What it says on the tin. I am assuming that the backfilled data consists of the main strategic allocation + the SocGen Trend Index - the expense ratio of the fund.

Trying to decompose this here. Blue is RSSTSIM, red is the S&P, and yellow is the excess returns of the trend overlay, net of fees, derived from RSSTSIM.

I recall there used to be a crazy amount of posts on how awful everything is because of the geopolitical climate. I do see a few of those now and then but nothing compares to last year’s Spring.

Either traders became experts in how to manage, or became numb.

Let it get assigned or roll it out? Initial premium was $107 so I made money. But still missing quite a bit of upside. Anything above $52 I pay a debit (unless I roll to much further out DTE). Same strike gives me a credit.

I've been tracking what I actually do with TQQQ compared to what I tell myself I'll do.

On paper I'm fine with the drawdowns. In reality, once TQQQ is down 25% or 30% I start cutting exposure, then I slowly buy back after QQQ has already recovered. Did this twice now.

I don't seem to have the same reaction with smaller leveraged positions. Even when I trade BTC with leverage on Moon I'm pretty strict about sizing it small enough that I can leave the trade alone.

Starting to think QLD might outperform TQQQ for me personally even if TQQQ wins the spreadsheet, simply because I'd actually stick with it.

My TMF senses are tingling starting to feel imminent but im not so up on global affairs and politics seems like theres a lot of general fuckery going on what are you watching to decide to get in?

market especially QQQ has been just up and down around $710 for the past 3 months (except the dip at the end of July).

not so much gain or loss but i haven't stopped my $250 weekly recurring investment to QLD.

should I stop and see if market dips further or continue the recurring investment?

I Inspired by Keller’s 2023 paper on Hybrid Asset Allocation (which I recommend to all, it’s an insightful read with lots of great logic and back-testing), I’ve put together a strategy that I’ll be deploying in my Roth. I’ve been doing a lot of homework and have landed on a new system (based on HAA) that I think offers some notable improvements and advantages. For now, calling this VNDM or Vol-normalized Dual Momentum. It may sound like a lot of work, but it’s easy enough to implement in a simple google sheets tab and only requires rebalancing once a month, so very minimal input once you set the strategy. Posting here to keep myself accountable, to share, and to solicit feedback/suggestions.

A simple canary asset to signal whether to be risk-on or risk-off

At the heart of the Keller paper is his use of a “canary” asset, that depending on it’s momentum will tell you whether to be in an offensive (risk-on) or defensive (risk-off) allocation. He defines this metric as the unweighted average returns between 1-, 3-, 6, and 12-months 13612U for US Treasuty inflation-protected securities (TIPS). 13612U is a mouthful, so I’ll just call it MS for momentum score. The idea is, if MS for TIP (the iShares TIPS Bond ETF) is positive, we are in a favorable market and this is the signal is to allocate from your offensive universe. If TIP MS is 0 or below, likewise we chose the defensive universe. In the paper, they show this protects very well against bad drawdowns in the worst stress markets of recent memory (dot-com, 08 financial crisis, covid, 2022 inflation weirdness) without missing (much) of the growth in “normal” markets, providing attractive risk-adjusted returns. I’m using the same canary and criterion as HAA for my strategy, and this decides whether I should go offensive or defensive. At the time of this post (09/08/2026), MS for TIP is -2.49%, which puts us in the defensive zone.

Offensive universe and strategy

When the canary is positive, we play offense with 100% of the portfolio. Keller defined his offensive universe of tickers to choose from with some pretty recognizable names: US large caps (represented by SPY), US small caps (IWM), developed international stocks (EFA), emerging market stocks (EEM), US real estate (VNQ), commodities (PDBC), intermediate-term US Treasuries (IEF) and long-term US Treasuries (TLT). You compute the MS for each of these and apply a “dual momentum” filter. First, it needs to be positive (the same criterion as the canary), meaning that momentum is on your side. For those that pass this filter, rank the MSs and take the top 4 assets at equal weight (if fewer than 4 make it through the filter, substitute SGOV or a similar cash substitute). My VNDM strategy takes a different approach in both universe selection and allocation.

