u/laurenthu

The chart nobody posts about their favorite strategy: same rules, every possible start date. I ran it for Golden Ratio Dual Gate, since this sub gave it a proper grilling at launch, and the honest version is more interesting than the headline.

The headline is real enough. The full backtest from April 2008 compounds at 19.9%. That's the number on the strategy page, and it's real. But nobody invests for 18 years starting at the exact bottom-adjacent month the backtest starts. So I replayed every completed 5-year and 10-year monthly start from the same production series, lump sum and DCA.

160 completed 5-year starts. CAGR ranged from 12.1% to 31.7%, median 21.1%. Same rules, same data, and the spread between a lucky entry and an unlucky one is 19 points a year. It beat SPY in 93.1% of lump-sum windows and 91.9% with monthly contributions, which sounds great until you notice that means roughly 1 in 12 5-year investors trailed a plain index fund the whole time while running a 50% UPRO strategy.

At 10 years the picture steadies: 100 starts, worst 14.1%, and every single one beat SPY. Before anyone quotes that back at me, those 100 windows overlap almost entirely and all come from one 18-year era that ends in a strong US equity and gold run. It's one historical record, not 100 experiments.

Rolling 5-year drawdowns ranged -25.3% to -5.6% depending on entry, against -37.3% for the full history. Your start date decides which of those you met.

Everything is in the full tables here: https://bestfolio.app/blog/golden-ratio-rolling-start-sensitivity (my site, founder disclosure)

If you're evaluating any levered strategy, ask for this chart. A single full-history CAGR is the least informative honest number a backtest can report.

I wanted to know how much of a monthly tactical-allocation backtest survives when you can't trade at the magic signal close...

Delay 0 here already means the signal is calculated at month-end close and the new allocation starts next session. I then pushed every trade 1 and 2 extra business sessions later. Same price data, same signals, 0.10% one-way base cost.

GEM went 9.83% CAGR to 9.54% to 9.75%.

HAA went 13.39% to 12.72% to 12.37%.

BAA went 10.90% to 10.29% to 9.87%.

So the sparse GEM switches were mostly noise. HAA and BAA each gave up about 1 CAGR point by the second extra session, which is more than I expected from monthly rules. Their max drawdowns barely followed the same order either. BAA return got worse while its historical max drawdown got slightly shallower.

The long history uses documented proxy chains before the ETFs existed, and the final partial month has no effect on a completed trade. I also kept the strategy parameters frozen.

For me this is enough to treat the execution timestamp as part of the rule. A backtest that says "month-end" still needs to say which tradable session actually owns the new position.

Last month's thread here about the year 2000 retiree turned into the best conversation I've had on this site, mostly about simulation methods: Monte Carlo versus historical replay, i.i.d. draws versus block bootstrap, whether Boldin-style guardrails change the answer. That discussion kept circling one assumption nobody named directly, so I wrote up the numbers on it.

Every classic retirement calculator, FireCalc, cFIREsim, Big ERN's sheet, replays a STATIC allocation. You give it 75/25 and it holds 75/25 through every 1966 and 2000 start in the dataset. Which is the right test... if that's what you actually run. But several people in that thread described rule-based approaches, and a portfolio that de-risks on a signal has a completely different NAV path through exactly the years that decide survival. The calculators structurally can't model it.

So we ran the classic Bengen math, rolling 30-year windows, inflation-adjusted withdrawals, on the backtested paths of rule-based strategies instead of static mixes. The gap is not subtle. On our 1990-2026 window a classic 60/40 supports a floor near 6.7%, and extending through the 1970s stagflation pulls it to 4.5%, Bengen's number rediscovered from the other direction. The rule-based paths support meaningfully higher floors on the same windows, with the honest caveat that most of those rules were published after the worst starts they're being graded on.

Full method and the floor-by-strategy table: https://bestfolio.app/blog/retirement-calculators-momentum (my site, disclosure as before)

For the spreadsheet builders here: has anyone wired a dynamic allocation into their own withdrawal model? The 2-tool approach I landed on, FireCalc for the conservative baseline plus strategy-level floors on top, still feels like a workaround rather than an answer.

"Why not hold long term" comes up here every week, and the answer is a number, not an opinion. I run BestFolio (disclosure up front) and I spent the spring testing what actually happens when you bolt an exit rule onto levered portfolios across 40 years, so here's the piece of it that's relevant to this sub.

