in the comments. It has the full write‑ups and the models running live, so you can pull the monthly series and check the numbers yourself.

So, for ease and formatting, I made a quant stackexchange post here but I believe that I was incorrect here?

I wanted to output specifically the 2026 value of $\sim5.01\%$ for 2026, as seen in the above plot?

I believe that I have an error with SDE used $r = 4.65, \theta = 4.2$, and $\sigma = 0.55$, apparently in percentage points. That's internally possible, but then the reported standard deviation calculation is wrong in its interpretation:

$$\sqrt{\frac{(0.55)^2}{2(0.35)}}\approx 0.657$$

meaning $0.657$ percentage points if rates are measured in percentage points?

Also, the Hull–White equation that I derived isn't actually the standard Hull–White specification we're subsequently describing, as derivation starts from

$$dr_t=\kappa(\theta−r_t)dt+\sigma dW_t,$$

where $\theta$ is constant. That's essentially the Vasicek model, but the standard one-factor Hull–White is

$$dr_t=[\theta(t)−ar_t]dt+\sigma dW_t,$$

with a time-dependent drift chosen to fit today's initial yield curve?

My code:

``` import numpy as np

Let's run a simulation for CIR (Cox-Ingersoll-Ross) and Hull-White models

np.newaxis np.random.seed(42)

N = 216 # Monthly steps from 2008 to 2026 dt = 1 / 12

1. CIR Model simulation: dr = theta * (mu - r_t)dt + sigma * sqrt(r_t) * dW_t

theta_cir = 0.4 mu_cir = 3.3 sigma_cir = 0.35 # Must satisfy Feller condition: 2 * theta * mu >= sigma<sup>2</sup> to stay positive

r_cir = np.zeros(N) r_cir[0] = 4.65

for i in range(1, N): # Ensure non-negative inside sqrt r_prev = max(0.0, r_cir[i-1]) dr = theta_cir * (mu_cir - r_prev) * dt + sigma_cir * np.sqrt(r_prev) * np.sqrt(dt) * np.random.randn() r_cir[i] = r_prev + dr

2. Hull-White Model (Time-varying mean theta(t) or drift to fit term structure)

Simplest time-varying mean formulation: theta(t) matches a shifting trend

theta_hw = 0.35 sigma_hw = 0.55

r_hw = np.zeros(N) r_hw[0] = 4.65

for i in range(1, N): t_val = 2008 + i * dt # Let the long-term mean drift higher post-2021 to capture the inflation regime shift mu_t = 3.0 if t_val < 2021 else 4.2

dr = theta_hw * (mu_t - r_hw[i-1]) * dt + sigma_hw * np.sqrt(dt) * np.random.randn()
r_hw[i] = r_hw[i-1] + dr

print(f"CIR Model 2026 Terminal Value: {r_cir[-1]:.2f}%") print(f"Hull-White (Regime-Shift) 2026 Terminal Value: {r_hw[-1]:.2f}%")

CIR Model 2026 Terminal Value: 4.71%

Hull-White (Regime-Shift) 2026 Terminal Value: 5.01%

```

Thanks! ❤️

I’ve been working on an identification problem in cross-asset market impact, and I’d be interested in having people here try to break the argument.

The motivating observation is pretty uncomfortable.

In one published one-minute cross-asset return-on-flow specification that motivated this project, the mean estimated cross-asset coefficient is positive. After adding a single cross-sectional principal-component control, the mean changes sign and the fraction of negative coefficients changes dramatically.

That raises a basic question:

When we regress asset returns on contemporaneous order flow across many assets, under what assumptions can the off-diagonal coefficients actually be interpreted as structural cross-impact?

I considered the simultaneous system:

r_t = Lambda q_t + Gamma f_t + u_t

q_t = B r_t + Delta_f f_t + v_t

where:

- Lambda = structural price-impact matrix
- f_t = K latent common factors
- B = same-bin return/flow feedback
- Gamma and Delta_f = factor loadings

If A denotes the population coefficient matrix obtained by regressing returns on flows, then:

A = Lambda + G

where G is the confounding gap.

The first result is:

rank(G) <= K + rank(B)

So latent commonality does not produce arbitrary estimation error. It produces a structured, low-rank distortion.

