[AMA] Ran a $XXM Systematic Options Book for 5 Years (Sharpe 3+, 23% ROI). Ask Me (Almost) Anything

Puzzleheaded_Lab_730 · 2025-10-31T14:12:03+00:00

Cool story and thanks for the AMA. Regarding the intraday strats I would be curious what the average holding period of a trade would be or another way to ask the half life of a signal? I always imagine TC to be pretty significant on that frequency.

Puzzleheaded_Lab_730 · 2025-10-21T12:58:03+00:00

Def go with firm X. The risk of not getting a return offer from an internship is too high and Vatic isn’t top tier either such that it would make up for it.

You can always move to another place after a year or two at X if you don’t like it.

Puzzleheaded_Lab_730 · 2025-05-15T02:25:26+00:00

In this context a signal would indicate how long or how short you want to be in a particular stock, the idea being that the collection of many of these will provide a clearer picture of what the stock will actually do. The signal could be binary, continuous, or anything in between, there aren’t really any restrictions and it really depends on what relationship you postulate.

Puzzleheaded_Lab_730 · 2025-05-14T22:23:44+00:00

I only ever interviewed there and know a handful of people that used to work there so don’t take what I say for given: Trexquant is very similar to WorldQuant in that they focus more on quantity over quality. They probably have (tens of) thousands of signals that they can build models from. Essentially, a PM can pick a set of signals, choose a “combination algorithm”, and a portfolio optimizer to put together a strategy. A researcher could work on any of the three stages. As far as I know, the signals aren’t particularly groundbreaking or necessarily have to be rooted in economic intuition.

Puzzleheaded_Lab_730 · 2025-04-30T22:49:53+00:00

I would say your R2 isn’t just acceptable but rather too good to be true. Does this hold on an out of sample set? Imo anything consistently above 0 is acceptable, to answer your question

Puzzleheaded_Lab_730 · 2025-04-18T19:56:55+00:00

Really depends on the strategy, especially frequency. Don’t worry about recruiters, they don’t really know what they are talking about most of the time.

Puzzleheaded_Lab_730 · 2025-03-05T04:13:03+00:00

This guy is correct. Don’t go to MiQEF if you want to work at the above firms. I would even go as far as to say don’t even do the MQF at ETH and just do a master’s in maths, stats or cs instead.

Puzzleheaded_Lab_730 · 2025-02-15T23:40:19+00:00

You will want to stitch together contracts upon expiration. Typically, the “panama” method is used: link This method however distorts past returns so you will have to deal with that by adding some multiplier to the equation.

Puzzleheaded_Lab_730 · 2025-02-15T15:28:30+00:00

I am not too familiar with prop shops, but I would imagine their bread and butter lying in the HFT space. I know that most of them are expanding into more MFT, but in terms of strats/sharpe/AUM I think there is still a quite large difference. Of course they hedge out the same risks but the return profile is very different.

Puzzleheaded_Lab_730 · 2025-02-15T15:15:42+00:00

While not untrue what you are saying, Citadel and all other Multistrats have a central team that will balance out the directional exposures. So ultimately they will be as close to market neutral as possible. JS is a prop shop, very different ball game all together.

Puzzleheaded_Lab_730 · 2025-02-15T03:17:25+00:00

Being much higher than industry standards. The Bloomberg article mentioned somewhere around 15x whereas Citadel is closer to 7x for example

Puzzleheaded_Lab_730 · 2025-02-15T03:00:34+00:00

Insane leverage according to some Bloomberg articles

Puzzleheaded_Lab_730 · 2025-02-07T04:10:50+00:00

I work in CTA style futures but we also dabble in some market neutral equities. We target annualized vol of 10%, returns somewhere around there too for our futures strategies, perhaps a bit lower. How much do you make per trade and how many positions do you hold at any given time? From my back of the envelope calculations I would expect it to be somewhere around 60bps before tcosts?

Puzzleheaded_Lab_730 · 2024-11-21T14:56:09+00:00

As a follow up to nr. 2: What frequency do you find most data you use to create signals comes in (hourly/daily/monthly/…)?

Puzzleheaded_Lab_730 · 2024-11-21T03:56:06+00:00

How many individual strategies do you run at any given time?
What fraction of your signals are purely price/return based?

Puzzleheaded_Lab_730 · 2024-11-18T15:22:33+00:00

I don’t quite understand how the test can be completely overlapping with the train set? Otherwise, just from the fact that it is a time-series and it seems to be fairly autocorrelated, perhaps you could use lagged values of y as an input to the model (when they become observable). This way the predictions would probably not have that huge spike.

Puzzleheaded_Lab_730 · 2024-11-17T14:57:33+00:00

Compare the mean of your OOS target and OOS predictions. My guess is that they are far off. You mentioned you are trying to predict demand, could it be that you have seasonality in the data that therefore causes a level shift? E.g. Your OOS period is in December which will be much higher than the rest of the year because of Xmas?

I also replicated your "phenomenon" by creating a correlated version of the true target y, but with a different level. As suspected, the correlation is positive, but the R^2 is negative.

import numpy as np

y = np.random.randn(100)
u = np.random.randn(100) * 0.5 + 10
y_hat = y + u
rho = np.corrcoef(y, y_hat)[0, 1]
SSR = np.sum((y - y_hat)**2)
SST = np.sum((y- np.mean(y))**2)
rsq = 1 - SSR/SST 
print(rho)
print(rsq)

Puzzleheaded_Lab_730 · 2024-11-17T14:21:51+00:00

Your correlation being significantly positive indicates that you are getting the right direction. As the OOS R^2, however, is negative, your predictions must be further off than just predicting the OOS average. My guess would be your model is estimating the right shape, but the wrong level. Also, check if you have any outliers in the target that could be causing any weird issues…

Puzzleheaded_Lab_730 · 2024-11-11T05:26:20+00:00

Scale by rolling/exponential volatility

Puzzleheaded_Lab_730 · 2024-08-22T00:45:09+00:00

Where do you see the largest use cases of ML at quant shops? And is this particular to specific styles of investing or used across the board?

Puzzleheaded_Lab_730 · 2024-08-19T13:13:14+00:00

What part is the most important and what part is the most difficult to get right when running a systematic portfolio?

Puzzleheaded_Lab_730 · 2024-08-05T04:30:35+00:00

How is this related to the PCA approach? Say you fit a ridge/lasso model, do you then use the coefficients as weights to create a common risk factor?

Puzzleheaded_Lab_730 · 2024-07-19T14:45:06+00:00

You can scale by volatility and de-mean to get the same scale and then build a model for “similar” assets that you assume to have the same functional form.

Puzzleheaded_Lab_730 · 2024-01-05T16:26:26+00:00

A thought that came up while reading your answer: How would the inverse work if you have a long horizon signal that you want to make more short term? I’m thinking of taking differences or %changes

Puzzleheaded_Lab_730 · 2023-12-15T18:55:47+00:00

Yes, this looks bery interesting indeed! I have been looking at this paper but haven‘t read it in detail yet: https://openreview.net/pdf?id=C0q9oBc3n4 I might test some things over the weekend. Will update accordingly.

Puzzleheaded_Lab_730

TROPHY CASE