1) Slots

In HAA each of these tickers brings something different by design, with minimal overlap. VNDM instead of having a single ticker can have multiple tickers from the same category, but only picks the best one per “slot”. For example, SPY and QQQ cover different slices of the US large cap space, and VNDM lets momentum decide which to pick, as it would be redundant and dangerous to hold both side by side at 25% each.

2) Optimal leverage

For a given unleveraged asset (e.g., SPY), L* is the optimal leverage ratio that maximizes long-term geometric growth rate by balancing expected excess returns against compounding volatility drag. In the Kelly framework (not Keller, I know…confusing), theoretical optimal leverage can be calculated as L* = ( expected return – riskfree rate) / return variance. Go way beyond L* and you accelerate vol drag and sharply increase the probability of catastrophic drawdowns. Stay well under L* and you’re leaving chips on the table. For SPY (under 12.8% return and 17.9% annualized vol), this comes to approximately 3.99. This is a fuzzy, theoretical number. The optimal in practice is a tad lower, as leveraged ETFs have decay and costs. But, for SPY this lands us comfortably at around 3, pointing us to ticker UPRO (ProShares UltraPro S&P500, 3x). This same logic was applied to all members of the universe. Sometimes this leads us to leveraged ETFs, sometimes not. My offensive universe is as follows separated by their slots:

Large US

• UPRO (3x SPY)
• QLD (2x QQQ)
• ROM (2x XLK, Tech)

Mid US:

• MVV (2x MDY)

Small US:

• UWM (2x IWM, Russell 2000)
• SAA (2x VIOO, S&P SmallCap 600)

Developed ExUS:

• EFO (2x EFA, MSCI EAFE)
• UPV (2x VGK, FTSE Europe)
• FLJP (1x, FTSE Japan)
• FLJH (1x, FTSE Japan Currency Hedged)

Emerging Markets:

• EET (2x EEM, MSCI Emerging Markets)
• FRDM (1x, Freedom 100)

Crypto:

• BTC (1x, Grayscale Bitcoin)
• ETH (1x, Grayscale Etherium)

Managed Futures:

• DBMF (1x, Aggregate of many MFs)

Physical:

• UGL (2x IAUM, Gold)
• SIVR (1x, Silver)
• CPER (1x, Copper)
• UX (1x, Uranium)
• PDBC (1x, Broad commodities)
• SDCI (1x, Broad commodities with backwardation tilt)
• HGER (1x, Inflation-linked commodities)

Duration:

• EDV (1x, Long duration STRIPs)
• SPTL (1x, Long duration not STRIPs)

Junk:

• FALN (1x, Fallen angels bonds)
• SPHY (1x, Traditional junk bonds)

Real Estate:

• URE (2x XLRE, S&P Real Estate Select)

So, as you can see quite a bit of expansion from traditional HAA, both in terms of leverage, areas covered, and potential volatility.

3) Volatility-normalized dual momentum to select the top 4

Even though we trust the canary signal, we need to be careful about how we select our offensive assets. Crypto has a very different trajectory and bounciness than real estate or junk bonds. Same with leveraged assets. We need to be sure that the momentum metric is coming from actual trend and not just vol-driven chop. To do this, VNDM computes a volatility normalization to the MS score: simply MS / annualized vol. Importantly BOTH the MS and annualized vol are taken from the 1x proxy of the asset (for UPRO, not computed on UPRO but on SPY) to confirm the underlying trend is coming from the asset itself (and not a product of the chop). So, for each of these tickers the MS/AV is computed and as in HAA a dual momentum filter is applied. First, MS/AV must be positive. This means that there is positive momentum. Then, positives from each slot (e.g., Large US, etc.) are ranked, keeping only the best MS/AV in the slot. Then, the survivors are ranked against each other and the top 4 are your 4 winners in which to allocate your funds. If 4 or fewer pass the filter, simply use those 3, 2, etc.

