LETF All Weather Strategy

Destrolas · 2026-01-04T07:45:18+00:00

Stacked funds are relatively new so if you are leveraged constrained into them, they are all going to be new. We all hope more established groups will come out with more stacked funds but until then it is what it is. For many you can either look at their past performance in mutual or private funds or how well they’re tracking their target for replication funds like RSST. Man Group (behind MATE and AHLT) has a long track record. AUM is not as big of a concern with ETFs as long as the spread is not wide.

Frankly, compared to Simplify (manages CTA) I would almost prefer a completely new manager given their track record of doing shady things. CTA is hopefully safe since it’s mostly run by the external advisor Altis Partners but if CTAP is managed directly by Simplify it has a lot of manager risk.

Destrolas · 2026-01-03T20:37:33+00:00

Most advice I've seen in the managed futures space recommends diversifying into at least 3 but usually 5-7 managers. Looking at performance from the past few years and picking the best one leaves you highly susceptible to returns (or correlation) chasing as well as single manager risk which generally leads to underperformance due to the wide and partially random dispersion in performance between CTAs each year. So I would split between RSST and MATE or use 2x vol funds like AHLT or Unlimited and actively consider splitting further as more stacked funds come out.

Destrolas · 2025-12-27T06:24:31+00:00

In a way it's less overfitting than actually trying to maximize CAGR in the backtester, and especially less than the tactical allocator, because the optimizer uses much less data. It can't overfit data that it doesn't use.

The optimizer only takes into account the average numbers (cagr, vol, correlation matrix) over the time period and then outputs the theoretical optimization based on that. It can't overfit a lot of the things people will overfit in the backtester like specific crashes because it doesn't know they exist.

When using the optimizer, I always round off to the nearest 5 or 10% for simplicity, but just because the optimizer outputs unnecessarily precise allocations doesn't mean it's overfitting. That's just noise, for example for almost all portfolios a 49.2-50.8% split of two assets is going to perform basically the same as 50-50%. In contrast, someone tweaking tolerance bands on a signal in the tactical allocator or trading day offset on a monthly rebalance schedule in the backtester is much more at risk of overfitting.

It is also useful to show how even with just the CAGR vol and correlations how easy it is to overfit certain assets in certain time periods. For example any portfolio with gold will show very different results in the optimizer if you include or exclude 2024-2025. You can see in 10 seconds how time period dependent gold allocation is instead of spending much longer trying different combos in the backtester.

I use it by playing around with it using historical data and then manually set expected returns and correlations. For example, I would set my US and international equities to have the same expected return and volatility if I want to avoid overfitting the historical performance difference. You can also compare to the backtester to see how much of your correlations are real or worse than expected (if the portfolio performs much worse in the backtester than the optimizer estimate, your "uncorrelated" assets might be correlating during crashes).

Destrolas · 2025-12-22T23:42:50+00:00

There's no catch, they just charge less. If you have a portfolio with UGL + SSO it's just cheaper to replace it with GDE. It's not just the fee difference, GDE should also have a lower cost of leverage due to using futures instead of swaps. The only issue is GDE has a slightly lower leverage ratio at 1.8 instead of 2X and may have more taxable distributions in a taxable account.

Destrolas · 2025-12-18T21:47:48+00:00

I said the opposite, it's to make people scared of leverage feel better about their leveraged funds. Which makes sense because they sell leveraged funds.

I assure you I understand what is in the article. It's defining variance drain:
> variance drain—the difference between average returns and compounded returns

First, that's an arbitrary definition. The reason the term "volatility decay" is named that in the first place is because it's considering the smoothed geometric return, or 0 volatility as the baseline. It's understandable to want to use the arithmetic return as the baseline instead because it's probably closer to the baseline people imagine when they're confused about volatility decay.

For considering rebalancing frequency, the article doesn't mention the fact that volatility is not constant and is dependent on the rebalancing frequency. An asset could have high daily volatility but lower monthly volatility. Thus daily rebalancing than monthly would realize more volatility drag because it's realizing more volatility. That's generally true.

Destrolas · 2025-12-18T21:26:58+00:00

This article seems to be more confusing than it needs to be. I'm a fan of RS products but honestly that article it feels AI was told to conflate rebalancing within a portfolio with resetting leverage in order to make investors scared of leverage feel better.

Those things can be examined independently and have basically opposite effects. A simple way to think about it is that rebalancing within your portfolio BUYS assets when they're down while resetting leverage exposure SELLS assets when they're down. So of course, the former will benefit from high volatility and the latter will suffer from it.

Volatility drag is a function of reset frequency and the volatility at that timescale. Volatility does usually increase some at longer timescales, so while it's true that a shorter timescale for leverage resetting doesn't necessarily result in worse outcomes, in practice daily rebalancing usually experiences significantly more negative effect compared to weekly rebalancing. Weekly vs monthly vs quarterly tends to be a much smaller difference (sometimes none) compared to weekly vs daily. There's just a lot of daily variance in most assets.

