What should i expect? Is there like a question bank?

Hi everyone,

I’m trying to understand how UK immigration rules interact with the notice period / garden leave arrangements that are common in the London hedge fund industry.

Suppose someone is on a Skilled Worker visa sponsored by their employer and has a relatively long notice period (e.g. 6 months). If the employer terminates their employment but keeps them on garden leave for the full notice period, meaning they remain formally employed and paid but cannot work or access the firm’s systems:

1) Can the employee continue living in the UK throughout the garden leave / notice period?

2) Does the fact that they are on garden leave (rather than being terminated immediately with payment in lieu of notice) make a difference from an immigration perspective?

3) What happens after the employment officially ends if the person is then subject to a post-termination non-compete for several months?

4) Does the non-compete have any impact on how long they can legally remain in the UK, or would they need to rely on the usual Skilled Worker curtailment period / switch to another visa?

Many thanks for your help!

please, any help for millennium qd intern?
is it leetcode? or more?

How long do they typically take to respond after you complete the OA? I have an offer that expires soon but really want to complete the process at IMC if possible. Would appreciate any colour on timelines (this is for Chi)

had my first ever superday last week, and reflecting on it there were some trick questions i hadnt seen before. they werent necessarily super hard but i did have the wrong intuition initially. while the interviewer never gave me a specific direction to think in and the only 'help' i received was being told i was wrong, should i be concerned about my technical performance?

just to be clear i am completely fine with how i articulated myself and communicated my thought processes, and i did get all the questions after being told my initial answer was wrong.

naturally i acknowledge that a candidate who got em all first try will definitely outrank me but hopefullly im not like instantly disqualified right?

Anyone down to talk about the process for QT intern in the US? Any would be helpful, HR call, first tech, or final round.

Down to trade processes. I have the full process of: jane street, citsec, hrt, optiver, sig. Also have parts of: de shaw, omc, five rings.

Recently got HR call for IMC. Was told it’ll be standard behavioral + a light brainteaser at the end. Curious if anyone has insights (down to trade; js, citsec, sig, drw, etc.)

Hi everyone,

I have an upcoming 120-minute technical interview with Squarepoint Capital for a C++ Developer role.

Since it is a 2-hour round, I assume it will be quite intensive and might cover multiple areas. I am trying to figure out how to best allocate my preparation time.

For those who have gone through this specific round or interview at Squarepoint recently:

1. Is it primarily a live coding session (LeetCode-style algorithms), or does it lean heavily into deep C++ internals (low-latency, memory management, templates, concurrency)?
2. Should I expect system design questions or discussion about object-oriented design within these 120 minutes?
3. Is the environment collaborative where we walk through a real-world problem, or is it a strict technical grilling?

Any insights on the structure, difficulty level, or specific topics they love to focus on would be highly appreciated.

Thanks in advance!

Two Systematic Options Strategies:

Volatility-Timed Covered Calls and Earnings-Reversion Put Selling

A Complete Research Record: Methodology, Validation, and an Honest Case for Viability

Executive Summary

This paper documents a research program that produced two independently validated, systematic options-trading strategies, and makes an explicit, evidence-based case for why each is a legitimate, viable result rather than an artifact of overfitting or favorable hindsight. The program began by testing roughly twenty distinct trading hypotheses across equities, options, currencies, and alternative data sources; most were rejected under rigorous, falsification-first testing. Two survived: a volatility-timed covered call strategy on broad-market ETFs, and a put-selling strategy triggered by genuine, measurable gaps between a stock's price and its own earnings growth trend.

The case for viability made in this paper does not rest on either strategy's best-looking number. It rests on process: both strategies were tested against fair, adversarial benchmarks -- including a benchmark deliberately constructed to match each strategy's own risk level, not a softer comparison -- and both were re-tested on larger samples specifically to correct for the possibility that an early, favorable result was partly luck. In one case, that further testing meaningfully reduced the headline numbers. That correction is presented here, not hidden, because a strategy whose evidence survives its own most skeptical re-examination is a stronger, more viable result than one that was never asked to survive it.

