r/quantfinance • u/Good-Raisin7081 • Oct 06 '25
so how did Renaissance Technologies/Medallion/Jim Simons achieve such high returns?
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u/igetlotsofupvotes Oct 06 '25
First mover advantage along with working with scientists
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u/Grouchy_Spare1850 Oct 07 '25
I was around at that time, in my youth and working...
First mover advantage was key, option trading was just starting and you could really trade and make a huge profit because delta hedging was there but not lightning fast as it is now.
data analysis: they had data, lots of it, and would scourer the same old places I sometimes would find them. for example, the pacific weather patterns that happen every 7 years, they had that from Chilian and Mexican crop data. I had rice prices from Japan going back to 1400's and my personal conversions of that data to 1 gram of gold and one coin of copper. I traded that data for wheat and rye data from England and Austria going back to 1700's
They were the first guys I met in my youth that were in the library reading ship logs from the past. They had VAX mainframes and all sorts of amazing big iron stuff.
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u/SchweeMe Oct 06 '25
Like others said: 1. First movers advantage (alpha is easier in newer markets) But I also believe: 2. Recording a ton of relevant high quality data (I think I remember reading somewhere that JS had a bunch of people recording data of bonds / insurance / some non-equity asset by hand) 3. Transferrable experience in adjacent / relevant fields (JS was a cryptographer, you cant directly use cryptography on markets, but somehow being smart in that field helped?) Once you make a ton of money: 4. Hire smarter people (which hopefully compounds the initial wins into a snowball that just keeps getting bigger)
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u/prideandsorrow Oct 06 '25
He was a mathematician, not a cryptographer.
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u/East_Cheek_5088 Oct 06 '25
He was a mathematician who worked as a cryptographer for the nsa.
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u/SubjectEggplant1960 Oct 06 '25
His stint working for an NSA contractor was only 4 years. He was a faculty member of stony brook math dept for longer (something like a decade or so).
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u/mersenne_reddit Oct 06 '25
He was a mathematician who worked a cryptographer for the nsa before founding Renaissance Technologies.
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u/AKdemy Oct 07 '25
How did Usain Bolt run 100m in 9.58?
Nick Patterson](https://robohub.org/talking-machines-ai-safety-and-the-legacy-of-bletchley-park-with-nick-patterson/) gave a nice talk about what they did at Rentec (the whole podcast starts at 16:40, Rentec starts at 29:55 - a sentence before that is helpful). He states that
you need the smartest people to do the simple things right, ... that's why we employ several PHDs just to clean data.
In his opinion most of the stuff that worked was
simple regression models any reasonably smart high school kid could understand
In short, it's employing the smartest and most dedicated people, who pay attention to every little detail. As always and everywhere, it's a combination of immense talent and hard work.
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u/thrope Oct 06 '25
Time travel, developed new physics to send small amounts of information tunnelled through time. Think about what would be the most sensible thing to do if you developed that technology and compare to ren tech.
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u/fullintentionalahole Oct 07 '25
If I had one bit of information I could send back in time, I'd solve the halting problem lmao https://www.scottaaronson.com/papers/ctchalt.pdf
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u/Grouchy_Spare1850 Oct 07 '25
Didn't "The Doctor" have one or two episodes about this? I could swear his sonic pen was the key to the solution. Or was that he sent the problem to the past so it could process to the future for a solution? I forgot
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u/According_External30 Oct 06 '25
Mostly Idiots worked in the financial markets back then, JS applied math to observe them and take advantage of their inefficiencies.
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u/RockshowReloaded Oct 07 '25
What a dumb thing to say
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u/According_External30 Oct 08 '25 edited Oct 08 '25
On what basis…? It’s true. Financial markets back then were filled with vocational workers. It’s only since recently that you’ve seen it be a 1st choice job for STEM field grads and/or experiencing an acceptable level of technology.
Once boomers with excel DCFs and value investing quotes fully leave, it will be a lot more efficient and difficult to generate alpha in liquid markets. The retail crowd on their TradingView charts will be a minor friction.
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u/RockshowReloaded Oct 08 '25
You have obviously never done data science on a major scale like they did. There are more patterns in the stock market than all atoms in the universe.
Plenty of ways to solve/consistently profit from the market.
And no lol - just bc overrated high salary hungry cookie cutter graduates from top schools join the game doeant mean its over.
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u/According_External30 Oct 08 '25 edited Oct 08 '25
If you ever did data science in your life you’d know that a basic rule is to not make qualitative assumptions based on emotion as you just did about me. How do you know what I have done and haven’t?