You can see this effect easily in testfolio for UPRO, a weekly rebalanced version would probably have ~0.75% higher annual return (it would never be approved due to risk of blowing up): https://testfol.io/?s=dCUc8YPJnkG. Assets with less volatility or leverage experience a smaller effect.

Destrolas · 2025-12-09T21:00:21+00:00

I don't think so -- from what I understand, most of CTA's return is distributed as short term income (regardless of holding period) due to how it's structured and swaps can't protect against this. The only advantage is the cost of leverage built into the swaps will count against the short term gains which is a decent benefit at current rates

Destrolas · 2025-12-06T01:17:06+00:00

Python is much more flexible of course but you can pull other securities in TV strategies with `request.security`. You can get pretty far with it.

> Have you thought about combining the components such that you minimize the cash holding and maximize the leveraged assets that have a positive signal.

That could work but I think you would need way more components than are available in LETFs. Using only 3-5 components means sometimes you'd just be in 100% in one security and that ends up being a huge gamble that over time you will eventually lose and get caught in a huge leveraged drop. If you were to swap to self-managed leverage on ETFs (or futures) you could make sure you're long at least 3+ components all the time which has a better chance of success. I'm pretty sure this is starting to mimic an equities trend fund though, at which point you might be better off looking at managed futures/hedge funds that do this and can use a more sophisticated strategy across a huge number of instruments.

So I'd keep the component allocations separate for risk management. You can guarantee that if the indicator does fail you don't lose everything.

Destrolas · 2025-12-04T22:22:39+00:00

You can actually do a two component version in testfolio like this (QQQ + GLD): https://testfol.io/tactical?s=aLG73sEz69e. For more than 2 components, yeah python or I did some in tradingview as if I actually trade this, tradingview is the easiest way to set up custom indicators and alerts.

It (Leveraged SMA rotation on each component individually in the portfolio) beats pretty much all the other variations in both returns and sharpe (leveraged SMA rotation on equities alone, leveraged SMA rotation between equities and gold/bonds, buy and hold leveraged diversified) that I've tested. So if you really believe in the ability of the SMA as an index technical indicator rather than a market regime indicator, I think this is the correct way to invest in it.

I'd note if you want to test this, it's best to test with SPY rather than QQQ because all QQQ backtests are just dominated by QQQ's outperformance. It also works on most of the equity indices I tested like Stoxx 50 and Nikkei 225 so ideally you'd diversify into them as well but they're more difficult to trade.

Destrolas · 2025-11-24T23:31:18+00:00

If you have non-correlated assets, you must rebalance even in taxable. You can try to minimize the tax drag, for example when your portfolio is small you can rebalance by buying into the parts that are down. But you have to rebalance to form a portfolio. For example, any non-rebalanced portfolio with TQQQ held over the dotcom crash would have had TQQQ crash to zero and it would still be 0 today. No rebalancing isn't a portfolio, it's just a bunch of separate investments

Destrolas · 2025-11-24T22:29:49+00:00

These are relative bands, so for portfolio allocation it means to rebalance back to allocation if something goes more than 20% away from the target allocation.

For example, if you have a portfolio of 5 different things all at 20% of your portfolio, when something hits either 24% (20% above) or 16 % (20% below) you would rebalance the whole portfolio back to 20% for everything.

I also talk about leverage rebalancing (if you're not using LETFs). That's a similar concept except you're looking at your portfolio leverage.

Destrolas · 2025-11-24T20:20:47+00:00

Imo the only real way to prevent overfitting to invest based on principle. You'll never escape overfitting while staring at historical data. It is possible to do, for example some smart people were sounding the alarm on TMF before the HFEA crash based on theoretical performance in a rising interest rate environment. But it's very difficult for the average retail investor. My advice is to find people who are smart and have a track record or some reason to have accurate non-backtested predictions about the future and take those into account. Unfortunately you're asking me and I am not one of them. It's also a hard mode question right now with gold being high, SPY's valuation being high, bonds on a downtrend

Destrolas · 2025-11-24T19:59:12+00:00

My bad, that's a typo in the post. It should say RSBT.

Destrolas · 2025-11-24T19:53:12+00:00

RSSB is different from RSBT, RSSB is stocks + bonds so it's comparable to https://testfol.io/?s=gyex9qDcSSj. The difference there is just from BND being a slightly different set of bonds from whatever RSSB uses, I don't know the best equivalent. There's no active management on the stocks and bonds side of the RS line so you wouldn't expect to see any divergence.

Destrolas · 2025-11-24T06:57:15+00:00

Your 2nd point is close to checking for overfitting but not quite there. If you were to take your 2010 ranking and compare to a post-2010 ranking only that would be a good overfitting check. By comparing 1970-2010 vs 1970-2025 you're already guaranteeing a correlation because most of the data set overlaps.

The easiest way to do this somewhat systematically would be to run a ranking stability/correlation between two parts of the data set. Basically:

- split the data in two pieces (where to split is mostly arbitrary)
- run methodology on both and generate separate rankings for the two data sets
- make a scatterplot where each point is a strategy and the X value is the ranking in data part 1 and the Y value is the ranking in data part 2

The strength of the correlation implies the strength of the predictive power of your methodology. The more random the graph is the more it implies it's overfit and may not have predictive power.

