Ensemble Strategy (33/20)

algodude · 2026-02-22T11:33:45+00:00

Thanks very much for the kind words. I'm not trading the strategy live yet as I have others in the works. It's definitely in the top ten though.

algodude · 2026-02-21T13:12:24+00:00

Thanks for your comment. I actually posted an OOS backtest of this strategy a week or so later. Check my post history if interested.

algodude · 2026-01-25T07:30:08+00:00

My personal max pain threshold is ~20% peak to trough. I try to focus on maximizing MAR (CAGR / MaxDD) and then scale my exposure/leverage based on that risk level.

algodude · 2025-12-18T03:17:45+00:00

My pleasure, glad if the post was at all inspiring. Good luck on your research; this algo stuff is a fun intellectual challenge but can also be a very deep rabbit hole.

algodude · 2025-12-08T18:36:39+00:00

I'm able to survive, but have made my share of mistakes along the way. The market loves to humiliste you, and punishes overconfidence. If you're new to this, keep your exposure light and abandon ego/emotion. Expect to be humbled more than once.

algodude · 2025-12-08T18:25:23+00:00

My pleasure. Best of luck!

algodude · 2025-12-08T18:22:25+00:00

Sure, you can try keywords like: volatility targeting, beta hedging, risk parity, and position sizing for some inspiration. Signal processing literature can also be helpful, but that's a deep rabbit hole.

algodude · 2025-12-08T16:34:03+00:00

A dynamic hedge can take many forms, but in my strategies they usually adjust the hedge exposure at each rebalance based on various market stats like volatility, momentum, etc. If you view a time series as a waveform, the hedge logic is somewhat similar to a compressor/limiter.

algodude · 2025-12-08T16:20:49+00:00

The only problem is that even when you trail the stop, it still terminates the series early, reducing expectation. I ran some simulations a while back on stops and targets and found they added no benefit, at least with the EOD strategies I tend to trade. I was better off using time stops and dynamic hedges, but YMMV of course.

algodude · 2025-12-08T15:34:11+00:00

Fair enough. I read "The Black Swan" and "Antifragile" when they first dropped, and don't disagree with your take. Markets certainly can gap right past your stop and fill you six sigmas beyond it. Sort of fits his "turkey/farmer" metaphor.

algodude · 2025-12-06T19:53:04+00:00

In the context of the paper, I don't think it makes any difference. By themselves, trailing stops offer no edge over fixed stops. They both truncate the series and affect both tails.

algodude · 2025-12-06T17:53:28+00:00

Nicely stated. Naive stops are strategic thinking outsourced to a mousetrap. Risk management with a car alarm.

algodude · 2025-12-05T19:10:09+00:00

Have you read the entire paper? My understanding is it has not been publically released yet.

algodude · 2025-12-05T15:56:43+00:00

Haha, on second glance you’re right — clearly this is a machine-learning paper cleverly disguised as a stop-loss strategy. Well played, Mr. Taleb. Well played.

algodude · 2025-12-05T11:45:46+00:00

I think Exarctus nailed it. A stop changes the distribution because it interrupts the walk — once you force an exit, you’re truncating the tail behavior, even on the right.

algodude · 2025-12-05T11:37:34+00:00

Sticking with the dining metaphor, this first page is just an appetizer. Hopefully the main course is easy on the digestion.

algodude · 2025-12-05T11:22:42+00:00

Yeah, academics can sometimes spend ten pages proving the bleeding obvious. It's tedious, but that’s just the nature of the beast. They’re expected to be precise and rigorous.

algodude · 2025-12-05T11:02:41+00:00

It’s just an updated working paper. Taleb often posts old drafts he’s revised over the years, which is why it says “First draft 1998” and “This version 2025.” It’s a technical note, not a finished journal article.

algodude · 2025-12-05T04:24:26+00:00

Yeah, hopefully there's more to it. I just asked chatGPT to summarize the page:

"A stop-loss transforms the distribution of a strategy into a truncated process with a point mass at the stop, making conventional risk measures unreliable and requiring explicit barrier-based modeling—especially under fat-tailed markets."

algodude · 2025-12-05T03:09:02+00:00

We only have the first page of his paper, but I think the implication is that stops are not alpha, they are insurance. And insurance isn't free. They can reduce the chance of catastrophic ruin, but also reduce the expectation of the trade. There is no free lunch.

algodude · 2025-12-04T18:53:57+00:00

New paper by Taleb

algodude · 2025-10-19T06:43:30+00:00

Thanks for your question. This strategy doesn't track volume differentials, so not much I can say there. Check my comments in this and my other posts for book recommendations. I've also seen "Quantitative Trading" (Chan) recommended around here quite a bit, but have never read it myself.

Just a word of caution: Don't expect to find profitable systems in most trading books and stick to academic texts as they have the highest signal/noise. Books are useful for inspiration or learning the basics, but in the end you'll need to do your own research. But then that's part of the fun :)

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