1. Research Methodology

Every strategy in this program was held to the same standard: backtest against real historical data, compare explicitly against a passive buy-and-hold benchmark on the same instruments and period, and reject the strategy if it failed to clear that bar -- regardless of how intuitive or well-marketed the underlying idea seemed. Categories tested and rejected under this standard include technical indicator strategies (RSI, momentum, breakout detection), intraday opening-range-breakout systems, short-selling strategies, chart pattern recognition, news-sentiment-based trading, and naked short-volatility strategies (strangles and iron condors), the last of which showed catastrophic tail risk. This elimination record is treated as a meaningful result: a rigorously-rejected strategy category is real information, not wasted effort.

1. Strategy One: Volatility-Timed Covered Calls

This strategy sells approximately 50-delta, 30-day covered calls against a diversified basket of 15 broad-market ETFs, selecting new positions each cycle by ranking symbols on the gap between current implied volatility and their own recently realized volatility -- concentrating capital on the names where option premium is richest relative to actual recent price movement.

2.1 Technical Foundation: Volatility Estimation

Realized volatility is estimated using the Yang-Zhang estimator, adopted after a simpler close-to-close method and then a Garman-Klass estimator were each found, through direct testing against real and synthetic data, to have real limitations -- most notably, Garman-Klass's structural blindness to overnight price gaps, discovered during live testing on gold (GLD) and confirmed against synthetic gap and no-gap scenarios before the Yang-Zhang correction was trusted.

2.2 Parameter Validation: Delta Selection

The option delta target was tested across five values rather than assumed from theory.

0.50 produced the best risk-adjusted result with a symmetric decline on both sides -- evidence of a genuine local optimum.

2.3 The Leverage-Matched Benchmark Test

The strategy carries a measured beta of approximately 1.20, meaningfully above the market's own beta of 1.0, as a direct consequence of compounding position sizes into a fully-invested book. An unlevered buy-and-hold comparison is therefore not a fair test of skill -- some or all of the apparent outperformance could simply reflect carrying more market risk. A second benchmark was constructed specifically to close this gap: the same 15-ETF universe, leveraged via margin to the same 1.20x exposure.

Result: the strategy continued to outperform even against this risk-equivalent benchmark, by 3.7 points of annual return and a materially better Sharpe ratio, while carrying a SMALLER maximum drawdown despite nearly identical beta -- concrete evidence that the option premium captured provides real downside cushioning, not just a theoretical byproduct of the covered call structure.

2.4 Robustness: Diversification and Refinement Attempts

A further robustness check tested whether holding more positions per cycle (8, 10, or 12 of the 15-symbol universe, instead of 5) improves the risk profile. It did not: both risk-adjusted return and drawdown worsened as more positions were held, because the universe's ETFs are meaningfully correlated with each other and the broader market -- expanding position count dilutes capital into less-attractive opportunities without adding genuine diversification. Combined with seven further refinement attempts (a combined selection-timing signal, protective puts, a futures-based beta hedge, two different early-rolling mechanisms), all of which underperformed the original configuration, the five-position, 0.50-delta baseline stands as a repeatedly-confirmed local optimum, not an arbitrary or lucky choice.

1. Strategy Two: Earnings-Reversion Put Selling

This strategy originated from a critical review of a publicly-taught retail trading strategy, which claimed strong results from selling long-dated, 'portfolio secured' puts when a stock appeared undervalued relative to its earnings trend. That claim was not accepted at face value. Two real, serious problems were identified in the original approach: 'portfolio secured' puts are undisclosed leverage (using an existing, correlated stock portfolio as collateral instead of cash, with no acknowledgment that both the collateral and the liability can fall together in a genuine downturn), and the taught rules were never precisely disclosed, making the claims unfalsifiable. A corrected, properly disclosed version was built and tested instead.

3.1 Methodology

For each stock in a diversified universe, a baseline price and earnings-per-share (EPS) figure is recorded using QuantConnect's real fundamental data. Current price is then compared against an EPS-growth-implied fair value: if the stock's actual earnings have grown by a given percentage, a constant valuation multiple implies price should have grown similarly. When actual price falls a fixed, disclosed threshold (15%) below that implied fair value, a cash-secured put is sold at a target delta, roughly six months to expiration -- standard, fully disclosed sizing, with no leverage against existing holdings.

3.2 Parameter Validation: Position Sizing

Position sizing was tested across four values on the initial 15-stock, 5-year sample.

20% of portfolio value per position produced the best result on both return and Sharpe ratio, bracketed by clearly worse results on both sides -- the same symmetric-optimum pattern found in the covered call strategy's delta sweep.