The problem with holding a 3x fund through everything is arithmetic, not courage. TQQQ's live record starts in 2010 and its worst fall so far is about -82%. Recovering from -82% needs +455%. QQQ itself fell 83% in the dot-com bust, and a daily-reset 3x of that path, financing costs included, lands near -99.9%. From there, recovery needs roughly a 1000x. That's not a drawdown anymore, that's a restart.

So I tested the same levered portfolios with and without a simple brake, the 10-month moving average rule Faber published in 2007. Sell when the index closes a month below it, come back when it closes above. On the 3x S&P side (UPRO-based mixes, closest cousin to what this sub runs), the brake cut the worst drawdown by 14 to 28 percentage points depending on the mix, while giving up surprisingly little CAGR. Not because it times tops. It's always late at tops. It just refuses to ride the middle of a multi-year decline, which is where levered funds do their dying.

The trade-off is real and worth stating plainly: you eat whipsaws in choppy sideways years, 2011 and 2015 style, and the brake does nothing for a crash faster than a month. What it buys you is the fat tail. The full numbers, all 40 years, every mix: https://bestfolio.app/blog/catastrophe-brake-leveraged-portfolios (free writeup, my site)

The SMA200 crowd here already runs a cousin of this. My honest question for the buy-and-hold side: what's your actual plan for the -80% print, hold through it, or is the plan that it won't happen again?

Everyone here holds bonds as crash insurance, so here's an uncomfortable stat: in the 5 fastest US equity selloffs since 2011, long Treasuries fell alongside stocks twice. They saved you in 2011, COVID and the 2024 yen-carry unwind, and they failed you in Volmageddon and the April 2025 tariff break.

The obvious follow-up I wanted answered: can you see the failures coming? The standard suggestion is stock-bond correlation... if SPY and TLT have already been moving together, trust TLT less. I run BestFolio (disclosure) and we tested exactly that, with the rule fixed in advance: 60-session correlation on the last trading day before each selloff, above zero means expect Treasuries to fail.

It got 2 of the 5 right. Worse, the clearest warning it ever gave was wrong: before the 2024 unwind, correlation sat at +0.35, more than a standard deviation above its recent average, screaming danger. TLT then gained 4.6% while SPY fell 6.1%. And in April 2025 the correlation was dead neutral at -0.004, no warning at all, and TLT dropped 3.4% anyway.

Then I ran the same trigger at every month-end since 2010. It warned 46 times. 2 of those warnings were followed by an actual 5% drawdown within 10 sessions. That's a 96% false-alarm rate, and moving the threshold around barely changes it.

So my takeaway is that trailing correlation describes the shock you just had, not the one you're about to get. I still find it useful for sizing (a long stretch of positive correlation genuinely means your 60/40 diversifies less), but as a binary crash switch it's noise. Full tables and the fixed methodology: https://bestfolio.app/blog/stock-bond-correlation-crash-hedge

Is anyone actually conditioning their bond allocation on correlation? I'd genuinely like to hear a version of this rule that worked out of sample.

Before anything else, I run BestFolio, so everything below is me scoring my own catalog against itself.

A few weeks ago someone here asked how I measure robustness, and while writing the answer down I ended up staring at a number that bugs me more than any single backtest. I've backtested 202 strategy variants that still live in my database. 150 are public on my leaderboard, the other 52 belong to research strategies I never released. If all 202 had zero real edge, pure random monthly returns, the best of them would still print an annualized Sharpe around 0.56. That's just what ranking 202 noise series does.

So a 0.6 Sharpe picked from a big menu of backtests is... basically nothing. And every platform, every spreadsheet with 30 testfolio tabs, every "I tried 50 variations and kept the best one" post is exactly that menu.

The correction I went with is the Deflated Sharpe Ratio (Bailey and Lopez de Prado, 2014). One number from 0 to 1: the probability the Sharpe is a real edge and not the luckiest pick of the batch. It adjusts for 3 things: how many variants were tested (202 in my case, released or not), how long the track record is, and the actual skew and fat tails of the monthly returns instead of assuming a bell curve.