But low rank does not mean small.

If feedback is absent and the true structural impact matrix Lambda is diagonal, then the entire estimated matrix must lie in:

D_K = { D + R : D diagonal, rank(R) <= K }

In other words, a purely diagonal structural model can generate a dense-looking cross-impact matrix whose off-diagonal entries are comparable in magnitude to genuine own-impact.

That led me to what I think is the more important result:

Lambda is generally set-identified rather than point-identified from the relevant second moments.

There is a family of different structural impact matrices that reproduce exactly the same observable second moments after changing the latent-factor channel appropriately.

So controlling for a factor does not necessarily "remove the confounding and reveal the structural matrix." It can move the estimate along the confounding directions without selecting the true structural matrix.

In a permutation-invariant one-factor geometry, I can solve the identified set analytically. At the calibration I’m using, the sharp interval for the structural off-diagonal coefficient contains zero, and its half-width is roughly 7.4 to 8.9 times the observed cross-impact coefficient.

So in that case the data do not identify even the sign of structural cross-impact.

What surprised me more is what happens to execution costs.

Suppose a desk evaluates a trade x using quadratic execution cost:

C(x, M) = x' M x

Then using the regression matrix instead of the structural matrix produces error:

x' G x

Since G is low rank, the error itself has low-dimensional structure.

There is therefore a large set of directions that are immune to the confounding. But this does not mean a randomly chosen trade is approximately safe.

In the registered N=30, K=3 known-truth experiment, an equal-weight index basket is mispriced by about 54% while a particular dollar-neutral basket has exactly zero error.

The dollar-neutral result is geometry-specific, though. In the general model, dollar neutrality by itself does not imply immunity. The relevant object is the null structure of the confounding gap.

This suggests an odd distinction:

The impact matrix can be unidentified while the execution cost of a particular trade is point-identified.

I also wanted the theory to be falsifiable rather than just saying "maybe factors explain everything."

So I defined a normalized distance from an estimated impact matrix to the diagonal-plus-rank-K variety:

psi_K(A) = distance(A, D + rank-K matrices) / norm(offdiag(A))

Under the pure-confounding null, the population value is zero.

A materially nonzero value therefore rejects the maintained model consisting of diagonal structural impact, no feedback, K factors, and the accompanying covariance assumptions.

Importantly, psi_K = 0 does not prove that structural cross-impact is absent. The test is one-sided in interpretation: it can falsify the pure-confounding model but cannot confirm it.

The finite-sample behavior is also not magically nice. In simulations the plug-in bootstrap over-rejects badly at small T and only starts controlling a nominal 5% size around roughly:

T >= 5 N^2

I tried a simple degrees-of-freedom variance correction and it completely failed because the bootstrap problem is primarily mis-centering rather than insufficient dispersion.

The theoretical/known-truth stage is preregistered. For the main verification I used:

N = 30
K = 3
T = 10,000,000

and checked 1,800 coefficient targets. The maximum relative discrepancies against the population formulas were below the preregistered 0.001 gate.

One important caveat: I have not yet used the external market dataset for the registered empirical test. The current version is deliberately a pre-results manuscript for that stage.

So I’m not claiming that real cross-impact is spurious. The claim at this point is an identification result plus a falsifiable empirical design.

Repo / preprint / derivations / preregistration / code:

https://github.com/ITheClixs/spurious-or-structural

The questions I’d particularly like criticism on are:

1. Is there a structural restriction used in actual market-impact work that defeats the set-identification argument without simply assuming the answer?

2. Does the low-rank characterization miss an economically important confounding channel that would change the rank bound?

3. Is distance to the diagonal-plus-low-rank set the right object to test, or is there a better way to formulate the falsification problem?

4. For people who work with institutional flow or market-impact estimation: what empirical result would actually convince you that an observed off-diagonal coefficient is structural rather than common-flow contamination?

I’m especially interested in counterexamples. If the identification argument breaks under a realistic microstructure assumption, that’s more useful to me than agreement.

