Here's my understanding of what's going on:

He has a dataset that links companies to each element of the periodic table (Intel would likely be highly linked to silicon, while not as linked to nitrogen as a fertilizer company). He then uses some clustering and whatnot to produce groups of stocks he is trading as a unit.

How he picks what to trade is kinda hand wavy (Is he trading all baskets and only reporting the good one?). He also mentions using Google trends data, which might be some signal for his clusters. Given the lack of detail, my gut reaction is to assume he made tens of these strategies and has kindly reported the best one to us.

At the end, you see he is a financial consultant hoping to build his resume up through posts like these.

So the author was able to beat S&P500 by 10% over a period from June-2016 to June-2017 using this solver. The most important question is whether the same underlying relationships will hold true for 2017-18.

This seems like a classic example of hindsight. There are many things one can tell on hindsight when the results are out. The key is if they will hold for the future too. Am I missing something?

It needs to be tested in both bull and bear markets. His time period was all bull (S&P +22%). I've been beating the S&P on a dividend play, but it's all in an up market as well, and the upward movement may be amplifying my choices on the positive side. A down market may have the opposite effect, and amplify on the negative. Also, I may have just hit a market rotation at the right time, which is not repeatable for me (since I don't know how to predict it). So I don't trust my results unless/until it also outperforms in a down market.

This is very much of how NNT taunts some reporter trying to explain the causality of Intra-day fluctuations of a Share. Humans obsession with establish wrong attribution in an attempt to express their domain expertise never expires! Now Data Science fuels it!

Yeah an the fact that he starts the post with "I finally beat the S&P 500 by 10%." suggests he has basically just tried a lot of techniques on historical data until he found one that worked. Almost certainly luck.

It doesn't matter if you can do it once. You have to do it consistently over years, which is not possible. I know about Renaissance Technologies, but I suspect they have insider information or some other angle and it's not all about their algorithms.

That is a rather large accusation. Can you restate that as something constructive or helpful to the conversation?

I didn't interpret it as an accusation. I interpreted it as he wrote: a suspicion that something other than RT's algorithms is a non trivial contributor to their success. He gave an example of insider trading, but it could be something else ranging from the equally sinister lying about outcomes (e.g. by creative accounting of what counts as a return) to the less sinister better access or timing to information anybody else has that makes the algorithms more effective and far less relevant, and many other things.

His other point about the rarity of regularly beating the market certainly supports some amount of skepticism of RT, in my view. It's extraordinary, and it's an extraordinary claim that suddenly the particular talent at computational trading at RT is better than the legion of super genius other quants and techies who have been doing this for years.

Disclaimer: I haven't a dog in the fight with respect to supporting or taking RT down. As I see it, being a small time player, my trading strategies are necessarily constrained to the point where RT's methods, legit or not, aren't applicable. So whether they win or lose doesn't make a difference to me.

They, and other hedge funds, apparently classify their earnings in a way the IRS objects to.

http://www.reuters.com/article/usa-irs-hedgefunds-idUSL3N0ZP...

Do you understand investing at all? People have been attempting to "beat the market" for decades and nobody has been successful. There is always the "reversion to the mean" issue in every case. People with algorithms think they are beating the market then something changes and their algorithm no longer works. Except for Renaissance--they have consistently been beating the market, or so it appears.

Does that help?

No, it doesn't help all. You have yet to provide any evidence for your accusation of insider trading (or "something else"). You can't really accuse RT of wrongdoing, just because you don't understand their methodology.

Also -- if I were a quant that managed to beat the market consistently, I would shut up and go straight to RT, for two reasons. To avoid toxic dubious comments like yours, and because RT can pay me better than your standard hedge fund.

Except that Renaissance trading doesn't really want to hire quants. They wish to hire scientists and mold them in the Renaissance way, which is something you'd know if you were a quant.

http://www.reuters.com/article/simons-hedge-idUSN21355752200...

It's a good article, but it's all that you will find about them, no real details on how they are beating the markets over and over again. It's simply not possible to be that successful for so long. The article states that some experiments succeed and some fail and that their strategies peter out over time and they have to develop new ones. Okay, but never booking a loss?

Speaking as a former quant myself (1 year), I believe that there are transient patterns in the market.