I now show it on every strategy as a Robustness column and flag anything under 0.90 as fragile. Current state of my own catalog: leaderboard median 0.99, about 3 in 10 at 1.0 (long histories with real edges get there), and 25 of the visible 150 below the line, plus 8 more in the unreleased pile you can't see. The lowest visible is the plain 200-day trend on TLT at 0.48, worse than a coin flip. The GLD version reads 0.51. White Knuckle, the 3x risk parity rotation, sits at 0.58. All flagged amber on my own leaderboard, which felt weird to ship but is kind of the point.

One property I really like: every variant I test raises the bar for all the others, released or not. I widened N from the 150 public variants to all 202 tested ones while writing this up, and watched 5 borderline strategies flip to fragile. My own research deflates my own numbers. One limitation I can't fix: the parameter variations I tried and binned aren't logged as separate trials, and this sub collectively has run way more than 202 backtests, so the true luck bar for "strategies you see posted online" is higher than anything I can compute.

Full writeup with the math and the fragile list: https://bestfolio.app/blog/robustness-score-deflated-sharpe

If you run your own testfolio marathons, a crude version of this correction is easy to bolt on. The paper is short and the formula fits in a spreadsheet cell.

The FMTM vs SPMO thread here last week turned into the best momentum discussion I've seen in this sub, and two questions kept coming back that my table didn't answer. One person pointed out that QMOM has rotated monthly since 2015 and still doesn't match FMTM's returns, so monthly rotation can't be the whole story. Another asked what to actually hold. So I extended the comparison.

Same method as before, everyone measured on the identical window since FMTM launched in March 2025, total return through late July: SPMO +60.7%, FMTM +56.1%, FDMO +44.4%, SPY +32.8%, and QMOM +28.9%.

That last number is the interesting one. QMOM rotates monthly, exactly like FMTM, and it TRAILED the index while the semi-annual SPMO led the whole group. So the zenyogi point from the thread holds up in the data: rebalance frequency isn't the driver. What separates them is what they hold. QMOM runs roughly 50 quality-screened names picked from the full market, deep concentration far from the index. FMTM holds 30 to 50 large caps. SPMO stays inside the S&P 500 and weights by momentum strength, so it never drifts far from the benchmark that's been winning.

Which means the honest conclusion is uncomfortable for stock pickers: in this stretch, the momentum funds won by being MORE like the hot index, not by clever selection away from it. Concentration away from the S&P has been a tax for over a decade. That's a regime, not a law, and if leadership ever broadens, the rankings above can invert.

Fuller history and the crash context for all of these is in the comparison I keep updated: https://bestfolio.app/blog/momentum-etfs-vs-tactical (my site, founder disclosure).

For the "what do I hold" question: I'd decide on the crash exposure, not the CAGR column. All of these are 100% long equities all the time. The century-long momentum data says the factor falls as hard as the market when everything goes down, and no rebalance schedule changes that.

The LVWC thread here last week kept circling one number: the 0.60% TER. I run BestFolio (disclosure) and we traced where the actual cost of the 2x MSCI World wrapper sits, because the TER turns out to be the smallest layer.

Layer one sits inside the benchmark itself, before Amundi touches anything. LVWC tracks the MSCI World Leveraged 2x Daily NET index, and MSCI's methodology charges a financing term on the borrowed half before any fund fee enters. The index borrows first. In the index currency, calendar days included... a weekend costs 3 days of financing. Whatever Amundi charges comes after that bill is already paid.

Layer two is the net-dividend convention. The leveraged index runs on net dividends, so comparing the fund against a gross World series makes the wrapper look worse than it is. Different dividend definitions. Not hidden fees.

Layer three is the fund's own tracking gap: the 0.60%, swap terms, implementation. I can't measure this cleanly for LVWC yet (first NAV 30 September 2025, and EU rules block official performance tables before 12 months). But Amundi's older 2x MSCI USA fund, CL2, publishes fund-versus-benchmark returns: it lagged its already-financed index by 1.11 points in 2024 and 0.61 in 2025, against a stated 0.50% fee. The wrapper gap moves around the fee. Never exactly equal to it.

So the honest answer to "is 0.60% not much?" is that 0.60% was never the bill. I wrote up the full trace with sources and the audit checklist here: https://bestfolio.app/blog/leveraged-ucits-financing-tracking-gap

LVWC crosses its first anniversary on 30 September 2026, and then we finally get the official fund-versus-index table. Until that lands I'd treat any precise "effective fee" estimate for it, including ours, as weaker than it sounds.