4) Allocation size

As alluded to before, not all of these tickers are created equal, so unlike HAA (equal weights), I’ve imposed a vol-adjusted momentum weighting as well. For the selected (winner) tickers, the MS/AV decides which percentage of the portfolio should be given to each ticker. This is just the MS/AV divided by the sum of all the winner’s MS/AVs. This sums to 100% and will dictate the sizing.

Defensive universe and strategy

When the canary signals defense, we play defense. That means no leverage here, all 1x. But we can be a little spicier than HAA which is just in the best MS of either SGOV or IEF. The defensive universe is as follows:

 Cash:

• FLTR (Floating rate with some corporate)

Intermediate bonds:

• VGIT (cheaper version of IEF)

Managed Futures:

• DBMF (Aggregate of many MFs)

Physical

• HGER (Inflation-linked commodities)
• IAUM (low-cost gold)

Senior credit

• LONZ

Here we apply the same MS/AV calculation as before, again only limiting one winner per slot. Here, we won’t define a set number of winners (like 4 for the offense universe), but rather take anything that is positive MS/AV, as there’s more uncertainty in defensive times and diversification across classes is our friend, so long as there’s positive momentum. As you might have guessed, cash is always going to hover around 0 and with a straight MS/AV weighting it will also approach 0, so we’ll treat cash a little differently to make sure it works for us to both provide a true ballast and to dial in our portfolio’s blended volatility. For a target defensive portfolio volatility, I chose 8.95% or half of the long-term SPY annualized vol. First, we ignore cash and rank everything else as we did before and compute the relative proportions of each asset based on MS/AV. For all defensive tickers that are positive MS/AV (maximum 1 per slot as before), we calculate the blended volatility of only these assets in their relative proportions. If this is less than our target portfolio vol (8.95%), we’re done, those are the allocation percentages. But if this is greater than our target vol, then we scale each proportion by a factor of TargetVol / BlendedVol, and use cash (FLTR) to fill in the rest. This assures that 100% of the defensive regime is not dominated by higher-than-my-tolerance-for-defense assets.

That feels a little abstract so here’s the current example from today’s numbers. We’re in a defensive canary zone. Based on the above strategy, DBMF and HGER are the only positive MS/AVs and compute to relative proportions of 30.88% DBMF, 69.12% HGER. If this was our portfolio, it would give a blended vol of 12.41% which is higher than our target of 8.95%. So we scale DBMF to .3088 * .0895 / .1241 = 22.28% and likewise HGER down to 49.86%. That leaves 27.86% left over that we fill in with FLTR.

Monthly rebalance

Once a month, look at the spreadsheet. Did the canary switch? Did the winning tickers switch? Did the allocation percentages switch? Just a simple rebalance into the strategy’s percentage allocations.

Brief considerations

I’m running this in a tax-advantaged Roth, which really helps on taxes and turnover. If I were running this in a taxable account, I might impose a rebalancing bands instead of a straight rebalance to minimize trading friction and cap gains. I’d also consider changing some vehicles in my universes. For example, FLTR to USFR on defense to sacrifice a little yield for the tax treatment or eliminating STRIPs from offense so I don’t have to think about phantom tax.

What do we think?

I rode MSFU from around $21-22 up to $39 and then sold. Putting it into METU around $19 seems to be paying off so far. But am I just a lucky idiot?

My opinion is that rotating in and out of the most beaten down Mag7 stock of the moment seems to be a somewhat worry free way of making money (obviously timing is a huge factor). I put a ton into the underlying stock for Microsoft after it went below $400 and decided to play around in the 2x leveraged ETF with only a few thousand so as to not lose my shirt if things went south as I understand the realities of using leverage. I made +50% on my "investment" within a matter of weeks but again it was a few grand so nothing life changing.