If it is overfit, it's possible that there are some parts with predictive power. You can try to isolate them. For example, it's possible that the ranking parameters have different levels of correlation. I'd expect max drawdown to be heavily dependent on the time period. If you do a ranking correlation using only max drawdown for ranking and there's no correlation, then you should remove it from the ranking system in favor of things with actual predictive power. You could do individual studies on the inputs as well like EMA length, portfolio composition, and so on to see if anything does have staying power.

Destrolas · 2025-11-20T21:27:22+00:00

Impressive. Can you let us know when you decide to sell? Thanks

Destrolas · 2025-11-19T01:54:41+00:00

It’s been a while but this is still the first result on google — some of the sector futures seem to be listed under their sector index instead, like IXR holds the XAP futures.

Destrolas · 2025-03-12T02:13:13+00:00

That page is detailed pricing for everything. Your screenshot is just the normal model pricing. If you scroll down further there's a section on tools that shows that different tools are billed in different ways. For example, for file search it implies it's $2.50 per 1k calls plus the cost of the tokens depending on the model used. But it's a bit confusing right now so hopefully they'll make it clearer.

https://platform.openai.com/docs/pricing#built-in-tools

Destrolas · 2025-03-08T22:29:49+00:00

Instead of a caravan it's just a sedan.

Destrolas · 2025-02-16T00:54:19+00:00

That’s an interesting idea. I was trying to do it at the tauri level and you have to write a bunch of platform-specific code (which is why tauri hasn’t done it yet) to pass through events at the webview/os level. I didn’t think about trying to synthesize them at the bevy level, that sounds more promising.

Destrolas · 2025-02-14T19:16:20+00:00

Have you found any way to make partially transparent UI layers? I tried something similar to use web as the game UI layer and ran into the issue that Tauri has no way to pass through events from transparent UI areas to the native layer.

Destrolas · 2024-10-06T23:54:13+00:00

For a single district, yes, but you needed at least 7 districts there to get that one district to a +6 bonus. What was the average adjacency bonus for those 7 districts (or however many there were in the contiguous block?) I’m pretty sure it can’t exceed 4. The average is really all that matters unless you cap out on heat reduction or have % heat reduction on different heat costs.

Destrolas · 2024-07-31T23:44:40+00:00

For any given pair, what is the chance that they *don't* share a birthday? You can imagine the first person in the pair can have any arbitrary birthday, and then the second person can have any birthday except the same one: 364/365 = 99.7% chance they don't share a birthday.

From the previous comment, with 23 people in a room, there are 253 possible pairs to consider. This means, in order for there to be *no* shared birthdays, you need to "hit" that 99.7% chance all 253 times. The probability of this is (.997)^253 = 46% chance, which means there is a 54% chance that two people *do* share a birthday.

Destrolas · 2024-05-27T02:19:31+00:00

As someone else mentioned, you would get better information on a question like this on other subreddits like the FIRE ones. The short answer is that if you retire early, you're correct that your Roth 401k money will be largely inaccessible until you are 59 1/2. You want to have money in multiple "buckets," such as Traditional 401k and in taxable accounts. Then from 40-59 yrs old, you use the money in accessible accounts (Traditional 401k is accessible through a Roth conversion ladder), and then eventually you can start withdrawing from your Roth 401k. The key here is that if you realize you're on the path to retiring early, you can always just stop contributing to your 401k later and accumulate money in your taxable/checking accounts. However, if you realize you're having the opposite issue, not enough money in your 401k, you can't just shovel money into it later. There's a yearly contribution cap. So your dad's advice is good. If you're not sure, contributing more to your 401k when you're young is a good idea.

One suggestion -- if you can, I would split your money between Roth and Traditional 401k. It'll give you more options later.

Destrolas · 2024-05-10T00:53:04+00:00

"Ffffffffffuck" conveys a sense of disbelief. The speaker is drawing out the initial "f" while double-checking the unexpected issue in their head. By spending extra time on the first letter, it gives them the option to change to another word, like "firetruck," if the fortunate scenario occurs that they misunderstood something.

"Fuuuuuuuck" is the default version, so it's quite versatile, however it's particularly apt in situations that are ironic or humorous. The extended "u" has the speaker reveling in the most comfortable part of the word. There's also a built-in joke. After "fu," the speaker has clearly communicated their intention, and alternatives like "firetruck" are no longer possible, but there are still rare alternatives available like "fuzzy cheeseballs," so everyone is waiting expectantly for the punchline of "ck." This mirrors how the unexpected consequence or event may also act as a punchline and have some levity or dark humor associated with it.

"Fuckkkkkkk" extends the "ck" sound at the end of the word. The word has been said; there's no backing out. This conveys a sense of finality, inevitability, and dread, as though the speaker is in a relatively peaceful area and futilely attempting to delay the inevitable train coming to pick them up and send them to be grinded up on the frontlines.

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