I kept seeing the argument that monthly contributions basically rescue a long-term 2x position after a crash, so I tried to give the strategy different return sequences and actually count the failures...

I built a synthetic 2x SPY, daily reset, paying 0.95% annual product cost plus financing on the borrowed dollar at the effective Fed Funds rate plus 0.50 points (time-varying, from FRED, not a flat guess). Same $1 contribution at the start of every month. Compare terminal wealth against the same dollars into plain SPY.

Across actual historical start months since 1993, the 2x route finished behind SPY in 31.08% of 15-year DCA starts. At 20 years that dropped to 7.41%, so continued buying genuinely helps at long horizons. The worst 15-year start still ended with 42 cents per SPY dollar.

Then I scrambled history. 12-month blocks resampled into 5,000 new 15-year paths, same contribution schedule. Keeps each year's daily compounding intact, changes the order of good and bad years. The 2x route still trailed in 24.74% of paths. Median 1.42x the SPY wealth, 5th percentile 0.63x, 95th percentile 3.09x. Same average market, wildly different outcomes, purely from sequence.

So DCA improves the odds a lot, but it never turns unmanaged 2x into a sequence-proof plan. The bad quarter of paths is exactly the kind you can't identify in advance.

Full writeup with the tables: https://bestfolio.app/blog/dca-leveraged-etf-path-dependence (my site, founder disclosure).

One thing I'd genuinely like input on... has anyone got a defensible historical financing spread over cash for a daily 2x product before the live LETF era? I used a flat 0.50 points because I didn't want to fit it by regime.

Since a lot of people here are sitting in drawdowns and doing the "will it come back" math, a piece of data worth having: what an actual worst-case start date did to a real retiree, followed all the way through.

The year 2000 cohort. $2M, 80/20, 4% plus inflation. By 2026 they technically survive with about $1.54M nominal, which is 39% of their starting purchasing power, and they spent 317 of 319 months below their starting balance. The killer was the order of returns... two deep bears inside the first nine years, withdrawals converting temporary losses into permanent ones the entire time.

The reason I bring it here specifically: a plan with no exit rule has to live through whatever sequence arrives. When I ran the faithful 9Sig rules through the dot-com window earlier this year, the sleeve printed a 99.7% drawdown before the math recovered on paper, and paper recovery assumes a holder who watches that and doesn't flinch for years. The 2000 cohort data says even a diversified 80/20 holder spends a quarter century underwater in the bad sequence. A concentrated levered sleeve without an exit lives the same story with the volume turned all the way up.

Full cohort numbers, extended past the usual 2023 cutoff, are here: https://bestfolio.app/blog/year-2000-retiree-replayed (my site, and I analyze 9Sig from the outside, not as a subscriber).

Not saying sell anything. Saying know which sequence your plan can't survive, before the market asks.

A few days ago a subscriber sent me an article where the author builds a portfolio on one of the paid strategy-tracking platforms. Three tactical strategies combined, and the combined numbers are genuinely impressive: about 16% a year since 1971 with a worst drawdown around -9%. They asked if I could add the missing piece to my site. Except it's a closed strategy. Its author gave the rules privately to the platform, the platform verified the track record, and the rules themselves stay secret. Nobody outside can implement it, including me.

So I tried the next best thing. Rebuild the whole portfolio, public rules only.

Two of the three pillars were already public anyway, Keller's Bold Asset Allocation and Hybrid Asset Allocation, both from published papers. The secret one is, by its own description, a fast momentum model: it looks at very recent returns and moves everything into bonds or cash at the first sign of trouble. The closest public strategy doing that job is Accelerating Dual Momentum, which ranks US large caps against international small caps on their last 1, 3 and 6 months, holds the winner, and steps out to long treasuries when both look weak. It switches once a month.

I weighted the three so each contributes about the same risk, which lands near 40/30/30. Result since 1993, monthly data, before costs: 14.5% a year, Sharpe 1.32, worst drawdown -9.2%, longest losing stretch 20 months. Not bad for rules anyone can read. The original reports 16.3% a year, Sharpe 1.31, worst drawdown -9.1%, from 1971 (windows aren't identical, and the 1970s flatter any backtest that includes them).