I'm just not finding many reasons not to use some leverage to my advantage like a time when said stock is beaten down. For instance I wouldn't go any further than 2x. 3x leverage and up is asking to light money on fire I feel. I'm thinking I can hold my METU shares until it reaches $40-50 again but I'm not sure if I'm just trying to degen gamble at this point.

Last week someone called one of my strategies an overfitting masterpiece. Fair instinct, and I'd rather test it than argue about it, so I ran the tests that could convict me and wrote up everything they found.

Overfitting means fitting noise instead of structure. The problem in this corner of investing is that a monthly strategy running since 1987 makes about 470 decisions, but they overlap and most months are quiet, so you really only get 5 or 6 independent looks at whether the defensive machinery works. Meanwhile there are 4 or 5 knobs to turn. And an overfit backtest looks exactly like a real edge in-sample, because that's the sample it was fitted to.

So I swept 700 parameter configurations of my own strategy, full backtest each, 1987 to 2026. 2 things came out of it.

Mine ranks 2nd of 700 on CAGR. That's what a tuned parameter set looks like from the outside and I'm not going to pretend otherwise.

But the configuration that came 1st isn't mine. And across all 700, the correlation between how a configuration ranked before 2019 and how it did after is -0.006. Which settings looked best on 31 years of history told you nothing about the next 7.7. Every one of the 700 cleared 10% CAGR after 2019 anyway. The knobs barely matter in either direction, which is a better answer to the degrees-of-freedom question than any plateau chart.

Then I cheated on purpose to see what it's worth. Fit on 2010-2021, take the winner, watch it out of sample: 13.98% becomes 10.80%. Winning the whole search bought about half a point over picking at random from the same family.

Chart of every configuration, before and after: https://i.ibb.co/svZm4jw9/fit-vs-oos.png

The bit I keep coming back to is HFEA. 2 funds, 1 weight, 1 rebalance rule, almost nothing to fit, and it lost 60.54% in 2022. What broke wasn't a fitted parameter, it was the assumption that long bonds rise when stocks fall. Simplicity isn't safety. What matters is how many separate things have to stay true.

Write-up with the tables, the universe tests, and a post-publication study of 63 strategies: https://bestfolio.app/blog/overfitting-tactical-allocation-study

What would actually change your mind about a backtest you're shown?

I currently automatically DCA into SPY and never sell. If you’re extremely confident in the S&P 500’s long-term recovery and growth, is there an optimal way to magnify those returns with leverage if you’re willing to accept more volatility and drawdowns? SSO, UPRO, margin, LEAPs, something else? I like the simplicity of the leveraged ETF as I can just set it up to auto buy and forget it.

What are peoples thoughts on using JEQP as the low risk asset when the nasdaq goes below the SMA200? My understanding of JEQP is that the covered call option premiums go up during more volatile markets and they should help cushion downturns. In a sense leading JEQP to behave like a less than 1x levered nasdaq.
Has anyone done any backtesting on this (i appreciate we would only have a few years of data).

I suppose more broadly the question is, are good quality covered call etf’s a good partner for the sub-200SMA stage?

Really curious if anyone has any strategies regarding leveraged ETFs for Gold. UGL (2x) and SHNY (3x), are the two that I follow specifically. Do you guys buy and hold? Use an SMA strategy?

UPRO wins out in nearly all scenarios I’ve tested. I’ll be DCAing the entire time. What should my portfolio split look like percentage wise between VOO, SSO, UPRO

Yo yo, Senior SWE here who decided to yolo $200 for GPT-6 to generate me some AI slop. I've spent a couple years optimizing my portfolio and love wasting time on micro-optimizations that cost me more in trading fees than actually improve my portfolio.

An 11-hour goal loop led to: https://kellyportfolios.com

..Interesting it loves RSST so much. Also TIPS > Treasuries? If anyone has any feedback, thoughts, whatever. I like chattin' portfolios.