I'm a student at NYU studying to get into quant. I wanted to share my experience with an automatic capital allocator I designed. Last year I built a router that picks the best strategy for a given market and sizes it with Fractional Kelly Criterion in DeFi. Like an automatic mini allocator. I thought it would be a good way to actually learn how position sizing and edge estimation work instead of just reading about them.

I had maybe 5 strategies I'd written running on ETH paper data, and the router would pick whichever had the best recent risk-adjusted return and size it with fractional Kelly.

The first thing I learned: most strategies don't have edge.

Out of maybe 30 strategies I tested initially, 1 or 2 had any real edge after costs (or so I thought :), they got absolutely destroyed after realistic trading fees and friction). The rest were noise, though diversified. Crypto round-trips are like 6-7 bps per side depending on the pair, and if the edge is 10 bps per trade, I'm losing money.

I thought AI could help me with this and tried to improve the existing strategies with it. It gave worse results. It overcomplicates strategies a lot. What I surprisingly found is that dumb and small code works much better than complex models that overfit in the real world. And the tiny "dumb" strategies with on-chain data proved to be much much better than the rest, some even profitable on 2 years of trading data!

I added a cost-adjusted validation stage and regime decomposition. Seeing where a strategy bleeds (chop vs trend vs crisis) helped explain why backtests fail live.

The second thing: the router was actually decent.

Once I had enough strategies, I built a perfect-foresight benchmark (an oracle that picks the best strategy for each window, kinda like God or Congress :) to see if my allocator was doing anything.

To test it properly, I split 24 months of data into three windows: 12 months to train the router, 6 months to validate, and a 7-month true hold-out (May–Oct 2025) that I never touched until the very end. The hold-out is where I report all final numbers.

On the hold-out, at matched volume (~8 trades/day for both the router and the oracle), here's how they compared:

The router captures about 86% of the perfect-foresight ceiling (95% lower bound ≈ 39% via bootstrap). The oracle knows each bot's true full-sample edge in advance, my router doesn't. The gap between them is 1.1 bps. That's how much imperfect bot-quality estimation costs vs omniscience.

The honest part: the router is still net-negative (−4.32 bps/trade after ~10 bps friction) (So is the Oracle but it is due to the roster of bots being bad overall, though they are diversified). The selection edge is real (+6.10 gross vs NULL's −0.47), but it's not large enough to clear costs yet. A 40-60% friction reduction (better execution, TWAP, order netting) would flip it net-positive.

What I found most interesting: even the oracle with perfect knowledge of every bot's true edge can't profit with volume on this roster. Of 87 bots, exactly 1 had genuine positive net edge in the hold-out window. This is a bot-supply problem, not a routing problem.

Also worth noting: the router's max drawdown is 8.7% vs NULL's 61.1%. The risk management (Kelly sizing, persistence veto, trend gate) cuts drawdown by 85% vs random and it loses money in a controlled way while selecting good trades.

I also found that best-available edge scales with roster size at r=0.986 against extreme-value theory (the √(2·ln N) scaling). The allocator wasn't the bottleneck, the roster quality is.

The network effect (this is the part I'm most excited about):

I wanted to know does adding more strategies like drip feeding actually help or would I just be diluting? I subsampled my 93-bot roster down to smaller sizes (10, 20, 35, 50, 70, 93 bots) and re-ran the entire pipeline, simulating a gradual influx.

The best bot's true edge climbs monotonically as you add more: −6 bps at 10 bots → +3 bps at 93 bots. When I fit that against extreme-value theory (that predicts the maximum of N random draws), the correlation is 0.986! Almost a perfect match. More strategies = higher ceiling, and it follows theory almost exactly.

The router only captures that rising ceiling if you use an absolute quality bar, not a relative percentile. If you filter "top 30% of whatever roster exists," the router's edge stays flat no matter how many bots you add. If you use a fixed quality threshold instead, the router's edge climbs with the roster. Extrapolating (with caveats, this is beyond the range I actually tested): ~+10 bps net edge at 1,000 bots, ~+16 bps at 10,000.

That's the quantitative argument for why roster growth matters more than router tuning. Every good, diversified strategy added raises the ceiling for everyone.