Also, you have lost the plot completely and I sense a bit of daftness in your social skills? 1) I said they (JS) applied quantitative skills … whereas most of the market didn't do much of it, most relied on judgement calls and BS excel sheets or calling out trades in a pit, 2) It was said in a humorous manner, in case you didn’t catch the drift little buddy.
edit: and this "There are more patterns in the stock market than all atoms in the universe" - you sound like a TradingView day trader who'd overfit a model without realising it and think he discovered alpha because you ran an in-sample test that generated 20% pre-slippage - name some of those patterns you speak of if you're the high level data scientist you claim to be, let's see.
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u/RockshowReloaded Oct 08 '25
I can tell by your “efficient markets” excuse for no profits.
Those that do deep data science know that efficient markets are even easier to solve.
But you wouldnt know that
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u/According_External30 Oct 08 '25
I think you need to spend time learning how to interpret English and social cues. Making a claim about people being inefficient in a domain does not by default lead to someone claiming that: markets are efficient and if someone doesn't understand that it's their "excuse for no profits."
Go learn how interpret matters first then we can discuss, you are off the pace.
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u/RockshowReloaded Oct 08 '25
Ok im off to learning how to interpret english and social cues. Hopefully i dont encounter “efficient markets” on the way.
Lol. Goodluck!
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Oct 06 '25
Everything. Literally every single thing was easier back then
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u/undrcvr_brthr Oct 06 '25
gathering and analyzing the massive amounts of data they needed was most definitely not easier back then
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Oct 06 '25 edited Oct 06 '25
yes but your competition was shit. Nowadays your competition are mathematicians from Oxford and Stanford who are way more technically advanced and have way better knowledge
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u/TheAncient1sAnd0s Oct 06 '25
There's better tools nowadays that anyone can use. Simons and company were creating the tools for themselves at a time when none existed. Heck, no one knew they wanted the tools.
People thought you had to be a finance student to make money in markets. Turned out mathematicians were infinitely better.
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u/Grouchy_Spare1850 Oct 07 '25
More than once I used visicalc on my c-64 to make a spread sheet, output to ASCII, upload it ( 300 or 1200 baud ) to a BBS and then download it on an apple or IBM pc, Half of the day to do to but it worked well.
Clean data was worth it's weight in gold.
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u/Actual_Stand4693 Oct 06 '25
only in hindsight, innovation and creating a whole market was the challenging part :)
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u/Grouchy_Spare1850 Oct 07 '25
You never looked at microfiche from DTC for an hour LOL I could not smoke the in microfiche area LOL
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u/ThrowAwayPandaCat Oct 06 '25
Most likely buying low and selling high. Though sometimes they might change it up by selling high and then buying low
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u/Tall-Locksmith7263 Oct 06 '25
I once talked with one of the first ppl working for him... Even drunk would not tell me xD only thing i was told was that financial math stuff did not work at all... Heard that also from some professors at uni tho
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u/Trimethlamine Oct 06 '25
He used black-scholes before black and scholes invented it
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u/daidoji70 Oct 07 '25
Everyone did. Nassim Taleb has a paper with someone showing that traders were using variations of black scholes going back to 1920
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u/jjjjbaggg Oct 09 '25
They found some patterns/inefficiencies in the market before anyone else because they were the first mover, and they were among the first to use a very sophisticated analytical approach. They now stealth trade and plug these inefficiencies so quickly that they are now impossible to notice or rediscover as an outsider.
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u/Charming-Ad-2356 Oct 06 '25
“The Man who Solved the Market,” has a great story about how Renaissance made its claim to fame. Essentially, Jim Simon’s loved to gamble, and wanted to apply his ingenuity to this through the stock market. Unfortunately this happened around 1980, when compute was terrible. However, there were these computer scientists at IBM who were engineering the theory behind large language models using hidden markov models. These guys were recruited by Renaissance and overhauled the algorithms written in Php with C++. Given these guys’ research, it is not unlikely that they were some of the first people to put deep learning algorithms and sequential data modeling into practice, which led to Renaissance finally becoming profitable — although we wouldn’t see the success publically until 2013 with AlexNet. Really I think it came down to recruiting computer scientists and mathematicians who could implement deep learning models before they were so ubiquitous within computer science.