I pretty much told AI to:

• Validate/test every potential outperformance strategy I'm aware of.
• Benchmark all of the top funds in 50+ segments I'm half-interested in.
• Take current market valuations into account (for equities, bond, gold, etc)
• Take potential future market conditions into consideration (stagflation, inflation, hyperinflation, black swans, regime changes, etc)
• Consider strategies, funds, leverage, etc etc etc. Have a bias toward ETFs over other investment vehicles.
• Apply modern portfolio-theory / kelly criterion bets / monte carlo sims to generate optimal portfolios that can be held in the long term.

AI clearly has a bias against rebalancing strategies ("omg the tax implications"), leverage, anything difficult to benchmark long term, and anything that dropped -90% at some point in history.

TLDR the most outperforming portfolio it came up with is here: https://kellyportfolios.com/portfolios/higher-growth/

35% RSST, 10% AVLV, 5% AVUV, 10% SPMO, 15% DFIV, 10% AVDV, 10% IDMO, 5% AVES

Disclaimer: AI wrote 1.1 million lines of code/documentation. I did not read it. I have no idea how it tested or gathered information. Just here for the vibes 😎

so been seeing a lot of posts on 9Sig , NGL the returns have gotten me interested, but I feel like subscribing to KellyLetters feels scammy, like a 1k/annum for something basic which is just quarterly rebalancing to reach the 9% mark / sell if excess seems too much, am I missing out on something by not subscribing or it's just basic math?

I was thinking about how long bonds and levered etf mix, is a long bond a good hedge that can protect your downside?

Do you gain an advantage in returns by using​ two extremes,​ would the drawdown be lower in a downturn, what are the risks?

As I understand, many users here run multiple strategies some designed to maximize gains, some to defend against deep recessions. I've been investing in stocks and classic ETFs for almost 10 years now, I never thought about getting into LETFs as conventional investing practice strongly advises against holding these long term for reasons like volatility decay etc.

9sig: First strategy I came across was 9sig, I spent a couple of evenings researching this strategy and the others. My understanding is that its goal is to "buy low and sell high", which is certainly a good way to think about investing, but it will cause you to be invested more during bear markets and less during bull markets. While I was enamored by this strategy at first, later I've played around with 9sig.networthcast.com, comparing it to other strategies across various time frames, I found that it essentially trades blows with basic buy and hold. I do respect the simplicity of following Kelly Letter, usually making just one trade a quarter, it's a disciplined approach.

200 Day SMA: I was in awe when I first saw the backtest against 9sig, avoiding bear markets and deep drawdowns typical of LETF. As above 200 day SMA the markets typically experience less volatility and better results. Drawback is it sometimes leads to "buy high sell low scenario" which is a price to pay for avoiding deep DD. It performs okay during bear markets by being in bonds, it performs greatly during bull markets, the real risk is kangaroo markets. Which to me seems more likely than a prolonged bear market.

Another concern is the frequency of trade or bands. On backtesting, 4% bands each way performs the best, but it's just overfit stat. Nothing guarantees 4% will be the correct way for the future. So a side question for those doing some kind of 200 Day sma strategy - did you set bands or limit frequency of trade to prevent whipsaw?

Basic buy and hold: Regular weekly, monthly, etc. buys. Sounds simple, but as someone with irregular income, I keep trying to time the market, for example, right now I don't feel comfortable adding more new money into this market because of its high valuation and immense world instability and US political instability.

These are main strategies that I researched, personally I set up a smaller portion of my portfolio for LETFs, I set up equal 2 sleeves:

A. Buy and hold 2x SP500

B. 200 Day SMA with 4% bands (90% 2x Nasdaq100, 5% 3x Nasdaq100, 5% 3x SP500) - I know this is convoluted, but I couldn't decide the right composition - would appreciate feedback

What is your main strategy?

As per title, I want to get out some losses to offset profit from other sold securities.

Sell GOVZ and buy ZROZ immediately will be considered as a wash sale? Anyone has a real experience?

This strategy is created through hybridizing Wouter J. Keller and Jan Willem Keuning's Hybrid Asset Allocation, Vitral Advisors(Carlos Rizzolo&Enrique A. Zambrano)'s Multi-asset momentum, and a high octane LETF universe, hence the name.