What surprised me is how exactly the risk side matched. Same Sharpe, same drawdown, almost to the decimal. The gap is all in the return: 1.8% a year. The secret strategy trades roughly 7 times more often than my monthly stand-in, and that activity apparently buys real extra return.

Could be wrong on one thing: I picked the stand-in from the closed strategy's public description. If it's doing something smarter than fast momentum, the real gap is bigger than my numbers show.

Full write-up with the correlations and what each weighting method picked: https://bestfolio.app/blog/replicating-a-closed-rules-taa-portfolio

I build BestFolio, for the record. All three strategies in the blend are public rules, so you can check every number yourself.

Would you pay 1.8% a year for rules you can actually read and audit, or rather trust the sealed version because a third party verified it?

Every few weeks someone here asks whether FMTM is the better momentum ETF now, and the thread usually turns into two people quoting CAGRs from completely different sample lengths at each other. I run BestFolio (disclosure, my site) and I just rebuilt our comparison table from scratch, so here is what the live records actually say as of last week.

Since each fund's own inception: SPMO 18.9% a year with a 30.9% max drawdown, MTUM 15.7% and 34.1%, FDMO 15.3% and 33.9%, QMOM 11.4% and 39.1%. SPY over its full 33 years, for scale, 10.8% and 55.2%.

Then FMTM, which prints 37.5% annualised with a max drawdown of only 12.2%... and that's close to meaningless. The fund started trading in March 2025. Its worst drawdown so far measures what hasn't happened yet, rather than how it handles a bear market. The fund hasn't met one.

The only honest comparison I can make is the window they all share. Since FMTM launched: SPMO +54.3%, FMTM +52.5%, FDMO +42.4%, SPY +33.7%. So on identical dates the active newcomer has slightly trailed the cheapest passive fund in the group while charging 0.45% against SPMO's 0.13%. Its monthly rebalance is a genuine methodological improvement over semi-annual reconstitution, that part of the pitch is real, but it hasn't shown up in the returns yet.

Worth pinning down since it trips up half these threads: FMTM is MarketDesk, not Fidelity. Fidelity's momentum ETF is FDMO, it launched in 2016 and it's a completely different fund with a 15.3% record. If you see a nine-year track record attached to FMTM anywhere, that's FDMO's. Full table with every fund and the tactical comparison: https://bestfolio.app/blog/momentum-etfs-vs-tactical (my site)

The bigger point survives all of this though. Every fund on that list is always long equities. They rank stocks against each other and never leave the asset class, so in a systemic drawdown the ranking can't save you, whatever the rebalance cadence.

I'm seriously considering replacing my Bluesound Node Icon with a T10 + Z10. Main source is Roon, TV through ARC, then Z10 directly into Purifi power amps and Focal Scala Utopia EVO speakers.

The hardware looks fantastic, but the day-to-day control seems surprisingly weak for a matched flagship combo... I'd love confirmation from someone who actually owns or tested both.

Does the Z10 HDMI ARC have any CEC support? In other words, can the TV remote change Z10 volume / mute, power it on, or automatically select ARC? One review says no, but I can't find much confirmation elsewhere.

Then the remotes. Can the play/pause/skip buttons on the Z10 V15 remote control the T10 or Roon, and can the T10 V16 remote control Z10 analogue volume, mute or inputs? Reviews seem to say they can't control each other at all, despite the Z10 remote having transport buttons... Very odd.

I'm also wondering about the app. Does Eversolo Control link both boxes as one system, or do you have to switch between two separate devices? And with trigger cables, can the Z10 reliably power the T10 and downstream amps together?

What I want should be simple enough. T10 fixed at 0 dB, Z10 handling volume in the analogue domain, Roon for browsing, and one remote handling the basic play/pause/skip/volume commands without having to juggle two Eversolo remotes plus the TV remote. But it looks like I'd need exactly that... Am I missing a setting or a firmware feature here?

With half this sub predicting a crash and the other half buying the dip, I keep thinking about the one retiree cohort that actually lived the nightmare scenario, because we extended their story past where the usual tools stop.

