Returns don't matter. People always compare strategies to the S&P 500's return, but it doesn't make any sense. In finance, strategies are compared on their Sharpe ratio, not on their returns. This is because leverage can be applied to make the returns better. For example, if you had a strategy that, unlevered, returned 5% with 2% volatility, that would be pretty amazing. If you wanted, you could lever up 4x and get around 20% return and 8% volatility (though, it's not that simple, since you are going to pay a volatility tax, but we'll ignore that).

Another thing to look at is correlation to the market. The less correlated to the market your strategy is, the more valuable it is. This is because investors like uncorrelated strategies. For example, lets say you have n strategies, each with a volatility of sigma and mean return of mu. Allocating all of your money to one strategy or two or all of them won't change your return, it will still be mu. But if the strategies are uncorrelated, and you equal weight each one, your vol will be sigma/sqrt(n) and your return will be mu. This is the essence of Modern Portfolio Theory (MPT): add as many uncorrelated assets that you can.

In no particular order, here's a list of things that matter when evaluating an (equity) strategy: turnover, size of alpha being exploited, Sharpe ratio, correlation to market and sector, correlation to style factors (value, momentum, oil, etc), and net exposure (long or short).

I mentioned the idea of adjusting for risk in the GP post, though you are correct that I didn't call out any specific measure like the Sharpe ratio by name. If your risk-adjusted returns are worse than S&P 500 then obviously leverage isn't going to fix that problem. Simply put, I don't think OP's strategy really has any of these desirable features like a good sharpe, market neutrality, low exposure, etc. I think OP is just naively building a portfolio based on price predictions extrapolated from price data, and it happens to work in a bull market. Since OP probably doesn't have the resources to really evaluate risk (good simulation tools, good historical data sets, & even the industry know-how of how to look at risk) it seems rather meaningless to hear "I used to have very good returns."

Apologies if I misinterpreted your comment, just my thoughts when reading it.

I guess all I'm saying is that a strategy that has a worse Sharpe ratio than the market but zero correlation could still be valuable to a lot of investors for the same reason that sometimes it makes sense to add a asset to your portfolio that lowers your expected return. It's possible that that one strategy with the worse Sharpe ratio when combined with your pure beta investments would yield a portfolio with a better Sharpe ratio than either allocation alone.

That's an interesting point that I didn't really consider. Thanks!

I think you're comparing apples to oranges here. These funds manage billions of dollars of client money, which forces them into highly liquid markets with scalable strategies. That's quite different from how individuals or smaller prop funds can operate, trading off capacity for higher returns by trading in less liquid markets or with strategies that are "not worth it" for large hedge funds. If you must manage billions of client money then you are right in terms of competition, but as someone who only trades his own capital, you can see a lot higher returns.

>These funds manage billions of dollars of client money, which forces them into highly liquid markets with scalable strategies.

Obviously this is true, but I think you're missing the point. Trading with ML on price data is a strategy that literally anyone can reproduce and, as is evident by reading the comments in this thread, is something that many people have tried to replicate. In that context, everyone using that strategy is effectively acting as a large fund. Further, a large fund or prop shop can deploy small-scale strategies, I think the limiting factor really tends to be leverage. But if they are just trying to make 5% returns for example, they can deploy a lot of small strategies that make ~5% returns. And that's not mentioning the countless tiny shops operating under the radar trading <10-50 million AUM (really, I think the average fund is much smaller than what you would imagine). What I'm getting at is that there are a lot more market players than the "big guys" and they will either have an equivalent strategy to you or will be better equipped to take advantage of that same alpha because of more capital/better data sources/smarter stats. With that in mind, it seems insane to suggest that you can find significant alpha in such a low-hanging fruit.

Remember that you are trading during one of the longest bull markets in history. It's not hard to make good returns, but it is hard to analyze risk. There are a million and one ways to make 100% y/y, but a fraction of a percent of those will continue to work in the long-term. With a black-box model you cannot properly assess risk. Even with well-understood models, this is something that real industry players struggle with: backtrading alpha != simulation alpha != profitable alpha != long-term alpha.

Also considering you're the OP & are trying to argue in favor of this type of trading, it would be very informative to disclose what kinds of returns you actually made. It's hard to expect people to listen to your opinion in a game where everyone successful is motivated towards secrecy.

Agreed. Many smaller, yet successful Hedge Funds limit the capital they manage for this very reason. Some strategies just don't work at certain scale.

If the above is true, why would a fund not just allocate a small amount of resources to trade on OP's strategies. Either:

(1) OP's strategy performs worse than the alternative (2) They already do this, and have resources that allow them to outperform OP at their own strategy

If the returns are really meaningful, i.e. better Sharpe ratio than just holding $SPY or some dead simple strategy like that, then (2) must be true at least _somewhere_.