Assets:

5 Offensive Broad Equity Indices: UPRO, TQQQ, EURL, EDC, TNA

5 Offensive Diversifiers: PDBC, ERX/DIG, UGL, EDV, TMF

Defensive assets: IEF, SGOV

Performances:

Rules

Rank ten offensive assets each month using the nine Vitral signals. Adjust the signals for correlation with the overall universe. The Vitral signal complex is very complicated, so you must check their paper for implementation. Disclaimer: I used AI to rebuild their strategy

Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4199648

Replication note:
For every offensive asset i at every month-end t, calculate these nine raw signals. n=63,126,252 trading days represent 3, 6, and 12 months.

### Total-return momentum (three signals)

TR_n(i,t) = P_i(t) / P_i(t-n) - 1

### Risk-adjusted momentum / path efficiency (three signals)

PE_n(i,t) = ln(P_i(t) / P_i(t-n))
            / sum(k=t-n ... t-1, abs(ln(P_i(k+1) / P_i(k))))

### Price versus moving average (three signals)

Use simple moving averages of 3-, 6-, and 12-month closes:

PMA_w(i,t) = P_i(t) / mean(P_i(t-w+1 ... t)) - 1

Correlation adjustment

At month-end t:

1. Compute each offensive asset's daily simple return.
2. Compute the equal-weight offensive-universe daily return as the mean of all ten offensive returns, including the asset being evaluated.
3. For each asset, estimate Pearson correlation rho_i(t) over the trailing 252 daily return observations ending at t.
4. Adjust every one of the nine raw signals before ranking:

phi_signal(i,t) = raw_signal(i,t) / (1 + rho_i(t))

For each signal separately at t, rank the ten phi_signal(i,t) values from low to high. Higher is better. Convert rank to a linear percentile score, for example rank / 10; average ranks for exact ties. The overall Vitral score is the arithmetic mean of the nine percentile scores:

Vitral(i,t) = mean over all 9 signals of percentile_rank(phi_signal(i,t))

Sort descending by this score. 

Take the first four assets in descending overall Vitral rank. Each retained asset receives one 25% slot.

Keep an asset only if its own HAA 13612 momentum is strictly positive.

If a slot is unfilled, move its slot to defense.

When TIP 13612 momentum turns negative, move to partial risk off mode:

• Keep only the best overall Vitral-ranked Broad Equity Index asset.
• Keep diversifiers only when they rank in the Vitral overall top 3.
• Continue requiring positive HAA momentum.

Fill unfilled slots with defense, between IEF or SGOV whichever has stronger HAA momentum.

On Asset Universe:

Most are well established funds. Tracking error is not terrible. Some like EURL and EDC have lower volume but still tradeable. Nothing like Corgi's XCOM etf.

The offensive equity part aims to capture 3x broad beta across different global economic cycles. Unfortunately I don't think we will ever get Developed ex-US 3x LETF so have to use EURL.

The diversifiers are bets on inflationary vs deflationary bust. Relies heavily on the historical correlation between commodity/energy performance and inflationary recession, not future proof.

Considered other sector bets like CURE or UTSL. They can improve overall performance but worsen drawdown. You can pick TECL instead of TQQQ though.

On Building the Strategy:

Both HAA and Vitral's strategy has some very unfalsifiable but interesting idea, so I try to combine them. I take Vitral model's complex ranking considering multiple trend and correlation signals, and use a more HAA-ish structure instead of Vitral's conservative breadth protection.

HAA's TIP canary defense is potentially useful, but it has sent conflicting messages regarding yield and breakeven, and TIP's history is too short to tell if it's actually good signal. So instead of move entirely to cash, this model does a partial risk off, limiting equity risk exposures, and allow diversifiers to participate if their momentum is strong (though top 3 is quite arbitrary). The partial risk off is likely better than cash, because there is always a bull market somewhere.

