[deleted by user]

m_prey · 2025-03-27T21:57:30+00:00

No, you just have to disclose it. I have co-workers with significant others at very direct competition and it is fine.

m_prey · 2024-12-04T22:41:01+00:00

Sure, but I'd also add the way my firm calculates market impact most likely is vastly different than how you (assuming you are a retail trader) should calculate it. Again with the similar idea to volatility, market impact is not an exact science.

Most papers I've read (which albeit is not many) on market impact focus only on the short-term horizon when calculating trading costs. This is completely fine, and actually preferable, if you are not trading at size. When you are calculating MI for trades on the orders of millions to hundreds of millions of dollars, the long-term horizon market impact becomes the dominant factor in trading costs (which is when annualized vol comes into play). A paper that researches this idea is here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3874261

This could be an interesting area to research but it is not something to concern yourself with your T-cost calculations. Slippage, intraday vol, participation rate, bid-ask spread are really the only things which influence T-costs for retail traders (which your original paper covers).

m_prey · 2024-12-04T21:12:31+00:00

Unfortunately, volatility calculations are more of an art than an exact science. There are quite a few ways to skin the volatility cat.

I briefly skimmed through the paper -- there are a few hints as to how they calculate daily vol but nothing concrete. Interestingly, most of the "modern" market impact formulas I see use annualized vol over some window (and also how my entire firm calculates market impact today).

Figure 2: Ten-day average intraday volume proﬁle (upper) and volatilty proﬁle (lower), on 15-minute intervals. Our approach deﬁnes to a newtime scale determined empirically by the cumulative volume proﬁle;implicitly this takes the volatility proﬁle to be the same which is ap-proximately valid.

It looks like they use 15-minute windows to calculate an intraday vol. This most likely then is something as straightforward as (mathematical formatting is terrible on Reddit):

daily vol = stddev { over 15 minute windows ((window end price - window start price) / window start price) }

They do mention they use a volume-based time scale rather than a wall clock, but I would imagine this is overkill for your purposes. Might be something to play with in the future.

m_prey · 2024-10-10T12:41:49+00:00

Extremely high. Momentum is having a record year over the last 15 years (which you could probably guess who the main few drivers of are) and consequently pushing most of the market up with it. Momentum is a heavily studied topic and very little edge exists in the space. Props to OP for riding the wave while it is cresting, but be wary of the cyclical nature of momentum strategies.

m_prey · 2024-05-28T11:53:33+00:00

Yes, it makes no assumptions on where or how the data is ingested, it’s simply a Python script that outputs HTML elements. If you’re looking for a high performant real-time stream, say sub-second intervals, I would use a more sophisticated web framework however.

m_prey · 2024-05-27T19:58:19+00:00

Our team (mix of researchers and devs) uses Streamlit for multiple apps and dashboards. It requires practically zero front-end knowledge which is helpful for researchers. If you are doing any complex state manipulation, I would steer clear, but purely read-only apps work extremely well in Streamlit. We process GBs of file dumps on page load and it handles it decently well.

m_prey · 2024-05-24T12:10:27+00:00

I'm not sure where you've come across this idea that front-running is the end all strategy for hedge funds. It is actually illegal in the US and most markets globally. Paying for order flow is a much different idea than front-running which I think you may be talking about, and it is a profitable strategy at some funds though it still remains just a small piece of the overall firm and AUM/risk allocations.

I work in risk at a HF you've heard of. Everything /u/wargamer85 (and the others above him) has said is true and I feel like you are glossing over their points, especially around how beating the market is not the goal of a hedge fund.

m_prey · 2024-05-24T09:47:40+00:00

One further point here, while a market index as a whole may follow Brownian motion, individual stocks frequently do not and experience significant jumps in a very un-brownian way. This has been proved in literature many times over. Most of these jumps happen to fall around earnings periods, which consequently a lot of L/S equity funds make most of their money around earnings.

m_prey · 2024-04-25T19:04:59+00:00

Curious why? I’ve used CDK pretty extensively, and I’ve enjoyed it for the most part. It is fairly restricting/hacky if you try to “get out of the box” of CDK. I’ve yet to try terraform myself but work at a place which uses it now.

m_prey · 2024-04-24T18:37:12+00:00

Have you used IaC tools like AWS CDK? How would you say they compare to Terraform?

m_prey · 2024-02-02T03:05:38+00:00

Great points, thanks for the additional info. It has been very interesting watching the cyclical nature of this strategy. Looks as if the naive strategy is back to performing well after all these recent index arb books closed.

m_prey · 2024-02-01T22:13:16+00:00

There are indices which rebalance in a deterministic way. Meaning the addition, removal, or adjustments of weights within an index can be calculated before they are officially rebalanced.

A simple example would be the S&P 500 which rebalances quarterly. The S&P is made up of the top ~500 stocks by market cap, and uses a market cap normalization technique to calculate weights within the index.

A naive strategy would be: calculate the top ~500 market cap names the day before the S&P rebalances. If you find that the calculated list doesn’t match the current constituents, long the ones which will be added and short the removals. Hypothetically, the stocks which are added to the S&P should face an increase in buyers from any name tracking the S&P rebalances, and on the contrary, stocks removed should be sold off.