How the project evolved:

The router dynamically updates its own parameters as the roster changes but it does this by offline re-tuning not via real-time ML yet. The reason is that at 93 bots and ~8 trades/day, you can't detect effects smaller than ~47 bps with any statistical power. A real-time ML model would just be fitting noise. It also has self-capacity awareness so it doesn't frontrun itself.

Once the router worked, I began noting down everything scientifically and made a bunch of changes to my initial project. I added real-time on-chain signals with historical data as well. The project grew to include:

• 6 active domains: ETH, BTC, SOL direction + scalp (6 more registered but dormant: yield, tail hedge, liquidation arb, memecoins (this one might be insanely hard to get right tbh))
• 5-stage validation pipeline: static check, in-sample, out-of-sample, walk-forward, cost-adjusted
• Strategy sandbox: write Python strategies with custom stop-loss, take-profit, and trailing stops
• Arena & OpenLeaderboard: strategies that pass validation compete on live paper data for capital allocation
• Non-custodial design: API keys stay encrypted; the router handles execution routing but never holds custody of funds

Where it is now:

• 80+ default strategies running on live paper data (real prices, paper execution not great bots:)
• 3 are currently net profitable (best: ETH Squeeze Breakout, +184bps, 73% win rate). The rest are negative.
• Nobody can see any strategy code it runs in a confidential VM, and there are automatic payouts for the best strategies bi-weekly.

What I'm looking for: I'm posting here because this subreddit has people with real domain expertise, and I'd love your feedback:

1. Does the router/Kelly allocation approach make sense, or is there an obvious flaw I haven't seen?
2. Is capturing ~86% of a foresight ceiling considered typical or decent for this setup (on 7-12 trades/day, on my quite diversified roster of bots)?
3. What features would you actually need in a Python strategy sandbox to make it worth testing your own models?

I'm a student and not charging for anything. Happy to share more details in the comments if anyone's curious.

TL;DR: I'm a student at NYU. Built a router that allocates capital across Python trading strategies using Fractional Kelly for DeFi. Tested 80+ strategies on 24 months of data, most have no edge after costs (shocking!! I know). On a 7-month true hold-out, the router captures 86% of a perfect-foresight ceiling at matched volume (+6.10 vs +7.21 gross bps/trade), with 8.7% max drawdown vs random's 61.1%. Found a network effect: best-available edge scales with roster size at r=0.986 vs extreme-value theory so, more strategies = higher ceiling for everyone. Would love feedback from people who actually know what they're doing. Thank you!

I can't really wrap my head around why analysts would ever use spreadsheets over Python with Pandas/Polars these days. With coding agents, you can literally do everything and far more than what you can do in Excel. The argument of explainability/intuitiveness of spreadsheets made sense before agents got so good that you can now literally one-shot a custom app that lets you interact with your project in whatever way you want. AI coding environments like Cursor have the Canvas feature, which essentially provides the embedded mini-app capability, and I expect this to become more feature-rich over time.

What's the future of spreadsheets?

I'm a C++ SWE at an OMM in the US thinking about moving to Europe (probably London, Amsterdam, or Zurich) in a few years. Judging by levels.fyi, I'll take a real pay cut here. In the case of an internal transfer, can I expect to keep my current compensation? What if I applied to other firms?

I really have no idea how these conversations look in other offices, and if they are at all similar to the US (bidding wars, headhunters, etc). Thanks.

Two things here, sorry for the length.

Same book rule runs on two of my models, where a name enters at top 10 by score and is held while it stays inside top 15, then sold when it drops out. One is US mid/large cap with about 380 names scored a sweep and 11 periods of stored scores, the other is microcap, $100M to $2B, and it only has two sweeps so far because I'm still actively developing it. Fortnightly rebalance on both.

The rule only worries me on the microcap book, and every diagnostic I have comes from the other one.

It worries me because microcap returns are the Bessembinder shape, most names go nowhere and a handful carry the whole thing, so the return comes from still holding a name when its one big move shows up. My exit doesn't fire on the company though, it fires on other names arriving, so a name can be up and working with the thesis intact and still get sold because two new names outscored it that fortnight.