Quick setup... the year 2000 retiree starts with $2M in an 80/20, taking 4% withdrawals plus inflation bumps. The Engaging Data visualizer everyone links cuts off in 2023 with the portfolio around $600k and no ending. We ran it through mid-2026. They survive, technically... about $1.54M nominal, which deflates to 39% of the starting purchasing power, funding a withdrawal that inflation has pushed to $158k a year. The low was $859k in February 2009.

The number that stuck with me is uglier than the balance: of 319 months retired so far, 317 were spent below the starting value. An entire retirement watching the account sit underwater against day one. On paper the 4% rule held. I don't believe many humans would have.

Then we replayed the exact same withdrawals on mechanical allocation rules, and the mildest example is the one worth repeating: Harry Browne's Permanent Portfolio, same cohort, same dates, spent roughly 45 of those 319 months below its starting value, bottoming near $1.8M in mid-2002. Slower engine, radically different experience. The trend-following variants ended far higher still, with the honest caveat that most of those rules were published after 2000, so treat their exact numbers as in-sample.

Full writeup with the cohort table: https://bestfolio.app/blog/year-2000-retiree-replayed (founder disclosure, my site).

The takeaway I'd defend either way: whether the crash comes or does not, sequence risk is the thing your allocation has to answer for, and CAGR tells you nothing about it.

Edit: I had the Permanent Portfolio number wrong when I posted this. I wrote 6 months below the starting value; rechecking the series properly it's about 45 months, with the low in 2002 rather than 2000. Corrected above. The contrast with the 80/20's 317 months still stands, but it's a smaller gap than I claimed and the correction cuts against my own argument, so it's only fair to flag it.

My backtest-window post earlier this week got a fair pushback: fine, the window hides things, but show the actual numbers. So here they are, for the momentum case specifically, since half the ETF subs are currently loading SPMO as a defensive core.

The visible record is genuinely good. Since its 2015 launch SPMO did about 19.6% a year against 14.9% for SPY with a slightly shallower max drawdown, and it fell less in both covid and 2022. If your sample starts in 2015, momentum looks like free lunch.

Extend the sample and I promise it stops being free. Ken French's top-decile momentum portfolio runs back to the 1920s: the winners fell roughly 48% through the dotcom bust, 51% through the GFC, about 70% in 1929-32, worst drawdown near 76%. A long-only momentum fund never de-risks... it rotates within equities, carries full beta the whole way down, then eats the momentum crash at the bottom when the destroyed names rip and the winners keep sinking. None of that fits inside an eight-year live record, which is my whole point about windows.

For levered folks I'd say the implication is direct: a momentum sleeve is a return engine, and levering a return engine without a separate exit rule just scales the part the backtest can't see. Full writeup with the tables: https://bestfolio.app/blog/spmo-core-holding-momentum-crash-record (founder disclosure, it's my site).

Still curious what people concluded from the last thread... did anyone actually stress their levered sleeves through pre-2010 regimes, or is the window still doing the heavy lifting?

First post here, been reading for a while. I build retirement math tools and this crowd seems like the right audience for a question that started on the FIRE subs and would not leave me alone: does the year 2000 retiree, the worst-case cohort everyone references, actually make it? The visualizers all stop at 2023, so I extended the run through mid-2026.

They make it, barely and joylessly. $2M at 80/20 with 4% plus inflation becomes about $1.54M nominal, which is 39% of starting purchasing power, with the withdrawal inflated to $158k a year. Low point $859k in February 2009. And the stat that reframed the whole exercise for me: 317 of the 319 months of retirement so far were spent below the starting balance. The plan survived on paper while feeling broken for 26 straight years.

Then I replayed identical withdrawals on mechanical allocation rules. The one worth mentioning here is the mild one, Harry Browne's Permanent Portfolio: same cohort, same dates, 6 months total below start in 26 years. It ends with a smaller pile than stocks in the good scenarios, so it's genuinely a trade, sleep for upside. But for a decumulation plan that trade reads very differently than it does for an accumulator.

Everything is written up with the tables here, free: https://bestfolio.app/blog/year-2000-retiree-replayed?utm_source=reddit&utm_campaign=year2000-diyret (my site, full disclosure).

What I'd love from this group... how do you all stress your withdrawal plans against a 2000-style start? Fixed floor, spending flex, bucket rungs? The math says the first five years decide almost everything.

Something I keep noticing on this sub, and I say it as someone who runs levered sleeves myself: almost every "this held up better in the crash" claim is really a claim about which crash happened to fit inside the backtest.