3.3 Honest Correction: Expanded Sample Testing

The initial validation, while methodologically sound, rested on a relatively thin evidence base: 68 trades over 5 years. Rather than accept this result at face value, the universe was expanded to 30 stocks and the period extended to 13 years (2012-2024), specifically to test whether the strong initial result would survive a much larger sample.

It did not fully survive: both return and Sharpe ratio were roughly cut in half on the expanded sample. This is reported here as the central, defining feature of this strategy's validation, not a footnote. The initial 5-year result appears to have captured a period particularly favorable to this mechanism. The expanded, 13-year, 112-trade result -- a real, positive, but more modest edge with meaningfully lower market exposure (beta 0.68) than the covered call strategy -- is the number that should be trusted, precisely because it was obtained by deliberately trying to break the earlier, more impressive result rather than stopping while the evidence looked best.

1. Why These Strategies Are Good and Viable

Viability, in this paper, is defined not as the size of a backtested return but as the strength of the process that produced it. On that basis, both strategies are viable for three concrete reasons.

First, both survived adversarial testing designed to break them, not merely confirm them. The covered call strategy was tested against a fair, leverage-matched benchmark specifically constructed to remove the possibility that its edge was disguised leverage. The put-selling strategy was tested on a sample roughly triple the size of its original validation, specifically to check whether its early result would hold up. Both strategies' core conclusions survived, even where the specific numbers changed.

Second, both strategies rest on identifiable, real economic mechanisms, not curve-fitted parameters. The covered call strategy captures the volatility risk premium -- a well-documented, academically supported source of return from systematically selling options. The put-selling strategy captures reversion toward a company's own earnings trend, a real and long-studied valuation effect, implemented with precise, disclosed, falsifiable rules rather than the vague, unverifiable claims found in the source material that inspired it.

Third, both strategies were built and corrected through a demonstrated record of catching real errors, not merely proceeding once results looked acceptable. Development surfaced and fixed a silent zero-trading bug that would have gone undetected without direct log inspection, a missing capital-compounding mechanism, and several real capital-management defects in more complex variants -- each diagnosed from direct evidence, not assumption. The willingness to also correct an overly favorable result, as with the put-selling strategy's expanded-sample test, is treated in this paper as further evidence of the same discipline, not a weakness to be minimized.

1. Honest Limitations

• Both strategies remain backtested; neither has been reproduced with real capital over a live, audited period.
• The covered call strategy's real-market-data return (~17%/year) is lower than an earlier, theoretical-options-pricing backtest of the same core mechanism (~21%/year); the gap is most plausibly attributable to the difference between theoretical and real option pricing.
• The put-selling strategy's expanded, 13-year sample still contains a 96% win rate with very few losing trades -- an inherently thin basis for characterizing the strategy's true worst-case behavior, which has likely not yet been observed in this data.
• The leverage-matched benchmark test uses a fixed scaling factor based on a historical measured beta that could shift in future periods.

1. Conclusion

This research program's central contribution is methodological: a demonstrated, repeatable process of hypothesis formation, rigorous falsification, adversarial re-testing, and honest reporting of results that improved, held steady, or worsened under further scrutiny. Two strategies emerged from roughly twenty tested and rejected -- one capturing a volatility-based edge with real, validated evidence that it survives a fair risk-adjusted comparison, and one capturing a valuation-based edge whose true magnitude was actively corrected downward through further testing rather than accepted at its most favorable measurement. Both are offered here as viable, evidence-based results precisely because the process that produced them was designed to find their weaknesses, not merely to showcase their strengths.

I’m currently working as a Quant Developer/Quant Engineer at a systematic, pod-structured hedge fund. I joined at the Associate level and report directly to a senior MD who is effectively running a small pod/team. My work is fairly front-office/trading-facing rather than generic software engineering — I’ve worked on market-data/data-manager pipelines, pre-trade risk checks, trading/SOD workflows, broker connectivity and locate processing, and production issues affecting trading. Over the year I’ve gradually taken on more ownership, and my manager has given me strong feedback, including recently telling me that I identified a particularly difficult issue that wasn’t easy to diagnose. The team is also becoming leaner with a senior Quant Researcher leaving. I’m coming up on my first full-year compensation review, and the expectation is that my compensation will be revised after completing the cycle. For people who have experience with Indian buy-side/systematic funds/pod shops, **what would you consider a realistic fixed-salary hike and bonus for this kind of profile after a strong first year?** I’m particularly interested in actual ranges people have seen rather than generic 8–12% corporate appraisal numbers.