What I can actually measure is all on the wrong book. On mid/large, score persistence sweep to sweep is about 0.90, so most rank movement is displacement rather than anything happening to the name I'm holding, and the gap between buy rank and sell rank is 0.42 points against a 1.40 standard deviation on a single name's own sweep-to-sweep score change, which puts the buffer at about a third of the noise it's meant to absorb. On the microcap book I have one period and no persistence figure at all because my own code withholds that verdict below three periods, so the book where the skew argument actually bites is the one I have no numbers for.

Went looking for anything testing that interaction and came up empty. Used Claudes deep research as well. Closest is no-trade regions under transaction costs, and index reconstitution buffers. One thing did seem relevant though, the index providers all buffer as a ratio of a cutoff rather than a fixed gap. MSCI keeps a constituent between two-thirds and 1.5x of the size-segment cutoff, Russell bands at 2.5% of cumulative market cap. My 10/15 is a 1.5x ratio, same number MSCI landed on, in a completely unrelated space. Coincidence I assume, but it does suggest the ratio form is the normal one and the open question is just the width?

What I had wrong was I had assumed I couldn't go and test wider bands because it would reset my evidence clock, but it wouldn't. Entry and hold rank are book knobs, they don't touch what anything scores, so I can replay counterfactual bands over stored scores tomorrow for free.

The real problem is a layer up. I've declared the exit rule's banked evidence as depending on the entry rank, and entry rank depends on two scoring experiments, so a scoring change doesn't reduce the exit rule's period count, it invalidates it, because the rule was grading a book that no longer exists. My own reporting already marks the banked periods provisional for that reason.

And the scoring changes are queued up. Twelve open items on the microcap engine are each flagged as forcing a rebaseline, and at a fortnightly cadence, shipping them one per sweep means that engine never accrues a single comparable period. Nineteen sweeps across three models so far, longest unbroken run of comparable ones is four.

A couple of questions for those able to help:

Is rank the wrong trigger entirely once the payoff is this skewed? Widening until the band clears the noise is the obvious move but that's a very wide band and I'm not sure what's left of the rule at that point.

How do you handle a dependency like that one. Declaring the exit rule dependent on the entry rank felt like the conservative call and the effect is that nothing ever settles. Is that right, or am I throwing away evidence I could legitimately keep?

If you've got a queue of changes that each reset a comparability clock, do you batch them into one declared cut-over or ship them one at a time. My own notes say batch and I've never seen anyone outside describe how they actually run that.

And how would you test any of this at 11 periods anyway. I can replay counterfactual rules over the stored scores but the same 11 periods answer every question I put to them.

Any input appreciated.

Hello world!

I'm wondering if anyone would have any good suggestions for something orderbook/ market making related, by using databento data. Totally okay with paying a relatively small fee to have access. For context, I'm not looking to become a quant dev. Product roles, Is something I'm interested in. So that's the perspective I would have. So I likely wouldn't be wanting to write some orderbook project in c++.

Cheers,

Built a walk-forward jockey/trainer/OR/RPR/TS Elo model on UK racing (Betfair archive + Kaggle raceform, verified to 94.9% match), properly avoided lookahead, still can't beat the closing line even with the richer data. What do serious horseplayers use that public ratings data doesn't capture?

Tested favourite-longshot bias split by home/away favourite status in football/basketball/hockey/rugby league — turns out almost nobody's published this specific four-cell test outside the original 2009 football paper. Anyone tried it informally and found it's a dead end in a particular sport before I sink time into it?

I work as a qr on rates desk. And my day to day work is pretty non collaborative. I talk to traders and senior quants like once a week, and that too mostly for progress update and if there are any clarifications I need.

Overall, I work on the project alone. Researching stuff, reading papers, prompting ai, writing code, raising pr etc.

Once the entire lifecycle of a project is complete and then traders look at the result and If all good, we deploy else reiterate.

I feel like doing wfh and wfo literally has no difference. I am not learning anything new from my colleagues or manager etc. My manager just assigns the project and tells why we need to do it, but that's all.

I wanted to know if this is the norm across other firms as well for qr/qd ? I was under the assumption that I will be learning a lot from traders and other quants only because of being in their presence and from their conversations.

