[nxzero]

Never understood why anyone would spend time creating any trading method given even if it did work (possible, but unlikely) the SEC would audit you and then leak how you were making the outperforming returns.

Welcome any thoughts, in part because legally beating the market is possible, just don't get the SEC & OPSEC aspect.

[lordnacho]

Why would the SEC audit you? Just by random chance there are many people outperforming the market. They can't audit you just for outperforming.

If they do audit you, how will they discover how you are generating your trading decisions? Their remit is to make sure you aren't doing something illegal. There's no reason they would understand what you were doing in anything other than a superficial way.

Also, something can be profitable, and obviously so, without being easily reproducible. For instance there are firms that do simple footrace arbitrage on the same security between different exchanges. Not hard to understand, but you still can't do it. There's a whole spectrum of strategies that are on a frontier on the map of easy-to-understand vs easy-to-implement.

Besides all that, I think even if you were to learn about a way to beat the market, the way you found out might lead you to be very skeptical of whatever was proposed. If a guy is selling it on a website, you will probably not believe him, right? And if he showed you backtests that worked, you would suspect they were generated from a random generator of some sort. And if he then shows you the math, you would almost certainly find fault with it. Why did he do this or that transformation on the data? Must be random...

[nxzero]

Few years back, SEC started being very agressive about finding entities making above average returns; my understanding is that if over a set amount of transactions you're making over 30% that you will get "knocked" and the auditors have zero reason not to leak the information. Best example I know is the Walmart parking lot satellite imagery analysis; happy to dig up a link.

[lordnacho]

That sounds like weeding out insider trading, not finding people with legitimate market beating strategies.

If you're trading on confidential information, your profile will look very interesting indeed. You'll be trading near announcements, and you'll be right all the time. Your turnover vs profit and number of trades will be through the roof. By contrast quant shops with real models will be using the law of large numbers.

[Bromskloss]

I'd love a link as well as additional examples, if you can think of any.

[partisan]

Googling "walmart parking lot analysis" yielded the following as the first result.

http://www.cnbc.com/id/38722872

[Bromskloss]

Thanks. It has no information about the technique being leaked by the SEC, though.

[eggy]

Not sure about SEC leaking anything, but satellite data is a tool for investors to use satellite imagery, and image processing to see things like how many cars are in several big branches, make assumptions and correlations to spending, and then act on the information before Walmart releases a quarterly statement, sort of.

Number of container ships docked or leaving port around China can forecast trends in China's exports, again before any official numbers are made available. I don't see this as insider trading. You pay for the satellite time, you gamble on your data analysis, and you either win or lose. If it were certain, others do the same, and the edge is lost quickly by market adjustments.

[ozi]

The problem isn't coming up with an algorithm that works (i.e. more wins than losses).

The difficulty is gaining confidence in your algo and determining when to move from paper trading to actual trading.

You run into counter-intuitive things while training a neural net, for example. You'd think more training data would be good, but when training neural nets, you actually want to use as little data as possible while still creating an ideal ROC curve.

[0xmohit]

> The problem isn't coming up with an algorithm that works (i.e. more wins than losses).

An algorithm that works would also include the ability to limit losses. An algorithm might be correct 9 out of 10 times, but may lose more in a single transaction than what it earned in those 9 winning transactions.