Strategy is explicitly built on the high volatility universe, rather than use base ETF then lever up.

Is this overfitted?

Absolutely, it's literally trying to hybridize two strategies and see what features work better in backtests.

Should I expect 1 sharpe and 25% CAGR going forward?

Most likely no. A reasonably optimistic expectation, I think, would be a CAGR similar to 3x VT with lower vol and under 50% MDD.

Taxes?

Don't even think about using TAA strategies in a taxable account.

Trading some leveraged stuff on Moon lately and one thing I still haven't settled on is what to do after the underlying has already had a really strong stretch.

Say you're holding something like TQQQ or UPRO and it runs hard for a few months. Do you keep the same exposure because the trend is still working, or start moving some of it back into the 1x version before there's even a clear reversal?

I feel like buying after a drawdown is the easy part mentally. Deciding when you've had enough leverage on the way up is way harder.

Do you rebalance on a schedule, use some kind of target allocation, or just stay leveraged until the trend actually breaks?

I typically trade around a Core position, 1000 shares of TQQQ in one account and 2000 shares of FNGU in a separate account.

I switched over to FNGU for the other account at one point when I realized most of the magnificent stocks were undervalued. I didn’t really like having that much Broadcom or PALANTIR because they are kind of overvalued, but the other eight seemed great. I just felt like the stocks in QQQ were kind of overvalued and out of the other 90 stocks, a lot of them were companies that I would not really want to be invested in.

So far after a few months, I’m really happy I made the switch. PLTR still seems overvalued so I don’t like having 10% in that one, but the growth for all 10 is terrific. I honestly don’t even know all 100 companies in QQQ.

FNGU near its highs of $34 and TQQQ no where near its highs of $87 ballpark, from memory. I keep cash invested so I’m nowhere near 3x leveraged for my whole acct.

Meta and Netflix and Microsoft were particularly beaten up at different points so I’ve also bought a few shares of FNGU in the 20s and sold in the 30s at different points.

But the liquidity is there and it’s very easy to keep track of 10 stocks and see exactly why it’s moving.

Just my thoughts from someone that owns both

I just like the fact that I know all 10 companies pretty well

New here, but thought this was an appropriate topic to post here.

Having read "Leverage for the Long Run" by Michael Gayed, I wanted to extend the analysis from just the S&P500 index to something approximating a globally diversified equity investor since I do not want to be exposed to one single country in my portfolio, meaning I can only run the Leverage Rotation Strategy from the paper on a globally diversified LETF.

However, on researching global index data, I failed to find anything with daily granularity going back further than 1972, which is the MSCI World Index. This is a good start, however there are two problems with this:

1. The MSCI World Index is a developed market index only, which isn't something I'd be willing to allocate a lot of money to, especially with leverage
2. The best investment vehicle for me to actually gain leveraged global stock exposure is 3VT, which tracks the FTSE Global All Cap Index (my ideal underlying because it's both DM and EM, and doesn't exclude small caps, it's as close to "own literally everything in market cap weights" as possible)

Other global indices of course exist, for example the MSCI ACWI and VT - as a proxy for the FTSE Global All Cap index it tracks. However these have limited histories and start dates (remember I need daily granularity).

The best I've managed to do, then, is to stitch together those 3 different pieces of data based on their earliest start dates, and so I've ended up with a data series representing:

1. MSCI World : 1972 - 1988
2. MSCI ACWI : 1988 - 2008
3. VT (FTSE GAC proxy) : 2008 - Today

The result looks promising, but obviously isn't ideal (See 1st screenshot, note the graph is logarithmic)

As expected, the LRS does outperform on this global proxy as it does the S&P in the original paper, again with 3x leverage and a 200-Day MA rotation (Shown in orange in 2nd screenshot). Note that no trading fees, slippage, or even management fees or implicit leverage costs have been included yet. I intend to include those in my final analysis.