The clearest current example is momentum. People point out that SPMO or a momentum tilt fell less than the S&P in the last downturn, and on the visible record that's true. But that record is basically 2015 onward for the ETF, or 2010 onward for most testfolio sims: a fast covid V and a 2022 rotation that happened to punish exactly what momentum was underweight. Neither is the regime that has historically hurt momentum.

I pulled Ken French's top-decile momentum portfolio back to the 1920s to see the parts our backtests skip. Momentum winners fell about 48% in the 2000-02 dotcom bust, 51% in the GFC, roughly 70% in 1929-32, with a worst drawdown near 76%. Long-only momentum never de-risks, it just rotates within equities, so you hold full beta the whole way down and then lag badly in the sharp recovery when the beaten-down names rip hardest.

Now layer leverage on top of that and the sample-size problem gets scarier, because a 2010-start backtest of anything levered has literally never met a 2000 or a 1973 or a stagflation decade.

So the question I'd put to the sub: for the levered strategies you actually hold, do you know how they behaved in the regimes your backtest can't see, or is the whole thesis resting on the friendly window the data happens to start in? Genuinely curious how people here stress that, because the answer changes how much leverage is sane.

Last week someone here asked a question that stuck with me: the 2000 retiree you can watch on Engaging Data, the one whose $2M turned into roughly $600k... do they actually make it through the full 30 years? The visualizer stops at 2023 and nobody in 246 comments had the ending. So I ran it myself.

They make it. 80/20 portfolio, 4% plus inflation bumps, monthly withdrawals: the cohort sits around $1.54M nominal as of this month. Sounds almost fine until you deflate it... that's 39% of the starting purchasing power, and the inflation-adjusted withdrawal has grown to about $158k a year against that shrunken base. The low point was roughly $859k in February 2009.

The number that actually got me is this one though. Out of 319 months of retirement so far, 317 were spent below the starting balance. Not below the peak. Below where they STARTED. Whatever the spreadsheet says about survival, I don't believe many humans hold a plan through 26 years of watching it stay underwater.

Why 2000 is the stress test: both bears hit the same cohort. Dotcom took about half, and 2008 arrived before the recovery finished. Two deep drawdowns inside the first nine years, which is precisely when withdrawals turn temporary losses into permanent ones.

Out of curiosity I also replayed the same withdrawals on a few mechanical allocation rules. The mildest example is Harry Browne's old Permanent Portfolio: same cohort, same dates, and it spent a grand total of 6 months below its starting value. Slower engine, but a retiree could actually live with it. The trend-following variants did far better still, with the honest caveat that most of those rules were published after 2000, so they got to "predict" a past their authors already knew.

For me the practical lesson is boring: margin in the withdrawal rate, spending you can actually cut, and knowing in advance what you'd do in year two of a bad start instead of deciding it mid-panic.

There was a question here last week (the flash crash protection thread) that I couldn't stop thinking about: if your signals lag, what do you actually hold in the gap? The comments had all the usual candidates and no numbers, so I measured every major defensive asset inside the five fastest selloffs of the ETF era: the Aug 2011 downgrade, Volmageddon, covid, the Aug 2024 yen unwind, and the April 2025 tariff week.

The results humbled a couple of my own assumptions. Long treasuries saved you in 3 of 5 (2011, covid, the yen unwind) but fell WITH stocks in Volmageddon and the tariff week... you don't get to pick which regime your crash arrives in. Gold was down in 4 of the 5 windows, including -3.6% during the covid crash leg. Managed futures (DBMF) were negative in all three windows they existed for, which surprised nobody who understands how slowly trend turns.

The one thing that rose in every single window it existed for? BTAL, the anti-beta fund. Up in all four. And it costs -3.8% CAGR since 2011 to hold, which is the whole insurance problem in one number.

I put the full table and window definitions here: https://bestfolio.app/blog/fast-crash-protection-event-study?utm_source=reddit&utm_campaign=fast-crash (founder disclosure, my site).

The takeaway that changed my own thinking: for a monthly system your true exposure is one month of beta on whatever sleeve is risk-on, and t-bills plus sleeve-sizing beat every exotic hedge on offer. Nothing spikes on demand except the stuff that bleeds you all year for the privilege.