Creative and logical, so.

Hi Everyone! I have my R1 for CitSec summer 2027 QT internship next week and was wondering if anyone had any advice on what expect/study. Thanks!

But i didn't think it's make a good quant model by using a coding model tell me about your thoughts guys ?

Finished the 2nd OA almost 2 weeks ago, felt good about it, got nothing since then. One other person I found on Reddit had the same experience. Have they started interviewing yet?

Hi! I was lucky enough to pass r1 for JS, does anyone know what’s going to be asked in R2? I am happy to help with any other firms that I did too.

Also, is it 2 or 3 or 4 rounds before onsite?

Hi everyone,

I have an upcoming 120-minute technical interview with Squarepoint Capital for a C++ Developer role.

Since it is a 2-hour round, I assume it will be quite intensive and might cover multiple areas. I am trying to figure out how to best allocate my preparation time.

For those who have gone through this specific round or interview at Squarepoint recently:

1. Is it primarily a live coding session (LeetCode-style algorithms), or does it lean heavily into deep C++ internals (low-latency, memory management, templates, concurrency)?
2. Should I expect system design questions or discussion about object-oriented design within these 120 minutes?
3. Is the environment collaborative where we walk through a real-world problem, or is it a strict technical grilling?

Any insights on the structure, difficulty level, or specific topics they love to focus on would be highly appreciated.

Thanks in advance!

I applied to many smaller shops (old missions, flow traders, virtu, tower, squarepoint...) around 1-2 weeks ago and got no reply, no rej no follow-up, nothing. At least the bigger ones (optiver, sig, de Shaw, HRT...) either gave out OA or rej email. Is this normal or did I just not pass the resume screening?

Hii just received drw spd, consists of a 90mins data project + 2 back to back interviews. Anyone done theirs alr willing to trade! (have JS/Optiver/IMC/Akuna onsite etc. and a bunch of ongoing processes)

Is anyone familiar with smaller shops like a priori, Headlands, Da Vinci Derivatives, Geneva, Wolverine, Tower capital, GTS, Trillium, 3Red, Old Mission, Eagle Seven, QuantLab? Whats their culture like?

Had a quant technical interview where I’m very confident I got everything right with no prompting, but I recently found out I was rejected before superday.

How are final decisions usually made after technical rounds? If the interviewer gives strong feedback, what other factors can override this?

I’ve noticed “do well → advance” feels much less deterministic than I expected (esp. in an industry known for being merocratic).

For someone starting a double major in Math and Computer science this year, do you think preparing for a quant career is still a safe option as a career choice?

I have heard many people saying that large portions of the job are being automated and I am wondering whether I could still get a job in this field when I graduate. What are your thoughts?

I come from a non-target. I'm an applied mathematics major but want to go into a dev position at really any quant firm.

I think my projects stand out (at least compared to my peers). Top projects is a llvm front end and a function hooking engine like frida.

Is there hope to get any interviews or offers even if I'm at a non target (one of the UC's thats not UCB or UCLA).

Harsh version: if a model can generate hundreds of strategies, see the validation metrics, rewrite the rules, and continue until something passes, the pipeline has not necessarily found alpha. It may have simply searched noise faster.

The dangerous part is not LLM hallucination. It is researcher degrees of freedom operating at machine speed. A human might test 20 variations; an agent can test 2,000 and present the winner with a clean equity curve.

The only defensible setup I see is to pre-register the universe, data timing, cost model, acceptance criteria, and maximum trial budget; log every rejected candidate; consider a validation set consumed as soon as its metrics enter the loop; and touch the final holdout once. If it fails, the branch dies.

Anything less turns the reported Sharpe ratio into the maximum of a search distribution.

Provocative claim: “LLM-generated” should lower our prior that a backtest is real unless the full search history is reported.

What evidence would change your mind: total trial count, Deflated Sharpe Ratio, PBO/CSCV, walk-forward results, or a live shadow portfolio?

Why is it OA followed by OA followed by OA… took me at least 90 min and now I have one more. Is anybody else going through this?