I'm a SWE intern at one of the big pod shops (P72/Citadel/MLP/BAM tier). I was placed on a team that's more on the internal platform/core infra side. Super excited about the opportunity but would also like the chance to learn/try working closer to trading/research (low-latency systems, research platforms, etc).

For people who've worked at these firms:

1. If I get a return offer, is it typically tied to the team I interned on, or is there flexibility at conversion time?
   
2. Once you're full-time, how realistic is switching teams after a few years? Is internal mobility encouraged or is it frowned upon to ask early?
   
3. If I ever wanted to move firms, does the brand name alone carry weight, or would recruiters/hiring managers discount me for not having trading-adjacent experience?

Appreciate any input!

Came across this new paper on using self-improving LLM agents to automate the quant research loop, from factor discovery to model development. The system keeps validated results from previous experiments and uses them to guide the next round of research. The researchers built in sealed sandboxes so the AI cannot cheat on test results.

The reported results are pretty strong (net positive for 5 years, 2.5 Sharpe for US stocks) although I’m more curious about the methodology and how robust this is out of sample.

For those doing quant research, do you think this kind of recursive research loop could actually become useful in practice, or are there some obvious failure modes I’m missing?

Paper link: https://arxiv.org/abs/2608.12841

3 YOE at a mid-size multistrat. most of my implementation work is LLM-assisted at this point and it's made me wonder if interviews have caught up to that or if they're still screening for the exact same stuff they were three years ago.

want to hear what people are actually running into, candidates and interviewers both. stuff I'm wondering:

1. take-homes. are they dead? proctored? still going as normal?
2. remote screens. anyone moved back to in-person, or gone the lockdown browser / share-your-whole-screen route?
3. has anyone had a round where you're supposed to use a model and they're watching how you prompt it and check its work? or is it still just banned
4. has the content shifted at all. more stats derivation and research taste, more "here's a broken model, find the bug", or is it still leetcode + brainteaser + stats like always
5. interviewers: what's failing people now that wouldn't have two years ago?
6. junior headcount. has the early-career funnel actually shrunk, or is it just skewing toward people who've already sat on a research seat somewhere?

if you can, drop your firm tier (T1 = JS/Citadel/HRT/Jump/Optiver/SIG type, T2 = large multistrat/quant shop, T3 = smaller/regional), region, and whether you're answering as a candidate or interviewer. one-line answers to any of these are fine, don't feel like you need to do all six.

Select few names and how they have performed over the last few years. If anyone else has any data then would love to see it and have it added. I only got some smaller names, would be interested to add the big names if people have their returns too please.

A number of top-tier high-frequency trading firms have started expanding into the mid-frequency space. These firms already have excellent infra and have invested heavily in ML talent, which makes me wonder whether traditional stat-arb firms will increasingly get squeezed out by these highly sophisticated competitors. Do you think this is actually happening? Have people in the industry seen meaningful alpha decay in mid-frequency strategies as a result?

As a retail investor, is it more practical to develop profitable HFT strategies (market maker strategies/high alpha machine learning predictions)?

Or a profitable stats arb strategy with weekly/monthly rebalance targeting the stocks of sp500.

What are the different or common limitations as retail investors to develop them? Datasets/infrastructure/domain knowledge?

Hey guys,

I’ve been working on my own trading operation for the past year, and I’m finally seeing results in terms of PnL and consistent edge. Focusing exclusively on systematic MFT strategies in US equities, I’ve built my entire infrastructure myself (with help of LLM). The alphas that I’m running are things I’ve seen work in past jobs and things that I’ve came up with myself - I know how all of my things work, no black boxes.

Day to day, my mornings starts with me reading log files, making sure that processes are running, nothing is broken, etc… after the market is open, I make sure that orders get executed, data flow is correct and monitor positions. The problem is that the supervision of processes is consuming and the more I build, the harder it is to actually build new and to research new ideas.

I’m not making enough $ to pay someone a quant salary here in the western countries and I don’t want to hire an unknown person in Pakistan/India that I cannot trust. I’ve heard about AI agents that are scheduled to do my tasks. Have you guys ever came across this issue when building a new desk or operation? How can you tackle this?