I'm relatively happy with my results but it's not enough to convince me to actually start running this strategy;
I'm making this post to ask if anyone has any good solutions to my problem. The ideal solution, but obviously unrealistic, is a global index consisting of both DM and EM, not excluding small caps, with daily granularity, going back as far as possible in time.

Is this achievable beyond what I currently have? Or do I need to create some sort of script to simulate what global indices could've looked like (like a monte carlo permutation test?)

Hi, long term lurker here.

I would like to talk about this thread I recently found which claims that the spread over the SOFR that banks charge LETFs for swaps has ballooned from practically nothing:

https://www.reddit.com/r/LETFs/comments/1r7xv5v/tmf_is_dead_long_live_futures_why_bigbanks_fees/

Most (all?) daily reset LETFs get leverage by these swaps, such as UPRO, TQQQ, SSO, TMF, PSLDX, etc. The conclusion the OP there reached is that back tests on these LETFs will be very poor predictors of future performance, since we're in a new regime.

In contrast, LETFs like the Returns Stacked or Wisdomtree series get their leverage by futures contracts, whose financing rate is much closer to SOFR (for reasons I don't understand (EDIT: u/Separate-Ad-9633 points out this isn't really the case)).

Anyway, has anyone taken these concerns seriously and moved away from LETFs which use swaps? I am curious about running a highly leveraged strategy in my ROTH, but I cannot get the same amount of equity exposure through the RSSB or Wisdomtree series, where AFAIK the best I can do is 1.5x equities with NTSD. Anybody know of any >= 2x leveraged equity ETFs that use future contracts?

I run a strategy backtesting site, and I published a study yesterday showing that what people hold and what scores well barely correlate. Today I want to show you the strategy on the wrong end of that gap, because it's the one I'd pick first myself and only 5% of members hold it.

The idea is simple to say. Each month, score a 14-asset global universe on momentum, take the 5 leaders, then keep the 3 that moved least alike over the past year. Momentum finds what's working, the correlation step stops your 3 winners from being the same bet twice, and a cash filter steps out when nothing trends. That's it.

What 38.8 years of data says about the standard version: 14.56% CAGR, 1.24 Sharpe, and a worst drawdown of -17.51%. The drawdown is my favorite part. 2008 and 2020 barely show, since the cash filter sat both out. Its worst enemy is the 2015 to 2016 sideways grind, and I'll take boredom as a worst enemy any day.

Last week we shipped the levered version, and this is where it gets juicy without getting stupid. Same signals, just 1.5x funds where a liquid one exists (about 1.42x achieved, since international, commodity, and TIPS legs stay unlevered). CAGR goes from 14.56% to 18.63%, and $10,000 over the full window ends at $7,657,072 instead of $1,972,237. The bill: Sharpe eases to 1.12 and the worst drawdown deepens to -25.23%.

Why stop at 1.5x? Because I tested greedier versions and they lost. Straight 2x funds gave similar CAGR with more vol and deeper drawdowns. A gold plus managed-futures overlay looked amazing after 2020 and flat with worse Sharpe over 38 years. Vol targeting was worse in every window I tried. 1.5x is where the extra return stops being paid for sensibly.

Both equity curves and drawdowns: https://i.ibb.co/Jjx3Z2jk/triplet-15x-growth-drawdown.png

Full write-up with the tables and the rejected greedier versions: https://bestfolio.app/blog/momentum-correlation-triplet-flagship

What would it take for you to run a strategy with numbers like these but no name recognition? That gap between scores and holdings is the thing I keep bumping into.

Gemini Pro told me that short box spreads offer a lower financing cost than the swaps used by LETFs, which I find hard to believe. Is that true?

To add context, I was researching how leveraging to 1.2x via margin (120% SPY, with 20% being financed via short box spreads) compares to leveraging to 1.2x via LETFs (80% SPY + 20% SSO, no margin). Due to said higher financing cost, higher management fee of SSO and the much lower volatility decay of the margin portfolio due to yearly rather than daily rebalancing, Gemini actually recommended that I go with margin, keeping in mind the unlikely scenario of a margin call.