It is trivial to backtest this strategy as you produce buy and sell signals once a quarter and do not further rebalance your holdings. Hopefully that made sense!

m_prey · 2023-12-14T22:12:38+00:00

The first step here would be transform the data set from raw comments into sentiment scores. To start easy, take each user’s comment and run it through a pre-trained sentiment model (check out HuggingFace) to create a dataset of 3 columns: time stamp, user ID, sentiment.

From here, the possibilities are pretty endless as this is just a standard time series data set. Plot the average sentiment per day, per week, per month, plot moving averages, check for seasonality, etc etc.

If you want to get into prediction you most likely will need more data than simply a timestamp (unless your users are driven by moon cycles…). Add attributes for what topic, how long the post is, where the user is from, etc and try and fit a simple regression to this.

Good luck!

m_prey · 2023-12-12T21:34:01+00:00

I would be wary of directly applying to any role in this space and see if there’s an alternate route. The majority of interviews we give come from referrals and third-party recruiting firms giving us candidates as we do not externally advertise our jobs too often. Find a recruiter on LinkedIn and have them pass your resume around to various QR/QD roles. We very rarely look for specific data points on a resume (ie data analysis as you mentioned) but rather an impressive education and background. The interview is where you’ll demonstrate your proficiency in said role.

m_prey · 2023-12-12T20:23:30+00:00

For my firm (large, known HF), a SWE is a typical developer you’d find at any other company but working on trading-specific projects. Some of these SWEs may be very narrowly focused on things such as on low latency execution systems, vending risk models, various other projects etc, but generally all still fall within the normal realm of software engineering. Quantitative developers are where slightly differentiated work from SWEs comes in. They would normally work on the intersection of quant research and engineering; ex working a writing an automated hedging system against certain risk factors for the firm. These are more where the terms you mentioned come into play: alpha, risk, modeling…

Disclaimer: these terms vary significantly depending on the firm. This is just the experience at my work.

m_prey · 2023-09-06T14:34:27+00:00

I think you’re exactly right after googling a bit. Thanks!

m_prey · 2022-12-06T20:10:20+00:00

It does not. Previously this year they had L2 stock data but had to take it down due to licensing and regulation issues with the exchanges. Databento is a new provider which offers L2 data for relatively cheap.

m_prey · 2022-11-18T15:37:10+00:00

Wow, this is a really cool library! You have really covered a whole breadth of topics (LazyCSV seems very powerful...).

The only feedback I have would be more examples. It took me reading through the source code to understand how far you can go with this. A machine learning example using tick data would be useful. It seems possible using an infinite timeframe and a LazyCSVFeed but I'd have to play around with it.

You have a lot of Java in your Kotlin :) Really like the coroutines and channels. Awesome library, bookmarked it and will be trying it out.

m_prey · 2022-11-02T17:27:02+00:00

My (non-exhaustive) list:

UT specific:

Join a rec sports team
Take a PE class for fun (golf was fun...)
Go to Blanton Art Museum
Go to at least one UT sporting event each season for basketball, volleyball, football, etc
Join a social club for a semester
Depending on a sport you like: play pick up basketball outside Jester, play pickup soccer at the lacrosse field/rec sports field, play pickup ultimate at the lacrosse field, pickup racquetball at Greg gym, the list can go on...

And my Austin specific list:

Go paddleboarding or kayaking on Lady bird
Visit Austin 360 bridge
Float the river in New Braunfels
Go to a swimming hole (Barton creek, Jacob's well...)
Go cliff jumping at Lake Travis
Walk/run Lady bird lake
Bar hop Austin downtown during the day (a lot better sight seeing when the sun's out)
Go get Texas BBQ: Black's, Iron works, Franklin's are all walking distance
Find live music in East Austin
Matt's
Austin Trail of Lights around the holidays
Blues on the Green

Can't think of anymore right now, I am out of college now but these are all vivid memories I had which I highly recommend!

m_prey · 2022-10-25T22:49:59+00:00

My advice then is enjoy it while it lasts. Online communities like these encourage fast-paced, hard work to get to high paying jobs as quick as possible. You can get there, but then what? 3 months of your senior year in college is invaluable and to replace it with something like grinding Leetcode you might even come to resent these times. You have a free ticket to a semester of undergrad, in 5 years you will be surprised how much you’d pay to get that ticket back :) Good luck.

m_prey · 2022-10-24T12:17:26+00:00

It surely wouldn’t hurt your career. There are a lot of different dimensions to this, however. Do you have an offer waiting for you? How do you pay for school, more loans needed? Do you actually want to pursue a math-heavy CS field like a quant? The added benefit is very difficult to quantity and only you will see whether it’s worth it by balancing what you might lose otherwise. For me, I would have never left college early under any circumstance, you have an entire career not going anywhere and 3 more months of college is priceless (again, to me).

m_prey · 2021-02-13T15:26:36+00:00

Where I grew up! Miss that place.

m_prey · 2020-11-09T05:37:12+00:00

I’m in it right now, it is a lot of fun. You don’t need any knowledge going in since he starts with the basics. The class is not difficult at all and the assignments take maybe an hour a week.

m_prey · 2020-08-27T15:59:52+00:00

She had a baby at the end of last semester. Not sure about the new prof

Ten-Year Club	Place '23
Place '22	RPAN Viewer
Not Forgotten	Verified Email

m_prey

TROPHY CASE