The challenge is that those patterns appear and then they disappear mostly forever because they are frequently created by someone else's mistakes like Nassim Taleb buying way out of the money put options that never really created any significant profit. Free Alpha, get your free Alpha, right there.

To tap into those patterns while they are profitable, you need a lot of smart people, and a great deal of infrastructure for experimentation and delivery of strategies that can tap into those patterns before they disappear. While you're right that it is possible that they cheat, it's also possible that they have a sufficiently sophisticated infrastructure to actually make this work. It's kind of like how Nintendo only ships 1 out of 3 video games they develop. All IMO of course.

It is not toxic nor dubious to point out the outlier and wonder why.

He simply asked you to backup your claim, which you don't seem to be able to otherwise you would have. Throwing around a "yeah they can do it, but they must be cheating" isn't exactly constructive.

I have no dog in the fight, but generally it is the extraordinary claims that need backing up.

Consistently beating the market would indeed be an extraordinary claim, because we havent seen that a lot (if at all).

Do you know the story of LTCM? If not, do not lecture me on this subject. I'm sure in time I'll be proved correct. I'm sure you've heard of Bernie Madoff and how he "beat the market" for so long, yes?

I don't have to "back up my claim" the math does it for me already! You're asking me to prove a negative--instead, show me one example of a firm that has beaten the market without resorting to funny business and I'm all ears.

I work at a hedge fund. I may not write the trading models, but I'd like to think I'm not totally ignorant.

So does your hedge fund beat the market every year like clockwork or do you have losses sometimes?

AlexTheParrot--then home come they are the only one?

I'm unsure what to make of this.

I can't help but feel that this is noise multiplied by noise. Regression to the mean ought to he coming up real soon.

This is systematic semi-blind guessing at what other people will guess in the future are good investments. It sprays investment capital equally at companies that are not in any way equivalent. Hypothetically, just because two companies both have names that begin with "Z" and sell product on Amazon, that is not an investment thesis for treating them alike. Treating them alike is a recipe for misallocation of capital, in the long run, which is what it seems sometimes we have in this economy in spades.

I don't get the strategy here. What's the investment thesis? Are they collecting news and trading on it? It doesn't seem to mention that.

I'm skeptical of the starmine dataset[0] being used:

"The dataset is based on relationships between elements in the periodic table and public companies."

This description is a bit vague.

[0] http://starmine.ai/datasets/dataset_builder.html

I'm having trouble deciding if this whole post is a sly commentary on how easy it is to get machine learning wrong.

Beat the market by 10% with k-means clustering and a feature set derived from companies and chemical elements! Hahahaha no.

I know, right? Now mapping it to the sequences coming from the numbers stations? Totally different story!

If this approach works, is it wise to be sharing it publicly?

Is there a danger than whatever gains you were able to capture with a unique insight will end up being shared with competitors?

Sharing it publicly is the author saying it does not actually work as he claims it does. You don't write a blog post on a method you created to make guaranteed money, you use it.

A couple years back there were some traders talking about trying to implement a really fast version of K-means for clustering stocks that are transiently correlated on some sub-reddit. I wonder if this guy is the one who started that thread.

Here's how this always goes: Someone thinks they have a killer algorithm and it works and makes them money...for a time. Then it suddenly doesn't work anymore and they lose more than they gained. It is not possible to beat the market over the long term unless you are trading on insider information, somehow manipulating markets, front-running orders, or have some other edge that is not universally available. There is no Python script that will simply print money for you year in and year out it doesn't matter what data you feed it or how smart your AI is.

Why is it impossible to beat the market over long term without insider information?

Because by definition in order to make money you are abusing a market inefficiency. If you use this inefficiency, you're effectively shrinking the inefficiency to closer to nothing every time its used.

I would argue that this scenario would actually be a positive for the author. If others start using the same methodology as him on a large enough scale and trading on the same data sets they would buy when he buys driving prices up.

On the other hand when he starts selling and others sell as well speed is a critical factor as the downward pressure will make the later movers the biggest losers.

It's just SEO.

Is this not a parody, making a joke about how sensitive to supervision "unsupervised" learning methods are?

If it's not a parody, it's one of the most unintentionally hilarious things involving data I have ever seen presented.

To anyone who takes this seriously: I have a price of butter in Bangladesh indicator which works REALLY WELL on the S&P500.