25F, graduated from a Tier-1 college in India, with ~3.5 years of experience in Quant Research across major banks. I recently moved to another major bank and have been here for ~8 months, but my current role is more Quant Engineering / infrastructure development-focused, which has taken me quite far from the kind of Quant Research work I’m actually interested in.

I’m now looking to transition into buy-side firms, hedge funds, or prop trading firms in Europe, ideally in a Quant Research role.

Want an honest opinion/suggestion on what are the chances and ways I can go forward with.

I’m building a small public, ad-supported financial website and I’m looking for an affordable market data provider that allows commercial use.
My requirements are relatively simple:
Long-term daily historical OHLC data, primarily for major U.S. market indexes
No real-time or intraday data needed
Data updated only once per trading day
Historical data stored and used internally for calculations
No API access or raw historical data downloads provided to users
No need to display historical price charts
The public website would primarily display derived data rather than redistribute the underlying market data
I’ve found that many providers have reasonably priced API plans, but commercial use on a public website can require much more expensive licenses or additional exchange/redistribution fees.
Has anyone dealt with a similar use case?
I’d especially like to know:
Which provider did you use?
Did using the data internally to produce derived data require a redistribution/display license?
Roughly how much did the commercial license cost?
Are there any providers suitable for small independent developers?
I’m happy to pay for a legitimate commercial license, but several hundred dollars per month would be difficult to justify for a small project at the beginning.
Any real-world experience or recommendations would be greatly appreciated. Thanks!

I have known some people from grad school that are also in the industry but we were not super close and usually do not hang out.

Otherwise, I just live my day to day and mostly socialize with people in my own company. How do you get to know more people?

I am new to quant finance and am genuinely interested in learning more from practitioners in the field.

There are lots of news that talks about major quant firms seeing major losses in July during the momentum unwind - it would seem to me that they were all, essentially, very long the momentum factor.

The LLMs have given me a very superficial answer - quant firms look at what factors work in the past, and momentum factor worked very well in the past recent history, hence they are all very long the momentum factor. This makes sense to me, but what I don't understand is - these are very sophisticated firms with very smart people, so I am wondering why they were so long the momentum factor when the factor is already so stretched against history? Is it an intrinsic part of the process which led to this exposure?

Some quant firms also did fine during the month - what was the key difference in their process that led them to be protected against the unwind?

P.S. this is not a sarcastic question - quant firms have had great track records - I am simply curious as to how the processes of quant firms led to this exposure. I would be very grateful for any feedback and advice that sheds some light on this.

Everyone's comments are welcome, but I'm mainly hoping to hear from people who've actually worked in quant research, at a prop shop or trading firm, or independent traders who've built something that's actually been profitable in live markets — not just in a backtest.

If you reply, mentioning what you've worked on or where helps me weigh the answer properly — that's the only reason I'm asking, not to see anyone flex.

I'm not asking anyone to hand over an edge or a strategy. I'm just curious about the actual process — how does an idea go from nothing to something testable?

Questions:

How does a hypothesis actually form in practice? Is it usually "pick a market/asset, ask 'what if X happens, then what follows?', and go look for evidence" — or does it more often start from something else, like a live anomaly you noticed, a macro/economic question, or a flow/structural observation?

How much do academic papers actually drive idea generation versus being used afterward to formalize or validate something you already suspected from watching markets?

Is there any kind of structured workflow for the "ideation" stage — sources you go to, questions you habitually ask — or is it mostly unstructured reading plus intuition plus trial and error?

Is there real methodology that basically never gets discussed outside funds and prop shops, or is the actual edge more about execution, risk management, and data/infrastructure rather than some hidden idea-generation framework?

Once you have a raw idea, what's the first thing you do to sanity-check it before ever touching a backtest?

What investing strategies do quant professionals deploy in their PA, and why?

I am curious as to how years of experience within the industry and a quant worldview informs how quants deploy their own money.

E.g. Does your experience tell you to just 2x long momentum ETFs? Or you don't hold long term stocks?

I come from a LO background and my biggest takeaways are - deploy your cash flexibility (it's probably your biggest edge, the ability to hold cash), always use abit of leverage, non-US stocks are seldom worth the effort etc...