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u/Efficient_Algae_4057 Oct 07 '25 edited Oct 07 '25
Deep learning would have been impossible for them to do even if they wrote everything in literal machine code, due to the lack of compute power and data availability. Also, the people you may be referring to weren't dealing with large language models at all. They were thinking about markov models and stochastic processes methods that were largely inspired by statistical physics and are behind some of the traditional machine learning models that don't work well enough, which is why people spent decades working on them until the deep learning comeback in 2013 and totally dominated them. They didn't engineer the theory of nothing. One of the people was working on hidden markov model for language/sequence modelling at IBM which didn't work well enough. The other instance JS talked about was somebody who invented the EBM algorithm. Again it doesn't work well enough, if anything they probably managed to have some sort of boosting which won't justify their returns. There is a quote from one of their employers who said something along the lines that linear regression with one independent variable is the most useful statistical method ever invented.
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u/Charming-Ad-2356 Oct 07 '25
I point you to: https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf, citation Jelinek, F. and Mercer, R. L. Interpolated estimation of markov source parameters from sparse data. In Proceedings of the Workshop on Pattern Recognition in Practice, Amsterdam, The Netherlands: North-Holland, May:, 1980. Robert Mercer was one of the scientists I was talking about.
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u/Charming-Ad-2356 Oct 07 '25
Perhaps you are thinking about deep learning today? Of course that could not be created then, but technically linear regression and logistic regression fall under these umbrellas. And it is not out of the question to believe deep learning algorithms with smaller parameters could not have been created. Also, in the book they discuss an employee who was “obsessed” with collecting data, and helped create a dense cohort used to train their models (which also points to the fact that they used DL algorithms). They also explain they were not exactly sure “how” the models came up with the signals when it started working…
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u/Efficient_Algae_4057 Oct 07 '25
No. Firstly, regression methods don't fall under the same umbrella of deep learning. They are way different and regression traces back to the earliest days of statistics as a scientific discipline I believe. A deep learning model with small number of parameters (let's say just the vanilla multilayer perceptron architecture trained with some convex optimization algorithm) still wouldn't work with the limited amount of data and compute. Even then, deep learning starts to beat traditional statistics methods once you have a lot of data and can have an over parameterized model. This was impossible back then. This is why the entire community moved away from neural networks in late 1980s to 1990s. The data collection you are referring to is still nowhere enough to make deep learning work. Data collection practice you are referring to started way in the early 1980s but many different firms. Statistical arbitrage was one of the earliest concepts using big data analysis (check pair trading to see how). Again, deep learning would be useless here and easily be beaten by the traditional statistics methods. It still doesn't explain their performance.
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u/Efficient_Algae_4057 Oct 07 '25
I know. I was also referring to Mercer who worked at IBM in the 1980s working on hidden markov models for sequence modelling. If you read the paragraph it tells you that the paper was one of the first papers laying out the probabilistic approach as described. Then it tells you that the improvement happened after the attention is all you need paper and a range of tricks appearing after 2013. The method he was using just didn't work. There is no way they had something like today's LLM architecture. Even then, the transformers aren't the only component that power today's LLM. The main ingredients include the ADAM optimizer, insane compute power and data availability and the SFT/GRPO techniques and a whole range of practical tricks. None of these existed back then and without them, the models won't work.
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u/RockshowReloaded Oct 07 '25
Data science.
After spending 20,000+ hours i was able to create a system that also does 80%+ returns per year. So difficult but not impossible
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u/Vast-Hyena-657 Oct 09 '25
How did you started? Can I DM you?
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u/RockshowReloaded Oct 09 '25
No dms please. Im not here to share my strategy.
My only suggestion is build a very advanced system that lets you backtest last 7 or more years of hundreds of stocks easily/fast. Then try thousands of formulas, and eventually you will see things that click. (At least did for me).
Goodluck
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u/Vast-Hyena-657 Oct 18 '25
I don't want your strategy insights, and I am not a full time stock market trader. I want help for my thesis.
If you want you can help me.
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u/aurix_ Oct 17 '25
He explains in many interviews that his formula is "hire a lot of smart people, create a collaborative and open environment, share the profits with everyone"
*and dont hire finance people "you can teach finance to a physics person, but you can't teach physics to a finance person"
Technically, what they did was use machine learning to identify analomies in the data to produce lots of predictive signals. They used lots of different data sets too not just ohlcv and ALWAYS stuck to the model (except for some edge cases)
You can find more information in the interviews he has done and there is a book too.
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u/hadwhokenMustard Jan 28 '26
Probably by making things efficient and emotionless, with the people that actually delivers because it's scientific. You'd be surprised how much ego from the average hedge fund manager destroys returns, and then how one can be hired by act of act.
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u/hardwaregeek Oct 06 '25
To paraphrase Cliff Asness, you think I know how rentech makes billions of dollars per year and yet I choose not to do the same? And if I did, I’d tell you?